Neuroimaging in Parkinson's Disease and Related Disorders 9780128216514

Table of contents :
Copyright
Contributors
Preface
Section I: Introduction
1. Parkinson's disease and related disorders: the pursuit for reliable readouts and the role of neuroimaging
Conclusion
References
The future of neuroimaging and the application of robust measures in clinical trials
Introduction
Clinical aspects of movement disorders
Hypokinetic movement disorders
Hyperkinetic movement disorders
Dystonia
Restless legs syndrome
2. Advances in magnetic resonance imaging
Introduction
Basic MRI physics
T1, T2, and T2∗ relaxation
Free induction decay
MRI scanner hardware
The magnet
Radiofrequency coils
Gradient coils
Shim coils
Image acquisition
Spin echo and gradient echo
Spatial localization gradients
Inversion recovery
Image reconstruction
Echoplanar imaging and turbo spin echo
Parallel imaging
MRI modalities
T1-weighted MR imaging
T2-weighted MR imaging
Diffusion-weighted imaging
Functional MRI
Arterial spin labeling
Neuromelanin-sensitive MRI
Susceptibility-weighted imaging
Quantitative susceptibility mapping
Magnetic resonance spectroscopy
Conclusion
References
3. Advances in molecular neuroimaging methodology
Introduction
PET acquisition
Quantitative PET analysis
Kinetic modeling analysis
Parametric modeling
Standardized uptake value
Partial volume correction
Advanced analysis methodology
Conclusion
References
Section II: Clinical Applications in Parkinson's Disease
4. Dopaminergic molecular imaging in familial and idiopathic Parkinson's disease
Introduction
Molecular biology and imaging of the dopaminergic system
Molecular imaging of the presynaptic dopaminergic terminals
Molecular imaging of the amino l-acid decarboxylase
Molecular imaging of the dopamine transporter
Molecular imaging of the vesicular Monoamine Transporter 2
Combined study of the AADC, DAT, and VMAT2 enzyme in idiopathic and familial PD
References
Molecular imaging of the postsynaptic dopaminergic terminals
Molecular imaging of the D1 receptors
Molecular imaging of the D2 receptors
Molecular imaging of the D3 receptors
Conclusive Remarks
5. Serotonergic molecular imaging in familial and idiopathic PD
Introduction
Serotonergic system changes in Parkinson's disease
Molecular imaging of serotonergic system
Serotonergic molecular imaging of motor symptoms
Serotonergic molecular imaging of nonmotor symptoms
Serotonergic molecular imaging of prodromal stages of Parkinson's disease
Conclusions
References
6. Molecular imaging beyond dopamine and serotonin in familial and idiopathic Parkinson’s disease
Introduction
Glucose metabolism
Synaptic pathology
Neurotransmitter systems
Cannabinoid
Cholinergic
Opioid
Noradrenergic
Glutamatergic
Phosphodiesterase
Neuroinflammation
Protein aggregation
Energy dysregulation
Conclusion
References
7. Structural MRI in familial and idiopathic PD
Introduction
Structural volumetric MRI
Gray matter volumes in idiopathic Parkinson’s disease
Association between gray matter changes with idiopathic Parkinson’s disease motor symptoms
Correlation between gray matter changes with nonmotor symptoms
Cortical thickness
Structural MRI changes in familial Parkinson’s disease
Diffusion MRI studies in idiopathic Parkinson’s disease
Free-water MRI changes in idiopathic Parkinson’s disease
Diffusion MRI changes and nonmotor symptoms of idiopathic Parkinson’s disease
Diffusion MRI changes associated with cognitive impairment in idiopathic Parkinson’s disease
Diffusion MRI changes within the glymphatic system in idiopathic Parkinson’s disease
Diffusion tensor imaging in familial Parkinson’s disease
Neuromelanin-sensitive MRI in idiopathic Parkinson’s disease
Neuromelanin-sensitive MRI in familial Parkinson’s disease
Iron-sensitive MRI studies in idiopathic Parkinson’s disease
Iron-sensitive MRI studies in familial Parkinson’s disease
Arterial spin labeling in idiopathic Parkinson’s disease
Summary
References
8. Functional MRI in familial and idiopathic PD
Introduction
rs-fMRI of motor symptoms in idiopathic Parkinson's disease
As PD progresses, different FC changes in the brain can occur: multiple cross-sectional and longitudinal studies
rs-fMRI of non-motor symptoms in idiopathic Parkinson's disease
rs-fMRI studies in familial Parkinson's disease
Task-based fMRI
Motor tb-fMRI in idiopathic Parkinson's disease patients
Motor skill learning
Cognitive tb-fMRI in idiopathic Parkinson's disease patients
tb-fMRI studies in familial Parkinson's disease
Conclusions
References
9. Molecular imaging in prodromal Parkinson’s disease
Introduction
Definition of prodromal Parkinson’s disease
Dopaminergic imaging of prodromal Parkinsonism
Molecular imaging of brain metabolism in prodromal Parkinson’s disease
Serotonergic molecular imaging of prodromal Parkinson’s disease
Molecular imaging of other neurotransmettitorial systems in prodromal Parkinson’s disease
Conclusions
References
Section III: Clinical Applications in Lewy Body Dementias
10. Molecular imaging evidence in favor or against PDD and DLB overlap
Introduction
PET markers of synaptic dysfunction
PET markers of amyloidosis
PET markers of tauopathy
PET markers of dopaminergic dysfunction
PET markers of cholinergic dysfunction
PET markers of noradrenergic dysfunction
Conclusions
References
11. Magnetic resonance imaging in Parkinson's disease with mild cognitive impairment, Parkinson's disease dementia, and dementi ...
Introduction
Structural MRI
Diffusion tensor imaging
Iron quantification MRI
Functional MRI studies
Conclusions
References
Section IV: Clincal Applications in Atypical Parkinsonian Disorders
12. Neuroimaging in multiple system atrophy
Introduction
Magnetic resonance imaging
Conventional MRI
Magnetic resonance volumetry
Diffusion tensor imaging and diffusion-weighted imaging
Magnetic resonance imaging of iron deposition
Functional magnetic resonance imaging
Magnetic resonance spectroscopy
Magnetization transfer imaging
Arterial spin labeling
Neuromelanin-sensitive magnetic resonance imaging
Molecular imaging techniques
Striatal dopaminergic imaging
Brain glucose metabolism
Perfusional SPECT imaging
Sympathetic imaging
Opioid system
Neuroinflammation imaging
Cholinergic activity
Tau, amyloid, and α-synuclein imaging
Conclusions and future directions
References
13. Neuroimaging in progressive supranuclear palsy
Introduction
Structural magnetic resonance imaging
Functional magnetic resonance imaging
Magnetic resonance spectroscopy
Molecular imaging studies in progressive supranuclear palsy
Single-photon emission computed tomography
Positron emission tomography
Brain metabolism
Dopaminergic system
Tau deposition
Other systems
Conclusion
References
14. Neuroimaging in corticobasal syndrome
Introduction
Structural magnetic resonance imaging
Functional magnetic resonance imaging
Single-photon emission computed tomography
Positron emission tomography
Brain metabolism
Dopaminergic system
Microglial activation
Cholinergic system
Tau deposition
Synaptic pathology
Conclusion and future directions
References
Section V: Clinical Applications in Other Movement Disorders
15. Molecular imaging in Huntington's disease
Introduction
Brain metabolism
Dopaminergic system
Neuroinflammation
Phosphodiesterases
Opioidergic system
GABAergic system
Cannabinoid system
Adenosinergic system
Synaptic pathology
Use of molecular imaging to track disease progression in Huntington's disease
Use of molecular imaging as outcome measure in Huntington's disease trials
Future perspectives of molecular imaging in Huntington's disease
References
16. Magnetic resonance imaging in Huntington's disease
Introduction
Structural MRI markers of disease pathology in Huntington's disease
Volumetric MRI
Diffusion tensor imaging
Magnetization transfer imaging
Functional MRI markers of disease pathology in Huntington's disease
Resting-state fMRI
Task-based fMRI
Arterial spin labeling
Iron
Magnetic resonance spectroscopy
The use of MRI to track disease progression in Huntington's disease
Macrostructural MRI measures
Microstructural MRI measures
Functional MRI measures
Magnetic resonance spectroscopy
The use of MRI as outcome measures and for stratification in Huntington's disease trials
Conclusion
References
17. Neuroimaging in essential tremor
Introduction
MRI markers of essential tremor
Structural MRI
Volumetric MRI
Cortical thickness
Diffusion tensor imaging
Iron-sensitive MRI
Neuromelanin MRI
Functional MRI
Task-based fMRI
Resting-state fMRI
Magnetic resonance spectroscopy
Molecular imaging markers of essential tremor
Dopaminergic imaging
Presynaptic
Postsynaptic
Perfusion and metabolic imaging
Molecular imaging of the GABAergic system in essential tremor
Conclusion
Neuroimaging to understand essential tremor pathophysiology
References
18. Neuroimaging of restless legs syndrome
Introduction
Molecular imaging in restless legs syndrome
Magnetic resonance imaging in restless legs syndrome
Structural MRI studies
Diffusion tensor imaging studies
MRI studies for iron quantification
Functional MRI studies
Magnetic resonance spectroscopy
Conclusions
References
19. Neuroimaging in dystonia
Introduction
Magnetic resonance imaging markers of dystonia
Structural MRI
Volumetric MRI
Cortical thickness
Diffusion tensor imaging
Iron-sensitive MRI
Functional MRI
Task-based fMRI
Resting-state functional MRI
Molecular imaging markers of dystonia
Molecular imaging of the dopaminergic system
GABAergic imaging
Metabolic and perfusion imaging
Cholinergic imaging
Conclusion
Neuroimaging to understand dystonia pathophysiology
References
20. Demyelinating syndromes and movement disorders
Introduction
Multiple sclerosis
Epidemiology and risk factors
Pathology and clinical features of multiple sclerosis
Multiple sclerosis diagnosis
Movement disorders in multiple sclerosis
Movement disorders in other primary demyelinating diseases
Neuromyelitis optica spectrum disorders
Movement disorders in neuromyelitis optica spectrum disorders
Acute disseminated encephalomyelitis
Movement disorders in acute disseminated encephalomyelitis
Osmotic demyelination syndrome
Movement disorders in osmotic demyelination syndrome
Diagnostic flowchart
Management
Summary
References
Index
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Citation preview

NEUROIMAGING IN PARKINSON’S DISEASE AND RELATED DISORDERS Edited by

MARIOS POLITIS Neurodegeneration Imaging Group and Mireille Gillings Neuroimaging Centre, University of Exeter, London, United Kingdom

Co-Editors

HEATHER WILSON Neurodegeneration Imaging Group and Mireille Gillings Neuroimaging Centre, University of Exeter, London, United Kingdom

EDOARDO ROSARIO

DE

NATALE

Neurodegeneration Imaging Group and Mireille Gillings Neuroimaging Centre, University of Exeter, London, United Kingdom

Academic Press is an imprint of Elsevier 125 London Wall, London EC2Y 5AS, United Kingdom 525 B Street, Suite 1650, San Diego, CA 92101, United States 50 Hampshire Street, 5th Floor, Cambridge, MA 02139, United States The Boulevard, Langford Lane, Kidlington, Oxford OX5 1GB, United Kingdom Copyright © 2023 Elsevier Inc. All rights reserved. No part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopying, recording, or any information storage and retrieval system, without permission in writing from the publisher. Details on how to seek permission, further information about the Publisher’s permissions policies and our arrangements with organizations such as the Copyright Clearance Center and the Copyright Licensing Agency, can be found at our website: www.elsevier.com/permissions. This book and the individual contributions contained in it are protected under copyright by the Publisher (other than as may be noted herein). Notices Knowledge and best practice in this field are constantly changing. As new research and experience broaden our understanding, changes in research methods, professional practices, or medical treatment may become necessary. Practitioners and researchers must always rely on their own experience and knowledge in evaluating and using any information, methods, compounds, or experiments described herein. In using such information or methods they should be mindful of their own safety and the safety of others, including parties for whom they have a professional responsibility. To the fullest extent of the law, neither the Publisher nor the authors, contributors, or editors, assume any liability for any injury and/or damage to persons or property as a matter of products liability, negligence or otherwise, or from any use or operation of any methods, products, instructions, or ideas contained in the material herein. ISBN: 978-0-12-821651-4 For information on all Academic Press publications visit our website at https://www.elsevier.com/books-and-journals Publisher: Nikki P. Levy Acquisitions Editor: Joslyn T. Chaiprasert-Paguio Editorial Project Manager: Kristi L. Anderson Production Project Manager: Stalin Viswanathan Cover Designer: Greg Harris

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Contributors Andrea Naldi Department of Neuroscience “Rita Levi Montalcini”, University of Turin, Turin, Italy

Christina Belogianni Prisma Health - Midland, Richland, University of South Carolina School of Medicine, Department of Neurology Residency Program, Columbia, United States

Flavia Niccolini King’s College Hospital NHS Foundation Trust, London, United Kingdom; Lewisham and Greenwich NHS Foundation Trust, London, United Kingdom; Department of Neurology, King’s College Hospital NHS Foundation Trust, London, United Kingdom; Department of Neurology, Queen Elizabeth Hospital, Lewisham and Greenwich NHS Foundation Trust, London, United Kingdom

Silvia Paola Caminiti In vivo human molecular and structural neuroimaging Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy Giulia Carli Department of Nuclear Medicine and Molecular Imaging, University of Groningen, University Medical Center Groningen, Groningen, Netherlands

Gennaro Pagano Neurodegeneration Imaging Group, University of Exeter Medical School, London, United Kingdom; Roche Pharma Research and Early Development (pRED), Neuroscience and Rare Diseases Discovery and Translational Area, Roche Innovation Center Basel, Basel, Switzerland

Antonio Carotenuto Multiple Sclerosis Clinical Care and Research Centre, Department of Neuroscience, Reproductive Science and Odontostomatology, ‘Federico II’ University, Naples, Italy Cristoforo Comi Parkinson’s Disease and Movement Disorders Centre, Neurology Unit, University of Piemonte Orientale, Novara, Italy

Marios Politis Neurodegeneration Imaging Group and Mireille Gillings Neuroimaging Centre, University of Exeter, London, United Kingdom; Neurodegeneration Imaging Group, Department of Clinical and Biomedical Sciences, Faculty of Health and Life Sciences, University of Exeter, United Kingdom

Edoardo Rosario de Natale Neurodegeneration Imaging Group and Mireille Gillings Neuroimaging Centre, University of Exeter, London, United Kingdom; Neurodegeneration Imaging Group, Department of Clinical and Biomedical Sciences, Faculty of Health and Life Sciences, University of Exeter, United Kingdom

Alana Terry Neurodegeneration Imaging Group, Department of Clinical and Biomedical Sciences, Faculty of Health and Life Sciences, University of Exeter, United Kingdom

Stefan Holiga Roche Pharma Research and Early Development (pRED), Neuroscience and Rare Diseases Discovery and Translational Area, Roche Innovation Center Basel, Basel, Switzerland

Giacomo Tondo Neurodegeneration Imaging Group, Department of Clinical and Biomedical Sciences, Faculty of Health and Life Sciences, University of Exeter, United Kingdom; School of Psychology, Vita-Salute San Raffaele University, Milan, Italy

Thomas Kustermann Roche Pharma Research and Early Development (pRED), Neuroscience and Rare Diseases Discovery and Translational Area, Roche Innovation Center Basel, Basel, Switzerland

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CONTRIBUTORS

Joji Philip Verghese Neurodegeneration Imaging Group, Department of Clinical and Biomedical Sciences, Faculty of Health and Life Sciences, University of Exeter, United Kingdom Heather Wilson Neurodegeneration Imaging Group and Mireille Gillings Neuroimaging Centre, University of Exeter, London, United Kingdom; Neurodegeneration Imaging Group, Department of Clinical and Biomedical

Sciences, Faculty of Health and Life Sciences, University of Exeter, United Kingdom Stefano Zanigni Roche Pharma Research and Early Development (pRED), Neuroscience and Rare Diseases Discovery and Translational Area, Roche Innovation Center Basel, Basel, Switzerland; Product Development Neuroscience, F. Hoffmann-La Roche Ltd, Basel, Switzerland

Preface Neuroimaging techniques play a pivotal role in the research and clinical landscape of Parkinson’s and related disorders by helping to understand biological signatures of the disease, to aid the diagnosis, disease monitoring, and pursuit for new therapeutic targets and novel drug development. The past 20 years has seen a rapid evolution of technological advances that has led to prompt developments in brain imaging techniques such as with magnetic resonance imaging (MRI), position emission tomography (PET), and single photon emission computed tomography (SPECT). Both academia and industry are increasingly employing neuroimaging tools to study changes within the brain which occur during disease states while using neuroimaging as markers and outcome endpoints in clinical trials. This book discusses the advances, and applications, of molecular, structural, and functional neuroimaging techniques in Parkinson’s and related disorders. This book captures the fundamental principles of neuroimaging techniques such with MRI, PET, and SPECT, and attempts to review the lessons learnt from these neuroimaging studies and their correspondence in the clinical presentation of patients with Parkinson’s disease and related disorders including those with Lewy body dementias, atypical parkinsonism, Huntington’s disease,

essential tremor, restless legs syndrome, dystonia, and demyelinating syndromes. Key features of this book include reviewing the biomarker value of neuroimaging of the brain to understand disease pathophysiology, aetiology, and progression in vivo in patients with Parkinson’s disease and related disorders, as well as the association between symptomatology and molecular, structural, functional changes, and the application of neuroimaging in drug development and clinical trials. The content offers a comprehensive overview and resource for neurologists, neuroscientists, neuroimagers, movement disorder specialists, and nuclear medicine professionals as well as students, teaching staff, basic and clinical scientists working on translational research, and across all levels of industry, including proof-of-concept, drug discovery, and clinical trials. The Editors wish to express their sincere gratitude to all the contributing authors and hope that readers find these articles informative, engaging, and helpful in their practices.

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Marios Politis MD, MSc, DIC, PhD, FRCP, FEAN Neurodegeneration Imaging Group and Mireille Gillings Neuroimaging Centre, University of Exeter, London, United Kingdom

C H A P T E R

1 Parkinson’s disease and related disorders: the pursuit for reliable readouts and the role of neuroimaging Edoardo Rosario de Natale*, Heather Wilson* and Marios Politis Neurodegeneration Imaging Group and Mireille Gillings Neuroimaging Centre, University of Exeter, London, United Kingdom

Introduction Neuroimaging techniques make it possible to investigate structural, functional, and biological processes in vivo. The past decades have seen a dramatic increase in the application of neuroimaging techniques, including magnetic resonance imaging (MRI), positron emission tomography (PET), and single photon computed tomography (SPECT), in clinical research and clinical trials. This has been driven by technological advances in scanner hardware, reduced costs and increased availability of tracer production, radiochemistry developments of new tracers, and methodological developments in acquisition and analysis protocols. Neuroimaging enables the quantitative evaluation of disease pathology and the spatiotemporal spreading of pathology in disease states. MRI techniques can study structural, functional, and microstructural changes, structural and functional connectivity, cerebral blood flow, and iron concentrations. While MRI has higher spatial resolution, PET and SPECT have the advantage to quantify biological processes at a molecular and cellular level. PET imaging has higher sensitivity compared with SPECT with a range of PET radiotracers validated for the investigation of neurotransmitter systems, neuroreceptors, protein aggregation,

*

Joint first authors.

Neuroimaging in Parkinson’s Disease and Related Disorders https://doi.org/10.1016/B978-0-12-821651-4.00011-7

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© 2023 Elsevier Inc. All rights reserved.

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1. Parkinson’s disease and related disorders: the pursuit for reliable readouts and the role of neuroimaging

neuroinflammation, glucose metabolism, synaptic integrity, enzymes, and energy regulation (Lu & Yuan, 2015; Wilson et al., 2020). The approval for neuroimaging applications in a clinical setting highlights the potential of MRI, PET, and SPECT to aid clinical practice and improve patient care. With the research and healthcare community striving toward personalized medicine approaches (Johnson et al., 2021), neuroimaging offers tools, which could potentially aid the identification of individual disease signatures and projected clinical outcomes for targeting therapeutic intervention (Cope et al., 2021). Movement disorders represent a large, heterogenous group of disabling neurological conditions that recognize a variety of neurodegenerative or nondegenerative causes. They are generally divided clinically into hypokinetic and hyperkinetic movement disorders. A key need across Parkinson’s disease (PD) and related movement disorders is to establish reliable biomarkers, including diagnostic, monitoring, response, predictive, and prognostic biomarkers. Neuroimaging techniques have the potential to meet the criteria to serve as multiple biomarker subtypes and offer reliable readouts in clinical trials (Shimizu et al., 2018). Diagnostic biomarkers are required to accurately identify individuals at risk and early preclinical disease stages to aid early diagnosis and therapeutic intervention; predictive biomarkers need to reliably predict an individual, or a cohort, response to an intervention, or clinical trajectory such as predicting the onset of symptomatology or phenoconversion; prognostic biomarkers should identify disease progression and identify higher-risk populations (Califf, 2018). PET imaging offers a valuable tool as a biomarker to evaluate the in vivo response to pharmacological treatment at a molecular level to understand and validate disease-modifying effects. Advances have been made to identify neuroimaging biomarkers linked to clinical endpoints (Cash et al., 2014; Muir & Macrae, 2016). For example, the National Institute on Aging and the Alzheimer’s Association (NIA-AA) research framework focuses on the AT(N) criteria (amyloid, tau, and neurodegeneration) for Alzheimer’s disease (AD), which includes neuroimaging biomarkers, for structural MRI, amyloid PET, and tau PET (Jack et al., 2013, 2018). The validation of neuroimaging biomarkers as reliable clinical endpoints holds valuable potential for future clinical trials. This chapter introduces clinical aspects of PD and related movement disorders, which are discussed in chapters throughout this book, and highlights the application of neuroimaging for robust measures in clinical trials.

Clinical aspects of movement disorders Hypokinetic movement disorders Parkinsonism is the most common hypokinetic movement disorder. The cardinal signs of Parkinsonism are akinesia, a complex symptom defined as a slowness (bradykinesia), poverty (hypokinesia), and difficulty in initiating movements; and rigidity, that is, an increased resistance of body segments to passive joint movements throughout the whole range of motion of agonist and antagonist muscles. PD is, by far, the most frequent form of Parkinsonism. PD is the most common movement disorder and the second most common neurodegenerative disease, after AD. Its frequency increases with age, being rare in people below 50 years but affecting up to 1% of the whole population over 60 years (de Lau & Breteler, 2006). I. Introduction

Clinical aspects of movement disorders

5

The clinical description of idiopathic PD still stands, in its core characterization, to the definition provided by James Parkinson in his 1817 essay on shaking palsy (Parkinson, 2002). Patients affected by PD show a general slowness of movement or an impairment of dexterity, a struggle in walking and loss of arm swing, or problems in the tone and articulation of the speech, which improve following dopaminergic therapy (Lees et al., 2009). These signs can be accompanied by tremor, which in PD takes the form of a “pill rolling” rest tremor of 4e6 Hz frequency, and postural alteration, which is a major predisposition to falls and subsequent disability. Alongside motor symptoms, nonmotor symptoms, such as loss of sense of smell, sleep problems, low mood and anxiety, autonomic dysfunction, psychosis, cognitive impairment, pain, and others, coexist and are a major source of burden to patients and caregivers (Lees et al., 2009). Progression is slow and characterized by the worsening of the motor symptoms, an unpredictable response to dopaminergic medication, and the onset and worsening of cognitive impairment and dementia. Research over the past two decades has demonstrated that the clinical diagnosis of PD is preceded by a phase, called “prodromal PD,” where symptoms, such as isolated REM sleep behavior disorder (iRBD), hyposmia, constipation, depression, and others, despite not fulfilling the criteria for “clinical PD,” reflect an ongoing neurodegenerative process (Heinzel et al., 2019). The study of this early stage is a major research field in PD to expand the temporal window to recognize the pathological process and for a timely pharmacological intervention. The role of molecular imaging in prodromal PD is discussed in Chapter 9 of this book. Familial PD is overall rare. A single, monogenic cause can be traced only in a small proportion of all PD cases, even when accounting those with a clear family history. There are, however, some populations that have clusters of mutations for PD. The G2019S mutation in the LRRK2 gene can be found in 37% of North African Arab and 23% of Ashkenazi Jewish familial PD populations, as well as in up to 16% of familial PD cases from Spain and Portugal, and 6%e7% of PD cases from Italy (Di Fonzo et al., 2005; Ferreira et al., 2007; Hulihan et al., 2008; Infante et al., 2006; Mata et al., 2006; Monfrini & Di Fonzo, 2017; Ozelius et al., 2006). Similarly, genetic variants of the GBA1 gene, the most common genetic risk factor for PD, can be found in up to 18% of Ashkenazi Jewish PD patients, as well as 15% of Dutch, and around 12% of French, Colombian, and Norwegian PD patients (den Heijer et al., 2020; Gan-Or et al., 2015; Lesage et al., 2011; Ruskey et al., 2019; Schapira, 2015). So far, mutations to more than 25 genes have been associated to either autosomal dominant of autosomal recessive familial forms of PD (Table 1.1). The problem of how to classify PD is a long one that has puzzled clinicians and researchers for many years (Calne, 1989). There is accumulating evidence from in vivo neuroimaging studies suggesting that patients with PD show distinct clusters of structural and functional changes, which may in turn reflect diverging patterns of pathological alterations (Horsager et al., 2022). The clinical observation of differences in the clinical presentation and progression of PD symptoms across patients has led to an ongoing and unresolved debate on which approach to use toward the definition of PD which, far from merely being philosophical, has extremely relevant implications on the selection of treatment targets and of the suitable population for clinical trials (Espay et al., 2017; Greenland et al., 2019). A further layer of difficulty comes when considering the pathological definition of PD. Definitive PD relies on the presence, in life, of a levodopa-responsive akinetic-rigid syndrome without red flags for alternative diagnoses, as well as on the pathological confirmation of a-synuclein deposition and

I. Introduction

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1. Parkinson’s disease and related disorders: the pursuit for reliable readouts and the role of neuroimaging

TABLE 1.1

List of genes associated with familial forms of Parkinsonism.

Gene

Locus

Phenotype

Inheritance

SNCA

4q22.1

Early or late onset typical or atypical PD with frequent dementia, pyramidal, and cerebellar signs.

AD

LRRK2

12.q12

Late-onset typical PD

AD

VPS35

16.q11.2

Late-onset typical PD

AD

GCH1

14q.22.2

Early-onset parkinsonism with dopa-responsive dystonia

AD

DNAJC13

3q22.1

Late-onset typical PD

AD

TMEM230 20p13p12.3

Late-onset typical PD

AD

RIC3

11p15.4

Early- or late-onset typical PD

AD

HTRA2

2p13.1

Late-onset typical PD

AD

GIGYF2

2q37.1

Late-onset typical PD

AD

CHCHD2

7p11.2

Early- or late-onset typical PD

AD

EIF4G1

3q27.1

Late-onset typical PD

AD

PTRHD1

2p21.3

Early-onset atypical PD with severe cognitive dysfunction and muscular weakness

AD

PODXL

7.32.3

Juvenile-onset levodopa-responsive parkinsonism

AD

DCTN1

2p13.1

Perry syndrome: early-onset atypical parkinsonism, with central hypoventilation, weight loss, insomnia, and depression

AD

ATXN2

12q24.12 Late-onset typical PD without dementia

AD

LRP10

14q11.2

Variants associated with late-onset PD and dementia

AD

GBA1

1q22

Association with late-onset typical PD and frequent dementia

AD

ATP1A3

19q.13.2

Associated with a rapid-onset dystonia parkinsonism

AD

Parkin

6q26

Early or juvenile typical PD with slow course, dystonia

AR

PINK1

1p31.12

Early or juvenile typical PD with slow course

AR

DJ-1

1p36.23

Early or juvenile typical PD with slow course

AR

PLAG26

22.13.1

Juvenile atypical parkinsonism with dementia, ataxia, pyramidal signs, and ocular disorders

AR

FBXO7

22q12.3

Rare juvenile, atypical parkinsonism with pyramidal signs and psychiatric features

AR

ATP13A2

1p36.3

Kufor Rakeb syndrome: parkinsonism, pyramidal signs, dystonia, supranuclear gaze palsy, dementia

AR

DNAJC6

1p31.3

Nonsense mutations: early or juvenile atypical parkinsonism with dementia, seizures, hallucinations, and epilepsy; missense and splicing mutations: early-onset typical PD

AR

I. Introduction

7

Clinical aspects of movement disorders

TABLE 1.1

List of genes associated with familial forms of Parkinsonism.dcont’d

Gene

Locus

Phenotype

Inheritance

VPS13C

15q22.2

Early-onset atypical parkinsonism with dementia and pyramidal signs

AR

SYNJ1

21q22.11 Early-onset atypical parkinsonism with dementia and epileptic seizures

AR

POLG

15q26.1

AR

Early- or late-onset atypical parkinsonism with dystonia, ophthalmoplegia, and peripheral neuropathy

AD, autosomal dominant; AR, autosomal recessive; ATP13A2, ATPase 13A2; ATP1A3, ATPase 1A3; ATXN2, ataxin 2; CHCHD2, coiled-coil-helix-coiled-coil-helix domain containing 2; DCTN1, dynactin 1; DNAJC6, DNAJ subfamily C member 6; DNAJC13, DNAJ subfamily C member 13; EIF4G1, eukaryotic translation initiation factor gamma1; FBXO7, F-box protein 7; GBA1, glucocerebrosidase 1; GIGYF2, GBR10 interacting GYF protein 2; GCH1, GTP cyclohydroxylase 1; HTRA2, HtrA serine peptidase 2; LRP10, LDL receptorerelated protein 10; LRRK2, leucine-rich repeat kinase 2; PD, Parkinson’s disease; PINK1, PTEN-induced kinase; PLA2G6, phospholipase A2, group 6: PODXL, podocalyxin like; POLG, DNA polymerase subunit gamma; PTRHD1, peptidylTRNA hydrolase domain containing 1; RIC3, acetylcholine receptor chaperone; SNCA, a-synuclein; SYNJ1, synaptojanin 1; TMEM230, transmembrane protein 230; VPS13C, vacuolar protein sorting 13C; VPS35, vacuolar protein sorting 35.

neuronal loss in the dopaminergic neurons of the substantia nigra pars compacta (Berg et al., 2014). However, the linearity between clinical history and synuclein pathology in Lewy bodies has been challenged by several studies on sporadic and familial cases of Parkinsonism (Gomez-Benito et al., 2020). Some familial forms (e.g., LRRK2 mutations) share similarities on their clinical manifestations but can be different in the presence and degree of a-synuclein brain deposition even in the same family (Ross et al., 2006; Zimprich et al., 2004). At the same time, patients carrying the same mutation in the SNCA gene may display individual differences in the age of onset, clinical presentation, and course, but on neuropathological examination present with a more homogenous widespread a-synuclein deposition in the brain (Spira et al., 2001; Zarranz et al., 2004). Familial PD has been defined therefore as the presence within a family with inheritance, of highly penetrant mutations in which most of the affected members meet the criteria for the diagnosis of clinical PD, removing the detection of a synucleinopathy at neuropathological examination (Berg et al., 2014). The role of neuroimaging in idiopathic PD and familial PD is discussed in Chapters 4, 5, 6, 7, and 8 of this book. A minority of patients presenting with Parkinsonism also display additional core symptoms, such as falls, early dementia, ataxia, oculomotor alterations, and dysautonomia. These patients are affected by a family of degenerative disorders, which are generally named atypical parkinsonism. These are sometimes challenging to distinguish from PD in the clinical practice, but, differently from PD, they have distinct presentations, progression, and neuropathology and require a different management approach. Dementia with Lewy bodies (DLB) is the second most common form of dementia in patients aged over 65, after AD (Vann Jones & O’Brien, 2014), but it is thought its real prevalence to be underestimated in the general population (Thomas et al., 2017). Cognitive impairment is the main clinical symptom of DLB and is characterized by confusion, deficit in attention, executive dysfunction, and visuospatial deficits (Vieira et al., 2011). Cognitive performances typically undergo marked variations within the same day, called fluctuations, which can sometimes resemble delirium, to which it should be differentiated (Morandi et al., 2017). Neuropsychiatric symptoms, such as depression and anxiety, and psychosis, are common in DLB. Visual hallucinations are complex and are characterized by vision of people or

I. Introduction

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1. Parkinson’s disease and related disorders: the pursuit for reliable readouts and the role of neuroimaging

animals. About 30% of DLB patients also report auditory hallucinations (Klatka et al., 1996). Parkinsonism is present in about 50% of DLB patients at diagnosis and in up to 85% during disease progression. Parkinsonism of DLB tends to be less severe than that of PD, but its responsiveness to levodopa therapy is scarce. Other symptoms of DLB are represented by hyposmia, RBD, autonomic dysfunction, and hypersensitivity to antipsychotics. Clinically, this picture strongly overlaps with Parkinson’s disease dementia (PDD), an endstage condition occurring in about 80% of cases of PD (Goetz et al., 2008), in which cognitive and behavioral symptoms ensue in a context of an established diagnosis of PD according to clinical criteria (Hely et al., 2008). Classically, apart from some differences in the clinical presentation of the core symptoms, e.g., cognitive impairment (Cagnin et al., 2015), these two entities are distinguished by the clinical criteria and the so-called one-year rule, an arbitrary threshold of one year between the onset of dementia and of Parkinsonism, which would identify a patient with DLB as opposed to PDD. This distinction is useful in the clinical practice but may not be precise in DLB for two reasons: first, in DLB, parkinsonism ensues on average 2 years after the onset of dementia (Ferman et al., 2011), and second, in about 15% of cases of DLB, Parkinsonian symptoms may never appear. Neuropathologically, both DLB and PDD are characterized by an increased cortical and subcortical deposition of Lewy bodies with DLB generally showing higher a-synuclein accumulation in the neocortex, transentorhinal and entorhinal cortex, amygdala, insula, and cingulate, as well as lower accumulation in subcortical areas such as the substantia nigra and the brainstem, compared with PDD (Gomez-Tortosa et al., 1999), although sometimes this difference may not be significant (Fujishiro et al., 2010). A strong neuropathological overlap is also seen when considering presence of AD-related pathology (amyloid-beta and tau deposition) in these conditions. DLB frequently presents, on neuropathological examination, amyloid pathology sufficient for a secondary neuropathological diagnosis of AD (Irwin & Hurtig, 2018). Similarly, up to 50% of PDD patients show a severe AD-like pathology (Irwin et al., 2012), and the extent of it can represent a major driver to conversion not PDD itself (Compta et al., 2011). When considering the different progression profile of these two entities and the differences in the pharmacological management, the issue of the clinicopathological distinction of DLB and PDD gains relevant clinical and management implications, and further clinical research is needed to understand similarities and differences between these two conditions. The role of neuroimaging in disentangling this problem is discussed in detail in Chapters 10 and 11 of this book. Multiple system atrophy (MSA) is characterized by autonomic failure accompanied by cerebellar or parkinsonian features, which prevalence defines the two clinical subtypes of cerebellar MSA (MSA-C) and parkinsonian MSA (MSA-P), although in clinical practice these two core signs significantly overlap in the same patient (Wenning et al., 2013). Onset is typically in the sixth decade, and the prevalence is of about 3/100,000 population per year (Bower et al., 1997), with an average survival of 6e10 years from diagnosis (Wenning et al., 2013). Autonomic dysfunction is an invariable and early symptom of MSA and is characterized by recurrent syncopes, orthostatic hypotension (complicated by a clinostatic hypertension), constipation, sexual dysfunction, cold extremities, and others (Coon & Singer, 2020). Cerebellar dysfunction in MSA manifests with gait and limb ataxia, dysarthria, nystagmus, and oculomotor abnormalities, whereas Parkinsonism in MSA is described as a symmetric and poorly levodopa-responsive akinetic-rigid syndrome, with tremor lacking the typical PD

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pill-rolling characteristics but rather being more a high-frequencyelow-amplitude tremor with, sometimes, a myoclonic component (Kollensperger et al., 2008). Other MSA symptoms include sleep abnormalities (a large majority of patients report a history of RBD), dystonia of the extremities and the orofacial region, frequent falls, dysphagia, pyramidal tract abnormalities, respiratory function alterations, and, although rarely at diagnosis, cognitive impairment characterized by frontoexecutive and visuospatial dysfunction (Brown et al., 2010). Neuropathologically, the presence of glial cytoplasmatic inclusions of a-synuclein in the oligodendroglia is pathognomonic for the disease (Trojanowski et al., 2007). This distinguishes MSA from other synucleinopathies, and although up to one MSA patient out of five can display Lewy body inclusions, this almost never is widespread (Homma et al., 2020, 2022; Miki et al., 2019). The role of neuroimaging in MSA is reviewed in Chapter 12 of this book. Progressive supranuclear palsy (PSP) is a primary tauopathy with a population prevalence of about 5/100,000, an age at onset over 50 years, and a mean survival after diagnosis of 5e8 years. Its defining symptoms are postural instability with falls, vertical supranuclear palsy, and progressive dementia. The first manifestations of PSP are unexplained and sudden backward falls, often leading to injury and hospitalization (McFarland, 2016). These are due to a combination of postural instability, parkinsonism with gait abnormalities, and progressive loss of postural reflexes. Parkinsonism in PSP is symmetric with axial predominance and poor response to levodopa (Hoglinger et al., 2017). Supranuclear gaze palsy, although not typical of this disorder, is most frequently seen in PSP and confers this clinicopathological entity its name. Patients have a limitation of vertical (downward more than upward) gaze, with involuntary horizontal saccades (square-wave jerks) and other oculomotor alterations. Dementia is characterized by alterations in attention and in shifting tasks. Frontal release signs, such as glabellar, snout, and grasp reflexes, are also frequent. Behavioral symptoms are also common in PSP and are characterized by depression, apathy, and dysphoria with disinhibition (Litvan et al., 1996). Emotional incontinence due to pseudobulbar palsy is also frequent (Strowd et al., 2010). The previous description is typical of the “classical” form of PSP, also called Richardson’s syndrome (PSP-RS), which represents by far the most common PSP clinical subtype. Other clinical subtypes can diverge significantly from this description. Parkinsonian PSP (PSP-P) presents with a predominance of parkinsonism over supranuclear gaze palsy, some response to levodopa, and a rather slower progression compared with PSP-RS; PSP-pure akinesia with gait freezing (PSP-PAGF) is characterized by predominant gait initiation difficulty and longer life expectancy compared with the other forms; PSP-corticobasal syndrome (PSP-CBD) presents with cortical sensory loss, dystonia, and speech apraxia; PSP-frontotemporal dementia (PSP-FTD) has a phenotype resembling a behavioral-type FTD with progressive emergence of PSP signs; PSP-cerebellar (PSP-C) with presence of cerebellar ataxia; PSP-oculomotor (PSPOM) shows predominant oculomotor signs; PSP-postural instability (PSP-PI), with frequent and predominant falls; PSP-frontal (PSP-F) featuring behavioral symptoms due to frontal lobe dysfunction; and PSP-primary lateral sclerosis (PSP-PLS), characterized by signs of degeneration of the upper motor neuron (Greene, 2019). Pathologically, PSP is a primary tauopathy characterized by its deposition in neurofibrillary tangles in a number of deep gray matter areas, and in astrocytes in characteristic inclusions called tufts (tufted astrocytes) (Kovacs, 2015).

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Corticobasal degeneration (CBD) is the rarest of the atypical parkinsonisms, with a prevalence of about 2/100,000, a disease onset at around 50e70 years, and a life expectancy after diagnosis of 6e8 years (Coyle-Gilchrist et al., 2016). CBD is characterized clinically by an asymmetric (often unilateral) parkinsonism with dystonia and ideomotor apraxia, and pathologically by 4-repeat tau deposition in the cortex and the basal ganglia. Common symptoms and signs of CBD are painful dystonia, ideomotor and speech apraxia, cortical sensory loss and cortical myoclonus, alien limb phenomenon, gait difficulty, postural instability with falls, and pyramidal tract involvement. Dementia is common but is usually a late feature of CBD (Graham et al., 2003). Some clinical variants can be encountered in CBD too and encompass a frontotemporal variant (characterized by predominant behavioral changes and frontal dysexecutive syndrome), a posterior cortical atrophy syndrome (with visuospatial disturbance, apraxia, and myoclonus), a progressive nonfluent/agrammatic apraxia (with marked apraxia of speech), and a PSP-like form with coexistence of CBD and PSP symptoms and signs (Armstrong et al., 2013). Pathologically, CBD is characterized by asymmetric cortical deposition of 4-repeat tau in neurons, glia, astrocytes, and corticobasal inclusions, while CBD typically lacks the tufted astrocytes usually present in PSP (Dickson et al., 2002). However, the clinical entity (corticobasal syndrome or CBS) only sometimes matches the neuropathological entity (CBD) (Armstrong et al., 2013). The role of neuroimaging in PSP and CBS is reviewed in Chapters 13 and 14, respectively, of this book.

Hyperkinetic movement disorders Hyperkinetic movement disorders encompass a broad family of involuntary degenerative and nondegenerative disorders of movement, with diverse characteristics and etiologies. Huntington’s disease (HD) is a devastating, progressive, autosomal dominant neurodegenerative disorder caused by a pathological expansion of a CAG trinucleotide repeat in the HTT gene. It is clinically characterized by the triad of a neuropsychiatric syndrome, a progressive movement disorder, and a progressive dementia. The neuropsychiatric symptoms may be the initial presentation and are the most burdensome symptom of HD (Wheelock et al., 2003). These include depression, apathy, and anxiety. Irritability is also common, with patients easily losing their temper and being very difficult to manage. Suicidal ideation is frequent, also in the premanifest stage of disease (Honrath et al., 2018). The principal movement disorder is a generalized chorea. Features on the neurological examination include motor impersistence (the inability of the patient to keep a sustained contraction) and parakinesia (the attempt of the patient to mask the involuntary movements into a purposeful one). Other motor symptoms include dystonia, gait impairment as well as, more rarely, tics and myoclonus. Ocular movement abnormalities (especially square jerks and slow-wave saccades) are also very frequent and may precede clinical diagnosis. Parkinsonism appears as an advanced, late-stage symptom of HD. Cognitive impairment can ensue early in the disease course, is slowly progressive, and includes dysexecutive problems, alterations of learning and planning, and social cognition. Typically, HD patients have a lack of insight of their cognitive symptoms. Other relevant symptoms are weight loss, sleep disturbance, and autonomic dysfunction (Goodman & Barker, 2010). The juvenile form of HD, also named Westphal variant, is characterized by a predominant parkinsonism with rigidity and tremor and almost

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no chorea (Quarrell et al., 2013). Other atypical symptoms of juvenile HD are ataxia, a high prevalence of obsessiveecompulsive disorder and psychosis, and seizures. The neuroimaging of HD is discussed in detail in Chapters 15 and 16 of this book. Other hyperkinetic movement disorders are characterized by either degenerative or nondegenerative causes. The most common hyperkinetic movement disorder is essential tremor (ET). ET is a nondegenerative syndrome characterized by an isolated, bilateral, action tremor typically of the upper extremities (Bhatia et al., 2018), which may or may not involve other body segments such as the lower extremities, the head, and the voice. Alcohol response (a transitory improvement of tremor after consumption of a small amount of alcohol) is a typical feature of ET that has some diagnostic utility in clinical practice but is not part of the definition of ET (Bhatia et al., 2018). When in combination with other signs, such as a dystonic posturing, or tandem gait impairment, the wording “ET-plus” is now used (Bhatia et al., 2018). In ET, tremor has a slightly faster frequency than parkinsonian resting tremor (4e12 Hz) and features a kinetic > postural component. Tremor generally worsens slowly over time, both in severity and in the number of body parts affected. There are a few red flags for an alternative diagnosis of ET toward other conditions, typically dystonia and PD. For example, unilaterality of the limb tremor or an isolated head and voice tremor, presence of parkinsonian signs, sensory trick, vocal spasm, or a rest or a task-specific tremor (Elble, 2022). Despite these distinctions, there is a recognized link between ET and both dystonia and PD, which may suggest some underlying pathophysiological commonalities. The role of neuroimaging in ET is discussed in Chapter 17 of this book.

Dystonia According to the Task Force of the International Parkinson’s and Movement Disorder Society, the definition of dystonia is of a “movement disorder characterized by sustained or intermittent muscle contractions causing abnormal, often repetitive, movements, postures, or both. Dystonic movements are typically patterned, twisting, and may be tremulous. Dystonia is often initiated or worsened by voluntary action and associated with overflow muscle activation” (Albanese et al., 2013). Dystonia is a complex movement disorder that can be present in isolation or in combination with other neurological symptoms, and that has opened the door to numerous classifications, according to the body segment affected, the clinical presentation, the etiology, and the inheritance pattern (Albanese et al., 2013). Its onset may range from the early childhood (where a genetic, detectable cause, and a progressive pattern is more likely) to adulthood (where the dystonic syndrome tends to remain more focal and tends not to progress (Grutz & Klein, 2021). As for the distribution, dystonia can be distinguished into focal dystonia, where only one body region is affected, as in the case of blepharospasm or cervical dystonia; segmental dystonia where two or more contiguous body segments are affected, as in the case of cranial dystonia; multifocal dystonia, where the body segments interested or may also be noncontiguous; generalized dystonia when the trunk and at least two other sites are affected; and hemidystonia when more regions are affected unilaterally as in the case of stroke. Dystonia can also be distinguished according to the temporal pattern: a dystonia continuously present at the same intensity is labeled persistent dystonia; if the intensity varies significantly

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throughout the day, it is present with diurnal fluctuations; if when the dystonic movements are present only in association of a specific task (e.g., exercise, playing an instrument), it is described as paroxysmal; and if triggered by external factors, it is described as actionspecific. Finally, dystonias are grouped according to the etiology (inherited or acquired) and to the copresentation (in isolation, or combined with other signs (e.g., dystonic tremor), or as part of a more complex neurological syndrome (e.g., with PD). A more comprehensive description of the clinical features of dystonias is provided here (Grutz & Klein, 2021). Neuroimaging of dystonia is reviewed on Chapter 19 of this book.

Restless legs syndrome Restless legs syndrome (RLS) is a frequent disorder, being found in up to 12% of the Caucasian population (Koo, 2015), characterized by an urge to move the legs (rarely other body segments), often associated with a sensorimotor discomfort of the affected limbs (Allen et al., 2005). This is triggered by rest and relieved by movement, and the symptoms are characteristically more intense or worsened in the evening or the night, although in severe or advanced cases, this symptom can also be present during daytime (Trotti, 2017). Together, these symptoms cause nighttime insomnia and severe daytime fatigue. Supporting features toward a clinical diagnosis of RLS represent the presence of a positive family history for RLS, a therapeutic response with dopaminergic medication, and the presence, on polysomnographic examination, of periodic leg movements (Benes et al., 2007). RLS can be present in about three-fourths of cases as idiopathic form and in the remaining cases in association with other medical conditions, most frequently iron deficiency, end-stage renal disease, pregnancy, and use of medications (Earley et al., 2011). Neuroimaging of RLS is discussed in detail in Chapter 18 of this book.

The future of neuroimaging and the application of robust measures in clinical trials The landscape of observational studies is shifting toward large, longitudinal cohort studies with multimodal assessments capturing in-dept imaging, genetic, clinical, and fluid data to identify robust biomarkers and readouts to design evidence-based interventional trials. For example, the Parkinson’s Progression Markers Initiative (PPMI) is a large multicenter, longitudinal, observational study employing multimodal imaging, genetic, fluid, and clinical assessments for the deep phenotyping of early PD patients, prodromal individuals at risk and healthy controls aiming to identify biomarkers of PD risk, onset, and progression (https://www.ppmi-info.org/). In HD, large observational studies, including TRACK-HD (Tabrizi et al., 2011, 2013), PREDICT-HD (NCT00051324), REGISTRY (NCT01590589), and Enroll-HD (NCT01574053), have enabled the characterization of phenotypic changes across disease stages. Initiatives such as the ENIGMA consortium (Bearden & Thompson, 2017) and the MRC Dementias Platform UK (DPUK; https://www.dementiasplatform.uk/) bring together large multicenter data sets, including imaging, clinical and genetic data, enabling big data analysis collaborations to understand disease mechanisms and pathophysiology across

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patient populations. Artificial intelligence methods, in large data sets, can help to identify specific disease patterns (Arabi et al., 2021; Arabi & Zaidi, 2020; Mei et al., 2021; Zaidi & El Naqa, 2021). Recent work by Mohan and colleagues highlights the application to machine learning techniques to characterize nine disease stages of increasing severity and presented a model to predict disease progression in HD (Mohan et al., 2022). Techniques such as these can help to improve the design of clinical trials and participant stratification. There are several hurdles to overcome for big data analysis research including the availability of multicenter clinical and imaging data for research purposes, regulatory and data governance, and security. A federated learning approach has been proposed to help overcome some of these challenges, whereby algorithms are shared to run analysis at a local level to avoid moving or sharing the primary data (Bron et al., 2022). With the urgent need for disease-modifying treatments in PD and related movement disorders, neuroimaging is increasingly used in clinical trials (Prasad & Hung, 2021). Neuroimaging has multiple potential applications for clinical trials from improving study design to offering robust outcome measures including efficacy and safety. Advanced analysis methods such as AmyloidIQ, TauIQ, and DATIQ quantify the level of target pathology with increased power and performance for AD (AmyloidIQ (Whittington et al., 2019) and TauIQ (Whittington et al., 2021)) and PD (DATIQ) over more traditional analysis methods, thus increasing the value of amyloid PET, tau PET, and DAT SPECT endpoints in clinical trials (https://invicro.com). Neuroimaging markers can be used for pathology-based phenotypic stratification aiming to improve cohort homogeneity. Some literature suggests that previous clinical trials could have failed due to heterogenous study populations with mixed pathological and clinical phenotypes (Gauthier et al., 2016). Stratifying a homogenous sample of patients designed for the target of interest could potentially demonstrate meaningful results. For example, the presence and degree of a-synuclein brain deposition varies across LRRK2 mutation carriers, in some cases even individuals within the same family (Ross et al., 2006; Zimprich et al., 2004). The development and application of an a-synuclein PET tracer would offer a breakthrough to stratify PD LRRK2 carriers depending on their a-synuclein status for enrollment clinical trials. Neuroimaging markers could help to identify biological changes, which precede clinical changes. The identification of a prominent prodromal and preclinical phase of PD has driven the focus of many clinical trials toward the earliest disease stage. Neuroimaging offers a valuable tool to aid the early identification of patients at risk in preclinical and prodromal stages and to predict clinical trajectories. Research studies are trying to identify and stratify these early disease stages where early intervention could produce a greater effect for disease modification (Hustad & Aasly, 2020; Meles et al., 2021). However, challenges remain in validating a reliable neuroimaging biomarker to identify preclinical and prodromal patients who will certainly develop PD. Due to the high sensitivity, neuroimaging-based outcome measures, most prominently PET with nanomolar sensitivity, could provide a stronger effect size, compared with clinical outcome measures, meaning fewer participants, thus improving the feasibility and economics of clinical trials (Valenzuela et al., 2011). However, neuroimaging outcome measures need to be selected carefully and validated; for example, imaging outcome measures should correlate with changes in clinical outcomes. Neuroimaging endpoints can also be affected by genetics, environmental interactions, and disease phenotypes; thus, it is important to understand these

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factors and obtain a multifaceted comprehensive clinical, genetic, and biological profile of patient populations. When using neuroimaging-based endpoints, the outcome measure and interpretation needs to be clear at the study design stage to enable unambiguous interpretation of trial findings. Using advanced analysis methodology can help to remove human bias, but these methods need to be applied with care, using the correct model, and ensuring the model assumptions are correctly met for the data set. While in-house analysis pipelines offer advantages in discovery and basic science research, for clinical trials, standard and validated analysis methods and software should be used to enable results to be independently validated and replicated (Valenzuela et al., 2011).

Conclusion This chapter introduces clinical aspects of PD and related movement disorders and highlights the application of neuroimaging in clinical trials. Continued developments in neuroimaging will enable the validation, and translation, of neuroimaging biomarkers and robust outcome measures from proof of concept and experimental medicine studies to clinical trials in PD and related disorders. Over the next decade, a multifaceted approach, encompassing neuroimaging, genetic, fluid, and clinical, is likely to play a central role in the development of precision disease models of PD and related disorders, including the use of artificial intelligence and advanced imaging analysis methodologies, to elucidate disease etiology and progression. These advances will help to drive the identification and translation of disease-modifying therapies and facilitate personalized medicine approaches. The role of neuroimaging in hypokinetic and hyperkinetic movement disorders is discussed in detail within dedicated chapters of this book.

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Ferreira, J. J., Guedes, L. C., Rosa, M. M., Coelho, M., van Doeselaar, M., Schweiger, D., Di Fonzo, A., Oostra, B. A., Sampaio, C., & Bonifati, V. (2007). High prevalence of LRRK2 mutations in familial and sporadic Parkinson’s disease in Portugal. Movement Disorders, 22, 1194e1201. Fujishiro, H., Iseki, E., Higashi, S., Kasanuki, K., Murayama, N., Togo, T., Katsuse, O., Uchikado, H., Aoki, N., Kosaka, K., Arai, H., & Sato, K. (2010). Distribution of cerebral amyloid deposition and its relevance to clinical phenotype in Lewy body dementia. Neuroscience Letters, 486, 19e23. Gan-Or, Z., Amshalom, I., Kilarski, L. L., Bar-Shira, A., Gana-Weisz, M., Mirelman, A., Marder, K., Bressman, S., Giladi, N., & Orr-Urtreger, A. (2015). Differential effects of severe vs mild GBA mutations on Parkinson disease. Neurology, 84, 880e887. Gauthier, S., Albert, M., Fox, N., Goedert, M., Kivipelto, M., Mestre-Ferrandiz, J., & Middleton, L. T. (2016). Why has therapy development for dementia failed in the last two decades? Alzheimers Dement, 12, 60e64. Goetz, C. G., Emre, M., & Dubois, B. (2008). Parkinson’s disease dementia: Definitions, guidelines, and research perspectives in diagnosis. Annals of Neurology, 64(Suppl. 2), S81eS92. Gomez-Benito, M., Granado, N., Garcia-Sanz, P., Michel, A., Dumoulin, M., & Moratalla, R. (2020). Modeling Parkinson’s disease with the alpha-synuclein protein. Frontiers in Pharmacology, 11, 356. Gomez-Tortosa, E., Newell, K., Irizarry, M. C., Albert, M., Growdon, J. H., & Hyman, B. T. (1999). Clinical and quantitative pathologic correlates of dementia with Lewy bodies. Neurology, 53, 1284e1291. Goodman, A. O., & Barker, R. A. (2010). How vital is sleep in Huntington’s disease? Journal of Neurology, 257, 882e897. Graham, N. L., Bak, T. H., & Hodges, J. R. (2003). Corticobasal degeneration as a cognitive disorder. Movement Disorders, 18, 1224e1232. Greene, P. (2019). Progressive supranuclear palsy, corticobasal degeneration, and multiple system Atrophy. Continuum (Minneap Minn), 25, 919e935. Greenland, J. C., Williams-Gray, C. H., & Barker, R. A. (2019). The clinical heterogeneity of Parkinson’s disease and its therapeutic implications. European Journal of Neuroscience, 49, 328e338. Grutz, K., & Klein, C. (2021). Dystonia updates: Definition, nomenclature, clinical classification, and etiology. Journal of Neural Transmission (Vienna), 128, 395e404. den Heijer, J. M., Cullen, V. C., Quadri, M., Schmitz, A., Hilt, D. C., Lansbury, P., Berendse, H. W., van de Berg, W. D. J., de Bie, R. M. A., Boertien, J. M., Boon, A. J. W., Contarino, M. F., van Hilten, J. J., Hoff, J. I., van Mierlo, T., Munts, A. G., van der Plas, A. A., Ponsen, M. M., Baas, F., … Groeneveld, G. J. (2020). A largescale full GBA1 gene screening in Parkinson’s disease in The Netherlands. Movement Disorders, 35, 1667e1674. Heinzel, S., Berg, D., Gasser, T., Chen, H., Yao, C., Postuma, R. B., & Disease, M. D. S. T. F. o. t. D. o. P. s. (2019). Update of the MDS research criteria for prodromal Parkinson’s disease. Movement Disorders, 34, 1464e1470. Hely, M. A., Reid, W. G., Adena, M. A., Halliday, G. M., & Morris, J. G. (2008). The Sydney multicenter study of Parkinson’s disease: The inevitability of dementia at 20 years. Movement Disorders, 23, 837e844. Hoglinger, G. U., Respondek, G., Stamelou, M., Kurz, C., Josephs, K. A., Lang, A. E., Mollenhauer, B., Muller, U., Nilsson, C., Whitwell, J. L., Arzberger, T., Englund, E., Gelpi, E., Giese, A., Irwin, D. J., Meissner, W. G., Pantelyat, A., Rajput, A., van Swieten, J. C., … Movement Disorder Society-endorsed, P. S. P. S. G. (2017). Clinical diagnosis of progressive supranuclear palsy: The movement disorder society criteria. Movement Disorders, 32, 853e864. Homma, T., Mochizuki, Y., Tobisawa, S., Komori, T., & Isozaki, E. (2020). Cerebral white matter tau-positive granular glial pathology as a characteristic pathological feature in long survivors of multiple system atrophy. Journal of the Neurological Sciences, 416, 117010. Homma, T., Mochizuki, Y., Tobisawa, S., Komori, T., & Takahashi, K. (2022). Tufted astrocyte-like glia in two autopsy cases of multiple system atrophy: Is it a concomitant neurodegenerative disorder with multiple system atrophy and progressive supranuclear palsy? Neuropathology, 42, 74e81. Honrath, P., Dogan, I., Wudarczyk, O., Gorlich, K. S., Votinov, M., Werner, C. J., Schumann, B., Overbeck, R. T., Schulz, J. B., Landwehrmeyer, B. G., Gur, R. E., Habel, U., Reetz, K., & Enroll, H. D.i. (2018). Risk factors of suicidal ideation in Huntington’s disease: Literature review and data from enroll-HD. Journal of Neurology, 265, 2548e2561. Horsager, J., Knudsen, K., & Sommerauer, M. (2022). Clinical and imaging evidence of brain-first and body-first Parkinson’s disease. Neurobiology of Disease, 164, 105626.

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C H A P T E R

2 Advances in magnetic resonance imaging Heather Wilson, Edoardo Rosario de Natale and Marios Politis Neurodegeneration Imaging Group and Mireille Gillings Neuroimaging Centre, University of Exeter, London, United Kingdom

Introduction The phenomenon of nuclear magnetic resonance, which laid the foundations for the development of magnetic resonance imaging (MRI), was first described by Bloch and Purcell in 1946 (Bloch et al., 1946; Purcell et al., 1946). Since then, technologically advances in MRI have paved the way for a rapidly growing field of research and clinical applications of MRI techniques. MRI is a noninvasive and safe technique, which only uses very small amount of energy, similar to energy in radiowaves, which is absorbed and emitted by the tissue of interest (Sobol, 2012). MRI does not affect any chemical processes, or the molecules themselves, within the body; rather the atomic nuclei within molecules provide a “report” of the local environment through their response to the electromagnetic energy applied by the MRI scanner. Hydrogen nuclei have the highest energy and therefore produce the largest signal at any given field strength (Grover et al., 2015). Thus, imaging with the hydrogen nucleus produces the highest resolution. The majority of clinical MRI techniques, and the techniques focused on in this chapter, target the hydrogen nuclei. However, in a research setting, other nuclei, such as sodium (23Na) and phosphorus (31P), can also be employed for MRI. The versatility of MRI stems from the range of ways in which MR signals can be manipulated and measured (McRobbie et al., 2017). A variety of different MR images can be acquired to measure a wide variety of properties within the brain, including neuronal activity, connectivity, macrostructural and microstructural alterations, iron levels, and perfusion. Advances in MRI hardware and software have led to the development of MRI scanners of increasing resolution, improved signal-to-noise ratio, reduced artifacts, and much more.

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Moreover, the evolution of analysis methodological approaches has enabled a range of different techniques to be applied to MRI data and the development of software packages to aid analysis and to standardize approaches. This chapter first introduces some basic concepts, principles of MRI physics, and MRI hardware, before going on to highlight key concepts for the acquisition, and analysis, of different MRI sequences. The concepts introduced in this chapter will lay the foundations of MRI discussed throughout this book and the application of these techniques across neurodegenerative movement, and related, disorders.

Basic MRI physics The fundamental principles of MRI are based on a property known as “spin” displayed by atomic nuclei, such as the hydrogen nucleus (1H), which consists of a single proton carrying a positive charge. The spin property of an atomic nuclei depends on the number of protons, which produce a nuclei’s positive charge. As a result of proton’s positive charge, protons are constantly spinning around their own axes. The moving electric charge generates a current, and an electric current generates a magnetic field. Therefore, a proton generates its own local magnetic field with north and south poles, which can be described as a dipolar magnet. Quantum mechanisms describe this dipolar magnet as comparable with classical mechanics of spinning objects with the dipole similar to a bar magnet, with magnetic poles aligning along its axis of rotation. When an external static magnetic field (B0) is applied, protons align either in parallel with (low-energy state) or perpendicular to (high-energy state) the external magnetic field (B0). Due to the rotation of an atomic nucleus, it has an angular momentum meaning it will rotate (or precess) around the axis of the external magnetic field (B0 axis). The term “spin” is often used to explain this quantum mechanical phenomenon. Precession is used to characterize the classical movement of a group of spinning protons in a static magnetic field (Currie et al., 2013). The velocity of the rotation around the B0 axis, also known as the precession frequency, is proportional to the field strength and is described by the Larmor equation (Deistung et al., 2017; Grover et al., 2015). The nuclear magnetic resonance phenomenon, first described in 1946 by Bloch and Purcell in work which was awarded the Nobel Prize for Physics in 1952 (Bloch et al., 1946; Purcell et al., 1946), is based on the precession of a nuclear spin when it is immersed in a static magnetic field. As a result of proton precessing in opposite directions (parallel and antiparallel to B0), they cancel each other out resulting in the sum magnetization (M0), also referred to as longitudinal magnetization. When a transient magnetic field (B1), referred to as a radiofrequency (RF) pulse, is applied perpendicular to a static magnetic field (B0), nuclei that possess a net nuclear spin change their energy state to become excited in a high energy state perpendicular to B0 (McRobbie et al., 2017). RF pulses take their name from the frequency of their electromagnetic energy, which lies within the megahertz (MHz) range of radiowaves. Upon absorption of the RF energy, nuclei transition from high to low energy levels, and vice versa on relaxation. When RF pulses stop, each nucleus relaxes, thus returning the system to thermal equilibrium. The energy required to induce the transition from high and low energy states of the nuclear spin is

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T1, T2, and T2* relaxation

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dependent on the strength of the static magnetic field (B0). The transfer of energy from the RF pulse to protons can only occur when the RF pulse has the same frequency as the processional frequency of the protons, i.e., the Larmor frequency (McRobbie et al., 2017). This phenomenon is called resonance. In a static magnetic field strength (B0), all nuclei precess at the same frequency, i.e., the spins resonate at the Larmor frequency meaning the spatial positioning of MR signal cannot be determined. Therefore, in addition to the static magnetic field (B0) and RF pulses (B1 field), spatial gradients of magnetic field (G) are also required to enable spatial localization of the MR signal. Using three sets of gradient coils (Gx, Gy, and Gz), a gradient can be applied in any orthogonal direction along the gradient coils (Grover et al., 2015; McRobbie et al., 2017). A gradient filed will add or take away from the static magnetic field at different points along the x-, y-, and z-axis. Therefore, applying gradients means that the field strength exerted on each nuclei depends upon its location within the tissue being studied. For example, Gx is a linear gradient along the x axis, which produces an extra field at point X (GxX). Together, the three gradient coils determine a unique point in three-dimensional space (x, y, z), which is commonly used to describe the location of a region of interest in MRI. Protons precessing faster or slower, depending upon their position, are detected as higher or lower frequencies in MR signal. Therefore, frequency measurements can be used to differentiate MR signals at different positions in space.

T1, T2, and T2* relaxation RF pulses have two main effects on protons (McRobbie et al., 2017). Firstly, an RF pulse reduces the overall longitudinal magnetization due to protons with spinning in opposite directions, which cancel each other out. Secondly, an RF pulse causes protons to move in the same direction at the same time, a process called in phase. Protons moving in phase result in transverse magnetization. This transverse magnetization is a moving magnetic field, which moves in line with the precessing protons at the Larmor frequency. When the RF pulse is turned off, protons return to a lower energy state, falling out of phase with each other, in a process called relaxation. There are two types of relaxation called transverse and longitudinal relaxation depending on the relaxation of transverse magnetization or longitudinal magnetization, respectively. Longitudinal and transverse relaxation, which can occur simultaneously and independently, are described by the time constants T1 and T2, respectively, which form the basis for T1-weighted and T2-weighted imaging (Currie et al., 2013). Longitudinal relaxation, also known as T1 relaxation, of photons occurs when an RF pulse is switched off and protons move from a high energy to low energy state restoring longitudinal magnetization along the Z axis (Currie et al., 2013; McRobbie et al., 2017). The rate of T1 relaxation is dependent on the rate of the molecular motion, known as the tumbling rate. The tumbling rate, and therefore the T1 relaxation rate, varies between molecules. The exchange of T1 relaxation energy, and hence its time, is most effective when the fluctuating magnetic field, generated when molecules tumble, is close to the Larmor frequency. For example, free water that has a small molecular size has a high tumbling rate and so it is not effective at T1 relaxation. Therefore, free water has a long T1 relaxation; thus, a long T1 value appears dark on T1-

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weighted images. Fat resonates with frequencies close to the Larmor frequency allowing effective transfer of energy. Therefore, fat has a short T1 value appearing bright on T1-weighted images. T1 relaxation is a continuous process, which when plotted against time, after the RF pulse is switched off, produces an exponential curve called the T1 curve. The T1 value is calculated as the length of time taken, following an RF pulse, for the system to return to approximately 63% of thermal equilibrium as an exponential function of time. Therefore, T1 can be manipulated by varying the time between the RF pulse, known as the repetition time (TR). Transverse relaxation occurs when protons fall out of phase in the X-Y plane resulting in decreased transverse magnetization (Currie et al., 2013; McRobbie et al., 2017). Transverse relaxation can be the result of either T2 relaxation or T2* (T2 star) relaxation (Fig. 2.1). The small magnetic fields generated by the spin of each proton can influence neighboring nuclei resulting in random fluctuations of individual proton’s Larmor frequency. This results in some proton exchanging energy; therefore, protons are not all moving in the same direction, which leads to a loss of phase coherence (Currie et al., 2013). This process, arising from internal inhomogeneity of spins, is called T2 relaxation or sometimes referred to as spinespin relaxation. T2 relaxation is random and occurs without the overall system losing energy, i.e., without the whole system returning to equilibrium. This contrasts with T1 relaxation in which energy transfer from the spin system occurs. Therefore, T2 relaxation is typically

FIGURE 2.1 Schematic illustration of a free induction decay, spin echo, and gradient echo. (A) A radiofrequency (RF) pulse, such a 90 degrees excitatory RF pulse, generates a free induction decay (FID) signal immediately after it is applied which decays with time constant T2* reflecting T2 relaxation plus increased signal loss due to magnetic field inhomogeneities. The amplitude of each echo is progressively smaller due to T2 decay. (B) Applying a second RF 180 degrees RF pulse regenerates spins, which have dephased due to static magnetic field inhomogeneities.

I. Introduction

The magnet

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faster than T1 relaxation with T2 values less than or equal to T1 values. Inhomogeneities within B0 can also result in dephasing as a result of differing Larmor frequencies of protons at different locations with the magnetic field. The combination of T2 relaxation and the dephasing due to inhomogeneity in B0 is referred to as T2* relaxation.

Free induction decay When an RF pulse is turned off, protons begin to lose phase coherence, resulting in an exponential decrease of the signal intensity to zero. This signal decay is called free induction decay (FID) (Currie et al., 2013). FID signal has a resonant frequency, which takes the form of a sine wave, which is at its highest immediately after the RF pulse is switched off and decreases as relaxation occurs (Fig. 2.1A). An RF pulse at any flip angle can create an FID because some protons in longitudinal magnetization are tipped into transverse magnetization. FID is also disrupted by dephasing of the magnetic field gradients. In practice, FID is not measured directly using MRI; instead, the MR signal is measured in the form of an echo such as a spin echo (SE) or a gradient echo (GRE).

MRI scanner hardware An MRI scanner is composed of a set of magnet coils including the main magnet coils, gradient coils, integral RF coils, and shim coils, and for neuroimaging MRI scans, an RF head coil is also used to improve the signal-to-noise ratio. The room in which the MRI scanner is housed is composed of special copper-lined walls to create a Faraday shield to keep the scanner RF inside and potential sources of electromagnetic noise out.

The magnet The principles of the Maxwell equations laid the foundations of the development of magnetic coils for MRI scanner. The Maxwell equations state that when an electric current flows through a wire, a magnetic field is induced around the wire. By cooling the magnetic conductor, the resistance to the flow of the electric current becomes close to negligible, therefore allowing the use of high electric currents to produce high-strength magnetic fields with little heat disposition. This principle has been employed to develop superconducting magnets, used in MRI scanners, with field strengths (B0) ranging from 0.5 Tesla (T) to 3 or 4 T, with new scanner reaching as high as 7T and even 10.5T (Nowogrodzki, 2018). The field strength of MRI magnets is significantly higher than the earth’s magnetic field strength, which is equivalent to 5  10 5 T. The main magnet of an MRI scanner is cooled to temperatures close to absolute zero ( 296 C) using a liquid helium cooling system (Jacobs et al., 2007). Higher field strengths provide improved signal-to-noise ratio, higher spatial and temporal resolution, and subsequently improved quantification. However, there are some disadvantages

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with higher field strengths that need to be taken into consideration such as magnetic susceptibility, eddy current artifacts, and magnetic field instability (Di Costanzo et al., 2003; Soher et al., 2007).

Radiofrequency coils The RF coils in an MRI system have two main roles; firstly, to transmit RF energy to the tissue, and secondly to receive the weak nuclear magnetic resonance signals generated by the tissue during a scan. In the context of neuroimaging, a separate RF receiver head coil is placed around the participants head to maximize the signal detected from the brain tissue. Advances in the RF coils have increased the signal-to-noise ratio. The MR signals detected by the receiver coils are sent to a computer system where they are processed using a series of mathematical algorithms to produce an image (Hahn, 1950). Head coils are usually coil arrays rather than single coils, allowing for increased sensitivity and more flexibility in geometry. Head coil arrays can increase the sign-to-noise ratio, thus reducing noise in an image. Head coils are also used to aid parallel or accelerated imaging to reduce scan time, as discussed in the following. Multiple head coils within a head coil array coil are often referred to a channel. Therefore, head coils are often referred to as the number of the channels within the head coils, e.g., 32-channel head coils. The most common head coils used are those with 8, 12, 16, 32, or 64 channels. The presence of more channels allows for accelerated imaging and higher signal-to-noise ratios.

Gradient coils Gradient fields are created by three electromagnets, called gradient coils, which are positioned left-to-right (x-axis), anterior-to-posterior (y-axis), and superior-to-inferior (z-axis) of the patient. Gradient coils generate a gradient magnetic field, which allows spatial localization and encoding of the MR signals, generated by the combination of B0 and B1, through a series of mathematical equations using Fourier analysis.

Shim coils The presence of an object, such as a head in the context of neuroimaging, within the static magnetic field (B0) of an MRI scanner creates local susceptibility effects and inhomogeneities. A uniform magnetic field is optimal for better MR signal. Therefore, local susceptibility effects and inhomogeneities can introduce artifacts into the MR image. Shim coils are used to minimize nonuniformities in the B0 field. Shimming can be active or passive. When an MRI magnet is installed, sheets at the bore of the magnet, close to the RF and gradient coils, are installed for passive shimming. Active shimming can be achieved using shim coils, which are activated by electric currents controlled by computer software and the MRI operator.

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Image acquisition

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Image acquisition MR signal intensity can be influenced by a number of factors such as proton density, blood flow, pulse sequence, TR, echo time (TE), inversion time (TI), magnetic susceptibility, T1, and T2 (Currie et al., 2013; McRobbie et al., 2017). The timing of parameters such as TR and TE, which are set by the operator, determines the desired image contrast of the resulting MR image. TR represents the time between consecutive 90 degrees RF pulses and affects the time to acquire an image. Long TR allows recovery of longitudinal magnetization; therefore, limiting T1 effects, long TR is typically used in T2-weighted imaging. Conversely, short TR allows differences in longitudinal magnetization to develop before the next 90 degrees excitation pulse; therefore, T1-weighted imaging typically uses short TR. TE represents the time from the center of the RF pulse to the center of the echo. T2 effects are limited by a short TE while with a long TE, T2 becomes more prominent. For sequences with multiple echoes between each RF pulse, several echo times are defined. The amount of rotation experience by the net magnetization after an RF pulse is referred to as the flip angle. An RF pulse that moves the net magnetization through exactly 180 degrees is called a 180 degrees pulse, while a 90 degrees RF pulse moves the net magnetization by 90 degrees into the transverse plan. The flip angle is influenced by the strength of the RF magnetic field and the duration of the pulse. If the RF pulse is left on for twice as long, or double the strength of the RF is applied, the net magnetization will move through exactly 180 degrees. In practice, to keep the MRI scan time at a minimum, the strength of the RF pulses is typically changed to produce different flip angles rather than increasing the duration of the RF pulse. Partial flip angles, commonly employed in GRE sequences to minimize signal loss from saturation effects, refer to RF pulses that tip longitudinal magnetization by less than 90 degrees into the x-y plane (Mills et al., 1987). The signal in an MR image refers to the brightness of a voxel within the image and is a measure of the nuclear magnetic resonance signal detected by the scanner receiver coils. All voxels within an MR image will contain a mixture of signal and noise. Noise can be introduced to an image from the electronic noise of the scanner as well as the electrically conducting tissue of a patient. The signal-to-noise ratio refers to the ratio between the signal intensity and the noise levels. Images with low signal-to-noise ratio will appear fuzzy and could be missing small details. Subtle contrast changes can also be affected by noise, giving rise to the terminology contrast-to-noise ratio (CNR). The signal-to-noise ratio can be improved by increasing slice thickness; however, thicker slices reduce contrast in a potential lesion. MR images can also be affected by artifacts such as inhomogeneity artifacts caused by hardware imperfections and susceptibility effects; motion artifacts from subject movement during a scan; phase wrap-around artifacts from the field-of-view set too small; gradient nonlinearity distortions caused by imperfections in gradient coils; and ghosting (Di Costanzo et al., 2003; Soher et al., 2007). Inhomogeneity artifacts typically cause alternations in signal intensity and image distortion, while motion artifacts can give a ghost-like appearance across the phase-encoding direction. Eddy current artifacts can also be introduced, from the rapid switching of gradients, and are most prominent in diffusion-weighted imaging (DWI) where preprocessing corrections are applied to correct for eddy currents (Andersson & Skare, 2002; Haselgrove & Moore, 1996; Rohde et al., 2004).

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Spin echo and gradient echo There are two main types of pulse sequences, which contain RF and gradient pulses, known as SE and GRE (Currie et al., 2013). SE sequences use two RF pulses to create the SE with the second RF pulse regenerating spins, which have dephased due to static magnetic field inhomogeneities (Fig. 2.1B). GRE sequences use one RF pulse, typically less than 90 degrees, and a subsequent gradient pulse with causes protons to rapidly dephase along the direction of the gradient, which results in a rapid decline in the FID signal. Applying a second magnetic field gradient in the opposite direction to the first gradient pulse causes the loss of phase coherence to be revered. Combined, this results in protons moving back into phase and creates the GRE. In GRE sequences, the TE is defined as the time from the beginning of the FID, after the initial RF pulse, to the maximum amplitude of the GRE. Both SE and GRE sequences can produce T1-, T2-, and proton densityeweighted images; and the echo measures the signal intensity. The influence of inhomogeneities in the magnetic field (T2*) is greater in GRE sequences, since the SE sequence compensates for magnetic field inhomogeneities by allowing protons to rephase. Therefore, GRE sequences are typically more affected by susceptibly and chemical shirt artifacts. Unlike SE sequences, GRE sequences do not need to wait for proton rephasing after the RF pulse; thus, GRE sequences typically have shorter TE. The majority of GRE sequences use an initial excitation pulse with a flip angle of less than 90 degrees. The use of a smaller flip angle means a larger amount of longitudinal magnetization remains ready for a next repetition, so a short TR can be used. Therefore, GRE sequence typically has shorter scan times compared with SE sequences. GRE sequences form the foundations for most fast imaging techniques due to the combination of low flip angle excitations, short TR, and short TE values (Elster, 1993; Felmlee et al., 1989; Winkler et al., 1988).

Spatial localization gradients By adding spatial localization gradients, using gradient coils (Gx, Gy, and Gz), to SE and GRE sequences, the spatial localization of the MR signal is incorporated in the pulse sequences (Currie et al., 2013; McRobbie et al., 2017). The application of gradients temporarily changes the resonant frequencies of protons along a specified direction. The slice-encoding gradient (Gz) determines which protons get excited, phase-encoding gradient (Gy) is used to generate phase shifts, and the frequency-encoding gradient (Gx) is used to alter the precession frequency during the echo (Fig. 2.2). Slice-encoding gradient, along the z-axis, alters the strength of the magnetic field (B0) so that protons in a single slice, within the gradient field, temporarily change their resonant frequencies. The RF pulse is usually applied in a range of frequencies, known as bandwidth, to excite protons within a specific slice. The thickness of slices is influenced by the bandwidth of the RF pulse and the steepness of the gradient field. Applying a phase-encoding gradient, along the y-axis, causes protons to process at different frequencies compared with other protons depending on their position within the gradient. When the gradient is switched off, protons return to their original precession frequencies; however, the protons accumulate a phase shift, so they are no longer in phase. The phase shirt is proportional to the strength of the gradient and the duration of time the gradient was

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Inversion recovery

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FIGURE 2.2 Schematic illustration of spatial encoding gradients for slice encoding along the z-axis, frequency encoding along the x-axis, and phase encoding along the y-axis.

applied. This phase shift enables the position of protons along the y-axis, within a slice, to be specified. Applying a frequency-encoding gradient along the horizontal x-axis results in protons along the x-axis, which all resonate at different frequencies relative to their position along the gradient, therefore enabling the position of protons along the x-axis to be specified. In a 2D image, frequency encoding is typically used for slice selection to define the position of entire slices, while in a 3D image, slice selection is based on phase encoding. In 3D imaging, two additional pulses are used to counteract any dephasing of the transverse magnetization. One pulse is applied immediately after the slice selection gradient and one immediately before the frequency-encoding gradient.

Inversion recovery Inversion recovery (IR) techniques are a variation of SE sequences which have an extra 180 degrees pulse separated by a timing parameter called inversion time (TI) before the 90 degrees pulse (x-y or transverse plane). The most common IR sequence in neurological disorders is called fluid-attenuated inversion recovery (FLAIR). FLAIR is a T2-weighted image with suppression of the cerebrospinal fluid (CSF) signal, which can be utilized to visualize lesions close to the ventricles (Pikus et al., 2006; Fig. 2.3). The inversion time (TI) is selected based on the T1 value of the CSF, with the TI set to about 70% of the T1 of the CSF. Any tissue types with a T1 value similar to the CSF will also be suppressed.

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FIGURE 2.3 Example of T1-weighted, T2-weighted, and inversion recovery images. The magnetizationprepared rapid gradient echo sequence (MPRAGE; Mugler & Brookeman, 1991) is one of the most commonly used gradient echo T1-weighted 3D sequences for structural anatomy due to its good gray and white matter contrast and relatively short scan times. In the T1-weighted MPRAGE, cerebrospinal fluid (CSF) appears dark, while in the T2weighted image, the CSF is very bright. T2-weighted image is commonly utilized to identify lesions within the brain such as demyelination. The T2 fluid-attenuated inversion recovery (FLAIR) is an example of a common T2-weighted spin echo inversion recovery image, with longer TE and TR. T2 FLAIR is commonly used in neuroimaging for highlighting pathological lesions (Pikus et al., 2006). The fast gray matter T1 inversion recovery (FGATIR; Sudhyadhom et al., 2009; Tanner et al., 2012) is an example of a T1-weighted with a short inversion time to nullify the white matter signal so the white matter appears dark, while the CSF is bright. The FGATIR image provides enhanced contrast for subcortical structures such as the globus pallidus internal and external, red nucleus, subthalamic nuclei, and the substantia nigra pars reticulata.

Image reconstruction A pulse sequence, composed of slice section, frequency-encoding, and phase-encoding gradients, is repeated multiple times to acquire sufficient phase-encoding information for an echo signal to be assigned to each location with a slice. During each repetition, the slice section and frequency encoding are kept the same while the phase-encoding gradient is increased by equal steps to create multiple signal echoes. Therefore, the phase-encoding step takes a significant amount of time to sample. A temporary virtual space, known as kspace, is used to store the raw data matrix of spatial frequencies from the image acquisition (McRobbie et al., 2017). In k-space, the raw data matrix is an array of numbers, which represent the spatial frequencies with lower frequencies closer to the center and higher frequencies toward the periphery. A series of mathematical processes, known as Fourier transformations, are applied to reconstruct an MR image (Ernst, 1975). Each point in k-space contains spatial frequency and phase information about every voxel in the final MR image, and each voxel in an MR image maps to a point in k-space, although points in k-space and voxels in the MR image do not correspond directly to each other. Voxels in an MR image, and the raw data matrix in k-space, are organized into rows and columns known as a matrix, for example, 256  128. Typically, the larger number in the matrix refers to the frequency-encoding matrix and the smaller number to the phase-encoding matrix. MR images also have a third dimension for the slice thickness. If the frequency encoding is set at 256, then each MRI echo will contain 256 columns in k-space, and if the phase encoding is set at 128, then the rows in kspace will have 128 echoes.

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Echoplanar imaging and turbo spin echo

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The scanner receiver boards contain an analog-to-digital converter, which samples frequency encoding (McRobbie et al., 2017). Therefore, the frequency-encoding sampling rate is determined by the analog-to-digital converter of the scanner, whereas the phaseencoding sampling rate is determined by the magnitude of shift in k-space dictated by the phase-encoding gradient. In SE and GRE sequences, one line of k-space represents one phase-encoding step, while in turbo spin echo (TSE) sequences, multiple lines of k-space can be acquired for each TR (Currie et al., 2013). Parallel imaging was developed to overcome the phase-encoding sampling rate aiming to reduce image acquisition time. Image resolution is determined by the highest spatial frequency sampled in k-space, known as Kmax, with Kmax inversely proportional to the image resolution. Isotropic 3D data sets comprise of voxels whose dimensions are the same in each direction, such as 1  1  1 mm; therefore, the image has the same resolution in any direction. Anisotropic describes voxels of different sizes in different directions, such as 1  1  3 mm. The field-ofview is determined by the sampling rate so that the spacing between k-space points in each direction and is inversely proportional to the field-of-view in that direction. If the spacing between k-space points is deceased in the x-direction, the field-of-view in the ydirection of the MR image will increase. Frequency encoding can be acquired along any of the three anatomical axes (superiore inferior, righteleft, or anterioreposterior). The frequency encoding is usually set along the longest anatomical axis to minimize the effects of phase wraparound artifacts from phase encoding on the shortest axis. For example, for a coronal neuroimaging scan, the frequency-encoding direction is set along the superior-inferior direction.

Echoplanar imaging and turbo spin echo Echoplanar imaging (EPI) and TSE sequences allow for significantly increased speed of data acquisition compared with SE and GRE sequences. EPI and TSE are commonly employed in DWI. The EPI method was introduced by Mansfield and colleagues in 1977 (Mansfield, 1977; Ordidge, 1999). EPI is based on the discovery that in addition to the frequency, the timing of signals at the same frequency, known as the phase, could be used to encode a second dimension. EPI enables both frequency and phase encoding to be observed simultaneously. Utilizing rapidly switching gradients allows the entirety of k-space, within one SE, to be acquired. In single-short EPI, a rapid GRE sequence, all phase-encoding steps can be acquired within a single TR. In multishort EPI, a rapid SE sequence, all phase-encoding steps are acquired within a few TRs. In TSE sequences, multiple 180 degrees pulses are applied following the initial 90 degrees RF pulse, with each 180 degrees pulse generating an echo, which creates a line of k-space. TSE sequences usually have lower susceptibility artifacts and image distortion compared with EPI sequences.

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Parallel imaging Parallel imaging can be employed with almost any pulse sequence without affecting the image contrast or signal-to-noise ratio. The majority of parallel imaging are in-plane methods such as sensitivity encoding (SENSE (Pruessmann et al., 1999)) or generalized autocalibrating partially parallel acquisition (GRAPPA). GRAPPA is a variation of simultaneous acquisition of spatial harmonics (SMASH (Sodickson & Manning, 1997)) but without the need for a separate coil sensitivity calibration acquisition due to the acquisition of additional lines of k-space (Griswold et al., 2002). In parallel imaging, the number of phase-encoding sampling steps is reduced by an acceleration factor, which typically ranges from 1.5 to 3. For example, an acceleration factor of 3 means only a third of k-space is sampled; therefore, the acquisition time is reduced threefold. The acceleration factor is limited by the number of receiver channels in the receiver coil array. Simultaneous multislice methods, such as multiband imaging, rely on using multiple coils in the head coil array being used in parallel allowing for the initial RF pulse to simultaneously excite 2 or more slices (Barth et al., 2016; Feinberg & Setsompop, 2013; Lee et al., 2005; Weaver, 1988). Multiband excitation methods combine slice excitation at different offresonance frequencies with demultiplexing based on the spatial sensitivity information of the receiver coil array (Larkman et al., 2001). Both simultaneous multislice and parallel imaging methods are designed to reduce acquisition time.

MRI modalities The acquisition parameters of MR images are constantly evolving with novel acquisition techniques and analysis methodologies driving the field forward enhancing the ability to extract quantitative measures of brain properties in physiological conditions and disease states. This section will focus on the most used MRI modalities, to date, in the field of neuroimaging research in movement disorders. MRI data can be affected by partial volume effects, which arise from the limitations of spatial resolution with respect to the structures of interest, which are often smaller than the resolution of the MR image (McRobbie et al., 2017). Therefore, some voxels contain a mixture of tissue types, such as some gray matter as well as some neighboring white matter or CSF. Therefore, the resulting signal from these voxels is a weighted average, of the signal from each tissue type, which is proportional to the fraction of the volume associated with each tissue type. Partial volume effects can affect how accurately a structural image is segmented into tissue types as well as into anatomical regions. Spatial smoothing is often applied to MRI data, most commonly to resting state functional MRI (rs-fMRI) data. Spatial smoothing creates a local weighted average, which acts to improve the signal-to-noise ratio by reducing the amplitude of noise and making the distribution of noise more Gaussian, which can improve statistical sensitivity. The degree of smoothing is defined by the full width at half maximum (FWHM) in millimeters, which sets the size of the Gaussian used to determine the local weights for the local averaging. While smoothing can improve the signal-tonoise ratio, large FWHM value can result in loss of sensitivity to detect changes in small

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T2-weighted MR imaging

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regions of interest. Therefore, FWHM is typically set at 1.5e2 times the voxel size to balance the benefits to the signal-to-noise ratio without compromising the sensitivity to changes within small regions.

T1-weighted MR imaging T1-weighted imaging refers to images in which the signal intensities between different tissues are predominantly explained by T1 relaxation times. T1-weighted images can be produced from either SE or GRE sequences. For SE sequences, T1-weighted images typically have a short TE and a short TR to enhance the T1 contrast between tissue. Short flip angles decrease the T1 contrast; therefore, for GRE sequences, a large flip angle and a short TE are often used for T1-weighted images. In T1-weighted images, CSF is very dark, while white matter is very bright and gray matter appears gray (Fig. 2.3). Areas of inflammation, such as infection or demyelination, appear dark on T1-weighted images. T1-weighted imaging produces structural MR images of the brain, which are frequently employed to study gross anatomy and macrostructural changes. Quantitative parameters, such as volumetric measures and cortical thickness, can be derived from T1-weighted images. Software packages such as FreeSurfer image analysis suite are commonly used for cortical surface modeling and measurements of cortical thickness (http://surfer.nmr.mgh.harvard. edu; Fig. 2.4F). Tissue-type segmentation of T1-weighted images allows normal appearing gray matter, white matter, and CSF to be segmented based on voxel intensities (Fig. 2.4BeE). Voxel-wise analysis methods of tissue volumes can be performed using voxel-based morphometry (VBM) methods aiming to determine anatomical differences in a voxel-wise manner. Toolboxes in SPM software (Statistical Parametric Mapping; http://www.fil.ion. ucl.ac.uk/spm/) and FSL (FMRIB Software Library; http://fsl.fmrib.ox.ac.uk/fsl/) are commonly used for VBM analysis. T1-weighted images are also frequently employed to aid the analysis of other modalities such as DWI, rs-fMRI, and PET data, for coregistration and normalization purposes, as well as to help delineate and define anatomical regions of interest (Fig. 2.4G).

T2-weighted MR imaging GRE and SE sequences can be used to acquire T2-weighted images. For SE sequences, a long TR and TE are required, while for GRE sequences, a small flip angle and a long TE are required for T2-weighted images. Magnetic field inhomogeneities, resulting in faster T2* relaxation, are promoted by susceptibility differences, which results in signal loss on GRE images. Conversely, the 180 degrees refocusing pulse in SE sequences compensates for this. T2*-weighted GRE sequences are often employed for susceptibility imaging of iron deposition. T2* decay starts from the excitation pulse and progresses with time. Therefore, T2*-weighted sequences typically have a longer TE allowing for greater signal loss, and a low flip angle and long TR to reduce the influence of T1. In T2-weighted images, CSF gives the brightest signal, while white matter is dark and gray matter is light gray (Fig. 2.3).

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FIGURE 2.4 Illustration of tissue-type segmentation, cortical thickness, and segmentation of anatomical regions of interest. (A) Original T1-weighted MPRAGE image; (B) gray matter (blue) and white matter (red) tissue segmentations; (C) white matter; (D) gray matter; and (E) cerebrospinal fluid maps from tissue-based segmentation of T1-weighted brain image; (F) FreeSurfer segmentation of gray and white matter to measure cortical thickness on a T1weighted MRI; (G) examples of regions of interest segmentation using FreeSurfer (top), multiatlas propagation with enhanced registration (MAPER) (Heckemann et al., 2010) (middle), and manual delineation of the striatum and globus pallidus (bottom).

Inflammation, such as infection or demyelination, appears bright on T2-weighted images. T2weighted MRI is commonly used to identify pathological lesions. For example, T2 lesions represent current or past inflammation in multiple Sclerosis (Rudick et al., 2006).

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Diffusion-weighted imaging

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Diffusion-weighted imaging DWI is based on quantifying the movement of water molecules, which diffuse following the principle of Brownian motion. Without restrictions, water molecules move equally and randomly in all directions; this is referred to as isotropic (Fig. 2.5). For isotropic diffusion, for example, in the CSF, the radius of the spherical range of motion defines the probability of motion in any given direction. In a restricted environment, such as white matter tracts, the movement of water molecules is confined by the physical surroundings forming an elliptical range of motion, referred to as anisotropic, which is not equal in all directions. The origin of diffusion-weighted sequences can be traced to the pulsed gradient SE technique, developed in the 1960s (Stejskal & Tanner, 1965), with a single-shot EPI readout. The main difference from structural imaging techniques is the application of diffusion-encoding gradients. Diffusion-sensitizing gradients, applied either side of the 180 degrees pulse, cause diffusion spins to be dephased with the first pulse and rephased with the second pulse, while stationary spins (nondiffusion molecules) are unaffected (Mori & Zhang, 2006). Increasing the

FIGURE 2.5 Diffusion tensor imaging. Illustration of tensor shapes (top right) showing example of anisotropic diffusion in white matter tracks where diffusion is restricted, and isotropic diffusion in gray matter. Regions with high fractional anisotropy (FA) represent areas which have a higher probability of diffusion in one direction, a more elongated probability of distribution. Regions with low FA have more spherical distribution. The direction of anisotropy can be plotted using color-coded maps showing the principle directions of diffusion within different white matter pathways (top left). Red, blue, and green colors are assigned to the x, y, and z axes, respectively. FA can be employed to measure of the organization of white matter and tissue microstructure integrity (top middle). The mean diffusivity (MD; bottom left) and the apparent diffusion coefficient (ADC) can be calculated from the average of the eigenvalues (l1 ; l2 ; l3 ). The radial diffusivity (RD; bottom middle) and the axial diffusivity (AD; bottom right) can also be extracted from the eigenvalues.

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time between two gradient pulses allows more time for water molecules to accumulate different phases resulting in loss of signal. For nondiffusion molecules, the magnetization effect will be maximally coherent, resulting in no signal loss from diffusion. The b-value is a factor, which reflects the strength and timing of the gradient pulses to generate a diffusion-weighted image. Depending on the b value, different degrees of signal loss are expected from the movement of water molecules. To determine diffusion coefficients, a minimum of two different b-values are applied during acquisition. A higher b-value reflects stronger diffusion effects, but increasing the b-value can result in mechanical vibration artifacts and a poor signal-to-noise ratio (Barrio-Arranz et al., 2015). A b-value of zero (b0) is used to describe the condition when a negligible amount of gradient is applied resulting in an image, which is insensitive to the diffusion process, i.e., a nonediffusion-weighted image (Fig. 2.5). When diffusion gradients are applied, for example, b-value of 1000 s/mm2, a diffusion-weighted image is obtained (Burdette et al., 2001; Kingsley & Monahan, 2004; Stejskal & Tanner, 1965). Measures of water motion are measured only along the applied gradient axis; therefore, multiple diffusion-encoding directions are often used to present diffusion in multiple directions (Basser & Pierpaoli, 1998; Hasan et al., 2001; Jones, 2004; Jones et al., 1999; Papadakis et al., 1999; Shimony et al., 1999). The acquisition of a nonediffusionweighted (b0) image is essential to calculate the relative reduction in intensity in the diffusion-weighted image and to control for the effects which are not of interest, in the context of diffusion imaging, such as changes due to proton density. Typically, multiple b0 images are acquired to calculate an average with lowest noise. While a single-short EPI acquisition protocol is the most widely used DWI method (Mansfield, 1984; Turner et al., 1990), partly due to its fast acquisition time and a good signal-tonoise ratio, there are limitations to consider (Haselgrove & Moore, 1996; Jezzard & Balaban, 1995). Distortions from eddy currents can wrap the DW images, which can result in inaccuracies and error in diffusion maps (Andersson & Skare, 2002; Haselgrove & Moore, 1996; Rohde et al., 2004). Eddy currents arise due to the rapid switching of the diffusionweighted gradients and are described in terms of translation, scaling, and shear in the phase-encoding direction. Magnetic field inhomogeneities, such as susceptibility-induced distortions (also called off-resonance field distortions), can cause nonlinear wrapping of the brain in the phase-encoding direction (Andersson et al., 2003; Graham et al., 2017). The acquisition of a pair of scans in opposite phase-encoding direction, known as blip-up-blip-down or a phase-encode reversed pair, is utilized to correct for susceptibility-induced distortions. Preprocessing steps are required prior to image analysis to correct for eddy currents and susceptibility-induced distortions (Soares et al., 2013). Advances in fast imaging techniques, such as parallel imaging and simultaneous multislice imaging, allow the acquisition time to be significantly reduced, using signals from multicoils in the head coil array, with little impact on the signal-to-noise ratio and resolution. A mathematical model, called the diffusion tensor, can be applied to characterize the diffusion signal to the direction of the fibers (Basser et al., 1994). The diffusion tensor describes the diffusion ellipsoid representing the 3D displacement of water as a function of their spatial location (Basser et al., 1994; Basser & Jones, 2002). In the diffusion tensor, information is enclosed in diffusion coefficients along three principle axes that define the shape, called eigenvalues (l1 ; l2 ; l3 ), and three axes that define the orientation, called eigenvectors (v1, v2, v3), of the ellipsoid (Alexander et al., 2007; Mori & Zhang, 2006). In diffusion tensor

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imaging, which assumes water movement following Gaussian properties, the diffusion tensor is calculated for each voxel within the image. An apparent diffusion coefficient (ADC) map can be generated from the diffusion coefficients in which the intensity of each voxel is proportional to the extent of diffusion, so that water molecules in bright regions diffuse faster than those in dark regions. In white matter tracts, water molecules move most freely in parallel to the dominant fiber orientation; therefore, the degree of anisotropy known as fractional anisotropy (FA) can be employed to measure of the organization of white matter and tissue microstructure integrity. Fitting the diffusion tensor allows quantitative parameters to be determined including FA, mean diffusivity (MD, also known as ADC), radial diffusivity (RD), and axial diffusivity (AD), which reflect various diffusion properties of a tissue (Fig. 2.5). MD is a measure of the average mobility of water molecules and represents a measure of membrane density. AD is a measure of axonal injury, where decreased AD is associated with axonal damage. RD is a measure of myelin injury, where increased RD is associated with demyelination. Therefore, both RD and AD can influence FA measures such that decreases in FA can be caused by either an increase in RD and/or a decrease in AD (Alexander et al., 2007). Typically, MD is higher in damaged tissues as a result of increased free diffusion; while FA decreases due to loss of coherence in the preferred diffusion direction (Beaulieu, 2002). Changes in tissue microstructure and organization result in alterations in the diffusion of water molecular; therefore, DTI is a powerful tool, which has been utilized to study and characterize microstructural changes across neurodegenerative disorders. Several toolboxes are available for the preprocessing and analysis of DTI data using region-of-interest-based approaches as well as voxel-by-voxel approaches such as tract-based spatial statistics (TBSS) (O’Donnell & Westin, 2011; Smith et al., 2006; Soares et al., 2013). When interrupting DTI parameters, it is important to remember the assumptions of the diffusion tensor model and, subsequently, the limitations of DTI parameters as a direct reflection of the underlying molecular biology. The interruption of DTI parameters is further complicated by the fact that approximately one- to two-thirds of all brain voxels contain multiple fiber orientations, such as crossing fibers. Therefore, in a region of crossing fibers, an increase in RD could be due to demyelination or other factors such as a less coherent alignment of fibers, axonal loss, or a combination of factors (Jones et al., 2013; Wheeler-Kingshott & Cercignani, 2009). Variations of the diffusion tensor model have been developed to overcome some of the limitations of DTI and provide a more detailed understanding of microstructural alterations reflecting molecular changes. Neurite orientation dispersion and density imaging (NODDI) has been developed to distinguish between changes in neurite density and orientation dispersion. NODDI models non-Gaussian water diffusion into thee compartments including isotropic diffusion (VISO) corresponding to free diffusion in the CSF; hindered anisotropic diffusion (VEC) corresponding to extracellular water; and restricted anisotropic diffusion (VIC) corresponding to intracellular water of axons and dendrites (Caverzasi et al., 2016; Chung et al., 2016; Zhang et al., 2012). Quantitative parameters can be extracted from NODDI data including the isotropic volume fraction (ISO), orientation dispersion index (ODI), and the neurite density index (NDI), respectively. NDI and ODI can provide more detailed understanding of changes in FA, which encompasses both neurite density and orientation dispersion (Hasan et al., 2001). NODDI can also provide better differentiation of white

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and gray matter with normal white matter displays higher NDI and lower ODI compared with normal gray matter. The development of fiber-tracking algorithms has opened a field of research to study brain microstructural connectivity in vivo (Mori & van Zijl, 2002). DTI tractography techniques have been applied to trace brain pathways using diffusion anisotropy and the principal diffusion direction to study white matter connectivity patterns. Fiber tracking can be performed using different algorithms, which can be divided into two main categories: deterministic and probabilistic (Descoteaux et al., 2009). Deterministic tractography, primarily based upon streamline algorithms, aims to reconstruct one fiber from each seed voxel (Jones, 2008; Lazar, 2010), while probabilistic tractography takes into account a degree of uncertainty, resulting in output probability maps representing the likelihood of a voxel being part of a fiber, and provides the multiple possible fiber direction emanating from each seed (Behrens et al., 2003a, 2003b). A potential limitation of DTI is the inability to describe fiber directionality in regions where two or more differently oriented fiber bundles are present within the same voxel (i.e., crossing, diverging, or kissing fibers); this can lead to incorrect estimations of fiber direction and pathways (Dell’Acqua & Catani, 2012; Descoteaux et al., 2009; Wiegell et al., 2000). New DTI methods have been developed, such as q ball imaging (QBI; Tuch et al., 2003), high angular resolution diffusion (HARDI; Alexander et al., 2002; Anderson, 2005; Frank, 2002), spherical deconvolution methods (Dell’Acqua et al., 2007; Tournier et al., 2007; Tournier et al., 2008; Yeh et al., 2011), diffusion spectrum imaging (DSI; Wedeen et al., 2005), and combined hindered restricted diffusion (CHARMED; Assaf & Basser, 2005). These techniques typically have longer acquisition times and have more sophisticated acquisition protocols such as multishell acquisition (for example, NODDI); however, they have shown promising results to resolve intersecting crossing white matter regions that have been developed.

Functional MRI Unlike structural and diffusion MRI, functional MRI (fMRI) measures dynamic changes to assess neuronal activity. fMRI is based on the principle that regional increases in cerebral blood flow (CBF) and cerebral blood volume (CBV) occur close to areas of neuronal activity to provide sufficient oxygen and glucose to active neurons. The discovery that oxygenated (diamagnetic) and deoxygenated (paramagnetic) blood interact with magnetic field differently laid the foundations for the development of blood oxygen leveledependent (BOLD) imaging or BOLD fMRI. The chain of events from a stimulus through to neuronal activity, which can be measured as MR signals, is characterized by the hemodynamic response function (HRF). There is a time delay, of several seconds, between the local neuronal activity and regional vasodilation with increased blood flow. Most fMRI methodologies model the HRF with a series of gamma functions, called canonical HRF. The HRF is composed of a gradual increase to a peak with a subsequent gradual decline to baseline followed by a small undershoot before stabilizing to baseline (Buxton et al., 2004; Handwerker et al., 2012; Miezin et al., 2000). When oxygen is released, deoxygenated hemoglobin is formed, which has four unpaired electrons causing the molecule to become strongly paramagnetic. Therefore, the

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deoxygenated hemoglobin causes the local magnetic field inhomogeneities, an effect not seen with oxygenated diamagnetic hemoglobin, which has no unpaired electrons. The presence of increased deoxygenated hemoglobin, causing increased local magnetic field inhomogeneities, results in proton going out of phase with one another, which reduces the total magnetization and therefore reduces the MR signal. This effect is measured by the T2* relaxation time. Therefore, fMRI sequences are sensitive to T2* relaxation. GSE sequences with TEs close to T2* of gray matter are more commonly employed for fMRI. SE sequences are less commonly employed for fMRI since they remove the majority of the T2* effects. A decrease in BOLD signal, reflecting T2* shortening, occurs when the concentration of deoxygenated hemoglobin is increased reflecting less neuronal activity (Glover, 2011). Conversely, when there is increased neuronal activity, the local MRI signal increases. Changes in BOLD signal are measured as a function of time; therefore, optimizing the temporal resolution usually takes president over spatial resolution. Fast acquisitions techniques, such as EPI, are commonly used for fMRI to increase temporal resolution and optimize detection of hemodynamically induced changes (Buxton et al., 2004). Slice timing correction, a preprocessing technique, is employed to correct the time course of the data at each voxel per slice to a reference slice by shifting the data in time according to the difference in the acquisition time of that slice compared with the reference slice (Sladky et al., 2011). fMRI can be affected by artifacts such as magnetic field inhomogeneities, geometric distortions, ghosting, and intensity spatial distortions. fMRI is especially sensitive to head motion during acquisition, which is corrected for using motion correction tools in preprocessing steps. A B0 field map, acquired alongside fMRI data, is used to aid correction of geometric distortions by predicting areas of signal loss in preprocessing analysis steps. There are two main types of experimental design used in fMRI acquisition: resting state (rs-fMRI) and task-based fMRI. Task-based fMRI is based on measuring the evoked activity during the performance of a task (Soares et al., 2016). The selection of tasks performed is based on the domain of interest such as cognitive, sensory, physiological, motor, or memory domains. Rs-fMRI is based on the intrinsic brain activity between regions, which are considered as a network of functionally connected and interacting regions, in the absence of an external stimuli. fMRI techniques are employed to study changes in the functional connectivity in neurodegenerative disorders, utilizing both rs-fMRI and task-based fMRI. A variety of validated data processing techniques, software, and toolboxes are available for the analysis of both rs-fMRI and task-based fMRI. Most task-based fMRI analysis methods are based on general linear modeling to derive undirected, i.e., functional connectivity, and directed or casual, i.e., effectively connectivity, associations between brain regions (Friston, 2011; Horwitz et al., 2005). The first methodology developed for rs-fMRI, which is still commonly used to-date, is seed-based correlation analysis. Seed-based analysis requires a prior region-of-interest to be selected, to map how that seed region-of-interest is functionally connected with all regions within the brain. Connectivity is measured indirectly based on correlations of signal fluctuations. Subsequently, three methods referred to as independent component analysis, principal component analysis, and clustering have been developed to overcome the limitations of data-driven methods. Principal component analysis methods, which can be applied to both rs-fMRI and task-based fMRI, are based on identifying the principal component that can maximize the explained variance of data and separate the relevant information from

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the noise (Smith et al., 2014; Viviani et al., 2005). Independent component analysis methods model fMRI data as a set of spatially or temporally independent components (Beckmann, 2012; Kiviniemi et al., 2003). Similar to principal component analysis and independent component analysis, clustering analysis is a data-driven approach using mathematical algorithms to group data into clusters, which are more similar to another compared with different clusters of voxels (Goutte et al., 1999; Heller et al., 2006; Lee et al., 2012; Mezer et al., 2009). Independent component analysis assumes that spatially independent regions exist, which form a network through a shared fMRI time course, whereas clustering approach does not rely on this assumption and rather groups voxels with similar time courses into a cluster. Graph theory is an increasingly common approach to investigate functional brain network (Farahani et al., 2019; Quante et al., 2018; Wang et al., 2010). Graph theories model the brain as a network of voxels or regions, referred to as nodes, and time series correlations, known as edges which connect nodes. This allows for a whole-brain network approach employing mathematical graph theory metrics to extract quantitative parameters of the functional connectome. Parameters such as clustering, coefficient, path length, centrality, efficiency, and modularity can be extracted to provide information on functional integration, segregation, resilience, and organization of the whole functional connectome or individual node (Bullmore & Sporns, 2009; Reijneveld et al., 2007; Stam & Reijneveld, 2007).

Arterial spin labeling Arterial spin labeling (ASL) is a noninvasive, nonionizing MRI method to study functional changes within the brain by measuring brain perfusion. ASL is advantageous over fMRI techniques, as a measure of perfusion, since ASL is quantitative allowing for hypo- or hyperprefusion states to be determined across physiological and pathological conditions (Deibler et al., 2008; Detre et al., 1994; Petersen, Zimine, et al., 2006; Pollock et al., 2009). CBF can be quantified from ASL data in tissue-specific units of ml of blood per 100g of tissue per minute. ASL techniques exploit the ability of MRI to magnetically label arterial blood as an endogenous tracer in the bloodstream, which changes the image intensity proportional to the blood delivered to the target tissue (Detre et al., 1994; Williams et al., 1992). A radiofrequency pulse, which inverts or saturates water protons, is applied to magnetically “tag” water molecules in the arterial blood to acquire a labeled image, often referred to as a tagged image. A baseline, or control, image is also acquired, without the endogenous tracer, in which the static tissue signals are identical, but the magnetization of the inflowing blood is different; therefore, the static signal can be eliminated to derive the perfusion signal which is proportional to the CBF (Petersen, Zimine, et al., 2006). Multiple pairs of control and tagged images are acquired, in a temporally interleaved fashion, and averaged to create CBF maps (Liu & Brown, 2007). The magnetic endogenous tracer decays with the longitudinal relaxation rate, the T1 relaxation time constant. The relaxation time for water is approximately 1e2 s, resulting in only small quantities of arterial spin-labelled water reaching the capillaries. As a result, the signal-to-noise ratio is inherently very low. The temporal resolution of ASL is also inherently poor, which combined with a low signal-to-noise ratio results in low contrast-to-noise ratio. Methods have been applied to improve the temporal resolution, such as turbo-ASL and

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single-shot ASL; however, these typically require more complex quantification approaches (Duyn et al., 2001; Hernandez-Garcia et al., 2005). EPI is commonly used for ASL acquisition due to its high signal-to-noise ratio and fast acquisition time. Parallel imaging, with a phase array receiver coil, can also be employed to increase the signal-to-noise ratio and shorter ASL acquisition time (Wolf & Detre, 2007). The arterial transit time is another important factor for consideration in ALS imaging and a frequent course of error in perfusion quantification (Buxton et al., 1998). The arterial transit time is the time it takes for blood to travel from the tagging/labeling region to the tissue in the imaging slices. This can be particularly problematic in patient populations, where vascular transit times are associated with disease pathology; under physiological conditions a time of 1 s is usually suitable (Petersen, Lim, & Golay, 2006). To overcome the arterial transit time, multiple TIs can be used during ASL acquisition (Golay et al., 2004); this allows for the arterial arrive time to be measured, which can also be useful to identify hemodynamically impaired regions (Bokkers et al., 2008; MacIntosh et al., 2010). Background suppression is also commonly used in ASL acquisition, which adds extra RF pulses, prior to image acquisition, to suppress the signal arising from the brain tissue that is not from the labeled blood. Background suppression can increase the sensitivity for detecting changes in CBF (Fernandez-Seara et al., 2007). ASL acquisition techniques are classified based on the magnetic labeling process including continuous ASL (CASL), pulsed ASL (PASL), pseudocontinuous ASL (PC-ASL), and velocityselective ASL (VS-ASL) (Liu & Brown, 2007; Petcharunpaisan et al., 2010; Pollock et al., 2009; Williams et al., 1992). In CASL, tagging is based on location and velocity by labeling bloode water continuously as it passes through a plane located in the neck; in PASL, tagging is based on location by applying an RF pulse over a selected region of the neck; and in VS-ASL, tagging is based on location and velocity. PC-ASL employs a train of discrete RF pulses together with a gradient wave applied between two consecutive RF pulses, which offers a CASL-style labeling method, with high signal-to-noise ratio, while taking advantage of PASL’s higher tagging efficiency (Wu et al., 2007). Territorial ASL (TASL), vessel-encoded ASL (VE-ASL), is a technique which can be employed to label and visualize the perfusion of the vasculature of individual arteries (Paiva et al., 2007; van Laar et al., 2008; Wu et al., 2008). Several preprocessing steps are required on ASL data. Typically, a separate field map is acquired, called a calibration image, which is used to correct for B0 distortions, most prominent in EPI sequences. Correction for susceptibility, flow, and motion artifacts is also applied to the raw data. To obtain a perfusion-weighted image, the tagged image is subtracted from the control image. A calibration image, usually a proton density or T1-weighted image, is acquired separately for scaling signal intensities. The calibration image is used to estimate the relationship between the signal intensity and the “concentration” of the labeled bloodewater. Time series data are often acquired to track the wash-in and washout of the tracer within the voxels. Tracer kinetic modeling can be applied to the time series in each voxel to derive how the tracer behaves within the tissue. Buxton and colleagues developed the general kinetic model, a single-compartment model, to obtain a quantitative perfusion map on a voxel-byvoxel basis (Buxton et al., 1998). Kinetic modeling is used to explain the relationship between the measured signal and the rate of labeled bloodewater delivery to calculate the perfusion. Kinetic modeling algorithms vary depending on the ASL acquisition protocol, e.g., PC-ASL

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versus PASL, and include corrections for the decay of the label with the relaxation time. In addition to the CBF, other perfusion parameters can also be extracted, using specific acquisition methods or quantification models, such as the arterial arrival time and the volume of arterial blood using multi-TI sequences.

Neuromelanin-sensitive MRI Neuromelanin, a dark brown polymer pigment, is most highly concentrated in catecholaminergic neurons of the substantia nigra pars compacta, locus coeruleus, and the solitary tract within the medulla oblongata (Fedorow et al., 2005; Zecca et al., 2008; Zucca et al., 2014, 2018). Neuromelanin is synthesized from iron-dependent oxidation of cytosolic dopamine and subsequent reaction with proteins and lipids in midbrain dopamine neurons (Zecca et al., 2008). The substantia nigra and locus coeruleus owe their names to the high concentration of neuromelanin giving their characteristic dark appearance (Zhang et al., 2011; Zucca et al., 2017). MRI can be used to image neuromelanin due to the paramagnetic properties of neuromelanineiron complexes, which form within autophagic organelles (Sasaki et al., 2006; Sulzer et al., 2018; Tosk et al., 1992; Zecca et al., 2004; Zucca et al., 2017). Neuromelanin-containing organelles possess abundant lipid bodies, as well as soluble and pigment bound proteins, and are thought to play a protective role against the accumulation of toxic catecholamine derivatives and oxidative stress (Mosharov et al., 2009; Sulzer et al., 2000; Zecca et al., 2000, 2008). MRI techniques that are sensitive to neuromelanin have been utilized to investigate neuromelanin concentrations in the substantia nigra and locus coeruleus (Sasaki et al., 2006; Trujillo et al., 2017; Zecca et al., 2002). The primary source of contrast for neuromelanin-sensitive MRI comes from a phenomenon called magnetization transfer, which results from the exchange between water protons and protons associated with macromolecules (Langley et al., 2015). The magnetization transfer effect is reduced in samples with paramagnetic substances, to the highest degree in melanine iron complexes (Tanttu et al., 1992). Melanineiron complexes exert an exceptional effect on T1 shortening, which enhances the signal intensity on neuromelanin-sensitive MRI (Trujillo et al., 2017). Therefore, both the magnetization transfer and T1 effects contribute to the contrast seen on neuromelanin-sensitive MRI. High-resolution fast SE T1-weighted sequences have been employed to capture neuromelanin signals (Sasaki et al., 2006). 3D pulse sequences are more prone to motion artifacts; therefore, 2D axial acquisitions are commonly acquired with a shorter field-of-view, focused on the brainstem nuclei, which also helps to reduce the acquisition time. Neuromelanin-sensitive MRI is commonly quantified using region of interest-based methods, using manual delineation, as well as the application of voxel-wise quantification more recently (Cassidy et al., 2019; Sulzer et al., 2018). Contrast ratio can be calculated using a validated reference region (Sasaki et al., 2006). In pathological processes affecting the substantia nigra pars compacta, there is a gradient of degeneration of this area where the lateral part is affected earlier and more severely than the medial part (Fearnley & Lees, 1991). For this reason, studies employing neuromelanin MRI often report alterations pertaining the medial and lateral regions of the substantia nigra pars compacta separately (Fearnley &

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Lees, 1991; Hatano et al., 2017). While manual region of interest-based approaches can provide accurate results, it requires the rater to have a good understanding of the substantia nigra anatomy and other dopaminergic and noradrenergic nuclei such as the ventral tegmental area and the locus coeruleus, respectively. Automated methods to define the substantia nigra and the locus coeruleus are being increasingly used for neuromelanin MRI analysis (Doppler et al., 2021).

Susceptibility-weighted imaging Susceptibility-weighted imaging (SWI) can be employed to investigate the accumulation of iron, as well as calcifications, in the pathophysiology of neurodegenerative disorders. SWI is typically acquired using high-resolution T2*-weighted GRE pulse sequences, which are sensitive to differences in tissue susceptibility due to their inability to refocus spins dephased by magnetic field inhomogeneities (Haacke et al., 2004). T2*-weighted contrast can be enhanced in GRE sequences by using a low flip angle, long TE, and long TR. Magnetic field inhomogeneities from susceptibility differences result in faster T2* relaxation and subsequently a loss of signal intensity on GRE images. Advances in SWI sequences have employed parallel imaging, to reduce scan time, and flow compensation in all three directions to reduce artifacts. Unlike T2*-weighted imaging technique, an important accept of SWI acquisition is that the phase and magnetic information are processed separately, as well as combined image that is commonly used for diagnostic purposes. General field inhomogeneities and distortions due to air and bones within the skull can introduce macrosusceptibility gradients in the raw phase image. The discovery, in 1997 (Reichenbach et al., 1997), that unwanted phase artifacts could be removed leaving just the local phase of interest opened a new dimension for susceptibility mapping (Haacke et al., 2005). A filtered phase image is produced after the application of local phase correction algorithms, to reduce artifacts, and digital high-pass filtering, to remove low-frequency fluctuations of the background field (Reichenbach et al., 1997). The phase shift represents the average magnetic field of protons in a voxel, which is dependent on the local susceptibility of the tissue. Paramagnetic substances increase the magnetic field resulting in a positive phase shift, while diamagnetic substances cause a negative phase shift, when compared with surrounding tissue. Several studies have illustrated a direct correlation between phase shifts and iron concentration (Ogg et al., 1999; Sehgal et al., 2005). Therefore, the filtered phase image can be used to distinguish paramagnetic substances, such as iron and blood, from diamagnetic substances, such as calcifications, with paramagnetic and diamagnetic substances displaying opposite signal intensities (Barbosa et al., 2015). The magnitude image reveals areas with a loss of T2* signal intensity. A phase mask is used to scale data from the filtered phase image over a range from zero to one to attenuate tissues with different susceptibilities (Barbosa et al., 2015; Yamada et al., 1996). The magnitude image is then combined with the phase mask to produce the susceptibility-weighted image, which contains both the magnitude and phase data (Haacke et al., 2004). The susceptibility-weighted image allows changes in signal intensity from both the T2*-based contrast and phase information to be exploited to detect susceptibility differences between tissues. The quality of SWI results depends on the accuracy of the postprocessing of the phase image. While mean phase-shift

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values can be quantified from filtered phase images, and converted to radians using a scanner specific algorithm, SWI only allows for the detection of the presence of tissue susceptibility and does not enable full quantification local magnetic susceptibility, which requires more advanced deconvolution approaches.

Quantitative susceptibility mapping Quantitative susceptibility mapping (QSM) is a promising technique to allow the absolute concentration of substances such as iron, calcium, and superparamagnetic iron oxide nanoparticles to be measured within the tissue using changes in local susceptibility (Deistung et al., 2017; Wang & Liu, 2015). In QSM, paramagnetic and diamagnetic substances within the tissue, which create local susceptibility field disturbances, are represented as a magnetic dipole. Therefore, QSM utilized both magnitude and phase images as well as performing dipole deconvolution to enable the quantification of local tissue susceptibility. Changes to the local susceptibility can be masked by the larger background field changes, thus requiring a series of background field removing algorithms. While SWI is implemented in clinical practice, currently QSM is prominently only featured in research setting due to the more complex acquisition and quantification methodology. The acquisition protocol for QSM is based on a 3D GRE sequence used for SWI but with the application of multiple echoes to detect weak susceptibility changes and to correct for multiexponential T2* decays. A pair of magnitude and phase images are acquired for each echo number, and for optimal phase reconstruction, multichannel data are acquired using multichannel phase array coils (Walsh et al., 2000). Phase images acquired on different channels may encompass different phase offsets associated with the respective coil sensitivity field. Several methods and toolboxes have been developed to process and analyze QSM data such as the morphology-enabled dipole inversion method (MEDI; de Rochefort et al., 2010; Liu et al., 2012, 2013), homogeneity-enabled incremental dipole inversion (HEDI; Schweser et al., 2012), truncated singular value decomposition (TSVD; Wharton et al., 2010); and multiscale dipole inversion (MSDI). Combined phase and magnitude images are first unwrapped, using methods such as the Laplacian approach (Schofield & Zhu, 2003). The local field map is then generated after removal of background effects, using methods such as projection onto dipole fields. Advanced mathematical algorithms can then be applied to calculate the source of each individual dipole. QSM data are typically quantified in parts per million. QSM maps can be quantified using both regional-based and voxel-wise whole brain approaches (AcostaCabronero et al., 2016, 2017).

Magnetic resonance spectroscopy Magnetic resonance spectroscopy (MRS) overlaps significantly with MRI; however, while MRI detects nuclear magnetic resonance signals produced by tissue water, MRS suppresses the water proton signal allowing the biochemical evaluation in situ via the detection of nuclear magnetic resonance signals produced by chemical compounds other than water. MRS

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is based on the concept that nuclei in different molecules experience a different magnetic field from each other, resulting in different resonance frequencies (Oz et al., 2014). The resonance frequency is changed by the intramolecular magnetic field around an atom providing details about the structure, dynamics, reaction state, and chemical environment of molecules. There are two main techniques in MRS: single-voxel techniques such as point-resolved spectroscopy (Ordidge et al., 1985) or stimulated echo acquisition mode sequences (Frahm et al., 1987), and multivoxel techniques such as chemical shift imaging and MR spectroscopic imaging (Verma et al., 2016). Inversion recovery or chemical shift selective techniques are used for water suppression, which is required to identify spectral signals. Unlike MRI acquisition, for MRS, a frequency gradient is not used to provide spatial information rather the frequency information is used to differentiate chemical compounds. In MRS, the resonances, or peaks, represent the signal intensity as a function of frequency, commonly expressed as parts per mission (ppm) (Oz et al., 2014). The TE determines the number of measurable metabolites with each molecule exhibiting its own fingerprint on the chemical shift spectrum, for example, glutamate resonates at 2.2e2.4 ppm, N-acetyl aspartate resonates at 1.48 ppm, choline resonates at 3.2 ppm, and myo-inositol resonates at 3.5 ppm (Oz et al., 2014). MRS has been employed to assess levels of metabolites in neurodegenerative diseases as highlighted throughout the chapters of this book.

Conclusion Since the introduction of MRI into the clinical setting in the early 1980s, MRI has seen tremendous growth becoming a valuable tool for diagnostic imaging. MRI has also become a valuable tool for clinical research in the pursuit of identifying reliable disease biomarkers to predict disease onset and monitor disease progression, unlocking disease mechanisms and evaluating response to disease-modifying therapies. To further our understanding of the brain in physiological and pathological conditions in vivo likely requires a multifaceted, analytical approach. Studies often employ several MRI techniques, and MRI outcome measures are frequently combined with other variables including clinical, genetic, and digital markers, as well as molecular neuroimaging techniques such as position emission tomography (PET) and single photon emission computed tomography (SPECT). Continuous advances in methodological approaches which integrate different components, and dimensions, from multimodal neuroimaging techniques alongside clinical, genetic, and biological factors, will be required in the future to build a comprehensive picture of disease progression and elucidate disease pathophysiology.

References Acosta-Cabronero, J., Betts, M. J., Cardenas-Blanco, A., Yang, S., & Nestor, P. J. (2016). In vivo MRI mapping of brain iron deposition across the adult lifespan. The Journal of Neuroscience, 36, 364e374. Acosta-Cabronero, J., Cardenas-Blanco, A., Betts, M. J., Butryn, M., Valdes-Herrera, J. P., Galazky, I., & Nestor, P. J. (2017). The whole-brain pattern of magnetic susceptibility perturbations in Parkinson’s disease. Brain, 140, 118e131. Alexander, D. C., Barker, G. J., & Arridge, S. R. (2002). Detection and modeling of non-Gaussian apparent diffusion coefficient profiles in human brain data. Magnetic Resonance in Medicine, 48, 331e340.

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I. Introduction

C H A P T E R

3 Advances in molecular neuroimaging methodology Heather Wilson and Marios Politis Neurodegeneration Imaging Group and Mireille Gillings Neuroimaging Centre, University of Exeter, London, United Kingdom

Introduction Molecular imaging encompasses techniques, such as positron emission tomography (PET) and single photon emission computed tomography (SPECT), which can capture real-time in vivo functions, changes, and biological processes at a molecular and cellular level. Molecular imaging is an extremely valuable tool in neuroscience, which has led to breakthroughs in understanding brain function in physiological conditions as well as disease states. PET and SPECT techniques are based on the measure of a radionuclide’s decay, which produces either a positron or g-rays, respectively. Using radiotracers with high affinity and specificity, PET and SPECT imaging can detect in vivo biochemical changes in specific molecular targets of interest (Phelps, 1977, 2000). Similar to PET, SPECT detects g-rays; however, the radioisotopes employed for SPECT decay via emission of a single g-ray (single photon), which is directly measured by the scanner. Furthermore, SPECT radioisotopes have a longer halflife, for example, 99mTc has a half-life of 6 hours and 123I has a half-life of 13.3 hours, compared with PET radioisotopes such as 11C, which has a half-life of 20.39 min and 18F with a half-life of 109.77 min (Table 3.1), therefore making SPECT imaging considerably cheaper and more feasible compared with PET. However, the sensitivity of SPECT is lower, in the range of 10
10 to 10
11, compared with the sensitivity of PET in the range of 10
11 to 10
12 (Lu & Yuan, 2015). Advances in imaging acquisition and analysis methodologies enable full quantitative analysis of molecular imaging data. Furthermore, advances in scanner technology by combining PET and SPECT with morphological modalities, either CT or magnetic resonance imaging (MRI), in a single scanner, known as PET/CT, SPECT/CT, and PET/MR, have removed the time and costs for a separate transmission scan required for attenuation correction (Beyer et al., 2000; Townsend, 2008). Furthermore, the development of PET/MR scanner enables the

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© 2023 Elsevier Inc. All rights reserved.

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TABLE 3.1

Common radioisotopes used for PET and SPECT.

Radionuclide

Half-life Positron emission tomography

11

20.39 min

18

109.77 min

15

2.04 min

13

9.97 min

64

13 hours

68

67.63 min

82

1.27 min

C F O N Cu Ga Rb

166

Ho

27 hours Single photon emission computed tomography

123

I

13.2 hours

99m

6.06 hours

67

3.26 days

67

3 days

Tc

Ga Cu

111

2.83 days

153

47 hours

159

20 hours

166

26 hours

177

7 days

In Sm Gd Ho Lu

simultaneous acquisition of PET and MRI data allowing for optimized colocalization of neurochemical changes and functional or structural changes, respectively, in one scanning session removing any potential bias from different scan days or times (Grant et al., 2016; Trefler et al., 2016). Technological advances have led to the development of total body PET scanners enabling low-dose, full-body dynamic acquisition with the entire body within the FOV in one scan (Cherry et al., 2018; Vandenberghe et al., 2020). Total-body PET/CT systems including the PeenPET Explorer and uExplorer have recently been applied in clinical studies (Pantel et al., 2020; Spencer et al., 2021). Across Parkinson’s and related disorders, radiotracers have been employed to study the neurotransmitter systems, neuroreceptors, protein aggregation, neuroinflammation, glucose metabolism, synaptic integrity, phosphodiesterases, and mitochondria. The application and

I. Introduction

PET acquisition

55

findings from these tracers in molecular imaging studies are discussed in chapters throughout this book. The concepts introduced in this chapter will lay the foundations for molecular imaging discussed throughout this book and the application of these techniques across movement disorders.

PET acquisition The radioisotopes employed in PET scanning, such as 11C, 15O, and 18F, follow bþ decay by emitting positively charged positrons, which travel a few millimeters in the tissue, losing kinetic energy, before interacting with electrons in the surrounding tissue to generate two grays with 511-keV traveling in nearly opposite directions, which is known as an annihilation radiation (Phelps, 2000). The scintillation crystals within the PET scanner detect high energy g-rays as coincidence events. A true coincidence event is the correction detection of a pair of g-rays, which originate from the same positron annihilation event (Fig. 3.1). The number of 511-keV photon pairs per unit time, detected by the PET scanner, for each unit of activity in a source determines the PET scanner sensitivity, typically expressed in counts per second per microcurie (or megabecquerel). Corrections for attenuation, random, scatter, dead time, and decay are applied to improve the accuracy of PET emission data (Heurling et al., 2017) (Fig. 3.1). A scatter event is a pair of g-rays that interact with the surrounding tissue and change direction due to Compton scatter. The loss of energy due to Compton scatter is typically low meaning g-rays can still be detected with approximately 511 keV energy. A random event is the detection of two single g-rays, which do not originate from the same positron annihilation but are incorrectly identified as a pair. Attenuation is the loss of detection of true coincidence events due to the photoelectric effect resulting in complete photon absorption or scattering with energy loss. To correct for attenuation, a low-dose CT scan is acquired at the start of a PET scanning session on a PET/CT scanner (Kinahan et al., 1998; Beyer et al., 2000). While it is not possible to create an attenuation map (m-map) on hybrid PET/MR scanners, several MR-based attention correction methods have been developed (Wagenknecht et al., 2013).

FIGURE 3.1

Correction for attenuation, scatter, and random events applied to PET emission data. The annihilation event is represented with a start symbol. Not that the annihilation event does not lie on the apparent line of response (LOR) between the two photon detections for random and scatter coincidence.

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The size of the crystal detection system is a major factor in determining the spatial resolution of the PET scanner. Spatial resolution is a measure of the ability of the PET scanner to accurately reproduce the image of an object, for example, the ability to accurately depict the variations in the distribution of radioactivity in the tissue. It is defined as the minimum distance between two points in an image that can be detected by the scanner. The intrinsic resolution of the scintillation detectors is best at the center of the field-of-view (FOV) and deteriorates toward the edge of the FOV. PET spatial resolution is also limited by the positron range and the noncollinearity of the annihilation (Fig. 3.1). The positron range is defined by the distance traveled by the positron in the tissue before annihilation. The distance traveled by the positron increases with its energy but decreases with the tissue density. The effective range traveled by the positron is given by the shortest perpendicular distance from the emitting nucleus to the positron annihilation line. Since coincidence detection is related to the location of annihilation, and not to the location of positron emission, an error occurs in the localization of the true positron emission, therefore resulting in the degradation of spatial resolution. Full width at half maximum (FWHM) of the positron count distribution, 0.2 mm for 18 F, contributes to the overall spatial resolution (Tarantola et al., 2003). Finally, noncollinearity arises from the deviation of the two 511 keV annihilation photons from the exact 180 degrees position due to small residual momentum of the positron at the end of the positron range. The maximum deviation from the 180 degrees direction is 0.25 degrees (i.e., 0.5 degrees FWHM). Therefore, the observed line-of-response (LOR) between two detectors does not intersect the point of annihilation but is slightly displaced. The detector ring diameter, i.e., the distance between two detectors, also contributes to the error due to noncollinearity. Raw listmode data format, without predefined time frames, is used to store millions of events for each detector-pair LOR through all the detectors. Listmode can be stored as parallel sets of LOR integrals at each angle, known as projections, with each projection entered as a row into a sinogram, which presents an array storing the number of coincidence events for each detector position and each angle (Fahey, 2002). Separate sinograms are acquired for each slice, with each pixel in a sinogram corresponding to a specific LOR. Applying correction factors, each sinogram can be reconstructed into a PET image (Kinahan et al., 1998). After reconstruction, each voxel in the PET image represents concentration of radioactivity as a function of time. There are different types of reconstruction algorithms, the most common are filtered backprojection (FBP) and iterative reconstruction, such as the ordered subset expectation maximization (OSEM) (Tong et al., 2010). Next-generation reconstruction algorithms, using artificial intelligence (AI) approaches such as machine learning and deep learningebased methods, are being developed to improve the accuracy and speed of analytical image reconstruction (Arabi et al., 2021; Wang et al., 2020). The duration of a PET scan is determined by the kinetic properties of the radiotracer and optimized analysis methodology, balanced with burden to the participant. To accurately estimate binding parameters, equilibrium of the radiotracer needs to be reached. Radiotracers with high affinity can take longer to reach equilibrium and thus require a long scan or longer uptake period. The ability of a participant, especially with an advanced neurodegenerative disorder, to tolerate a long scan time needs to be considered within the study design. Another important factor when designed a PET study is the type of PET acquisition. PET acquisition can be either static or dynamic with coincidence events binned into a single frame or into multiple time frames, respectively. Dynamic imaging acquires data continuously, in multiple

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predefined number of time frames, collecting information on the tracer concentration and distribution over time enabling full quantitative kinetic analysis to be applied using specific kinetic models depending on the tracer. For dynamic scans, the radiotracer is injected when the participant is on the scanner bed at the start of the PET acquisition. Conversely, static imaging is acquired over a fixed time period producing a single PET image and therefore provides no temporal details about the tracer distribution. For a static scan, the radiotracer is typically injected off the scanner bed to allow for a predefined uptake period (e.g., 30 or 90 min) before PET scanning acquisition starts.

Quantitative PET analysis The analysis of PET analysis can be conducted as either region-based or voxel-based methods. For region-based analysis, the more traditional analysis method for PET data, regions-of-interest are defined using an anatomical atlas (Lancaster et al., 2000, 2007; Tziortzi et al., 2011), automated segmentation methods (Desikan et al., 2006; Heckemann et al., 2010, 2011), or manual delineation using coregistered structural MRI (Tziortzi et al., 2011) to quantity the mean activity of all the voxels within that region. Conversely, in voxel-based analysis, each voxel in the image is considered independently providing a spatial distribution of the radiotracer binding. Voxel-based analysis typically has higher noise, compared with region-based analysis methods, and careful consideration in statistical analysis methods must be taken to reduce the false positive rate (Ganz et al., 2021).

Kinetic modeling analysis PET analysis methodology can provide quantitative outcomes measures using mathematical models to measure the distribution of the radiotracer over time. The type of model used depends on the kinetics of the radiotracer, such as whether it binds reversibly or irreversibly to the specific target of interest, and whether a linear or nonlinear model is used. In 1984, Mintun proposed the first compartmental modeling approach to quantify radioligand density for receptor ligand binding (Mintun et al., 1984). This three-tissue compartmental model (3TCM) requires a plasma input function and assumes that the radioligand comes from the plasma, as a single source, into the tissue. In the 3TCM, the radioligand can be represented as three different forms in the tissue: the concentration of radioligand free within the tissue (CF), the concertation of the radioligand specifically bound to the target (CS) and the concentration of radioligand nonspecifically bound to other proteins within the tissue (CNS) (Fig. 3.2A). The concentration of radioligand in the plasma is quantified using arterial blood data, and it is assumed that the unmetabolized parent radioligand in the plasma equilibrates rapidly with the plasma protein so that the free fraction is constant over time. Moreover, the radioligand is reversible between the compartments and is the concentration between each compartment is controlled by six different rate constants (K1eK6), which characterize the transfer of the radioligand between compartments. The 3TCM is not feasible in practice due to the high number of rate constants, which are required to be estimated, and the fact that compartments for specific (CS) and nonspecific binding (CNS) are kinetically indistinguishable in vivo. The two-tissue compartmental model (2TCM) can be simplified into the 2TCM by combining the concertation of nonspecifically bound (CNS) and free (CF) radioligand into a single compartment representing the concentration of nondisplaceable I. Introduction

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3. Advances in molecular neuroimaging methodology

FIGURE 3.2 Schematic representation of compartmental models. (A) The three-tissue compartmental model (3TCM) composed of three tissue compartments: (i) the concentration of free radioligand in the tissue (CF), (ii) the concentration of radioligand specifically bound to the target (CS), and (iii) the concentration of nonspecifically bound radioligand (CNS). (B) The two-tissue compartmental model (2TCM) composed of two tissue compartments: (i) the nondisplaceable fraction of the radioligand (CND ¼ CF þ CNS) and (ii) the concentration of radioligand specifically bound to the target (CS). (C) The one-tissue compartmental model (1TCM) composed of only one tissue compartment: the concentration of the radioligand in the target tissue (CT ¼ CF þ CNS þ CS). (D) Simplified reference tissue compartment model. CF, concentration of free radioligand in the tissue; CND, concentration of nondisplaceable radioligand in the tissue; CNS, concentration of nonspecifically bound radioligand in the tissue; CP, concentration of the radioligand in the plasma; CS, concertation of radioligand specifically bound to the target; CT, concentration of radioligand in the target tissue region; K1, influx rate of radioligand diffusion from the plasma to the tissue; K2, efflux rate of diffusion from the tissue to the plasma; K3, rate of association to specific target; K4, rate of dissociation from the specific target; K5, association rate constant for nonspecific binding; K6, dissociation rate constant for nonspecific Ref binding; KRef 1 , influx rate of radioligand diffusion from the plasma to the reference tissue; K2 , efflux rate of diffusion from the reference tissue to the plasma.

radioligand (CND ¼ CNS þ CF), based on the assumption that a rapid equilibrium occurs between these compartments (Fig. 3.2B). Furthermore, the concentration of nondisplaceable radioligand (CND ¼ CF þ CNS) and the concertation of specifically bound radioligand (CS) at equilibrium can be combined into one single compartment representing the concentration of the radioligand in the tissue (CT) (Fig. 3.2C). The appropriate kinetic model is fitted to the regional timeeactivity curve for quantification within each region-of-interest. Assuming that there is no irreversible uptake in any compartment, the total volume of distribution (VT) can be derived from 2TCM and

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Quantitative PET analysis

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one-tissue compartmental model (1TCM) as the outcome macroparameter. VT is defined at equilibrium as the ratio of the radioligand concentration in the target tissue region (CT) to the metabolite-corrected plasma reference concentration (CP). Other outcome macroparameter including the distribution volume ratio (DVR) and a variety of binding potential estimates such as the concentration of nondisplaceable tracer concentration in the tissue (BPND; the ratio of the concentration of the radiotracer specifically bound to the target to the concentration of the nondisplaceable radiotracer at equilibrium) can be derived from compartmental models (Innis et al., 2007). Microparameters can also be derived, known as rate constants; however, these are less common and can be affected by high noise (Heurling et al., 2017). Graphical analysis methods, generally classified as the Logan graphical model for radiotracers with reversible binding (Logan et al., 1990, 1996) or the Patlak graphical model for radiotracers with irreversible binding (Patlak & Blasberg, 1985; Patlak et al., 1983), offer a simplification of compartmental models using linear least-square fitting procedures to estimate outcome parameters. In graphical analysis methods, when a steady state is reached, the reversible radiotracer concentration in the region-of-interest and the arterial plasma function are combined into a single curve, which approaches linearity. An advantage of graphical analysis methods is that they do not depend on any priori model structure. The Patlak model assumes that when all the reversible compartments are at equilibrium, after a defined linear time point (t*), only the irreversible compartments influence the apparent distribution volume (Patlak & Blasberg, 1985; Patlak et al., 1983). At equilibrium, the Patlak plot is linear with the slope of the linear phase representing the net transfer rate (Ki), i.e., the influx constant. The Logan plot with plasma input assumes that after t* the radiotracer concentration in the tissue is at equilibrium with the concentration in the plasma (Logan et al., 1990). The slope of the linear regression line in the Logan plot represents the VT. The multilinear analysis-1 (MA1) method is a development of the Logan plot method aiming to reduce noise bias, which can be applied to determine the VT for reversible radiotracers using an arterial input function (Ichise et al., 2002). Compartmental models and graphical methods require a plasma input function, which is obtained from arterial blood samples collected from the subject, via arterial cannulation, throughout the PET scan acquisition. The concentration of the free nonmetabolized tracer in the arterial plasma can be calculated using blood analysis to create an input function curve, which represents the available parent tracer in the plasma as a function of time (Tonietto et al., 2015). To obtain the corrected plasma input function, the plasma is first separate from whole blood activity, using mechanical centrifugation of whole blood samples, before using high-performance liquid chromatography to isolate the nonmetabolized parent compound, from other radioactive metabolites due to peripheral metabolism, to correct for the presence of plasma metabolites. The plasma free fraction is defined as the proportion of free nonmetabolized parent compound not bound plasma proteins at equilibrium, which can freely diffuse in the plasma and tissue. A correction for temporal delay between the arterial sampling and arrival of the radiotracer in the tissue is also applied. An appropriate mathematical model is fitted to derive a metabolite corrected arterial plasma input function for kinetic modeling analysis (Tonietto et al., 2016). The plasma free fraction can have a large influence on the VT. Therefore, in cases where there are differences in the plasma free fraction, the VT should be normalized by the plasma free fraction (Innis et al., 2007). Although use of an arterial plasma input function is considered as the gold standard ,the collection of arterial blood can be considered invasive due to the use of arterial cannulation. I. Introduction

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Therefore, as an alternative to using a plasma input function, noninvasive reference region models have been developed in which the arterial input function is replaced by the timee activity curve of the reference region. Reference tissue models are based on the presence of a reference region within the brain, which behaves similarly to the target region except that it is devoid of specific binding sites; therefore, the signal in the reference region is solely a nonspecific component (Gunn et al., 2001). The simplified reference tissue compartmental model (SRTM) allows for the estimation of the BPND using a suitable reference region devoid of specific binding and therefore does not require blood sampling (Lammertsma & Hume, 1996) (Fig. 3.2D). Variations of the graphical analysis models, including the Logan (Logan et al., 1996) and the Patlak (Patlak & Blasberg, 1985) reference models, which use a reference region as the input function has also been developed for noninvasive quantification using linear estimations after a set time period. The Patlak reference model has been applied for the analysis of [18F]FDOPA (Sossi et al., 2003). The outcome parameter from the Logan plot reference model is distribution volume ratio (DVR) from which the BPND can be derived (Innis et al., 2007). The multilinear reference tissue method (MRTM), for reversible tracers, has been described, which combines the benefits of linear graphical analysis with reference tissue region instead of a plasma input function (Ichise & Ballinger, 1996; Ichise et al., 2002, 2003). An image-derived input function offers a potential noninvasive alternative to arterial blood samples; however, caution should be taken when implementing image-derived input function, which has only been validated for a few radiotracers (Christensen et al., 2014; Zanotti-Fregonara et al., 2011, 2014).

Parametric modeling Kinetic models can be applied to fit a timeeactivity curve separately to each voxel in the PET image to create a map of the radiotracer spatial distribution known as a parametric image. Each voxel in a parametric image represents the defined physiological outcome parameter such as BPND or VT. Therefore, parametric images can be used for voxel-based statistical image analysis techniques such as statistical and biological parametric mapping, which are available in most neuroimaging software packages, including SPM (https://www.fil.ion. ucl.ac.uk/spm) and FMRIB Software Library (https://fsl.fmrib.ox.ac.uk). Similar to kinetic modeling for region-based analysis, parametric analysis methods depend on the kinetic properties of the radiotracer, such as whether it has reversible or irreversible uptake, and whether a plasma input function is required or the presence of a validated reference region. For example, radiotracers with reversible uptake parametric models using a plasma input function include the 1CTM and Logan plot graphical analysis, while the SRTM and Logan plot reference model can be applied when there is a validated reference tissue as the input function (Gunn et al., 2001; Lammertsma & Hume, 1996; Logan et al., 1990). For radiotracers with irreversible uptake, Patlak graphical analysis can be applied with either a plasma input function or a reference region (Patlak & Blasberg, 1985; Patlak et al., 1983).

Standardized uptake value Standardized uptake value (SUV) is a common semiquantitative measure, predominantly used in clinical practice due to its simple computation. SUV can be derived from by normalizing the measured tissue activity concentration at a defined time window by the injected

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dose divided by the participant’s body weight (Boellaard, 2009). SUVmean and SUVmax are used as global indices of the tracer uptake within a region-of-interest. SUV ratio (SUVR) can be calculated when the system is at equilibrium, as a ratio of the tracer activity concentration in the tissue of interest to the activity in a reference region (Heurling et al., 2017). At transient equilibrium, BPND is theoretically equal to SUVR-1 (Ito et al., 1998). SUV measures provide useful measures in clinical practice to aid diagnosis due to simple acquisition and quantification requirements; however, they do not provide any information about the kinetics of the system. Research studies provide validation of the use of SUV outcomes measures, against full kinetic analysis methods (Koole et al., 2019; Naganawa et al., 2021).

Partial volume correction A limiting factor of accurate PET quantification is the scanner resolution. Regions-of-interest, which are smaller than twice the FWHM of the scanner resolution, are most affected by a phenomenon called partial volume effect (PVE) (Hoffman et al., 1979). The PVE can result in spill-out of radioactivity from a region-of-interest with high radioactivity into surrounding tissue, leading to an underestimation of the uptake in the region-of-interest (Aston et al., 2002). Conversely, spill-in (also known as spill-over) arises from high radioactivity in surrounding tissue, leading to an overestimation of uptake in the region of interest. Often both spill-out and spill-in are present. Several partial volume correction (PVC) methods have been described, including using the point-spread function (Erlandsson et al., 2012; Rousset et al., 2007). PVC can increase noise and artifacts in data and so need to be applied correctly with care and consideration (Yang et al., 2017).

Advanced analysis methodology Advances in AI approaches, including deep learning and machine learning algorithms, have drawn increasing attention and application across molecular neuroimaging from data acquisition, correction for noise or artifact, and analysis methods (Arabi et al., 2021; Arabi & Zaidi, 2020; Wang et al., 2020). AI models have been developed aiming to aid clinical research by providing desired outcome variables such as disease classification or stratification, severity scoring, clinical outcome prediction, and monitoring response to treatments (Arabi & Zaidi, 2020; Jo et al., 2019; Mei et al., 2021; Termine et al., 2021; Wang et al., 2017). For example, AI-based algorithms have been shown to detect and classify early Alzheimer’s disease using glucose metabolism patterns obtained from [18F]Fluorodeoxyglucose (FDG) PET (Duffy et al., 2019; Lu et al., 2018). Furthermore, AI approaches using SPECT with a dopamine transporter (DAT) tracer can aid the early diagnosis of Parkinson’s disease, in some cases at premotor stage, and can improve the accuracy of parkinsonism differential diagnosis (Antikainen et al., 2021; Mei et al., 2021; Zhang, 2022). Commonly AI approaches use multimodal data, combining imaging (PET, SPECT, MRI), clinical, biological data (blood, CSF), and increasingly genetic data, aiming to further optimize the accuracy and reliability of these models. AI could also play an important role as the research and clinical landscape across neurodegenerative diseases moves toward precision medicine (Johnson et al., 2021; Termine et al., 2021). Molecular network-based and connectivity-based analysis methods have been developed, in part based on techniques employed for the analysis of functional connectivity MRI data, to I. Introduction

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identify disease-related patterns in pathology using molecular imaging data (Sala & Perani, 2019). [18F]FDG PET has most widely been employed to study metabolic connectivity, identifying covariance across subjects, using modeling techniques such as independent component analysis (ICA), graph theory, seed correlation or interregional correlation analysis (IRCA) and sparse inverse covariance estimation (Yakushev et al., 2017). For example, using sparse inverse covariance estimation method and graph theory, Caminiti and colleagues reported alterations in whole-brain metabolic connectivity as well as altered metabolic connectivity in dopaminergic and cholinergic pathways and in relation to a-synuclein pathology in dementia with Lewy bodies (Caminiti et al., 2017). Connectivity and network-based analysis approaches have also extended to other PET targets including neurotransmitter systems (Sala & Perani, 2019). Using graph-based methods, covariance analysis of PET data has been applied to [18F]FDOPA for dopa decarboxylase, and [11C]SB217045 for serotonin type 4 receptor density, aiming to understand the organization of biological functions across the brain (Veronese et al., 2019). ICA has been applied to [11C]PHNO PET data, targeting both dopamine type 2 and type 3 receptors, in blocking studies to identifying separate patterns of dopamine type 2 and type 3 receptor availability from [11C]PHNO PET data (Smart et al., 2020). Furthermore, Fang and colleagues utilized ICA to identify organized covariance spatial patterns of synaptic density, using [11C]UCB-J PET, a radiotracer specific for synaptic vesicle protein 2A (SV2A) (Fang et al., 2021). With the availability of PET tracers for tau and amyloid proteins, connectivity and network-based analysis methods have been applied to model covariance patterns of connectivity-based pathology spreading in vivo (Franzmeier et al., 2019; Vogel et al., 2021). Data-driven voxel-based spatial patterns analysis methods, such as principal component analysis (PCA), have been applied to PET data to develop image-based prediction models of clinical outcomes (Blazhenets et al., 2021; Klyuzhin et al., 2018). For example, Fu and colleagues identified unique Parkinson’s disease-related patterns (PDRP) using PCA of [11C]DASB PET data, for serotonin transporter, in idiopathic and symptomatic and asymptomatic genetic cohorts of Parkinson’s disease patients (Fu et al., 2018). Furthermore, the application of advanced methodological approaches, utilizing large data sets, has enabled to development of automated algorithms, such as AmyloidIQ (Whittington et al., 2019), TauIQ (Whittington et al., 2021), and DATIQ (Fran et al., 2021), which have increased power over traditional analysis methods to detect meaningful outcome measures and endpoints in clinical trials.

Conclusion Molecular PET imaging offers valuable biomarkers for diagnostic and prognostic application across neurodegenerative disorders. Validated analysis techniques enable full quantitative outcomes measures derived from PET data, representing biological processes at a molecular level, to be applied alongside genetic, clinical, and biosample data to build a comprehensive multifactorial model of disease states striving to deepen our understanding of disease pathophysiology. Continued advances in hardware technology, including the new total body PET/CT scanners, alongside computational methodological advances, and radiochemistry developments, of novel PET tracers for new biological targets, increase the potential of PET imaging and extend the frontiers of knowledge across neurodegeneration. The utilization of molecular imaging techniques in AI models and multivariate molecular connectivity methods highlights pioneering research, across neuropathology and I. Introduction

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neurotransmission systems, which has the potential to provide novel insights into disease pathogenesis and pathophysiology. The future application of these methods to newly validate PET tracers, across disease stages, offers an exciting opportunity. Developments in PET analysis methodology offer an innovative platform to improve diagnostic tools and to provide meaningful outcome measures and endpoints in clinical trials.

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I. Introduction

C H A P T E R

4 Dopaminergic molecular imaging in familial and idiopathic Parkinson’s disease Edoardo Rosario de Natale, Heather Wilson and Marios Politis Neurodegeneration Imaging Group and Mireille Gillings Neuroimaging Centre, University of Exeter, London, United Kingdom

Introduction Parkinson’s disease (PD) is a progressive neurodegenerative disorder affecting about 2%e3% of the population over 65 years of age and significantly hinders quality of life (Politis et al., 2010). The clinical diagnosis relies heavily on the presence of classic motor symptoms bradykinesia, rigidity, rest tremor, and postural instability (Berg et al., 2018; Postuma et al., 2018), which, for the vast majority, arise as a result of the progressive degeneration of dopaminergic substantia nigra pars compacta neurons that, in turn, causes a misconnection in forebrain areas along the striatothalamocortical pathway. PD is also characterized by a wide range of nonmotor symptoms, which cause relevant burden and are thought to be determined by complex functional and structural alterations involving different neurotransmitter deficits, some of which can be present many years before the onset of motor symptoms (Schapira et al., 2017). Neuropathologically, PD is characterized by the deposition of fibrillary protein aggregates named Lewy bodies (LBs) whose principal component is a-synuclein (SNCA) (Spillantini et al., 1997). According to the neuropathological model proposed by Braak, the spatial distribution of LBs proceeds in a stereotyped, cumulative fashion, where areas of the caudal brainstem are affected first, followed by forebrain areas and, finally, by the neocortex (Braak et al., 2003). This is in accordance with the temporal succession of motor and nonmotor symptoms in the natural history of the disease. Additionally, this model provides a pathological justification to the occurrence of a number of nonmotor symptoms, which arise before the clinical diagnosis is

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performed, in a stage called prodromal PD, a still poorly characterized phase in which signatures of disease are supposed to be already present, and more susceptible to be modified by therapeutic intervention. A florid line of research is currently focused on identifying clinical biomarkers that can recognize PD at this early stage and can monitor disease progression. The great majority of PD cases are idiopathic, meaning that no determinant cause can be identified, and it is currently thought that idiopathic PD arises as consequence of a complex combination of genetic and environmental triggers, resulting in the development of neurodegenerative processes (Kalinderi et al., 2016). However, about 15% of PD cases display a family history, and in about 5%e10%, a mendelian pattern of inheritance can be identified (Deng et al., 2018). Since the first description of mutations in the SNCA gene, as the cause of an autosomal dominant form of familial PD in a large Mediterranean kindred (Polymeropoulos et al., 1997), numerous subsequent studies have increased the pool of genetic mutations responsible for monogenic Parkinsonism, providing significant insights about the mechanisms of PD-related damage (Bonifati, 2014; Houlden & Singleton, 2012). However, a great deal of PD cases with clear patterns of familiarity still goes undiagnosed on genetic testing, suggesting that other determinant or susceptibility factors need to be found to achieve a complete vision of the genetic alterations underlying PD. A summary of the genes associated with familial PD is available in Chapter 1 of this book. Molecular imaging has provided potential means to understand the pathogenesis and track the progression of PD (Strafella et al., 2018). By employing specific radiolabeled tracers, positron emission tomography (PET), as well as single photon emission computerized tomography (SPECT), imaging allows the direct in vivo study of altered metabolic processes at a molecular level in resting conditions, during tasks or following pharmacological interventions. This has considerably increased our knowledge of mechanisms, leading to accumulation of disease burden in idiopathic (Politis, 2014) as well as in familial parkinsonism (Matarazzo et al., 2018; Varrone & Pellecchia, 2018). Compared with SPECT, PET provides a wider range of available radiotracers, an improved image quality, higher spatial localization, and shorter imaging protocols (Bateman, 2012). By contrast, SPECT is much more widely available in research and clinical centers compared with PET mainly due to the lower costs and availability of radiotracers with longer half-lives, such as 123I or 99mTc, which do not require a cyclotron within close proximity. Research PET and SPECT imaging has concentrated on the study of the pre- and postsynaptic dopaminergic function, regarded as the signature PD-related functional alteration underlying motor symptoms such as rigidity and bradykinesia, as well as several nonmotor symptoms (de Natale et al., 2018). This chapter will review the most recent advances, obtained by means of PET and SPECT imaging, in the understanding of the molecular pathophysiology of the dopaminergic system, in both idiopathic and familial PD.

Molecular biology and imaging of the dopaminergic system The pars compacta of the substantia nigra projects dopaminergic neurons to the basal ganglia along the medial forebrain bundle to the medial striatal neurons, where the presynaptic dopaminergic axons arborize extensively to take contact with a high number of postsynaptic striatal terminals. The integrity and function of the nigrostriatal dopaminergic pathway

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can be studied with PET and SPECT molecular imaging by targeting enzymes or receptors located on either the presynaptic or postsynaptic end. Presynaptic targets are the amino Lacid decarboxylase (AADC), the dopamine transporter (DAT), and the vesicular monoamine transporter 2 (VMAT2) enzymes. AADC is an essential enzyme that converts 3,4dihydroxyphenylalanine (levodopa) into dopamine and therefore participates in a critical process of the final biosynthesis of dopamine. For its critical metabolic role, AADC is also a potential target for disease-modifying gene therapy in PD (Christine et al., 2009). [18F] DOPA is a PET radiotracer that has been developed in the 1980s to target the function of AADC. It has been demonstrated that the measure of AADC correlates with the postmortem nigral cell count (Snow et al., 1993), and therefore, it has been long used as measure of the presynaptic dopaminergic integrity (Deng et al., 2002). DAT is a presynaptic symporter that reuptakes dopamine secreted in the synaptic cleft. By doing this, DAT modulates the overall availability of active dopamine in the brain. The activity of DAT is influenced by a number of external cellular factors that regulate the levels of dopamine according to short-term and long-term physiological demands (Vaughan & Foster, 2013). a-Synuclein has been found to bind to DAT and may directly exert a detrimental effect on its function (Swant et al., 2011). Mutations on the Parkin gene may also impair the function of DAT through multiple mechanisms, which include loss of ubiquitylation, and failure to suppress a-synuclein-mediated DAT neurotoxicity (Jiang et al., 2004; Moszczynska et al., 2007). Cocaine, a natural blocker of DAT (Gainetdinov et al., 2002), has provided the molecular substrate for several PET and SPECT radiotracers, which all show preponderant specificity for DAT in the basal ganglia, a region in which this enzyme is preferentially present over the serotonergic (SERT) and noradrenergic (NART) transporters. The affinity of the majority of these tracers also for SERT and NART does preclude their utilization of the study of DAT in extrastriatal regions. VMAT2 is expressed in all monoaminergic neurons of the brain and plays a critical role in the sorting, storing, and releasing of dopamine from the cytosol to the synaptic vesicles (Eiden et al., 2004). The levels of VMAT2 are decreased in PD, and it has been postulated that increasing the levels of this enzyme could exert a potentially protective role against PD (Glatt et al., 2006). High-affinity PET tracers selective or VMAT2 are [11C]DTBZ and [18F]AV-133 and have been used to study the integrity of this enzyme in both idiopathic and familial PD. The dopamine receptors represent the targets for the study of the postsynaptic dopaminergic system. The dopamine receptors are G-coupled proteins (labeled as D1 to D5), grouped into two families, the D1-like receptors, which contain the D1 and D5 receptors, and the D2-like receptors, which contain the D2, D3, and D4 receptors. The dopamine receptors most expressed in the striatum are the D1, D2, and D3 (Mishra et al., 2018) receptors. D1 receptors are widely expressed in the striatum and in the accumbens, as well as in other areas such as the SN pars reticulata (SNr), the dorsolateral prefrontal cortex, the cingulate cortex, and the hippocampus (Boyson et al., 1986). They mediate the dopaminergic direct pathway, a basal ganglia tonic inhibitory circuit in which the striatal neurons project directly to their output nuclei (internal pallidus and SNr) and are suppressed by activating inputs from the cortex to the striatum, which occur during movement (Chevalier et al., 1985). PET ligands for the D1 receptors include the noncompetitive, high-affinity antagonists [11C]SCH23390, [11C] SCH39166, [11C]NNC112, and [11C]NNC756 (Sioka et al., 2010). [11C]NNC756 has the highest

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affinity; however, its use is hindered by a slow kinetics. For this reason, this tracer has been overshadowed by [11C]SCH23390 in PD research. However, the use of this PET ligand has been limited, because of a concomitant affinity for 5-HT2 receptors (Bischoff et al., 1986). Newer radioligands, including agonists, have been developed for D1 receptors, but they have not found yet any application in clinical research (Cervenka, 2019). The D2 receptors are expressed in the stratum, the external pallidum, the amygdala, cortex, and hippocampus (Jackson & Westlind-Danielsson, 1994). The D2 receptors mediate the indirect dopaminergic pathway, an output circuit from the striatal neurons that, passing through the external pallidum, travels either to the subthalamic nucleus to the internal pallidum and SNr, or directly to these latter nuclei and is suppressed when movement is concluded. The direct and indirect dopaminergic pathways work in opposite ways to select motor programs that are not competing with each other (Mink, 2003). In PD, there is an imbalance between these two segregated and opposite pathways, where the dopaminergic depletion causes a loss of activity of the direct dopaminergic pathway, in favor to the hyperactivation of the indirect dopaminergic pathway, with consequent higher rates of inhibition of the thalamic activity and generation of motor symptoms. In addition, the loss of dopamine impairs the ability of the direct and indirect dopaminergic pathways to induce synaptic plasticity through long-term potentiation and depression, respectively (Shen et al., 2008); an aberrant plasticity could explain some of the common motor and nonmotor symptoms of PD (Dujardin & Laurent, 2003). Numerous PET and SPECT radioligands exist for the study of the D2 receptors and are constituted mainly by the agonist or antagonist [11C]Raclopride, [11C]NMSP, [18F]Fallypride, [18F]DMFP, [11C]NPA, [11C]MNPA, [11C] FLB457, [123I]IBZM, and [123I]Epidepride. Of these, the tracer that has found the most application in clinical research is the D2/D3 antagonist [11C]Raclopride, because of its high affinity to striatal D2 receptors and its advantageous kinetics. The property of dopamine to inhibit competitively [11C]Raclopride binding to D2 receptors has been used to study the levels of tracer displacement in response to a pharmacological challenge or a motor or cognitive task. This paradigm has been extensively studied to understand, in physiological and pathological conditions, the endogenous capacity to release dopamine along the nigrostriatal pathway (Ginovart, 2005). [11C]FLB457 and [18F]DMFP, D2/D3 tracers with low affinity for the D4 receptors show compared with [11C]Raclopride, a higher affinity for the extrastriatal D2 receptors, and can be used to selectively study the density of D2 receptors in those areas. The D3 receptors are abundant in the striatum and in the cerebral cortex and mediate a plethora of physiological functions not limited to motor symptoms, but including also cognition, attention, impulse control, and sleep symptoms, and the regulation of food intake, all aspects that are profoundly altered in PD (Mishra et al., 2018). In particular, the modulation of the D3 receptors in PD has been found to have a role in the reversal of motivational loss (Carnicella et al., 2014). Radioligands developed for the study of the dopaminergic receptors are either agonists or antagonists of these receptors. PET tracers with higher selectivity for D3 over D2 receptors are more scarce, and so far, [11C]PHNO is the only radiotracer with such characteristics that has found application in PD clinical research (Doot et al., 2019; Rabiner et al., 2009). An overview of the PET and SPECT radiotracers available for the molecular study of the dopaminergic system is available in Table 4.1.

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Molecular imaging of the presynaptic dopaminergic terminals

TABLE 4.1

List of available PET and SPECT radiotracers for the molecular study of the dopaminergic system.

Site

Target

PET tracers

SPECT tracers

AADC

[18F]DOPA [18F]FMT

DAT

[11C]PE2I [11C]CFT [11C]FP-CIT [11C]MP [11C]NM [11C]RTI-32 [18F]CFT [18F]FP-CIT [18F]PR04.MZ

VMAT2

[11C]DTBZ [18F]DTBZ [18F]AV-133

D1 receptors

[11C]SCH23390 [11C]SCH39166 [11C]NNC112 [11C]NNC756

Intrastriatal D2 receptors

[11C]Raclopride [11C]NMSP [11C]DMFP [11C]MNPA [11C]NPA [18F]Fallypride [18F]Spiperone

[123I]IBZM [123I]Epidepride [123I]Altropane [123I]Iodospiperone

Extrastriatal D2 receptors

[11C]FLB457

[123I]Iodolisuride [123I]IBF

D3 receptors

[11C]PHNO [18F]FTP

Presynaptic markers

[123I]FP-CIT [123I]b-CIT [123I]IPT [123I]PE2I [99Tc]TRODAT1

Postsynaptic markers

AADC, amino acid decarboxylase; D1, dopamine type 1 receptor; D2, dopamine type 2 receptor; DAT, dopamine transporter; VMAT2, vesicular monoamine transporter 2.

Molecular imaging of the presynaptic dopaminergic terminals Molecular imaging of the amino L-acid decarboxylase PET studies with [18F]DOPA have demonstrated that patients with established idiopathic PD show a severe presynaptic dopaminergic dysfunction in the putamen and, to a minor degree, in the caudate (Brooks et al., 1990; Morrish et al., 1996), which extent correlates with the staging of the disease as measured with the Hoehn and Yahr scale (Broussolle et al., 1999),

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and the presence and severity of bradykinesia and rigidity, but not tremor (Pikstra et al., 2016). The loss of striatal dopaminergic terminals functionally impairs striatocortical connections that cause a less efficient interplay between areas devoted to motor processing and generates the impairment in motor control seen in PD (Ruppert et al., 2020). An early loss of dopaminergic terminals can also be seen in motor areas at early stages of PD (Moore et al., 2008). Areas of extrastriatal dopaminergic loss in PD are the hypothalamus, and, in advanced stages, the SN, the locus coeruleus, and the midbrain (Moore et al., 2008). Although the level of [18F]DOPA influx constant (Ki) in the striatum does not predict disease severity as expressed with the Unified Parkinson’s Disease Rating Scale (UPDRS) (Li et al., 2018), low levels of [18F]DOPA Ki are generally associated with disease duration (Hilker et al., 2005), presence of microstructural alterations in the substantia nigra, the putamen, and the nigrostriatal projections on diffusion tensor magnetic resonance imaging (MRI) (Shang et al., 2021), and with higher probability to develop dyskinesias in the course of the disease (Löhle et al., 2016). The annual progression of dopaminergic terminals loss in PD has been estimated to be 6.3%e8.9% per year in the putamen, and 3.5%e4.4% in the caudate (Hilker et al., 2005; Morrish et al., 1998), and PD patients with tremor-dominant disease show significantly lower rates of progression of caudate pathology (Hilker et al., 2005). Regional decrease in [18F]DOPA Ki in PD has been also associated with nonmotor symptoms. Drug-naïve PD patients with high [18F]DOPA putaminal turnover are at higher risk of later neuropsychiatric fluctuations (Löhle et al., 2019). Another consistent association has been found with cognitive performances. Poor performances on tests for executive function, visual memory, working memory, and processing time have been associated, in idiopathic PD patients, with a loss of dopaminergic terminals in the caudate (Brück et al., 2001; Holthoff-Detto et al., 1997; Jokinen et al., 2009; Rinne et al., 2000). Cognitive deficits can also be associated also with extrastriatal dopaminergic dysfunction. Jokinen and colleagues found, in a group of 23 drug-naïve idiopathic PD patients, that signs of cognitive impairment were associated with loss of dopaminergic terminals in the anterior cingulate, an area associated with decision-making and impulse control (Jokinen et al., 2013), and that is affected in patients with Parkinson’s disease dementia (PDD) (Ito et al., 2002). Symptomatic carriers of mutations for the autosomal dominant familial PD genes SNCA, LRRK2, and GBA show similar degrees of [18F]DOPA striatal loss, and similar posterior-toanterior gradient of putaminal pathology, compared with idiopathic PD (Adams et al., 2005; Goker-Alpan et al., 2012; Greuel et al., 2020; Hasegawa et al., 2009; Krüger et al., 2001; Lopez et al., 2020; Samii et al., 1999; Saunders-Pullman et al., 2010; Sossi et al., 2010; Yoshino et al., 2017). Findings among symptomatic SNCA mutation carriers are similar with each other, independently from the mutation (missense or multiplication) (Krüger et al., 2001; Samii et al., 1999; Yoshino et al., 2017). Two studies have investigated [18F] DOPA in an asymptomatic carriers, summing two unaffected carriers of the A53T SNCA mutation and one carrier of the A30P SNCA mutation. Both subjects at risk for familial PD showed values of [18F]DOPA Ki overlapping with those of healthy controls (Krüger et al., 2001; Samii et al., 1999). Studies on unaffected LRRK2 mutation carriers show either similar (Sossi et al., 2010; Wile et al., 2017), or decreased (Nandhagopal et al., 2008), striatal values of [18F]DOPA compared with controls. These subjects seem to show progressive decreases at an older age, presumably because of a compensatory upregulation of ADCC (Sossi et al., 2010;

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Wile et al., 2017). In affected LRRK2 carriers, the dopaminergic deficit seems to progress in a similar fashion compared with idiopathic PD patients (Wile et al., 2017). PD patients carrying the GBA gene variants p.E365K and p.T408M show greater degrees of dopaminergic denervation, as seen with [18F]DOPA, compared with idiopathic PD patients, suggestive of more aggressive disease (Greuel et al., 2020). One longitudinal study is available on a cohort of affected and unaffected carriers of GBA mutations (Lopez et al., 2020). In this study, the degree of progression of [18F]DOPA striatal uptake loss was similar to that observed in idiopathic PD patients, being it approximately 4% per year in the caudate and 5% per year in the putamen. Conversely, unaffected GBA carriers showed, both at cross-sectional and at longitudinal observations, levels and trajectories of dopaminergic denervation similar to those of healthy controls (Lopez et al., 2020). Symptomatic autosomal recessive gene mutation carriers show profound decreases of [18F]DOPA uptake in the striatum quantifiable, in the putamen, to 40%, 28%, and 15% of that of the control values in PINK1, Parkin, and DJ-1 mutation carriers, respectively, which are significantly lower compared with idiopathic PD, despite a milder degree of motor disability (Broussolle et al., 2000; Eggers et al., 2010; Hilker et al., 2001; Khan, Valente, et al., 2002). Parkin PD patients show a preserved posterior-to-anterior putaminal gradient (Scherfler et al., 2004). The asymmetry of the dopaminergic deficit in the striatum seems to be preserved in DJ-1 (Dekker et al., 2003), whereas it is less pronounced in Parkin (Khan, Brooks, et al., 2002). When compared with young onset idiopathic PD, affected Parkin PD did not show differences in the degree of striatal [18F]DOPA deficit (Ribeiro et al., 2009). PINK1 PD patients seem to display a peculiar spatial localization of extrastriatal dopaminergic deficit, compared with idiopathic PD. PINK1 PD pathology seems to involve more the midbrain and the locus coeruleus, compared with idiopathic PD; on the contrary, idiopathic PD patients show alterations of the hypothalamus, which is spared in PINK-determined PD (Pavese et al., 2010). A similar pattern of peculiar midbrain involvement has also been found in Parkin PD (Scherfler et al., 2004). These features have been interpreted as possibly the effect of a different monoaminergic alterations in these monogenic forms. In line with the mild clinical progression of disease, a 5-year longitudinal study on three symptomatic compound heterozygous Parkin mutation carriers showed a mean annual reduction of [18F]DOPA uptake of 0.5% in the putamen and 2% in the caudate (Pavese et al., 2009). The study of heterozygous mutation carriers can provide information about the possible biological role of a single mutation to one of these genes. Single Parkin mutation carriers show subclinical reductions of [18F]DOPA striatal uptake compared with controls, and slow rates of deficit progression (Pavese et al., 2009); similarly, single PINK1 carriers, show 20% reduction in [18F]DOPA uptake (Eggers et al., 2010; Khan, Valente, et al., 2002). These results suggest that carrying heterozygous mutations for autosomal recessive familial PD genes results in subtle degrees of dopaminergic deficit and may pose at higher risk of developing symptoms in the future. A single case report is available of a symptomatic c.238G > A PLA2G6 gene mutation carrier (Agarwal et al., 2012), which presents with a form of atypical parkinsonism with dystonia, and cognitive decline. Despite the peculiar clinical picture, the pattern of presynaptic degeneration as studied with [18F]DOPA overlaps that of idiopathic PD patients (Agarwal et al., 2012) (Table 4.2).

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TABLE 4.2

Summary of the main findings from the molecular imaging studies of the presynaptic dopaminergic system in idiopathic and familial PD.

Molecular target AADC

Main findings

References

Idiopathic Severe, asymmetric, loss in PD putamen > caudate, with posterior-toanterior gradient. Associated with severity of rigidity and bradykinesia and disease duration. In extrastriatal areas: decrease in hypothalamus, SN, LC, midbrain. Annual progression 6.3%e8.9% per year in the putamen, 3.5%e4.4% per year in the caudate.

Brooks et al., 1990; Broussolle et al., 1999; Brück et al., 2001; Hilker et al., 2005; Holthoff-Detto et al., 1997; Ito et al., 2002; Jokinen et al., 2009, 2013; Kaasinen et al., 2000; Lee et al., 2000; Li et al., 2018; Löhle et al., 2016, 2019; Moore et al., 2008; Morrish et al., 1998; Morrish et al., 1996; Pikstra et al., 2016; Ruppert et al., 2020; Shang et al., 2021

SNCA

In affected carriers, asymmetric loss in putamen > caudate with posterior-toanterior gradient. In unaffected carriers, uptake within values of healthy controls.

Krüger et al., 2001; Samii et al., 1999; Yoshino et al., 2017

LRRK2

In affected carriers, asymmetric loss in putamen > caudate with posterior-toanterior gradient. In unaffected carriers, values within or below those of healthy controls.

Adams et al., 2005; Nandhagopal et al., 2008; Sossi et al., 2010; Wile et al., 2017

GBA

Goker-Alpan et al., 2012; Lopez et al., 2020; In affected carriers, asymmetric loss in Saunders-Pullman et al., 2010 putamen > caudate with posterior-toanterior gradient. In unaffected carriers, uptake within values of healthy controls. In unaffected heterozygous, subclinical loss in the striatum. Annual progression in affected carriers 5% per year in the putamen and 4% per year in the caudate.

Parkin

In affected carriers, severe, slightly Broussolle et al., 2000; Hilker et al., 2001; Khan, Brooks, et al., 2002; Pavese et al., 2010; asymmetric, loss in the putamen. Annual progression in affected carriers 0.5% per Ribeiro et al., 2009; Scherfler et al., 2004 year in the putamen and 2% per year in the caudate.

PINK1

In affected carriers, severe asymmetric loss in the putamen. Involvement of the midbrain and LC. In unaffected heterozygous, subclinical loss in the striatum.

Eggers et al., 2010; Khan, Valente, et al., 2002; Pavese et al., 2010

DJ-1

In affected carriers, severe loss in the putamen

Dekker et al., 2003

PLA2G6

In an affected carrier, asymmetric loss in putamen > caudate with posterior-toanterior gradient.

Agarwal et al., 2012

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Molecular imaging of the presynaptic dopaminergic terminals

TABLE 4.2

Summary of the main findings from the molecular imaging studies of the presynaptic dopaminergic system in idiopathic and familial PD.dcont’d

Molecular target DAT

77

Main findings

References

Idiopathic Severe, asymmetric, loss in PD putamen > caudate, with posterior-toanterior gradient. Associated with severity of rigidity and bradykinesia and disease duration. Young patients show greater loss in the caudate. Loss of DAT associated with disease progression, and progression of severity of motor symptoms. Association with speech problems, hyposmia, sleep problems, impulse control disorder. Contrasting results regarding the association with depression and anxiety.

Arnaldi et al., 2015; Bohnen et al., 2007; Caspell-Garcia et al., 2017; Ceravolo et al., 2013; Chou et al., 2004; Cilia et al., 2010; Colloby et al., 2005; Di Giuda et al., 2012; Djaldetti et al., 2011; Felicio et al., 2010; Filippi et al., 2005; Fois et al., 2021; Frosini et al., 2015; Guttman et al., 1997; Happe et al., 2007; Hesse et al., 2009; Hirata et al., 2020; Hong et al., 2014; Huang et al., 2001; Jaakkola et al., 2017; Juri et al., 2021; Kaasinen et al., 2014; Kiferle et al., 2014; Kim et al., 2019; Koros et al., 2020; Lee et al., 2000; Li et al., 2018; Lorio et al., 2019; Maillet et al., 2021; Marek et al., 2001, 1996; Marié et al., 1999; Martín-Bastida et al., 2019; Martini et al., 2018; Mito et al., 2020; Moccia et al., 2014; Moriyama et al., 2011; Mozley et al., 2000; Murakami et al., 2021; Navalpotro-Gomez et al., 2019; Nozaki et al., 2021; Orso et al., 2021; Pagano et al., 2019; Palermo et al., 2020; Palmisano et al., 2020; Pirker et al., 2003; Polychronis, Dervenoulas, et al., 2019; Polychronis, Niccolini, et al., 2019; Ravina et al., 2012; Remy et al., 2005; Rinne et al., 2000, 1999; Roussakis et al., 2019; Schwarz et al., 2000; Siepel et al., 2014; Son et al., 2019; Takahashi et al., 2019; Troiano et al., 2009; Umehara et al., 2021; van Deursen et al., 2020; Vriend et al., 2014; Weintraub et al., 2005; Weng et al., 2004; Xu et al., 2021; Yang et al., 2019; Yoo et al., 2019; Yousaf et al., 2019, 2018

SNCA

In affected carriers, asymmetric loss in caudate and putamen with posterior-toanterior gradient. In unaffected carriers, uptake within values of healthy controls. Multiplication carriers show asymmetric or symmetric loss in the striatum. In unaffected carriers, uptake within healthy control values.

Ahn et al., 2008; Fuchs et al., 2007; Koros et al., 2018; Martikainen et al., 2015; Olgiati et al., 2015; Puschmann et al., 2009; Ricciardi et al., 2016; Wilson et al., 2019; Xiong et al., 2016; Yoshino et al., 2017

LRRK2

In affected carriers, asymmetric loss in putamen > caudate with posterior-toanterior gradient. In unaffected carriers, about 11% show loss in the striatum.

Adams et al., 2005; Artzi et al., 2017; Bergareche et al., 2016; Hasegawa et al., 2009; Isaias et al., 2006; McNeill et al., 2013; Pont-Sunyer et al., 2017; Sánchez-Rodríguez et al., 2021; Sierra et al., 2017; Simuni et al., 2020; Varrone et al., 2004 (Continued)

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TABLE 4.2

Summary of the main findings from the molecular imaging studies of the presynaptic dopaminergic system in idiopathic and familial PD.dcont’d

Molecular target

VMAT2

Main findings

References

GBA

In affected carriers, asymmetric loss in Greuel et al., 2020; Ichinose et al., 2019; Lee putamen > caudate with posterior-toet al., 2021; Simuni et al., 2020 anterior gradient. In unaffected carriers, about 3% show loss in the striatum

Parkin

In affected carriers, severe, rather symmetric Guo et al., 2011; Ribeiro et al., 2009; Shyu loss in the striatum. No correlation with et al., 2005; Varrone et al., 2004; Varrone & disease severity. Annual progression 0.9% Pellecchia, 2018 per year in the striatum.

PINK1

In affected carriers, severe, rather symmetric Albanese et al., 2005; Guo et al., 2011; loss in the striatum. No correlation with Kessler et al., 2005; Rango et al., 2013; disease severity. Annual progression 1.7% Samaranch et al., 2010; Weng et al., 2007 per year in the striatum. In unaffected heterozygous, increased levels of DAT.

DJ-1

In affected carriers, severe, rather symmetric Guo et al., 2011 loss in the striatum.

ATP13A2 In affected carriers, severe, bilateral, symmetric loss in the striatum

Brüggemann et al., 2010; Santoro et al., 2011

PLA2G6

In an affected carrier, severe loss in the striatum. In an unaffected carrier, slight loss in the putamen.

Shi et al., 2011

ATP1A3

Normal levels in the striatum

Brashear et al., 1999

POLG

Severe, bilateral, symmetric loss in the striatum putamen > caudate

Tzoulis et al., 2013

Cho et al., 2019; Fu et al., 2018; Hsiao et al., Idiopathic Severe, asymmetric, loss in 2014; Lee et al., 2000; Lin et al., 2014; Shi PD putamen > caudate, with posterior-toanterior gradient. Associated with severity et al., 2019 of rigidity and bradykinesia and disease duration. In extrastriatal areas, loss in the substantia nigra and pallidum. Correlation with severity of disease and with increasing burden of nonmotor symptoms. SNCA

In affected carriers, severe, asymmetric, loss Yoshino et al., 2017 in putamen > caudate, with posterior-toanterior gradient.

LRRK2

In affected carriers, severe, asymmetric, loss Adams et al., 2005; Hasegawa et al., 2009; in putamen > caudate, with posterior-toNandhagopal et al., 2008; Sossi et al., 2010; anterior gradient. Wile et al., 2017 In unaffected carriers, modest loss in the striatum.

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Summary of the main findings from the molecular imaging studies of the presynaptic dopaminergic system in idiopathic and familial PD.dcont’d

Molecular target

Main findings

References

DNAJC13 In affected carriers, severe, asymmetric, loss Appel-Cresswell et al., 2014 in putamen > caudate, with posterior-toanterior gradient. PLA2G6

In affected carriers, severe, asymmetric, loss Agarwal et al., 2012 in putamen > caudate, with posterior-toanterior gradient.

DCTN1

In affected carriers, severe, symmetric, loss in putamen > caudate, with posterior-toanterior gradient.

Felicio et al., 2014

AADC, amino acid decarboxylase; ATP13A2, ATPase 13A2; DAT, dopamine transporter; DCTN1, dynactin 1; DNAJC13, DNAJ subfamily C member 13; GBA1, glucocerebrosidase 1; LC, locus coeruleus; LRRK2, leucine-rich repeat kinase 2; PD, Parkinson’s disease; PINK1, PTEN-induced kinase; PLA2G6, phospholipase A2, group 6; POLG, DNA polymerase subunit gamma; SN, substantia nigra; SNCA, a-synuclein; VMAT2, vesicular monoamine transporter 2.

Molecular imaging of the dopamine transporter The wide availability of SPECT across research centers, the use of [123I]FP-CIT in the clinical setting and the selection of PET and SPECT radiotracers, has produced a great number of studies investigating the role of DAT in both idiopathic and familial forms of PD. From a range of studies, we have detailed information about how DAT changes over the course of the disease, and about the possible causal associations with a number of motor and nonmotor symptoms of PD. The advent of new-generation, high-sensitivity fluorinated DAT PET radiotracers, such as [18F]PR04.MZ, can significantly extend the application of this method of study in patients with PD and other movement disorders (Juri et al., 2021; Lehnert et al., 2022). Both PET and SPECT imaging for DAT can distinguish patients with degenerative parkinsonism from healthy controls by a selective striatal loss in PD, with high sensitivity and specificity (Chou et al., 2004; Eshuis et al., 2009; Guttman et al., 1997; Huang et al., 2001; Mozley et al., 2000; Rinne et al., 1999; Weng et al., 2004). In PD, loss of DAT has been found also in other regions such as the pallidum and the SN (Pagano et al., 2019). The putamen is consistently more affected than the caudate, and within the putamen, there is a posterior-to-anterior gradient of DAT pathology. Patients with unilateral motor symptoms, and patients with mild, early drug-naïve idiopathic PD, can show significant levels of DAT loss in the ipsilateral putamen to the clinical symptoms (Filippi et al., 2005; Marek et al., 1996; Schwarz et al., 2000; Weng et al., 2004), indicating that molecular imaging of DAT could flag subclinical stages of dopaminergic terminal loss. Loss of DAT uptake in drug-naïve early PD patients has also been associated with glucose hypometabolism in deep and cortical gray matter nuclei (Orso et al., 2021). DAT loss in the putamen of established idiopathic PD patients correlates with the disease stage as assessed with the Hoehn and Yahr staging (Takahashi et al., 2019), and the severity of bradykinesia and rigidity, as measured with the UPDRS, but not of tremor (Kaasinen et al., 2014; Moccia et al., 2014; Pagano et al., 2019; Rinne et al., 1999). This finding indicates that tremor may underlie a different, nondopaminergic pathophysiology. This

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notion is corroborated by the less efficacy of dopaminergic replacement therapy in clinical setting for this symptom. A recent work, however, seems to indicate a possible role played by striatal dopaminergic denervation to contribute to rest tremor, at least in some cases (Fois et al., 2021). The akinetic-rigid syndrome in PD can underlie peculiar spatial aspects of dopaminergic denervation: for example, upper extremity rigidity is associated with a higher loss of [123I]FP-CIT uptake in the dorsal putamen, whereas hypomimia is associated with DAT deficiency in the caudate nucleus (Mäkinen et al., 2019). A loss of striatal DAT has also been associated with other motor symptoms, such as specific gait abnormalities (Hirata et al., 2020; Palmisano et al., 2020), as well as dysphagia, speech difficulties, and drooling (Mito et al., 2020; Palermo et al., 2020; Polychronis, Dervenoulas, et al., 2019; Polychronis, Niccolini, et al., 2019). The age of onset of idiopathic PD has been put in relationship with different characteristics of DAT loss in the striatum. Patients with PD motor symptoms onset before 50 years of age show greater [11C]CFT uptake in the caudate compared with PD patients with onset after 50 years of age, whereas the levels of DAT loss in the putamen overlapped (Yang et al., 2019). This could be due to different degrees of neuronal damage along the nigrostriatal pathway, as suggested by the different projections that the caudate and putamen receive from the SN (Fearnley & Lees, 1991). This hypothesis could be corroborated by a recent study, in which topographical loss of SN contrast ratio as expressed with neuromelanin MRI correlated with [11C]PE2I PET loss in both the putamen and caudate (Martín-Bastida et al., 2019). Longitudinal observation has demonstrated a fast and progressive loss of striatal DAT loss in idiopathic PD with disease progression (Colloby et al., 2005; Li et al., 2018; Marek et al., 2001; Pagano et al., 2019; Pirker et al., 2003; Rinne et al., 2000). In one study using [123I]b-CIT SPECT, the rate of progression of DAT loss did not correlate with the annual loss of performances of clinical function (Marek et al., 2001). In a more recent study, using the more selective PET tracer [11C]PE2I, it was found that the degree of DAT decline was associated with motor progression over time (Li et al., 2018). Despite different initial degrees of dopaminergic denervation in the striatum, patients with young-onset and lateonset PD do not seem to display different rates of progression of [123I]FP-CIT progression over a follow-up of up to 4 years (Koros et al., 2020). A detectable progression of DAT loss could also be found in advanced patients with PD dementia (Colloby et al., 2005), as well as in a population of advanced PD patients with mean disease duration of 20.6 years (Djaldetti et al., 2011). In this latter study, a preserved asymmetry of striatal dopaminergic degeneration and a dorsal-to-ventral putaminal gradient of DAT loss could still be detected (Djaldetti et al., 2011). The degree of striatal DAT loss may be a harbinger of disease progression and faster reaching of clinical milestones (Ravina et al., 2012). In combination with other imaging modalities such as structural and microstructural MRI, DAT imaging could also predict future progression of the disease. In one large study of 205 drug-naïve PD patients, a combination of putaminal loss of [123I]FP-CIT uptake and MD increase in the operculum was strong predictor of worsening on the UPDRS part-II scores, at a 1-year follow-up (Lorio et al., 2019). The association between DAT loss and emergence of motor complications such as levodopa-induced dyskinesias (LIDs) has been studied by some PET and SPECT DAT imaging studies (Hong et al., 2014; Roussakis et al., 2019; Troiano et al., 2009). One PET study using [11C]MP has found that PD patients with LID show greater loss of DAT in striatal

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dopaminergic terminals, as the possible effect of a compensatory attempt of DAT to increase the dopaminergic pool in the synapses, which results in high, oscillating levels of dopamine (Troiano et al., 2009). The ability of DAT imaging to predict those PD patients who will develop LIDs later in time, however, has produced contrasting results. In a first retrospective study, Hong and colleagues found that the baseline levels of [18F]FP-CIT uptake in the putamen were predictive of the future development of LIDs (Hong et al., 2014). A more recent, prospective study over 5 years using [123I]FP-CIT SPECT did not find any predictive power of striatal DAT density in differentiating idiopathic PD patients who will go on to develop LIDs from those who will not (Roussakis et al., 2019). Dopaminergic denervation as assessed with DAT molecular imaging has been extensively studied, in idiopathic PD, in association with the presence and development of nonmotor symptoms. Patients with low density of striatal DAT are considered at higher risk of developing PD-related complications. In a large imaging study employing [123I]b-CIT SPECT on a group of 537 PD patients followed up for 5.5 years, it was found that lower striatal binding at baseline was independently associated with higher risk of falls, cognitive impairment, psychosis, and depression (Ravina et al., 2012). The study of changes of presynaptic DAT availability in association with nonmotor symptoms can also provide useful information about the compensatory changes that the synapses undergo during the course of this disease. In idiopathic PD, lower DAT binding in the striatum has been consistently associated with hyposmia, sleep problems, psychiatric symptoms, and cognitive disturbance (Arnaldi et al., 2015; Bohnen et al., 2007; Happe et al., 2007; Jaakkola et al., 2017; Kiferle et al., 2014; Maillet et al., 2021; Siderowf et al., 2005; Siepel et al., 2014; Umehara et al., 2021; van Deursen et al., 2020; Yousaf et al., 2019). PET and SPECT imaging studies have demonstrated that reduced DAT density in the striatum is associated with the presence of hyposmia (Bohnen et al., 2007; Siderowf et al., 2005), REM sleep behavior disorder (RBD) (Arnaldi et al., 2015; Xu et al., 2021), and impulse control disorder (ICD) (Martini et al., 2018). A loss of DAT availability in the caudate has been associated with the presence of excessive daytime sleepiness (Yousaf et al., 2018), psychosis (Kiferle et al., 2014), autonomic dysfunction (van Deursen et al., 2020), and orthostatic hypotension (Umehara et al., 2021), as well as with presence of cognitive alterations in specific cognitive domains, such as frontal executive tasks, processing speed, executive function, but not memory (Kim et al., 2019; Marié et al., 1999; Siepel et al., 2014; Son et al., 2019; Vriend et al., 2020). Finally, the presence of psychosis and visual hallucinations has been associated with the loss of DAT in the presynaptic dopaminergic terminals of the ventral striatum (Cilia et al., 2010; Navalpotro-Gomez et al., 2019), and the presence of pareidolia, a visual phenomenon that is regarded as a minor hallucination (Ffytche & Aarsland, 2017) has been associated with a loss of [123I]FP-CIT uptake in the in the right caudate and putamen nuclei (Murakami et al., 2021). A few observational studies have found that detection of lower DAT levels in the caudate can be associated with the future development of RBD and cognitive impairment (Arnaldi et al., 2015; Caspell-Garcia et al., 2017; Yousaf et al., 2019), suggesting that this functional alteration can be linked with a more aggressive phenotype in PD. Inconclusive associations have been found between changes of DAT and other nonmotor symptoms such as depression and anxiety. A few SPECT and PET studies have linked depression with either increased (Ceravolo et al., 2013; Felicio et al., 2010; Moriyama et al., 2011) or decreased (Di Giuda et al., 2012; Frosini et al., 2015; Hesse et al., 2009; Remy

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et al., 2005; Vriend et al., 2014; Weintraub et al., 2005; Yoo et al., 2019) DAT availability in the striatum. The use of heterogenous populations, and of different tracers, makes it difficult to compare these studies directly; however, it can be speculated that those findings could be the effect of two different functional events in the pathophysiology of this symptom in PD: on one side, the progressive synaptic deterioration causes the onset of depressive symptoms in PD patients; on the other side, the relative increase in DAT may be due to a lack of compensatory downregulation of DAT at the synaptic level that produces a net reduction of dopaminergic tone in the synaptic cleft (Ceravolo et al., 2013). Recently, dopamine transporter molecular imaging has been studied in relation to the clinical outcome following deep brain stimulation (DBS) of the subthalamic nucleus (STN) in PD patients. Nozaki and colleagues studied 10 PD patients who underwent STN-DBS and [11C] CFT PET after 12 months from surgery. They found an increase of DAT binding in the contralateral anteroventral putamen and ipsilateral ventral caudate to the treated side, which correlated with preoperative [11C]CFT uptake in numerous striatal regions and with the degree of postoperative motor recovery, thus possibly suggesting a neuromodulatory effect of surgical therapy in these patients (Nozaki et al., 2021). Affected carriers of mutations for familial forms of PD consistently show a marked reduction of DAT on molecular imaging studies, to an extent similar to that of idiopathic PD patients (Adams et al., 2005; Brüggemann et al., 2010; Hasegawa et al., 2009; Ichinose et al., 2019; Kessler et al., 2005; Koros et al., 2018; McNeill et al., 2013; Ribeiro et al., 2009; Shi et al., 2011; Varrone et al., 2004; Wilson et al., 2019; Xiong et al., 2016). Alike idiopathic PD, a degeneration of the putamen relative to the caudate, with a posterior-to-anterior gradient of putaminal degeneration, has been found in SNCA (Nishioka et al., 2009; Olgiati et al., 2015; Puschmann et al., 2009; Wilson et al., 2019), LRRK2 (Adams et al., 2005; Hasegawa et al., 2009; Isaias et al., 2006; Pont-Sunyer et al., 2017), and GBA (Ichinose et al., 2019) mutation carriers, indicating that in the autosomal dominant forms of familial PD, the functional effects of presynaptic dopaminergic degeneration are comparable with those of idiopathic PD. SNCA PD patients carrying the missense mutation A53T show an asymmetric dopaminergic deficit similar to that observed in idiopathic PD (Puschmann et al., 2009; Ricciardi et al., 2016; Wilson et al., 2019). Recently, our group tested seven affected A53T SNCA carriers with [123I]FP-CIT SPECT and found, compared with idiopathic PD, a greater degree of DAT loss in the caudate, but not in the putamen (Wilson et al., 2019). This finding was reported also by Koros and colleagues in 11 A53T SNCA PD and was interpreted as a possible floor effect (Koros et al., 2018). A picture of profound, symmetrical, DAT loss compared with idiopathic PD has also been found in two affected carriers of the A53E SNCA mutation with severe bradykinesia but little or no tremor (Martikainen et al., 2015). Three studies are available on a handful of affected carriers of SNCA multiplications (Ahn et al., 2008; Fuchs et al., 2007; Olgiati et al., 2015). Those studies all showed severe asymmetric (Ahn et al., 2008; Olgiati et al., 2015), or symmetric (Fuchs et al., 2007) degrees of striatal DAT loss. There are a few studies investigating the DAT integrity in unaffected mutation carriers for autosomal dominant familial PD. A large imaging study conducted on 208 LRRK2 and 184 GBA unaffected carriers showed that about 11% of LRRK2 and 3% of GBA display a significant DAT reduction, in the absence of motor symptoms, but in the presence of higher rates of prodromal signs such as hyposmia (Simuni et al., 2020). A finding of reduced DAT in

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unaffected LRRK2 carriers in the putamen or in the right striatum has also been reported in two studies (Artzi et al., 2017; Bergareche et al., 2016). This finding has been associated with the presence of prodromal signs such as hyposmia and may be a harbinger of future motor phenoconversion. This hypothesis is corroborated by two longitudinal studies (Lee et al., 2021; Sierra et al., 2017). In the first study, 29 unaffected LRRK2 carriers, in which the three that phenoconverted at 4 years’ follow-up showed lower striatal [123I]FP-CIT SPECT DAT binding at baseline compared with nonconverters (Sierra et al., 2017). A large longitudinal study including unaffected LRRK2 and GBA mutation carriers, as well as idiopathic PD patients, showed that LRRK2 mutation carriers display similar degrees and trajectories of striatal dopaminergic denervation, compared with idiopathic PD, whereas the presence of GBA N370S variant was associated with faster deterioration of putaminal dopaminergic function (Lee et al., 2021). A cohort of asymptomatic LRRK2 G2019S mutation carriers from Spain has been followed up for up to 8 years with serial [123I]FP-CIT SPECT scans (Sánchez-Rodríguez et al., 2021; Sierra et al., 2017). The yearly rate of DAT density decline in this cohort has been calculated as 3.5% (Sánchez-Rodríguez et al., 2021). Carriers who phenoconverted showed lower striatal [123I]FP-CIT SPECT DAT binding at baseline compared with nonconverters (Sierra et al., 2017), and none of those with an undefined threshold between 0.5 and 0.8 developed symptoms ascribable to PD (Sánchez-Rodríguez et al., 2021). These results indicate that, in LRRK2, as well as in GBA mutation carriers, low DAT binding can be present years before development of motor symptoms and represents a harbinger of proximity to PD phenoconversion. A few studies investigated unaffected SNCA carriers with [123I[FP-CIT SPECT. Both A53T carriers and a single duplication carrier did not show any abnormalities of striatal [123I [FP-CIT uptake (Fuchs et al., 2007; Ricciardi et al., 2016; Wilson et al., 2019). Interestingly, seven A53T SNCA-unaffected carriers with normal DAT binding showed a significantly decreased serotoninergic synaptic degeneration in the raphe nuclei, caudate, putamen, thalamus, hypothalamus, amygdala, and brainstem, suggesting that serotonergic pathology may take place before dopaminergic pathology (Wilson et al., 2019). Carriers of mutations for autosomal recessive PD such as Parkin, PINK1, and DJ-1 tend to display a more symmetric degree of DAT loss in the striatum (Albanese et al., 2005; Guo et al., 2011; Kessler et al., 2005; Rango et al., 2013; Samaranch et al., 2010; Shyu et al., 2005; Weng et al., 2007). Compared with age-matched idiopathic PD, Parkin PD seems to show higher degrees of [123I]FP-CIT SPECT uptake reduction (Varrone et al., 2004). In another study that used [11C]PE2I PET, however, the severity of DAT loss of Parkin PD patients was comparable with that of other gene-negative, young-onset PD patients with long disease duration (Ribeiro et al., 2009). Despite the degree of DAT loss in these forms can be severe, no correlation has been found between the severity of dopaminergic denervation and disease duration in both Parkin (Varrone et al., 2004) and PINK1 mutation carriers (Samaranch et al., 2010). Longitudinal observation of the course of DAT reduction in autosomal recessive PD is in line with the slow clinical progression of this disease. Parkin PD patients show an annual reduction of [123I]FP-CIT striatal uptake of 0.9% as opposed to 5.9% of idiopathic PD (Varrone & Pellecchia, 2018). In another study, Weng and colleagues reported that three homozygous PINK1 PD showed a slower annual rate of progression of striatal degeneration (1.7%) compared with idiopathic PD (4.1%), indicating how the slow clinical course of these forms is reflected in a slow dopaminergic functional decline (Weng et al., 2007). In one SPECT study, heterozygous unaffected PINK1 mutation carriers showed significantly increased

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levels of [123I]FP-CIT uptake in the striatum (Kessler et al., 2005). This finding is of unclear significance and may reflect some form of preclinical functional adaptation; however, it has not been replicated since. A few imaging studies are also available on carriers of genetic mutations for rare forms of autosomal dominant or recessive familial atypical parkinsonisms. Homozygous carriers of mutations of ATP13A2 gene, causing Kufor-Rakeb syndrome, showed bilateral and symmetric reductions of striatal DAT density, whereas a heterozygous carrier had normal DAT binding (Brüggemann et al., 2010; Santoro et al., 2011). One affected carrier of a p.D331Y mutation on the PLA2G6 gene showed a substantial reduction of [11C]CFT binding in the striatum, whereas his younger, unaffected sister, had only a slight reduction of putaminal [11C]CFT uptake (Shi et al., 2011). Two brothers with different phenotypical manifestations of PLA2G6 mutations have been more recently studied with [18F]FP-CIT PET. One sibling, with an adult-onset dystonia-parkinsonism had a severe DAT loss in the putamen; his brother, with a childhood onset atypical neuroaxonal dystrophy, had a normal DAT, indicating how the clinical phenotype with or without parkinsonism may predict, in PLA2G6 carriers, the levels of dopaminergic deterioration (Kim et al., 2015). This has not proven true for three family members with mutation on the autosomal dominant ATP1A3, which causes a rapid onset dystonia-parkinsonism. Despite the presence of parkinsonism, [11C]CFT PET did not show any uptake difference compared with controls (Brashear et al., 1999). On the other hand, a group of 11 carriers of POLG mutation, despite none of them showed motor signs suggestive of parkinsonism, displayed on [123I]FP-CIT SPECT, a marked, bilateral, symmetric, putaminal higher than caudate, reduction of DAT compared with controls (Tzoulis et al., 2013).

Molecular imaging of the vesicular Monoamine Transporter 2 Molecular imaging studies about the pathology of VMAT2 in idiopathic and familial PD are less available in the literature. Patients with idiopathic PD, studied with the PET tracer [18F] DTBZ, show an asymmetric, marked reduction of the availability of VMAT2 enzyme in the presynaptic striatal terminals, with a preserved rostrocaudal gradient of putaminal pathology severity (Cho et al., 2019; Fu et al., 2018; Hsiao et al., 2014; Lin et al., 2014). The extent of [18F] DTBZ-specific uptake ratio loss in critical regions such as the putamen, caudate, and SN was proportional to the severity of the disease reaching, in the most advanced stages, reductions of 83.2%, 63.9%, and 44% compared with controls (Hsiao et al., 2014). Together with the degeneration of the nigrostriatal pathway, PD patients also show a corresponding reduction of VMAT2 density along the nigropallidal pathway, in which the external pallidum displays degeneration across all stages of PD, whereas the internal pallidum degenerates only in the late stages of disease (Cho et al., 2019). A selective decrease of VMAT2 activity, as examined with [11C]AV-133, has been associated in two recent studies, with the presence of freezing of gait (Zhou et al., 2019), and with increased presence of nonmotor symptoms (Shi et al., 2019). A number of PET studies have investigated the integrity of VMAT2 in affected and unaffected carriers of mutations for familial PD. The extent, symmetry, and gradient of VMAT2 density loss in the striatum of affected carriers for both autosomal dominant (LRRK2, SNCA, DNAJC13), and autosomal recessive (PLA2G6) closely resembles what described for the patients with idiopathic PD (Adams et al., 2005; Agarwal et al., 2012; Appel-Cresswell et al., 2014; Felicio et al., 2014; Hasegawa et al., 2009; Nandhagopal et al., 2008; Sossi et al., 2010;

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Wile et al., 2017; Yoshino et al., 2017). In two affected carriers of mutations in the DCTN1 gene (Perry Syndrome), the degree of VMAT2 density loss was symmetrical, in line with the rather symmetrical characteristics of DCTN1-mediated parkinsonism (Felicio et al., 2014). These results suggest that, despite the differences in the age at onset, clinical characteristics, and disease course, affected patients with familial PD share a similar functional biology of dopaminergic denervation in the basal ganglia, to idiopathic PD. Asymptomatic carriers of LRRK2 mutations can show some degree of VMAT2 functional alteration in the striatum (Sossi et al., 2010; Wile et al., 2017). In a group of eight subjects with mean age of 48 years, a small but significant reduction of striatal [11C]DTBZ was detectable, as possible sign of an ongoing subclinical dopaminergic degeneration (Sossi et al., 2010). LRRK2 carriers may display a, perhaps compensatory, increase of serotonergic activity as seen with [11C]DASB, when VMAT2 activity is still in the normal range, suggesting that serotonergic functional alterations may precede dopaminergic deterioration (Wile et al., 2017). It must be born in mind, however, that LRRK2 mutations do not have full penetrance; therefore, longitudinal studies on large cohorts are needed to understand possible signatures toward phenoconversion and to assess whether early mechanisms of dopaminergic compensation may be effective and prevent future development of clinical features.

Combined study of the AADC, DAT, and VMAT2 enzyme in idiopathic and familial PD A combined molecular study of the presynaptic dopaminergic damage could provide key information about the biological basis of dopaminergic loss along the nigrostriatal system in idiopathic and familial PD, highlighting possible similarities or differences across disease entities. The extent by which the loss of dopaminergic presynaptic enzymes represents a functional adaptation to the synaptic terminal loss has been first explored in one important study by Lee and colleagues on idiopathic PD (Lee et al., 2000). In this study, they used [18F]DOPA, [11C]MP, and [11C]DTBZ for the study of the AADC, DAT, and VMAT2 receptors in a group of 35 patients, subgrouped for disease duration, dopaminergic therapy, and the findings divided between subregions of the striatum. They found that AADC density in the PD patients showed less reduction compared with the other targets, and the AADC/VMAT2 ratio was higher, across all striatal subregions studied, whereas DAT showed the highest degree of reduction, with a low DAT/DTBZ ratio. This is evidence of an upregulation of the AADC enzyme, which may contribute toward increasing the synthesis of dopamine as the dopaminergic terminals in the striatum degenerate, and a downregulation of DAT enzyme, which may contribute to reducing the reuptake of synaptic dopamine into nerve terminals, thus prolonging its biological effect (Lee et al., 2000). This interesting study paradigm has been employed in studies on patients with familial PD, thus providing a useful method of comparing not only of the extent of the presynaptic dopaminergic damage in these conditions, but also the functional biological adaptation taking place in monogenic PD. Affected LRRK2 mutation carriers show similar degrees of AADC and DAT enzyme activity loss predominantly in the putamen compared with the caudate, similarly to what happens in idiopathic PD (Adams et al., 2005). In addition to that, two out of four unaffected LRRK2 mutation carriers also showed abnormally low levels of VMAT2 and DAT enzymes, although in the presence of still normal AADC activity. These findings have been interpreted as an

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initial stage of functional readaptation of the presynaptic terminals, where AADC, consistently with other studies on unaffected familial PD (Sossi et al., 2010; Wile et al., 2017), increases its activity as opposed to VMAT2 and especially DAT, which downregulate to ensure preservation of a proper dopaminergic tone as long as possible. Using a similar imaging paradigm, a significant complete dopaminergic deficit was seen in one affected homozygous p.A53V SNCA carrier with advanced PD, whereas her unaffected son aged 36 years still showed values of [18F]DOPA, [11C]MP, and [11C]DTBZ PET in the normal range (Yoshino et al., 2017). Similar findings mirroring the functional adaptations of the AADC, DAT, and VMAT2 in the striatum were found in a family carrying the p.N855S DNAJC13 mutation (Appel-Cresswell et al., 2014), and in a 24-year-old young gentleman with mutations in the PLA2G6 gene (Agarwal et al., 2012). Despite the atypical clinical presentation of PLA2G6associated parkinsonism, the imaging findings suggest that in this genetic form, the biological basis of the parkinsonism is similar to that of idiopathic PD. This hypothesis is also corroborated by the reported good response of the DNAJC13 and PLA2G6 patients to levodopa administration (Agarwal et al., 2012; Appel-Cresswell et al., 2014).

Molecular imaging of the postsynaptic dopaminergic terminals Molecular imaging of the D1 receptors PET tracers specific for the D1 receptors have been available for at least 30 years; however, their lack of a satisfying signal-to-background ratio, and the presence of some nonspecific offtarget binding, has limited their use in clinical research of PD. Given these limitations, the results obtained from previous studies should be interrupted with caution and should not be deemed as conclusive of the understanding of the pathophysiology of D1 receptors in this disease. The few studies available on early idiopathic PD patients indicate that the striatal distribution of D1 receptors is bilaterally preserved, with negligible differences compared with healthy controls (Laihinen et al., 1994; Rinne et al., 1990; Shinotoh et al., 1993). In a more recent study, a group of 15 nondemented PD patients showed no differences of [11C] NNC112 binding in frontal regions compared with controls (Cropley et al., 2008). More studies are warranted when more specific PET tracers with higher signal-to-noise ratio are available, to fully elucidate the pathophysiology of D1 receptors in idiopathic and familial forms of PD (Table 4.3).

Molecular imaging of the D2 receptors Early studies conducted in cohorts of drug-naïve idiopathic PD patients using the early PET tracer [11C]NMSP and the more diffused tracer [11C]Raclopride have consistently shown an increase of the levels of D2 receptors in the putamen contralaterally to the most affected side (Hägglund et al., 1987; Kaasinen, Ruottinen, et al., 2000; Rinne et al., 1993; Rinne et al., 1995; Sawle et al., 1993; Schwarz et al., 1994). In these patients, the degree of increase of [11C] Raclopride-binding potential in the putamen was similar to the degree of decrease of [18F] DOPA in the same region, indicating that the increase of density could represent an upregulatory adaptation to the progressive loss of presynaptic dopaminergic terminals (Sawle et al.,

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TABLE 4.3

Summary of the main findings from the molecular imaging studies of the postsynaptic dopaminergic system in idiopathic and familial PD.

Molecular target

Main findings

References

D1 receptors

Idiopathic Bilateral preservation in the striatum and in Cropley et al., 2008; Laihinen et al., 1994; PD frontal regions. Annual progression 6.3%e8.9% Rinne et al., 1990; Shinotoh et al., 1993 per year in the putamen, 3.5%e4.4% per year in the caudate.

D2 receptors

Idiopathic In drug-naïve patients, increase in the putamen PD contralaterally to the most affected side. In patients taking levodopa therapy, progressive decrease in the putamen contralaterally to the most affected side. In extrastriatal regions, decrease, in advanced patients, in the dorsolateral prefrontal cortex, anterior cingulate, medial thalamus, and hypothalamus. Progression of 2%e4% per year in the striatum and of 6%e11% per year in extrastriatal areas. Excessive decrease after pharmacological stimulation with dopamine, and metamphetamine. Patients with LIDs show excessive, temporary reduction after stimulation with levodopa. Patients with ICD and DDS show abnormal reduction in the ventral striatum.

Hägglund et al., 1987; Rinne et al., 1995, 1993, Sawle et al., 1993; Antonini et al., 1997, 1994; Schwarz et al., 1994; Tedroff et al., 1996; Turjanski et al., 1997; Kaasinen et al., 2003; de la FuenteFernández et al., 2001a, b; de la FuenteFernández et al., 2004; Piccini, Pavese and Brooks, 2003; Thobois et al., 2004; Evans et al., 2006; Koochesfahani et al., 2006; Pavese et al., 2006; Steeves et al., 2009; O’Sullivan et al., 2011; Ray et al., 2012; Wu et al., 2015; Politis et al., 2017; Mann et al., 2021; Mihaescu et al., 2021

SNCA

In affected carriers, increase in the putamen Krüger et al., 2001; Samii et al., 1999 contralaterally to the most affected side. In unaffected carriers, uptake within values of healthy controls. Multiplication carriers show asymmetric or symmetric loss in the striatum. In unaffected carriers, uptake within healthy control values.

LRRK2

In affected carriers, increase in the putamen contralaterally to the most affected side.

Adams et al., 2005

DCTN1

In affected carriers, increase in the putamen contralaterally to the most affected side.

Felicio et al., 2014

Parkin

In drug-naïve affected carriers, increase in the Hilker et al., 2001; Ribeiro et al., 2009; Scherfler et al., 2004, 2006 putamen contralaterally to the most affected side. In patients taking levodopa therapy, severe decrease in the putamen contralaterally to the most affected side. In extrastriatal areas, reduction in temporal, orbitofrontal, parietal, and frontal cortex.

PINK1

In affected carriers and in heterozygous unaffected carriers, binding comparable with healthy controls.

Kessler et al., 2005

(Continued)

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TABLE 4.3

Summary of the main findings from the molecular imaging studies of the postsynaptic dopaminergic system in idiopathic and familial PD.dcont’d

Molecular target D3 receptors

Main findings

References

Idiopathic Severe decrease in the pallidum and slight Boileau et al., 2009; Pagano et al., 2016; PD decrease in the ventral striatum. Increased Payer et al., 2016 levels in the putamen. Patients with LID have increased binding in the pallidum and decreased binding in the ventral striatum. Low binding in the hypothalamus associated with excessive daytime sleepiness. DCTN1

In affected carriers, severe, symmetric, loss in putamen > caudate, with posterior-to-anterior gradient.

D1, dopamine type 1 receptor; D2, dopamine type 2 receptor; D3, dopamine type 3 receptor; DCTN1, dynactin 1; DDS, dopamine dysregulation syndrome; ICD, impulsive compulsive disorder; LC, locus coeruleus; LID, levodopa-induced dyskinesias; LRRK2, leucine-rich repeat kinase 2; PD, Parkinson’s disease; PINK1, PTEN-induced kinase; PLA2G6, phospholipase A2, group 6; SNCA, a-synuclein.

1993). Therapy with lisuride, or levodopa, causes a decrease in both putaminal and caudate [11C]Raclopride-binding potential (Antonini et al., 1994, 1997; Politis et al., 2017; Thobois et al., 2004; Turjanski et al., 1997). From the moment that advanced PD patients show a progressive loss of striatal D2 receptors (Antonini et al., 1997), it was debated whether this progressive loss could be ascribed to the dopaminergic supplementation therapy, or could represent the effect of neurodegeneration. Thobois and colleagues demonstrated, in a group of advanced PD patients who underwent deep brain stimulation (DBS) of the subthalamic nucleus, that those who remained drug-free after the surgical procedure had [11C]Raclopride putaminal binding similar to that of controls, and 24% higher compared with those who continued levodopa therapy postsurgery, indicating that the downregulation of D2 receptors may relate to the long-term dopaminergic treatment, rather than to disease progression (Thobois et al., 2004). More recently, Politis and colleagues, studying 68 idiopathic PD patients at different stages of disease, found that the decrease of D2 receptors availability correlated with disease duration, but chronic treatment with dopamine agonists, more than levodopa, causes the decrease of the availability of striatal D2 receptors suggesting a preferential role of this drug category in the suppression of D2 density (Politis et al., 2017). Thanks also to the use of the PET tracer [11C]FLB457, a number of studies have investigated the changes in D2 availability in extrastriatal areas. Kaasinen and colleagues used this tracer in 14 drug-naïve PD patients and 14 levodopa-treated advanced PD. Drug-naïve PD patients did not show changes in [11C]FLB457 binding compared with controls; while advanced, levodopa-treated patients had significant decreases of D2 receptors density in the dorsolateral prefrontal cortex, anterior cingulate, and medial thalamus, suggesting a similar behavior of D2 receptors with disease course across extrastriatal areas (Kaasinen, Någren, et al., 2000). Politis and colleagues found significant decrease of D2 receptors availability measured with [11C]Raclopride in the hypothalamus, an important area for homeostasis of physiological body functions, although their finding did not correlate with disease severity

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(Politis et al., 2008). Longitudinal studies have observed the rate of progression of D2 receptor alterations in idiopathic PD. Antonini and colleagues found, in a 5-year follow-up study of drug-naïve PD patients, a significant 2%e4% annual decrease of putaminal and caudate [11C]Raclopride binding (Antonini et al., 1997). Kaasinen and colleagues used [11C]FLB457 in eight drug-naïve PD patients at baseline, and after a mean 3.3 years, when six patients had started levodopa therapy. They found a significant 6%e11% annual decrease of D2 receptors in the left dorsolateral prefrontal cortex, the left temporal cortex, and medial thalami. However, these rates were not corrected for the use of levodopa; therefore, the dopaminergic supply could have influenced the fast rate of D2 decrease (Kaasinen et al., 2003). [11C]Raclopride PET imaging has also been employed to study the functional activity changes of the dopaminergic synapses following the administration of levodopa (dopa challenge), or drugs that stimulate the endogenous release of dopamine. The paradigms of these studies stem from the fact that [11C]Raclopride competes with endogenous dopamine for the postsynaptic D2 receptors (Endres et al., 1997), and a 10% decrease of [11C]Raclopride binding corresponds to a 500% net increase of dopamine in the synaptic cleft (Breier et al., 1997). Tedroff and colleagues studied 10 PD patients with [11C]Raclopride at baseline (in OFF state), and after 5 min from intravenous infusion of levodopa. They found that the infusion of levodopa induced an 8%e18% decrease of [11C]Raclopride in the striatum with an anteroposterior gradient and that correlated with the degree of motor disability measured in OFF state (Tedroff et al., 1996). A similar paradigm was used by Pavese and colleagues, who employed 250/25 mg levodopa/carbidopa. They found that the degree of [11C]Raclopride displacement correlated with the motor improvement caused by the levodopa administration, limitedly to bradykinesia and rigidity scores, but not to tremor (Pavese et al., 2006). Piccini and colleagues used methamphetamine, a short-acting and potent stimulant of dopaminergic release, on six advanced PD patients. Despite advanced disease, they showed a detectable displacement of [11C]Raclopride, as sign of a still preserved capacity to release endogenous dopamine, which extent was proportional to the severity of dopaminergic denervation, as measured with [18F] DOPA (Piccini et al., 2003). Koochesfahani and colleagues tried to elicit a similar response by using methylphenidate, a blocker of presynaptic DAT, but did not find any change in dopamine synaptic concentration (Koochesfahani et al., 2006). This finding has practical implications as it suggests that pharmacological attempts at increasing the dopamine levels by blocking the DAT may not be successful in PD patients. These studies also revealed an interesting functional phenomenon, that is, the PD patients that displayed the largest putaminal [11C]Raclopride displacement levels were those that developed dyskinesias (Pavese et al., 2006). LIDs are characterized by involuntary movements temporally associated with the intake of dopaminergic medications and are part of a broad range of aberrant motor fluctuations arising as consequence of long-term levodopa supplementation therapy (Martignoni et al., 2003). Previous studies indicated that PD patients with LIDs show abnormally low levels of presynaptic DAT, as a possible role of an abnormal reuptake of dopamine in those patients (Troiano et al., 2009). However, the findings obtained by the challenge studies suggest that the postsynaptic dopaminergic system is sensitive to externally administered levodopa, rather than by the pharmacological modulation of DAT (Koochesfahani et al., 2006). For this reason, the functional study of PD patients with motor fluctuations and LIDs has concentrated on the investigation of the functional shifts of D2 receptors in association with the intake of dopaminergic agents. De la Fuente-

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Fernandez and colleagues found that PD patients with LIDs had an immediate (1 hour) significant decrease of D2 receptors density in the putamen compared with PD patients with stable response to levodopa, which was not present after 4 hours (de la Fuente-Fernández et al., 2001a). The same research group replicated those findings by using the short-acting dopamine agonist apomorphine (de la Fuente-Fernández et al., 2001b) and found that the swings in [11C]Raclopride in the putamen and caudate elicited by the administration of levodopa, lending support to a progressive functional imbalance of the dopaminergic system to chronic use of levodopa therapy, as a contributor to the development of LIDs (de la FuenteFernández et al., 2004). A functional aberrant adaptation of the dopaminergic terminals to dopamine supplementation has been called for the onset of other two common complications of PD, impulse control disorders (ICDs), and dopamine dysregulation syndrome (DDS). The former are addictive behaviors such as pathological gambling, binge eating, hypersexuality, and compulsive shopping; the latter are a compulsive tendency toward an excessive intake of levodopa, regardless of its clinical effects. A number of studies employing PET imaging of the D2 receptors have shed light on the biological basis of these two disorders in PD. Steeves and colleagues studied idiopathic PD patients with pathological gambling using [11C] Raclopride PET and a gambling task, as opposed to a control task. Idiopathic PD patients with ICD had lower levels of D2 receptor density in the ventral striatum, which further decreased during the gambling task, indicative of a greater dopaminergic release, to an extent similar to what happens during drug addiction (Steeves et al., 2009). O’Sullivan and colleagues used a levodopa challenge coupled with neutral or reward-directed cues. The reward-directed cue, but not the neutral cue, caused a significant 16.3% decrease of D2 receptors availability in the ventral striatum, compared with only 5.8% of PD patients without ICD, highlighting that the driver of the compulsive behavior is the reward associated to it, rather than the compulsive behavior itself (O’Sullivan et al., 2011). In another study, the severity of ICD, expressed to the number of impulsive behaviors in the same patient, was not associated to any greater decrease of D2 receptor loss in the ventral striatum (Wu et al., 2015). However, idiopathic PD patients with more than one ICD display higher rates of depression, suggesting that the severity of the ICD may be also driven by molecular mechanisms taking place in critical areas for depression (Wu et al., 2015). This could be put in relationship with the results of one [11C]FLB457 PET study, which identified two areas, specifically the midbrain and the anterior cingulate, as associated with wider changes of D2 receptors in gamblers, which also correlated with the degree of impulsivity (Ray et al., 2012). Finally, Evans and colleagues have studied the mechanisms underlying the development of DDS. They demonstrated that, in response to a single dose of levodopa, idiopathic PD patients with DDS showed significant decreases of [11C]Raclopride binding in the ventral striatum (Evans et al., 2006). This finding puts DDS phenomenon in relationship with the impulsive addictive behaviors previously described, linked to self-reward-oriented behavior rather to a will-directed behavior (Evans et al., 2006). Postsynaptic extrastriatal dopaminergic circuits have been also studied in relation to the presence of PD-related nonmotor symptoms, such as sleep disturbances and cognitive disturbances. Patients with PD and RBD, as opposed to PD patients without RBD, show lower levels of D2 receptor availability within the uncus parahippocampus, superior, lateral, and inferior temporal cortex, as indicative of more profound dopaminergic deficit not only in the striatum,

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but also in extrastriatal areas, associated with RBD phenotype (Valli et al., 2021). With reference to cognitive symptoms, in one [11C]FLB457 PET and MRI study with graph theory analysis, PD patients with cognitive impairment showed lesser small-worldness, altered clustering, and local efficiency in the mesocortical dopaminergic network compared with PD patients without cognitive impairment, suggesting a deterioration of dopaminergic connections associated to cognitive impairment, or a compensatory action of mesocortical D2 dopaminergic receptors in the maintenance of optimal cognitive performances in early stages of PD (Mihaescu et al., 2021). Another recent study explored the role of dopaminergic system underlying response inhibition. Response inhibition is a frequent disturbance of executive function in patients with PD, and evidence point toward a beneficial effect of dopaminergic supplementation therapy on this cognitive symptom (Manza et al., 2017). Mann and colleagues studies this phenomenon on a cohort of 17 PD patients with a stop-signal cognitive task and [18F]Fallypride PET. They found an association between faster stop-signal reaction time and greater [18F]Fallypride nondisplaceable binding potential in the amygdala and the hippocampus, suggesting a role for mesolimbic dopamine in response inhibition (Mann et al., 2021). [11C]Raclopride PET and [123I]IBZM SPECT have been used in a few studies to understand the pathophysiology of the D2 receptors in carriers of mutations for familial PD (Adams et al., 2005; Felicio et al., 2014; Hilker et al., 2001; Kessler et al., 2005; Krüger et al., 2001; Ribeiro et al., 2009; Samii et al., 1999; Scherfler et al., 2004, 2006). Affected carriers of A53T and A30P SNCA missense mutations show levels of putaminal D2 receptors comparable with those of idiopathic PD and, similar to what happens in idiopathic PD, a higher putamento-caudate [11C]Raclopride binding ratio (Krüger et al., 2001; Samii et al., 1999). These findings are comparable to those reported in affected G2019S LRRK2 mutation carriers (Adams et al., 2005). Two clinically affected patients with Perry syndrome and mutations to the DCTN1 gene showed an increase of D2 receptors in the same striatal areas that display a pathological presynaptic dopaminergic denervation as assessed with [18F]DOPA and [11C] DTBZ PET (Felicio et al., 2014). This is also in line with what happens in idiopathic PD and suggests that the upregulation of postsynaptic D2 receptors is a common early compensatory response to dopaminergic terminals loss along the nigrostriatal pathway. Drug-naïve carriers of mutations to the autosomal recessive gene Parkin show similar increases of D2 receptors in the putamen. When started on levodopa therapy, however, these D2 receptors levels decrease to values below those of both healthy controls and idiopathic PD patients (Scherfler et al., 2006). This finding has been interpreted as the effect of a greater susceptibility of Parkin mutation carriers to the exposure to dopaminergic medication than idiopathic PD patients (Scherfler et al., 2006). As a further evidence to this hypothesis, affected Parkin patients show reductions of D2 receptors in cortical areas, namely temporal, orbitofrontal, parietal, and frontal cortices, as opposed to young patients with idiopathic PD (Ribeiro et al., 2009; Scherfler et al., 2004). It is worth noting that Parkin patients show high rates of dystonia and of LIDs at very low levodopa doses and this could be put in relationship with a different response of the postsynaptic D2 receptors to exposure to levodopa. As of today, no studies have been performed on homozygous or heterozygous Parkin mutation carriers with the pharmacological challenge paradigms as described earlier in this paragraph. A single study has employed [123I]IBZM SPECT on three affected homozygous carriers and four heterozygous unaffected carriers of a PINK1 mutation. In all, the levels of D2 receptor were within the range of healthy subjects (Kessler et al., 2005).

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Molecular imaging of the D3 receptors A limited number of PET studies, using the high-affinity tracer [11C]PHNO, are available only in cohorts of idiopathic PD patients (Boileau et al., 2009; Pagano et al., 2016; Payer et al., 2016). In one study, 10 drug-naïve PD patients demonstrated significantly decreased (
42%) levels of D3 receptors in the pallidum, and a trend toward a decrease (
11%) in the ventral striatum, two regions physiologically rich in D3 receptors. On the contrary, the levels of [11C]PHNO binding were increased, similar to [11C]raclopride, in the D2-rich putamen, suggesting that, in this region, the D2 receptors could undergo an upregulatory process similar to what documented for the D2 receptors (Boileau et al., 2009). The changes in the regional density of D3 receptors have also been studied in relationship with clinical symptoms. Payer and colleagues found that PD patients with LIDs had increased binding of [11C]PHNO, as possible expression of upregulation, in the pallidum, and decreased binding in the ventral striatum, possibly as an effect of increased dopamine synaptic levels (Payer et al., 2016). Pagano and colleagues found, in 12 PD patients, that severity of excessive daytime sleepiness was associated with low D3 density in the hypothalamus, providing evidence of a possible role of a postsynaptic dopaminergic alteration in the generation of this frequent symptom of PD (Pagano et al., 2016). These findings have been confirmed by a recent postmortem study showing loss of D3 receptors in the nucleus accumbens of PD patients with ICD (Barbosa et al., 2021). More studies are needed to fully elucidate the role of the degeneration of D3 receptors in idiopathic and familial PD, especially, given the preferential localization of this receptor, in relationship with behavioral nonmotor symptoms.

Conclusive remarks The study in PD, with molecular imaging, of the functional alterations of the dopaminergic system in response to the degeneration of the nigrostriatal pathway and to the pharmacological attempts to restore the dopaminergic tone has allowed a leap forward in the understanding of the pathophysiology of PD, and in the identification of possible routes for therapeutic intervention. Nevertheless, idiopathic PD is still a largely mysterious disease, for which both the etiology and the pathogenesis are yet to be fully elucidated. The consequence of this is that, despite decades of efforts, there is no therapy targeting the cause of PD with real disease-modifying potential available yet. People with genetic mutations for familial PD currently carry the burden of being at increased risk to develop PD during their life, compared with the general population, and no current practical advantage in terms of access to therapies. However, one potentially crucial advantage is that, despite similarly unclear mechanisms of disease, the cause of their ailment is known. This has opened doors, on one hand, toward a better understanding of the processes that, starting from the genetic mutation, lead to the development of symptoms; on the other hand, it can be possible to design targeted approaches directed toward the mutated gene product, with potentially higher chances to be successful. As importantly, unaffected carriers could potentially benefit from such an approach since the preclinical stages of their disease, further increasing the chances to delay or prevent the onset of the disease. The large similarities, obtained through PET and SPECT imaging, of the molecular alterations taking place in the idiopathic and familial forms of PD, give hope that some of these approaches, if successful, could be effective also for the large population of idiopathic PD patients.

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C H A P T E R

5 Serotonergic molecular imaging in familial and idiopathic PD Gennaro Pagano1, 2 1

Neurodegeneration Imaging Group, University of Exeter Medical School, London, United Kingdom; 2Roche Pharma Research and Early Development (pRED), Neuroscience and Rare Diseases Discovery and Translational Area, Roche Innovation Center Basel, Basel, Switzerland

Introduction Parkinson’s disease (PD) is characterized by a loss of dopaminergic neurons in the substantia nigra pars compacta, and the main pathological process is the accumulation of Lewy body pathology (Ozansoy & Basak, 2013). Postmortem studies showed that Lewy body pathology and neuronal loss are common in other pathways beyond dopaminergic, such as noradrenergic and serotonergic (Buddhala et al., 2015). The damage of these systems might be one of the early signs of neurodegeneration, present already in the prodromal stage of PD even prior to the damage of the dopaminergic system (Braak et al., 2003; Halliday et al., 2006; Maillet et al., 2021). The serotonergic pathway is releasing serotonin, also called 5-hydroxytryptamine (5-HT), essentially in most of the areas of the brain. 5-HT is one of the oldest neurotransmitter systems in evolutionary terms and has thus had the longest to diversify with more than 10 distinct subtypes of the 5-HT receptor, and many more isoforms (Fox et al., 2009). In the past decades, several positron emission tomography (PET) molecular imaging tools have been developed to detect in vivo changes in the serotonergic pathway (Fazio et al., 2020; Fu et al., 2018, 2021; Pagano et al., 2016b, 2016c, 2017a, 2017b, 2018; Pagano & Politis, 2018; Politis et al., 2017; Roy et al., 2016). These tools aid in the understanding of the role of 5-HT in several diseases, including PD (Pagano et al., 2018; Pagano & Politis, 2018). This chapter reviews the main findings from serotonergic PET imaging studies in idiopathic and familial Parkinson’s disease, such as asymptomatic LRRK2 and A53T SNCA mutation carriers, discussing how serotonergic imaging alterations are linked to motor and nonmotor symptoms, motor complications, and presymptomatic stage.

Neuroimaging in Parkinson's Disease and Related Disorders https://doi.org/10.1016/B978-0-12-821651-4.00018-X

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Serotonergic system changes in Parkinson’s disease Neurons that produce 5-HT are mostly located in the brainstem and are organized in what is called raphe nuclei. Raphe nuclei have a superior part, defined as rostral raphe, with major projections innervating the striatum and the limbic system (hypothalamus, amygdala, cingulum, the medial cerebral cortex, and part of the hippocampus), and an inferior part, defined as caudal raphe, with major projections to the caudal brainstem and to the spinal cord (Hornung, 2003). 5-HT has been implied in many brain functions, including cognition, emotion, and motor behavior; in PD, 5-HT plays a role in the control of motor and nonmotor functions (Pagano et al., 2018; Pagano & Politis, 2018). Postmortem evidence showed severe loss of 5-HT in the hypothalamus, caudate, and frontal cortex of people with PD (Hartmann, 2004), but with relatively lesser reduction of 5-HT (66%) compared with dopamine loss (98%) (Kish et al., 2008). Several evidences suggest the caudal raphe is damaged prior to the dopaminergic cells in the substantia nigra as the pathological process in PD is supposed to happen in an ascending fashion, starting from the olfactory nucleus and the medulla in presymptomatic stages and spreading to the pons and midbrain later (Braak et al., 2003; Halliday et al., 2006). In Braak stage 2, Lewy body and Lewy neurite deposition occurs within the median raphe nuclei containing the serotonergic neurons of the caudal brainstem (Braak et al., 2003; Halliday et al., 2006).

Molecular imaging of serotonergic system PET is used to in vivo measure presynaptic transporters and postsynaptic receptors levels. [11C]McN5652 was the first PET tracer developed to bind presynaptic serotonergic transporter (SERT) (Suehiro et al., 1993) showing high specificity-to-selectivity ratio (Szabo et al., 1999) and great brain distribution, mirroring the postmortem SERT brain 5-HT innervation (Scheffel et al., 1998). A total of 13 people with sporadic PD were compared with 13 healthy volunteers in their level of SERT and dopamine transporter (DAT) by using [11C]McN5652 PET and [11C] WIN35428 PET, respectively (Kerenyi et al., 2003). The SERT and DAT levels in the striatum of PD population were lower than controls, with DAT (but not SERT) showing the characteristic disproportionately reduction contralateral to the more clinical affected side of the body. Greater SERT loss was associated with greater impairment of motor symptoms. Of note, [11C]McN5652 showed several shortcomings, including a high nonspecific binding and long imaging sessions required (Frankle et al., 2004); thus to overcome these limitations, a tracer called [11C]-3-amino-4-(2-dimethylaminomethylphenylsulfanyl)-benzonitrile ([11C]DASB) was developed (Wilson et al., 2000). Compared with [11C]McN5652, [11C]DASB is three times more selective for SERT than for DAT or noradrenaline transporter (Wilson et al., 2000, 2001) and has a higher specific-to-nonspecific binding ratio (Frankle et al., 2004). More than a hundred studies have been published using [11C]DASB PET imaging over more than 20 different PET centers across the world, showing reliable results when a given preprocessing pipeline was applied (Norgaard et al., 2019).

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In PD populations, a decrease in SERT levels has been shown in the caudate, thalamus, hypothalamus, and anterior cingulate cortex at the early stage of PD, and further decrease in the putamen, insula, posterior cingulate cortex, and prefrontal cortex at the middle stage of PD, and in the ventral striatum, raphe nuclei, and amygdala at the advanced stage of PD (Politis et al., 2014). A metaanalysis of all [11C]DASB PET imaging studies performed in PD up to 2017 investigated the serotonergic system in a total of 234 individuals with sporadic PD across 20 PET studies, showing a reduction of serotonergic terminals in the raphe nuclei, thalamus, hypothalamus, ventral striatum, caudate, and putamen, which correlates with the duration of the disease (Pagano et al., 2017a). Previous studies included small sample populations of people with PD; thus, this metaanalysis was able to show significance where previously there were trends, e.g., for the thalamus. The main conclusion is that there is a widespread and progressive loss of synaptic terminals in the 5-HT pathways in PD, and not only in dopamine pathways. To note, levodopa-induced dyskinesias (LIDs) were associated with preserved SERT binding indicating that, though the progressive neurodegeneration of the 5-HT pathways is heterogenous in PD, those who develop LIDs might have an aberrant spreading of serotonergic terminals or a faster degeneration of dopaminergic than serotonergic terminals (Roussakis et al., 2016). Changes in the SERT have been associated with the development of tremor, dyskinesias, and nonmotor symptoms such as cognitive impairment, sleep problems, and neuropsychiatric symptoms (Pagano et al., 2017a). Fig. 5.1 shows a schematic representation of the SERT levels during the different stages of PD. Several PET imaging probes have been developed to measure in vivo serotonergic receptors, such as [18F]MPPF for 5-HT1A, [18F]setoperone for 5-HT2A, and [11C]AZ10419369 for 5HT1B receptors (Ballanger et al., 2010; Varnas et al., 2011). Serotonergic receptors are G

FIGURE 5.1 SERT levels at different stages of PD showing a progressive reduction until the onset of motor complications where an abnormal sprouting is observed and associated with the onset of levodopa-induced dyskenia. Further reduction is observed at the onset of cognitive impairment. II. Clinical applications in Parkinson disease

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proteinecoupled receptor or ligand-gated ion channels, which are present in the central and peripheral nervous systems (Beliveau et al., 2017). 5-HT receptors are activated by the neurotransmitter 5-HT and involved in both excitatory and inhibitory neurotransmission, modulating the release of glutamate, gamma-aminobutyric acid (GABA), dopamine, noradrenaline, and acetylcholine and, thus, influencing several processes including aggression, anxiety, appetite, cognition, learning, memory, mood, nausea, sleep, and thermoregulation (Johnston et al., 2014).

Serotonergic molecular imaging of motor symptoms Impairment of 5-HT pathway has been associated in PD with the onset and the severity of tremor (Loane et al., 2013) and the motor complications LIDs and graft-induced dyskinesias (GIDs) (Pagano et al., 2017a, 2018). A “classical” 4e6 Hz pill-rolling resting tremor is a cardinal motor feature of PD. However, people with PD may also display less common types of tremor, including on the “wrong side” of the body (contralateral to the side of more severe bradykinesia), such as “action,” postural, and reemergent tremor. Tremors in PD have a poor and unpredictable response to dopaminergic treatment compared with bradykinesia and rigidity (Koller & Hubble, 1990). Evidence from PET and single-photon emission computerized tomography (SPECT) molecular imaging studies have shown that dopamine transporter is less affected in tremor-dominant compared with akinetic-rigid PD patients and that tremor does not correlate with dopamine transporter levels, assessed with [123I] FP-CIT SPECT (Asenbaum et al., 1998), dopamine terminal capacity, assessed with [18F]FDOPA PET (Brooks et al., 1992), nor dopamine D2 receptor availability, assessed with [11C]raclopride PET (Pavese et al., 2006), which suggests that this symptom could be associated with nondopaminergic mechanisms. PET imaging studies have been performed in PD patients with tremor using [11C] WAY100635 (Doder et al., 2003), a selective marker of 5-HT1A receptors, and [11C]DASB (Loane et al., 2013), a selective marker of SERT levels. [11C]WAY100635 was tested in 26 people with PD compared with 8 healthy volunteers showing that midbrain raphe 5-HT1A binding was reduced by 27% in PD patients and lower midbrain raphe 5-HT1A binding was associated with greater severity of resting tremor (Doder et al., 2003). [11C]DASB was tested in 12 people with PD with tremor-dominant motor phenotype, compared with 12 with akinetic-rigid ones, and 12 healthy volunteers, showing that SERT was reduced by 30% in the caudate, putamen, raphe nuclei, thalamus, and Brodmann areas 4 and 10 in tremordominant ones and lower [11C]DASB binding in the caudate, putamen, and raphe nuclei was associated with greater severity of postural and action tremors (Loane et al., 2013). It is important to underline that the scale used to quantify the tremor (MDS-UPDRS) cannot differentiate between postural and “reemergent tremor”; thus, it is possible to speculate that the findings of the [11C]DASB study could actually show a correlation between SERT and this specific form of resting tremor, in line with the findings of [11C]WAY100635 study. However, in the [11C]DASB study, no correlations were found between SERT levels and tremor at rest scores, which further confirms the complexity of tremor in PD suggesting that more than one mechanism is involved in the development of this symptom.

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Serotonergic pathology has also been associated with the development of the motor complications LIDs and GIDs (Pagano et al., 2018). People with PD who experienced LIDs and GIDs might have an aberrant spreading of serotonergic terminals, which lead to an increased serotonergic/dopaminergic terminal ratio within the putamen. The chronic administration of levodopa induces sharp increases in striatal dopamine levels, which are particularly elevated in people with PD who experience LIDs and GIDs (Pagano et al., 2017a). However, a moderate-to-severe loss of dopaminergic terminals in the dorsal putamen is a necessary condition for the development of LIDs. Since 5-HT neurons lack of an autoregulatory feedback of dopamine release, serotonergic terminals convert exogenous levodopa into dopamine in a nonphysiological manner and release an abnormal amount of dopamine without an autoregulatory feedback. This results in higher swings in synaptic levels of dopamine, which leads to the development of LIDs and GIDs. The modulation of serotonergic terminals with 5-HT1A and 5-HT1B receptor agonists partially reduced these motor complications. However, other systems also play a role in the development of these motor complications (Pagano et al., 2018). The inability of remaining dopaminergic terminals to remove the released dopamine and to store it into the presynaptic vesicles also provides a key contribution to the development of LIDs/GIDs. In these circumstances, the same amount of levodopa administered induces higher release of dopamine in the extracellular space (augmented dopamine percent change from basal levels) (Lindgren et al., 2010). This results in higher swings in synaptic levels of dopamine and pulsatile stimulation of postsynaptic receptors located on striatal projection neurons. [11C]DASB PET studies in PD patients showed that people with PD experiencing LIDs showed higher striatal SERT levels and higher striatal dopamine release than those with stable response to levodopa (Politis et al., 2014). 5-HT terminals and dopamine release were measured by using [11C]DASB PET imaging and [11C]raclopride PET, respectively. Changes in D2 receptor availability, as reduction of baseline [11C]raclopride levels after levodopa administration, allow an indirect measure of synaptic dopamine release. People with PD experiencing LIDs showed increased dopamine release after the administration of levodopa compared with those with stable response to levodopa, with a relative preservation of serotonergic terminals in the putamen (Politis et al., 2014). Oral administration prior to levodopa of the 5-HT1A receptor agonist buspirone, a presynaptic modulator of serotonergic system, reduced levodopa-evoked striatal synaptic dopamine release and attenuated LIDs (Politis et al., 2014). To note, among PD with LIDs, the antidyskinetic effect of buspirone was greater in those with higher levels of serotonergic terminals, who also exhibited a greater decrease in dopamine release after buspirone pretreatment (Politis et al., 2014). Overall, the findings from this study provided the first human evidence suggesting that striatal serotonergic terminals contribute to LIDs pathophysiology via aberrant processing of exogenous levodopa and release of dopamine as false neurotransmitter. The results also support the development of selective 5-HT1A receptor agonists for use as antidyskinetic agents. Another study investigated the role of serotonergic innervation of the globus pallidus in the development of dyskinesias (Smith et al., 2015). Density of serotonergic terminals and the striatal dopamine release were measured in the globus pallidus of people with PD experiencing LIDs compared with those with stable response to levodopa by using [11C]DASB PET and [11C]raclopride challenge, respectively. People with PD experiencing LIDs showed preserved serotonergic terminals in the globus pallidus, with a level similar to healthy volunteers. Higher density

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of serotonin terminals in the globus pallidus was associated with a greater amount of dopamine released and greater severity of LIDs. This indicates either that the serotonin terminal function in the globus pallidus in patients with LIDs is spared or that an adaptive terminal sprouting of remaining serotonergic projections occurs not only in the putamen but also in the globus pallidus. Taking together this (preserved serotonergic terminals in the globus pallidus) and the previous finding (preserved serotonergic terminals in the putamen), it is possible to hypothesize that an imbalance caused by the normalization of serotonin terminals in the dopamine-denervated striatum creates increased dopamine release after levodopa administration, resulting in an increased negative input to the globus pallidus neurons controlling thalamic output. Greater LIDs might be the result of increased dopamine release at presynaptic dopaminergic receptors located at the synapses of striatopallidal GABAergic neurons in the globus pallidus. These neurons control the projection neurons to the thalamus and thereby the thalamic output. By overinhibition of these neurons, the dysregulated basal ganglia output then results in LIDs. This is in line with the preclinical evidence of a profound suppression of globus pallidus output activity in monkeys experiencing LIDs (Papa et al., 1999). A third study investigated the interaction between serotonergic and dopaminergic terminals in the development of LIDs (Roussakis et al., 2016). They measured the density of serotonergic and dopaminergic terminals in the striatum of people with PD experiencing LIDs and of PD with stable response to levodopa by using [11C]DASB PET and [123I]FPCIT SPECT, respectively. They found that higher putaminal serotonergic-to-dopaminergic terminal ratio was associated with longer disease duration, indicating that, as PD progresses, the ratio between serotonergic and dopaminergic terminals becomes higher, as reflected by the higher [11C]DASB PET to [123I]FP-CIT SPECT binding ratio. This might be due to a faster progression of dopaminergic terminals compared with serotonergic ones, or to an aberrant sprouting of serotonergic innervation in the people who will experience LIDs, as previously demonstrated in animal studies (Carta & Bezard, 2011; Carta et al., 2007). In parallel, a fourth PET study from another team has also shown that, compared with nondyskinetic patients, people with PD experiencing LIDs had a higher striatal serotoninergic-to-dopaminergic terminal availability, as reflected by the higher [11C]DASB to [18F]FP-CIT PET binding ratio, with no difference in striatal dopaminergic terminals (Lee et al., 2015). Overall, these findings suggest that when the dopaminergic innervation in the striatum is critically low, the serotonergic system plays an important role in development of LIDs. These findings support the role of serotonergic terminals in the aberrant release of striatal dopamine and in promoting the development of LIDs in patients with PD. Another troublesome involuntary movement associated with the serotonergic system are GIDs. Transplantation with fetal ventral mesencephalic tissue aimed to restore the dopaminergic terminals in advanced cases of PD. This treatment showed tremendous efficacy in some patients, which showed remarkable improvement of motor symptoms. However, some patients developed GIDs, a complication similar to LIDs but which is also present while patients are “off” their dopaminergic treatment. Grafted tissue contained a varied proportion of nondopaminergic cells including serotonergic neurons. Thus, striatal graft tissue containing high levels of serotonin neurons leads to mishandling of striatal dopamine levels resulting in the occurrence of GIDs (Politis et al., 2011, 2014; Politis, 2010). It has been postulated that the same serotonergic mechanisms, such as excessive striatal serotonergic innervation and high serotonin-to-dopamine striatal terminal ratio, are pivotal in the development of GIDs in people

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with PD who underwent striatal transplantation with fetal ventral mesencephalic tissue (Politis et al., 2011, 2014; Politis, 2010). A study evaluated the density of serotonergic terminals, by using [11C]DASB PET imaging, and the presynaptic dopaminergic amino acid aromatic decarboxylase terminals’ activity, by using [18F]DOPA PET imaging, in three people with PD who received striatal transplantation with fetal ventral mesencephalic tissue and exhibited GIDs. All three people with PD showed an excessive graft-derived serotonergic innervation (Politis et al., 2014) and high serotonin-to-dopamine terminal ratio (Politis et al., 2011). Furthermore, administration of small, repeated doses of 5-HT1A receptor agonist buspirone was able to attenuate GIDs possibly by attenuating the abnormal serotonin terminal-derived dopamine release. These findings support the involvement of the serotonergic system in the development of GIDs and indicate that a “close-to-normal” striatal serotonin/dopamine ratio in the transplanted fetal ventral mesencephalic tissue should be necessary to avoid the development of GIDs.

Serotonergic molecular imaging of nonmotor symptoms Impairment of 5-HT pathways has been associated in PD with the onset and the severity of cognitive impairment (Kotagal et al., 2012), sleep problems (Wilson et al., 2018), fatigue (Pavese et al., 2010), and neuropsychiatric symptoms, including depression (Politis et al., 2010), apathy (Prange et al., 2022), and psychosis (Ffytche et al., 2017). Cognitive decline is very common in PD with approximately 8 out of 10 patients developing at some point in the disease (Aarsland & Kurz, 2010). This is a multifactorial process which involves a-synuclein, b-amyloid, tau misfolded pathogenic proteins, development of neuronal and synaptic dysfunctions of limbic system (Niccolini et al., 2017; Schulz et al., 2018; Wilson et al., 2016, 2019b) that leads to reduced glucose metabolic and cerebral blood flow, and impairment of brain network connectivity (Kalia, 2018). Overactivation of 5-HT receptors has been linked to increased generation of b-amyloid pathogenic species at the synaptic level (Shen et al., 2011), and administration of selective serotonin reuptake inhibitors (SSRIs) seems to reduce this process (Cirrito et al., 2011) via G proteinecoupled receptors (Femminella et al., 2013) inducing the cleavage of amyloid precursor protein by a-secretase, leading to a reduction in b-amyloid 1e42 generation and reduced plaque formation (Cirrito et al., 2011). A total of 13 people with PD were evaluated using [11C]DASB and amyloid PET and showed an inverse correlation between SERT levels in the striatum and amyloid levels in the neocortex (Kotagal et al., 2012). In the same study, it has been demonstrated that the use of SSRIs for at least 6 months was associated with a lower level of cerebrospinal fluid (CSF) b-amyloid 1e42 and with lower risk of cognitive decline (Kotagal et al., 2018). This confirms the hypothesis that serotonergic terminals may play a role in the clearance of b-amyloid and indirectly to the development of cognitive impairment. The relationship between serotonergic system and accumulation of misfolded proteins opens to a fascinating hypothesis on its potential connection with the deposition of Lewy bodies. However, to test this hypothesis in vivo, it will be necessary to have, alongside with [11C]DASB, an a-synuclein PET tracer, which is currently unavailable. Twelve people with PD patients were examined with PET using the 5-HT1B radioligand [11C]AZ10419369 showing a low 5-HT1B receptor availability in the right orbitofrontal cortex. Lower 5-HT1B receptor availability in right midbrain and left parahippocampal gyrus was associated with older age (Varrone et al., 2014).

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Degeneration of raphe nuclei has also been associated with the development of sleep disturbances in PD (Jouvet, 1972). After sleep deprivation, the anterior olfactory nucleus and substantia nigra show lower SERT levels, assessed by using [11C]DASB PET in rats (Hipolide et al., 2005). In humans, the use of SSRIs in the treatment of depression is commonly associated with the development of sleep alterations (Rush et al., 1998), which further confirms the theory that serotonergic system is relevant for the development of sleep disturbances. Sleep disturbances in PD might both have a presynaptic and a postsynaptic component (Yousaf et al., 2018a, 2018b; Pagano et al., 2016a). People with PD and sleep disturbances show low levels of SERT in the midbrain raphe, basal ganglia, and hypothalamus, assessed by use of [11C]DASB PET imaging (Wilson et al., 2018). Lower SERT in the hypothalamus, thalamus, and striatum was associated with greater severity of sleep symptoms, assessed with Parkinson Disease Sleep Scale. By using [18F]altanserin PET imaging, it has been shown that a single night of sleep deprivation induces an increase in cortical 5-HT2A receptors (medial inferior frontal gyrus, insula, anterior cingulate, parietal, sensorimotor, and ventrolateral prefrontal cortices) in healthy volunteers (Ballanger et al., 2010). This confirms a role for the serotonergic dysfunction in the onset of insomnia. A study using combined [18F]DOPA and [11C]DASB PET investigated the role of 5-HT in people with PD and fatigue (Pavese et al., 2010). People with PD patients and fatigue showed severe decrease in SERT levels in the striatum, thalamus, and limbic system compared with the people with PD without fatigue, but striatal [18F]DOPA uptake was similar (Pavese et al., 2010). This observation suggests that the presynaptic component of serotonergic system plays a significant role in fatigue in PD. However, further research using other ligands, such as postsynaptic 5-HT receptors tracer, is needed to further investigate this area. More than half of people living with PD develop neuropsychiatric disturbances in the course of the disease (Zhuo et al., 2017). SERT levels are increased in the insula, thalamus, and striatum of people with major depression (Cannon et al., 2007) and with bipolar (Cannon et al., 2006, 2007) as with [11C]DASB PET imaging, suggesting that serotonergic system could account for symptoms of depression by leading to increased clearance of serotonin from the synapse. However, the serotonergic component of depression in PD might involve both presynaptic and postsynaptic terminal dysfunction. Compared with PD patients without depression, people with PD and depression showed a reduction in CSF 5-hydroxyindoleacetic acid levels, the principal metabolite of 5-HT (Mayeux et al., 1986). In PET studies using [11C] DASB, people with PD and depression have reported relative increases of SERT binding in limbic structures compared with nondepressed PD individuals (Politis et al., 2010; Boileau et al., 2008). The increase was greater in the dorsolateral (37%) and prefrontal (68%) cortices and correlated with depressive symptoms (assessed by the Hamilton Depression Rating Scale) (Boileau et al., 2008). An increase in subcortical SERT levels, in the amygdala, hypothalamus, raphe nuclei, and posterior cingulate cortex was also demonstrated in PD patients with depressive symptoms compared with matched-PD patients without depression. This increase in [11C]DASB binding was indeed correlated with depressive symptoms (as assessed by Beck Depression Inventory-II and Hamilton Depression Rating Scale) (Politis et al., 2010). In the limbic regions of depressed PD patients, it has also found a decrease in 5-HT1A receptors by using [18F]MPPF PET imaging (Ballanger et al., 2012), which is in agreement with postmortem evidence (Sharp et al., 2008) and supports the hypothesis that there is also a postsynaptic in the development of depression in PD (Ballanger et al., 2012).

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Overall, these findings suggest that in PD, as in major and bipolar depression, an excessive 5-HT clearance by SERT induces a reduced extracellular concentration of 5-HT, leading to the development of depressive symptoms. However, the prevalence of depression in PD is higher compared with non-PD populations, and this could be explained by the concurrent decrease in 5-HT1A receptors and loss of serotonergic terminals in limbic regions. Thus, depression in PD could be the result of a combined effect of serotonergic terminal loss, inappropriate upregulation of SERT, and reduced postsynaptic efficacy of the already depleted 5-HT. However, it should be noted that the pathophysiology of depression is complex and involves changes in multiple circuits and neurotransmitters beyond the serotonergic system likely in a dynamic and interactive way. In addition to depression, several neuropsychiatric disturbances are common in PD, such as visual hallucinations and psychosis. A PET study with [18F]setoperone, a selective 5-HT2A receptor radioligand, showed that PD patients with visual hallucination have an increased 5-HT2A binding in ventral visual pathway, dorsolateral prefrontal cortex, medial orbitofrontal cortex, and insula (Ballanger et al., 2010). These findings suggested that abnormalities in serotonin neurotransmission could be involved in the neural mechanisms underlying the development of visual hallucinations that is associated with PD and support the use of selective 5-HT2A receptor antagonists in the treatment of visual hallucinations in PD (Ballanger et al., 2010). Psychosis occurs in a third of people with PD, leading to increased nursing home placements and higher rates of morbidity (Marsh et al., 2004). The treatment of psychosis in PD is complicated by the D2 antagonist effect of typical and atypical antipsychotics, which may worsen motor and cognitive PD symptoms (Ffytche et al., 2017). There is evidence suggesting that the serotonergic system may be involved in the pathophysiology of psychosis in PD (Ffytche et al., 2017). Pimavanserin is a selective 5-HT2A antagonist/inverse agonist that showed to be efficacious on psychotic-like behavioral deficits in a 6-week, randomized, double blind, placebo-controlled phase 3 study (Cummings et al., 2014). These findings confirm a key role of serotonergic system in the development of psychosis in PD.

Serotonergic molecular imaging of prodromal stages of Parkinson’s disease Carriers of autosomal dominant mutation genes associated with PD, such as LRRK2 and Ala53Thr (A53T; 209G/A) SNCA, are the ideal population to evaluate in vivo the Braak’s hypothesis that serotonergic system is affected earlier than dopaminergic one. A cohort of presymptomatic LRRK2 mutation carriers was evaluated with [18F]DOPA, [11C]DTBZ, [11C]d-threo-methylphenidate, and [11C]DASB PET imaging to evaluate the dopaminergic and serotonergic loss (Wile et al., 2017). Presymptomatic LRRK2 mutation carriers showed reduction of DAT, assessed with [11C]d-threo-methylphenidate PET scans, but no reduction in dopaminergic [18F]DOPA and [11C]DTBZ binding in the striatum. They also showed an increase in [11C]DASB binding in the hypothalamus, striatum, and brainstem (Wile et al., 2017). These findings challenge the classic pathological interpretation of disease progression. If the midbrain raphe nucleus is affected in a presymptomatic stage according to Braak, and LRRK2 pathological features are similar to those seen in idiopathic PD, serotonergic PET would have shown decreases rather than increases. As described above, serotonergic PET studies in idiopathic PD have shown a progressive and nonlinear loss of serotonergic II. Clinical applications in Parkinson disease

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terminals (Politis et al., 2014). PET studies have reported relative increases in the expression of [11C]DASB in the hypothalamus, striatum, and brainstem in people with sporadic PD and depressive symptoms (Politis et al., 2010; Boileau et al., 2008) compared with individuals with idiopathic PD but without depressive symptoms. This finding suggests that elevated expression of serotonin transporters was probably a concurrent occurrence with serotonergic terminal loss in the individuals with idiopathic PD (Politis et al., 2017). Data for depression were reported in only five presymptomatic LRRK2 mutation carriers who were assessed with the self-reported Beck Depression Inventory, with no data related to weight changes or other nonmotor symptoms, therefore limiting interpretation. Increased expression of SERT in presymptomatic LRRK2 mutation carriers may also suggest that serotonergic terminals are affected by a-synuclein pathology (Wile et al., 2017). In such a scenario, upregulation of serotonin transporters might be secondary to decreases in synaptic serotonin levels and, therefore, indirect PET measures of synaptic serotonin release could help elucidate mechanisms in presymptomatic LRRK2 mutation carriers. However, to better understand these findings, additional studies are needed in a larger cohort of presymptomatic LRRK2 mutation carriers, including thorough clinical assessments for depression, and other nonmotor symptoms, and perhaps longitudinal observations to elucidate molecular changes over time and their associations with clinical symptoms. Another key limitation of the presymptomatic LRRK2 mutation carrier population is that the penetrance of PD is pretty low, approximately 25% (Bonifati, 2007; Iwaki et al., 2020; Ruiz-Martinez et al., 2010; Wang et al., 2022), and population of the highly penetrant gene mutation should be studied, such as presymptomatic mutation carriers of the A53T point mutation in the SNCA gene. They are an ideal population to study the premotor phase and evolution of PD. A cross-sectional study was performed recruiting seven presymptomatic carriers of the A53T SNCA mutation from specialist Movement Disorders clinics in Athens, Greece, and Salerno, Italy, and used [123I]FP-CIT SPECT and [11C]DASB PET imaging to assess whether dopaminergic and serotonergic abnormalities were present (Wilson et al., 2019a). Compared with healthy controls, premotor A53T SNCA carriers showed lower SERT levels in the raphe nuclei, brainstem, amygdala, hypothalamus, thalamus, and striatum, corresponding to Braak stages 1e3, and no differences in DAT levels (Wilson et al., 2019a). This study suggests that the presence of serotonergic damage in presymptomatic A53T SNCA carriers preceded development of dopaminergic damage, and molecular imaging of SERT could be used to identify individuals at the prodromal stage of PD.

Conclusions Serotonergic pathology plays a key role in the progressive neurodegenerative process in the course of PD, and PET imaging probes provide the means for direct visualization and quantification in vivo of serotonergic alterations in PD. Although several studies have been performed using serotonergic PET ligands, the vast majority of associations between serotonergic pathology and PD symptoms require further validation. Currently, all studies have been cross-sectional, and new longitudinal designs, perhaps employing various PET ligands for the quantification of serotonergic markers, are needed to better understand the progression of serotonergic pathology and the relation to symptoms and complications in people with PD. The presence of serotonergic damage preceding the dopaminergic damage in

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presymptomatic A53T SNCA carriers may be considered as an in vivo proof of the Braak’s theory and molecular imaging of SERT could be used to identify individuals at the prodromal stage of PD. Future work might establish whether serotonin transporter imaging is suitable as an adjunctive tool for screening and monitoring progression for individuals at risk or people with PD to complement dopaminergic imaging, or as a marker of PD burden in clinical trials.

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C H A P T E R

6 Molecular imaging beyond dopamine and serotonin in familial and idiopathic Parkinson’s disease Heather Wilsona, Alana Terrya and Marios Politis Neurodegeneration Imaging Group, Department of Clinical and Biomedical Sciences, Faculty of Health and Life Sciences, University of Exeter, United Kingdom

Introduction Positron emission tomography (PET) and single photon emission computerized tomography (SPECT) imaging techniques are highly valuable, noninvasive in vivo tools for molecular neuroimaging to study disease etiology and pathophysiology aiming to identify biomarkers to track disease progression, aid differential diagnosis, and evaluate response to novel therapeutic treatments. Advances in radiochemistry have sparked the development of a variety of radiotracers, intravenously administered, to quantity-specific molecular targets of interest with nanomolar affinity (Phelps, 2000). In idiopathic and familial Parkinson’s disease (PD), the dopaminergic system is one of the most widely studied systems, employing a variety of PET and SPECT tracers for presynaptic and postsynaptic dopaminergic targets (de Natale et al., 2018; Politis, 2014). Molecular imaging has also been utilized to investigate other neurotransmitter systems, including the serotonergic, cholinergic, cannabinoid, opioid, noradrenergic, and glutamatergic, as well as several other molecular pathways and pathologies, for which validated PET radiotracers are available, including glucose metabolism, neuroinflammation, protein aggregation, phosphodiesterase (PDE) enzymes, synaptic integrity, and mitochondrial and energy dysregulation. This chapter will discuss PET imaging techniques to study these molecular pathologies in idiopathic and familial PD. Separate chapters in this book focus on dopaminergic and serotonergic molecular imaging, as well as findings from structural and functional magnetic resonance imaging (MRI), in idiopathic and familial PD.

a

Joint first authors.

Neuroimaging in Parkinson’s Disease and Related Disorders https://doi.org/10.1016/B978-0-12-821651-4.00002-6

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Bradykinesia, tremor, and rigidity represent the cardinal motor symptoms of PD defining the clinical onset (Bloem et al., 2021). A plethora of nonmotor symptoms including sleep disturbance, cognitive deficits, mood changes, and autonomic dysfunction present a significant impact on patients’ quality of life (Politis et al., 2010). While multiple pathologies and pathways have been implicated in PD, the presence of abnormal fibrillar aggregates of a-synuclein, forming Lewy bodies and Lewy neurites, is considered the cardinal pathological event for PD (Braak et al., 2003; Spillantini et al., 1997). Molecular imaging studies strive to link neuropathology with motor and nonmotor symptomatology in vivo. While the pathogenesis of PD is unknown, it likely involves a multifaceted, complex process arising from a combination of genetic, environmental, and lifestyle factors. To date, genetic variation has been linked to approximately 25% of overall risk of developing PD (Day & Mullin, 2021; Nalls et al., 2019). There are two main hypotheses regarding the genetic component of complex diseases such as PD, the common diseaseecommon variant (CDCV) hypothesis (Reich & Lander, 2001), and the common diseaseerare variant (CDRV) (Pritchard, 2001; Pritchard & Cox, 2002). The CDCV hypothesis proposes that a significant proportion of risk for diseases such as PD is mediated through common genetic variants, defined as a genetic allele with a frequency of greater than 1% within a population (Pritchard & Cox, 2002; Reich & Lander, 2001). GWAS studies have made testing for the CDVC hypothesis feasible, leading to the identification of several common risk loci. Conversely, the CDRV hypothesis proposes that a risk component will be a rare genetic variant, defined as a genetic allele with a frequency of 1% of less within the population (Pritchard, 2001; Pritchard & Cox, 2002). Second-generation sequencing methods have allowed for rare alleles to be studied in the large cohorts required for sufficient power (Jonsson et al., 2013). These two hypotheses are not mutually exclusive; therefore, it is likely that both play a role in the genetic components underlying PD. Genome-wide association studies (GWAS) have led to the identification of genes containing risk variants overlapping with monogenic genes, containing diseasecausing mutations (Hernandez et al., 2016; Nalls et al., 2019). Following the identification of the first genetic cause of PD in 1997 (Polymeropoulos et al., 1997), over 25 mutated genes have been linked with familial forms of parkinsonism (Hernandez et al., 2016). Familial forms of PD can be classified, based on inheritance patterns, as dominantly inherited mutations (such as SNCA and LRRK2) or recessively inherited mutations (such as PINK1, DJ-1, and PARKIN) (Day & Mullin, 2021; Klein & Westenberger, 2012). Genetic factors offer a variable for the stratification of PD into subgroups, with different etiology, clinical trajectory, prognosis, and treatments, striving toward a landscape of precision medicine based on specific disease subtypes (Bloem et al., 2021; Crosiers et al., 2011; Marras & Lang, 2013; Wilson, Politis, et al., 2020). Studying genetic factors offers a unique opportunity to help identify causative pathways linked with disease pathogenesis, which has the potential to identify targets for therapeutic intervention. This chapter will highlight insights from molecular imaging studies to unlock disease pathology, beyond the dopaminergic and serotonergic systems, in idiopathic and familial PD, and point toward potential further applications.

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Glucose metabolism It has been established that neuronal cells uphold antioxidant status through the preferential metabolism of glucose through the pentose phosphate pathway (PPP). Inhibiting this can cause the cells to die (Dunn et al., 2014; Herrero-Mendez et al., 2009). In turn, this has led to the investigation of this pathway within sporadic PD in postmortem studies, which found that PPP downregulation may be considered a primary occurrence within PD pathology (Dunn et al., 2014). Consequently, molecular neuroimaging to investigate glucose metabolism within PD is a highly valuable tool for understanding these findings further. [18F]Fluorodeoxyglucose (FDG), radiolabeled glucose, PET has been widely employed to investigate regional cerebral metabolic rates of glucose metabolism in parkinsonism (Poston & Eidelberg, 2010). This nuclear medicine neuroimaging technique can detect radioligand accretion by measuring g (gamma)-ray pairs emitted from the radiodecay of [18F]Fluorodeoxyglucose (Lu & Yuan, 2015). This can be translated to the level of glucose usage, in turn directly reflecting the regional activity and metabolic processes within the brain (Verger & Guedj, 2018). Characteristic metabolic changes in early-moderate PD stages of bilateral hypermetabolism in the globus pallidus (external) and putamen have been identified (Borghammer et al., 2012). Further increased metabolism was found within the cerebellum, thalamus, and motor cortex in a study of 10 early PD patients and 10 healthy controls (Tan et al., 2018). Contrastingly, hypometabolism was observed in the dorsolateral prefrontal and paretooccipital association regions (Eckert et al., 2005; Jin et al., 2018). Corroborating hyper- and hypometabolic findings were also presented by Tripathi et al. as well as noting a hallmark feature of either preserved or hypermetabolic activity in the lentiform nuclei within idiopathic PD. This was bilateral or unilateral in hemiparkinsons (Tripathi et al., 2013). Borghammer et al. used high-resolution research tomograph (HRRT) [18F]FDG PET to enable further investigation into smaller structural basal ganglia changes. Alongside extensive decreased cortical metabolism (frontotemporaleparietoocciptal regions), they noted increased metabolism in the putamen, internal pallidum, thalamic subnuclei, and external globus pallidus, which had the most significant relative hypermetabolism (Borghammer et al., 2012). Hypometabolism of the parietal region (bilaterally) in idiopathic PD (Zhao et al., 2012) and of the inferior parietal lobe has also been demonstrated (Firbank et al., 2017). Similarly, a metaanalysis, consisting of 74 studies comprising 2323 patients and 1767 controls, noted extensive decreased metabolism in the caudate nucleus (left) and inferior parietal cortex (bilaterally). Motor symptoms were related to hypometabolism in the caudate nucleus and cognitive deficits to hypometabolism in the inferior parietal cortex (Albrecht et al., 2019). Findings of occipital lobe hypometabolism in PD compared with controls have also been reported (Bohnen et al., 1999) associated with neurophysiological test performance (Firbank et al., 2017). Huang et al. found that specific pattern changes in cerebral metabolism correlated with specific cognitive, motor, and emotional dysfunction (Huang et al., 2013). Several studies have utilized spatial covariance analysis within PD. This has enabled the identification of a covariance pattern within PD, known as the Parkinson’s diseaseerelated pattern (PDRP) of preserved or increased metabolism in thalamic, cerebellar, pons, and lentiform nucleus areas but decreased metabolism within regions of the premotor cortex, parietal,

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and occipital areas (Eidelberg et al., 1994; Ma et al., 2007; Tomse et al., 2017). A PD-related cognitive pattern (PDCP) has also been illustrated by relative hypermetabolism of cerebellum and dentate nuclei and relative hypometabolism of parietal and frontal areas (Eidelberg, 2009; Huang, Mattis, et al., 2007). In addition, Lozza et al. identified topographically separate networks related to executive function and bradykinesia within PD (Lozza et al., 2004). PDRP has been found to be associated with PD severity (Hoehn and Yahr score) (Eidelberg et al., 1994), with a positive correlation with UPDRS scores (Wu et al., 2013). Longitudinal changes of progressive PRDP expression increase in PD compared with healthy controls over time have also been demonstrated. Specifically, an association between disease progression and increases in metabolic function of dorsal pons, primary motor cortex, internal globus pallidus, and subthalamic nucleus as well as reducing metabolic function in parietal areas (prefrontal and inferior) was noted (Huang, Tang, et al., 2007). Meles et al. further investigated PRDP through spatial covariance analysis on three European PD cohorts (Spain, Italy, and Netherlands) identifying significant expression of PDRP in PD participants relative to the controls in all three cohorts. They did, however, note the need for future investigation into topographic changes in particular subtypes of PD (Meles et al., 2020). Previous studies have examined [18F]FDG PET metabolic changes in specific subtypes of PD. Two heterogenic phenotypes of PD, tremor-dominant and akinetic-rigid, were studied to determine metabolic differences. Glucose metabolism rate (GMR) was decreased in frontal regions of the akinetic-rigid group relative to the tremor-dominant group. There was a correlation between GMR in BA7 and cognitive performance for both tremor-dominant and akinetic-rigid PD. A correlation between posterior cingulate and parietal GMR with cognitive impairment severity was noted in akinetic-rigid participants, whereas a cognitive test performance correlated with frontal, parietal, temporal, and anterior cingulate GMR in tremordominant group. The authors suggested that this may indicate disparate pathogenetic/ logical mechanisms underpinning different PD subtypes (Miliukhina et al., 2020). Cerebral metabolism in PD with different levels of cognitive dysfunction has also been further studied. Tang et al. investigated three PD cohorts with differing cognitive functions: no cognitive impairment (PD-NC), mild cognitive impairment (PD-MCI), and dementia (PDD) groups. Relative to the PD-NC group, PDD showed more metabolic reductions (primarily within the posterior cortex) than those with MCI. Thus, the authors postulated a possible link between severity of cognitive impairment and posterior cortex region glucose hypometabolism (Tang et al., 2016). Homenko et al., in a study of PD patients including those without cognitive disorders and those with MCI, observed hypometabolism in the cingulate cortex, parietal, and frontal areas in the latter relative to the normal cognition group (Homenko et al., 2017). Similar findings were noted by Khomenko et al. who found temporal (posterior) and parietal as well as frontal and cingulate region hypometabolism in PD with cognitive impairment, with glucose metabolism of such regions correlating with UPDRS score (Khomenko et al., 2020). Such changes were also observed in those with PD with dementia (PDD) compared with controls. Here, PDD was compared with Alzheimer’s disease (AD) groups noting a relative preservation of medial temporal metabolism but a larger decrease in visual cortex metabolism in the PDD patients. As a result, they concluded that these disparate regional changes in PDD and AD indicate differing combinations and levels of underlying neuropathological mechanisms (Vander Borght et al., 1997). Correlations between cognitive function determined by Raven’s Colored Progressive Matrices (RCPM)

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scores and relative regional cerebral metabolic rates for glucose (rrCMRglc) have also been established. A positive correlation between right retrosplenial cortex and left middle frontal gyrus rrCMRglc and RCPM score was found (Nagano-Saito et al., 2004). It is also important to note hypometabolic changes in the neocortex of PD participants without any tangible cognitive impairment. The largest differences in cerebral metabolic rates for glucose (rCMRglc) were in the thalamus and posterior areas compared with controls (Eberling et al., 1994). Glucose metabolism in specific genetic mutations has also been investigated. Three studies have employed [18F]FDG PET to investigate glucose metabolism in A53T, A30P, and duplication SNCA mutation carriers (Kruger et al., 2001; Nishioka et al., 2009; Xiong et al., 2016). Two A30P symptomatic SNCA mutation carriers, who had neuropsychological symptoms, showed reduced cortical [18F]FDG uptake, and one A30P premotor SNCA mutation carrier, who also had neuropsychological symptoms, showed some degree of hypometabolism in the temporal and medial frontal cortices and caudate (Kruger et al., 2001). The authors suggested that hypometabolism reflected the severity of neuropsychological deficits in these SNCA mutation carriers. A symptomatic A53T SNCA mutation carrier, who did not display significant neuropsychological symptoms, presented with a similar PD-related spatial covariance pattern (PDRP) of [18F]FDG uptake as seen in idiopathic PD patients (Matthews et al., 2018), showing hypometabolism in the parietal cortex and relative hypermetabolism in pallidothalamic, pontine, and cerebellar areas (Xiong et al., 2016). In symptomatic duplication SNCA mutation carriers, [18F]FDG uptake was reduced bilaterally in the parietotemporooccipital cortex (Nishioka et al., 2009). Reduced [18F]FDG uptake in the parietal cortex has been reported in one asymptomatic A53T SNCA mutation carrier; however, the metabolic pattern was different from the PDRP pattern commonly observed in idiopathic PD (Xiong et al., 2016), while in three asymptomatic duplication SNCA mutation carriers, [18F]FDG uptake was in line with controls (Nishioka et al., 2009). While preliminary, the presence of alerted glucose metabolism detected in one asymptomatic A53T SNCA mutation carrier supports the hypothesis that pathological-related changes start during preclinical disease stages and that it could be possible to detect, and monitor the progression of, these preclinical changes in vivo using molecular imaging techniques. Two studies have also investigated [18F]FDG changes in PARK-7 PD (DJ-1 gene mutation) (Dekker et al., 2003, 2004). Here, cerebellar hypermetabolism was demonstrated in homozygotes (Dekker et al., 2003, 2004) as well as thalamic, occipital, and parietal decreased metabolism (Dekker et al., 2004). Caudate and putamina hypermetabolism was also noted in DJ-1 mutation homozygote parkinsonism participants (Dekker et al., 2004). It is reasonable to posit that this could be due to either a phenomenon of the primary disease mechanism or also a secondary consequence of the deafferentation of the striatum (Dekker et al., 2003). In research involving five patients heterozygous for GBA mutations, [18F]FDG revealed hypometabolism within the supplementary motor areas (SMA) and, in carriers who had parkinsonism, the parietooccipital cortices additionally (Kono et al., 2010). Here, it is postulated that since the decreased SMA metabolism was seen in both GBA carriers who had parkinsonism and those who were asymptomatic, this may indicate that these metabolism alterations are not associated with akinetic mechanisms. Rather, it may be that in GBA carriers manifesting parkinsonism, there may be modulation of such phenotype by the preestablished hypometabolic SMA, which may instigate akinetic presentation (Kono et al., 2010).

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Mao et al. studied glucose metabolism using [18F]FDG in PD participants with CHCHD2 p.Thr61Ile mutations, noting that the glucose metabolic changes found were consistent with PDRP patterns (Mao et al., 2016). However, they also noted there was not a correlation between PDRP activity and disease severity, contrary to other research such as that by Eidelberg et al. (Eidelberg et al., 1994). They hypothesized an explanation for this being the effect of a compensatory mechanism. The authors suggest CHCHD2 mutation may result in dysfunction of mitochondria, which play a role in glucose metabolic processes. In initial disease stages, there may be mild mitochondria dysfunction as a result of the mutation but with high levels of compensatory hypermetabolism within the globus pallidus and putamen. However, with progression of the disease and thus mitochondrial dysfunction, this could lead to loss of compensatory processes and a lower amount of hypermetabolism (Mao et al., 2016). Further research is needed to support and authenticate this premise. The notion of mitochondrial dysfunction relating to metabolism changes has also been noted by Han et al. (2021). Dunn et al. also theorized the occurrence of damage of the mitochondria as a direct consequence of altered regulation of PPP (Dunn et al., 2014). Some studies have also investigated metabolic changes in response to therapeutic intervention. Lee et al. used serial PET imaging and statistical parametric mapping on PDD patients before and after cholinesterase inhibitor treatment to evaluate any association between cerebral metabolism and cognitive improvement. They found significant increases in left middle orbitofrontal, right superior frontal, and left angular gyri metabolism (which extended to the left superior and middle frontal gyri and supramarginal area) posttherapeutic intervention. There was, however, reduced right fusiform gyrus metabolism after treatment relative to baseline. There was a significant association between posttherapy cognitive improvement (assessed using the minimental state examination) and cingulate, orbitofrontal, and supramarginal metabolic increases (Lee et al., 2008). Metabolic responses to levodopa and deep brain stimulation (unilateral ablation of the subthalamic nucleus) have also been shown with posttreatment decreases in activity within the PD-related metabolism patterns/networks (Feigin et al., 2001; Su et al., 2001). With regard to future research and clinical application, several studies have noted the potential utility for glucose metabolism patterns as a diagnostic or differential tool for parkinsonisms (e.g., progressive supranuclear palsy, multiple systems atrophy [MSA], and corticobasal syndrome), particularly in early/preclinical stages, and in the monitoring/control of therapeutic intervention efficiency (Eckert et al., 2005; Homenko et al., 2017; Tripathi et al., 2013). In support of this, a systematic review and metaanalysis conducted by Gu et al. to evaluate the utility of PD [18F]FDG PET within clinical practice concluded that differential diagnostic accuracy between parkinsonism is high in [18F]FDG PET metabolism pattern analysis (Gu et al., 2019).

Synaptic pathology The notion of synaptic pathological changes within PD is an area of great interest and evolving research. It has been suggested that several genes often affected in familial PD (i.e., PARKIN, PINK1, DJ-1, and LRRK2) are crucially implicated in synaptic regulation,

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structure, and function (Bellucci et al., 2016; Esposito et al., 2012; Plowey & Chu, 2011). This has been further corroborated by knockout animal studies, which have shown a resulting dysregulation of neurotransmitter functionality and the plasticity of synapses (Belluzzi et al., 2012). The existing research base further hypothesizes the impact of a-synuclein accumulation and its effect on synaptic and axonal physiology and integrity (Taoufik et al., 2018). Therefore, further examination of synaptic pathology and its role within PD pathogenesis may hold great potential in advancing the understanding of disease etiopathogenesis, disease monitoring, and assessing the efficacy of therapeutic intervention within PD (Matuskey et al., 2020). Levetiracetam-based PET radiotracers have been developed, which can target the synaptic vesicle glycoprotein 2A (SVA2) enabling the quantification of synaptic density in vivo (Finnema et al., 2016). To date, the most widely employed PET radiotracer for SV2A in disease models in vivo is [11C]UCB-J. At present, four studies (Andersen et al., 2021; Delva et al., 2020; Matuskey et al., 2020; Wilson, Pagano De Natale, et al., 2020) have used [11C]UCB-J PET scanning to assess synaptic integrity, identifying synaptopathy in idiopathic PD patients showing consistent, reproducible reduction in synaptic integrity. Matuskey et al. used [11C] UCB-J PET to study synaptic density in 12 moderate to advanced idiopathic PD patients and 12 healthy controls alongside postmortem putamen and substantia nigra autoradiography in a separate cohort consisting of 15 PD and 13 controls. Using post hoc analysis, they found a decreased red nucleus, locus coeruleus, parahippocampal gyrus, and substantia nigra SV2Aspecific binding in the PD group. Autoradiography also demonstrated decreased substantia nigra SV2A binding in PD tissue (Matuskey et al., 2020). Significant SV2A binding losses were also demonstrated within the substantia nigra in 30 early treated PD patients in a [11C]UCB-J PET study by Delva et al. (2020). Here, Delva et al. also used [18F]FE-PE2I PET (a radioligand for the dopamine transporter [DAT]) finding a positive relationship between this and [11C]UCB-J binding potential within the substantia nigra and caudate (Delva et al., 2020). Such findings can thus help to understand potential PD etiology. For example, it is thus plausible to conclude that in early PD, there is loss of both presynaptic axonal terminals, which provide innervation to the substantia nigra as well as neurones of the nigrostriatum. This may be explicated as either a transsynaptic model of pathology process propagation or a response mechanism as a result of target deprivation (Delva et al., 2020). A study from our group observing both cross-sectional and longitudinal changes in 12 drug-naïve PD participants and 16 controls found a decrease in cortical, thalamic, dorsal raphe, brain stem, caudate, and putamen [11C]UCB-J volume of distribution in PD versus controls (Wilson, Pagano, et al., 2020). Therefore, it can be postulated that aberrations of synaptic function occur within the early disease stages, likely playing a role in symptomatology. However, in longitudinal follow-up of drug-naïve PD participants, no significant differences were shown relative to baseline. Possible explanations for this may include the fact that very early stages of PD were investigated, the sample size and the timing of follow-up (average follow-up 11 months) (Wilson, Pagano, et al., 2020). Andersen et al. assessed synaptic density in PDD or dementia with Lewy bodies (DLB), nondemented PD, and control subjects using [11C]UCB-J PET. Decreased standardized uptake ratio (SUVR)-1 values in the substantia nigra were noted in the nondemented PD, DLB, and PDD group compared with controls. The DLB and PDD group also showed decreased SUVR-1 in all other cortical regions, with the exception of the amygdala and

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hippocampus. Executive function test scoring correlated significantly with SUVR-1 in the middle frontal gyrus (Andersen et al., 2021). It has also been noted that future longitudinal research with larger sample sizes may be necessary to further validate their findings and advance the current understandings of synaptic pathogenesis in PD (Andersen et al., 2021). Similarly, a case report on two participants with DLB found reduced binding of [11C] UCB-J, particularly so within occipital and parietal areas in such patients relative to healthy controls (Nicastro et al., 2020). The evidence base of [11C]UCB-J imaging in genetic PD cohorts remains limited. However, recent research on mutation carriers of other neurodegenerative conditions highlights the potential application of such studies within familial PD as an exciting area of possible future research. For example, Malpetti et al. used [11C]UCB-J PET to evaluate synaptic density in 3 asymptomatic C9orf72 mutation carriers (with possible frontotemporal dementia risk), 1 clinically diagnosed probable frontotemporal dementia (behavioral variant), and 19 control subjects. Decreased thalamic synaptic density was observed in the asymptomatic C9orf72 mutation carriers as well as frontotemporal loss in the frontotemporal dementia patient, compared with healthy controls (Malpetti et al., 2021). It is possible that loss of synaptic integrity is an early phenomenon, which could be detected in asymptomatic genetic carriers of PD mutations. The application of independent component analysis (ICA) for the identification of spatial covariance patterns may be an area of potential interest within future synaptic pathology research. Fang et al. used a sample of 80 healthy control subjects, performing ICA on two subsets, each consisting of 40 participants, to explore the presence of spatial patterns or networks of coherent synaptic density variability (Fang et al., 2021). Through this method, they were able to demonstrate novel findings of organized covariance networks/patterns characterizing density of synapses within the human brain. The presence of age-related changes in some synaptic density networks highlights the possible usefulness of ICA in the recognition of synaptic density patterns or networks with biological, and potentially pathological, significance (Fang et al., 2021). ICA has previously been applied in disease states in functional MRI studies to statistically separate neuronal networks (Savio et al., 2017) and [18F]FDG PET (Di et al., 2019) as well as [11C]PHNO PET (Smart et al., 2020). Furthermore, a study by Fu et al. identified different spatial covariance patterns of serotonergic pathology, in unaffected LRRK2 carriers compared with idiopathic PD patients (Fu et al., 2018). The participants involved included 15 idiopathic PD patients, 8 LRRK2 mutation carriers with manifest PD, 9 nonmanifesting LRRK2 mutation carriers, and 9 controls. Here the authors employed principle component analysis noting possible explicit and distinct spatial serotonergic patterns in nonmanifesting LRRK2 mutation carriers, compared with serotonergic spatial covariance patterns observed in idiopathic PD and LRRK2 mutation carriers with manifest PD (Fu et al., 2018). The presence of distinct serotonergic spatial covariance patterns in individuals at risk of PD due to LRRK2 mutations highlights the need to further explore disease mechanisms in early premanifest stages. It would be interesting to explore the presence of distinct or overlapping spatial covariance patterns of synaptic integrity, identified using ICA of [11C]UCB-J PET, in disease cohorts of idiopathic PD and familial PD to help unravel disease pathologies. Further future paradigms and directions of synaptic pathology research are ever evolving. Possible applications of other radiotracers for SV2A, including fluorinated-18 compounds, are an area of interest as such compounds are advantageous with regard to their half-life. A longer half-life relative to carbon-11 compounds circumvents the necessity for on-site

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cyclotron production, thus expanding the opportunity for wider application and research (Warnock et al., 2014). For example, in vivo preclinical animal studies have identified the potential utility of [18F]MNI-1126 and [18F]UCB-J, novel tracers found to be comparable with [11C]UCB-J with regard to its in vivo properties, pharmacokinetics, and SV2A specificity. This therefore shows great promise for the potential application of [18F]MNI-1126 and [18F] UCB-J PET to investigate SV2A changes within future human clinical studies (Constantinescu et al., 2019; Li et al., 2019). Moreover, Naganawa et al. performed the first human comparison of [18F]SynVesT-1 and [11C]UCB-J tracers in eight healthy participants (Naganawa et al., 2021). They established very promising findings of the kinetic and binding characteristics of [18F]SynVesT-1 in vivo, supporting its potential utility for visualizing and quantifying synaptic density within future studies (Naganawa et al., 2021). Additionally, the role of SV2C in the mediation of dopaminergic function, regulation, and thus PD pathophysiology has also been suggested (Dunn et al., 2017). SV2C is another integral surface protein prominently expressed in the basal ganglia involved (Dardou et al., 2013). Genetic knockout of SV2C in animal studies further highlights this functional role (Dunn et al., 2017). Dunn et al. and Rai et al. further noted that in alpha synuclein mutation, the expression of SV2C is significantly altered (Dunn et al., 2017; Rai et al., 2018). While, at present, there is currently no available PET tracer for SV2C, the notion of targeting and further exploration of SV2C presents a novel and exciting potential for ongoing research (Rai et al., 2021). With regard to optimization of therapeutic management, Tusi and Isacson present the rationale and theoretical neurobiological mechanisms of cell therapy for the repair of synaptic and cellular function, with a view to this as an exciting potential in future PD therapy (Tsui & Isacson, 2011). Recently, Barbuti et al. have reviewed the contemporary advances in such stem cellebased research such as grafting and transplantation implicating the importance and potential utility of neuroimaging alongside this. For example, in the follow-up of posttransplant patients, MRI could be useful in monitoring the sizing and development of grafts. Similarly, PET scanning may be able to detect synaptic changes or, by utilizing [18F]fluorothymidin, could monitor graft cell proliferation. Dopaminergic function may also be evaluated using tracers such as [18F]DOPA and [18F]GE180 (Barbuti et al., 2021).

Neurotransmitter systems Cannabinoid The endocannabinoid system, encompassing endocannabinoids, endogenous lipid messengers, as well as cannabinoid type 1 (CB1) and type 2 (CB2) receptors, has been implicated in a wide range of physiological functions such as motor skills, memory, mood, appetite, and inflammation (Lu & Mackie, 2016). CB1 receptors are predominately presynaptic and expressed in the hippocampus, cerebral neocortex, hypothalamus, cerebellum, and basal ganglia (Jiang et al., 2007). The basal ganglia play a crucial role in the integration and coordination of signals from the cortex to regulate motor activity, and it is thought that endocannabinoids play a prominent role within the functioning of such circuits, particularly dopaminergic activity modulation (Benarroch, 2007; El Manira & Kyriakatos, 2010; MoreraHerreras et al., 2012). This is supported by evidence of significant basal ganglion CB1 signal

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changes in states where dopamine is depleted implicating the role of the endocannabinoid system in such disease processes and highlighting why is it of such interest in PD (MoreraHerreras et al., 2012). Furthermore, postmortem and preclinical animal studies in PD have shown changes in CB1 and CB2 receptors. For example, Casteels et al. used [18F]MK-9470 PET to investigate cannabinoid receptor changes in the 6-hydroxydopamine (6-OHDA) rat model of PD. They noted alterations within endocannabinoid transmission involving primarily caudate and putamina areas, however, also within cerebellar, somatosensory, and motor networks (Casteels et al., 2010). Moreover, within animal models, studies have demonstrated that modulating the endocannabinoid system may provide therapeutic bearing in PD (Morera-Herreras et al., 2012) as well as showing the role of CB2 receptors in dopaminergic modulation (Zhang et al., 2014). Overexpression of CB2 receptors has been shown to be linked with neurodegeneration and conditions including PD, AD, and Huntington’s disease (Teodoro et al., 2021). Several studies have furthermore suggested the role of CB2 receptors in neuroinflammation an established pathogenic mechanism within PD (Wang et al., 2015), with glial CB2 receptor upregulation noted in postmortem PD samples, which illuminates the potential for CB2 receptors to be pharmacologically targeted in future antiinflammatory therapeutic intervention (Gómez-Gálvez et al., 2016). The aforementioned evidence has highlighted the need for further in vivo PET studies in PD patients to attempt to recapitulate such findings and to furthermore substantiate the understanding of the role of endocannabinoids within PD pathophysiology. Van Laere et al. attempted to investigate the regional availability of CB1 receptors in 29 idiopathic PD patients (9 early PD drug-naïve, 10 advanced PD with levodopa-induced dyskinesias [LIDs], and 10 advanced PD without LIDs) and 12 control subjects. Using [18F] MK-9470 PET, they demonstrated reduced substantia nigra CB1 availability in PD patients. Contrastingly, there was an increase in the mesocortical dopaminergic projection, nigrostriatal, and mesolimbic regions of CB1 availability. There was no significant difference between advanced PD patients with LIDs and those without. Similarly, in the cohort with LIDs, no correlation between this and the severity of LIDs was noted (Van Laere et al., 2012). Moreover, Ceccarini et al. also used [18F]MK-9470 PET studying 38 PD patients with 10 matched controls, noting an association between visuospatial and executive functioning as well as episodic memory disturbances and a reduced prefrontal and midcingulate cortices CB1 receptor availability in the PD group (Ceccarini et al., 2019) implicating the postulation of endocannabinoid system involvement within PD pathophysiology. With regard to future research, the need for development of new tracers such as those targeting CB2 receptors is of great interest. For example, Teodoro et al. biologically evaluated a novel tracer [18F]LU14 demonstrating, in PET imaging of a rodent model, good tracer selectivity and high affinity to CB2 receptors (Teodoro et al., 2021). Similarly, while information regarding the exact mechanisms of action and effective dosing regimens from clinical trials on cannabinoid treatment for LIDs still remain unknown (Patricio et al., 2020), the potential for disease-modifying therapeutic interventions in PD targeting the endocannabinoid system (due to its association with modulation of neuroinflammation) mechanisms has been suggested (Cilia, 2018). Further development of this research and investigation of the potential utility of CB2 receptors as a therapeutic target is necessary and may provide particularly useful with hope to elucidate our understanding of this and its role in PD further (Cilia, 2018; Teodoro et al., 2021).

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Cholinergic The cholinergic system consists of basal forebrain and mesopontine tegmental nuclei (which project to the cortex and both thalamic and subcortical regions, respectively) as well as additional projecting interneurone/neurone groups (Pasquini et al., 2021). It is considered this system plays a critical role in higher-order functioning of the brain, memory, attention, and learning (Hampel et al., 2018). Cholinergic receptors intercede effects of the neurotransmitter acetylcholine and can be divided into two subtypes on the basis of binding predilection: muscarinic or nicotinic (Broadley & Kelly, 2001). Importantly, the role of the cholinergic system within PD pathophysiology has been implicated. For example, postmortem studies have demonstrated in PD patients, there was reduced hippocampal, and frontal and temporal cortical muscarinic- and nicotinic-binding capacities or sites relative to controls (Aubert et al., 1992). As such, there is great interest in further in vivo neuroimaging of the cholinergic system within PD to attempt to further establish a greater understanding of its role within PD etiology. There are several different targets each with a selection of tracers that can be used with PET and SPECT imaging to investigate the cholinergic system (Bohnen et al., 2018). These include nicotinic (using tracers such as 5[123I]5IA-85380, 2[18F]FA-85380, and [18F]ASEM) and muscarinic receptors (using tracers such as [11C]NMPB, [11C]benztropine, [123I]QNB), the vesicular acetylcholine transport (VAChT, a glycoprotein that regulates the presynaptic vesicular packing of acetylcholine (Giboureau et al., 2010)) for which [123I]IBVM and [18F] FEOBV tracers can be used, and acetylcholinesterase (AChE, the main enzyme involved in the metabolic hydrolysis of acetylcholine to produce acetate and choline (English & Webster, 2012)) for which [11C]PMP, [11C]MP4A, and [11C]donepezil tracers can be used (Bohnen et al., 2018). Bohnen et al. studied 12 AD, 14 PDD patients, and 11 PD patients without dementia using [11C]PMP (Bohnen et al., 2003). They noted that average activity of cortical acetylcholinesterase (AChE) remained relatively conserved for AD participants but was decreased the most in PDD participants, then in PD patients without dementia, suggesting that decreased cholinergic activity is more characteristic to those with PDD relative to AD patients (Bohnen et al., 2003). Interestingly, an association between cholinergic hypofunction and fall status was also established by Bohnen et al. (2009). Lui et al. also used [11C]PMP in PD patients with LRRK2 compared with idiopathic PD patients demonstrating significant thalamic, cortical, limbic, and default mode network-related intergroup differences between 14 LRRK2 mutation carriers with manifest PD, 16 premanifest LRRK2 mutation carriers without PD, 8 idiopathic PD, and 11 controls. They used an outcome measure of acetylcholinesterase hydrolysis rates finding cortical increases in LRRK2 carriers without PD compared with controls. They also noted significantly greater activity in thalamic and cortical areas of LRRK2 carriers with manifesting PD relative to with idiopathic patients (Liu et al., 2018). Here, it is suggested that the cholinergic activity alterations (lowest activity within idiopathic patients and highest within LRRK2 carriers both manifesting and premanifest relative to controls) could be a reflection of compensatory mechanisms in response to functional aberrations as a result of the LRRK mutation (Liu et al., 2018). This notion can be supported by other imaging modalities including [18F]FDOPA and vesicular monoamine transporter 2 PET (using [11C]DTBZ radiotracers) in another study, whereby premanifest

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LRRK2 carries had higher levels of hypothalamic, brain stem, and striatal serotonin transporter binding (relative to sporadic and LRRK2 manifest PD) potentially indicative of serotonergic compensation, which predates PD motor manifestation, thus accounting for the disparities in clinical presentation of LRRK2 PD (Wile et al., 2017). Moreover, in a review by Pasquini et al., they suggest there is a vast research base that posits the role of aberrant cholinergic function in PD symptoms including depression, visual hallucinations, gait freezing, impairment of cognition, and rapid eye movement (REM) sleep behavior disorder (RBD) (Pasquini et al., 2021). Bohnen et al. also used [11C]PMP in PD patients noting a greater propensity for gait freezing in those with reduced cholinergic innervation of the neocortex, however, not within the thalamus (Bohnen et al., 2014). Such findings support the postulation that cholinopathy may underpin PD pathophysiology and its presenting manifestations (Bohnen et al., 2014; Pasquini et al., 2021).

Opioid Opioid receptors are described as G proteinecoupled receptors (Thobois et al., 2018) considered to mediate pain modulation and processing, neuropsychiatric disorders, eating and food-related disorders as well as also movement disorders (Rosenbaum et al., 2009). In animal studies, opioid intervention using nalbuphine proved to have effective therapeutic benefit in LID (Potts et al., 2015). Similarly, in a rodent study, an association was found between opioid transmission changes in the cortex and LID (Johansson et al., 2001). Moreover, it has been established that there is striatal circuit expression of opioid receptors, which regulate spiny projection neurone activity within motor control and movement (Sgroi & Tonini, 2018). Such findings have catalyzed scientific interest in furthering the understanding of such mechanisms with relation to PD and its pathogenesis. While there are a range of opioid PET tracers, for human studies, those commonly utilized include [11C]CAF (carfentanil, a selective m receptor agonist) and the nonselective tracer [11C] DPN (diprenorphine) (Thobois et al., 2018). [11C]DPN PET studies have noted significant decreases in opioid binding within the cingulate, striatum, and thalamus and prefrontal increases for LID patients; however, this was not found in those without dyskinesia. Such findings may therefore suggest the role of opioidergic transmission changes within LID (Piccini et al., 1997). Pagano et al. (2017) have also noted the influence of opioidergic alterations within LID development. Investigating opioid system changes may also have potential utility with regard to aiding the differential diagnosis of neurological conditions. For example, Burn et al. used [11C]DPN PET imaging in eight PD, seven MSA (striatonigral degeneration), and six Steele-Richardson-Olszewski (SRO) syndrome patients. Here, binding to opioid receptors in the caudate was over 2.5 standard deviations less than normal mean values in four SRO patients, whereas this was not the case in any of the MSA or PD patients (Burn et al., 1995). With regard to future directions within opioid system molecular imaging, there are limited data regarding the relationship between opioidergic changes and the manifestation of nociceptive and neuropathic pain, depression and anxiety, and disordered impulse control within PD (Thobois et al., 2018).

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Noradrenergic The noradrenergic system can be described as a neurotransmitter network that facilitates the release and transmission of noradrenaline (Bylund, 2003). Such nuclei can be found within the medulla and pons, with the locus coeruleus acting as the principal noradrenaline source (Ranjbar-Slamloo & Fazlali, 2020). Within the brain, noradrenaline plays a role in attention, vigilance, arousal, and anxiety (Ranjbar-Slamloo & Fazlali, 2020). In current research, it is widely accepted that the noradrenergic system may be affected within PD and could explicate some of its clinical symptoms and manifestations (Paredes-Rodriguez et al., 2020). This has been furthermore supported by preclinical studies. For example, in MPTP (1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine)-induced parkinsonism primate models, significant loss of noradrenergic cells was demonstrated (Masilamoni et al., 2017). Similarly, in MPTP-administered rodents, a reduction of somatosensory and medial prefrontal cortices norepinephrine concentration was noted (Nayyar et al., 2009). Postmortem findings have also shown extensive decreases in norepinephrine as well as locus coeruleus pathology (Sommerauer et al., 2018). As such, there is great interest in the further investigation into noradrenergic changes within PD and how this may relate to its pathogenesis. [11C]methylreboxetine ([11C]MeNER) is a norepinephrine transporter antagonist radiotracer with high selectivity, which can be used with PET imaging when studying the noradrenergic system in PD in vivo (Nahimi et al., 2018). Such neuroimaging techniques have shown significant functional norepinephrine terminal loss (Kinnerup et al., 2021) and decreased [11C]MeNER binding potential in the nucleus ruber and thalamic regions of PD patients versus controls, coherent with prior in vitro research (Nahimi et al., 2018). Studies have also investigated noradrenergic changes in REM sleep behavior disorder (RBD) patients using [11C]MeNER. For example, Knudsen et al. did not find any significant intergroup changes in RBD patients relative to those with PD (Knudsen et al., 2018). However, Sommerauer et al. (who investigated the availability of norepinephrine transporters in 16 PD participants who had RBD and 14 who did not) noted extensive decreased tracer binding in the former group (Sommerauer et al., 2018). It is therefore reasonable to posit that the reduction of noradrenergic terminals is subordinate in RBD alone relative to PD with RBD (Knudsen et al., 2018). Moreover, it may be evident that there is a link between RBD within PD and aberrant noradrenergic functionality, with this potentially underpinning the pathophysiology of such nonmotor PD symptomology. Consequently, targeting such systems could be an area of importance to take into consideration with regard to future therapeutic interventions (Sommerauer et al., 2018). Kinnerup et al. furthermore found differences in [11C]MeNER distribution volume ratios between PD patients who had rest tremor and those who did not, noting significantly greater thalamic and locus coeruleus mean values in the former group relative to the latter (Kinnerup et al., 2021). Here, it may be postulated that locus coeruleus and thalamic noradrenergic alterations may therefore underpin the etiology of tremor phenotype in PD (Kinnerup et al., 2021).

Glutamatergic Glutamate is considered to be the key neurotransmitter with excitatory function within the brain (Meldrum, 2000). The glutamatergic system regulates several physiological

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mechanisms within the brain including development of neurones, plasticity of synapses, and memory (Crupi et al., 2019). Glutamate (alongside dopaminergic reciprocation) also plays a role in the modulation of mesolimbic, nigrostriatal, and mesocortical neurotransmission networks that have been found to be implicated in PD (Kang et al., 2019). There is a growing evidence base that glutamatergic aberrations may be linked to PD and/or LID pathophysiology (Bastide et al., 2015). For example, in postmortem research of both animal model and human PD, significant striatal glutamatergic synapse pathology was noted (Villalba et al., 2015). Moreover, it was found that PD patients demonstrated significantly greater serum glutamate concentrations relative to healthy participants (Mironova et al., 2018), also highlighting the potential for future therapeutic targets such as the restoration of physiological glutamatergic function (Zhang et al., 2019). Consequently, in vivo evaluation of the glutamatergic changes within PD is an area of great interest. [18F]FPEB is a selective glutamate type 5 receptor radiotracer and has therefore been used to investigate the glutamatergic system within PD in vivo (Wong et al., 2013). Kang et al. performed a pilot study using [18F]FPEB PET in nine PD participants as well as eight controls, demonstrating greater amygdala, hippocampal, and bilateral putamina mean tracer binding potentials for the former group relative to controls (Kang et al., 2019). Here, it is evident that there is an upregulation of glutamate type 5 receptors in mesotemporal and striatal PD gray matter, implicating the role of the glutamatergic system within PD pathophysiology (Kang et al., 2019). In vivo research on the glutamatergic system in PD remains relatively limited; further research is necessary to validate previous findings and deepen understanding. An interesting future direction for research paradigms may be the refinement or development of additional biomarkers or pharmacodynamic markers for the longitudinal evaluation of neuroprotective therapies (Kang et al., 2019).

Phosphodiesterase Cyclic nucleotide phosphodiesterase (PDE) intracellular enzymes modulate intracellular signaling cascades via the hydrolysis of cyclic adenosine monophosphate (cAMP) and cyclic guanosine monophosphate (cGMP). Evidence from PET imaging studies has highlighted alterations in the expression of phosphodiesterase 10A (PDE10A) and phosphodiesterase 4 (PDE4) linked with symptomatology in idiopathic PD patients. Using the [11C]IMA107, selective for PDE10A, our group reports loss of PDE10A in the caudate, putamen, and globus pallidus in 24 levodopa-treated moderate to advanced PD patients compared with healthy controls (Niccolini et al., 2015). Reduced [11C]IMA107 update in the caudate, putamen, and globus pallidus correlated with longer disease duration and with motor symptom severity of bradykinesia and rigidity but not tremor, assessed using MDS-UPDRS part III. In the subcohort of 12 PD patients with LIDs, lower PDE10A in the caudate and putamen was associated with the severity of dyskinesias, assessed using the Unified Dyskinesia Rating Scale (Niccolini et al., 2015). PDE10A hydrolyzes cAMP and CGMP regulating dopaminergic signaling, in the direct and indirect striatal pathways, contributing toward the tightly controlled balance required for motor control (Girault, 2012; Nishi et al., 2008). The in vivo relationship between PDE10A loss with motor symptom severity (Niccolini et al., 2015)

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supports the theory that loss of PDE10A leads to functional imbalance between the striatal dopaminergic pathways, reducing motor activity, consequently contributing to the development of PD motor symptoms. Koole et al. reported lower [18F]JNJ42259152 nondisplaceable binding potential (BPND) values, a tracer for PDE10A, in nine early PD patients compared with controls but did not reach statistical significance possibility due to small sample size (Koole et al., 2017). In a cohort of 17 early de novo, idiopathic PD patients, lower [11C] IMA107 uptake was observed in the caudate, putamen, and ventral striatum compared with healthy controls, with no significant difference in the globus pallidus and motor thalamic nuclei (Pagano et al., 2019). Furthermore, striatal loss of PDE10A was associated with loss of DAT, measured using [11C]PE2I PET. These findings support the role of PDE10A as an early phenomenon in PD with relevance to symptomology and support preclinical studies, which have reported decreased striatal and pallial PDE10A mRNA and protein levels arising from lesions of dopaminergic neurons (Giorgi et al., 2008, 2011). The PET tracer [11C]Rolipram has been developed to quantify PDE4 levels in vivo (ZanottiFregonara et al., 2011). PDE4 hydrolyzes cAMP regulating the cAMP-protein kinase A (PKA) and cAMP response elementebinding protein (CREB) signaling pathway to modulate proteins involved in cognition, mood, and daytime sleepiness (Baquet et al., 2005; Izquierdo et al., 2002; Nishi et al., 2008). In a cohort of 12 levodopa-treated idiopathic PD patients, we reported loss of PDE4 expression in the caudate, prefrontal, and temporal thalamic nuclei, hypothalamus, frontal cortex, and supplementary motor area compared with healthy controls (Niccolini et al., 2017). Furthermore, PDE4 loss in the frontal cortex, supplementary motor area, precentral gyrus, and caudate and prefrontal thalamic nuclei was associated with deficits in spatial working memory. Stratifying PD patients into those with (n ¼ 5) and without (n ¼ 7) excessive daytime sleepiness (EDS) identified increased PDE4 in the caudate, hypothalamus, hippocampus, and limbic striatum in PD patients with EDS (Wilson, Pagano, et al., 2019). Elevated PDE4 levels were associated with the severity of EDS, assessed using the Epworth sleepiness scale. Taken together with findings from PDE10A studies suggests that in idiopathic PD, PDE10A may play a more prominent role in motor symptoms with PDE4 alterations more closely linked to nonmotor symptoms of cognition and sleep dysfunction. To date, in vivo PET studies of PDEs in familial forms of PD are lacking. However, preclinical studies have investigated PDEs in A53T SNCA transgenic mice (Tozzi et al., 2012) and in LRRK2 G2019S knock-in mice (Russo et al., 2018). Tozzi et al. reported that the inhibition of PDE5, with zaprinast, fully restored normal long-term depression of synaptic plasticity, which is reduced in the striatum (Kurz et al., 2010), in old A53T SNCA transgenic mice via the activation of the intracellular PKG signaling pathway (Tozzi et al., 2012). The authors suggest that in A53T SNCA transgenic mice, the modulation of the cGMP/PKG intracellular signaling pathway restores physiological striatal synaptic function and could represent a potential target for therapeutic intervention, although further work is required to understand if PDE inhibition alleviates motor symptoms in this PD model (Tozzi et al., 2012). Russo et al. report the downregulation of PKA activation, resulting in reduced PKAmediated NF-kB inhibitory signaling, which subsequently leads to increased inflammation due to the pathological mutation LRRK2 G2019S (Russo et al., 2018). Furthermore, LRRK2 modulates PKA, in microglia, by affecting PDE4 activity and cAMP degradation. The identification of PDE4 as a novel LRRK2 effector protein in microglia is an important finding since

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the LRRK2 G2019S pathological mutation could favor microglia activation to proinflammatory state, which in turn may exacerbate inflammation and neurodegeneration in LRRK2related PD (Russo et al., 2018).

Neuroinflammation Neuroinflammation is hypothesized to play an important role in the onset and progression of neurodegenerative disorders including PD, with a number of genetic factors related to PD having been linked to neuroinflammation (Belloli et al., 2020; Yao et al., 2021). Postmortem studies have identified activated microglia in the substantia nigra of PD patients (Banati et al., 1998; McGeer et al., 1988). Furthermore, in vitro studies suggest microglia activation occurs early in disease pathology, preceding neuronal death, although the exact role of microglia in disease pathophysiology and progression remains unclear (Belloli et al., 2020; Doorn et al., 2014; Nissen et al., 2019). To date, PET studies have predominately focused on the application of first- and second-generation tracer for translocator protein (TSPO), such as [11C]PK11195, [11C]PRB28, and [18F]DPA-714 expressed in the outer mitochondria member of activated microglia (Wilson, Politis, et al., 2020). Gerhard et al. reported increased [11C]PK11195 binding in frontal and temporal cortices as well as in the substantia nigra and striatum in idiopathic PD patients, with the absence of a relationship between [11C]PK11195 binding with clinical severity or striatal dopaminergic deficits, detected using [18F]FDOPA PET (Gerhard et al., 2006). Over a 2-year follow-up, [11C]PK11195 binding remained stable in eight PD patients, suggesting that microglia activation could be an early phenomenon with activation level potentially remaining relatively stable (Gerhard et al., 2006). In 10 early drug-naïve PD patients, Ouchi et al. reported increases between [11C]PK11195 binding in the midbrain contralateral to the clinically most affected side (Ouchi et al., 2005). Furthermore, increases between [11C]PK11195 binding in the midbrain correlated with loss of DAT, measured using [11C]CFT PET, in the putamen and with worse motor symptoms (Ouchi et al., 2005). Using [11C]DPA-713 PET, Terada et al. observed increased microglia activation in the occipital, temporal, and parietal cortex in early PD patients (n ¼ 11) with increased [11C]DPA-713 signal, predominantly in the temporal and occipital cortex, detected at a 1-year follow-up (Terada et al., 2016). The authors suggest the results indicate the spread of microglia activation, across extrastriatal regions, as an early phenomenon in the pathophysiology of PD. In PD-MCI and PDD, increased cortical [11C] PK11195 binding has been associated with cognitive decline and with reduced glucose metabolism, measured using [18F]FDG PET (Edison et al., 2013; Fan et al., 2015). TSPO PET has been employed as an outcome measure in clinical trials. A phase 2a randomized placebocontrolled multicentre trial, reported by Jucaite et al., employed [11C]PBR28 PET at baseline and after 4 and 8 weeks of treatment with verdiperstat (AZD3241), an irreversible myeloperoxidase inhibitor (Jucaite et al., 2015). A reduction in [11C]PBR28 binding in the substantia nigra and striatum was observed after 8 weeks of treatment in PD patients (Jucaite et al., 2015). The application of third-generation TSPO PET radiotracers, such as [11C]ER176 (Fujita et al., 2017), overcomes some of the limitations of second-generation TSPO PET radiotracers and warrants application in PD. However, to aid the correct interpretation of TSPO PET studies, much work is needed to understand the biology underlying TSPO changes, including the function

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and role of TSPO changes. Furthermore, the ability to image different isoforms of microglia, neuroprotective versus neurotoxic, in vivo would greatly advance our understanding of the immune response over the disease course and aid the development of targeted therapies. Beyond TSPO PET, several radiotracers have been developed to investigate other components of the immune response, and inflammatory pathways, such as cyclooxygenase (COX)-1 with [11C]PS13, COX-2 with [11C]MC1, and purinergic receptor P2X7 with [11C]SMW139 (Wilson, Politis, et al., 2020). The PET radiotracer [11C]BU99008 for imidazoline 2 binding sites (I2BS) expressed on activated astroglia (Tyacke et al., 2018) has been employed to investigate astroglial activation in PD (Wilson, Dervenoulas, et al., 2019). Cross-sectional bidirectional changes in astroglia activation were observed in PD patients, with increased [11C] BU99008 binding in early stages, while moderate-to-advanced PD patients showed decreased [11C]BU99008 binding (Wilson, Dervenoulas, et al., 2019). The increase in [11C]BU99008 binding in early disease stages could reflect a neuroprotective response, while as the disease advances, astroglia could lose their reactive function. Furthermore, loss of [11C]BU99008 binding correlated with longer disease duration and increased global disease burden (Wilson, Dervenoulas, et al., 2019). Longitudinal studies are required to observe changes in astroglia over time in relation to the course of PD. Furthermore, investigating astroglia pathology in asymptomatic genetic carriers, as well as prodromal and at-risk populations, could provide important insights into how early in disease pathophysiology reactive astrogliosis occurs.

Protein aggregation Histopathological studies suggest Alzheimer’s-related pathology including amyloid-b plaques and tau neurofibrillary tangles coexists with a-synuclein pathology in PD pathophysiology and dementia related to PD (Compta et al., 2014; Horvath et al., 2013). Therefore, it could be hypothesized that amyloid-b and tau pathology could underlie the development of cognitive impairment in idiopathic PD, with up to 80% of patients developing PDD (Aarsland et al., 2003), and hypothesized that AD-like pathology is more prominent in familial forms, which frequently present with cognitive impairment, such as SNCA mutation carriers (Spira et al., 2001). This hypothesis can be supported by the proposal that the overexpression of SNCA can promote the pathological aggregation of tau (Giasson et al., 2003; Kotzbauer et al., 2004; Oikawa et al., 2016; Riedel et al., 2009; Wills et al., 2011). Furthermore, 79% of LRRK2 mutation carriers have been reported to have some degree of tau pathology (Poulopoulos et al., 2012), with immunohistochemistry analysis reporting the presence of AD-like tau pathology in LRRK2 PD mutation carriers (Henderson et al., 2019). Irwin et al. showed that the presence of AD-like pathology in synucleinopathies is linked with worse prognosis, that is, shorter survival and a shorter time interval from the onset of motor symptoms to the onset of dementia, suggesting that the cooccurrence of both pathologies has an aggravating effect on disease progression (Irwin et al., 2017). There are a number of PET radiotracers to quantify in vivo amyloid-b pathology, such as [11C]Pittsburgh compound-B (PIB), [18F]Florbetaben, [18F]Florbetapir (formerly [18F]AV45) and [18F]Flutemetamol, and tau pathology, such as [18F]Flortaucipir (formerly [18F]AV1451 or [18F]T807), [11C]PBB3, [18F]THK5351. The use of second-generation tau PET

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radiotracers, such as [18F]PI-2620, [18F]MK-6240, and [18F]RO948, overcomes the limitations of first-generation tau radiotracers with reduced off-target binding (Leuzy et al., 2019). In vivo amyloid and tau PET imaging studies have investigated amyloid-b and tau pathology in synucleinopathies, including PDD, DLB, and in some PD-MCI patients (Gomperts et al., 2016a,b; Kantarci et al., 2017). While some studies report increased amyloid-b, associated with cognitive decline, in PDD and PD-MCI patients (Akhtar et al., 2017; Gomperts, Marquie, et al., 2016), other studies show no relationship (Gomperts et al., 2008, 2012; Melzer et al., 2019; Winer et al., 2018). Increased tau pathology, assessed using [18F]Flortaucipir PET, has been reported in PD patients with cognitive impairment with increases most prominent in the inferior temporal and precuneus regions (Gomperts, Locascio, et al., 2016). To date, only one study has investigated tau pathology in three duplication SNCA mutation carriers, two symptomatic and one asymptomatic, using [11C]PBB3 PET (Perez-Soriano et al., 2017). Increased [11C]PBB3 binding was observed in the striatum, thalamus, globus pallidus, substantia nigra, pedunculopontine nucleus, and occipital lobe in the three duplication SNCA mutation carriers (Perez-Soriano et al., 2017). Little difference was observed between the two symptomatic and one asymptomatic SNCA mutation carriers. While these preliminary findings are interesting and highlight the potential in vivo presence of tau pathology in SNCA mutation carriers, [11C]PBB3 has been shown to have some affinity for a-synuclein in brain homogenates from DLB patients where the burden of a-synuclein is very high (Ono et al., 2017). Therefore, the increased [11C]PBB3 binding in SNCA mutation carriers could at least in part reflect the presence of a-synuclein pathology. Future in vivo PET studies, in particular using second-generation tau radiotracers, are warranted in SNCA mutation carriers, as well as other familial forms of PD, to help disentangle the presence of tau and amyloid-b pathology and their role in disease progression and symptomology. The community is endeavoring to develop an a-synuclein-specific PET radioligand, which would prove a breakthrough in the field, allowing the in vivo quantification of a-synuclein pathology in healthy humans and disease conditions, including idiopathic PD, prodromal stages, at-risk populations, and familial forms of PD.

Energy dysregulation Energy dysregulation, including mitochondria and the endoplasmic reticulum (ER), has been linked with abnormal protein aggregation, such as a-synuclein, as well as synaptic loss and neuroinflammation (Fujita et al., 2014). Mitochondrial dysfunction represents a common finding in postmortem and preclinical animal studies of sporadic and familial PD (Flones et al., 2018; Schapira, 1993; Schapira et al., 1989, 1990), with several PD-related genetic mutations, including PINK-1, Parkin, DJ-1, and SNCA, linked to mitochondrial dysfunction and oxidative stress (Cookson, 2012; Ge et al., 2020; Martin et al., 2006; Moon & Paek, 2015; Ryan et al., 2015). However, the spatiotemporal distribution of disease pathology, and the sequence of events in the pathological cascade, remains unclear. While some preclinical studies indicate that mitochondrial dysfunction is the critical step promoting downstream events resulting in the accumulation of a-synuclein and synaptic dysfunction (Tamagno et al., 2005; Zaltieri et al., 2015), other evidence indicates that the accumulation of a-synuclein,

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oxidative stress, and ER dysfunction precede mitochondrial dysfunction (Ferrer et al., 2011; Hunn et al., 2015; Paillusson et al., 2017). Therefore, investigating mitochondria, ER, synaptic integrity, and neuroinflammation, using available PET radioligands, in symptomatic and asymptomatic PD mutation carriers, could provide key insights into the etiology and progression. Furthermore, studying asymptomatic PD mutation carriers, with longitudinal followups, could help to disentangle the temporal sequences of events in the pathological cascade as well as the relationship with disease progression and symptomatology. The PET radioligand [18F]BCPP-EF has been validated to assess mitochondrial complex 1 (MC1) activity in humans (Mansur et al., 2021). Preclinical studies in a nonhuman primate model of PD reported reduced [18F]BCPP-EF uptake in cortical regions, as well as in the striatum, raphe, and substantia nigra (Kanazawa et al., 2017; Tsukada et al., 2016). A recent study from our group investigated MC1 levels, using [18F]BCPP-EF, alongside [11C]SA-4503 for Sigma 1 receptor (S1R), a chaperone protein residing at the mitochondrion-associated ER membrane (MAM) (Hayashi & Su, 2007), in 12 early drug-naïve idiopathic PD patients compared with healthy controls at baseline cross-sectional analysis, and at a 1-year longitudinal follow-up (Wilson, Pagano, et al., 2020). The lack of significant cross-sectional or longitudinal changes in [18F]BCPP-EF and [11C]SA-4503 could be due to the very early disease stage, small sample size, and short follow-up period. Mishina et al. reported loss of S1R, measured using [11C]SA-4503, in the more affected anterior putamen, compared with the less affected side, in six moderate stage, treated, idiopathic PD patients but no cross-sectional differences compared with healthy controls (Mishina et al., 2005). Given the potential role of mitochondrial and ER dysregulation in disease mechanisms of familial and idiopathic PD, further in vivo PET studies are warranted to fully elucidate the relevance to disease etiology, progression, and symptomatology.

Conclusion Employing PET imaging to study asymptomatic genetic PD mutation carriers allows the in vivo investigation of molecular changes in preclinical stages, striving to identify disease biomarkers, track progression, and disentangle disease pathogenesis for potential therapeutic intervention to slow, and ultimately halt, disease progression. With advancements in radiochemistry, there are several validated PET tracers, which warrant further application in idiopathic PD and could provide important insights into the pathogenesis and pathophysiology of genetic forms of PD. Future studies, in asymptomatic and symptomatic genetic forms of PD, should strive to utilize PET tracers targeting neurotransmitter systems beyond dopamine such as cholinergic and noradrenergic, neuroinflammation pathways, protein aggregation, synaptic integrity, and mitochondrial and ER dysregulation. The development of an a-synuclein specific PET radioligand would present a breakthrough in the field for application in idiopathic and familial PD. Longitudinal follow-ups will allow for changes in these molecular targets to be tracked over time to provide insights into the evolution and progression of the disease from early preclinical stages. The continued study of neuropathological changes, clinical trajectories, and genetic factors could help with the stratification of PD patients striving toward a landscape of precision medicine with targets for therapeutic intervention.

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C H A P T E R

7 Structural MRI in familial and idiopathic PD Joji Philip Verghese1, Edoardo Rosario de Natale2 and Marios Politis2 1

Neurodegeneration Imaging Group, Department of Clinical and Biomedical Sciences, Faculty of Health and Life Sciences, University of Exeter, United Kingdom; 2Neurodegeneration Imaging Group and Mireille Gillings Neuroimaging Centre, University of Exeter, London, United Kingdom

Introduction Parkinson’s disease (PD) is a common neurodegenerative disorder with a prevalence of more than 1% in the population older than 60 years (Tysnes & Storstein, 2017). The vast majority of PD cases is idiopathic (idiopathic PD or iPD). In about 10%e15% of cases, a clear family history can be recognized, and in a few of them, it can be traced back to pathogenic mutations (familial PD, or fPD) with either autosomal recessive (e.g., Parkin/PARK2, PINK1/PARK6, DJ-1, ATP13A2/PARK9) (Lin & Farrer, 2014; Lohmann et al., 2003; Nuytemans et al., 2010; Samaranch et al., 2010) or autosomal dominant (LRRK2/PARK8, SNCA/PARK1/ PARK4) (Kasten & Klein, 2013; Lin & Farrer, 2014; Nuytemans et al., 2010; Sidransky et al., 2009) mode of inheritance, and with varying levels of penetrance, as well as susceptibility risk loci (e.g., on the GBA gene). In PD, motor symptoms (bradykinesia, rigidity, resting tremor, and postural instability) occur as a result of gradual loss of the dopaminergic neurones of the substantia nigra (SN) (Jankovic, 2008; Tysnes & Storstein, 2017). PD patients also suffer from a great deal of nonmotor symptoms, a few of which (poor sleep, hyposmia, mood disorders, dysautonomia) can often precede the onset of motor symptoms (Jankovic, 2008; Tysnes & Storstein, 2017). Clinical examination still remains the gold standard in diagnosis (Gelb et al., 1999), despite a wide variability of biological and pathological alterations being progressively identified by recent PD research (Leclair-Visonneau et al., 2020). Nevertheless, at present, there are no biomarkers that can help identify the disease or provide us with information about its severity and progression. It is thought that the characterization

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of such biomarkers would significantly increase our knowledge about the mechanisms leading to PD and provide us with means for a disease-modifying intervention. The study of the rare cases of fPD, alongside iPD, where asymptomatic carriers have a high chance to develop motor symptoms in the future and may represent ideal models of the preclinical stage of PD, could expand our time window for this understanding and may provide critical information about PD pathogenesis (results summarized in Table 7.1). Magnetic resonance imaging (MRI), a nonionizing imaging modality that provides good soft-tissue contrast and anatomic visualization, has played a critical role in PD research, for its ability to noninvasively study structural, microstructural, and functional alterations without entailing any exposure to ionizing radiations. Over time, a number of diverse structural sequences, and analysis methods such as voxel-based morphometry (VBM) (Ashburner & Friston, 2000), have been employed in PD research for the study of regional and global gray matter volume (GMV) and cortical thickness (Elster, 1988), as well as diffusion sequences for the study of white matter tracts integrity (Huisman, 2010), up to more recently developed sequences for the quantitative assessment of regional iron deposition (Chavhan et al., 2009; Yan et al., 2018). Neuromelanin-containing catecholaminergic neurons in the substantia nigra pars compacta (SNPc) and locus coeruleus (LC) (Cassidy et al., 2019), and for regional perfusion and cerebral blood flow (CBF) changes in brain regions (Petcharunpaisan et al., 2010). This chapter will focus on recent findings from research in iPD and fPD using structural MRI sequences. A detailed review of findings using functional MRI sequences in PD is provided in Chapter 7 of this book.

Structural volumetric MRI Gray matter volumes in idiopathic Parkinson’s disease iPD is characterized by a progressive and profound cellular loss in the dopaminergicproducing neurons within the SN (Kish et al., 1988). It has been thought that the classical motor symptoms leading to the clinical diagnosis of iPD emerge when at least 50% of these neurons have degenerated. A number of MRI studies have sought to understand the mechanisms and dynamics of SN atrophy in relation to disease stage and phenotype. However, due to its size and location, high-resolution mapping of the SN to measure GMV still remains a difficult task. Prior studies utilized operator-dependent segmentation of the regionsof-interest (ROIs), such as SN (Bae et al., 2021). However, the use of VBM and higherresolution imaging has allowed for automated segmentation and more accurate GMV calculations (Bae et al., 2021). Studies have shown SN atrophy could be seen in iPD patients when compared with healthy controls (HCs) (Menke et al., 2009; Poston et al., 2020; Ziegler et al., 2013). One of the studies identifying Hoehn and Yahr (H&Y) stage 1 iPD patients had significantly reduced SN volumes compared with HCs, with H&Y stage 2 and 3 PD patients showing nonsignificant atrophy compared with H&Y stage 1 patients (Ziegler et al., 2013). A cross-sectional study, using a 7-Tesla scanner (Poston et al., 2020), revealed lower SN volumes were correlated with longer disease duration as well as greater Movement Disorder SocietyeUnified Parkinson’s Disease Rating Scale (MDS-UPDRS) (global motor and bradykinesia-rigidity subscores) scores in iPD. A bulk of recent studies have quantified the degree of striatal gray matter (GM) loss across the stages of iPD. The putamen is the main receiver of motor inputs from the nigrostriatal II. Clinical applications in Parkinson disease

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TABLE 7.1 Cohort

Summary of the main findings from structural volumetric MRI studies in idiopathic and familial PD.

Main findings

References

Idiopathic Cortical and PD subcortical changes

SN atrophy is seen in iPD patients when compared with HCs. Posterior putaminal volume loss is seen in early stages of iPD patients. The greatest rate of putamen volume loss occurs in the early stages of the disease together with a floor effect after 5 years. Specific volumetric changes in iPD patients have varied due to disease staging. No atrophic changes within the CN are seen between de novo iPD patients and HCs. Significant CN atrophy can be seen in iPD patients at the later stages of disease compared with HCs. Greater rate of CN atrophy in iPD patients when compared to HC, with greater GMV loss within the earlier stages of iPD, which slows down in the later stages of the disease. GMV reductions could be seen in frontal, temporal, parietal, limbic, and occipital regions of iPD subjects. Atrophy restricted the left limbic lobe, left parietal lobe, and bilateral occipital lobe in iPD patients with disease duration under 3 years. Subcortical volume loss was identified within the hippocampus and the thalamus within moderate iPD patients, with additional volume loss in the caudate, putamen, and amygdala in severe iPD subjects (based on H&Y scale).

(Menke et al., 2009; Poston et al., 2020; Ziegler et al., 2013) (Geng et al., 2006; Khan et al., 2019; Sterling et al., 2013) (Fioravanti et al., 2015; Lewis et al., 2016; Tessa et al., 2014) (Cui et al., 2020; Khan et al., 2019; Lee et al., 2011; Lin et al., 2021) (Tessa et al., 2014) (Lin et al., 2021; Naduthota et al., 2017) (Lewis et al., 2016; Tessa et al., 2014) (Burton et al., 2004; Morgen et al., 2011; Nagano-Saito et al., 2005; Pagonabarraga et al., 2014; Pan et al., 2012; Ramírez-Ruiz et al., 2005; Rosenberg-Katz et al., 2013; SanchezCastaneda et al., 2010; Shao et al., 2015; Shin et al., 2021; Tinaz et al., 2011; Weintraub et al., 2011; Yang, Deng, et al., 2020) (Shao et al., 2015) (Wilson et al., 2019)

Disease changes in early iPD

Posterior putaminal volume loss is seen in early stages of iPD. The greatest rate of putamen volume loss occurs in the early stages of the disease together with a floor effect after 5 years. No atrophic changes within the CN are seen between de novo iPD patients and HCs. Atrophyrestricted the left limbic lobe,

(Geng et al., 2006; Khan et al., 2019; Sterling et al., 2013) (Fioravanti et al., 2015; Lewis et al., 2016; Tessa et al., 2014) (Tessa et al., 2014) (Shao et al., 2015)

(Continued)

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TABLE 7.1 Cohort

Summary of the main findings from structural volumetric MRI studies in idiopathic and familial PD.dcont’d

Main findings

Disease changes in moderate stages of iPD

References left parietal lobe, and bilateral occipital lobe in iPD patients with disease duration under 3 years. Subcortical volume loss was identified (Wilson et al., 2019) within the hippocampus and the thalamus within moderate iPD patients (based on H&Y scale).

Disease changes in advanced stages of iPD

Significant CN atrophy can be seen in (Lin et al., 2021; Naduthota et al., 2017) (Wilson et al., 2019) iPD patients at the later stages of disease compared with HCs. Subcortical volume loss was identified within the hippocampus, the thalamus, the caudate, putamen, and amygdala in severe iPD subjects (based on H&Y scale).

Volumetric changes with iPD progression

Atrophy of the putamen, CN, and SN reflects a greater motor burden. However, due to early striatal GM atrophy and subsequent floor effect, it has been difficult to monitor the progression of motor symptoms solely based on striatal volumes. The greatest rate of putamen volume loss occurs in the early stages of the disease together with a floor effect after 5 years. Greater rate of CN atrophy in iPD patients when compared with HCs, with greater GMV loss within the earlier stages of iPD, which slows down in the later stages of the disease. Cerebellar atrophy is present in two out of three stage-specific GMV atrophy patterns seen in the later stages of iPD (based on H&Y stage). Increased cortical atrophy is associated with worsening CI in iPD patients.

(Lewis et al., 2016; Poston et al., 2020) (Fioravanti et al., 2015; Lewis et al., 2016; Tessa et al., 2014) (Lewis et al., 2016; Tessa et al., 2014) (Lin et al., 2021) (Pan et al., 2012; Pereira et al., 2014; Shin et al., 2021; Tinaz et al., 2011)

Volumetric changes in relation to motor symptoms in iPD

Atrophy of the putamen, CN, and SN reflects a greater motor burden. However, due to early striatal GM atrophy and subsequent floor effect, it has been difficult to monitor the progression of motor symptoms solely based on striatal volumes. Cerebellar atrophy could be seen in iPD

(Lewis et al., 2016; Poston et al., 2020) (Lin et al., 2021; Xia et al., 2013; Zeng et al., 2017) (Lin et al., 2021) (Wen et al., 2015) (Carriere et al., 2014) (Surdhar et al., 2012; van Mierlo et al., 2015)

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Structural volumetric MRI

TABLE 7.1 Cohort

Summary of the main findings from structural volumetric MRI studies in idiopathic and familial PD.dcont’d

Main findings

Volumetric changes in relation to motor phenotypes in iPD

Volumetric changes in relation to cognition

References patients when compared with HCs. Cerebellar atrophy is present in two out of three stage-specific GMV atrophy patterns seen in the later stages of iPD (based on H&Y stage). Progressive frontal lobe atrophy was associated with MCI conversion earlystage NC-iPD cohort. Greater atrophy in nucleus accumbens correlates to the severity of apathy in iPD. The degree of amygdala and hippocampal atrophy negatively correlates to severity of depression. (Piccinin et al., 2017) TD-iPD subjects had reduced GM (Rosenberg-Katz et al., 2013) volume in cerebellar lobules VIIIa compared with PDAR-iPD subjects and HCs. PIGD-iPD patients had greater atrophy in the cerebellum, the primary motor area, and frontal regions compared with TD-iPD patients. Increased cortical atrophy is associated with worsening CI in iPD patients. Increased cortical atrophy in the frontal, temporal, parietal, and limbic regions in MCI-iPD patients compared with NCiPD patients and HCs in MCI-iPD patients compared with NC-iPD patients and HCs. Atrophy within precuneus, angular gyrus, and cerebellum is seen in MCIiPD patients when compared with NCiPD patients and HCs. Temporal lobe atrophy within NC-iPD patients is harbinger of subsequent conversion to PD-MCI. Hippocampal atrophy is associated with cognitive decline in iPD. In drug-naïve iPD subjects, right entorhinal cortex atrophy in MCI-iPD patients compared with NC-iPD patients. Atrophy of the superior frontal gyrus and the cerebellum in MCI-iPD when compared with NC-iPD (in iPD disease

(Pan et al., 2012; Pereira et al., 2014; Shin et al., 2021; Tinaz et al., 2011) (Donzuso et al., 2021; Jia et al., 2019; Melzer et al., 2012) (Donzuso et al., 2021) (Melzer et al., 2012) (Biundo et al., 2013; Mak et al., 2015) (Jia et al., 2019) (Donzuso et al., 2021) (Wen et al., 2015) (Hanganu et al., 2013; Mak et al., 2015) (Melzer et al., 2012; Pereira et al., 2014) (Shin et al., 2021)

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TABLE 7.1 Cohort

Summary of the main findings from structural volumetric MRI studies in idiopathic and familial PD.dcont’d

Main findings

Volumetric changes in relation to RBD

Volumetric changes in relation to neuropsychiatric disorders

References duration under 2 years). Progressive frontal lobe atrophy was associated with MCI conversion earlystage NC-iPD cohort. Atrophy of the nucleus accumbens was seen in patients with MCI-iPD when compared with HCs. Increased atrophy of the frontal, temporal, occipital (intracalcarine and lingual gyri) lobes along with posterior cingulate gyrus and caudate nuclei is seen in PDD patients compared with MCI-iPD patients. GM loss of frontal and angular gyrus as predictors of future conversion to PDD in MCI-iPD cohort. (Yang, Deng, et al., 2020) GMV reduction of right superior temporal gyrus iPD patients with RBD (Lim et al., 2016) than those without RBD. GM atrophy of the frontal gyrus, posterior cingulate cortex, hippocampus, cuneus, precuneus, and inferior parietal lobe can be seen in iPD patients with RBD compared with HCs. The degree of volume loss was associated with progressive CI in RBDiPD patients. Greater atrophy in nucleus accumbens in apathetic iPD patients compared with HCs, with the degree of atrophy correlated to the severity of apathy. Amygdala atrophy was associated with increased anxiety in iPD patients. Progressive anterior cingulate cortex and precuneus atrophy were associated with progression of anxiety in iPD patients. Reduced amygdala and hippocampal GMV in depressed iPD patients. The degree of atrophy negatively correlates to severity of depression. Parietal and occipital lobe atrophy is observed within iPD patients with minor hallucinations when compared with HCs. De novo iPD who developed minor

(Carriere et al., 2014) (Vriend et al., 2016) (Wee et al., 2016) (Surdhar et al., 2012; van Mierlo et al., 2015) (Pagonabarraga et al., 2014) (Bejr-Kasem et al., 2021)

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Summary of the main findings from structural volumetric MRI studies in idiopathic and familial PD.dcont’d

Main findings

References

hallucinations over a 5-year follow-up period had greater atrophy of left lingual gyrus, the left middle occipital gyrus, the left middle temporal gyrus/ temporal pole, and the right precuneus than nonconverters at baseline. Cortical thickness Cortical thickness changes have changes: Stages and garnered variable results in the early disease severity stages of iPD when compared with HCs. In a recent study, regional cortical thinning of the left medial orbitofrontal lobe in early iPD patients compared with HCs. UPDRS-III scores were associated with cortical thinning in bilateral fusiform gyri and the left temporal pole in early iPD patients. Progressive cortical thinning and subcortical volume loss in nondemented iPD patients correlated with H&Y staging. Thinning of the orbital cortex was seen in early iPD (H&Y scores 1e2), additional thinning frontal and inferior parietal cortex in moderate iPD (H&Y scores 2e3), and further cortical thinning in the temporal and occipital cortices were seen in severe iPD (H&Y scores 3e4). Degree of temporal thinning correlates with disease duration. Greater rate thinning can be seen in frontotemporal regions in early-iPD subjects compared with HCs. Greater rate of cortical thinning of temporal, parietal, and occipital brain regions in the iPD subjects when compared with HCs. Increased cortical thinning is associated with progressive motor and cognitive decline in iPD. Cortical thickness changes: Cognition

Cortical thickness reductions were seen in temporal, parietal, and frontal areas in nondemented iPD patients when compared with HCs. Hippocampal atrophy and temporal,

(Ibarretxe-Bilbao et al., 2012; Worker et al., 2014; Imperiale et al., 2018) (Fu et al., 2020; Jubault et al., 2011; Mak et al., 2015; Pereira et al., 2014; Tinaz et al., 2011; Uribe et al., 2018) (Fu et al., 2020) (Gao et al., 2018) (Wilson et al., 2019) (Jubault et al., 2011) (Ibarretxe-Bilbao et al., 2012) (Leocadi et al., 2022) (Leocadi et al., 2022)

(Biundo et al., 2013; Uribe et al., 2018; Zhang, Wang, et al., 2018) (Wilson et al., 2019) (Pereira et al., 2014) (Leocadi et al., 2022) (Continued)

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TABLE 7.1 Cohort

Summary of the main findings from structural volumetric MRI studies in idiopathic and familial PD.dcont’d

Main findings

Progressive cortical thickness changes

References parietal, frontal, and cingulate cortices thinning is associated with lower cognitive scores in iPD. Cortical thinning was restricted more toward the temporal lobe in NC-iPD patients, while MCI-iPD was associated with temporal, parietal, and frontal thinning. Increased cortical thinning is associated with progressive motor and cognitive decline in iPD. Temporal thinning was seen in early non-MCI iPD patients who developed MCI. MCI in iPD were associated with frontal and temporaleparietal thinning compared with HCs. Greater rate of cortical thinning in frontal and temporoparietal cortices in MCI-iPD patients compared with NCiPD patients. NC-iPD patients who were converted to MCI-iPD subjects had cortical thickness thinning of bilateral temporal lobes compared with nonconverters. MCI-iPD patients who have thinning of the angular gyrus and frontal lobes along with thickening of the right anterior cingulate cortex and left parahippocampal gyrus have a higher probability to develop PDD. A potential correlation may be seen between cortical thinning (orbitofrontal cortex, ventrolateral prefrontal cortex, and occipitoparietal areas) along with subcortical and striatal volume loss and emotional dysregulation early in iPD. UPDRS-III scores were associated with cortical thinning in bilateral fusiform gyri and the left temporal pole in early iPD patients. Progressive cortical thinning and subcortical volume loss in nondemented iPD patients correlated with H&Y staging. Increased cortical thinning is associated

(Mak et al., 2015) (Mak et al., 2015; Pereira et al., 2014) (Mak et al., 2015) (Mak et al., 2015) (Shin et al., 2021) (Tinaz et al., 2011)

(Gao et al., 2018) (Wilson et al., 2019) (Leocadi et al., 2022)

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TABLE 7.1 Cohort

LRRK2

GBA

SNCA

Summary of the main findings from structural volumetric MRI studies in idiopathic and familial PD.dcont’d

Main findings

LRRK2-NMC

References with progressive motor and cognitive decline in iPD. Results have varied. From small increases in GMV within the striatum and cuneus in LRRK2-NMC when compared with HCs, to no significant volumetric differences between LRRK2NMC and HCs.

(Brockmann et al., 2011; Reetz et al., 2010) (Thaler et al., 2014, 2018; Vilas et al., 2016)

LRRK2-PD

Results have varied. From small increases in GMV within cerebellum and precentral gyrus in LRRK2-PD when compared with iPD, to no significant volumetric differences between LRRK2-PD and iPD.

(Brockmann et al., 2011) (Thaler et al., 2018)

GBA-NMC

No significant GMV changes between GBA-NMC and HC were detected.

(Thaler et al., 2018)

GBA-PD

No significant GMV changes between GBA-PD and iPD patients. Significant cortical thinning of left temporal, parietal, and occipital gyri GBA-PD subjects at baseline when compared with HCs and iPD subjects.

(Thaler et al., 2018) (Leocadi et al., 2022)

SNCA-NMC

No volumetric differences could be seen (Szamosi, Nagy, and Kéri, 2013; Burciu between SNCA-NMC and HCs. et al., 2018)

SNCA-NMC

(Szamosi et al., 2013) SNCA-NMC patients who developed SNCA-PD had reduced caudate volume (in the absence of cortical atrophy) when compared with HCs.

ATP13A2 ATP13A2- NMC

When compared with HCs, increased GMV of the striatum was seen in ATP13A2-NMC.

Parkin

Parkin-NMC

When compared with HCs, increased (Reetz et al., 2010) GMV of the striatum was seen in (Binkofski et al., 2007) Parkin-NMC. Increased GMV in the putamen and the internal globus pallidus within ParkinNMC and PINK1-NMC patients when compared with HCs.

Parkin-PD

iPD and symptomatic Parkin carriers both had reduced basal ganglia volumes compared with HCs. The

(Reetz et al., 2010)

(Bilgic et al., 2012; Reetz et al., 2009)

(Continued)

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TABLE 7.1 Cohort

PINK1

Summary of the main findings from structural volumetric MRI studies in idiopathic and familial PD.dcont’d

Main findings

PINK1-NMC

References degree of caudate atrophy correlates to UPDRS scores Parkin-PD (Reetz et al., 2010) When compared with HCs, increased (Binkofski et al., 2007) GMV of the striatum was seen in (Reetz et al., 2008, 2010) PINK1-NMC. Increased GMV in the putamen and the internal globus pallidus within ParkinNMC and PINK1-NMC patients when compared with HCs. Atrophy of the hippocampal and limbic areas (Reetz et al., 2008, 2010) as well as the frontal cortex area (Reetz et al., 2008) in PINK1-NMC patients when compared with HCs.

CN, caudate nucleus; GM, gray matter; GMV, gray matter volume; H&Y stage, Hoehn-Yahr stage; HCs, healthy controls; iPD, idiopathic Parkinson’s disease; MCI, mild cognitive impairment; NC, normal cognition; NC-iPD, normal cognition iPD; NMCs, nonmanifest carriers; PD, Parkinson’s disease; PDAR, predominant akinetic/rigidity; PDD, Parkinson’s disease dementia; PIGD, posture instability gait difficulty; RBD, rapid eye movement sleep behavior disorder; SN, substantia nigra; TD, tremor dominant; UPDRS, Unified Parkinson’s Disease Rating Scale.

pathway and is found to be heavily affected in iPD (Kish et al., 1988). Atrophy of the putamen is believed to be caused by the denervation of the dopaminergic neurones, causing dopamine depletion and the initial onset of motor symptoms in iPD. Putaminal volume loss can be identified from the early stages of iPD (Geng et al., 2006; Khan et al., 2019; Sterling et al., 2013), with preferential atrophy of the posterior putamen in motor disease onset (Sterling et al., 2013). Longitudinal studies (Fioravanti et al., 2015; Lewis et al., 2016; Tessa et al., 2014) have identified the greatest rate of putamen volume loss occurs in the early stages of the disease (approximately
1%/year) together with a floor effect after 5 years (Lewis et al., 2016), reflecting the limitations of using putamen GMV for long-term correlation of symptoms progression. Changes to the caudate nucleus in iPD have varied according to studies, in relation to disease staging (Cui et al., 2020; Khan et al., 2019; Lee et al., 2011; Lin et al., 2021). Despite no atrophic changes within the caudate being seen between de novo iPD patients and HC (Tessa et al., 2014), studies with iPD patients at the later stages of disease (Lin et al., 2021; Naduthota et al., 2017) did identify significant caudate atrophy. Interestingly, longitudinal studies (Lewis et al., 2016; Tessa et al., 2014) revealed a greater rate of caudate atrophy in iPD patients when compared with HCs, with greater GMV loss within the earlier stages of iPD, which plateaus in the later stages of the disease. Further analysis from one of the studies identified caudate volume changes were inversely correlated with levodopa equivalent daily dose (LEDD) (Lewis et al., 2016). Depending on the stage and severity of iPD, patterns of cortical and subcortical GM changes have been identified in iPD. A few studies (Brenneis et al., 2003; Ellfolk et al., 2013; Ibarretxe-Bilbao et al., 2009; Melzer et al., 2012; Pezzoli et al., 2019; Weintraub et al., 2011) did not identify any significant volumetric structural changes in iPD patients when

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compared with HCs; though it is believed heterogeneity of the population, the severity of disease at assessment and procedural issues (image acquisition issues, distortions, software issues, etc.) may have contributed to the variability of results. A recent study found early drug-naïve iPD patients (H&Y  2) with normal cognition exhibited atrophy of the left primary motor cortex and bilateral amygdala (Jia et al., 2019). A study exploring GMV changes in early (H&Y  2) and late (H&Y > 2) stage iPD patients identified three stage-specific GM atrophic patterns unique to this study (Lin et al., 2021). Two of the atrophic patterns found in this study greater atrophy within frontal, temporal, parietal, limbic, and occipital regions in late-stage iPD, with atrophy to these regions seen in the advanced stages of iPD in other studies (Burton et al., 2004; Morgen et al., 2011; Nagano-Saito et al., 2005; Pagonabarraga et al., 2014; Ramírez-Ruiz et al., 2005; Rosenberg-Katz et al., 2013; Sanchez-Castaneda et al., 2010; Shin et al., 2021; Tinaz et al., 2011; Weintraub et al., 2011). Three VBM metaanalysis studies identified atrophy of the frontal lobe (Pan et al., 2012; Shao et al., 2015) and superior temporal gyrus (Pan et al., 2012; Yang, Chang, et al., 2020) in iPD patients compared with HCs. These works, however, deal with studies performed on diverse cohorts of iPD patients and thus may not reflect changes seen at specific timepoints such as in early disease. Interestingly, in a subanalysis from one of the studies (Shao et al., 2015), atrophy was restricted to the left limbic lobe, left parietal lobe, and bilateral occipital lobe in iPD patients with disease duration under 3 years, while GMV reductions in frontal, parietal, occipital, and limbic lobes were seen in iPD patients with disease onset under 5 years (results summarized in Table 7.1). Pathological changes in GMV have also been identified in the cerebellum of iPD patients, with cross-sectional studies (Lin et al., 2021; Xia et al., 2013; Zeng et al., 2017) identifying cerebellar atrophy in iPD patients compared with HC, with one of the studies (Lin et al., 2021) identifying cortical and cerebellar atrophy as a feature seen in the later stages of iPD (based on H&Y stage) in two out of three study-specific GM atrophy patterns.

Association between gray matter changes with idiopathic Parkinson’s disease motor symptoms Motor impairment in iPD has been closely associated with the atrophy of the putamen, caudate, and SN (Lewis et al., 2016; Poston et al., 2020), with greater atrophy reflecting a greater motor burden. Due to early striatal GM atrophy and subsequent floor effect seen in iPD, it has been difficult to monitor the progression of motor symptoms solely based on striatal volumes (Lewis et al., 2016). Along with changes within the striatum, GMV loss can be used as a marker to distinguish iPD and the presenting motor phenotype. In studies exploring GMV changes in different motor phenotypes, tremor-dominant (TD) iPD subjects had reduced GMV in cerebellar lobules VIIIa compared with akinetic/rigidity-predominant-iPD (PDAR) subtype subjects and HC (Piccinin et al., 2017), while postural instability gait difficulty (PIGD) iPD patients had greater atrophy in the cerebellum, the primary motor area, and frontal regions compared with TDiPD patients (Rosenberg-Katz et al., 2013). Further cross-sectional and longitudinal studies are required to determine whether cerebellar GMV changes correlate to disease progression, which can help determine if these GMV changes are pathological or compensatory.

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Correlation between gray matter changes with nonmotor symptoms Cortical and subcortical structural changes can also occur as iPD progresses, leading to the presentation and progression of nonmotor symptoms. RBD, hyposmia, autonomic dysfunction, cognitive impairment, mood, and neuropsychiatric disorders are some of the prominent nonmotor symptoms seen in iPD (Postuma & Berg, 2016; Schapira et al., 2017; Sveinbjornsdottir, 2016). Rapid eye movement (REM) sleep behavior disorder (RBD) is defined by a dream-enacting behavior during REM sleep, with a loss of muscle atonia during REM sleep representing its neurophysiological underpinning (Schenck & Mahowald, 2002). The presence of symptoms ascribable to RBD can be traced back to 20%e77% of patients with iPD before their clinical diagnosis (Baumann-Vogel et al., 2020; Hawkes, 2008; Kim & Jeon, 2014; Postuma et al., 2019). A cross-sectional study (Lim et al., 2016) identified reduced GMV (in frontal gyrus, posterior cingulate cortex, hippocampus, cuneus, precuneus, and inferior parietal lobe) in iPD patients with RBD compared with HC, with the degree of atrophy in the iPD with RBD population associated with progressive cognitive impairment (CI). A recent metaanalysis (Yang, Chang, et al., 2020) identified a significant reduction of right superior temporal gyrus volume in RBD-iPD patients compared with non-RBD-iPD patients. CI is a frequent nonmotor symptom of PD and constitutes a milestone of disease progression (Emre, 2003; Gonzalez-Latapi et al., 2021). Deterioration of one or multiple cognitive domains can be defined as mild cognitive impairment (MCI) in iPD (Gonzalez-Latapi et al., 2021). Dementia can be defined as an impairment in two or more cognitive domains with impairment to baseline functions (Elahi & Miller, 2017), with Parkinson’s disease dementia (PDD) having a point prevalence of 30% in iPD population (Hanagasi et al., 2017). The degree of cortical atrophy and changes in cortical thickness can impact CI in iPD (Pan et al., 2012; Pereira et al., 2014; Shao et al., 2015; Shin et al., 2021; Tinaz et al., 2011). Studies have shown that the degree of atrophy depends upon the degree of CI, the clinical stage of iPD, and the time from onset (Mak et al., 2015), with several cross-sectional studies identifying that increased cortical atrophy is associated with worsening CI (Pan et al., 2012; Pereira et al., 2014; Shin et al., 2021; Tinaz et al., 2011). Multiple studies (Donzuso et al., 2021; Jia et al., 2019; Melzer et al., 2012) have shown increased cortical atrophy in the frontal, temporal, parietal, and limbic regions in MCI-iPD patients compared with normal cognition iPD (NC-iPD) patients and HC, with the more recent study (Donzuso et al., 2021) also identifying atrophy within the cerebellum in the MCI-iPD cohort. One of the most important cortical regions in this regard is the temporal lobe. Temporal lobe atrophy has been found in cognitively unimpaired iPD patients to represent a harbinger of subsequent progression to PD-MCI in a longitudinal study that monitored 66 normal cognition iPD patients over 18 months (Melzer et al., 2012). Hippocampal atrophy is associated with cognitive decline (Biundo et al., 2013; Mak et al., 2015), with studies identifying no cognitive decline in the absence of hippocampal atrophy (Almeida et al., 2003; Carlesimo et al., 2012; Cordato et al., 2002; Surdhar et al., 2012). A cross-sectional study with a drug-naïve iPD cohort (Jia et al., 2019) identified right entorhinal cortex atrophy in MCI-iPD patients compared with NC-iPD patients, highlighting a potential early biomarker for the onset of MCI in NC-iPD. VBM analysis study (Donzuso et al., 2021) revealed that in iPD patients with disease duration under 2 years, atrophy of the superior frontal gyrus and the cerebellum was seen in the MCI-iPD cohort when compared with

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the NC-iPD cohort. Interestingly, a longitudinal study revealed progressive frontal lobe atrophy was associated with MCI conversion in a group of early-stage NC-iPD patients (Wen et al., 2015), which supports the subanalysis findings of a metaanalysis study (Pan et al., 2012) that revealed atrophy in inferior frontal gyrus/orbitofrontal cortex was associated with CI and autonomic dysfunction in iPD (results summarized in Table 7.1). Interestingly, atrophy of the nucleus accumbens (NAc) was seen in patients with MCI-iPD when compared with HC (Hanganu et al., 2013; Mak et al., 2015). The NAc is believed to work with other cerebellar regions to regulate reward-related behavior and different aspects of memory (Burton et al., 2015); thus, atrophic changes within NAc may play a key role in developing CI symptoms in iPD. Patients with PDD display further loss of cortical and subcortical GM structures compared with MCI-iPD patients, with a significant reduction in GMV detected within the frontal, temporal, and occipital (intracalcarine and lingual gyri) lobes along with posterior cingulate gyrus and caudate nuclei (Melzer et al., 2012; Pereira et al., 2014; Weintraub et al., 2011). A recent study by Shin et al. on a group of MCI-iPD patients identified GM loss of frontal and angular gyrus as predictors of future conversion to PDD in this population (Shin et al., 2021). Neuropsychiatric symptoms (NPS) such as depression, apathy anxiety, and hallucinations are symptoms commonly associated with iPD, with CI in iPD associated with a greater NPS burden (Simon-Gozalbo et al., 2020). A previous study (Carriere et al., 2014) found greater atrophy in the NAc in apathetic iPD patients compared with HC, with the degree of atrophy correlated to the severity of apathy. Cross-sectional studies (Vriend et al., 2016) have shown amygdala atrophy was associated with increased anxiety in iPD patients. However, a longitudinal study (Wee et al., 2016) identifying progressive anterior cingulate cortex and precuneus atrophy was associated with progression of anxiety within iPD patients, with authors theorizing impaired functional network between the amygdala and the precuneus modulating the onset of anxiety. Alteration of limbic circuits has been theorized to cause the onset of depressive symptoms in PD, with studies that have identified reduced amygdala and hippocampal GMV in depressed iPD patients (Surdhar et al., 2012; van Mierlo et al., 2015), with the degree of atrophy negatively correlating to severity. Cortical changes have been observed in iPD patients with minor hallucinations (mH-iPD), with a cross-sectional study (Pagonabarraga et al., 2014) identifying GMV loss in the parietal and occipital areas associated with the dorsal visual stream, in mH-iPD patients when compared with HC. A longitudinal study (Bejr-Kasem et al., 2021) identified de novo iPD who converted to mH-iPD over a 5-year follow-up period had greater atrophy the of left lingual gyrus, the left middle occipital gyrus, the left middle temporal gyrus/temporal pole, and the right precuneus than nonconverters at baseline. Further analysis revealed greater rates of atrophy within occipital and temporal regions of mH-iPD converters during 1- and 2-year follow-up scans (Bejr-Kasem et al., 2021).

Cortical thickness Cortical thickness changes in iPD have yielded varying results depending on the stage and the severity of iPD study population. Several studies have identified significant reductions in

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regional cortical thickness in the early (based on H&Y stage) iPD groups (Fu et al., 2020; Jubault et al., 2011; Mak et al., 2015; Pereira et al., 2014; Tinaz et al., 2011; Uribe et al., 2018) compared with HCs, with one single work being unable to identify any significant difference (Ibarretxe-Bilbao et al., 2012). Earlier works (Biundo et al., 2013; Jubault et al., 2011; Tinaz et al., 2011) have demonstrated cortical thinning of the orbitofrontal cortex, ventrolateral prefrontal cortex, and occipitoparietal and temporal areas in early iPD patients compared with HCs, but a more recent study (Fu et al., 2020) identified significant regional cortical thinning has restricted the left medial orbitofrontal lobe in early iPD patients (H&Y stage 1e2). Subanalysis from the prior studies (Jubault et al., 2011) revealed the degree of temporal thinning correlated with disease duration, revealing a potential biomarker to monitor disease progression (Jubault et al., 2011). A longitudinal study (Ibarretxe-Bilbao et al., 2012) involving 16 early-PD patients (H&Y stage  II and disease duration  5 years) and 15 HC over 35month period identified greater cortical thinning within the frontotemporal regions in iPD subjects compared with HC. Another longitudinal study (Leocadi et al., 2022) revealed, after a 5-year follow-up, widespread cortical thinning of temporal, parietal, and occipital brain regions in the iPD cohort when compared with HC, despite having a similar cortical thickness at baseline. Subanalysis revealed that increased cortical thinning was associated with progressive motor and cognitive decline in iPD, revealing that observing cortical thickness can be a key tool to monitor iPD progression. Several studies have identified cortical thickness in several cortical and subcortical brain regions correlates with disease severity. One study has identified that UPDRS-III scores were associated with cortical thickness in bilateral fusiform gyri and the left temporal pole in a group of early iPD patients (Gao et al., 2018). Another cross-sectional study (Wilson et al., 2019) identified a correlation between H&Y staging and progressive cortical thinning and subcortical volume loss in nondemented iPD patients. This study (Wilson et al., 2019) revealed thinning of the orbital cortex was seen in early iPD (H&Y scores 1e2), additional thinning frontal and inferior parietal cortex in moderate iPD (H&Y scores 2e3), and further cortical thinning in the temporal and occipital cortices was seen in severe iPD (H&Y scores 3e4). Interestingly, along with cortical thinning, subcortical volume loss was identified within the hippocampus and the thalamus within moderate iPD patients, with additional volume loss in the caudate, putamen, and amygdala in severe iPD subjects (results summarized in Table 7.1). Cross-sectional studies have found a correlation between the cortical thickness reduction in specific regions in early iPD and the risk of these patients developing MCI or dementia (Pereira et al., 2014; Tinaz et al., 2011; Zhang, Wang, et al., 2018). A recent study (Wilson et al., 2019) identified a correlation between lower cognitive scores to hippocampal atrophy and temporal, parietal, frontal, and cingulate cortices thinning, with the authors suggesting the pathological changes may predate the onset of CI. In the absence of CI, cortical thinning has been observed in the temporal lobe in NC-iPD patients when compared with HC, while MCI-iPD was associated with temporal, parietal, and frontal thinning (Biundo et al., 2013; Mak et al., 2015; Pereira et al., 2014; Uribe et al., 2018). Early NC-iPD patients who developed MCI in an 18-month longitudinal study had temporal thinning at the baseline visit, with MCI-iPD patients exhibiting greater frontal and temporoparietal thinning at follow-up when compared with HCs and NC-iPD patients (Mak et al., 2015). A previous study (Biundo

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et al., 2013) identified early MCI-iPD patients have cortical thinning of frontal, parietooccipital, and temporal regions when compared with HC but showed cortical thickening in the parietalefrontal and temporaleoccipital areas when contrasted to early NC-iPD patients, indicating a potential degree of neuroplasticity of the brain to combat CI in the early stages of the disease. Another study of note (Uribe et al., 2018) compared cortical thickness change seen in early iPD patients and classified them using hierarchical cluster analysis. This analysis identified two patterns of atrophy: an anterior atrophic pattern categorized by orbitofrontal, anterior cingulate, and temporal cortical thinning (bilateral orbitofrontal, anterior cingulate, and lateral and medial anterior temporal gyri) and a posterior atrophic pattern, characterized by occipital and parietal cortical thinning (bilateral occipital gyrus, cuneus, superior parietal gyrus, and left postcentral gyrus). Further analysis revealed iPD patients with the posterior atrophic pattern had worse cognitive phenotypes and a greater proportion of MCI-iPD patients (Uribe et al., 2018). A longitudinal analysis study (Mak et al., 2015) identified MCIiPD patients had reduced frontal and temporoparietal cortical thickness when compared with HC, with subanalysis revealing a greater rate of cortical thinning in frontal and temporoparietal cortices in MCI-iPD patients compared with NC-iPD patients over an 18-month period. Interestingly, NC-iPD patients who converted to MCI-iPD subjects during the follow-up had cortical thinning of bilateral temporal lobes compared with nonconverters (Mak et al., 2015), with significant frontal cortical thinning being seen in NC-iPD patients during follow-up when compared with HC. The authors of this study have theorized that progressive frontal and temporal cortical thinning may be a predictor of PDD conversion in MCI-iPD (Mak et al., 2015). Cortical thickness changes were found to have a direct correlation to changes in cognition, memory, and other nomotor symptoms, thus highlighting the potential use of cortical thickness to detect and understand potential cognitive decline as iPD progresses. A study (Shin et al., 2021) utilizing machine learning identified that MCI-iPD patients who have thinning of the angular gyrus and frontal lobes along with thickening of the right anterior cingulate cortex and left parahippocampal gyrus have a higher probability to develop PDD. Utilizing this change in cortical thickness, in conjunction with the machine learning software, was shown to have good predictive power to detect the probability of conversion of MCI-iPD to PDD in this study (Shin et al., 2021).

Structural MRI changes in familial Parkinson’s disease As mentioned prior, cortical atrophy of frontal, temporal, and parietooccipital regions reflects the pathological process seen in iPD (Lee et al., 2013). The degree of atrophy seen reflects the severity of the disease and the degree of CI present in iPD patients. In a study comparing HC with asymptomatic single mutation carriers for fPD (Reetz et al., 2010), increased GMV of the striatum was seen in asymptomatic nonmanifest carriers (NMCs) of Parkin, PINK1, ATP13A2, and LRRK2 (LRRK2-NMC had a smaller GMV increased compared other variants mentioned). It may be hypothesized that changes in striatal volume may represent a dynamic process at this stage, whereby atrophy may be preceded by an initial volume gain as a compensatory process. A second study (Binkofski et al., 2007) also

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identified increased GMV in the putamen and the internal globus pallidus within ParkinNMC and PINK1-NMC patients when compared with HCs, reflecting the initial compensatory process to adjust for subclinical dopaminergic deficiency. A few studies have explored GMV changes in symptomatic and nonmanifest LRRK2 carriers (Brockmann et al., 2011; Reetz et al., 2010; Thaler et al., 2014, 2018), but they have varied in results. One study (Brockmann et al., 2011) found increased GMV of the cerebellum and precentral gyrus of symptomatic LRRK2-PD subjects compared with iPD, while increased GMV was identified in the cuneus of LRRK2-NMC subjects compared with HCs. However, several other studies could not find any significant GMV differences between symptomatic LRRK2-PD and iPD subjects (Thaler et al., 2018) and LRRK2-NMC carriers and HCs (Thaler et al., 2014, 2018; Vilas et al., 2016). Cross-sectional studies exploring volumetric changes in symptomatic Parkin carriers found that both iPD and symptomatic Parkin carriers had reduced basal ganglia volumes compared with HCs (Bilgic et al., 2012; Reetz et al., 2009), with subanalysis revealing the degree of caudate atrophy correlating to UPDRS scores Parkin-PD. Interestingly, increased right globus pallidus external GM volumes in iPD and Parkin-PD were seen in one study (Reetz et al., 2009), reflecting a potential compensatory change. GM changes have been seen in PINK1-NMC patients, with studies identifying reduced GMV in the hippocampal and limbic areas (Reetz et al., 2008, 2010) as well as the frontal cortex area (Reetz et al., 2008). Atrophy in these GM areas may reflect why neuropsychiatric symptoms can present in PINK1 carriers. Studies analyzing GMV differences in symptomatic GBA-PD and GBA-NMC have obtained variable results. A cross-sectional study (Thaler et al., 2018) found no significant GMV changes between subgroups of symptomatic (GBA-PD and iPD) and asymptomatic (GBA-NMC and HC) cohorts. A recent longitudinal study (Leocadi et al., 2022) identified significant cortical thinning of left temporal, parietal, and occipital gyri in GBA-PD subjects at baseline when compared with HCs and iPD subjects. After a 5-year follow-up, GBA-PD patients had greater thinning of the occipital and frontal regions compared with iPD patients, while the follow-up scan of iPD patients revealed similar cortical thinning seen in GBA-PD patients during their baseline scan, which helps confirm GBA-PD is a more aggressive form of parkinsonism compared with iPD. Interestingly, similar rates of subcortical, hippocampal, and amygdala volume loss were seen in GBA-PD and iPD subjects.

Diffusion MRI studies in idiopathic Parkinson’s disease Using diffusion-weighted imaging (DWI) and diffusion tensor imaging (DTI), fractional anisotropy (FA) and mean diffusivity (MD) changes can assess microstructural alterations within the SN of iPD patients. Multiple studies have identified reduced FA of the SN in iPD patients (Nagae et al., 2016; Vaillancourt et al., 2009; Zhan et al., 2012; Zhang et al., 2015). Several metaanalysis studies (Atkinson-Clement et al., 2017; Cochrane & Ebmeier, 2013; Deng et al., 2018; Schwarz et al., 2013) identified increased MD (Deng et al., 2018; Schwarz et al., 2013) and reduced FA in the caudal SN (Atkinson-Clement et al., 2017; Cochrane & Ebmeier, 2013; Deng et al., 2018) could be used as a surrogate marker to

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differentiate iPD subjects from HCs. One of the mentioned metaanalyses (Schwarz et al., 2013) found increased MD in the SN of iPD compared with HCs with ROI analysis but no difference with voxel-based analysis (VBA), which could infer that VBA may be less sensitive to microstructural changes when compared with ROI analysis and tract-based special statistics (Loane et al., 2016). The use of DTI to monitor progressive changes in iPD has been controversial. However, a recent systematic review (Zhang & Burock, 2020) has recognized in light of other crosssectional and longitudinal studies (Agosta, Canu, et al., 2013; Burciu et al., 2017; Loane et al., 2016; Ofori et al., 2015; Zhang et al., 2016); specific DTI changes in the SN could be used to monitor progressive changes in iPD. Two of the longitudinal studies (Loane et al., 2016; Zhang et al., 2016) identified progressive FA reductions and MD increases within the SN of iPD subjects when compared with HC, over a 19.3- and 12.6-month follow-up period, respectively. Interestingly, one of the studies (Zhang et al., 2016) identified that the largest DTI changes in the SN were correlated with increased dopamine deficiency and reduced cerebrospinal fluid (CSF) a-synuclein. Another longitudinal study (Pozorski et al., 2018) identified reduced FA and increased MD in the rostral brainstem, SN, and deep subcortical WM in iPD patients when compared with age-matched HC over an 18-month period, with the greatest DTI change occurring within the early stages of iPD (results summarized in Table 7.2). The DTI changes in putamen have also been investigated in several studies. A crosssectional research study (Theisen et al., 2017) identified that reduced bilateral streamline density (percentage of seeded voxels that create streamlines) of the putamen to the SN could help to differentiate iPD patients from HCs. However, the degree of streamline density loss did not correlate to the degree of motor symptoms in this study. A prior cross-sectional study (Zhan et al., 2012) identified FA reductions of the SN and MD increases in the putamen were associated with increased disease severity in iPD patients. Another study (Nagae et al., 2016) found iPD patients had a greater increase in SN MD, with greater MD in the SN and globus pallidus in PIGD-iPD patients compared with TD-iPD patients. Subanalysis revealed MD increased in these areas correlates to progressive disease stage and motor severity in PIGD-iPD motor phenotype (Nagae et al., 2016). However, due to the heterogeneity of the iPD population, DTI changes in the putamen may not be a consistent tool to differentiate iPD patients from HCs. The caudate nucleus was found to have no increase in MD when comparing iPD patients with HCs (Gattellaro et al., 2009; Loane et al., 2016). However, the caudate FA was increased in iPD patients in a few studies (Nagae et al., 2016). A metaanalysis (Atkinson-Clement et al., 2017) identified that increased FA and normal MD can be seen in the caudate of iPD patients, which may be due to compensatory reorganization of functional connectivity (Hou et al., 2016). On top of FA change in subcortical areas such as SN, putamen, and the posterior striatum, DTI changes can also be detected in cortical regions (Zhan et al., 2012). Several cross-sectional studies (Karagulle Kendi et al., 2008; Zhan et al., 2012) have identified reduced FA within the supplementary motor areas (SMAs), an area responsible for controlling motor planning and movement execution. Interestingly, one study (Zhan et al., 2012) found that increased disease severity, as measured with UPDRS scores, was associated with reduced SN FA and increased FA of the postcentral gyrus, reflecting potential factors for progressive motor symptoms and compensatory mechanisms, respectively. Reduced FA in SMA, prefrontal areas, and anterior

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TABLE 7.2 Cohort

Summary of the main findings from diffusion MRI studies in idiopathic and familial PD.

Main findings

References Reduced FA of the SN in iPD patients compared with HCs. Increased MD and reduced FA in the caudal SN can help to differentiate iPD subjects from HCs. Progressive reductions in FA and increases MD of the SN may be seen longitudinally in iPD patients. Reduced FA and increased MD in the rostral brainstem, SN, and deep subcortical WM in iPD patients as disease duration increases. The greatest DTI change occurs within the early stages of iPD. FA reductions of the SN and MD increases in the putamen were associated with increased disease severity. Increased FA and normal MD can be seen in the CN of iPD patients when compared with HCs. FA reduction in the SMA can be seen in iPD patients. Increased UPDRS scores were associated with reduced SN FA and increased FA of the postcentral gyrus. Reduced FA in SMA, prefrontal areas, and anterior cingulate cortex were seen in nondemented iPD patients when compared with HCs. MD changes could be more sensitive than FA in early iPD.

(Nagae et al., 2016; Vaillancourt et al., 2009; Zhan et al., 2012; Zhang et al., 2015) (Atkinson-Clement et al., 2017; Cochrane & Ebmeier, 2013; Deng et al., 2018; Schwarz et al., 2013) (Agosta, Canu, et al., 2013; Burciu et al., 2017; Loane et al., 2016; Ofori et al., 2015; Zhang et al., 2016) (Pozorski et al., 2018) (Zhan et al., 2012) (Atkinson-Clement et al., 2017; Gattellaro et al., 2009; Loane et al., 2016) (Karagulle Kendi et al., 2008; Zhan et al., 2012) (Zhan et al., 2012) (Karagulle Kendi et al., 2008) (Koshimori et al., 2015)

Diffusivity changes with iPD progression

FA reductions of the SN and MD increases in the putamen were associated with increased disease severity.

(Zhan et al., 2012)

Motor phenotypes

Greater MD in the SN and globus (Nagae et al., 2016) pallidus in PIGD-iPD patients compared with TD-iPD patients. MD increased in these areas’ correlates to progressive disease stage and motor severity in PIGD-iPD motor phenotype.

Idiopathic Cortical and PD subcortical areas

Diffusion changes in Increased MD bilaterally in the corona (Minett et al., 2018) relation to motor radiata, corpus callosum, forceps minor, symptoms cingula, internal and external capsule, inferior and superior frontooccipital

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Diffusion MRI studies in idiopathic Parkinson’s disease

TABLE 7.2 Cohort

Summary of the main findings from diffusion MRI studies in idiopathic and familial PD.dcont’d

Main findings

References

fasciculi as well as superior and inferior longitudinal fasciculi in iPD patients compared with HCs. With these MD changes at baseline, a predictor of progressive worsening motor symptoms in iPD. Diffusion changes in Increased MD of the medulla oblongata (Pyatigorskaya et al., 2016) relation to of iPD compared with HCs. Increased MD changes in this area were autonomic associated with progressive autonomic dysfunction dysfunction. Diffusion changes in FA reduction olfactory circuits and relation to hyposmia primary olfactory areas changes could be seen early in hyposmic-iPD when compared with HCs.

(Ibarretxe-Bilbao et al., 2010; Nigro et al., 2021; Rolheiser et al., 2011)

Diffusion changes in Increased MD and reduced GM volume relation to cognition of the nucleus basalis of Meynert in MCI-iPD patients compared with NCiPD patients and HCs. FA reduction in occipital WM, anterior cingulate bundle, and prefrontal regions in NC-iPD. Reduced FA and increased MD in white matter structures of the frontoparietal and frontotemporal along with interhemispheric WM tracts (anterior inferior frontooccipital, uncinate, insular cortices, superior longitudinal fasciculi, corona radiata, genu, and the body of the corpus callosum) are seen in MCIiPD patients when compared with NCiPD patients and HCs. FA reductions of the frontal, temporal, and anterior cingulated bundles in MCIiPD and PDD when compared with HCs, with the degree of FA changes linked to the severity of CI seen in iPD cohort. Reduced FA in the anterior superior longitudinal fasciculus and the genu of the corpus callosum in PDD patients in comparison with NC-PD patients. Reduced FA in bilateral posterior cingulate bundles in PDD compared with nondemented iPD patients.

(Schulz et al., 2018) (Deng et al., 2013; Price et al., 2016) (Agosta et al., 2014; Galantucci et al., 2017; Gorges et al., 2019; Koshimori et al., 2015; Pu et al., 2020; Wang et al., 2020) (Deng et al., 2013) (Kamagata et al., 2013) (Matsui et al., 2007) (Atkinson-Clement et al., 2017)

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TABLE 7.2 Cohort

Summary of the main findings from diffusion MRI studies in idiopathic and familial PD.dcont’d

Main findings

References

Reduced FA and increased MD of the corpus callosum, especial within the genu, were associated with developing executive dysfunction, attention dysfunctions, and PDD within iPD patients. Diffusion changes in Depression: relation to Results have varied from no diffusivity changes to increased axial diffusivity in neuropsychiatric frontal and limbic WM tracts and FA disorders reduction in bilateral mediolateral thalami in iPD patients with depression than those without depression. Reduced FA in the thalami and long WM tracts to frontal areas may be associated with depression in iPD. Apathy: Results have varied from no diffusivity changes to reduced FA in bilateral corona radiata, left superior corona radiata, left cingulum as well as genu and body of corpus callosum in iPD patients with apathy than those without apathy. ICB: Increased FA in the anterior corpus callosum, right internal capsule posterior limbs, right posterior cingulum, and right thalamic radiations in ICB iPD subjects compared with nonICB iPD patients and HCs. Increased MD of the genu of the corpus callosum, right uncinate fasciculus, left parahippocampal tract, and right pedunculopontine tract along with reduced FA of left uncinate fasciculus and parahippocampal tract in ICB iPD compared with non-ICB iPD patients and HCs.

(Li et al., 2010, 2020; Surdhar et al., 2012; Zhang & Burock, 2020) (Zhang & Burock, 2020) (Carriere et al., 2014; Zhang, Wu, et al., 2018) (Yoo et al., 2015) (Canu et al., 2017; Imperiale et al., 2018)

Diffusion changes in Results have varied from no diffusivity (Ansari et al., 2017; Ford et al., 2013; relation to RBD changes iPD patients with and without Lim et al., 2016) RBD, to FA reductions in bilateral cingulum pathways, bilateral inferior frontooccipital fasciculi, longitudinal fasciculi, bilateral corticospinal tracts, middle cerebellar peduncles along with

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TABLE 7.2 Cohort

Summary of the main findings from diffusion MRI studies in idiopathic and familial PD.dcont’d

Main findings

References

genu, body, and splenium of the corpus callosum within RBD iPD subjects. Free-water diffusion Increased FW in the posterior SN in iPD changes patients compared with HCs. There is a positive correlation between age, H&Y stage, and MDS-UPDRS III score to SN FW. Significant FW increase in the posterior SN within iPD subjects longitudinally, while no FW changes were seen in HC. The steepest FW increases of posterior SN occurred early on in the earlier stages of follow-up compared with the later stages. Increased FW in posterior SN was correlated with increased bradykinesia scores and reduced MoCA values in iPD, while increased posterior SN FW was associated with reduced DAT scan striatal binding ratio of the putamen. Most parkinsonian syndromes have increased FW within the SN. iPD does not have increased FW within the midbrain, caudate nucleus, putamen, or cerebellum structures, which are seen mainly in atypical parkinsonian syndromes. Progressive freewater diffusion changes

(Burciu et al., 2017; Mitchell et al., 2021; Ofori et al., 2015; Planetta et al., 2016; Yang et al., 2019) (Yang et al., 2019) (Burciu et al., 2017; Ofori et al., 2015) (Burciu et al., 2017) (Burciu et al., 2017; Ofori et al., 2015) (Mitchell et al., 2021; Planetta et al., 2016)

There is a positive correlation between (Yang et al., 2019) (Burciu et al., 2017; Ofori et al., 2015) age, H&Y stage, and MDS-UPDRS III (Burciu et al., 2017; Ofori et al., 2015) score to SN FW. Significant FW increase in the posterior SN within iPD subjects longitudinally, while no FW changes were seen in HC. Increased FW in posterior SN was correlated with increased bradykinesia scores and reduced MoCA values in iPD, while increased posterior SN FW was associated with reduced DaT scan striatal binding ratio of the putamen.

Diffusion changes in Lower ALPS index in iPD subjects was the glymphatic seen when compared with HCs and system in iPD essential tremor patients. HCs have increased ALPS index than late-stage iPD patients. Studies have varied if ALPS can be used to

(Chen et al., 2021; McKnight et al., 2021) (Chen et al., 2021; Ma et al., 2021) (Chen et al., 2021)

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TABLE 7.2 Cohort

Summary of the main findings from diffusion MRI studies in idiopathic and familial PD.dcont’d

Main findings

References differentiate early- and moderate-stage iPD patients. Lower ALPS scores can be associated with progressive UPDRS-II scores and lower MMSE values. No significant difference in FA between (Thaler et al., 2014) LRRK2-NMC and HCs.

LRRK2

LRRK2-NMC

GBA

GBA-PD

Reduced FA within the olfactory tract, (Agosta, Kostic, et al., 2013) cingulum, corpus callosum, parahippocampal tract, parietal aspect of the superior longitudinal fasciculus, occipital WM, the internal and external capsule in the GBA-PD group compared with iPD group and HCs.

Parkin

Parkin-PD

Decreased FA, increased MD, and radial (Andica et al., 2020; Koinuma et al., 2021) diffusivity within the corona radiata, internal capsule, thalamic radiation, splenium of the corpus callosum, corticospinal tracts, frontooccipital fasciculus, longitudinal fasciculus, posterior corona radiata, uncinate fasciculus of Parkin-PD patients compared with HCs.

ALPS, analysis along the perivascular space; CI, cognitive impairment; CN, caudate nucleus; DaT scan, dopamine transporter scan; DTI, diffusion tensor imaging; FA, fractional anisotropy; FW, free water; GM, gray matter; HCs, healthy controls; H&Y stage, Hoehn-Yahr stage; ICDs, impulse control disorders; iPD, idiopathic Parkinson’s disease; MCI, mild cognitive impairment; MD, mean diffusivity; MMSE, mini mental state examination; MoCA, Montreal cognitive assessment; NC, normal cognition; NC-iPD, normal cognition iPD; NMC, nonmanifest carrier; PIGD, posture instability gait difficulty; PD, Parkinson’s disease; PDD, Parkinson’s disease dementia; RBD, rapid eye movement sleep behavior disorder; SN, substantia nigra; SMA, supplementary motor area; TD, tremor dominant; MDS-UPDRS, Movement Disorder SocietyeUnified Parkinson’s Disease Rating Scale; WM, white matter.

cingulate cortex (in the absence of GMV changes in said areas) was seen in nondemented iPD patients compared with HCs in one cross-sectional study (Karagulle Kendi et al., 2008).

Free-water MRI changes in idiopathic Parkinson’s disease Free water (FW) is defined as the diffusivity of the water molecules, which do not undergo diffusion restriction from its environment. Free-water MRI (FW-MRI) removes partial volume effects of extracellular FW from surrounding tissue, allowing for FW effect on the tissue to be measured (Pasternak et al., 2009). Identifying FW changes within the tissue, abnormalities such as neuroinflammation and neurodegeneration are to be assessed with greater precision. Cross-sectional (Planetta et al., 2016; Yang et al., 2019) and longitudinal (Burciu et al., 2017; Ofori et al., 2015) studies have identified increased FW in the posterior SN in iPD patients compared with HCs. One of the studies (Yang et al., 2019) also identified an inverse

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correlation between posterior SN FW with [11C]DTBZ positron emission tomography (PET) (a marker of presynaptic monoamine release) of the putamen, with a positive correlation to age, H&Y stage, and MDS-UPDRS III score. Two longitudinal studies were done by the same team (Burciu et al., 2017; Ofori et al., 2015) comparing FW changes in de novo iPD patients compared with HCs over 1- and 4-year follow-up, respectively. Both studies identified a significant FW increase in the posterior SN within iPD subjects longitudinally, while no changes were identified in HC. Baseline FW levels predicted the rate of change of posterior SN over the 4-year follow-up. Subanalysis revealed an inverse correlation between 4-year FW changes within the posterior SN and 4-year putaminal [123I]FP-CIT single-photon emission computed tomography (SPECT) (a marker of dopamine transporter density) striatal binding ratio changes (Burciu et al., 2017). Despite progressive posterior SN FW increases, the steepest rate of FW change was observed within the first 2 years of follow-up, with the greater posterior SN FW increases over the first 2 years associated with progressive 4-year changes on the H&Y scale (Burciu et al., 2017). Further analysis from these studies also showed increased FW in posterior SN was correlated with increased bradykinesia scores and reduced Montreal cognitive assessment values in iPD patients (Ofori et al., 2015), while increased posterior SN FW was associated with reduced DAT scan striatal binding ratio (SBR) of the putamen (Burciu et al., 2017). Despite initial studies showing the limited scope of DTI to monitor disease progression, recent studies have shown that longitudinal DTI/FW changes within the SN of iPD subjects can be used as a potential biomarker for disease progression. Although the increase in SN FW is also seen in several parkinsonian syndromes (Mitchell et al., 2021; Planetta et al., 2016), iPD does not have increased FW within the midbrain, caudate nucleus, putamen, or cerebellum structures, which are seen mainly in atypical parkinsonian syndromes (results summarized in Table 7.2).

Diffusion MRI changes and nonmotor symptoms of idiopathic Parkinson’s disease Degenerative lesions to the medulla oblongata, which contains nucleus tractus solitarius, may be present in the initial pathological stages of iPD (Braak et al., 2003). Studies have identified autonomic dysfunction as a common nonmotor feature of iPD, which often precedes motor symptoms (Khoo et al., 2013). A cross-sectional study on a group of iPD patients (Pyatigorskaya et al., 2016) identified increased MD correlated with lower heart rate and respiratory frequency variability during REM sleep. The authors of the study concluded that microstructural neuronal damage of the medulla oblongata potentially underlies the onset of autonomic dysfunction in iPD. Hyposmia is recognized as a prodromal symptom of PD, being described many years motor symptoms manifest (Haehner et al., 2009). Cross-sectional studies (Ibarretxe-Bilbao et al., 2010; Nigro et al., 2021; Rolheiser et al., 2011) have identified FA reduction in the primary olfactory areas and tracts of patients with iPD at the early stage of disease, as assessed with the H&Y scale, with greater FA reduction in hyposmic-iPD patients. Metaanalysis studies (Atkinson-Clement et al., 2017) have recognized that reduced MD can be seen in the olfactory cortex within iPD subjects when compared with HC, but it was not recognized as a definitive feature to differentiate the two groups.

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DTI changes in RBD-iPD patients have been explored, in comparison with non-RBD-iPD, in a few cross-sectional studies, although results have varied. A study (Ford et al., 2013) found significant FA reductions in right inferior frontooccipital fasciculus, longitudinal fasciculi (left inferior and superior longitudinal fasciculi), and right corticospinal tracts in RBD-iPD patients with a later study (Ansari et al., 2017) identifying significant FA reductions in bilateral cingulum pathways, bilateral inferior frontooccipital fasciculi, bilateral corticospinal tracts, middle cerebellar peduncles along with genu, body, and splenium of the corpus callosum within RBD iPD subjects. Though another work found no FA difference between iPD patients with and without RBD, the iPD cohort had reduced FA in bilateral frontal lobes along with widespread MD increases compared with HCs (Lim et al., 2016). Though LC degeneration has been associated with iPD, none of the aforementioned studies identified any significant DTI change in the LC. Several studies have explored DTI changes in iPD patients with anxiety, depression, and apathy, but most studies have been limited by small sample sizes. Cross-sectional studies had produced variable results from no significant FA/MD differences between depressed, nondepressed iPD subjects, and HCs (Surdhar et al., 2012), to studies identifying FA reduction in bilateral mediolateral thalami in depressed iPD patients compared with nondepressed iPD patients (Li et al., 2010), and significantly increased axial diffusivity in frontal and limbic WM tracts in connecting structures (Li et al., 2020). Interestingly, a recent systematic review (Zhang & Burock, 2020) found that reduced FA in the thalami and long WM tracts to frontal areas may be associated with depression in iPD. A previous cross-sectional study (Surdhar et al., 2012) identified reduced amygdala volume in depressed iPD patients when compared with HC; however, only six depressed iPD subjects were enrolled in this study. A study (Carriere et al., 2014) exploring structural changes in apathetic iPD patients found no significant FA reduction in HCs and nonapathetic and apathetic iPD patients, despite significant atrophy of NAc observed in apathetic iPD compared with HCs and nonapathetic iPD subjects. A study of note (Zhang, Wu, et al., 2018) identified reduced FA in bilateral corona radiata, left superior corona radiata, left cingulum as well as genu and body of corpus callosum in apathetic iPD patients compared with nonapathetic iPD patients. Impulsiveecompulsive behaviors (ICBs) and punding are known complications of dopaminergic treatment in iPD. Studies have indicated that iPD subjects without dopaminergic treatment do not increase the risk of ICBs (Weintraub et al., 2013). Cross-sectional DTI studies have identified significant FA/MD changes in ICB iPD subjects compared with non-ICB iPD and HCs. One study (Yoo et al., 2015) identified increased FA in the anterior corpus callosum, right internal capsule posterior limbs, right posterior cingulum, and right thalamic radiations in ICB iPD subjects, while other studies (Canu et al., 2017; Imperiale et al., 2018) have shown increased MD of the genu of the corpus callosum, right uncinate fasciculus, left parahippocampal tract, and right pedunculopontine tract along with reduced FA of left uncinate fasciculus and parahippocampal tract in ICB iPD (Imperiale et al., 2018). The authors of one of the studies (Canu et al., 2017) have stated that the impaired connection between the WM tracts connecting the mesolimbic and frontal cortical areas may suggest repetitive behavior without the lack of rewards.

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Diffusion MRI changes associated with cognitive impairment in idiopathic Parkinson’s disease From several studies, different key microstructural and structural changes in MRI can be seen as iPD progresses to cause CI. A prospective study (Schulz et al., 2018) found increased MD and reduced GMV of the nucleus basalis of Meynert (NBM) in MCI-iPD patients compared with NC-iPD patients and HCs. The NC-iPD cohort was followed up after 36 months, where some of the patients developed MCI. Analysis revealed GMV and increased MD in the NBM were predictive of the development of MCI in NC-iPD patients, with authors theorizing degeneration of cholinergic neurones of NBM may underline the onset of CI (Schulz et al., 2018). Several DTI cross-sectional studies (Agosta et al., 2014; Galantucci et al., 2017; Gorges et al., 2019; Koshimori et al., 2015; Pu et al., 2020; Wang et al., 2020) have acknowledged MCI-iPD patients have reduced FA and increased MD in the frontoparietal and frontotemporal white matter structures, along with the interhemispheric WM tracts (anterior inferior frontooccipital, uncinate, insular cortices, superior longitudinal fasciculi, corona radiata, genu, and the body of the corpus callosum). In a prior cross-sectional study (Agosta et al., 2014), despite no significant GMV changes observed within HC, NC-iPD and MCI-iPD patients who were enrolled in this study, FA reduction of the frontoparietal, frontotemporal, and interhemispheric WM tracts was identified in MCI-iPD patients when compared with the HCs and NC-iPD patients, with authors theorizing WM tract damage may precede atrophic changes in iPD. Utilizing FA and MD values from the basal ganglia and frontotemporoparietal areas, MCI-iPD patients could be differentiated from NC-PD patients and HCs (Galantucci et al., 2017). In some cases, MCI-iPD patients may not have significant FA/MD differences from NC-iPD patients (Koshimori et al., 2015), as the degree of neuronal degeneration may be limited. The authors of this study found reduced MD in specific white matter tracts in MCI-iPD subjects but no significant changes to WM FA. A Longitudinal DTI study (Minett et al., 2018) explored progressive DTI in early MCI-iPD, early NC-iPD, and HC subjects. At baseline, no significant MD differences were seen between NC-PD and MCI-iPD groups; however, both iPD groups had increased MD bilaterally in the corona radiata, corpus callosum, forceps minor, cingulate, internal and external capsule, inferior and superior frontooccipital fasciculi as well as superior and inferior longitudinal fasciculi compared with HCs. On follow-up, MD significantly increased from baseline for both iPD subgroups in the frontal lobes (Minett et al., 2018). Though the study did not find a correlation between longitudinal FA and MD in both iPD groups to cognitive scores, baseline MD was a predictor of progressive worsening motor symptoms in iPD (Minett et al., 2018). Along with volumetric changes, FA change can be seen in PDD. One cross-sectional study identified FA reductions of the frontal, temporal, and anterior cingulated bundles in MCI-iPD and PDD when compared with HCs, with the degree of FA changes linked to the severity of CI seen in iPD cohort (Deng et al., 2013). Another cross-sectional DTI study (Kamagata et al., 2013) identified reduced FA in the anterior superior longitudinal fasciculus and genu of the corpus callosum in PDD patients in comparison with NC-PD patients, while a similar crosssectional study (Matsui et al., 2007) revealed reduced FA in bilateral posterior cingulate

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bundles in PDD compared with nondemented iPD patients. Posterior cingulate bundles have been associated with task and attention domains (Zheng et al., 2014). Therefore, degenerative changes to the posterior cingulate bundles, along with other cortical changes, play vital roles in the onset of developing dementia in iPD patients. A 2017 metaanalysis (Atkinson-Clement et al., 2017) revealed that reduced FA and increased MD of the corpus callosum, especial within the genu, were associated with developing executive dysfunction, attention dysfunctions, and PDD within iPD patients. Significant FA reduction to the splenium of the corpus callosum was identified in cross-sectional studies (Deng et al., 2013) and confirmed by metaanalysis (Atkinson-Clement et al., 2017), which can be a useful tool to differentiate PDD from MCI and NC iPD subjects (results summarized in Table 7.2).

Diffusion MRI changes within the glymphatic system in idiopathic Parkinson’s disease The glymphatic system is a recently discovered waste removal system of soluble proteins and metabolites of the brain, with astroglia forming the perivascular channels (Jessen et al., 2015). The glymphatic system mainly works when subjects sleep, reflecting that sleep may be essential for the removal of neurotoxins. Insomnia and RBD are frequently associated with iPD (Stefani & Högl, 2020), with sleep disorders potentially linked with reduced metabolic clearance of a-synuclein (Sundaram et al., 2019). A recent metaanalysis (Zhang et al., 2020) has revealed iPD patients have reduced quantity and quality of sleep compared with HCs. Interestingly, the metaanalysis (Zhang et al., 2020) identified that reduced sleep duration and slow sleep waves were associated with CI in iPD. Diffusivity changes within the perivascular space (PVS) of iPD patients have been used to determine the effectiveness of the glymphatic system. To standardize the diffusivity changes, DTI analysis along the perivascular space (ALPS) index has been used. The ALPS index is a ratio of the diffusivity in the direction of the PVS, against the diffusivity in the direction perpendicular to both major fiber tracts and perivascular space (Yang, Deng, et al., 2020). Lower ALPS index in iPD subjects was seen when compared with HCs (Chen et al., 2021) and essential tremor subjects (McKnight et al., 2021). The ability to differentiate HCs from iPD patients has garnered variable results, with one study (Chen et al., 2021) finding significantly higher ALPS index in the HC group when compared with iPD cohort, while a second study (Ma et al., 2021) only finding late-stage iPD patients had significantly ALPS index compared with HCs. These studies (Chen et al., 2021; Ma et al., 2021) could not identify significant differences in iPD subgroups (from H&Y staging and cognitive groups) based on the ALPS scores, although one of the studies (Chen et al., 2021) identified an association between lower ALPS score to progressive UPDRS-II scores and lower Mini-Mental State Examination (MMSE) values.

Diffusion tensor imaging in familial Parkinson’s disease One cross-sectional study (Agosta, Kostic, et al., 2013) compared DTI changes in a cohort of 16 HCs, 16 iPD patients, and 15 patients with GBA-PD. Results revealed significantly

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reduced FA within the olfactory tract, cingulum, corpus callosum, parahippocampal tract, parietal aspect of the superior longitudinal fasciculus, occipital WM as well as the internal and external capsule in the GBA-PD group compared with iPD group and HCs. A cross-sectional study (Thaler et al., 2014) analyzed the differences in tract-based spatial statistics (TBSS) and VBM of 25 LRRK2-NMC (G2019S mutation) and 30 HCs (who are firstdegree relatives of patients with LRRK2-PD). The study revealed no significant difference in FA or GMV between LRRK2-NMC and HCs. However, a trend was identified with increased FA and reduced MD of the SN, and several WM tracts (anterior thalamic radiation bilaterally, bilateral corticospinal tracts, right superior longitudinal fasciculus, right inferior frontooccipital fasciculus, right cingulate, and the forceps major) of LRRK2-NMC compared with HC may represent early WM compensatory changes in LRRK2-PD. Another cross-sectional study using DTI (Koinuma et al., 2021) compared nine early-onset Parkin-PD patients against 15 age-matched healthy controls. The results from this study showed decreased FA, increased MD, and radial diffusivity (RD) within WM microstructures (corona radiata, internal capsule, thalamic radiation, splenium of the corpus callosum, corticospinal tracts, frontooccipital fasciculus, longitudinal fasciculus, posterior corona radiata, uncinate fasciculus) of Parkin-PD patients compared with HCs. The white matter microstructural changes seen in Parkin-PD patients in this study were similar to changes in iPD (Andica et al., 2020), despite previous studies (Ahlskog, 2009) linking Parkin with greater affinity to SN dopaminergic neurodegeneration, indicating specific changes in Parkin-PD could not be detected or nonmotor and motor symptoms seen in iPD cannot specifically be attributed to substantia nigral dopaminergic neurodegeneration alone.

Neuromelanin-sensitive MRI in idiopathic Parkinson’s disease Neuromelanin (NM) is a by-product of oxidative polymerization of dopamine or norepinephrine, thus finding in high concentrations within the SNPc and LC, within which it is thought to exert a neuroprotective role against intracellular oxidative stress (Wakamatsu et al., 2014). Postmortem studies have revealed a selective loss of NM-containing neurons of the SNPc, and LC in tissue samples from iPD patients (Fearnley & Lees, 1991). There is an increasing bulk of research indicating that NM and a-synuclein form a vicious cycle of reciprocal propagation that can lead to dopaminergic neuronal loss in PD (Xu & Chan, 2015). Utilizing NM’s paramagnetic proprieties, neuromelanin-sensitive MRI (NM-MRI) is able identify NM rich structures in vivo as hyperintense areas (Cassidy et al., 2019; Sasaki et al., 2008). In vivo MRI studies on iPD patients have shown a loss of NM-MRI signal-to-noise ratio (SNR) within the SNPc when compared with HCs (Castellanos et al., 2015; Isaias et al., 2016; Ohtsuka et al., 2014; Porter et al., 2020; Schwarz et al., 2011; Wang et al., 2018, 2019), which can be spotted since the de novo stage (Wang et al., 2018). Cross-sectional (Schwarz et al., 2011) and longitudinal (Biondetti et al., 2020; Gaurav et al., 2021; Matsuura et al., 2016) studies have identified a negative correlation between NM-MRI SNPc SNR loss with iPD duration and disease severity, with a greater SNR loss associated with more advanced disease. However, a prior ROI-based NM-MRI study (Ohtsuka et al., 2014) was unable to find significant differences in signal intensity between early and advanced iPD, when

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comparing the contrast-to-noise ratio (CNR) in three regions (medial, lateral, and central) of the SNPc. NM-MRI CNR changes in the SN were unable to differentiate between motor and nonmotor phenotypes of iPD (Wang et al., 2018). The diagnostic ability of NM-MRI was analyzed in a narrative review publication (Feraco et al., 2021), which identified NM-MRI has high diagnostic accuracy for iPD, with high sensitivity and specificity values. A quantitative analysis study (Pyatigorskaya et al., 2018) found a combination of SN fractional anisotropy, volume, and NM-MRI signal changes provided excellent diagnostic accuracy (even though NM-MRI signal loss to detect iPD had very good diagnostic potential). Longitudinal studies (Matsuura et al., 2016) have shown total area and contrast ratio (CR) of the SNPc of iPD patients have reduced in follow-up NM-MRI scan, with subanalysis revealing the area and CR of the SNPc loss were negatively correlated with disease duration (results summarized in Table 7.3). The LC, the principal site of noradrenaline production, is known to play a key role in arousal/sleep cycles and emotions (Benarroch, 2009). Degenerative changes in the LC can be associated with RBD. Reduced NM-MRI signal can be seen in idiopathic RBD (iRBD) when compared with HC (Ehrminger et al., 2016), as well as iPD patients with RBD, in contrast to iPD patients without RBD (García-Lorenzo et al., 2013; Sommerauer et al., 2018). Cross-sectional studies have shown that reduced NM-MRI signals in the LC in iPD subjects are associated with increased muscle tone in RBD-iPD patients (García-Lorenzo et al., 2013), a greater frequency of CI, orthostatic hypotension, and reduced EEG activity (Sommerauer et al., 2018) as well as depression (Wang et al., 2018). Studies have shown that pathologies of the LC not only result in reduced NM-MRI signals but also could exhibit reduced FA and reduced binding of [11C]MeNER, a PET marker of noradrenaline transporter availability, in iPD patients with RBD (Sommerauer et al., 2018).

Neuromelanin-sensitive MRI in familial Parkinson’s disease Two cross-sectional studies (Castellanos et al., 2015; Correia Guedes et al., 2017) have identified reduced SN NM-MRI signal of iPD, LRRK2-PD, and Parkin-PD cohorts when compared with HC, with one of the studies identifying no significant SN signal change between the LRRK2-PD and iPD groups, despite the iPD group being older with greater motor scores (Correia Guedes et al., 2017). Though one of the studies (Castellanos et al., 2015) did not explore whether there was any significant signal change between the iPD, Parkin-PD, and LRRK2-PD, it did identify reduced NM-MRI of SN and LC could be used to differentiate iPD from HC, with greater diagnostic accuracy seen when measuring SN NM-MRI signal.

Iron-sensitive MRI studies in idiopathic Parkinson’s disease Iron is believed to play a key role in the pathogenesis of PD (Mochizuki et al., 2020), with postmortem analysis identifying iron deposition within the SN of PD patients (Langkammer et al., 2010). Susceptibility-weighted imaging (SWI) and T2* MRI allow for areas of iron deposition and hemorrhagic changes to be identified (Chavhan et al., 2009), while quantitative

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Iron-sensitive MRI studies in idiopathic Parkinson’s disease

TABLE 7.3 Cohort

179

Summary of the main findings from neuromelanin MRI studies in idiopathic and familial PD.

Main findings

Idiopathic Cortical and PD subcortical changes

References Loss of NM-MRI signal within the SNPc of iPD patients compared with HCs. Studies identified a negative correlation between NM-MRI SNPc signal loss with iPD duration and disease severity, with the greater signal loss associated with more advanced disease. No significant contrast-to-noise ratio in medial, lateral, and central regions of the SN in early and advanced iPD patients. Reduced total area and contrast ratio of the SNPc of iPD patients has been as disease duration increases.’ with ’As disease duration increases, the total area and contrast ratio of SNPc reduces in patients with iPD.

(Castellanos et al., 2015; Feraco et al., 2021; Isaias et al., 2016; Ohtsuka et al., 2014; Porter et al., 2020; Pyatigorskaya et al., 2018; Schwarz et al., 2011; Wang et al., 2018, 2019) (Biondetti et al., 2020; Gaurav et al., 2021; Schwarz et al., 2011) (Ohtsuka et al., 2014) (Matsuura et al., 2016)

NM-MRI changes in Reduced NM-MRI signals in the LC in (García-Lorenzo et al., 2013; the locus coeruleus iPD subjects are associated with increased Sommerauer et al., 2018; Wang et al., of iPD patients muscle tone, a greater frequency of CI, 2018) orthostatic hypotension, reduced EEG activity well as depression in RBD-iPD patients. Disease changes in early iPD

Significant NM-MRI signal loss can also be overserved in de novo iPD patients.

(Wang et al., 2018)

(Biondetti et al., 2020; Gaurav et al., NM-MRI changes Greater NM-MRI SNPc signal loss 2021; Schwarz et al., 2011) with iPD progression associated with more advanced disease (Matsuura et al., 2016) As disease duration increases, the total area and contrast ratio of SNPc reduces in patients with iPD. NM-MRI changes in NM-MRI signal changes in the SN were (Wang et al., 2018) relation to motor unable to differentiate between motor and phenotypes in iPD nonmotor phenotypes of iPD. NM-MRI differences Essential tremor: to other neurological Essential tremor subjects do not have conditions significant NM-MRI signal loss within the SNPc, which is seen in iPD patients. PSP: Increased SNPc area and reduced CR of the LC in iPD patients when compared with PSP patients. MSA: Results have varied, with increased and decreased contrast ratios of the LC in iPD patients compared with MSA patients. Increased contrast if the internal and external SN of MSA patients when compared with iPD patients.

(Reimão et al., 2015; Wang et al., 2019) (Ohtsuka et al., 2014; Taniguchi et al., 2018; Matsuura et al., 2021) (Matsuura et al., 2013; Ohtsuka et al., 2014) (Simões et al., 2020)

(Continued)

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TABLE 7.3

Summary of the main findings from neuromelanin MRI studies in idiopathic and familial PD.dcont’d

Main findings

References

NM-MRI changes in Reduced NM-MRI signal can be seen in relation to RBD in iPD patients with RBD compared with iPD iPD patients without RBD.

(García-Lorenzo et al., 2013; Sommerauer et al., 2018)

LRRK2

LRRK2-PD

Reduced SN NM-MRI signal in LRRK2PD patients when compared with HCs. No significant SN signal change between the LRRK2-PD and iPD patients.

(Castellanos et al., 2015; Correia Guedes et al., 2017) (Castellanos et al., 2015)

Parkin

Parkin-PD

Reduced SN NM-MRI signal in Parkin-PD (Castellanos et al., 2015; Correia patients when compared with HCs. Guedes et al., 2017)

Cohort

CI, cognitive impairment; CR, contrast ratio; EEG, electroencephalogram; HCs, healthy controls; iPD, idiopathic Parkinson’s disease; LC, locus coeruleus; MSA, multiple system atrophy; NM, neuromelanin; NM-MRI, neuromelanin-sensitive MRI; PD, Parkinson’s disease; PSP, progressive supranuclear palsy; RBD, rapid eye movement sleep behavior disorder; SN, substantia nigra; SNPc, substantia nigra pars compacta.

susceptibility mapping (QSM) and R2* are quantitative MRI sequences, which can measure iron content within tissues (Yan et al., 2018). Both cross-sectional analysis (Azuma et al., 2016; Langkammer et al., 2016; Lewis et al., 2018) and longitudinal (Du et al., 2018) studies have identified that QSM is more sensitive to iron-specific changes in iPD than R2*, even at early disease stages, which was confirmed in narrative review (Feraco et al., 2021) and metaanalysis (Pyatigorskaya et al., 2020) studies. Interestingly, R2* was found to be a superior modality to detect complex microstructural changes within the neurones than QSM in cross-sectional (Lewis et al., 2018) and longitudinal (Du et al., 2018) studies. A study utilizing SWI (Zhang et al., 2010) identified that increasing iron levels within the SN in iPD patients correlated to greater UPDRS motor scores. Interestingly, this study did not find any correlation between the duration of disease and iron deposition of the SN, with the authors suggesting environmental, lifestyle, age-related, and genetic factors can lead to progressive iron deposition. As seen with SWI, studies (Langley et al., 2017) looking at T2* signal changes have identified significantly reduced signal intensity in SNPc in iPD patients compared with HC. A cross-sectional study (Tambasco et al., 2019) exploring iron accumulation seen in T2* MRI identified a positive correlation with iron levels in the SN and UPDRS III scale, along with an inverse correlation of iron accumulation in the SN and cognitive scoring scales. Prior studies (Arabia et al., 2010) have commented on the limited sensitivity of T2* imaging in iPD, but more recent iPD studies utilizing radiomics signatures (Liu et al., 2020) obtained good diagnostic performance to differentiate iPD subjects from HC. A longitudinal study utilizing QSM (Bergsland et al., 2019) identified significantly raised iron levels in the dorsal SN in iPD compared with HC, with a further increase in iron deposition in the SN with increased disease duration. SN R2* has been detected in several longitudinal analysis (Du et al., 2018; Hopes et al., 2016; Ulla et al., 2013), with one study (Ulla et al., 2013) identifying progressive R2* signal increase in SN and caudal putamen in iPD patients over 3 years, with no significant R2* changes in HC over that time frame. Another longitudinal study (Hopes et al., 2016) found that early iPD subjects had increased R2* signal in

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the SN, putamen, and caudate over a 2-year period. Further analysis of the diverse iPD cohort revealed that iron overload was not linearly correlated with disease progression; the initial iron accumulation occurs within the first 3e5 years of disease with a later slow down as disease progresses, which may explain the absence of linear correlation of iron overload and disease progression in this study. The authors of the study (Hopes et al., 2016) have highlighted that atrophy of the striatum may interfere with measuring the iron content of nigrostriatal structures. The later longitudinal study (Du et al., 2018) found R2* MRI could detect longitudinal changes in the SNPc within the iPD cohort, while QSM could not, highlighting that R2*can be used as a modality to monitor iPD progression. Further analysis revealed that increased SNPc R2* signal in later iPD correlated with increasing nonmotor symptoms, while a nonlinear decrease in R2* and QSM substantia nigra pars reticulata (SNPr) signals seen in late iPD was linked with greater motor symptoms. This study identified increased iron levels of SNPr in the early stages of iPD with reduced iron in later stages, with authors theorizing an early compensatory method in early iPD and a countercompensation method later on in the disease (Du et al., 2018) (results summarized in Table 7.4). Another structure affected by iron overload in iPD patients is the red nucleus. An excess of iron accumulation in the red nucleus has been associated with iPD in two SWI cross-sectional works (Kolpakwar et al., 2021; Shah et al., 2020). A recent study (Xiong et al., 2020) identified PDAR-iPD patients had significantly lower SWI signal in the globus pallidus compared with HC and TD-iPD subjects, indicating increased iron deposition in globus pallidus is associated with PDAR phenotype in iPD.

Iron-sensitive MRI studies in familial Parkinson’s disease Results from a cross-sectional study (Pyatigorskaya et al., 2015) explored R2* changes in a cohort of NMC (Parkin-NMC and LRRK2-NMC), fPD patients (Parkin-PD and LRRK2-PD), iPD patients, and HC found in comparison with HC, iPD, fPD, and HC cohorts had significantly raised SN R2* values. Despite NMC subjects displaying higher SN R2* values than HC, no significant difference was seen in comparison with iPD cohort. This indicates that iron deposition occurs in the preclinical phase in NMC, which is comparable with the levels of patients with iPD. The fPD cohort displayed greater an SN R2* signal than iPD patients but did not differ from NMC. Within this study (Pyatigorskaya et al., 2015), researchers did not find a correlation between SN R2* changes to disease duration, age, or disease severity.

Arterial spin labeling in idiopathic Parkinson’s disease Both SPECT and PET imaging modalities have indicated abnormal perfusion and metabolic patterns in iPD (Hosokai et al., 2009; Nagamachi et al., 2008; Vander Borght et al., 1997). Previous PET studies in iPD (González-Redondo et al., 2014) have identified areas of hypometabolism that could predate structural changes that can be seen in iPD. Arterial spin labeling (ASL) allows for perfusion and cerebral blood flow (CBF) changes to be detected without the use of exogenous contrast agents (Petcharunpaisan et al., 2010).

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TABLE 7.4

Summary of the main findings from iron-sensitive MRI studies in idiopathic and familial PD.

Cohort and MRI sequence Main findings Idiopathic Cortical and PD: SWI subcortical changes

Swallow tail sign (STS)

References Increasing iron levels within SN in iPD patients correlate to greater UPDRS motor scores. No correlation between disease duration and iron deposition of the SN found in this study.

(Zhang et al., 2010)

Good concordance between SWI N1 signal loss and reduced striatal PET tracer uptake of 6-[18F]FDOPA and [18F] FP-(þ)-DTBZ in iPD patients. Loss of N1 is more likely to be a predictor of parkinsonism, with SWI N1 signal loss in present in iPD, PSP, and MSA patients. Loss of STS has a high diagnostic potential in detecting iPD from HCs. Quantifying the degree of STS loss (STS scale) has yielded high sensitivity and specificity rates to diagnose iPD from HCs (iPD subjects have lower STS values than HCs). A negative correlation between STS scale to both Hoehn and Yahr stage and disease duration was seen in iPD patients.

(Michler et al., 2021; Wang et al., 2021) (Wang et al., 2012; Meijer et al., 2015; Wang, Luo, and Gao, 2016; Simões et al., 2020; Michler et al., 2021) (Schwarz et al., 2014; Stezin et al., 2018; Cheng et al., 2019; Chau et al., 2020; Cheng et al., 2020; Xiong et al., 2020; Michler et al., 2021) (Lin et al., 2021; Wang et al., 2021) (Lin et al., 2021)

SWI changes with iPD duration was found to be correlated (Kolpakwar et al., 2021; Shah et al., 2020) iPD progression to increased red nucleus volumes. SWI changes in PDAR-iPD patients had significantly (Xiong et al., 2020) relation to motor lower SWI signal in the globus pallidus phenotypes in iPD compared with HC and TD-iPD subjects. (Wang et al., 2012; Meijer et al., 2015; SWI differences to Loss of N1 is more likely to be a other parkinsonian predictor of parkinsonism, with SWI N1 Wang, Luo, and Gao, 2016; Simões et al., disorders signal loss in present in iPD, PSP, and 2020; Michler et al., 2021) MSA patients. Idiopathic Cortical and PD: T2* subcortical changes T2* changes with iPD progression

Reduced signal intensity in SNPc in iPD (Langley et al., 2017) patients compared with HCs. A positive correlation with iron levels in (Tambasco et al., 2019) the SN and UPDRS-III scale, plus an inverse correlation of iron accumulation in the SN and cognitive scoring scales.

Idiopathic Strengths of QSM QSM is more sensitive to iron-specific PD: QSM and R2* imaging changes in iPD, while R2* is better a and R2* in iPD modality to detect complex microstructural changes with the neurones.

(Du et al., 2018; Lewis et al., 2018) (Azuma et al., 2016; Du et al., 2018; Langkammer et al., 2016; Pyatigorskaya et al., 2020)

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TABLE 7.4

Summary of the main findings from iron-sensitive MRI studies in idiopathic and familial PD.dcont’d

Cohort and MRI sequence Main findings

Cortical and subcortical changes

References QSM had greater sensitivity in detecting iron overload than R2* in iPD (even in the early stages). Increased iron levels (seen in QSM) in (Bergsland et al., 2019) the dorsal SN in iPD compared with HCs.

QSM and R2* With increased disease duration, R2* changes with iPD signal increases in SN and caudal progression putamen in iPD patients. Early iPD subjects had increased R2* signal in the SN, putamen, and CN as disease duration increased. Increased SNPc R2* signal in later iPD correlated with increasing nonmotor symptoms. A nonlinear decrease in R2* and QSM SNPr signals seen in late iPD were linked with greater motor symptoms. Increased iron levels of SNPr in the early stages of iPD with reduced iron in later stages. Increased iron levels (seen in QSM) in the dorsal SN in iPD compared with HCs, with a further increase in iron deposition in the SN with increased disease duration. LRRK2

Parkin

183

(Du et al., 2018; Hopes et al., 2016; Ulla et al., 2013) (Hopes et al., 2016) (Du et al., 2018) (Du et al., 2018) (Du et al., 2018) (Bergsland et al., 2019)

LRRK2-NMC

Increased SN R2* values in iPD patients, (Pyatigorskaya et al., 2015) LRRK2-PD, and LRRK2-NMC compared with HCs.

LRRK2-PD

Increased SN R2* values in iPD patients, (Pyatigorskaya et al., 2015) LRRK2-PD, and LRRK2-NMC compared with HCs.

Parkin-NMC

Increased SN R2* values in iPD patients, (Pyatigorskaya et al., 2015) Parkin-PD, and Parkin-NMC compared with HCs.

Parkin-PD

Increased SN R2* values in iPD patients, (Pyatigorskaya et al., 2015) Parkin-PD, and Parkin-NMC compared with HCs.

CN, caudate nucleus; HC, healthy controls; iPD, idiopathic Parkinson’s disease; MSA, multiple system atrophy; N1, nigrosome-1; NMC, nonmanifest carrier; PET, positron emission tomography; PD, Parkinson’s disease; PDAR, predominant akinetic/rigidity; PSP, progressive supranuclear palsy; QSM, quantitative susceptibility mapping; SN, substantia nigra; SNPc, substantia nigra pars compacta; SNPr, substantia nigra pars reticulata; STS, swallow-tail sign; SWI, susceptibility-weighted imaging; UPDRS, Unified Parkinson’s Disease Rating Scale.

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Multiple cross-sectional analyses utilizing ASL have looked at regions of altered blood flow in the brain of iPD patients. No significant perfusion changes were seen in early iPD subjects when compared with HCs (Pelizzari, Laganà, Di Tella et al., 2019). However, in studies that included iPD patients with PDD and increased disease duration, reduced perfusion in the frontal lobe (Lin et al., 2017; Pelizzari et al., 2020), posterior cingulate gyrus (Le Heron et al., 2014; Rane et al., 2020), parietooccipital cortex, precuneus, and cuneus regions (Barzgari et al., 2019; Kamagata et al., 2011; Le Heron et al., 2014; Melzer et al., 2011; Pelizzari et al., 2020; Rane et al., 2020) has been observed. These results may be a reflection of the different stages, the severity of iPD, and the different manifestations of iPD, leading to variable patterns of perfusion that are seen. Along with cerebral blood flow (CBF) value, arterial spin labeling can detect arterial arrival time (AAT) and cerebrovascular reactivity (CVR). iPD has been shown to have increased AAT throughout the whole brain (Al-Bachari et al., 2014, 2017) when compared with HCs. Interestingly, one study (Al-Bachari et al., 2017) identified similar AAT in controls with cerebrovascular disease (CVD) and iPD (who had no other health issues), indicating that other abnormalities other than conventional CVD processes have caused raised AAT in iPD. Studies looking at CVR in iPD study population (Al-Bachari et al., 2017; Pelizzari, Laganà, Rossetto, et al., 2019) did not show any difference in CVR in iPD group and HC, indicating that any perfusion alteration in iPD was not due to impaired blood vessel dilation. A few cross-sectional studies have explored how perfusion variation affects the severity of motor symptoms. One study (Barzgari et al., 2019) identified iPD patients who had a greater increase in rCBF during motor tasks as a marker of disease severity, along with differential regional connection within the brain in the iPD group to compensate for striatal dysfunction. Another cross-sectional study (Pelizzari, Laganà, Rossetto, et al., 2019) identified a significant positive correlation between UPDRS III scores and CBF in the precentral gyrus, postcentral gyrus, supplementary motor areas, SN, striatum, pallidum, thalamus, and red nucleus, reflecting the degree of the compensatory regional hyperperfusion as a marker for disease severity in iPD. These studies have highlighted the degree of regional hyperperfusion as a potential biomarker to monitor disease progression (results summarized in Table 7.5). Some studies explored the lateralization of motor symptoms using a laterality index, which is a ratio of the least affected contralateral and affected ipsilateral CBF (Yamashita et al., 2017). Lateralization of motor symptoms can be detected in early nondemented iPD, with asymmetrical motor symptoms, utilizing putaminal laterality index (Shang, Wu, Zhang, et al., 2021); laterality index in the caudate was significantly elevated in the early stages of iPD, and it could decline with increasing severity of the disease. The studies have highlighted the potential use LI in identifying lateralization of motor symptoms and monitoring disease progression, but further longitudinal analysis with larger cohorts is needed to identify the exact regions of altered perfusion to utilize the LI. Perfusion changes may precede the atrophic changes in the brain of iPD and the degree of atrophic change impacting the severity of CI. One study (Pelizzari et al., 2020) identified hypoperfusion of the parietal lobes of nondemented iPD patients was seen when compared with HCs together with hypoperfusion not accompanied by atrophic changes in this study, indicating that hypometabolism/hypoperfusion may precede atrophic changes, which has been shown in previous PET exploring cognitive iPD (González-Redondo et al., 2014). A cross-sectional study (Al-Bachari et al., 2014) identified hypoperfusion of parietal and

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Arterial spin labeling in idiopathic Parkinson’s disease

TABLE 7.5 Cohort

Summary of the main findings from arterial spin labeling studies in idiopathic and familial PD.

Main findings

Idiopathic Regional and global PD perfusion changes

Disease changes in early iPD

References Results have varied from no significant perfusion changes to reduced perfusion in the frontal lobe, posterior cingulate gyrus, parietooccipital cortex, precuneus, and cuneus regions. It is theorized that different stages, the severity of iPD, and the different manifestations of iPD lead to variable patterns of perfusion. Increased AAT throughout the whole brain in iPD patients compared with HCs. No significant difference in CVR was seen between iPD patients and HCs. Increase in rCBF during motor tasks as a marker of disease severity in iPD patients. Early asymmetrical iPD which can be detected with putaminal LI, when compared with HCs.

(Pelizzari, Laganà, Di Tella, et al., 2019) (Barzgari et al., 2019; Kamagata et al., 2011; Le Heron et al., 2014; Melzer et al., 2011; Pelizzari et al., 2020; Rane et al., 2020) (Al-Bachari et al., 2014, 2017) (Al-Bachari et al., 2017; Pelizzari, Laganà, Rossetto, et al., 2019) (Barzgari et al., 2019) (Shang, Wu, Zhang, et al., 2021)

LI in CN was significantly elevated in the early stages of iPD.

(Shang, Wu, Zhang, et al., 2021)

Perfusion changes Increase in rCBF during motor tasks as with iPD progression a marker of disease severity in iPD patients. A positive correlation between UPDRSIII scores and CBF in the precentral gyrus, postcentral gyrus, supplementary motor areas, substantia nigra, striatum, pallidum, thalamus, and red nucleus (regional hyperperfusion as a potential biomarker to monitor disease progression). LI in CN was significantly elevated in the early stages of iPD, and it could decline with increasing severity of the disease.

(Barzgari et al., 2019) (Pelizzari, Laganà, Rossetto, et al., 2019) (Shang, Wu, Zhang, et al., 2021)

Perfusion changes in Hypoperfusion of the parietal lobes of relation cognitive nondemented iPD patients were seen change in iPD when compared with HCs. Hypoperfusion of parietal and occipital regions in iPD subjects was correlated to cognitive decline. Hypoperfusion of the precuneus was associated with MCI in iPD subjects.

(Pelizzari et al., 2020) (Al-Bachari et al., 2014) (Jia et al., 2018) (Suo et al., 2019) (Le Heron et al., 2014) (Shang, Wu, Chen, et al., 2021)

(Continued)

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TABLE 7.5 Cohort

Summary of the main findings from arterial spin labeling studies in idiopathic and familial PD.dcont’d

Main findings

References

Significantly raised regional ATT in the right thalamus in MCI-iPD subjects compared with NC-iPD patients and HCs. Both PDD and AD patients exhibit similar posterior hypoperfusion patterns. Reduced normalized rCBF of bilateral putamen, left precentral gyrus, left middle cingulate gyrus, and right middle frontal gyrus in MCI-iPD cohort compared with amnestic MCI (a precursor form to AD). Perfusion changes in Hypoperfusion of the insula and the (Lin et al., 2017) relation to autonomic frontal lobes were significantly dysfunction in iPD associated with autonomic dysfunction and iPD progression. AAT, arterial arrival time; AD, Alzheimer’s disease; CBF, cerebral blood flow; CN, caudate nucleus; CVR, cerebrovascular reactivity; HCs, healthy controls; iPD, idiopathic Parkinson’s disease; LI, laterality index; MCI, mild cognitive impairment; NC-iPD, normal cognition iPD; PD, Parkinson’s disease; PDD, Parkinson’s disease dementia; rCBF, regional cerebral blood flow; UPDRS-III, Unified Parkinson’s Disease Rating Scale Part III.

occipital regions in iPD subjects was correlated to cognitive decline, with another study (Jia et al., 2018) revealing that hypoperfusion of the precuneus was associated with MCI in iPD subjects. However, another study (Suo et al., 2019) revealed that utilizing AAT may be a more sensitive biomarker for MCI in the earlier stages of iPD than CBF, with significantly raised regional ATT in the right thalamus in MCI-iPD subjects. When comparing perfusion changes in PDD and Alzheimer’s disease (AD), a previous study (Le Heron et al., 2014) could not identify significant perfusion variations with both groups exhibiting similar posterior hypoperfusion patterns. However, a recent study (Shang, Wu, Chen, et al., 2021) comparing MCI-iPD subjects to amnestic MCI (aMCI), both precursor forms of PDD and AD, respectively, detected reduced normalized rCBF of bilateral putamen, left precentral gyrus, left middle cingulate gyrus, and right middle frontal gyrus in MCI-iPD cohort, indicating different mechanisms of disease progression to cause dementia in PD and AD. Autonomic dysfunction has been identified as a significant feature that may develop in iPD. A study (Lin et al., 2017) identified that hypoperfusion of the insula and the frontal lobes was significantly associated with autonomic dysfunction and iPD progression. It can be theorized that as iPD progresses, more neuronal loss of autonomic areas occurs, resulting in regional hypoperfusion and eventual manifestation of autonomic dysfunction.

Summary As each structural MRI sequence looks at detailed structural changes within the brain, specific pathophysiological changes can be detected in different sequences. From the early to an II. Clinical applications in Parkinson disease

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advanced stage, definitive structural changes can be detected, with potential premanifested changes seen in certain sequences. Diffusion-MRI, NM-MRI, iron imaging, and GMV change within the SN have been identified as useful tools to assist the diagnosis of PD alongside clinical assessment. Studies have also highlighted the potential of MRI in monitoring specific iPD symptom progression via FW and R2* changes of the SN, along with cortical FA/MD reductions, hypoperfusion, and atrophy. Identifying MRI-specific changes in slower progressive forms of fPD can recapitulate iPD changes over a larger time window, while MRI changes in more aggressive forms of fPD can give insight into how specific patterns of MRI changes iPD patients may be seen in more advanced stages. It is predicted that future application of MRI may offer greater insight into disease-specific structural changes seen in PD, to aid not only clinical diagnosis but also a way to monitor symptoms progression alongside diseasemodifying therapies.

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C H A P T E R

8 Functional MRI in familial and idiopathic PD Joji Philip Verghese, Edoardo Rosario de Natale and Marios Politis Neurodegeneration Imaging Group, Department of Clinical and Biomedical Sciences, Faculty of Health and Life Sciences, University of Exeter, United Kingdom

Introduction Parkinson’s disease (PD) is a progressive neurodegenerative disorder pathologically characterized by the intracellular buildup of a-synuclein to form aggregates named Lewy bodies, which, over time, lead to neuronal damage and neuronal death (Braak et al., 2004). Clinically, PD is recognized by bradykinesia, associated with resting tremor and rigidity, which have been classically associated with selective degeneration of the dopaminergic neurons of the substantia nigra pars compacta. PD is also characterized by a plethora of nonmotor symptoms, such as anxiety and depression, cognitive disturbances, psychosis, sleep disturbances, hyposmia, autonomic dysfunction, and pain, which may precede or accompany the onset of motor symptoms. These are thought to arise as the result of a combination of dopaminergic and nondopaminergic neurotransmitter system alterations, namely serotonergic, cholinergic, noradrenergic, and others, which can involve widespread areas of the nervous system (Sveinbjornsdottir, 2016). Most cases are sporadic (idiopathic Parkinson’s disease, iPD). In around 15% of cases, clear family history can be recognized; in some, pathogenic mutations to single, causative genes, with autosomal dominant (SNCA, LRRK2) or recessive (Parkin, PINK-1, DJ-1, and others), patterns of inheritance have been described (familial PD, fPD) (Nuytemans et al., 2010). Recent research has revolutionized our approach toward PD. From this being a homogenous disorder merely defined by clinical assessment, now PD is a heterogenous biological entity that may recognize different clinical (Sauerbier et al., 2016) and pathogenetic aspects (Borghammer & Van Den Berge, 2019), which can open new avenues toward the characterization of its pathophysiology and can have ground-breaking implication for a

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disease-modifying approach. Neuroimaging has played a decisive role in these new findings (Horsager et al., 2020), by allowing the structural, microstructural, and functional assessment, in vivo, of metabolic and physiological phenomena at a molecular level, thus providing a temporal and spatial platform to cellular alterations thought to contribute in determining PD phenotype (de Natale et al., 2021; Strafella et al., 2018). Functional MRI (fMRI) exploits the changes in blood oxygenation of metabolically active brain areas. This leads to a signal change named blood oxygen leveledependent (BOLD) response on T2*sequences. This can be elicited, in brain cells, either at rest (rs-fMRI), by looking for low-frequency oscillations (0.01e0.1Hz) representing BOLD signal alterations of spontaneous neuronal activity at rest (Yousaf et al., 2018), or while performing motor or cognitive tasks (task-based fMRI), which elicit changes of the BOLD-dependent response of specific, activated brain areas (Soares et al., 2016). After the inception of the imaging technique (Biswal et al., 1995), multiple approaches are available to analyze the data by comparing functional connectivity (FC) within regions, identifying neural networks, changes in the spontaneous intensity of resting BOLD signal, synchrony of adjacent regions, and graph analysis (Lv et al., 2018; Smitha et al., 2017). Therefore, fMRI provides insight into how different areas of the brain connect to one another, thus allowing the study of neuroplasticity or neurodegeneration in PD (Filippi et al., 2018; Meijer & Goraj, 2014). This chapter will outline the main insights on PD pathophysiology that have been achieved through the use of fMRI in human research in both idiopathic and familial PD.

rs-fMRI of motor symptoms in idiopathic Parkinson’s disease There is a great deal of rs-fMRI studies analyzing functional connectivity alterations in iPD associated with its motor manifestations. Multiple studies have identified both increased and decreased activity and FC of the cerebellothalamocortical circuits of iPD patients compared with controls (Agosta et al., 2014; Akram et al., 2017; Anderkova et al., 2017; Badea et al., 2017; Choe et al., 2013; de Schipper et al., 2018; Esposito et al., 2013; Fang et al., 2017; Gao et al., 2017; Hacker et al., 2012; Helmich et al., 2010; Kwak et al., 2010; Kwak et al., 2012; Luo et al., 2014; Manza et al., 2016; Ng et al., 2017; Simioni et al., 2017; Suo et al., 2017; Tuovinen et al., 2018; Wu, Long, et al., 2009; Wu, Wang, et al., 2009; Xu et al., 2016; Yang et al., 2013; Yu et al., 2013). Metaanalyses have demonstrated that iPD patients show that FC alteration within the sensorimotor network (SMN), represented by the premotor cortex (PMC), the primary motor cortex (M1), the supplementary motor areas (SMA), and the postmotor gyrus (Pan, Zhan, et al., 2017; Pan, Zhang, et al., 2017; Wang, Zhang, et al., 2021), can be attributed to motor dysfunction. Hypoconnectivity within regions of the SMN has been seen in the early stages of iPD (Tuovinen et al., 2018), with progressive hypoconnectivity of the putamen and SMN regions correlating with progressive motor score impairment (Pan, Zhan, et al., 2017; Pan, Zhang, et al., 2017). A recent study (Caspers et al., 2021) has shown that the degree of correlation between SMN hypoconnectivity and UPDRS-III scores may be less pronounced in later stages of iPD, with FC changes within these networks reflecting a more generalized effect of disease and less dependent on motor severity. Progressive cerebellar hyperconnectivity to regions within the SMN and the inferior parietal lobules

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(IPL) has been associated with higher UPDRS-III scores in both cross-sectional (Li, Liu, et al., 2019) and longitudinal (Tuovinen et al., 2018) studies. Areas within the SMN and IPL are used to process sensorimotor feedback and executive control of movements. Increased FC in IPL suggests that, in iPD patients, increased sensorimotor feedback is required to initiate motor responses (Tahmasian et al., 2017). Due to reduced FC between putamen and SMN, increased FC between SMN and the cerebellum may represent compensatory changes to reduced motor severity (Tuovinen et al., 2018). In a study with 62 iPD patients OFF their medications for at least 12 hours, Palmer and colleagues found that increased cerebellar connectivity of frontoparietal and default mode network (DMN) was associated with worsening MDS-UPDRS III and MoCA scores in iPD; further highlighting the potential pathophysiological role of misconnection between the cerebellum and other subcortical and cortical areas in the progression of PD-associated symptoms (Palmer et al., 2020). Hyperconnectivity of the cerebellum to other cortical regions has been seen, with one study (Liu et al., 2013) identifying hypoconnectivity of the IPL and dentate nucleus in iPD subjects, with compensatory hyperconnectivity of cerebellum and the dentate nucleus. The dopaminergic state of PD patients can have a role in modulating FC across striatocortical circuits (Agosta et al., 2014; Akram et al., 2017; Esposito et al., 2013; Gao et al., 2017; Hacker et al., 2012; Kwak et al., 2010; Kwak et al., 2012; Luo et al., 2014; Ng et al., 2017; Wu, Long, et al., 2009; Wu, Wang, et al., 2009). When compared with controls, iPD patients OFF levodopa display hypoconnectivity within frontostriatal areas, basal ganglia (BG) circuits, and SMN, along with increased cerebellar FC, with changes present even in the drug-naïve stage (Esposito et al., 2013; Wu, Wang, et al., 2009). The progressive FC changes within these areas and networks often reflect the degree of motor impairment, seen with UPDRS-III scores, in iPD patients. Cross-sectional studies (Esposito et al., 2013; Wu, Wang, et al., 2009), further corroborated by metaanalysis data (Tahmasian et al., 2015), have shown that administration of dopamine normalizes the abnormal FC changes seen in PD, suggesting that abnormal FC in PD reflects the chronic state of dopaminergic deficiency. One longitudinal study (Li, Lao-Kaim et al., 2020) has investigated the potential of rs-fMRI to track disease progression. In this 20-month follow-up work, the authors found reduced FC from the posterior putamen to the thalamus, midbrain, SMA, and sensorimotor cortex was correlated with DAT density and motor disability over the follow-up period, indicating that rs-fMRI may represent a tool to monitor disease progression. Interestingly, a metaanalysis study (Tahmasian et al., 2017) revealed increased IPL activation in iPD patients OFF levodopa when compared with HC, but reduced activity within the IPL when ON levodopa, which may reflect increased activity within IPL as a compensatory mechanism to improve sensorimotor feedback in PD (results of rs-fMRI changes associated with motor symptoms of iPD are summarized in Table 8.1).

As PD progresses, different FC changes in the brain can occur: multiple cross-sectional and longitudinal studies Agosta et al. (2014), Esposito et al. (2013), and Luo et al. (2014) have shown impaired cerebellothalamocortical FC in drug-naïve iPD patients, with reduced FC in SMN, corticostriatal

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8. Functional MRI in familial and idiopathic PD

Summary of the main findings from resting state fMRI studies in idiopathic and familial PD. Main findings

Cohort Idiopathic PD

References

iPD regional changes Decreased activity and FC of the cerebellothalamocortical circuits in iPD patients compared with HCs. Hypoconnectivity of the IPL and dentate nucleus with compensatory hyperconnectivity of cerebellum and the dentate nucleus in iPD patients compared with HCs. Hypoconnectivity of the putamen, and the SMN in iPD patients compared with HCs. Hypoconnectivity within the SMN, corticostriatal loops, and mesolimbicstriatal circuits, along with increased striatal and thalamic FC with temporal, parietal, occipital, and cerebellar regions in iPD patients compared with HCs. Hyperconnectivity from the cerebellum to SMN, DMN, FPN, and the IPL in iPD patients compared with HCs.

(Agosta et al., 2014; Akram et al., 2017; Anderkova et al., 2017; Badea et al., 2017; Choe et al., 2013; de Schipper et al., 2018; Esposito et al., 2013; Fang et al., 2017; Gao et al., 2017; Hacker et al., 2012; Helmich et al., 2010; Kwak et al., 2010; Kwak et al., 2012; Luo et al., 2014; Manza et al., 2016; Ng et al., 2017; Simioni et al., 2017; Suo et al., 2017; Tuovinen et al., 2018; Wu, Long, et al., 2009; Wu, Wang, et al., 2009; Xu et al., 2016; Yang et al., 2013; Yu et al., 2013) (Liu et al., 2013) (Caspers et al., 2021; Pan, Zhan, et al., 2017; Pan, Zhang, et al., 2017) (Agosta et al., 2014; Manza et al., 2016; Tuovinen et al., 2018) (Li, Liu, et al., 2019; Tahmasian et al., 2017; Tuovinen et al., 2018)

Disease changes in early iPD

Reduced FC in SMN, visual networks, corticostriatal loops, and mesolimbicstriatal circuits. Impaired cerebellothalamocortical FC in drug-naïve iPD patients, with reduced FC in SMN, corticostriatal loops, and mesolimbic-striatal circuits. Hypoconnectivity within frontostriatal areas, BG circuits and SMN, along with increased cerebellar FC. Increased cerebellar ReHo in early stages of iPD.

(Agosta et al., 2014; Esposito et al., 2013; Fang et al., 2017; Luo et al., 2014) (Agosta et al., 2014; Esposito et al., 2013; Luo et al., 2014) (Esposito et al., 2013; Wu, Wang, et al., 2009) (Li et al., 2021; Wu, Long, et al., 2009; Yang et al., 2013)

Disease changes in moderate stage iPD

(Sheng et al., 2021) Hypoconnectivity between the left substantia innominate and the left frontal inferior opercularis and hyperconnectivity between the substantia innominate, the left cingulum middle, right primary motor, and sensory areas.

Disease changes in advanced stage iPD

Increased FC in right primary motor and (Sheng et al., 2021) sensory areas. (Hacker et al., 2012)

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As PD progresses, different FC changes in the brain can occur: multiple cross-sectional and longitudinal studies

TABLE 8.1 Cohort

Summary of the main findings from resting state fMRI studies in idiopathic and familial PD.dcont’d Main findings Reduced striatal FC to the thalamus, midbrain, upper pons, and cerebellum. Reduced FC in the corticostriatal loop and the posterior putamen and increased FC anterior putamen and the sensorimotor cortex. Decreased FC in the sensorimotor and visual cortex. Progressive functional Progressive hypoconnectivity of the changes putamen and SMN regions correlating with progressive motor score impairment in PD patients. However, the correlation becomes less pronounced in the later stage of disease. Progressive hypoconnectivity within the SMN, corticostriatal loops, and mesolimbic-striatal circuits, along with increased striatal and thalamic FC with temporal, parietal, occipital, and cerebellar regions, is associated with progressive motor scores. Progressive hyperconnectivity from the cerebellum to SMN, DMN, FPN, and the IPL has been associated with higher UPDRS-III scores. Progressive hypoconnectivity within frontostriatal areas, BG circuits, and SMN, along with increased cerebellar FC, is associated with higher UPDRS-III scores. Progressive hypoconnectivity of the posterior putamen to the thalamus, midbrain, SMA, and sensorimotor cortex is associated with higher motor scores. Progressive hypoconnectivity putameneM1 reflected poorer motor performance. Progressive hyperconnectivity of the putamen and BG reflects compensatory mechanisms to worsening motor symptoms. Progressive putaminal ALFF reductions in iPD patients associated with increased disease duration. Increased putaminal ALFF in medicated subjects correlating levodopa equivalent daily dose, HY stage, and disease

References (Helmich et al., 2010) (de Schipper et al., 2018; Hacker et al., 2012)

(Caspers et al., 2021; Pan, Zhan, et al., 2017; Pan, Zhang, et al., 2017) (Agosta et al., 2014; Manza et al., 2016; Tuovinen et al., 2018) (Li, Liu, et al., 2019; Tahmasian et al., 2017; Tuovinen et al., 2018) (Esposito et al., 2013; Wu, Wang, et al., 2009) (Li, Lao-Kaim et al., 2020) (Simioni et al., 2016) (Pan, Zhan, et al., 2017; Simioni et al., 2016; Tuovinen et al., 2018) (Hou et al., 2014; Skidmore et al., 2013; Wen et al., 2013) (Wang et al., 2020) (Wu, Long, et al., 2009; Yang et al., 2013) (Li et al., 2021; Wu, Long, et al., 2009; Yang et al., 2013; Zeng et al., 2017) (Amboni et al., 2015; Chen et al., 2015; Díez-Cirarda et al., 2018; Olde Dubbelink et al., 2014; Rektorova et al., 2012; Ruppert et al., 2021; Wolters et al., 2019; Zarifkar et al., 2021; Zhan et al., 2018)

(Continued)

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206 TABLE 8.1

8. Functional MRI in familial and idiopathic PD

Summary of the main findings from resting state fMRI studies in idiopathic and familial PD.dcont’d Main findings

Cohort

Dopamine use

ALFF

duration. Negative correlation between putaminal ReHo and UPDRS scores. Increased cerebellar ReHo was correlated with higher UPDRS-III scores and motor progression. Progressive hypoconnectivity within DMN is associated with MCI-iPD and PDD. PD patients OFF levodopa display hypoconnectivity within frontostriatal areas, basal ganglia (BG) circuits and SMN, along with increased cerebellar FC. Dopamine modulates FC across striatocortical circuits. Dopamine normalizes the abnormal FC changes associated with PD. If dopamine does not normalize FC in PD patients, it reflects the chronic state of dopaminergic deficiency. Increased IPL activation in iPD patients OFF levodopa when compared with HC, but reduced activity within the IPL when ON levodopa. Reduced putaminal ReHo in iPD subjects OFF medication compared with HC. Levodopa has been shown to normalize abnormal putaminal ReHo changes in iPD subjects. Increased ALFFs in the right inferior parietal gyrus alongside reduced ALFFs in the bilateral SMAs, left putamen, left lateral premotor cortex, and left inferior parietal gyrus in iPD patients compared with HCs. Reduced putaminal ALFF in iPD patients compared with HCs. Increased slow-5 band (0.01e0.027 Hz) ALFF activity was seen in the putamen when levodopa is used in iPD patients compared with HCs. Reduced FC and ALFF in SMA, PMC, and M1 iPD patients compared with HCs.

II. Clinical Applications in Parkinson disease

References

(Esposito et al., 2013; Wu, Wang, et al., 2009) (Agosta et al., 2014; Akram et al., 2017; Esposito et al., 2013; Gao et al., 2017; Hacker et al., 2012; Kwak et al., 2010; Kwak et al., 2012; Luo et al., 2014; Ng et al., 2017; Wu, Long, et al., 2009; Wu, Wang, et al., 2009) (Esposito et al., 2013; Wu, Wang, et al., 2009) (Tahmasian et al., 2015) (Tahmasian et al., 2017) (Wu, Long, et al., 2009; Yang et al., 2013) (Shen et al., 2020; Wu, Long, et al., 2009)

(Wang, Zhang, et al., 2018) (Hou et al., 2014; Skidmore et al., 2013; Wen et al., 2013) (Pan, Zhang, et al., 2017; Wang, Zhang, et al., 2018) (Wang et al., 2020) (Pan, Zhan, et al., 2017; Pan, Zhang, et al., 2017)

207

As PD progresses, different FC changes in the brain can occur: multiple cross-sectional and longitudinal studies

TABLE 8.1

Summary of the main findings from resting state fMRI studies in idiopathic and familial PD.dcont’d

Cohort ReHo

Main findings

References

Increased ReHo in the cerebellum and bilateral IPL and medial prefrontal cortices in iPD subjects compared with HCs. Decreased ReHo in the SMA, DMN, thalamus, putamen, precentral gyrus, and lingual gyrus in iPD subjects compared with HCs. Reduced putaminal ReHo in iPD subjects OFF medication compared with HCs. Levodopa has been shown to normalize abnormal putaminal ReHo changes in iPD subjects. Increased cerebellar ReHo in early stages of iPD. Increased cerebellar ReHo was correlated with higher UPDRS-III scores and motor progression. Reduced ReHo of the DMN and sensorimotor cortex with increased ReHo in SMA, bilateral temporal gyrus, and bilateral hippocampus as PD progresses. TD-iPD patients had significantly reduced ReHo in bilateral putamen and cerebellar posterior lobe with increased ReHo in the parahippocampal gyrus compared with HCs. PIGD-iPD patients had increased ReHo in right putamen, right superior temporal gyrus, left thalamus, and right superior parietal gyrus with reduced ReHo in right parahippocampal gyrus, bilateral superior occipital gyrus, and middle occipital gyrus when compared with HCs. Reduced ReHo was located in the left posterior cerebellar lobe in iPD patients compared with HCs. Increased ReHo in the limbic lobe in MCI-iPD compared with HC. Increased ReHo in the left lingual gyrus, bilateral precuneus, left superior parietal lobe, and left IPL with decreased ReHo in the left insula in MCI-iPD patients compared with NCiPD.

(Choe et al., 2013; Li et al., 2016; Li et al., 2021; Li, Zhao, et al., 2020; Pan, Zhan, et al., 2017; Wang, Zhang, et al., 2021; Wu, Long, et al., 2009) (Guo et al., 2021; Harrington et al., 2017; Luo et al., 2021; Pan, Zhan, et al., 2017; Shang et al., 2021; Wang, Zhang, et al., 2021; Wu, Long, et al., 2009) (Wu, Long, et al., 2009; Yang et al., 2013) (Shen et al., 2020; Wu, Long, et al., 2009) (Li et al., 2021; Wu, Long, et al., 2009; Yang et al., 2013) (Li et al., 2021; Wu, Long, et al., 2009; Yang et al., 2013; Zeng et al., 2017) (Zeng et al., 2017) (Hu et al., 2019) (Hu et al., 2019) (Li, Liu, et al., 2020; Shang et al., 2021; Xing et al., 2021)

Motor phenotypes (Continued)

II. Clinical Applications in Parkinson disease

208 TABLE 8.1

8. Functional MRI in familial and idiopathic PD

Summary of the main findings from resting state fMRI studies in idiopathic and familial PD.dcont’d

Cohort

Freezing of gait

Main findings

References

Increased FC of the globus pallidus internal and subthalamic nucleus to the cerebellothalamocortical circuits in TDiPD when compared with HCs. Increased FC globus pallidus internal and subthalamic nucleus to the cerebellothalamocortical circuits associated with worse tremor scores. Reduced FC between the cerebellar posterior lobe and the dentate nucleus in TD-iPD patient when compared with AR-iPD patients. Reduced inferior parietal cortex and posterior cingulate gyrus as well as hypoconnectivity of right frontoinsular cortex and regional nodes in AR-iPD patients compared with TD-iPD patients. Increase in bilateral STN connectivity to the left anterior and posterior cerebellar lobes in TD iPD patients compared with HCs. Increased STN connectivity with the visual cortex and lower STN connectivity with the putamen and pons in PIGD-iPD patients compared with HCs. TD-iPD patients had significantly reduced ReHo in bilateral putamen and cerebellar posterior lobe with increased ReHo in the parahippocampal gyrus compared with HCs. PIGD-iPD patients had increased ReHo in right putamen, right superior temporal gyrus, left thalamus, and right superior parietal gyrus with reduced ReHo in right parahippocampal gyrus, bilateral superior occipital gyrus, and middle occipital gyrus when compared with HCs. Loss of FC within the DMN, the visual associative network, and the executive attention network in iPD patients with FoG compared with patients without FoG. Loss of FC within SMA and the corticopontineecerebellar pathways in iPD patients with FoG compared with patients without FoG.

(Dirkx et al., 2016; Helmich et al., 2011; Wang, Chen, et al., 2016) (Helmich et al., 2011; Wang, Chen, et al., 2016) (Liu et al., 2013) (Wang, Shen, et al., 2021) (Wang, Chen, et al., 2016) (Wang, Chen, et al., 2016) (Hu et al., 2019) (Hu et al., 2019)

II. Clinical Applications in Parkinson disease

(Bharti et al., 2020; Canu et al., 2015; Tessitore, Amboni, et al., 2012) (Bharti et al., 2019; Canu et al., 2015; Fling et al., 2014; Lench et al., 2020; Wang, Jiang, et al., 2016) (Potvin-Desrochers et al., 2019)

209

As PD progresses, different FC changes in the brain can occur: multiple cross-sectional and longitudinal studies

TABLE 8.1

Summary of the main findings from resting state fMRI studies in idiopathic and familial PD.dcont’d Main findings

Cohort

Levodopa-induced dyskinesias

Cognitive

References

Increased FC in bilateral thalamus and globus pallidus external to visual areas in iPD patients with FoG compared with patients without FoG (reflecting potential compensatory mechanisms). Reduced FC from the inferior frontal (Cerasa et al., 2015; Herz et al., cortex to the, the cerebellum, and the 2016; Yoo et al., 2019) SMA in LID-iPD patients compared with non-LID-iPD patients. Progressive hyperconnectivity from the cerebellum to DMN and FPN has been associated with worsening cognitive score scores. Reduced FC within the DMN, auditory network, and FPN in iPD patients compared with HCs. Reduced functional connectivity and activity within the cerebellum, prefrontal cortex, superior frontal gyrus, inferior frontal gyrus, marginal division/the caudal border of the neostriatum, insula, precuneus, and posterior cingulate cortex in MCI-iPD patients compared to nonMCI-iPD patients. Reduced ReHo were located in the left posterior cerebellar lobe in iPD patients compared with HCs. Increased ReHo in the limbic lobe in MCI-iPD compared with HC. Increased ReHo in the left lingual gyrus, bilateral precuneus, left superior parietal lobe, and left IPL with decreased ReHo in the left insula in MCI-iPD patients compared with NCiPD. Increased FC in the execution network in NC-iPD may increase the risk of developing MCI-iPD patients. Reduced FC in the nucleus basalis of meynert to superior parietal lobe and the postcentral gyrus was associated with MCI-iPD. Reduced FC within DMN was seen in MCI-iPD compared with NC-iPD. Progressive hypoconnectivity within DMN is associated with MCI-iPD and PDD.

(Palmer et al., 2020) (Amboni et al., 2015; Baggio, Segura, Sala-Llonch et al., 2015; Bezdicek et al., 2018; Chen et al., 2020; Díez-Cirarda et al., 2018; Putcha et al., 2015; Rektorova et al., 2012; Ruppert et al., 2021; Tessitore, Esposito, et al., 2012; van Eimeren et al., 2009; Wolters et al., 2019; Zarifkar et al., 2021; Zhan et al., 2018) (Chen et al., 2015; Li, Chen, et al., 2019; Li, Liu, et al., 2020; Maiti et al., 2020; Mu et al., 2020; Nagano-Saito et al., 2019; Palmer et al., 2020; Shuai et al., 2020; Zhan et al., 2018) (Li, Liu, et al., 2020; Shang et al., 2021; Xing et al., 2021) (Shuai et al., 2020) (Zhang et al., 2020) (Wolters et al., 2019) (Amboni et al., 2015; Chen et al., 2015; Díez-Cirarda et al., 2018; Olde Dubbelink et al., 2014; Rektorova et al., 2012; Ruppert et al., 2021; Wolters et al., 2019; Zarifkar et al., 2021; Zhan et al., 2018) (Chen et al., 2015; Olde Dubbelink et al., 2014; Zhan et al., 2018) (Baggio, Segura, Sala-Llonch et al., 2015) (Dirnberger & Jahanshahi, 2013; Lebedev et al., 2014)

(Continued)

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210 TABLE 8.1

8. Functional MRI in familial and idiopathic PD

Summary of the main findings from resting state fMRI studies in idiopathic and familial PD.dcont’d

Cohort

Hyposmia

RBD

Main findings

References

Reduced FC in posterior cingulate cortex and posterior cortical areas is associated with progressive cognitive decline and progression from MCI-iPD to PDD. Compared with HC, MCI-iPD with VS/ VP decline showed increased FC between DMN and occipitoparietal cortical regions, whereas MCI-iPD with attention and executive dysfunction displayed increased FC in dorsal attention network (DAN) and frontoinsular regions. Impaired FC in FPN in MCI-iPD is associated with progressive executive and attentional dysfunction. Impaired decoupling between DMN and FPN in MCI-iPD patients when compared with HCs. Reduced FC within limbic/paralimbic cortices in iPD patients compared with HCs, with reduced FC between amygdala to parietooccipital regions correlating with progressive hyposmia and cognitive decline in iPD patients. Lower FC in the olfactory bulb to striatothalamofrontal regions, orbitofrontal cortex to striatofrontal regions, and left frontotemporal area in PDD patients compared with HCs.

(Baggio, Segura, Sala-Llonch et al., 2015; Boord et al., 2017)

Reduced FC in pontostriatocortical circuits in RBD-iPD patients compared with HCs. Hypoconnectivity of the BG and striatothalamopallidal can be used to differentiate iPD and RBD patients when compared with HCs. Reduced ALFF in the primary motor cortex extending to the premotor cortex, reduced pedunculopontine nucleus eanterior cingulate gyrus FC along with increased ReHo in the left cerebellum, the right middle occipital region, and the left middle temporal region, and decreased ReHo in the left middle frontal region in RBD-iPD patients when compared with non-RBD-iPD patients.

II. Clinical Applications in Parkinson disease

(Yoneyama et al., 2018) (Lee et al., 2020)

(Dayan & Browner, 2017; Gallea et al., 2017; Li, Zeng, et al., 2020; Liu, Koros, et al., 2021; Rolinski et al., 2016) (Dayan & Browner, 2017; Gallea et al., 2017; Li, Zeng, et al., 2020; Liu, Koros, et al., 2021; Rolinski et al., 2016) (Gallea et al., 2017; Li, Huang, et al., 2017; Liu, Koros, et al., 2021)

211

As PD progresses, different FC changes in the brain can occur: multiple cross-sectional and longitudinal studies

TABLE 8.1

Summary of the main findings from resting state fMRI studies in idiopathic and familial PD.dcont’d Main findings

Cohort

References

Pain

(Polli et al., 2016) Reduced FC between the nucleus (Engels et al., 2018) accumbens and the left hippocampus, reduced ALFF in the left frontal inferior (Yu et al., 2019) orbital, and increased ALFF bilateral cerebellar areas and right temporal inferior areas in iPD patients with persistent pain when compared with iPD subjects without pain. Increased global brain efficiency of hub regions within pain networks in iPD patients when compared with HCs. Increased FC in the left middle frontal gyrus and the right precentral gyrus iPD acupuncture group compared with nonacupuncture iPD group.

Fatigue

Hyperconnectivity of the DMN, (Li, Yuan, et al., 2017; Tessitore hypoconnectivity of SMN, increased et al., 2016) activity within the salience network, and reduced activity within the attention network in iPD patients with fatigue when compared with iPD patients without fatigue.

Depression

Impaired FC within the prefrontal elimbic systems and amygdala in depressed iPD patients compared with nondepressed iPD patients and HCs. Abnormal connectivity within DMN, salience network, visual network, and posterior regions in depressed iPD patients when compared with nondepressed iPD patients and HCs.

(Baggio, Segura, Garrido-Millan et al., 2015; Liao et al., 2020; Liao et al., 2021; Lin et al., 2020; Lou et al., 2015; Qiu et al., 2021; Skidmore et al., 2013; Wang, Chen, et al., 2018; Wen et al., 2013; Zhang et al., 2021; Zhu et al., 2016) (Liao et al. 2020, 2021; Lin et al., 2020; Qiu et al., 2021; Zhang et al., 2021)

(Hepp et al., 2017) Visual hallucinations Global reduction of FC in frontal, temporal, occipital, and striatal regions (Dujardin et al., 2020; Shine et al., 2014; Walpola et al., 2020) when compared to iPD patients with visual hallucinations when compared with HCs. Hypoconnectivity between dorsal and ventral attention networks and increased coupling of the DMN to the visual network is associated with presence and severity of VH in iPD. ICD (Continued)

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212 TABLE 8.1

8. Functional MRI in familial and idiopathic PD

Summary of the main findings from resting state fMRI studies in idiopathic and familial PD.dcont’d

Cohort

LRRK2

LRRK2-NMC

Main findings

References

Increased FC in the salience network, DMN and sensorimotor network, along with decreased FC in the central executive network in ICD-iPD patients compared wiith non-ICD-iPD patients. Increased FC in DMN and central executive network was associated in developing ICD in drug-naïve iPD patients. Reduced FC between the rostral anterior cingulate cortex and the nucleus accumbens was associated with increasing ICD severity in ICD-iPD patients. Hyperconnectivity in the habenula and amygdala with thalamus and striatum bilaterally, along with hypoconnectivity between bilateral habenula and left frontal and precentral cortices in ICDiPD patients compared with non-ICDiPD patients. Increased FC within ventroanterior putamen and reduced FC in the SMN, DAN, and salience network along with reduced FC in IPL and the dorsoposterior putamen and in the caudal motor part of the left striatum, the ipsilateral precuneus, and superior parietal lobe in missense mutations LRRK2-NMC compared with HCs.

(Imperiale, Agosta, Canu, et al., 2018; Tessitore, Santangelo, et al., 2017) (Tessitore, De Micco et al., 2017) (Hammes et al., 2019) (Markovic et al., 2017)

(Helmich et al., 2015; Jacob et al., 2019; Vilas et al., 2016)

LRRK2-PD

(Hou et al., 2018) Reduced FC within the sensorimotor(Hou et al., 2018) striatal and posterior putamen-striatal circuits in LRRK2-PD compared with HCs. Reduced FC between the putamen and bilateral superior frontal gyri, precuneus, and calcarine gyri LRRK2-PD group when compared with drug-naïve iPD patients.

GBA

GBA-PD

Weak FC in the caudate nuclei and occipital cortex in GBA-PD patients when compared with iPD patients.

(Greuel et al., 2020)

SNCA

Nonpathogenic SNCA PD

Increased ALFF in right angular gyrus and reduced ALFF in bilateral fusiform gyri in rs11931074 and rs894278 SNCA

(Si et al., 2019; Zhang et al., 2019)

II. Clinical Applications in Parkinson disease

As PD progresses, different FC changes in the brain can occur: multiple cross-sectional and longitudinal studies

TABLE 8.1

Summary of the main findings from resting state fMRI studies in idiopathic and familial PD.dcont’d Main findings

Cohort

Parkin and PINK1

213

PINK1-PD and Parkin-NMC PINK1-PD and Parkin-PD

References

PD patients, respectively, when compared with HCs. Reduced FC within the precuneus of the (Makovac et al., 2016) DMN in PINK1-PD and Parkin-NMC when compared with HCs. Reduced FC within the precuneus of the (Makovac et al., 2016) DMN, the frontal pole of the executive network, the right angular gyrus, and the right superior frontal gyrus in PINK1-PD and Parkin-PD when compared with HCs.

loops, and mesolimbic striatal circuits. Reduced FC in SMN and visual networks is seen in the early stages of PD (Fang et al., 2017), along with functional decoupling of the anterior putamen and the midbrain (Manza et al., 2016). Progressive hypoconnectivity within these regions, along with increased striatal and thalamic FC with temporal, parietal, occipital, and cerebellar regions, is associated with progressive motor scores at baseline and longitudinal follow-up (Agosta et al., 2014; Manza et al., 2016; Tuovinen et al., 2018). Increased FC from the midbrain to DMN and cerebellum has been seen as compensatory changes to adjust for impairments in the sensorimotor and visual network in PD (Fang et al., 2017; Tuovinen et al., 2018). A study (Simioni et al., 2016) exploring FC changes in mild-to-moderate iPD patients found increased putameneM1 connectivity reflected poorer motor performance, while increased cerebellar FC to the putamen and BG reflects compensatory mechanisms to adjust for impaired motor function (Pan, Zhan, et al., 2017; Simioni et al., 2016; Tuovinen et al., 2018). Another study (Sheng et al., 2021) found that early-to-moderate stage of iPD was also associated with reduced FC between the left substantia innominate and the left frontal inferior opercularis when compared with HC, whereas FC was increased between the substantia innominate and the left cingulum middle as well as right primary motor and sensory areas. In the iPD cohort, those with advanced stages had increased FC in right primary motor and sensory areas, with the authors (Sheng et al., 2021) theorizing that these changes are due to increased cholinergic hyperconnectivity. In advanced stages of PD, reduced striatal FC to the thalamus, midbrain, upper pons, and cerebellum has been observed (Hacker et al., 2012), along with reduced FC in the corticostriatal loop and the posterior putamen and increased FC anterior putamen and the sensorimotor cortex (Helmich et al., 2010), with the last finding reflecting a compensatory mechanism for the reduced connectivity of the posterior putamen. Although a few other studies (de Schipper et al., 2018; Hacker et al., 2012) identified decreased FC in the sensorimotor and visual cortex in advanced iPD subjects, this finding may mirror histopathological data, which postulate that sensorimotor and visual cortices are the latest structures to be affected by Lewy bodies in PD (Braak et al., 2004) (results summarized in Table 8.1).

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214

8. Functional MRI in familial and idiopathic PD

Amplitude of low-frequency fluctuation (ALFF) is a measure of spontaneous fluctuations of activity during the resting state, resulting in local BOLD-fMRI changes (Mao et al., 2015). Cross-sectional (Skidmore, Yang, Baxter, von Deneen, Collingwood, He, White, et al., 2013) and metaanalysis data (Pan, Zhang, et al., 2017) have shown increased ALFFs in the right inferior parietal gyrus alongside reduced ALFFs in the bilateral SMAs, left putamen, left lateral premotor cortex, and left inferior parietal gyrus tool to differentiate iPD subjects from HC. Another metaanalysis study (Wang, Zhang, et al., 2018) found reduced putaminal ALFF was the most consistent finding in iPD subjects. Multiple studies (Skidmore, Yang, Baxter, von Deneen, Collingwood, He, Tandon, et al., 2013; Wen et al., 2013; Hou et al., 2014) have identified reduced putaminal ALFF in iPD subjects, with progressive ALFF reductions correlated with disease duration. Post hoc analysis from a recent study (Wang et al., 2020) found increased slow-5 band (0.01e0.027 Hz) ALFF activity was seen in the putamen when levodopa is used in iPD subjects, with increased putaminal ALFF in medicated subjects correlating levodopa equivalent daily dose (LEDD), HY stage, and disease duration. Interestingly, reduced FC and ALFF within the putamen was identified as one of the most consistent findings in iPD subjects within a few metaanalysis studies (Pan, Zhang, et al., 2017; Wang, Zhang, et al., 2018). Several other studies (Herz et al., 2014; Pan, Zhang, et al., 2017) have theorized functional and ALFF changes reflect motor deficits and compensatory mechanisms in PD, with the reduced functional activity of the putamen reflecting increased denervation of striatal motor areas, thus correlating with increasing motor impairment. Reduced FC and ALFF in motor cortical areas such as SMA, PMC, and M1 have been identified (Pan, Zhan, et al., 2017; Pan, Zhang, et al., 2017), reflecting potential dopamine deficiency; thus, specific target therapies or stimulation may improve motor symptoms of PD (Lindenbach & Bishop, 2013) (results summarized in Table 8.1). Regional homogeneity (ReHo) is an index of the local FC between a given node and its nearest neighboring nodes, which has been used as a measure of the centrality of the node of interest in the functional connectome (Jiang & Zuo, 2016). Studies exploring ReHo in different iPD subgroups have garnered some variable results, from increased ReHo in the cerebellum, right temporal pole, angular gyrus, IPL, primary sensorimotor cortex, premotor area, and dorsolateral prefrontal cortex (DLPFC), along with reduced ReHo in medial frontal gyrus, postcentral gyrus, precentral gyrus, superior temporal gyrus, Rolandic operculum, putamen, thalamus, SMA, and primary sensory cortex (Choe et al., 2013; Guo et al., 2021; Harrington et al., 2017; Hu et al., 2019; Li et al., 2016; Li et al., 2021; Li, Liu, et al., 2020; Li, Zhao, et al., 2020; Luo et al., 2021; Shang et al., 2021; Shen et al., 2020; Wang, Zhang, et al., 2021; Wu, Long, et al., 2009; Xing et al., 2021; Zeng et al., 2017). A metaanalysis (Pan, Zhan, et al., 2017) found increased ReHo in the left cerebellum and bilateral IPL and medial prefrontal cortices in iPD subjects compared with HC, along with decreased ReHo in the right putamen, right precentral gyrus, and left lingual gyrus. Earlier studies (Wu, Long, et al., 2009; Yang et al., 2013) have identified reduced putaminal ReHo in iPD subjects OFF medication, with a negative correlation of putaminal ReHo with UPDRS scores. Administration of levodopa has been shown to normalize abnormal putaminal ReHo changes in iPD subjects (Shen et al., 2020; Wu, Long, et al., 2009). Multiple cross-sectional studies (Li et al., 2021; Wu, Long, et al., 2009; Yang et al., 2013) have identified increased cerebellar ReHo in early stages of iPD, with increased ReHo in the more advanced motor stages of iPD, reflecting potential increased cerebellar compensation in response to motor symptom progression (Liao et al., 2021). A longitudinal analysis study (Zeng et al., 2017) found that increased cerebellar

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As PD progresses, different FC changes in the brain can occur: multiple cross-sectional and longitudinal studies

215

ReHo was correlated with more severe motor symptoms in PD, as per UPDRS-III scores. Interestingly, this study found reduced ReHo cerebellum within the iPD group, along with increased clinical improvement over a 2-year window, with authors theorizing use of dopaminergic medication has reduced compensatory increased activity within the cerebellum. Subanalysis from this study (Zeng et al., 2017) revealed during PD diseases progression, reduced ReHo of the DMN and sensorimotor cortex was seen along with increased ReHo in SMA, bilateral temporal gyrus, and bilateral hippocampus, with reduction and increases of regional ReHo reflecting loss of efficacy and compensatory changes, respectively (results summarized in Table 8.1). PD motor phenotypes have been investigated by several cross-sectional rs-fMRI studies to determine if specific resting-state functional connections are responsible for the onset of certain motor clinical subtypes. iPD patients with tremor dominant (TD) phenotype show increased FC of the globus pallidus internal and subthalamic nucleus to the cerebellothalamocortical circuits (Dirkx et al., 2016; Helmich et al., 2011; Wang, Chen, et al., 2016), with increasing connection corresponding to increased tremor scores (Helmich et al., 2011; Wang, Chen, et al., 2016). Compared with patients affected by the akinetic-rigid (AR) iPD phenotype, iPD patients with TD display reduced FC between the cerebellar posterior lobe and the dentate nucleus (Liu et al., 2013). Conversely, iPD patients with AR show reduced activity in the DMN, specifically within the inferior parietal cortex and posterior cingulate gyrus (Karunanayaka et al., 2016), as well as reduced right frontoinsular cortex FC with regional nodes (Wang, Shen, et al., 2021) when compared with TD-iPD; these changes possibly represent an early biomarker for cognitive decline in AR-iPD subjects. A few rs-fMRI works have compared iPD patients with TD with those showing the postural instability gait difficulties (PIGD) phenotype. In one study, Wang and colleagues found an increase in bilateral subthalamic nuclei (STN) connectivity to the left anterior and posterior cerebellar lobes in TD-iPD patients in comparison with HC, while PIGD-iPD patients had increased STN functional connectivity with the visual cortex with reduced STN functional connections to the pons and the putamen, with the former FC changes reflecting a potential compensatory mechanism taking place in PIGD motor phenotype (Wang, Chen, et al., 2016). Intestinally, a study found TD-iPD patients had increased ALFF in bilateral putamen and the cerebellar posterior lobe with reduced ALFF in bilateral temporal gyri and the left superior parietal lobule in comparison with PIGD-iPD patients (Chen, Wang, et al., 2015). When compared with HC, another work (Hu et al., 2019) showed TD-iPD patients had significantly reduced ReHo in bilateral putamen and cerebellar posterior lobe with increased ReHo in the parahippocampal gyrus, while PIGD-iPD patients had increased ReHo in right putamen, right superior temporal gyrus, left thalamus, and right superior parietal gyrus with reduced ReHo in the right parahippocampal gyrus, bilateral superior occipital gyrus, and middle occipital gyrus. TD-iPD patients were also shown to have increased right parahippocampal gyrus ReHo compared with PIGD-iPD, with authors theorizing these changes may represent a protective compensatory mechanism slowing cognitive decline in the TD subtype compared with the PIGD subtype (Hu et al., 2019). Freezing of gait (FoG) is a frequent complication characterized by sudden stops during gait, especially when passing through narrow spaces or changing directions. Crosssectional analysis of iPD subjects with FoG has demonstrated a loss of FC within several networks involved in sensorimotor control, such as the DMN (Bharti et al., 2020), the visual

II. Clinical Applications in Parkinson disease

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associative network (Canu et al., 2015), and the executive attention network (Tessitore, Amboni, et al., 2012). Loss of FC was also seen in the SMA (Canu et al., 2015; Fling et al., 2014; Lench et al., 2020) and the corticopontineecerebellar pathways (Bharti et al., 2019; Wang, Jiang, et al., 2016). Some works also detected an increased FC in bilateral thalamus and globus pallidus external to visual areas (Potvin-Desrochers et al., 2019). It has been hypothesized that these latter increases in FC could represent compensatory neural mechanisms for sensorimotor deficits in iPD with FoG (Potvin-Desrochers et al., 2019). A few rs-fMRI studies have explored changes in FC of iPD patients affected by levodopainduced dyskinesias (LIDs). These works have detected LID-iPD subjects have reduced FC from the inferior frontal cortex to the putamen (Cerasa et al., 2015; Herz et al., 2016), the cerebellum (Yoo et al., 2019), and the SMA (Gan et al., 2020) in comparison with non-LID-iPD patients, reflecting abnormal FC and neural pathways within striatomotor circuitry and corticocortical network due to altered dopaminergic modulation (Gan et al., 2020).

rs-fMRI of non-motor symptoms in idiopathic Parkinson’s disease PD is associated with a variety of nonmotor symptoms, few of which may predate the onset of motor symptoms. Notable nonmotor symptoms include cognitive impairment, autonomic dysfunction, hyposmia, rapid eye movement (REM), sleep behavior disorder, fatigue, depression, and pain (Postuma & Berg, 2016; Schapira et al., 2017; Sveinbjornsdottir, 2016). Around 2%e40% of iPD patients show cognitive impairment with disease progression (Emre, 2003; Gonzalez-Latapi et al., 2021). Mild cognitive impairment in PD (MCI-PD) is defined as a decline in performance in one or more cognitive domains without causing disability in individuals with a confirmed diagnosis of PD (Gonzalez-Latapi et al., 2021). About 10% of PD subjects develop Parkinson’s disease dementia (PDD) every year, with the lifelong prevalence of developing PDD being approximately 80% (Aarsland et al., 2001). MCI-iPD is more likely to develop PDD and develop PDD at an earlier stage compared with iPD patients with normal cognition (NC iPD) (Hely et al., 2008; Janvin et al., 2006). In NC iPD, an increased FC in the execution network may represent a potential harbinger for future onset of MCI (Shuai et al., 2020). Recent cross-sectional studies have associated cognitive decline in iPD with reduced functional connectivity and activity within the cerebellum (Maiti et al., 2020; Mu et al., 2020; Shuai et al., 2020), prefrontal cortex (Nagano-Saito et al., 2019; Palmer et al., 2020), superior frontal gyrus (Palmer et al., 2020), inferior frontal gyrus (Wang, Jia, et al., 2018), marginal division/the caudal border of the neostriatum (Li, Chen, et al., 2019), insula (Li, Liu, et al., 2020), precuneus (Li, Liu, et al., 2020), and posterior cingulate cortex (Chen, Fan, et al., 2015; Mu et al., 2020; Zhan et al., 2018). Reduced FC in the nucleus basalis of Meynert to the superior parietal lobe (SPL) and the postcentral gyrus was associated with MCI in iPD subjects (Zhang et al., 2020). This finding confirms the key role that reduced cholinergic connectivity plays in the generation of cognitive decline in iPD (Schulz et al., 2018). The DMN involves several key areas such as the prefrontal cortex, anterior and posterior cingulate cortex, retrosplenial cortex, precuneus, angular gyrus, IPL, temporoparietal junction, parahippocampal, and lateral temporal cortex (Andrews-Hanna et al., 2010; Greicius

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et al., 2009; Raichle et al., 2001; Raichle & Snyder, 2007); these connections play a key role in multiple cognitive processes. Impaired connectivity within DMN has been associated with aging, psychiatric conditions, and cognitive impairment (Dennis & Thompson, 2014; Grieder et al., 2018; Qin et al., 2021; Zhou et al., 2020; Zovetti et al., 2020). Multiple cross-sectional studies (Amboni et al., 2015; Baggio, Segura, Sala-Llonch et al., 2015; Bezdicek et al., 2018; Chen et al., 2020; Díez-Cirarda et al., 2018; Putcha et al., 2015; Rektorova et al., 2012; Ruppert et al., 2021; Tessitore, Esposito, et al., 2012; van Eimeren et al., 2009; Zarifkar et al., 2021; Zhan et al., 2018) and metaanalysis studies (Wolters et al., 2019) have identified that cognitive decline in iPD patients can be attributed with reduced FC within the DMN, auditory network, and frontoparietal network (FPN) when compared with HC. Interestingly, several studies have identified reduced FC in DMN (Putcha et al., 2015; Tessitore, Esposito, et al., 2012; Zarifkar et al., 2021) and increased nodal centrality (Chen et al., 2020) in NC iPD subjects, indicating these changes may be a potential early biomarker prior to the onset of cognitive decline. A significant reduction in FC within DMN was seen in iPD patients with cognitive impairment when compared with NC iPD subjects (Wolters et al., 2019). Progressive changes in DMN connectivity have been associated with MCI-iPD (Amboni et al., 2015; Díez-Cirarda et al., 2018; Olde Dubbelink et al., 2014; Ruppert et al., 2021; Wolters et al., 2019; Zarifkar et al., 2021; Zhan et al., 2018) and PDD (Chen, Fan, et al., 2015; Rektorova et al., 2012; Zarifkar et al., 2021; Zhan et al., 2018). Cross-sectional data (Chen, Fan, et al., 2015; Zhan et al., 2018) have revealed that reduced FC in posterior cingulate cortex is associated with progressive cognitive decline and progression from MCI-iPD to PDD. Longitudinal study (Olde Dubbelink et al., 2014) has identified reduced FC in the posterior cortical areas associated with cognitive decline as well as development and progression to PDD. Increased FC between prefrontal corticalecerebellar regions has been identified as potential compensatory mechanism to combat cognitive impairment (Wolters et al., 2019; Zhan et al., 2018). MCI-iPD patients have also been studied by means of rs-fMRI according to the altered cognitive domain. MCI-iPD with visuospatial/visuoperceptual (VS/VP) decline showed increased FC between DMN and occipitoparietal cortical regions, whereas MCI-iPD with attention and executive dysfunction displayed increased FC in dorsal attention network (DAN) and frontoinsular regions (Baggio, Segura, Sala-Llonch et al., 2015). The FPN is located anatomically between DMN and DAN nodes, with theories suggesting the FPN offers a degree of connective plasticity between specific areas when performing certain tasks (Spreng et al., 2013). Progressive executive and attentional dysfunctions in MCI-iPD have been associated with impaired FC in FPN (Dirnberger & Jahanshahi, 2013; Lebedev et al., 2014). Several studies have also pointed out to the impaired decoupling between DMN and FPN in MCI-iPD (Baggio, Segura, Sala-Llonch et al., 2015; Boord et al., 2017), with recent results showing increased interregional FC in the FPN and reduced FC in the DMN of MCIiPD patients (Ruppert et al., 2021). Hyposmia and olfactory dysfunction are common in many neurodegenerative conditions (Doty, 2017) such as PD, with metaanalysis data (Rahayel et al., 2012) suggesting low-level perceptual olfactory tasks may be the cause of hyposmia in PD. A longitudinal study (Gjerde et al., 2018) identified that the rate of cognitive decline in iPD patients with hyposmia was greater than in normosmic iPD subjects over a 7-year follow-up period. These data have found some confirmation in rs-fMRI research, in that PDD subjects show lower FC in the

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olfactory bulb to striatothalamofrontal regions, orbitofrontal cortex to striatofrontal regions, and left frontotemporal area (Lee et al., 2020). Studies have shown iPD subjects with hyposmia have reduced FC within limbic/paralimbic cortices (Su et al., 2015) and with reduced FC between amygdala and parietooccipital regions correlating with progressive hyposmia and cognitive decline (Yoneyama et al., 2018). REM sleep behavior disorder (RBD) is seen in PD patients with a frequency of 27%e42.3% (Monderer & Thorpy, 2009; Zhang et al., 2017), with RBD being a relatively common prodromal symptom in PD subjects prior to the onset of motor symptoms (Baumann-Vogel et al., 2020; Postuma & Berg, 2016; Schapira et al., 2017; Sveinbjornsdottir, 2016). Reduced FC in pontostriatocortical circuits has been detected in iPD patients with RBD compared with HC (Dayan & Browner, 2017; Gallea et al., 2017; Li, Zeng, et al., 2020; Liu, Shuai, et al., 2021; Rolinski et al., 2016). Interestingly, hypoconnectivity of the BG and striatothalamopallidal network has been shown to differentiate iPD and RBD patients when compared with HC (Dayan & Browner, 2017; Gallea et al., 2017; Li, Zeng, et al., 2020; Liu, Shuai, et al., 2021; Rolinski et al., 2016). As opposed to iPD patients without RBD, iPD patients with RBD displayed reduced ALFF in the primary motor cortex extending to the premotor cortex (Li, Huang, et al., 2017), reduced pedunculopontine nucleuseanterior cingulate gyrus FC (Gallea et al., 2017) along with an increase in ReHo in the left cerebellum, the right middle occipital region and the left middle temporal region, and decreased ReHo in the left middle frontal region (Liu, Shuai, et al., 2021). Alterations in nociceptive processing may lead to a reduced pain threshold to different stimuli (Rukavina et al., 2019). rs-f-MRI studies have shown that iPD patients with persistent pain, when compared with iPD subjects without pain, have reduced FC between the nucleus accumbens and the left hippocampus, reduced ALFF in the left frontal inferior orbital, and increased ALFF bilateral cerebellar areas and right temporal inferior areas, with the authors theorizing functional changes may be responsible for PD-associated pain (Polli et al., 2016). Another cross-sectional study (Engels et al., 2018) revealed increased global brain efficiency of hub regions within pain networks in iPD subjects, with a positive association between pain and integration of pain networks (insula, anterior cingulate gyrus, prefrontal cortex, thalamus, and secondary somatosensory cortex) in iPD subjects, while a negative association was seen in HC, suggesting that functional network topology in iPD differs from HC. Interestingly, in a study exploring the use of acupuncture in reducing PD-related pain, the acupuncture iPD group was correlated to reduced King’s Parkinson’s Disease Pain Scale’s and increased FC in the left middle frontal gyrus and the right precentral gyrus when compared with the nonacupuncture iPD group (Yu et al., 2019). The authors of the study theorized modulation of both sensory-discriminative and emotional regulating regions to play a key role in pain regulation in iPD. PD-related fatigue is seen in 33%e70% of cases (Friedman et al., 2011), but the cause still remains relatively uncertain. Studies have identified hyperconnectivity of the DMN, hypoconnectivity of SMN, increased activity within the salience network, and reduced activity within the attention network play a key role in PD-related fatigue (Li, Yuan, et al., 2017; Tessitore et al., 2016). It has been theorized by authors that divergences of motor and cognitive networks connectivity and their adaptive or maladaptive functional changes are responsible for increased fatigue in iPD (Tessitore et al., 2016).

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Depression is a common symptom in PD, being present in about 40%e50% of patients (Reijnders et al., 2008). Depression can have a negative impact on patients’ quality of life (Marsh, 2013). Multiple studies have identified depressed iPD patients have altered FC within the prefrontal-limbic systems (Skidmore, Yang, Baxter, von Deneen, Collingwood, He, Tandon, et al., 2013; Wen et al., 2013; Baggio, Segura, Garrido-Millan et al., 2015; Lou et al., 2015; Zhu et al., 2016; Wang, Chen, et al., 2018; Liao et al., 2020; Lin et al., 2020; Qiu et al., 2021; Liao et al., 2021; Zhang et al., 2021) and amygdala (Skidmore, Yang, Baxter, von Deneen, Collingwood, He, Tandon, et al., 2013; Wen et al., 2013; Baggio, Segura, Garrido-Millan et al., 2015; Lou et al., 2015; Lin et al., 2020). Reduction in the degree of centrality, the number of connections per node, between the prefrontal and limbic systems can help predict the severity of depression in iPD (Liao et al., 2021). Recent studies have also identified abnormal connectivity within DMN (Liao et al., 2020; Lin et al., 2020), salience network (Liao et al., 2021), and visual network and posterior regions (Liao et al. 2020, 2021; Qiu et al., 2021; Zhang et al., 2021) in depressed iPD patients when compared with nondepressed iPD patients and HC. Utilizing a radiomic model that combined connectivity and activity changes associated with depression in iPD, a machine learning study (Zhang et al., 2021) was able to differentiate depressed iPD, nondepressed iPD, and HC with high diagnostic accuracies. In this work, lasso, random forest, and support vector machine were implemented for training sets. Each method had a predictive accuracy greater than 82% of cases when differentiating patient subgroups, with the exception of the support vector machine differentiating nondepressed iPD subjects, which yielded a predictive accuracy of 65% (Zhang et al., 2021). Psychosis, often in the form of visual hallucinations (VH), is commonly seen in late-stage PD patients. It has been postulated that increased 5-HT2A binding in prefrontal and visual processing areas plays a key role in the onset of VH in PD (Ballanger et al., 2010; Borgemeester et al., 2016). rs-fMRI studies have posited that VH is associated with a global reduction of FC in frontal, temporal, occipital, and striatal regions (Hepp et al., 2017). Studies have identified that the presence and severity of VH in iPD are determined by the degree of hypoconnectivity between dorsal and ventral attention networks (Shine et al., 2014) and increased coupling of the DMN to the visual network (Dujardin et al., 2020; Walpola et al., 2020). Impulsiveecompulsive disorders (ICDs), characterized by failure to resist urges, lead to behavioral issues such as excessive gambling, compulsive buying, binge-eating disorder, and compulsive sexual behaviors (Weintraub et al., 2010). In iPD, ICD is frequently seen as a side effect of chronic dopamine agonist use. The theorized pathophysiology of ICD is due to the upregulation of D3 receptor, which has a greater affinity for dopamine agonists in the reward center, and the downregulation of the dopamine 2 receptors (Maréchal et al., 2015). A study found increased FC in the salience network and DMN, along with decreased FC in the central executive network in ICD iPD (Tessitore, Santangelo, et al., 2017). A longitudinal study from the same team (Tessitore, De Micco et al., 2017) found that increased FC in DMN and central executive network (CEN) were key factors in developing ICD in drug-naïve iPD. In physiological conditions, there is an anticorrelation between DMN and CEN in response to external stimuli; therefore, the authors theorized the loss of this anticorrelation at baseline may lead iPD patients to become more susceptible to developing ICD (Tessitore, De Micco et al., 2017). Other studies have identified increased FC in the sensorimotor network in iCD-PD subjects, with severity and duration of ICD symptoms modulating the functional connectivity with sensorimotor, visual, and cognitive networks (Imperiale, Agosta, Canu, et al., 2018). A study found that reduced

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FC between the rostral anterior cingulate cortex and the nucleus accumbens was associated with increasing ICD severity (Hammes et al., 2019), with authors theorizing dysfunctional connectivity in these regions plays a key role in ICD development in iPD. Punding is a rare complication of iPD characterized by stereotyped complex, repetitive activities that are not goal oriented and that may occupy a large part of the patients’ time (Fasano et al., 2011). Specific FC changes have been associated with punding in iPD, with hyperconnectivity in the habenula and amygdala with thalamus and striatum bilaterally, and hypoconnectivity between bilateral habenula and left frontal and precentral cortices in iCD-PD patients compared with non-iCDPD patients (Markovic et al., 2017) (results of rs-fMRI changes associated with non-motor symptoms of iPD are summarized in Table 8.1).

rs-fMRI studies in familial Parkinson’s disease Neuroimaging studies in nonmanifest carriers (NMCs) of gene mutations for fPD may reveal critical early pathological changes at a prodromal stage. Leucine-rich repeat kinase 2 (LRRK2) is an autosomal dominant gene mutation causing a late-onset PD, which is clinically indistinguishable from iPD (Gandhi et al., 2009). Several studies (Helmich et al., 2015; Jacob et al., 2019; Vilas et al., 2016) have compared connectivity changes between NMCs of LRRK2 carrying the G2019S, G2385R, R1441G, R1441C, and R1628P missense mutations, and HC. These studies demonstrated increased FC within ventroanterior putamen and reduced FC between IPL and the dorsoposterior putamen (Helmich et al., 2015), as well as reduced FC in the caudal motor part of the left striatum, the ipsilateral precuneus, and superior parietal lobe in LRRK2-NMC compared with HC (Vilas et al., 2016). As dopaminergic depletion starts in the posterior putamen, increased connectivity to the ventroanterior putamen may indicate compensatory changes to maintain normal function in the presence of ongoing dopaminergic terminal degeneration. In addition, increased FC in the ventroanterior putamen in LRRK2 NMC is consistent with what is seen in iPD (Helmich et al., 2010) and increases with age. A recent study on 44 LRRK2 NMCs (Jacob et al., 2019) found they had reduced FC in the DMN, salience network, and DAN networks. Using a machine learning method and regional FC changes as predictors for LRRK2 mutation carriers, the authors could distinguish HCs from LRRK2 NMCs and showed an accuracy rate above 80%. A cross-sectional study (Hou et al., 2018) comparing 22 HCs and 22 PD patients (11 drugnaïve iPD subjects and 11 manifest LRRK2-PD with R1628P or G2385R mutations) showed reduced FC within the sensorimotor-striatal and posterior putamen-striatal circuits within both PD groups in comparison with healthy controls, highlighting primary pathological in the early stage of PD is associated with FC reductions in the striatal circuits. Results also showed reduced FC between the putamen and bilateral superior frontal gyri, precuneus, and calcarine gyri LRRK2-PD group when compared with drug-naïve iPD. Interestingly, FC between the putamen and the bilateral superior frontal gyri reduced with progressive age in the LRRK2 mutation carriers but not in the noncarriers (Hou et al., 2018). Mutations in the GBA gene (L444P and N370S) are one of the most common risk factors for fPD, with manifest heterozygous carriers presenting with PD at a younger age and with a more aggressive phenotype (Sidransky et al., 2009). A recent study (Greuel et al., 2020) identified 12 GBA-PD patients had weak FC in the caudate nuclei and occipital cortex compared,

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as opposed to a cohort of 42 iPD. The FC changes seen in the GBA-PD cohort (Greuel et al., 2020) were similar to previous studies exploring connectivity changes in PDD (Rektorova et al., 2012) and PD patients with hallucinations (Hepp et al., 2017), which may explain the increased susceptibility to hallucinations and cognitive decline seen in GBA-PD patients (Jesús et al., 2016). SNCA gene mutations cause an autosomal dominant form of fPD associated with earlier onset and more aggressive development of symptoms compared to with idiopathic counterpart (Delamarre & Meissner, 2017). Several SNCA pathogenic mutations, such as A53T, A30G, A30P, E46K, G51D, H50Q, and A53E (Conway et al., 1998; Dorszewska et al., 2021; Liu, Koros, et al., 2021; Zarranz et al., 2005), have been discovered alongside nonpathogenic mutations. Most rs-fMRI studies on SNCA carriers have focused on nonpathogenic SNCA mutations (rs11931074 and rs894278) in PD subjects (Si et al., 2019; Zhang et al., 2019). Results from these studies have shown that nonpathogenic SNCA PD subjects, rs11931074 and rs894278 carriers, have increased ALFF in right angular gyrus (Si et al., 2019) and reduced ALFF in bilateral fusiform gyri (Zhang et al., 2019), respectively, compared with HC. As the results are from nonpathogenic mutation carriers, it is difficult to ascertain if FC changes are responsible for pathological changes seen in PD. Mutations to the Parkin and PINK1 genes are associated with a young-onset, slowly progressive autosomal recessive form of fPD (Delamarre & Meissner, 2017). A study (Makovac et al., 2016) compared homozygous mutation carriers (5 PINK1-PD and 3 Parkin-PD) and heterozygous mutation carriers (2 PINK1-PD, 8 PINK1-NMCs, and 2 Parkin-NMCs) against 22 HCs. One of the limitations of this study (Makovac et al., 2016) was the inclusion of two PINK1-PD patients, who were heterozygous mutation carriers, in the heterozygous cohort group. Without environmental and epigenetic factors, heterozygous carriers of autosomal recessive genes do not normally develop the disease (Coppedè, 2012); thus, including heterozygous carriers with disease may lower the detection of significant fMRI changes within the study (Makovac et al., 2016). All but two PINK1-PD patients (one homozygous carrier and a heterozygous carrier) from both of the mutation carrier cohorts had cognitive impairment (Makovac et al., 2016), despite cognitive decline being rare in Parkin (Benbunan et al., 2004) and far more common in PINK1 (Maynard et al., 2020). Subanalysis revealed greater visuospatial impairment in the homozygous cohort compared with heterozygous cohort. Both genetic groups had reduced FC within the precuneus of the DMN when compared with HC (Makovac et al., 2016). Additionally, within the homozygous cohort, FC reductions in the frontal pole of the executive network, the right angular gyrus, and the right superior frontal gyrus were observed when compared with HC (Makovac et al., 2016). This study also revealed a positive correlation between DMN to the salience network and right FPN within heterozygous patients, which is opposite to what is seen in physiological conditions (Kelly et al., 2008). This change was not observed within the homozygous cohort, with authors theorizing that the genetic condition or the phenoconversion could play a role in the reversal of this functional correlation (Makovac et al., 2016) (results of rs-fMRI changes seen in fPD is summarized in Table 8.1).

Task-based fMRI Task-based fMRI (tb-fMRI) evaluates functional activity changes within the brain using motor, cognitive, or other performance measures. In PD patients, a wide number of studies II. Clinical Applications in Parkinson disease

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are available, which have employed a large number of paradigms to evaluate the function of pathways pertaining to the motor, associative, limbic, and other circuits. The following is a description of recent research utilizing such paradigms in PD.

Motor tb-fMRI in idiopathic Parkinson’s disease patients tb-fMRI can reveal functional changes, elicited by motor tasks, in iPD patients since the early, drug-naïve, stage. In an earlier study (Buhmann et al., 2003), drug-naïve iPD patients had hypoactivity in the M1 and SMA areas compared with HC, when asked to complete finger opposition tasks. A more recent study (Martin et al., 2019) identified that drug-naïve iPD patients had increased DLPFC activity, alongside hypoactivity of the putamen, M1, and the cerebellum when compared with HC during the execution of a prelearned fourfinger sequence task, with ROI analysis revealing hypoactivity within the putamen and SMA. Despite a few motor tb-fMRI studies showing reduced activity and connectivity SMA and M1 in iPD patients (Buhmann et al., 2003; Wu et al., 2011), a metaanalysis revealed it is not a consistent finding in all studies due to the task- and medication-specific variation between trails (Xing et al., 2020). This metaanalysis found bilateral putamen, left SMA, left subthalamus nucleus, right thalamus, and right medial globus pallidus hypoactivation in iPD patients OFF their medication compared with HC, with pre-SMA hypoactivation correlated with disease severity (Xing et al., 2020). Areas of increased and decreased activation in iPD patients from tb-fMRI studies have varied as a result of the use of dopaminergic medication during the trial, the complexity of the motor task, externally paced movements, and varying degrees of PD progression. A study exploring fMRI changes during externally guided and internally guided limb movements (Drucker et al., 2019) demonstrated that during task-based movements, iPD patients with mild-to-moderate stage disease had reduced activity in the striatum and motor areas when compared with age-matched HC. Interestingly, further analysis revealed reduced hypoactivation of the cerebellum of iPD group during internal guided movements when compared with external guided movements, with authors theorizing that the results were a result of more complex motor tasks (Drucker et al., 2019). Multiple motor tb-fMRI studies have identified hypoactivity of the putamen as the most consistent finding in iPD patients OFF their medication compared to HC (Caproni et al., 2013; Drucker et al., 2019; Wu et al., 2016; Wurster et al., 2015; Yan et al., 2015), which was confirmed with the most recent meta-analysis (Herz et al., 2021; Xing et al., 2020). As the motor loop connects the putamen to cortical areas, it has been theorized that denervation of the posterior putamen in iPD patients may reflect both hypoactivity of the putamen and cortical areas during motor tasks, with progressive motor impairment associated with greater hypoactivity within these areas (Herz et al., 2014). Dopaminergic medication has shown increased activity within pre-SMA and putamen during motor tasks in iPD patients compared with HC (Eckert et al., 2006; Mohl et al., 2017; Wu et al., 2016). PD patients may require external cueing/goal-directed behaviors to aid and facilitate movement, resulting in impaired spontaneous verbal or motor behavior (Ginis et al., 2018). Due to impaired automaticity and overreliance on goal-directed movement in PD, increased

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activation of the frontal, parietal, occipital, and cerebellar regions is seen during motion and while managing obstacles. Reduced automated motor behavior in PD may be also a result of impaired utilization of sensorimotor and visuospatial inputs, with a recent fMRI (Goelman et al., 2021) study revealing reduced connectivity from sensorimotor afferents to the SMA and prefrontal cortex. A metaanalysis (Herz et al., 2014) had identified increased parietal activity in motor movement in iPD subjects OFF medication compared with HC, with subanalysis revealing increased inferior parietal cortex activity during externally specified movements. Another study (Gilat et al., 2017) explored the use of dopamine via virtual reality (VR) gait paradigm, to measure changes in lower limb automaticity, found iPD patients who were OFF their medications had increased activation in cortical regions associated with cognition during the task, while the ON medication state had increased cerebellar activity with increased FC in striatal-limbic system. The authors theorized dopamine-induced increased connectivity to the cerebellum and striatal-limbic system, which coupled with the cognitive corticostriatal pathway leads to better motor control during task activity (Gilat et al., 2017). It is believed that the reduction in spontaneity is due to a reduced FC across the striatocortical loop, which is partially compensated by increased FC in cortical regions as well as in the cerebellum (Martin et al., 2019; Palmer et al., 2020; Wu et al., 2011). However, compensatory changes in iPD may lead to abnormal connections, which may negatively impact the patient. A study (Cerasa et al., 2012) exploring activity change in LID iPD patients and nondyskinetic iPD patients OFF their medication found increased activity within the SMA in the LID group during visuomotor tasks, revealing potential abnormal connectivity changes due to compensatory mechanisms. Along with hypoactivity of the BG, cortical and cerebellar activity changes are seen in iPD. In the early stages of PD, hypoactivation of the primary sensorimotor cortex is seen in the response to the motor task, with increased activity of FPN, M1, and SMA representing increased disease severity (Tessa et al., 2012). Multiple tb-fMRI studies have identified increased cerebellar activity in iPD patients completing motor tasks, reflecting a potential compensatory mechanism to combat BG dysfunction (Cerasa et al., 2006; Wu et al., 2016; Wurster et al., 2015; Yan et al., 2015; Yu et al., 2007). Interestingly, a recent metaanalysis (Solstrand Dahlberg et al., 2020) found no significant association between iPD duration and cerebellar activity, reflecting a potential compensatory activity peak in the initial stages of iPD, which either reaches an asymptote or regresses in the later stages of the disease. The same metaanalysis study also found a negative association between UPDRS scores and activity within Vermal VII, VIII, and Lobule VI in iPD subjects, which implies reduced cerebellar involvement in these regions as disease severity increases (Solstrand Dahlberg et al., 2020). Gait impairment is one of the major causes of daily disabilities in PD, but the exact pathophysiological cause still remains uncertain (Gilat et al., 2019). Studies utilizing motor imagination (MI) of gait in PD have shown increased SMA activity in iPD subjects compared with controls (Crémers et al., 2012; Snijders et al., 2011). A tb-fMRI study (Peterson et al., 2014) utilized MI and VR to stimulate imagined gait tasks in a cohort of iPD patients both with and without FoG. In this study (Peterson et al., 2014), FoG-iPD patients showed hypoactivation of the SMA, along with increased FC in the cerebellar locomotor region (CLR) and mesencephalic locomotor region (MLR) compared with non-FoG-iPD patients. A recent crosssectional analysis (Piramide et al., 2020) utilizing alternate dorsal/plantar foot flexion found

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FoG-iPD patients had hyperactivity of the parietooccipital and cerebellar areas compared with HC, but reduced BG activity when compared with iPD patients without FoG. Subanalysis from this study also revealed that reduced recruitment of frontoparietal areas was associated with more severe FoG scores in the iPD-FoG cohort (Piramide et al., 2020). Studies exploring upper limb freezing found similar patterns of reduced BG activity and increased activation of sensorimotor areas, revealing similar pathophysiological causes of freezing of gait in the upper limb (Vercruysse et al. 2012, 2014). Metaanalysis data (Gilat et al., 2019) revealed hypoactivity of SMA, due to denervation of the SMA, may be a potential cause of gait disturbance in iPD, while a recent systematic review (Song et al., 2021) found reduced frontostriatal network activity plays a key role in FoG load in iPD. Both CLR and MLR are known to modulate gait; thus, increased connectivity to these regions may represent compensatory changes to counter FoG or maladaptive processes, which may cause FoG (Peterson et al., 2014; Bharti et al., 2019, 2020). Though the metaanalysis (Gilat et al., 2019) found a strong association with CLR to modulate gait disturbances, this finding is not specific to iPD gait as it can commonly be seen in elderly controls. There have been limited studies exploring tb-fMRI changes in different iPD phenotypes (Lewis et al., 2011; Mohl et al., 2017; Planetta et al., 2014; Prodoehl et al., 2013). One of the earlier cross-sectional studies (Lewis et al., 2011) identified that TD and AR-iPD subjects could be differentiated by differences in striatothalamocortical (STC) and cerebellothalamocortical (CTC) circuits activity during finger tapping task, with reduced activity within the lentiform nucleus of the BG in the TD-iPD group during the motor task, while increased cerebellar and thalamic activity is seen in the AR-iPD group. Compared with controls, TD-iPD patients had increased contralateral STC and CTC pathway activity during the motor task, while the AR-iPD group had increased activity only within contralateral CTC pathways (Lewis et al., 2011). Another tb-fMRI study (Prodoehl et al., 2013) compared activity changes in TD-iPD, non-TD-iPD, and HC groups during the grip task. Despite non-TD-iPD cohort having more widespread reduced activity within STC, DLPFC, SMA, IPL, and globus pallidus compared with TD-iPD patients and HC, increased activity within the contralateral DLPFC was seen TD-iPD cohort in comparison with non-TD-iPD patients and HC (Prodoehl et al., 2013). A study (Mohl et al., 2017) found divergent cortical activity in different motor phenotypes of iPD in response to levodopa during a taping task, with levodopa causing hyperactivity of contralateral putamen in PIGD-iPD patients compared with TD-iPD patients and HC during right finger tapping, while levodopa increased FC between the posterior putamen and the motor network in TD-iPD compared with PIGD-iPD and HC (Motor tbfMRI changes seen in iPD is summarized in Table 8.2).

Motor skill learning Motor skill learning (MSL) is defined as the ability to learn a novel skill and to improve upon it with practice (Seidler, 2010). Motor learning is dependent on the cerebellum, striatum, and frontoparietal regions; as PD is associated with functional alteration in connectivity within these regions, motor learning may be difficult in PD patients (Nieuwboer et al., 2009). Motor skill learning has three proposed phases: a cognitive/acquisition phase, an associative phase, and an autonomous/retention phase (Dahms et al., 2020; Weaver, 2015). The first phase involves instructions regarding how motor action is completed, requiring high

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TABLE 8.2

Summary of the main findings from task-based fMRI studies in idiopathic and familial PD.

Cohort and task Idiopathic PD: motor tasks

Main findings

References

iPD regional changes Reduced activity and connectivity SMA during motor and M1 in iPD patients compared with movements HCs. Bilateral putamen, left SMA, left subthalamus nucleus, right thalamus, and right medial globus pallidus hypoactivation in iPD patients OFF their medication compared with HCs. Hypoactivity of the putamen in iPD patients OFF medication compared with HCs. Automated motor behavior: reduced FC from sensorimotor afferent areas to the SMA and prefrontal cortex in iPD patients compared with HCs. Spontaneity of movement: reduced FC across the striatocortical loop with increased FC in cortical regions as well as in the cerebellum (partial compensation) in iPD patients compared with HCs. Motor imagination of gait: increased SMA activity in iPD subjects compared with HCs.

(Buhmann et al., 2003; Wu et al., 2011) (Xing et al., 2020) (Caproni et al., 2013; Drucker et al., 2019; Herz et al., 2021; Wu et al., 2016; Wurster et al., 2015; Xing et al., 2020; Yan et al., 2015) (Goelman et al., 2021) (Martin et al., 2019; Palmer et al., 2020; Wu et al., 2011) (Crémers et al., 2012; Snijders et al., 2011)

Disease changes in early Complete finger opposition task: stages iPD (during hypoactivity in the M1 and SMA areas motor movements) in drug-naïve iPD patients compared with HCs. Prelearned four-finger sequence task: increased DLPFC activity, alongside hypoactivity of the putamen, M1 and the cerebellum areas in drug-naïve iPD patients compared with HCs. Hypoactivation of the primary sensorimotor cortex in early stage iPD patients compared with HCs.

(Buhmann et al., 2003) (Martin et al., 2019) (Tessa et al., 2012)

Disease changes in moderate stage iPD (during motor movements)

Externally guided and internally guided (Drucker et al., 2019) limb movements: reduced activity in the striatum, motor areas, and cerebellum in mild-to-moderate stage iPD patients when compared with age-matched HCs.

Progressive functional Pre-SMA hypoactivation correlated with (Xing et al., 2020) changes (during motor disease severity during motor tasks in (Herz et al., 2014) movements) iPD patients OFF their medication. (Tessa et al., 2012) (Continued)

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8. Functional MRI in familial and idiopathic PD

Summary of the main findings from task-based fMRI studies in idiopathic and familial PD.dcont’d

Cohort and task

Main findings Progressive hypoactivity of putamen and cortical areas is associated with progressive motor impairment in iPD patients. Increased activity of FPN, M1, and SMA representing increased disease severity in iPD patients. Initial cerebellar hyperactivity in the initial stages of iPD, which either reaches an asymptote or regresses in the later stages of the disease. Reduced cerebellar activity is associated with increased iPD during motor tasks. Dopamine use (during Increased activity within pre-SMA and motor movements) putamen during motor tasks in iPD patients ON medication compared with HCs. Increased parietal activity in motor movement in iPD subjects OFF medication compared with HCs. Lower limb automaticity during virtual reality gait paradigm: increased activation in cortical regions associated with cognition in iPD patients OFF medication compared with HCs. Increased cerebellar activity with increased FC in striatal-limbic system in iPD patients ON medication compared with HCs. Motor phenotypes (during motor movements)

References (Cerasa et al., 2006; Solstrand Dahlberg et al., 2020; Wu et al., 2016; Wurster et al., 2015; Yan et al., 2015; Yu et al., 2007) (Solstrand Dahlberg et al., 2020)

(Eckert et al., 2006; Mohl et al., 2017; Wu et al., 2016) (Herz et al., 2014) (Gilat et al., 2017)

(Lewis et al., 2011) Finger tapping task: reduced activity within the lentiform nucleus of the BG (Prodoehl et al., 2013) in the TD-iPD group during the motor (Mohl et al., 2017) task, while increased cerebellar and thalamic activity seen in the AR-iPD group. Grip task: increased activity within the contralateral DLPFC was seen in TDiPD patients compared with non-TDiPD patients and HC. Reduced activity within STC, DLPFC, SMA, IPL, and globus pallidus in non-TD-iPD patients compared with TD-iPD patients and HCs. Finger taping task with levodopa use: During the motor task, levodopa

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TABLE 8.2

Summary of the main findings from task-based fMRI studies in idiopathic and familial PD.dcont’d

Cohort and task

Main findings

Freezing of gait (during motor movements)

Levodopa-induced dyskinesias (during motor movements) Idiopathic PD: motor skill learning

increased activity of contralateral putamen in PIGD-iPD patients compared with TD-iPD patients and HCs. During the motor task, levodopa increased FC between the posterior putamen and the motor network in TDiPD compared with PIGD-iPD and HCs. Motor imagination and virtual reality gait tasks: Hypoactivation of the SMA, along with increased FC in the cerebellar locomotor region and mesencephalic locomotor region in FoGiPD patients compared with non-FoGiPD patients. Alternating dorsal/plantar foot flexion task: Hyperactivity of the parietooccipital and cerebellar areas in FoG-iPD patients compared with HCs. Alternating dorsal/plantar foot flexion task: Reduced basal ganglia activity in FoG-iPD patients compared with nonFoG-iPD patients. Repetitive finger movement and upper limb motor blocks: reduced BG activity in FoG-iPD patients compared with non-FoG-iPD patients. Repetitive finger movement and upper limb motor blocks: increased activation of sensorimotor areas in FoG-iPD patients compared with HCs. Visuomotor tasks: Increased activity within the SMA in LID-iPD patients and nondyskinetic iPD patients (both groups OFF their medication).

iPD regional changes Retention of hippocampal activity was during MSL associated with higher learning performances in iPD. Overactivation of the substantia nigra during MSL in iPD patients when compared with HCs. During MSL, greater substantia nigra activation correlated with increased daily dopamine dose and disease

References

(Peterson et al., 2014) (Piramide et al., 2020) (Piramide et al., 2020) (Vercruysse et al. 2012, 2014) (Vercruysse et al. 2012, 2014)

(Cerasa et al., 2012)

(Carbon et al., 2010; Tzvi et al., 2021) (Tzvi et al., 2021) (Tzvi et al., 2021) (Nackaerts et al., 2018) (Duchesne et al., 2016) (Wu & Hallett, 2008; Nieuwboer et al., 2009; Maidan et al., 2016; Vervoort et al., 2016; Nieuwhof (Continued)

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8. Functional MRI in familial and idiopathic PD

Summary of the main findings from task-based fMRI studies in idiopathic and familial PD.dcont’d

Cohort and task

Idiopathic PD: cognitive tasks

Main findings

Executive function

Working memory deficits in iPD

References

duration in iPD patients. Intense visually cued handwriting training: increased FC between left hemispheric visuomotor stream to the SMA in early iPD patients along with improvement of the motor skill. High-intensity aerobic training: iPD patients with limited improvement of aerobic functions predominantly utilized cerebellar hyperactivity in MSL. iPD patients with increased aerobic functions had increased hippocampal and striatal activity but limited cerebellar hyperactivity. Dual-task learning: Hypoactivity within the frontostriatal network and increased motor network activity in iPD subjects compared with HCs. Longitudinal changes seen in action observation training and motor imagination dual-task gait training: Hypoactivity of the frontal areas with hyperactivity of the cerebellum in iPD patients. Longitudinal changes seen in action observation training dual-task gait training: Increased temporoparietal activity along with reduced SMA and cerebellar activity in iPD patients. Dysexecutive function with a reduced activity within the frontostriatal network in iPD patients. Set-shifting tasks: Hyperactivity of the hippocampus and of FPN in iPD patients with optimal executive function compared with HCs (potential compensatory measure). Tower of London task: reduced FC in FPN in drug-naïve iPD patients compared with HCs.

et al., 2017; imperiale, Agosta, Markovic, et al., 2018) (Sarasso et al., 2021) (Sarasso et al., 2021)

Visuospatial task: reduced FC in the FPN and hyperactivation in DLPFC, middle frontal gyrus, and parietal lobule in iPD patients compared with

(Kawashima et al., 2021; Parkin et al., 2021; Trujillo et al., 2015) (Kawashima et al., 2021; Trujillo

(Monchi et al. 2006, 2007; NaganoSaito et al., 2014) (Gerrits et al., 2015; Nagano-Saito et al., 2014) (Trujillo et al., 2015)

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TABLE 8.2

Summary of the main findings from task-based fMRI studies in idiopathic and familial PD.dcont’d

Cohort and task

Main findings

MCI-iPD

HCs. Visuospatial task: FPN hypoactivity associated with reduction of working memory in iPD patients. Behavioral task þ spectroscopy: iPD subjects with greater working memory error rates had functional hypoactivity and reduction of GABA levels in frontoparietal areas. Attention network tests: During the task, hypoactivity of the left postcentral gyrus was seen in MCI-iPD patients compared with NC-iPD patients and HCs. Compensatory cerebellar hyperactivity in iPD patients compared with HCs during the task. Set-shifting tasks: hypoactivity of the prefrontal cortex and caudate nucleus in MCI-iPD patients compared with NCiPD patients and HCs. Hippocampal hyperactivity was associated with greater memory scores. Visuospatial task: activity in the MFG and IPL in MCI-iPD patients compared with NC-iPD patients.

References et al., 2015) (Parkin et al., 2021)

(Yang et al., 2021) (Nagano-Saito et al., 2014) (Kawashima et al., 2021)

(Farid et al., 2009) Dopamine use (during Go/no-go task: iPD patients OFF cognitive task) levodopa therapy showed activation in (Simioni et al., 2017) (Dan et al., 2019) the frontal lobe, superior and medial temporal cortex, caudate, and cerebellum compared with HCs. Levodopa partially normalized brain activation and induced changes in the pattern of cingulate cortex activity, which was more pronounced in the rostral part in the drug-off state and in the caudal part after levodopa intake. N-back task: increased activation of the left ventrolateral prefrontal cortex, increased FC between the caudate and parietal lobe, and increased working memory performance in iPD patients ON dopaminergic medication state compared with iPD patients in OFF state. Emotional face-matching task: (Continued)

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8. Functional MRI in familial and idiopathic PD

Summary of the main findings from task-based fMRI studies in idiopathic and familial PD.dcont’d

Cohort and task

Main findings

LRRK2

LRRK2-NMC

GBA

GBA-NMC

hyperactivity of the visual cortex in iPD patients compared with HCs. Improved performance on the cognitive task and hypoactivity of posterior cingulate gyrus was seen in iPD patients in the ON state compared with the OFF state. Motor imagination task: hypoactivity of the right caudate nucleus and increased activity in the right dorsal premotor cortex in LRRK2-NMC patients compared with HCs. N-Back test: No differences in behavioral performances or neural activity in LRRK2-NMC, GBA-NMCs, and HCs. Stroop task: Hyperactivity of IPL, precuneus, and the fusiform gyrus, along with increased FC of the IPL and precuneus to cortical and subcortical areas in LRRK2-NMC patients compared with HCs. Stroop task: Hyperactivity of right precentral gyrus, left medial frontal gyrus, left superior frontal gyrus, and left precentral gyrus in LRRK2-NMC and GBA-NMC in comparison with HC. Gambling task: In comparison with HC, LRRK2-NMCs have a smaller BOLD response in the left nucleus accumbens and right insula in anticipation of risk, with a higher BOLD response in the right insula in anticipation of reward. N-Back test: No differences in behavioral performances or neural activity in LRRK2-NMC, GBA-NMC, and HC. Stroop task: Hyperactivity of right precentral gyrus, left medial frontal gyrus, left superior frontal gyrus, and left precentral gyrus in LRRK2-NMCs and GBA-NMCs in comparison with HCs. Hyperactivity of right medial frontal gyrus and lingual in GBA-NMC

References

(van Nuenen et al., 2012) (Bregman et al., 2017; Thaler et al., 2016) (Thaler et al., 2013) (Bregman et al., 2017) (Thaler et al., 2019)

(Bregman et al., 2017) (Bregman et al., 2017)

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Motor tb-fMRI in idiopathic Parkinson’s disease patients

TABLE 8.2

Summary of the main findings from task-based fMRI studies in idiopathic and familial PD.dcont’d

Cohort and task

Parkin

PINK1

231

Main findings

Parkin-NMC

References

patients compared with LRRK2-NMCs and HCs. Right finger-to-thumb opposition task: (Buhmann et al., 2005) Hyperactivity of right rostral cingulate (Anders et al., 2012) motor area and left dorsal premotor cortex of Parkin-NMCs compared with HCs. Facial gesture processing task: reduced activation of the right fusiform gyrus when processing neutral facial gestures and increased activation right ventrolateral premotor cortex in ParkinNMCs compared with HCs. Increased activity of the ventrolateral premotor cortex in Parkin-NMCs was associated with better facial emotion recognition.

Parkin-PD

Sequential finger movement task: No (van Eimeren et al., 2010) significant difference in movementrelated neuronal activation between iPD and Parkin-PD patients.

PINK1-NMC

Three thumb-to-finger opposition tasks: (van Nuenen et al., 2009) Hyperactivity within rostral SMA and rostral premotor areas in Parkin-NMC and PINK1-NMC compared with HCs. Further activation of frontoparietal areas in PINK1-NMC patients.

ACC, anterior cingulate cortex; AD, Alzheimer’s disease; ALFF, amplitude of low-frequency fluctuation; AR, akinetic rigidity; BG, basal ganglia; CLR, cerebellar locomotor region; CN, control network; CTC, cerebellothalamocortical; DAN, dorsal attention network; DLPFC, dorsolateral prefrontal cortex; ECN,executive control network; FoG, freezing of gait; fPD, familial Parkinson’s disease; FPN, frontoparietal network; GPe, globus pallidus externus; GPi, globus pallidus internus; HC, healthy controls; HY stage, Hoehn-Yahr stage; ICD, impulse control disorders; IFC, inferior frontal cortex; iPD, idiopathic Parkinson’s disease; IPL, inferior parietal lobe; LID, levodopa-induced dyskinesias; M1, primary motor cortex; MCI, mild cognitive impairment; MFG, middle frontal gyrus; MLR, mesencephalic locomotor region; MSL, motor skill learning; NCs, normal controls; NMCs, nonmanifest carriers; PD, Parkinson’s disease; PDAR, primary akinetic/rigidity; PDD, Parkinson’s disease dementia; PIGD, posture instability gait difficulty; pre-SMA, presupplementary motor area; RBD, REM sleep behavior disorder; ReHo, regional homogeneity; rs-fMRI, resting-state fMRI; SMA, supplementary motor area; SMN, sensorimotor network; SN, substantia nigra; STC, striatothalamocortical; STN, subthalamic nucleus; tb-fMRI, task-based fMRI; TD, tremor dominant; UPDRS, Unified Parkinson’s Disease Rating Scale; VH, visual hallucinations; VN, visual network; VS/VP, visuospatial/visuoperceptual.

attentional demand. The second stage involves practising and stabilizing the learned motor process; with an increasing training regime and practice, the motor movements become more accurate. All three stages utilize corticostriatal loop, with a transition into the later stage, leading to reduced corticocerebellar loop activity and increased engagement of the subcortical motor areas (Dahms et al., 2020). Due to the pathophysiological processes of PD, there is reduced activity from the BG, thus reducing motor activation, which impairs the transition of the second to third stage of MSL.

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In drug-naïve and early-stage iPD, the first stage of motor learning is preserved, but the ability to retain the skill and progress into subsequent stages is impaired, especially in later stages of iPD (Doyon et al., 1997; Marinelli et al., 2017; Wu & Hallett, 2008). A tb-fMRI study (Nackaerts et al., 2018) revealed intense MSL (visually cued handwriting training) leads not only to improvement of the motor skill but also to increased FC between left hemispheric visuomotor stream to the SMA, revealing certain intense MSL methods may modulate SMA connectivity in early iPD. Compensatory hyperactivity within the cerebellum may help initially in MSL in iPD, but these mechanisms fail to establish the later stages of motor learning due to impaired corticostriatal loop activity in later stages of iPD; thus impairing the ability to recall and improve new motor skills (Carbon et al., 2010; Duchesne et al., 2016; Wu & Hallett, 2013). An fMRI study (Duchesne et al., 2016) found that high-intensity aerobic training can increase MSL in iPD patients. Results revealed iPD patients who had limited improvement of aerobic functions predominantly utilized cerebellar hyperactivity in MSL, but iPD patients with increased aerobic functions had increased hippocampal and striatal activity but limited cerebellar hyperactivity, suggesting hippocampal activity changes were the more durable compensatory mechanisms for motor learning. Interestingly, both cross-sectional (Tzvi et al., 2021) and longitudinal (Carbon et al., 2010) studies found retention of hippocampal activity was associated with higher learning performances in iPD. Due to the pathological processes of PD, Lewy bodies may build up within the hippocampus, leading to atrophy and reduced function (Brück et al., 2004). This longitudinal study (Carbon et al., 2010) found iPD patients who had high learning performances in a 2-year follow-up had greater hippocampal activity, which infers reduced hippocampal atrophy. Another tb-fMRI study assessing MSL (Tzvi et al., 2021), revealed overactivation of the substantia nigra (SN) during MSL in iPD patients when compared with HC, with greater SN activation correlated with increased daily dopamine dose and disease duration. Further analysis revealed increased FC between SN and the putamen modulates impaired MSL in iPD (Tzvi et al., 2021). Single-task learning is usually preserved within the early stage of the disease despite corticostriatal loop dysfunction in iPD (Nieuwboer et al., 2009). Studies have shown that increased disease progression (Muslimovic et al., 2007) and cognitive decline (Vandenbossche et al., 2009) were associated with worse serial reactions (Muslimovic et al., 2007) times and sequence learning. A literature review (Nieuwboer et al., 2009) has shown that early iPD subjects have short-term retention of learned motor skills, but results varied depending on the task, the amount of practice, cognitive impairment, and the time frame when the skill was retested. Dual-task learning, where both motor and cognitive tasks are done together, is found to be impaired in iPD patients due to executive dysfunction secondary to hypoactivity within the frontostriatal network (Nieuwboer et al., 2009). Along with increased motor network activity, altered activity within the prefrontal, temporoparietal, occipital, and cerebellar areas (Wu & Hallett, 2008; Maidan et al., 2016; Vervoort et al., 2016; Nieuwhof et al., 2017; imperiale, Agosta, Markovic, et al., 2018) has seen iPD subjects during dual task when compared with HC, reflecting potential compensatory mechanism. Cues have been shown to assist both single and dual tasks in iPD (Nieuwboer et al., 2009), potentially due to the potential reorganization of motor networks by incorporating different external stimuli. Automation of motor skills can be observed in iPD, though the time needed to achieve this goal may be increased in iPD patients (Nieuwboer et al., 2009). Dual-task training had

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233

been identified as a more efficient method of integration and inducing automatization (Sarasso et al., 2021; Strouwen et al., 2017). A recent study (Sarasso et al., 2021) found iPD patients who had dual-task gait training with action observation training (AOT) and MI had better gait and balance control than the group with only dual-task gait training over a 2-month period. Analysis of the fMRI data revealed AOT þ MI dual-task gait training group revealed hypoactivity of the frontal areas with hyperactivity of the cerebellum, while the only dual-task gait training group had increased temporoparietal activity along with reduced SMA and cerebellar activity, revealing variable cortical modulation of different MSL methods in iPD (Motor skills learning changes seen in iPD is summarized in Table 8.2).

Cognitive tb-fMRI in idiopathic Parkinson’s disease patients Utilizing different task-based methods, several studies have been able to identify dynamic changes in the cognitive domain in PD. Impaired executive function can be seen since the early stage of iPD and may impact significantly both motor and nonmotor symptoms (Champod & Petrides, 2007; Collette et al., 2006; Dirnberger & Jahanshahi, 2013; Gratton et al., 2018; Lewis et al., 2004; Owen, 2004). Areas of preferential involvement in executive function are the frontal cortex, medial temporal cortex, hippocampus, entorhinal cortex, cingulate gyrus, amygdala, and posterior parietal cortex (Rabinovici et al., 2015). Several tb-fMRI studies in iPD have associated dysexecutive function with a reduced activity within the frontostriatal network (Monchi et al. 2006, 2007; Nagano-Saito et al., 2014). Early iPD patients, with optimal executive function, have shown in fMRI studies, using set-shifting tasks as a cognitive paradigm, hyperactivity of the hippocampus and of FPN that has been interpreted as a possibly compensatory measure (Gerrits et al., 2015; Nagano-Saito et al., 2014). Conversely, Trujillo and colleagues found a reduced FC in FPN in a group of 17 drug-naïve iPD patients, which was correlated with reduced task-related network activity (for both planning and increase task loads), suggesting the presence of a goal-directed behavior impairment since the early stages (Trujillo et al., 2015). The dopaminergic state of iPD patients may influence cognitive performances (Cools et al., 2003; Farid et al., 2009; Simioni et al., 2017). Prefrontal areas, implied in executive function tasks such as action planning, receive dense dopaminergic innervation. One study found iPD patients OFF levodopa therapy showed activation in the frontal lobe, superior and medial temporal cortex, caudate, and cerebellum during go/no-go task, while levodopa intake partially normalized brain activity and modified the pattern of activation of the cingulate cortex, compared with the OFF medication state, indicating a role of dopaminergic status in the functional activation of brain structures implicated in the processing of executive function tasks (Farid et al., 2009). In another work, iPD patients in ON dopaminergic state showed increased working memory performance and increased activation of the left ventrolateral prefrontal cortex compared with patients in OFF state (Simioni et al., 2017). Additionally, iPD patients in ON state showed increased FC between the caudate and parietal lobe, indicating a compensatory activity by levodopa across striatocortical networks to support working memory performances (Simioni et al., 2017). In another fMRI work, using an emotional face-matching task as cognitive fMRI paradigm found that iPD patients displayed

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hyperactivity of the visual cortex, irrespective of their cognitive status. Impaired emotion performance was dependent on dopaminergic availability, as iPD patients in the ON state showed better performances on the cognitive task compared with the OFF state, which was accompanied by a reduction of posterior cingulate activity (Dan et al., 2019). Task-based fMRI studies have sought to assess functional performances in iPD patients with MCI, yielding different results according to the cognitive domain tested. A recent study identified MCI-iPD patients had reduced activity in the left postcentral gyrus during attention network tests compared with NC-iPD and HC patients (Yang et al., 2021). Interestingly, a possibly compensatory increased activation of the bilateral cerebellum crus 1 has been detected in both MCI-iPD and NC-iPD, suggesting that this change may precede the onset of cognitive impairment in iPD (Yang et al., 2021). Another study (Nagano-Saito et al., 2014) found MCI-iPD patients had reduced activity within the prefrontal cortex and caudate nucleus during the planning of set-shifting tasks, with reduced activity of the premotor cortex and posterior putamen in task activity, reflecting impaired cognitive and motor corticostriatal loops in MCI-iPD patients. In this study, NC-iPD patients showed increased hippocampal activity, which may be explained as a functional compensation of the ongoing striatal dysfunction (Nagano-Saito et al., 2014). Working memory deficits are commonly seen at all stages of iPD (Nagano-Saito et al., 2014). Visuospatial task-fMRI studies (Kawashima et al., 2021; Parkin et al., 2021; Trujillo et al., 2015) have identified iPD patients have reduced FC in the FPN and hyperactivation in DLPFC, middle frontal gyrus (MFG), and parietal lobule. A more recent visuospatial study (Kawashima et al., 2021) identified MCI-iPD patients had reduced activity in the MFG and IPL when compared with NC-iPD patients. Results from these studies imply that impaired activity within the FPN underlies the reduction of working memory in iPD patients (Kawashima et al., 2021; Trujillo et al., 2015). A study combining tb-fMRI and magnetic resonance spectroscopy (Parkin et al., 2021) found iPD subjects with greater working memory error rates had functional hypoactivity and reduction of GABA levels in frontoparietal areas, highlighting reduced GABAergic activity in these areas is associated with impaired working memory (Cognitive tb-fMRI changes seen in iPD is summarized in Table 8.2).

tb-fMRI studies in familial Parkinson’s disease Multiple studies have explored tb-fMRI changes in both manifest and nonmanifest carriers of genetic mutations for fPD using a variety of motor and nonmotor tasks. Using a motor imaginary tb-fMRI study, 34 LRRK2-NMCs and 28 HCs were asked to judge hand laterality of hand pictures, with results revealing similar behaviour-based performances (error rates, reaction time) (van Nuenen et al., 2012). The LRRK2-NMCs were found to have reduced imagery-related activation of the right caudate nucleus and increased activity in the right dorsal premotor cortex. Interestingly, FC between the right dorsal premotor cortex and the right extrastriatal body became stronger with increasing striatal impairment, revealing the increased connectivity changes as compensatory changes for early striatal dysfunction in LRRK2 carriers (van Nuenen et al., 2012).

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tb-fMRI can assess cognitive functions using certain tasks such as working memory NBack test (participant to recall the Nth stimuli present from current stimuli) to assess recall and Stroop test (ability to differentiate the mismatch between the name of a color/object to the printed color/object) to assess attention and interference resolution, since these domains potentially impaired in PD (Litvan et al., 2012). Two separate N-Back tb-fMRI studies done by the same team could not identify any altered and behavioral performances or neural activity within the whole brain and ROI in cohorts of 38 LRRK2-NMCs and 39 controls (Thaler et al., 2016) as well as another cohort LRRK2-NMC, GBA-NMC, and controls (Bregman et al., 2017). These results reveal working memory is relatively preserved in both NMC of LRRK2 and GBA and not all cognitive domains may be impacted in NMCs at risk of developing PD. Neural activation changes in GBA-NMC and LRRK2-NMC when compared with HC can be seen using fMRI and a cognitive task (the Stroop test) despite no significant change in the cognitive performance itself (Bregman et al., 2017; Thaler et al., 2013). The first study (Thaler et al., 2013) found when compared with 21 HCs, 19 LRRK2-NMCs had increased neuronal activity within the IPL, precuneus, and the fusiform gyrus when asked to perform the Stroop task. Further analysis revealed right IPL and precuneus have increased FC with cortical and subcortical areas in LRRK2-NMC subjects compared with HCs. As both IPL and the precuneus are key nodes within the ventral attention system, increasing FC within these areas infers compensatory changes in LRRK2-NMC to maintain cognitive performance (Thaler et al., 2013). The second study revealed (Bregman et al., 2017) when compared 22 HCs, 10 GBANMCs and 21 LRRK2-NMCs had significantly raised activity in the right precentral gyrus, left medial frontal gyrus, left superior frontal gyrus, and left precentral gyrus during Stroop task. Interestingly, post hoc analysis revealed GBA-NMC had increased activation of right medial frontal gyrus and lingual gyrus during the task, with these changes not being present in LRRK2-NMC and HC. Though there was some overlap, there were some variations in areas with increased activity in the LRRK2-NMC cohort. GBA-NMC had a similar outcome with a further increase in neuronal activity when compared with LRRK2-NMC (Bregman et al., 2017; Thaler et al., 2013), revealing either a potential common compensatory at this stage within both gene mutation profiles. PD is commonly associated with impaired processing of both risk and reward (Frank, Seeberger, and O’Reilly R 2004; Torta & Castelli, 2008). Utilizing a gambling task, the integrity of the reward system was studied in monogenic forms of Parkinsonism by comparing 34 LRRK2-NMCs and 32 HCs (Thaler et al., 2019). Results from this work showed that, in comparison with HCs, LRRK2-NMCs have a smaller BOLD response in the left nucleus accumbens and right insula in anticipation of risk, with a higher BOLD response in the right insula in anticipation of reward. Further analysis revealed LRRK2-NMCs have reduced connectivity from the insula during punishing outcomes compared with HCs, which leads to reduced processing of risk (Thaler et al., 2019). A reorganization and establishment of a new striatocortical motor loop in the midst of latent nigrostriatal dysfunction of NMCs of pathogenic mutations for fPD may represent early compensatory changes to preserve motor task functions, as suggested by a study on Parkin-NMC. In a tb-fMRI study that used visual cues to determine the application of a right finger-to-thumb opposition task (Buhmann et al., 2005); despite similar task performances between the 12 Parkin-NMCs and 12 HCs, increased neuronal activity in the right rostral cingulate motor area and left dorsal premotor cortex was observed within the Parkin-NMCs when

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8. Functional MRI in familial and idiopathic PD

compared to the HCs. A similar tb-fMRI study using three thumb-to-finger opposition using visual cues compared changes in 9 PINK1-NMCs, 13 Parkin-NMCs, and HCs (van Nuenen et al., 2009). Both Parkin-NMC and PINK1-NMC groups had increased activity within rostral SMA and rostral premotor areas during the motor task compared to HC, highlighting similar compensatory mechanism between the two mutation groups (van Nuenen et al., 2009). When comparing the two mutation carrier groups, PINK1-NMCs had increased frontoparietal activation during the motor task relative to Parkin-NMCs (van Nuenen et al., 2009). 11 patients iPD and 9 Parkin-PD nondyskinetic patients who were on dopaminergic therapy were asked to undertake sequential finger movement tb-fMRI (van Eimeren et al., 2010), revealing no significant difference in movement-related neuronal activation or task performance in both groups. The results highlighted similar pathophysiological changes in early iPD and Parkin-PD, with similar responses to dopaminergic therapy (van Eimeren et al., 2010). A tb-fMRI study assessing facial gesture processing (Anders et al., 2012), found the eight Parkin-NMCs on average had reduced facial recognition compared with eight HCs. In comparison with HCs, Parkin-NMCs showed reduced activation of the right fusiform gyrus when processing neutral facial gestures and increased activation right ventrolateral premotor cortex, specifically inferior frontal gyrus pars orbitalis, during processing positive facial gestures processing. Further analysis revealed increased activity of the ventrolateral premotor cortex in Parkin-NMCs was associated with better facial emotion recognition. PD is associated with disrupted facial emotion recognition (Argaud et al., 2018). Therefore, the loss of ventrolateral premotor cortex hyperactivity in Parkin-NMCs may lead to a similar loss of facial expression seen in PD (tb-fMRI changes seen in fPD is summarized in Table 8.2).

Conclusions fMRI has garnered a promising role with a dynamic understanding of neural activity and connectivity changes in neurodegenerative diseases, and in PD in particular. Research into cognitive and sensorimotor changes in PD has given greater insight into specific disease process changes. Studies have identified FC and activity changes within DMN, FPN, SMN, BG, and cerebellum, and these, combined with other imaging and clinical tools, can help to monitor the progression of disease through its clinical milestones. To further ascertain this role, longitudinal studies and automated methods of analysis (e.g., machine learning) are warranted to help identify early patterns of functional alteration at the early stages of the disease, as well as to identify functional changes that could represent predictive aspects of disease progression. Furthermore, the evidence that FC may change by means of pharmacological intervention, such as dopaminergic therapy, can further increase the potential application of fMRI research in PD. fMRI can be employed to better understand the mechanisms of action of new drugs as well as represent an outcome measure to test the efficacy of an intervention to modify or revert changes of FC in brain areas related to specific symptoms. It is envisaged that more studies in the future may focus on these new aspects of evaluating functional pathology in PD with the ultimate aim of providing a diseasemodifying compound for this much-studied but still incurable disease.

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C H A P T E R

9 Molecular imaging in prodromal Parkinson’s disease Edoardo Rosario de Natale1, Joji Philip Verghese2, Heather Wilson1 and Marios Politis1 1

Neurodegeneration Imaging Group and Mireille Gillings Neuroimaging Centre, University of Exeter, London, United Kingdom; 2Neurodegeneration Imaging Group, Department of Clinical and Biomedical Sciences, Faculty of Health and Life Sciences, University of Exeter, United Kingdom

Introduction Current Parkinson’s disease (PD) diagnostic criteria rely on the recognition of a spectrum of cardinal motor symptoms, which constitute the clinical hallmark of the disease (Gelb et al., 1999). However, multiple research evidence indicates that clinical diagnosis corresponds to a biological stage where pathological alterations likely responsible for PD have been ongoing for several years or even decades (Kalia et al., 2015; Postuma & Berg, 2016) and have already caused significant structural and functional neuronal damage (Mahlknecht, Seppi, & Poewe, 2015). Neuropathological models of a-synuclein deposition and spreading in PD postulate two possibly different pathways in PD (Borghammer & van den Berge, 2019): a “brainfirst” subtype in which spreading starts in the brain to subsequently involve the peripheral autonomous system, and a “body-first” subtype in which a caudorostral temporal involvement of brainstem and other structures from the peripheral nervous system give rise to nonmotor and motor symptoms of PD (Braak et al., 2003). This latter model has been called for the emergence of a plethora of nonmotor symptoms of PD that may precede the onset of motor symptoms by years or decades. Reflection of this early stage is a range of clinical markers such as rapid eye movement (REM) sleep behavior disorder (RBD), hyposmia, constipation, orthostatic hypotension and other autonomic symptoms, excessive daytime somnolence, depression, anxiety, and others (Hustad & Aasly, 2020), which, despite not being specific for PD, nevertheless constitute the effect of an ongoing neurodegeneration, termed “prodromal PD.” The recognition of prodromal PD in the general population is thought to represent a

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major goal of neurodegeneration research, as it is thought that an intervention at this stage could slow down, or potentially revert the course of cellular damage (Lin et al., 2019). To do so, research has sought to identify and characterize candidate markers to recognize and track prodromal PD in the general population in view of their predictive value to future phenoconversion to clinical PD, and research diagnostic criteria are now available (Heinzel et al., 2019; Postuma et al., 2015), which have shown potential to identify patients at risk of short-term conversion to PD (Mahlknecht et al., 2016; Obeso et al., 2017). Particular populations at risk, such as asymptomatic carriers of genetic mutations for familial Parkinsonism, have been particularly studied in the hypothesis that these could constitute a pathophysiological model that could be transposed to PD. Molecular imaging using positron emission tomography (PET) or single photon emission computerized tomography (SPECT) possesses unique features, which allows the study, in vivo, of the function of single cellular metabolic activities and of the density of specific enzymes or receptors. PET imaging has demonstrated ability to disclose molecular alterations due to neurodegeneration years before the predicted onset of symptoms (Wilson et al., 2017). For this reason, molecular imaging has been employed widely as an ideal tool to investigate underlying pathology and in the characterization of biomarkers of prodromal PD. The development and availability of carbon-11 and fluorinated-18 radiotracers targeting neurotransmitter systems such as the dopaminergic, serotonergic, cholinergic, and noradrenergic, as well as to study complex biological phenomena such as neuroinflammation, has helped in the early identification and monitoring of cellular alterations with potential to predict the future conversion to full-blown PD in populations identified as having prodromal PD (Table 9.1). This chapter will review the principal findings obtained by molecular imaging research in cohorts representative of prodromal PD.

Definition of prodromal Parkinson’s disease Before discussing recent research findings on prodromal PD, it is essential to briefly discuss what constitutes a cohort of prodromal PD subjects. Despite the identification of several clinical, as well as fluid markers associated with prodromal PD (Hustad & Aasly, 2020), the majority of these is very common in the general population and therefore can be associated with the disease, or recognized only retrospectively in the single patient, that is, only after the clinical diagnosis has been made. From a clinical standpoint, the most consistent symptoms associated with prodromal PD are represented by isolated RBD (iRBD) and hyposmia. iRBD is a parasomnia characterized by the acting out of the dream content during REM sleep, derived by the lack of the muscle atonia normally associated with this sleep phase, which can be present either in isolation (isolated RBD, or iRBD) or in the context of a neurodegenerative disease (secondary RBD). The former has been recognized as an early manifestation of a synucleinopathy (Berg et al., 2021), with up to 91% of RBD patients developing symptoms related to a neurodegenerative disease over long follow-up times (Iranzo et al., 2014). Longitudinal studies have identified and characterized a number of clinical, neurophysiological, fluid, and imaging markers associated with high predictive value of future phenoconversion from iRBD to a Lewy body disease (de Natale et al., 2022). However, there

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Definition of prodromal Parkinson’s disease

TABLE 9.1 Tool PET PET

Overview of the radiotracers employed in molecular imaging research in prodromal Parkinson’s disease.

Radiotracer

Molecular system

Target

11

Presynaptic dopaminergic system

Dopamine transporter

11

Presynaptic dopaminergic system

Dopamine transporter

[ C]CFT [ C]MP 123

I]FP-CIT

Presynaptic dopaminergic system (low affinity for Dopamine transporter (low affinity the presynaptic serotonergic system) for the serotonin transporter)

SPECT

[

SPECT

[123I] Nortropane

Presynaptic dopaminergic system

Dopamine transporter

PET

[18F]DOPA

PET PET PET

Presynaptic dopaminergic system

Amino acid decarboxylase

18

Presynaptic dopaminergic system

Amino acid decarboxylase

11

Presynaptic dopaminergic system

Vesicular monoamine transporter 2

18

Presynaptic dopaminergic system

Vesicular monoamine transporter 2

11

[ F]FMT [ C]DTBZ [ F]AV-133

PET

[ C] Raclopride

Postsynaptic dopaminergic system

Dopamine receptor D2

SPECT

[123I]IBZM

Postsynaptic dopaminergic system

Dopamine receptor D2

SPECT

[99mTc]ECD

Regional brain perfusion

Distribution in the brain proportional to blood flow

SPECT

[99mTc] HMPAO

Regional brain perfusion

Distribution in the brain proportional to blood flow

PET

[18F]FDG

PET PET PET

Regional brain metabolism

Glucose analogue

11

Presynaptic serotonergic system

Serotonin transporter

11

Presynaptic cholinergic system

Vesicular acetylcholine transporter

11

Cholinergic synapse activity

Acetylcholinesterase

11

Central and peripheral cholinergic synapse activity

Acetylcholinesterase

[ C]DASB [ C]FEOBV [ C]PMP

PET

[ C] Donepezil

PET

[11C]PK11195 Neuroinflammation

PET SPECT

11

[ C]MeNER 123

[

I]MIBG

Microglial translocator protein

Presynaptic noradrenergic system

Noradrenergic transporter

Postganglionic sympathetic neurons

Norepinephrine transporter

PD, Parkinson’s disease; PET, Positron Emission Tomography; SPECT, Single Photon Emission Computerized Tomography.

is also compelling evidence supporting the notion that iRBD may constitute by itself the manifestation of a more aggressive form of PD (Kim & Jeon, 2014), making it difficult to generalize findings from this cohort to the entire biological spectrum of PD. Furthermore, RBD is not specific of PD being a frequent occurrence of the prodromal phases of other rare synucleinopathies such as dementia with Lewy bodies (DLB) and multiple system atrophy (Skorvanek et al., 2018), posing further concerns on the utility of generalizing findings especially from cross-sectional studies, which would be informative to understand common

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disease mechanisms shared by synucleinopathies but may be less helpful in isolating the trajectory proper of the single pathological entity. Hyposmia is present in up to 80% of patients with PD, and its emergence has been described as one of the earliest features associated with prodromal PD (Postuma et al., 2012), in line with the proposed “body-first” hypothesis of PD a-synuclein spreading, which postulates the olfactory bulb to be one of the earliest central regions involved (Braak et al., 2003). The presence of hyposmia has been associated with a 7.3-fold increased risk of future conversion to PD in the following 5 years (Mahlknecht, Iranzo, et al., 2015). However, longterm longitudinal studies in aging populations, as well as research in first-degree relatives of PD patients, have demonstrated a heterogenous ability of the presence of hyposmia to represent a harbinger of future conversion to PD (Ponsen et al., 2010; Savica et al., 2018). Asymptomatic carriers of genetic mutations conferring high risk of developing familial forms of PD (familial PD) have also been identified as potential population models for the study of prodromal PD. Despite its relative rarity, this constitutes a privileged population to study, in that they possess unique characteristics of a more direct connection between the likely cause (genetic) and the future development of PD and may even constitute a potential model of preclinical PD, that is, a stage preceding prodromal PD whereby biological changes can be spotted using biomarker techniques in the absence of any clinical feature (Siderowf & Lang, 2012). Genetic loci associated with PD may range from common risk variants, which are present with high frequency in the general population and confer a low additional risk of PD, to rare gene mutations with high penetrance and responsible for autosomal dominant or autosomal recessive forms of familial PD (Cherian & Divya, 2020). The most common monogenic cause of familial PD is represented by point mutations to the LRRK2 gene, encoding for the leucine-rich repeat kinase 2. The mutation G2019S is the most frequent LRRK2 mutation associated with PD in the Caucasian population, with incomplete penetrance, and a phenotype resembling a late-onset, idiopathic form of PD with somehow slower progression, and a lower prevalence of RBD and cognitive impairment compared with its sporadic counterpart (Heinzel et al., 2019; Trinh et al., 2016). Much rarer than LRRK2 are mutations to the SNCA gene, encoding for the a-synuclein protein, which encompass missense mutations as well as multiplications (duplications, triplications, and more). In this population, the phenotype can vary consistently even in the same family; however, common features are an age of onset slightly earlier than that of idiopathic PD, a prominence of nonmotor symptoms (especially autonomic disturbance), and a faster decline. Heterozygous mutations to the GBA1 gene, encoding for the enzyme glucocerebrosidase, constitute the most common genetic risk factor for PD. It has been estimated that the relative risk of developing PD in heterozygous GBA1 mutations carriers is increased sevenfold with a cumulative lifetime risk at age 80 years of up to 30% (McNeill et al., 2012). Despite patients carrying GBA1 gene variants present with a late-onset disease, recent findings also suggest that a relevant share of early-onset PD cases may carry GBA1 variants (den Heijer et al., 2020). GBA1 variants (more than 400 identified so far) have been associated with either milder of faster progressive PD, with the latter being more associated with RBD and dementia. Despite varying degrees of penetrance, which require nonetheless long follow-ups, the risk of asymptomatic carriers to develop PD in the future is considerably higher than other forms of prodromal PD and are well studied. However, the main downsize is that clinical, biological, and pathological features of genetic forms of parkinsonism vary considerably according

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to the interested gene and may also differ from idiopathic PD, rising open questions on how much information inferred by studies on monogenic parkinsonism can be transposed to the general population with the idiopathic form of PD (Barber et al., 2017).

Dopaminergic imaging of prodromal Parkinsonism Molecular imaging of the dopaminergic system uses a wide weaponry of PET and SPECT presynaptic and postsynaptic radioligands (Table 9.2). The main targets of presynaptic radioligands are the enzymes amino L-acid decarboxylase (AADC), vesicular monoamine transporter 2 (VMAT2), and dopamine transporter (DAT), which mediate the production, storage in vesicles, and reuptake of dopamine in the presynaptic end, respectively. A more detailed description of the function of these enzymes is contained in Chapter 4. Molecular imaging studies using radiotracers targeting DAT (principally [123I]FP-CIT) conducted on patients with iRBD have demonstrated that around 20%e40% of these subjects show a significant reduction of striatal uptake compared with healthy controls, but still significantly higher compared with patients with PD (Albin et al., 2000; Arnaldi et al., 2015; Eisensehr et al., 2000, 2003; Iranzo et al., 2010, 2011; Stiasny-Kolster et al., 2005). Similar to PD patients, the pattern of striatal dopaminergic loss affects the putamen more than the caudate (Iranzo et al., 2010; Kim et al., 2010). The clinical characteristics of iRBD patients showing decreased DAT binding have been investigated in a number of studies. The demographic characteristics or the clinical features of iRBD patients with low striatal [123I]FP-CIT uptake do not seem to differ significantly compared with those with normal dopaminergic function (Chahine et al., 2019), although in another work the extent of striatal loss correlated with the presence of mild motor symptoms (Rupprecht et al., 2013). With regard to neurophysiological characteristics, a small study on eight iRBD found a correlation between presynaptic dopaminergic denervation and the level of muscle activity during REM sleep (Eisensehr et al., 2003), but a more recent study on 14 iRBD did not detect such correlation, suggesting that dopaminergic dysfunction may not be essential for the development of motor iRBD symptoms (Kim et al., 2010). A great deal of molecular studies has sought to determine the rate of progression of striatal dopaminergic loss in iRBD patients, and to understand its potential as short-term and, possibly, long-term predictors of disease conversion to PD. Iranzo and colleagues studied prospectively 43 iRBD patients with a combined assessment of [123I]FP-CIT SPECT and transcranial sonography, and followed up for an average 2.5 years. At baseline, 40% showed reduced [123I]FP-CIT striatal binding. After follow-up, eight of the patients with dopaminergic denervation and/or hyperechogenicity of the substantia nigra at baseline, but none of the others converted to a synucleinopathy, and the sensitivity of having both reduced [123I]FP-CIT striatal binding, and substantia nigra hyperechogenicity was of 100% at 2.5 years (Iranzo et al., 2010). The hypothesis that low striatal uptake of [123I]FP-CIT in iRBD could represent a predictor of short-term conversion of these patients to a synucleinopathy has been confirmed by two successive longitudinal studies from the same research group, in which it was demonstrated that iRBD patients showing low [123I]FP-CIT striatal dopamine concentration at baseline had significantly higher risk of phenoconverting at a follow-up of

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9. Molecular imaging in prodromal Parkinson’s disease

Principal molecular alterations associated with prodromal Parkinson’s disease.

Alteration

Significance

References

Decrease of striatal density of DAT

Denervation of presynaptic dopaminergic terminals; downregulation of DAT activity.

(Iranzo et al., 2011, 2017, 2010; Adams et al., 2005; Albin et al., 2000; Arnaldi et al., 2021, 2015; Artzi et al., 2017; Bergareche et al., 2016; Chahine et al., 2019, 2016; Eisensehr et al., 2003, 2000; Gersel Stokholm, Garrido, et al., 2020; Jennings et al., 2014; Kim et al., 2010; Marrero-González et al., 2020; Nandhagopal et al., 2008; Ponsen et al., 2004; Rupprecht et al., 2013; SánchezRodríguez et al., 2021; Shin et al., 2020; Siderowf et al., 2020; Sierra et al., 2017; Sommer et al., 2004; Sossi et al., 2010; Stiasny-Kolster et al., 2005; Wile et al., 2017)

Decrease of striatal density of VMAT2

Denervation of presynaptic dopaminergic terminals.

(Albin et al., 2000; Beauchamp et al., 2020)

Expression of the RBDRP

Combination of initial degenerative metabolic alterations with concomitant compensatory regional changes; possibly early manifestation of the PDRP.

(Arnaldi et al., 2019; Han et al., 2020; Meles et al., 2018; Shin et al., 2021; Wu et al., 2014; Yoo et al., 2021; Yoon et al., 2019)

Decrease of brain metabolism in the occipital regions

Suffering of vulnerable brain areas (Baril et al., 2020; Carli, Caminiti, associated with cognitive impairment. Galbiati, et al., 2020; Carli, Caminiti, Sala, et al., 2020; Hanyu et al., 2011; Vendette et al., 2012)

Increased density of SERT in the hypothalamus and brainstem of LRRK2 NMC

Possible compensatory change of serotonergic innervation preceding motor onset.

(Fu et al., 2018)

Decreased density of SERT in Serotonergic terminal degeneration (Wilson et al., 2019) raphe nuclei, striatum, and other following a “body-first” degeneration subcortical areas in SNCA NMC pattern. Increase of cholinergic presynaptic terminals and decrease of AChE hydrolysis

Compensatory changes of aimed at contrasting impending cholinergic denervation from the NBM.

(Bedard et al., 2019; Gersel Stokholm, Iranzo, et al., 2020; Liu et al., 2018)

Increased microglial activation in Neuroinflammation in vulnerable (Gersel Stokholm, Garrido, et al., 2020; the SN brain areas, often in conjunction with Mullin et al., 2021; Stokholm et al., dopaminergic neuron suffering. 2017; Stær et al., 2020) Decreased noradrenergic activity in the LC

Reduction of noradrenergic (Andersen et al., 2020; Knudsen et al., innervation from the LC in line with 2018) the “body-first” degeneration pattern.

DAT, dopamine transporter; LC, locus coeruleus; LRRK2, leucine-rich repeat kinase 2 gene; NBM, nucleus basalis of Meynert; NMC, nonmotor carrier; RBDRP, REM sleep behavior disordererelated pattern; SERT, serotonin transporter; SN, substantia nigra; SNCA, synuclein gene; VMAT2, vesicular monoamine transporter 2.

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up to 5.7 years (Iranzo et al., 2011, 2017). That presynaptic dopaminergic denervation in iRBD patients strongly foreshadows future phenoconversion has been further confirmed in a recent longitudinal study on 24 iRBD followed up for up to 10 years with serial PET scans with [18F] FMT, a PET tracer with affinity for the AADC enzyme (Miyamoto et al., 2020). In this study, iRBD patients with higher negative rate of decline in [18F]FMT uptake in the putamen developed either PD or DLB during the follow-up observation (Miyamoto et al., 2020). Low DAT striatal binding does not constitute by itself the only predictive factor of short-term phenoconversion in iRBD patients. This finding has been associated with age over 70, as well as with other prodromal PD signs such as constipation (Arnaldi et al., 2021). The presence of other prodromal signs in addition to DAT presynaptic loss can indeed increase the risk of future phenoconversion. In one recent SPECT study, baseline low striatal [123I]FP-CIT uptake and hyposmia at baseline predicted 67% of future iRBD converters, as opposed to 58% of low striatal [123I]FP-CIT uptake alone (Shin et al., 2020). A few studies have studied the integrity of the presynaptic nigrostriatal pathway in iRBD by investigating the density of the VMAT2 enzyme using the PET tracers [11C]DTBZ and [18F] AV-133, respectively (Albin et al., 2000; Beauchamp et al., 2020). These studies found a loss of striatal density of VMAT2 in iRBD patients, with putamen-to-caudate gradient, confirming the previous results obtained with molecular imaging of the DAT enzyme (Albin et al., 2000; Beauchamp et al., 2020). Cohorts of patients with hyposmia, as clinical model of prodromal PD, have been extensively studied with DAT imaging to assess whether this group demonstrates early signs of striatonigral dopaminergic degeneration. Sommer and colleagues studied 10 patients with idiopathic olfactory loss and hyperechogenicity of the substantia nigra on transcranial sonography, using [123I]FP-CIT SPECT imaging, and found that these displayed variable degrees of striatal dopaminergic loss, ranging from normal values, to borderline values, up to (50% of the group) pathological uptake (Sommer et al., 2004). Higher prevalence of DAT deficit in the putamen had been described in idiopathic hyposmia patients compared with controls, with a percentage ranging from 11% to 16% (Jennings et al., 2014; Marrero-González et al., 2020). This prevalence is lower compared with figures reported for iRBD, perhaps because a small percentage of hyposmic patients fulfill the Movement Disorder Society research criteria for prodromal PD compared with iRBD (Marrero-González et al., 2020). The association between altered clinical and imaging findings could help, however, identifying subgroups at high risk of short-term conversion to clinical PD. In a 2-year prospective work studying patients with and without hyposmia, only those with hyposmia and low striatal [123I]FP-CIT striatal uptake progressed to develop clinical PD, suggesting that a combination of these clinical and imaging findings could indeed identify people at high risk of phenoconversion to PD (Ponsen et al., 2004). Longitudinal findings from the Parkinson-Associated Risk Syndrome (PARS) study confirmed that patients with idiopathic hyposmia show increased prevalence of other nonmotor symptoms associated with prodromal PD (Chahine et al., 2016) and that those with a combination of hyposmia and DAT deficit at baseline tend to progress faster toward clinical PD (Siderowf et al., 2020). A number of cross-sectional and longitudinal studies have been conducted on nonmotor carriers (NMCs) of genetic mutations for autosomal dominant forms of familial PD. NMCs of LRRK2 point mutations (principally G2019S and R1441R) may show alterations of presynaptic nigrostriatal dopaminergic function (Adams et al., 2005; Artzi et al., 2017; Bergareche

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et al., 2016; Gersel Stokholm, Garrido, et al., 2020; Nandhagopal et al., 2008; Sossi et al., 2010; Wile et al., 2017) sitting to an intermediate degree between noncarrier healthy controls and patients with idiopathic PD (Artzi et al., 2017; Wile et al., 2017). Similar to the sporadic PD patients, the putamen seems to be the most affected striatal area (Artzi et al., 2017; Bergareche et al., 2016), and similar to studies in the sporadic form (Lee et al., 2000), the earliest indicator of dopaminergic dysfunction seems to be a reduction of DAT activity (Nandhagopal et al., 2008), suggesting that similar mechanisms of downregulation of this enzyme may take place in the prodromal stage of LRRK2-mediated familial PD. A few studies have observed longitudinally over up to 8 years groups of NMC LRRK2 subjects, in the attempt to detect molecular imaging harbingers of phenoconversion. Low values of striatal specific binding ratio on [123I]FP-CIT SPECT among subjects at baseline have been described as predictive of future conversion to PD (Sierra et al., 2017). However, the rate of annual decline of DAT binding has been calculated, after an 8-year follow-up, as 3.5%, a slower rate compared with that reported in cohorts of early PD patients (Simuni et al., 2018) with, in addition, a wide heterogeneity across subjects (Sánchez-Rodríguez et al., 2021). This may reflect either the incomplete penetrance of the LRRK2 gene mutations, and consequently a variable biological response to the presence of the mutation in these carriers, or alternatively the early stage in which the NMC cohort had been investigated (Sánchez-Rodríguez et al., 2021). A few cross-sectional and longitudinal works are available in the literature focusing on dopaminergic imaging of GBA NMCs. Values of both DAT binding in the striatum, as measured with either [11C]CFT PET or [123I]FP-CIT SPECT, and of AADC activity, as measured with [18F]DOPA PET, have been reported to be within normal limits in the majority of the subjects studied (Goker-Alpan et al., 2012; Kono et al., 2010; Mullin et al., 2021), with only rare individual cases displaying striatal reduction of dopaminergic neurons functionality (Goker-Alpan et al., 2012; Kono et al., 2010). In the only longitudinal study available so far, conducted on 13 GBA NMCs, Lopez and colleagues did not detect any significant difference in striatal [18F]DOPA uptake or in the longitudinal change in [18F]DOPA Ki compared with healthy controls, suggesting that dopaminergic degeneration in this group may constitute a slow process, likely influenced by the low penetrance of these mutations in conferring PD risk (Lopez et al., 2020). The phenotype of SNCA mutation carriers is heterogenous, with evidence, in the described kindreds, of a variability of symptoms even in the same family. This is, however, contrasted by a homogeneity of the pathological findings (Houlden & Singleton, 2012). Asymptomatic carriers of A53T or A30P missense mutations have shown on dopaminergic imaging employing [18F]DOPA PET or [123I]FP-CIT SPECT, with no evidence of degeneration of the presynaptic dopaminergic terminals (Krüger et al., 2001; Wilson et al., 2019). These subjects did not display any symptom or sign suggestive of impending motor decline. One single A53T asymptomatic subject from the Contursi kindred showed signs of DAT abnormalities on [123I]FP-CIT SPECT, in conjunction with olfactory dysfunction (Ricciardi et al., 2016). In this case, the presence of the loss of smell identification together with the subtle evidence of dopaminergic denervation could be interpreted as a possible sign of an underlying, albeit still subclinical, neurodegenerative process (Ricciardi et al., 2016). Scarce information is available on molecular imaging studies of the postsynaptic dopaminergic system in prodromal PD. In the idiopathic form of PD, a consistent increase of the availability of dopamine D2 receptors in the contralateral putamen to the clinically most

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affected side is seen, as an effect of upregulation of the receptor in response to the loss of the presynaptic terminals (de Natale et al., 2018). Studies in cohorts of iRBD (Eisensehr et al., 2000), as well as of subjects with the A30P missense mutation in the SNCA gene (Krüger et al., 2001), and heterozygous carriers of GBA mutations (Kono et al., 2010), did not detect any abnormality in the binding of either the PET radiotracer [11C]Raclopride or the SPECT radiotracer [123I]IBZM, sensitive for the D2 postsynaptic dopaminergic receptors. Despite the very small bulk of studies, it may be hypothesized that the D2 receptor upregulation seen in early PD cases may represent a phenomenon taking place only close to the predicted phenoconversion.

Molecular imaging of brain metabolism in prodromal Parkinson’s disease Quantitative measures of in vivo brain metabolism have been achieved with the use of PET and SPECT radioligands sensitive to the regional cerebral blood flow, and to the level of glucose consumption, as a surrogate marker of regional neuronal integrity (Attwell & Laughlin, 2001; Cohen et al., 1986; Vallabhajosula et al., 1989). Studies on patients with prodromal PD have employed the SPECT radioligands [99mTc]HMPAO, [99mTc]ECD, and [123I]IMP, and the PET radioligand [18F]FDG. Molecular studies testing regional metabolism in iRBD patients have showed a consistent pattern of increased metabolism in the brainstem, hippocampus, and the putamen, contrasted by a decrease of metabolism in cortical regions such as the medial and parietal frontal cortex, as well as the temporal and parietal cortices (Ge et al., 2015; Liguori et al., 2019; Mazza et al., 2006; Vendette et al., 2011). Some imaging findings seem to recapitulate similar spatial alterations of brain metabolism seen in overt synucleinopathies. Hanyu and colleagues detected a loss of brain perfusion using [99mTc]ECD SPECT in the precuneus and cerebellum, a finding that been consistently detected across synucleinopathies and that could represent a harbinger of ongoing degeneration (Hanyu et al., 2011). In addition to this, Vendette and colleagues demonstrated that iRBD patients with cognitive impairment showed hypometabolism in posterior cortical regions, a finding generally associated with phenotypes of PDD and DLB (Vendette et al., 2012). Cognitive impairment is found in about half of iRBD patients and is a risk factor for future conversion to DLB. Hypometabolism in this region could therefore represent an early marker of future phenoconversion to either DLB or PD. A finding of hypometabolism in occipital brain regions has been confirmed by more recent works examining iRBD patients at a single-subject level (Carli, Caminiti, Galbiati, et al., 2020). In this study, the detection of low [18F]FDG uptake in the occipital cortex has been interpreted as the effect of a selective vulnerability of this region, which may take place early in the neurodegenerative process, leading to synucleinopathies, and individual differences across subjects in the degree and extent of glucose hypometabolism as the effect of early and distinct pathophysiological events due to the underlying synucleinopathy (Carli, Caminiti, Galbiati, et al., 2020). The hypothesis that metabolic alterations in iRBD patients may represent potential distinct endophenotypes pointing to a specific neurodegenerative outcome has been further investigated by the same research group. Patients with iRBD, on [18F]FDG PET imaging, showed a distinct hypometabolism of the Chapters 5 and 6 divisions of the cholinergic

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networks, a characteristics shared by DLB patients but not by PD patients, suggesting this finding may distinguish iRBD patients with a peculiar biological alteration that may develop a dementia phenotype (Carli, Caminiti, Sala, et al., 2020). Attempts have been made to identify areas of altered metabolism as predictors of future phenoconversion to a synucleinopathy. In one longitudinal study on a cohort of 20 iRBD of which, after a 36-month followup, half converted to either PD or DLB, presence of increased hippocampal perfusion was correlated with motor and nonmotor scores and predicted phenoconversion (Dang-Vu et al., 2012). Baril and colleagues followed up a cohort of 37 iRBD patients for an average 17 months and detected a heterogenous pattern of normalization of initially hypometabolic cortical brain areas over time, which may reflect a process of compensation of initially affected areas (Baril et al., 2020). Neurodegenerative disorders such as PD feature disease-specific complex patterns of concomitant regional alterations of [18F]FDG PET uptake, which can be extracted using principal component analysis (PCA). These are named PD-related pattern (PDRP) and are characterized by an increased metabolic activity of the lentiform, and thalamus, associated with decreased activity of lateral frontal, paracentral, inferior parietal, and parietooccipital areas (Eidelberg et al., 1994). In patients with iRBD, the extent of the expression of the PDRP has been found to be intermediate between that of healthy subjects and of patients with PD and has been associated with a high risk of developing a synucleinopathy within 5 years (Holtbernd et al., 2014). Patients with RBD show a peculiar RBD-related pattern (RBDRP) characterized by hypermetabolism in the brainstem, thalamus, medial frontal and sensorimotor areas, hippocampus, supramarginal and inferior temporal gyri, posterior cerebellum, and concomitant hypometabolism in cortical areas, namely temporal, parietal, and occipital regions (Wu et al., 2014). In consideration that this pattern was highly expressed also in PD patients, it was concluded the RBDRP could represent an early manifestation of the PDRP (Meles et al., 2018). This pattern should be considered as a spectrum and a dynamic process where one person displays the pattern at increasing levels of expression, as disease progresses. In turn, this determines, at a group level, a heterogeneity of results, which translates to a low discriminating ability between RBD and PD patients (Arnaldi et al., 2019; Han et al., 2020). A molecular imaging study has sought to understand the correlation of these covariance patterns with the clinical picture of iRBD patients and its predictive value toward the conversion to PD (Yoon et al., 2019). A recent study identified a covariance pattern in a group of PD patients and RBD (PD þ RBD-related pattern, PDRBD-RP) and assessed its expression in a group of 28 polysomnography-confirmed RBD patients with no imaging evidence of loss of dopamine transporter as seen by [123I]Nortropane SPECT. In this latter group, the degree of expression of the PDRBD-RP was negatively correlated with frontal executive function and hyposmia; at follow-up, 5 out of 11 participants showing the PDRBDRP, against none of those who did not, converted to a synucleinopathy, indicating that the PDRBD-RP may represent a candidate marker of early neurodegeneration in iRBD patients (Yoon et al., 2019). Patients with iRBD also display higher expression of a de novo PD with RBD pattern (dnPDRBD-RP), which may represent a prodromal metabolic pattern with predictive value toward phenoconversion, indicative of a similar, yet distinct phenotype of patients with RBD eventually developing an overt synucleinopathy (Shin et al., 2021), and able to predict cognitive deterioration in iRBD patients, thus representing a potential marker of progression toward Lewy body disease (Yoo et al., 2021).

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There is scarce literature evidence on glucose metabolism alterations in NMC of mutations for familial parkinsonism, and the few data available may be influenced by the low penetrance of some mutations. An NMC of the A30P mutation in the SNCA gene showed, on [18F]FDG PET imaging, a decrease of glucose metabolism in the left temporal cortex with additional signs of low uptake in the left frontal medial cortex and caudate nucleus, which were associated with the presence of low scores on cognitive tests (Krüger et al., 2001). However, NMCs of a duplication in the SNCA gene displayed normal [18F]FDG PET scans also at an old age, possibly reflecting the low penetrance of this mutation (Nishioka et al., 2009). A single study is available on three asymptomatic carriers of GBA risk mutations for PD, who showed a hypometabolism localized in the supplemental motor area, a critical region for the generation of voluntary movements, and implicated in the generation of akinesia in the Parkinsonian phenotype (Kono et al., 2010). This alteration, therefore, may represent in the GBA NMC an early biological expression of the future manifestation of akinesia after phenoconversion (Kono et al., 2010).

Serotonergic molecular imaging of prodromal Parkinson’s disease The presynaptic integrity of the serotonergic system can be studied with selective tracers targeting the serotonin transporter (SERT), which carries out mirroring functions to the DAT enzyme for the dopaminergic system. The principal radiotracer employed is the PET ligand [11C]DASB, which displays high affinity for SERT binding. Given the similar structure, SERT availability can also be measured in extrastriatal areas, with low affinity, by radiotracers usually employed for the visualization of DAT density. In this instance, [123I]FP-CIT SPECT has found some application for the study of extrastriatal SERT. There is limited information regarding the integrity of the serotonergic system in patients with iRBD. In one study on 43 iRBD patients using [123I]FP-CIT SPECT, a negative correlation between the [123I]FP-CIT uptake in the dorsal raphe nucleus and the severity of apathy, a symptom associated with a serotonergic dysfunction in early PD, was detected, highlighting the possible role of a dysfunction of this neurotransmitter in the generation of this frequent symptom of synucleinopathies and of PD in particular, also in the prodromal stage (Barber et al., 2018). More data are available on NMC of genetic mutations for familial PD. Nine LRRK2 NMC showed, using [11C]DASB and PET imaging, an increase of serotonergic terminal density in the hypothalamus compared with PD patients and controls, and in the brainstem, compared with LRRK2 carriers with PD, which could be interpreted as a possible compensatory change of serotonergic innervation in the stages preceding the motor onset of PD (Wile et al., 2017). The presence of changes in serotonergic innervations preceding motor onset of PD in the premanifest stages of monogenic forms has also been suggested by a successive study on SNCA A53T NMC (Wilson et al., 2019). In this study, seven SNCA NMCs showed loss of [11C]DASB binding in the dorsal and ventral raphe nuclei, caudate nucleus, putamen, thalamus, hypothalamus, amygdala, and brainstem when compared with healthy controls, in presence of preserved dopaminergic innervation as demonstrated by unremarkable striatal [123I]FP-CIT binding, thus suggesting serotonergic terminal degeneration may precede in time dopaminergic alterations and thus motor symptoms in PD course, with potentially critical

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implications with regard of the nonmotor, prodromal symptoms of PD (Wilson et al., 2019). Changes of serotonergic innervations have also been studied in the light of the presence of a spatial covariant serotonergic pattern. In a study on nine LRRK2 NMCs, a covariance pattern composed of a decreased [11C]DASB binding in the pons, PPN, thalamus, and rostral raphe nucleus, and preserved [11C]DASB binding in the hypothalamus, amygdala, hippocampus, and substantia nigra was identified in a cohort of nine LRRK2 NMCs. This pattern was not expressed in healthy controls, possibly meaning a compensatory change of serotonergic brain innervation, and in LRRK2-PD, possibly reflecting in this case the loss of the compensatory change at the time of phenoconversion (Fu et al., 2018).

Molecular imaging of other neurotransmettitorial systems in prodromal Parkinson’s disease Alterations of nondopaminergic neuronal systems, within and outside the basal ganglia, play a decisive role in PD pathogenesis (Sanjari Moghaddam et al., 2017), and current therapeutic approaches are concentrating on the use of drugs acting on nondopaminergic pathways for the management of both motor and nonmotor symptoms (Cenci et al., 2022; Titova & Chaudhuri, 2018). It has become therefore of primary importance, to understand at which point of the PD temporal spectrum alterations of neurotransmettitorial systems such as the cholinergic and noradrenergic system take place and what is their role in the determination of PD symptoms, as well as to clarify the role complex biological responses such as neuroinflammation play in modulating cellular damage. Molecular imaging can provide in vivo spatiotemporal evidence of their abnormalities since the prodromal stage of PD. Several PET radioligands have been designed to study the cholinergic system integrity in the central nervous system in the past few years (Pasquini et al., 2021). In research works on models of prodromal PD, cholinergic pathology has been assessed by either measuring the degeneration of cholinergic axon terminals, with the ligand [18F]FEOBV, which binds to the vesicular acetylcholine transporter (VAChT), or measuring the activity of the acetylcholine esterase (AChE) enzyme, a synaptic extracellular enzyme that degrades acetylcholine secreted into the synaptic cleft into acetate and choline. PET ligands with high affinity for the AChE are [11C]PMP and [11C]donepezil. An alteration of cholinergic circuits in the basal forebrain has been put in relationship with the emergence of cognitive and neuropsychiatric symptoms in Lewy body diseases (Barrett et al., 2020). In PD, a degeneration of cholinergic nuclei and projections from the nucleus basalis of Meynert (NBM) has been put in relationship, in both postmortem and in vivo studies, with future development of cognitive impairment and dementia (Schulz et al., 2018; Wilson et al., 2021), Moreover, about 50% of iRBD patients display alterations of cognitive performances (Gagnon et al., 2009). Bedard and colleagues performed a pilot study on five iRBD patients and five healthy controls, using PET and [18F]FEOBV. They detected an increase of cholinergic terminal density in the patients relative to controls in regions critical for the pathophysiology of RBD such as the bulbar reticular formation, the locus coeruleus/subcoeruleus complex, and cerebellum, as well as the thalamus, precentral cortex, anterior cingulate, and prefrontal cortex, all areas known to receive extensive cholinergic innervation (Bedard et al., 2019). They also detected

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a direct correlation between the degree of muscle activity during REM sleep, as assessed with polysomnography, and [18F]FEOBV uptake increase in the mesopontine area and the paracentral cortex (Bedard et al., 2019). Studies employing PET tracers to measure AChE have demonstrated a reduction of its activity. Gersel Stokholm and colleagues found, in a group of 17 iRBD, a global reduction of AChE levels, as measured with [11C]Donepezil PET, in the neocortex, particularly in the superior temporal, occipital, cingulate, and dorsolateral prefrontal cortex, with iRBD patients with lower Montreal Cognitive Assessment (MoCA) scores showing the lowest uptake levels (Gersel Stokholm, Iranzo, et al., 2020). This finding suggests that, as also suggested by reports of concomitant dopaminergic and cholinergic abnormalities in iRBD patients (Valerio et al., 2013), the iRBD stage is already characterized by a multisystem disorder and that the degeneration of cholinergic terminals arising from the NBM may have critical implications in the generation of the subsequent phenotype of synucleinopathy. A research study is available on the integrity of the cholinergic system in NMCs of genetic mutations for familial PD (Liu et al., 2018). In this work, Liu and colleagues investigated 16 LRRK2 NMCs using the tracer [11C]PMP. They detected increased AChE hydrolysis rates in the cortex, including regions involved in the default mode and the limbic networks, as well as in the thalamus of this group, compared with health controls. These changes may be interpreted as an early and sustained attempt to compensate for LRRK2-related dysfunction in a way similar to what reported, in the same population, for the serotonergic system (Fu et al., 2018). Studies on the role of neuroinflammation in the promotion of neurodegeneration and of synucleinopathies in particular have bloomed in the past few years. It is thought that, in vulnerable areas such as the dopaminergic neurons of the substantia nigra, an imbalance toward proinflammatory insults (deposition of a-synuclein, iron accumulation, release of neuromelanin, and ultimately, neuroinflammation itself), could cause a self-maintaining vicious circle that promotes cellular damage as well as the perpetuation and propagation of this process in other areas (Sian-Hulsmann & Riederer, 2021). First- and second-generation PET tracers have been designed to target the translocator protein (TSPO) present on activated microglia and hence a marker of active neuroinflammation in brain regions. Gersel Stokholm and colleagues have studied 20 iRBD patients and 19 healthy controls using the firstgeneration tracer [11C]PK11195, detecting increased binding on the left side of the substantia nigra of iRBD patients as opposed to controls. This was coupled with reduced dopaminergic function, as measured with [18F]DOPA, in the bilateral putamen, as sign of impending dopaminergic suffering (Stokholm et al., 2017). In a successive study from the same group, the level of inflammation in the substantia innominata, a major source of cholinergic inputs from the basal forebrain to the cortex, was correlated with a decrease of cholinergic activity in the frontal and temporal lobes as assessed with [11C]Donepezil, indicating neuroinflammation as a possible contributing factor to cholinergic dysfunction in the brain in iRBD patients (Stær et al., 2020). Studies on asymptomatic carriers of genetic mutations for autosomal dominant forms of familial PD seem to confirm the presence of increased regional inflammation in brain regions also in this group. By using [11C]PK11195 and PET imaging on eight LRRK2 NMC, an increased binding in the substantia nigra was detected in three participants, which colocalized with reduction of dopamine synthesis as measured with [18F]DOPA (Gersel Stokholm,

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Garrido, et al., 2020). In another recent work, increased [11C]PK11195 binding potential was detected in the bilateral substantia nigra of nine GBA NMC as well as in the occipital and temporal lobes, the hippocampus, mesencephalon, and cerebellum (Mullin et al., 2021). In the GBA cohort, increased inflammation was not matched by a corresponding reduction of dopamine synthesis, suggesting that neuroinflammation may represent an early pathological occurrence preceding dopamine synthesis failure in nigral dopaminergic neurons in carriers of GBA risk mutations (Mullin et al., 2021). One of the most relevant areas affected in RBD is the locus coeruleus/subcoeruleus complex (Fraigne et al., 2015), which hosts the majority of the noradrenergic neurons of the brain (Boeve, 2013). According to the “body-first” hypothesis of pathological spreading in PD, this area is one of the earliest affected in PD, being involved before the emergence of motor symptoms. Therefore, it is hypothesized that a noradrenergic deficit arising from alteration of this brainstem region could underlie the phase of prodromal PD, before dopaminergic alterations ensue. To test this hypothesis, Knudsen and colleagues investigated a group of 22 iRBD patients using the PET tracer [11C]MeNER, sensitive for the presynaptic noradrenergic transporter (NART), coupled with assessment of dopaminergic ([18F]DOPA), coupled with the investigation of peripheral cholinergic ([11C]Donepezil), and cardiac sympathetic function (using SPECT and the tracer [123I]MIBG) coupled with the assessment of the integrity of the catecholaminergic neurons (using magnetic resonance imaging for neuromelanin). The iRBD group interestingly displayed a reduced sympathetic and peripheral cholinergic innervation, and a reduction of the noradrenergic neurons of the locus coeruleus and of noradrenergic thalamic innervation, in presence of still preserved dopaminergic storage capacity, in line with the hypothesis of a caudorostral gradient of pathology in patients with iRBD as prodromal synucleinopathy (Knudsen et al., 2018). A further research shed light on the peculiar role that noradrenergic pathology may play in the RBD phenotype. In this study, [11C] MeNER and [18F]DOPA have been employed in a group of 17 iRBD patients, as opposed to PD patients with or without RBD (Andersen et al., 2020). They detected a reduction of noradrenergic innervation in the primary sensorimotor region of both iRBD and PD patients with RBD, with additional correlation between deficit in dopamine synthesis and thalamic noradrenergic innervation in iRBD patients. This finding may represent the in vivo evidence of ongoing noradrenergic alteration underlying the RBD phenotype (Andersen et al., 2020).

Conclusions The recognition of the clinical characteristics and of the biological underpinning of prodromal PD has critical implications. It could significantly expand the time window of diagnosis of PD at a time where some cellular alterations have not become irreversible yet, thus allowing margin of operation for neuroprotective intervention. Molecular imaging has played a significant role in disclosing subclinical degrees of alterations of neurotransmitter pathways, which may precede in time the onset of dopaminergic denervation and, therefore, the onset of motor symptoms (Table 9.2). Molecular imaging also possesses unique characteristics of sensitivity toward its studied molecular targets, thus allowing attaining significant differences between groups with high effect sizes. This is relevant in rare subjects such as genetic cohorts, which nevertheless could constitute an essential model not only of prodromal, but

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10 Molecular imaging evidence in favor or against PDD and DLB overlap Silvia Paola Caminiti1 and Giulia Carli2 1

In vivo human molecular and structural neuroimaging Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy; 2Department of Nuclear Medicine and Molecular Imaging, University of Groningen, University Medical Center Groningen, Groningen, Netherlands

Introduction Parkinson’s disease (PD), Parkinson’s disease with dementia (PDD), and dementia with Lewy bodies (DLB) are characterized by the accumulation of misfolded a-synuclein in the form of Lewy bodies (LBs) (Spillantini & Goedert, 2016). In synucleinopathies, the spatial and temporal distribution of pathological a-synuclein in the brain involves only a few types of vulnerable and axonally interconnected projection neurons within the human nervous system (Braak et al., 2004). According to Braak’s proposed staging of a-synuclein spreading, it is possible to distinguish six stages of a-synuclein deposition and spreading (Braak & Del Tredici, 2009; Braak et al., 2003). The first a-synuclein-positive structures in the brain usually are the olfactory bulb and/or the dorsal motor nucleus of the glossopharyngeal and vagal nerves (stage 1). In stage 2, Lewy pathology develops in the medulla oblongata and the pontine tegmentum. By stage 3, pathology has reached the amygdala and the substantia nigra. Generally, at some point during this stage, the motor symptoms of PD (bradykinesia, with at least one of the three features of rigidity, rest tremor, or gait disturbance) start to be evident. As the pathology worsens, the a-synuclein inclusions reach the temporal cortex (stage 4). During stages 5 and 6, LBs and Lewy neurites (LNs) reach the neocortex, accounting for many of the cognitive problems associated with advanced PD. Of note, several studies have reported that w20%e50% of PD patients do not conform to the Braak staging scheme (Beach et al., 2009; Kalaitzakis et al., 2008; Parkkinen et al., 2008; Zaccai et al., 2008), and 7%e17% of cases do not exhibit a-synuclein pathology in the lower brainstem (Beach et al., 2009; Kalaitzakis et al., 2008; Parkkinen et al., 2008; Zaccai et al.,

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2008). It was suggested that Braak staging seems valid for young-onset PD patients with long-lasting disease and prevalent motor symptoms, but less so for late onset, rapid course PD, which develop dementia (Jellinger, 2019). Recently a new hypothesis emerged to explain the discrepancies related to these neuropathology findings, suggesting that a-synuclein pathology may have two different starting points: (1) a periphery nervous system (PNS)-first and (2) a central nervous system (CNS)-first (Borghammer & Van Den Berge, 2019). In the first case, patients have significant damage to the autonomic PNS following the prototypical Braak staging scheme. In the latter case, a-synuclein accumulation early affects CNS structures, including the substantia nigra, while the autonomic PNS seems to be initially spared (Borghammer & Van Den Berge, 2019; Knudsen et al., 2021). PDD is a late complication of PD and shows remarkably convergent neuropathological changes at autopsy with DLB (see Gomperts, 2016 for a review). Since PDD and DLB substantially overlap, there is increasing controversy about whether the distinction itself is still valid (Berg et al., 2014). In PDD, a-synuclein pathology almost invariably extends to the limbic system or neocortex, with neocortical involvement more frequent in PDD than in PD (Compta et al., 2011; Horvath et al., 2013; Irwin et al., 2012; Kempster et al., 2010). However, some data have challenged this view, showing that 94% of PD patients without dementia reached the highest cortical Braak’s stages (stages 5 and 6) (Compta et al., 2011). This may suggest that quantitative/semiquantitative measurements of a-synuclein inclusions are more informative than the sole topographic assessments of LB and LNs. Quantitative methods can overcome the ceiling effect of the Braak’s PD staging system, where a single LB in first-order sensory association areas in the neocortex or premotor area is sufficient to promote a patient from stage 5 to stage 6 (Compta et al., 2011). Accordingly, limbic and neocortical LB counts were found to be approximately 10 times higher in dementia cases (Apaydin et al., 2002), with cortical LB burden reaching an excellent accuracy to discriminate PDD from PD (Hurtig et al., 2000). Similarly, in DLB, LB pathology mainly involves cortical and limbic structures, with studies suggesting that high neocortical and limbic LB burden is an independent predictor of dementia (Jellinger, 2018; Ruffmann et al., 2016). Notably, in the nucleus basalis, substantia nigra, and midbrain, no differences in a-synuclein burden were seen between PD without dementia, PDD, and DLB (Apaydin et al., 2002; Sierra et al., 2016). Consistently, the Braak staging of sporadic PD does not entirely fit DLB, where LBs are found largely in the neocortex from the beginning (Arnaoutoglou et al., 2019). DLB is characterized by greater global a-synuclein pathology than PDD, but differences were usually not significant (Fujishiro et al., 2010; Halliday et al., 2011; Ruffmann et al., 2016). Differences in the spreading of a-synuclein pathology and its interaction with concomitant misfolded proteins, such as amyloid-beta (Ab) and tau, neurotransmitter systems alterations, and genetic factors might produce divergent clinical phenotypes in Lewy body disorders (LBDs) (Irwin et al., 2013). In PDD, the combination of LB and Alzheimer’s disease (AD) pathologydlevels of Ab plaques and tau neurofibrillary tangles (NFTs)dhas the most robust correlation with the severity of cognitive decline (Compta et al., 2011; Irwin et al., 2012). One study of pathologically confirmed PD and PDD cases found that cortical Ab burden and Braak tau stages significantly correlated with the development of cognitive impairments (Compta et al., 2011). Similarly, some studies found that AD pathology (Ab and tau NFTs) is an important predictor of dementia in DLB (Deramecourt et al., 2006; Jellinger, 2018).

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Accordingly, a synergy between these pathologic proteins has been proposed, suggesting that abnormal a-synuclein aggregation promotes the phosphorylation of tau (Guo et al., 2013). Of note, some data show that DLB presents a higher AD pathology prevalence than PDD at postmortem examination (Irwin et al., 2017; Jellinger & Attems, 2006). This difference may contribute to explaining the earlier onset of cognitive impairment in DLB compared with PDD. Moreover, in vivo neuroimaging studies showed overlapping patterns of regional hypometabolism, and cholinergic and dopaminergic neurotransmitter changes between PDD and DLB (Shimada et al., 2009; Klein et al., 2010; Watson et al., 2012; Pilotto et al., 2018). It is possible that the pathologic overlap of DLB and PDD represents the confluence of separate initial pathological mechanisms at the time of the emergence of dementia. Alternatively, it is conceivable that DLB and PDD represent phenotypic variations of the identical underlying disease process (Frey & Petrou, 2015). This chapter will provide an overview of the advances in molecular neuroimaging for the study of LBD. We will report evidence in vivo in favor or against the DLB and PDD overlap.

PET markers of synaptic dysfunction The [18F]FDG-PET signal reflects astrocyte/neuron coupled energy consumption (Kadekaro et al., 1987; Lundgaard et al., 2015; Pellerin & Magistretti, 1994; Sokoloff, 1981). Pioneering studies conducted by Sokoloff many years ago clearly showed that glucose is the obligatory energy substrate for the brain (Sokoloff, 1977). Pellerin and Magistretti (1994) proposed that lactate is produced glycolytically by astrocytes and is transported to neurons to be used as a significant source of energy (Pellerin & Magistretti, 1994). Recently, Zimmer et al. provided evidence that astrocytes account for a substantial proportion of glucose consumption, thus better defining their coupling (Zimmer et al., 2017). [18F]FDG-PET is a marker of synaptic integrity (Attwell & Laughlin, 2001; Rocher et al., 2003). Decrease of brain metabolism, assessed by [18F]FDG-PET, is considered a surrogate of synaptic dysfunction, which might be related to a variety of neuropathological events, including altered intracellular signaling cascades and mitochondria bioenergetics, impaired neurotransmitter release, and accumulation of neurotoxic protein species (Kato et al., 2016; Perani, 2014). a-Synuclein oligomers accumulate in synaptic terminals and spread in a prion-like manner through vulnerable synaptic circuits. Synaptic pathology plays, therefore, a central role in the pathogenesis of a-synucleinopathies (Calo et al., 2016). Specifically, the alterations in synaptic structure and function caused by the accumulation of abnormal a-synuclein are reported as a primary event in the pathogenesis of a-synuclein-related diseases (Calo et al., 2016). This synaptic dysfunction can be measured by [18F]FDG-PET, which allows the detection of patterns of brain hypometabolism with disease-specific topography within the LBD spectrum. DLB condition is characterized by a selective reduction of [18F]FDG uptake in the occipital cortex, specifically in the associative occipital cortex and primary visual cortex (McKeith et al., 2017). DLB shares with AD a similar hypometabolic pattern in the temporoparietal cortices (Caminiti et al., 2019; Perani et al., 2014; Presotto et al., 2017; Teune et al., 2010). In terms of differential diagnosis with AD, pathological reduction of metabolism in lateral occipital cortex showed the highest sensitivity (88%), whereas the relative preservation of posterior

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cingulate metabolism (the “cingulate island sign”) achieved the highest specificity (100%) (Lim et al., 2009). A recent study showed that occipital lobe hypometabolism has the highest discriminative power distinguishing DLB from AD and PD conditions with an accuracy of >90% (Caminiti et al., 2019). It has been also demonstrated that a posterior hypometabolism pattern represents an early marker able to distinguish patients with PD at higher risk of progression to dementia (i.e., PDD/DLB) after 4-year follow-up from those who will remain with normal cognition (Pilotto et al., 2018). Klein et al. (2010) directly compared regional brain hypometabolism in DLB and PDD patients. The hypometabolism patterns obtained at the group level in the two clinical cohorts showed an identical topographical distribution of reduced [18F]FDG signal. The hypometabolism pattern involved the occipital, temporoparietal, and prefrontal cortices bilaterally in both DLB and PDD groups (Klein et al., 2010). Yong et al. reported a more extended hypometabolism in the anterior cingulate in DLB than in PDD (Yong et al., 2007). Recent neuroimaging progresses allow the in vivo quantification of synapses by means of synapse-specific PET radioligand able to bind a protein ubiquitously present on synaptic vessels (Cai et al., 2019). Specifically, recently, synaptic density PET measurements, targeting the synaptic vesicle glycoprotein 2A (SV2A) (i.e., [11C]UCB-J tracer), are starting to be investigated, providing promising results in idiopathic PD (Matuskey et al., 2020; Wilson et al., 2020). Matuskey et al. (2020) have reported a synaptic loss in several brainstem regions of PD patients, including substantia nigra, red nucleus, and locus coeruleus. Accordingly, Wilson et al. (2020) demonstrated synaptic dysfunction in drug-naive PD patients, where lower brainstem [11C]UCB-J volume of distribution correlated with Movement Disorder Society Unified Parkinson’s Disease Rating Scale part III and total scores (Wilson et al., 2020). Reduced synaptic density was also found in the substantia nigra of both PD and DLB/ PDD patients. Of note, the DLB/PDD cohort was also characterized by widespread cortical loss of synaptic density using in vivo [11C]UCB-J PET imaging (Andersen et al., 2021). Probably due to the small sample size of the DLB/PDD group (N ¼ 13), the current study did not directly compare the PDD and DLB cohorts.

PET markers of amyloidosis To date, several tracers are available to assess Ab plaque burden in vivo, with [11C]PiB being the pioneering and most frequently used one for research purposes. [11C]PiB binds to both extracellular Ab plaques and vascular Ab deposits (Bacskai et al., 2003). This tracer has a very high specificity for Ab plaques, and it does not bind to non-Ab inclusions such as neurofibrillary tangles or LBs (Fodero-Tavoletti et al., 2012; Lockhart et al., 2007). Three other [18F]-labeled Ab tracers, [18F]florbetapir, [18F]florbetaben, and [18F]flutemetamol, have then received Food and Drug Administration and European Medicines Agency approval for clinical use (Villemagne et al., 2018). Ab and a-synuclein may interact synergistically to enhance each other’s aggregation and accelerate cognitive decline in the LBD spectrum. The mechanisms by which these two aggregation-prone proteins interact remain unclear. However, growing evidence suggests that Ab may influence a-synuclein pathology by modulating protein clearance, driving

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inflammation, activating kinases, or directly altering a-synuclein aggregation (Marsh & Blurton-Jones, 2012). In DLB, which often presents also in comorbidity with AD pathology, positivity to Ab -PET was reported frequently (Ossenkoppele et al., 2015). Pure DLB casesdwith exclusive a-synuclein brain pathologydare relatively rare (w20%), and Ab deposition is often present (Marsh & Blurton-Jones, 2012; McKeith et al., 2017). The presence of Ab pathology in DLB may be the trigger of dementia onset, which may influence the severity of cognitive impairment, as well as dementia progression (Foster et al., 2010; Gomperts et al., 2012; Pletnikova et al., 2005). Four imaging studies have compared Ab brain deposition in DLB and AD; one found low global [11C]PiB retention ratio in DLB (Kantarci et al., 2012); two other studies have also found lower cortical [11C]PiB uptake in DLB compared with AD (Rowe et al., 2007; Villemagne et al., 2011); and one study found no difference between DLB and AD, with very similar results in both groups in all cortical areas (Gomperts et al., 2008). Of note, increased [11C]PiB binding was found in DLB patients with a pattern of distribution similar to AD (Burack et al., 2010; Edison et al., 2008; Foster et al., 2010; Gomperts et al., 2008; Maetzler et al., 2009; Rowe et al., 2007). As estimated by a meta-analysis, the prevalence of [11C]PiB positivity is 68% in DLB, 34% in PDD, and 5% in PD patients with mild cognitive impairment (Petrou et al., 2015). In PDD, [11C]PIB binding was reported not to differ from PD patients or healthy individuals (Gomperts et al., 2008). However, cortical [11C]PIB retention has been shown to predict cognitive decline in PD (Gomperts et al., 2012). Neuropathology studies have confirmed these findings by demonstrating greater Ab pathology in DLB versus PDD/PD (Harding & Halliday, 2001) and PDD versus PD (Irwin et al., 2012). A recent longitudinal study (12-month follow-up) by Biundo et al. showed that PET Ab positivity is associated with worse global cognition, executive, and language deficits and that Ab positive PD patients have a higher risk of conversion to demented than Ab negative PD (Biundo et al., 2021).

PET markers of tauopathy One of the most recent advances in in vivo PET imaging is the evaluation of cerebral tau burden (Okamura et al., 2016; Okamura et al., 2014; Villemagne & Okamura, 2014, 2015). Notwithstanding tau biological complexity, the synthesis of some radioligands is now available and is rapidly entering into research use and possibly for potential validation in clinical practice (Okamura et al., 2016). To date, three broad groups of radioligands are under extensive evaluation, i.e., [18F]THK5351, [18F]THK5117 or [18F]THK5105 (Okamura et al., 2014; Harada et al., 2015, Harada et al., 2016), [18F]T807 (also known as [18F]AV1451 or [18F]flortaucipir) (Chien et al., 2013), and [11C]PBB3 (Maruyama et al., 2013). Furthermore, novel secondgeneration tau-PET tracers, including [18F]MK-6240 and [18F]PI-2620, are coming into the research field with the aim of solving the shortcomings of first-generation tau tracers (no off-target binding). [18F]MK-6240 has a high affinity for neurofibrillary tangles with negligible off-target binding to monoamine oxidases (MAO)-A/B, as well as high brain uptake, homogenous distribution, and rapid clearance (Hostetler et al., 2016; Pascoal et al., 2018).

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[18F]PI-2620 has a high signal-to-noise ratio and high affinity for neurofibrillary tangles with no MAO-A/B off-target binding and low affinity binding to Ab (Mueller et al., 2017). Using [18F]AV-1451 PET, increased neocortical tau retention was reported in DLB, which also correlated to cognitive performances (Gomperts et al., 2016; Kantarci et al., 2017; Smith et al., 2018). The pattern of tau distribution mainly involved temporoparietal cortices (Gomperts et al., 2016; Kantarci et al., 2017; Smith et al., 2018), but also the precuneus (Gomperts et al., 2016) and occipital cortex (Kantarci et al., 2017). However, tau binding in DLB patients was relatively lower than AD (Kantarci et al., 2017) and posterior cortical atrophy (Nedelska et al., 2019). Notably, in one study, 4 out of 17 DLB patients presenting with low amyloid burden had elevated [18F]AV-1451 uptake in the inferior temporal cortex, suggesting an independent effect of tau pathology with respect to amyloid burden (Gomperts et al., 2016). The reported evidence suggests that DLB patients might be characterized by varying degrees of AD pathology in addition to LB disease, representing the pathological hallmark. A pattern similar to that of DLB, but with lower magnitude and extension, was observed in PD in the presence of cognitive decline ranging from mild to severe impairment (Gomperts et al., 2016). Lee et al. studied 12 PD patients, 22 PD with cognitive impairment, and 18 DLB patients using [18F]AV-1451, for tau, and [18F]florbetaben, for detection of Ab deposits; the cohort also included 25 AD and 25 healthy controls (Lee et al., 2018). DLB showed slightly increased [18F]AV-1451 binding in the sensorimotor, parietotemporal, and visual cortices, compared with normal controls, and lower binding in temporal cortices when compared with AD. DLB with Ab-positive scans also showed an increase of tau deposition in the same cortical regions (Lee et al., 2018). Overall, tau PET studies have reported a gradient of pathology binding from cognitively normal PD, showing absent to lowest tau tracer binding, to cognitively impaired PD, with low binding, to DLB with intermediate tau tracer binding, lower than AD levels (Gomperts et al., 2016; Hansen et al., 2017).

PET markers of dopaminergic dysfunction The integrity and the number and density of presynaptic dopaminergic terminals and postsynaptic dopamine receptors have been assessed using both PET and SPECT imaging approaches in parkinsonian disorders. These approaches may be used to measure striatal aromatic amino acid decarboxylase (AADC) activity, providing an indirect estimation of the dopaminergic storage pool (Dexter & Jenner, 2013); the presynaptic dopamine transporters (DAT) availability, responsible to the reuptake of dopamine from the synaptic cleft (Saeed et al., 2017); and the density of vesicular monoamine transporter (VMAT2), implicated in the transport of dopamine from the cytoplasm into secretory vesicles (Hughes et al., 1992). Consistent changes in the dopaminergic system have been observed using PET and SPECT neuroimaging and different tracers (see Cummings et al., 2011 for a review). For the assessment of presynaptic dopaminergic functions, the AADC density can be imaged by using [18F] fluorodopa PET; the VMAT2 density with [11C]DTBZ PET; the dopamine transporter density with [11C]CIT PET, [18F]FP-CIT PET, [18F]PE2I PET, [123I]b-CIT SPECT, [123I]FP-CIT SPECT [123I]IPT SPECT, [123I]altropane SPECT [123I]PE2I SPECT, and [99Tc]m-TRODAT SPECT. For the assessment of postsynaptic functions, the density of the D2-like receptors can be

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assessed with [11C]raclopride PET, [18F]fallypride PET, [18F]DMFP PET, [123I]IBZM SPECT, [123I]IBF SPECT, and [123I]epidepride SPECT, the density of the D3 receptors with [11C] PHNO PET, and the D1-like receptors density with [11C]SCH23390 PET and [11C]NNC112 PET (Cummings et al., 2011; Searle et al., 2010). Presynaptic dopaminergic terminal measures show asymmetric radioligand uptake reductions in the striatum of PD patients, with a more pronounced decrease in the putamen than in the caudate. This reduction is correlated with the disease severity (Brück et al., 2009; Hsiao et al., 2014; Tatsch, 2008). The postsynaptic dopaminergic system can be studied by using radioligands binding to the dopamine receptors. Early PD is characterized by elevated D2-like receptor availability in the striatum contralateral to the more affected limb, greater in the putamen than in the caudate (Tatsch, 2008). The augmented binding has been related to a compensatory mechanism due to the presynaptic denervation. Preserved/upregulated postsynaptic receptor binding has been proposed to discriminate PD from other atypical parkinsonian disorders, such as DLB, in which the postsynaptic side is also affected (Schwarz et al., 1998). DLB is characterized by progressive disruption of the nigrostriatal dopaminergic pathway (O’Brien et al., 2004; Colloby et al., 2005; Walker et al., 2007; Klein et al., 2010). If impairment in presynaptic dopaminergic activity in DLB has been widely reported, studies on the postsynaptic integrity of dopaminergic neurons are scarce. A few ex vivo studies reported altered receptor densities in the striatum and thalamus (Piggott et al., 1999, Piggott et al., 2007; Sun et al., 2013). Piggot et al. (1999) analyzed brain samples from 25 DLB subjects and found decreased 3H-Raclopride binding in the caudal portion of the putamen and in the overall caudate nucleus, in DLB compared with PD and controls, suggesting a downregulation of D2-like receptors expression. Striatal D3 receptor density, as measured with 3H-7-OHDPAT, was unchanged (Piggott et al., 1999). Conversely, Sun et al. (2013) reported unchanged D2-like and increased D3-like striatal receptor densities in a small group of DLB/ PDD subjects. These findings might be related either to the medication history of some patients included in the sample or to a compensatory mechanism consequent to the presynaptic dopaminergic deficit (Sun et al., 2013). Dopaminergic assessment in DLB proved to be of particular importance at the clinical level in distinguishing DLB from AD, with [123I]FPCIT scans achieving very high diagnostic specificity and sensitivity (Lim et al., 2009; O’Brien et al., 2004; Walker et al., 2007). On the other hand, due to the pathological and clinical overlap between DLB, PDD, and PD, similarities have been reported in these conditions (Tatsch & Poepperl, 2013). Comparable levels of [123I]FP-CIT striatal binding have been observed in DLB and PD patients (Colloby & O’Brien, 2004; O’Brien et al., 2004; Walker et al., 2004). A similar progression of striatal dopaminergic degeneration in DLB, PD, and PDD is present, with comparable decline rates for the three groups, as assessed by a 1-year longitudinal study (Colloby et al., 2005). Few studies, however, reported some differences. In a work by Klein et al. (2010) in a group of PD, PDD, and DLB with asymmetric deficit of [18F]fluorodopa striatal uptake, PD and PDD showed severe Ki reductions both ipsilaterally and contralaterally to the clinically most affected side, whereas DLB patients did not show significant reduction of [18F] fluorodopa in the ipsilateral striatum. Walker et al. (2004) detected lower caudate binding in DLB as compared with PD, whereas no significant difference was found in putamen between the two groups (Siderowf et al., 2014; Walker et al., 2004). O’Brien et al. (2004) reported similar loss in putamen and caudate in DLB, whereas putamen, compared to caudate, was markedly more affected in PD. These inconsistencies could be related to confounding factors,

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such as age, disease duration, or severity of symptoms. Accordingly, differences in [123I]b-CIT caudate and putamen bindings have been reported in DLB subjects with different age and severity of disease (Lim et al., 2009).

PET markers of cholinergic dysfunction In addition to the loss of dopaminergic function, other neurotransmitter system dysfunctions, including the cholinergic system, have been evaluated in the pathophysiology of PD. In postmortem tissue, cholinergic cell loss is assessed by measuring the activity of choline acetyltransferase (ChAT), the enzyme that catalyzes the synthesis of acetylcholine. In patients with PD, postmortem studies have documented a selective loss of cholinergic neurons in the basal forebrain and a significant reduction in hippocampal ChAT selectively associated with dementia occurring in patients with pure LB PDdno other co-occurrent neuropathologies (Hall et al., 2014). Although there are no PET radiotracers for ChAT, there are radiotracers for acetylcholinesterase (AChE), or the acetylcholine vesicular transporterdthe latter two having been shown to map acetylcholine cells in the brain and to have a good correspondence with ChAT (Mesulam & Geula, 1992; Weihe et al., 1996). AChE is an enzyme that catalyzes the hydrolysis of acetylcholine to choline and acetic acid. Though AChE is anchored in presynaptic membranes in the acetylcholinergic neuron as well as in postsynaptic membranes and in the intersynaptic space, it has a very good correspondence with ChAT (Volkow et al., 2001). The activity of AChE can be measured with PET using labeled acetylcholine analogs that serve as substrates for AChE (i.e., [11C]PMP) and hydrolyze to a hydrophilic product (i.e., [11C]MP4A) that is trapped in the cell (Volkow et al., 2001). Another method is to use radioligands that bind to AChE (Brown-Proctor et al., 1999; Pappata et al., 1996). Cholinergic PET imaging studies with [11C]MP4A or [11C]PMP have shown that loss of cortical AChE activity occurs early in PD and is more evident in PDD than in non-demented PD (Bohnen et al., 2003; Hilker et al., 2005; Shimada et al., 2009). Although the cholinergic denervation occurs early in PD, no significant difference in brain AChE activity levels has been reported between patients with early PD and patients with advanced PD (Shimada et al., 2009). This data suggests that cholinergic deficits do not progress in the PD disease course without dementia, even at advanced stages. A large crosssectional study of 143 PD patients with and without dementia (Bohnen et al., 2015), and a smaller earlier study (Bohnen et al., 2006), demonstrated that a worse cognitive performance was strongly associated with reduced cortical AChE density, as assessed with [11C]PMP PET. This is consistent with postmortem evidence suggesting loss of forebrain cholinergic function is associated with PDD (Bohnen et al., 2003; Hilker et al., 2005; Shimada et al., 2009). Another study, tracing M1/M4 subtype muscarinic receptor with [123I]QNB SPECT, found evidence of decreasing binding in basal forebrain, temporal cortex, striatum, insula, and anterior cingulum in PDD (Colloby et al., 2016). Using [11C]MPA4 PET, pathological reduced cortical AChE activity has also been demonstrated in DLB patients (Shimada et al., 2015). These in vivo molecular imaging data highlight the susceptibility of the cholinergic system to degeneration in DLB.

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Two studies directly compared the reduction in AChE activity in the PDD and DLB and found that there was no significant difference between the two clinical conditions in terms of both intensity and topography distribution (Klein et al., 2010; Shimada et al., 2009). Moreover, PDD patients exhibited higher cholinergic dysfunction than AD patients, matched according to the degree of cognitive impairment, suggesting that different mechanisms may underlie cognitive decline in PD and AD, where the cholinergic model of dementia may be more applicable to PDD than prototypical AD (Bohnen et al., 2003). By using [11C]MP4A PET, DLB exhibited a greater reduction of cortical AChE activity compared with AD, showing 28% and 8% of reduction in [11C]MPA4 uptake compared with controls, respectively (Shimada et al., 2015). All the aforementioned suggest that DLB and PDD are essentially the same disease in terms of brain cholinergic dysfunction and differ substantially from AD in terms of cholinergic neuron degeneration. The widespread and profound cerebral cortical cholinergic deficits associated with both may explain why cholinesterase inhibitors often bring about favorable responses in the treatment of dementia in both PDD and DLB patients (Gomperts, 2016).

PET markers of noradrenergic dysfunction Noradrenergic innervation of the brain arises almost exclusively from the neuromelanincontaining cells of the locus coeruleus. Depositions of aggregated a-synuclein in the locus coeruleus occur early in Braak stage 2 of PD (Braak et al., 2004). Postmortem studies have detected a substantial noradrenergic deficit in PD patients (Kish et al., 1984). Moreover, in vitro studies have shown significant degeneration of noradrenergic neurons in the locus coeruleus and loss of noradrenergic projections to cortical and subcortical structures (Zarow et al., 2003). However, in vivo assessment of the noradrenergic system has been hampered by the lack of suitable imaging tools, and very few imaging studies have attempted to quantify this loss of noradrenaline in PD patients using PET ligands that were not specific to components of noradrenergic synapses (Pavese et al., 2011; Remy et al., 2005). Recently, neuromelanin-sensitive MRI sequences have been introduced, capable of delineating the pigment in the cell bodies in the locus coeruleus (García-Lorenzo et al., 2013; Keren et al., 2009; Sasaki et al., 2006). Additionally, [11C]MeNER, a reboxetine analog with high specificity for noradrenaline transporters (NETs), has been developed as a novel PET tracer for the study of the presynaptic noradrenergic system (Schou et al., 2003). In particular, the preliminary studies suggest that [11C]MeNER has the potential to be a suitable measure of the density of NETs in the human brain (Nahimi et al., 2018). These imaging modalities have been successfully used to study locus coeruleus changes and noradrenergic transporter function in PD (García-Lorenzo et al., 2013; Nahimi et al., 2017). With regard to the molecular PET findings, a recent study shows a reduction of [11C]MeNER binding potential in PD patients compared with age-matched healthy controls in high-binding regions of the brain (i.e., raphe nucleus, locus coeruleus, and hypothalamus), and attained significance in the thalamus and nucleus ruber (Nahimi et al., 2018). The [11C]MeNER binding potential did not correlate with disease duration or severity, but patients with tremor had more preserved noradrenergic innervation in the thalamus compared with patients without tremor (Nahimi et al., 2018). This is in line

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with previous electrophysiological studies, which suggested that parkinsonian tremor may originate in basal ganglia circuits, specifically in the thalamus, and further implicate the noradrenergic system in tremor genesis (Isaias et al., 2011, Isaias et al., 2012). The tremordominant PD subtype displays fewer and milder nonmotor symptoms and possibly a slower disease progression rate (Fereshtehnejad et al., 2015; Paulus & Jellinger, 1991). The noradrenergic system mediates cognitive attention, regulates the sleep cycle, and influences cardiac function via widespread projections from the locus coeruleus to the cortex, brainstem nuclei, and the hypothalamus (Rodovalho et al., 2006; Vazey & Aston-Jones, 2012). Specifically, as the locus coeruleus norepinephrine system is part of the sleepewake cycle through projections to wake-promoting regions (Szabadi, 2013), deficits in this system lead also to sleep disturbances, such as sleep-disordered breathing, excessive daytime sleepiness (Chaudhuri et al., 2006), and rapid eye movement (REM) sleep behavior disorder (RBD) (Knudsen et al., 2018). RBD is a parasomnia characterized by a lack of normal muscle atonia during REM sleep and consequently enactment of dream content (Högl et al., 2017). RBD is a frequent finding in PD patients (Boeve et al., 2003; Galbiati et al., 2018). More than 80% of idiopathic RBD cases will eventually be diagnosed with PD, DLB, or, rarely, multiple system atrophy (Iranzo et al., 2013; Postuma et al., 2015). The RBD phenotype is believed to be caused by damage to the locus subcoeruleus and magnocellular nucleus (Boeve, 2013). Several studies have suggested that RBD-positive PD patients are at a higher risk of developing nonmotor symptoms, including autonomic dysfunction and cognitive impairment (Fereshtehnejad et al., 2015; Rolinski et al., 2016). In this context, another [11C]MeNER study recruited PD patients with RBD or without RBD to elucidate the potential role of noradrenaline in RBD and nonmotor symptoms of PD. RBD-positive PD patients displayed significantly reduced [11C]MeNER distribution volume ratios in the thalamus, locus, hypothalamus, and raphe nuclei compared with RBD-negative PD patients (Sommerauer et al., 2018). The [11C]MeNER distribution volume ratios in the locus coeruleus and hypothalamus correlated with the drop in systolic and diastolic blood pressure upon standing, implicating the noradrenergic system in autonomic dysfunction in PD patients. In addition, reduced [11C]MeNER binding in the locus coeruleus correlated with decreased global cognition scores. A reduction of high frequency (alpha and beta) and increases in low frequency (theta and delta) activity in the brain, as measured with standard electroencephalography, is related to an increased risk of cognitive impairment in PD patients (Klassen et al., 2011). The slowing of the electroencephalography was more pronounced in RBD-positive PD patients and correlated with [11C]MeNER distribution volume ratios in the locus coeruleus (Sommerauer et al., 2018). These findings support the claim that damage to the noradrenergic neurons may be involved in cognitive decline in PD. There is a lack of in vivo molecular PET studies assessing noradrenergic innervation integrity in the whole a-synuclein spectrum, including PDD and DLB, due to the problematic issues of suitable imaging tools. However, as mentioned earlier, postmortem data showed that a-synuclein burden in the locus coeruleus not only precedes (stage 2) but may even be of greater magnitude than that of the substantia nigra (stage 3) in PD, DLB, and PDD (Del Tredici & Braak, 2013). Future longitudinal studies with [11C]MeNER PET are needed to characterize in vivo the relationship between the integrity of noradrenergic innervation and disease progression and nonmotor symptoms in PD, PDD, and DLB patients.

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Conclusions

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Conclusions The accurate tracking of pathological mechanisms and brain vulnerability are fundamental targets of in vivo imaging. Molecular imaging studies, performed so far in the LBD spectrum, have targeted brain metabolic activity, proteinopathies, and neurotransmitter systems. Similar metabolic topographical changes have been reported in both DLB and PDD, including marked deficits in occipital regions, and relative sparing of the medial temporal lobe compared with AD. PET studies tracing amyloid and tau pathology evidenced that there is greater amyloid pathology in DLB versus PDD/PD. It is conceivable that comorbid AD pathology (amyloidopathy and tauopathy) in the LBD spectrum is associated with more severe clinical outcomes, faster conversion to dementia, and shorter survival compared with more “pure” LB pathology. Nigrostriatal dopaminergic impairment is another shared pathophysiology of PDD and DLB that does not allow accurate discrimination between these two conditions.

FIGURE 10.1 Neurotransmitter and proteinopathies changes in Lewy body diseases. Top panel of the figure shows Braak staging scheme of a-synuclein spreading in PD, PDD, and DLB. The middle panel depicted tau and amyloid burden topography in PD, PDD, and DLB according to PET molecular evidence. PET studies tracing amyloid pathology evidenced that there is greater amyloid pathology in DLB versus PDD/PD. The pattern of tau distribution mainly involves temporoparietal cortices, precuneus, and occipital cortex in DLB. A similar pattern, but with lower magnitude and extension, was observed in PD in the presence of cognitive decline ranging from mild to severe impairment (Gomperts et al., 2016). The bottom panel of the figure shows neurotransmitter impairments characterizing PD, PDD, and DLB. PD without cognitive impairment presents severe dopaminergic nigrostriatal impairments, noradrenergic denervation, and only moderate cholinergic system impairment. DLB and PDD are essentially the same disease in terms of brain neurotransmitter dysfunction showing a severe cortical and subcortical dopaminergic, noradrenergic, and cholinergic impairments.

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Molecular imaging studies confirmed the heterogenous etiology underlying the cognitive impairment syndrome in LBD, where neurotransmitter and proteinopathy changes showed incremental and possibly interactive effects (Fig. 10.1). Variable presence and combinations of proteinopathies and neurotransmitter changes may define endophenotypes within the cognitive impairment syndrome of LBD. Accordingly, a multitracers analysis showed that cortical cholinergic activity and global amyloid burden predict global cognitive dysfunction. Specific cognitive domains were predicted by both neurotransmitter deficits and AD proteinopathy. The verbal learning cognitive domain was predicted by cortical amyloid binding and cholinergic activity. Instead, the attention cognitive domain was predicted by striatal amyloid binding and caudate nucleus dopaminergic activity (Shah et al., 2016). These data suggest that the combined effect of neurotransmitter and proteinopathy changes has a role in cognitive impairment in PD at risk of PDD. PDD and DLB present a more profound reduction of ChAT activity compared with PD. The noradrenergic system seems to be associated with cognitive decline in the LBD spectrum, but further study should better clarify this aspect. Future molecular imaging studies targeting a-synuclein pathology are expected to better define the early pathological changes characterizing the LBD spectrum, and the interaction between this hallmark pathology and other changes has emerged so far. Moreover, molecular imaging studies with larger cohorts and longitudinal follow-up are needed to improve our understanding of the temporal sequence of pathological events in the LBD spectrum and its effects on the different phenotypical expressions.

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Striatal and Cortical b-Amyloidopathy and Cognition in P arkinson’s Disease. Movement Disorders, 31(1), 111e117. Wiley Online Library. Shimada, H., Hirano, S., Shinotoh, H., Aotsuka, A., Sato, K., Tanaka, N., Ota, T., Asahina, M., Fukushi, K., Kuwabara, S., Hattori, T., Suhara, T., & Irie, T. (2009). Mapping of brain acetylcholinesterase alterations in Lewy body disease by PET. Neurology, 73(4), 273e278. AAN Enterprises. Shimada, H., Hirano, S., Sinotoh, H., Ota, T., Tanaka, N., Sato, K., Yamada, M., Fukushi, K., Irie, T., Zhang, M. R., Higuchi, M., Kuwabara, S., & Suhara, T. (2015). Dementia with Lewy bodies can be well-differentiated from Alzheimer’s disease by measurement of brain acetylcholinesterase activityda [11C] MP4A PET study. International Journal of Geriatric Psychiatry, 30(11), 1105e1113. Wiley Online Library. Siderowf, A., Pontecorvo, M. J., Shill, H. A., Mintun, M. A., Arora, A., Joshi, A. D., Lu, M., Adler, C. H., Galasko, D., Liebsack, C., Skovronsky, D. 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C H A P T E R

11 Magnetic resonance imaging in Parkinson’s disease with mild cognitive impairment, Parkinson’s disease dementia, and dementia with Lewy bodies Thomas Kustermann1, Stefan Holiga1, Stefano Zanigni1, 2 and Gennaro Pagano1, 3 1

Roche Pharma Research and Early Development (pRED), Neuroscience and Rare Diseases Discovery and Translational Area, Roche Innovation Center Basel, Basel, Switzerland; 2 Product Development Neuroscience, F. Hoffmann-La Roche Ltd, Basel, Switzerland; 3 Neurodegeneration Imaging Group, University of Exeter Medical School, London, United Kingdom

Introduction Abnormal accumulation of aggregated a-synuclein into Lewy bodies and Lewy neurites is the pathological hallmark of Lewy body diseases, which includes, among others, Parkinson’s disease (PD) with mild cognitive impairment (PD-MCI), Parkinson’s disease dementia (PDD), and dementia with Lewy bodies (DLB) (Seidel et al., 2015). The differential diagnosis among them is mostly based on the degree of cognitive impairment (MCI vs. dementia) and the temporal relationship between the onset of cognitive impairment versus motor dysfunction with DLB showing cognitive decline before or in concurrence with parkinsonisms, while in PDMCI and PDD, dementia develops at least one year after parkinsonian symptoms manifest (Emre et al., 2007; McKeith et al., 2017).

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However, despite extensive work, the exact pathophysiology of PD-MCI, PDD, or DLB is still largely unknown, and the diagnostic confirmation is still based on postmortem evaluations, with Lewy bodies and neurites often cooccurring with amyloid-b plaques and tau neurofibrillary tangles (Elahi & Miller, 2017). Diagnostic criteria for DLB show acceptable sensitivity but low specificity (Emre et al., 2007; McKeith et al., 2017); thus, in the clinical setting, DLB is often misdiagnosed (Zahirovic et al., 2016). Although a relative preservation of medial temporal lobe (MTL) structures in brain imaging is common and has been included added as a supportive biomarker feature in the DLB criteria (McKeith et al., 2017), it has not been proven to increase diagnostic specificity (Filippi et al., 2012). Diagnostic criteria include the use of molecular imaging (e.g., dopamine transporter deficit) as supportive or indicative diagnostic features without allowing for differentiation from other parkinsonian disorders (Emre et al., 2007; McKeith et al., 2017). Novel biomarkers based on magnetic resonance imaging could contribute to an enhanced diagnostic accuracy by investigating (micro-)structural and functional brain alterations manifest in PD-MCI, PDD, and DLB. The Lewy body disease spectrum has been the object of intense research using structural, diffusion, iron-sensitive, functional MRI with the purpose of detecting anatomical and functional alterations and their relationship with disease severity and duration. They can increase diagnostic accuracy made at the bedside and potentially monitor disease progression or being used as diagnostic biomarkers to test novel therapeutics. This chapter will review the major contributions of MRI neuroimaging research to the understanding of Lewy body dementias.

Structural MRI Structural MRI imaging, generally using 3D T1-weighted sequences, aims to detect subtle anatomical changes in cortical and subcortical gray matter (GM) regions. Visual evaluation of structural MRI has been widely used to compare regional structural changes in PD-MCI, PDD, and DLB against AD and healthy controls (HCs). More quantitative methods assessing brain structure take advantage of identifying typical anatomical landmarks (such as GM, white matter [WM], and cerebral spinal fluid [CSF]) boundaries using automated image processing algorithms. For instance, voxel-based morphometry (VBM) (Ashburner & Friston, 2000) automatically segments and classifies GM, WM, and CSF tissue to detect regional structural differences at a voxel level, allowing statistical group comparisons between or within subjects across the entire brain. Another major step in automated quantitative structural images was the development of FreeSurfer primarily focused on automated unfolding and flattening of the cortical surface, thus allowing the quantification of cortical thickness. On visual evaluation of structural MRI, it is possible to detect cerebrovascular diseases and space-occupying lesions such as brain tumor or hematoma, which are part of the differential diagnosis of Lewy body disease and Alzheimer’s disease (AD) (Barber et al., 1999; O’Brien et al., 2003). People living with DLB show less brain atrophy than AD, with a relative preservation of the MTL in DLB compared with AD (Orad & Shiner, 2022). However, postmortem data from DLB cases have highlighted a progressive atrophy, in particular in individuals older than 85 years (Burton et al., 2004). Higher levels of amyloid-b plaques and

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neurofibrillary tangles but not Lewy body-associated neuronal inclusions have been associated with greater atrophy of the hippocampus, suggesting that GM loss in DLB could be due to concomitant AD pathology (Burton et al., 2004). Relative hippocampal atrophy in DLB, AD, and HCs has also been investigated, showing less severe atrophy in DLB than in AD when compared with HCs (Beyer et al., 2007). Despite the relative preservation of the MTL and the hippocampus in DLB, when hippocampal atrophy occurs in DLB, it is associated with more severe cognitive deficits (Burton et al., 2004). Similarly, other studies have demonstrated that the entorhinal cortex, CA1, and subiculum areas of the hippocampus may be most affected in AD compared with DLB (Kamagata et al., 2017; Lee et al., 2010). In parallel with the MTL findings, people with DLB generally demonstrate relative preservation of episodic memory capabilities compared with AD while experiencing impairments in attentional and visuospatial tasks. Longitudinal and cross-sectional evidence consistently show that DLB is associated with lower global atrophy compared with AD. A study in 184 individuals with various dementias that underwent structural MRI and had their brain assessed postmortem showed that pathological groups were identifiable based on global atrophy with an accuracy ranging from 86% to 97% from HCs. DLB was differentiated from AD with a sensitivity of 64%, specificity of 82%, and balanced accuracy of 73%, as well as from frontotemporal lobar degeneration with a sensitivity of 93%, specificity of 89%, and balanced accuracy of 91% (Harper et al., 2016). The low sensitivity in distinguishing DLB from controls or AD ultimately emphasizes the elevated number of false negatives attached to DLB diagnosis, which is likely due to the large degree of pathophysiological overlap, which exists between DLB and AD, as demonstrated by the fact that more than half of DLB cases exhibit significant amyloid-b pathology (Petrou et al., 2015). In a comparison between DLB and PDD, global atrophy was more pronounced in DLB, which is in line with the fact that DLB shows greater amyloid-b levels (Mak et al., 2014). Studies investigating correlation patterns between brain structure and clinical and neuropsychiatric manifestations of the disease revealed that atrophy of the anterior cingulate, right hippocampus, and amygdala was associated with cognitive performance (SanchezCastaneda et al., 2009), while atrophy in the left precuneus and inferior frontal lobe correlated with visual hallucinations in DLB, but not in PDD (Sanchez-Castaneda et al., 2010). By using a VBM approach, studies have also demonstrated greater atrophy in the midbrain of people with DLB compared with AD, with the substantia innominata as the area showing the greatest atrophy (Hanyu et al., 2005; Whitwell et al., 2007). The substantia innominata contains the nucleus basalis of Meynert, which is highly involved in the cholinergic neurotransmitter system and known to be subject to degenerative processes in DLB (Klein et al., 2010) and PDD (Bohnen et al., 2003). In addition, an increased dorsal mesopontine atrophy was present in DLB compared with AD (Hanyu et al., 2005; Whitwell et al., 2007) and allowed the differentiation between DLB and AD (Whitwell et al., 2007). These findings may suggest a greater cholinergic dysfunction in DLB, perhaps related to the presence of midbrain a-synuclein pathology. No distinct cortical atrophy profile was identified as typical of DLB compared with PDD (Burton et al., 2004; Klein et al., 2010), but greater volume reductions in the temporal, parietal, and occipital lobes have been shown in people with DLB (Beyer et al., 2007). Alongside the temporal and parietal atrophy, occipital and striatal atrophy has also been reported in DLB (Lee et al., 2010), particularly affecting the caudate, putamen, and pallidum. The atrophy in those areas was higher in DLB than AD, III. Clinical applications in lewy body dementias

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when normalized to total intracranial volume (Hanyu et al., 2005; Lee et al., 2010). Interestingly, such atrophy has not been described in people with PDD (Colloby et al., 2013; SanchezCastaneda et al., 2010). A meta-analysis of VBM studies in DLB, AD, and HCs found that there were no significant structural differences between DLB and HCs (although temporal and insular atrophy was reported in Zhong et al., 2014), but a relative sparing of parahippocampal regions was characteristic of DLB patients when compared with people with AD (Ma et al., 2022). Conversely, a meta-analysis comparing PD-MCI patients with PD patients revealed insular and temporal atrophy, which was linked to disease severity (Qin et al., 2020). One study presented evidence that pathogenic a-synuclein levels increase significantly with age in the striatum and hippocampus, making these regions more vulnerable to Lewy body pathology. A working hypothesis suggests that, when the levels of a-synuclein in these regions exceed the pathogenic threshold, it may lead to the formation of Lewy body pathology and neurodegeneration, which is primarily evident in DLB (Lebedev et al., 2013). However, why such atrophy is not present in PDD patients remains unclear (Zhong et al., 2014). The typical cortical GM changes related to the neuropathological alterations can also be detected by assessing cortical thickness on T1-weighted MRI sequences with high accuracy and sensitivity. Hence, this method has been applied to structural MRI as a way of differentiating DLB from AD, PDD, and HCs. Investigations into cortical thickness alterations in DLB revealed relatively small GM changes, primarily affecting the posterior parietal areas, as opposed to the patterns of GM change affecting the temporoparietal association cortices in AD (Watson et al., 2015). These findings corroborate the notion that DLB is a result of neuronal synaptic dysfunction, not neuronal loss. A multivariate classification study of cortical thickness demonstrated 82% sensitivity and 85% specificity for differentiating AD from DLB (Lebedev et al., 2013). AD was characterized by regional thinning of the parahippocampal, subgenual cingulate regions and temporal pole, whereas cortical thinning in DLB was localized in the middle and posterior cingulate, superior temporooccipital, and lateral orbitofrontal regions. It is interesting to note that the greater temporal involvement in AD compared with DLB has been one of the most consistent findings on structural imaging whether on visual inspection or VBM studies (Ballmaier et al., 2004; Barber et al., 2000). Longitudinal brain atrophy rates are used as outcome measures to evaluate the potential of disease-modifying agents in AD, but the atrophy rate in DLB has been reported to be analogous to or marginally greater than HCs (Mak et al., 2015). Despite the challenge of conducting longitudinal studies of DLB given the higher mortality rates compared with AD (Williams et al., 2006), further long-term investigations would be valuable in elucidating the neurobiological underpinnings of disease heterogeneity in DLB. Evidence of subcortical involvement in DLB has revealed the vulnerability of the thalamus, striatum, and brainstem to Lewy-related pathology. Studies have demonstrated that thalamic diffusion and perfusion deficits are associated with DLB (Shimizu et al., 2008), and striatal volumetric loss appears to be more affected in DLB than AD (Watson et al., 2016), with prominent nigrostriatal dysfunction (Piggott et al., 1999). Significant reductions in brainstem volume in DLB have also been reported (Watson et al., 2016), with a study showing marked to severe neuronal loss in the ventral tegmental, pedunculopontine nucleus, and locus coeruleus regions in DLB (Piggott et al., 1999). III. Clinical applications in lewy body dementias

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Diffusion tensor imaging Diffusion tensor imaging (DTI) studies the random (Brownian) movement of water molecules in the brain. The microarchitecture of the brain limits the water diffusion. Depending on the tissue microarchitecture, the diffusion of the water molecules is restricted to a varying degree. If the tissue is ordered and aligned (such as WM tracts), the water tends to diffuse along the structure, resulting in anisotropic diffusion. DTI can answer two principal questions about the water diffusion process: direction of water diffusion and the amount of diffusion (isotropic vs. anisotropic), both of which can be combined in an algebraic object: tensor. The most common ways of quantifying the shape of the tensor in a voxel are fractional anisotropy (FA) as a summary measure of microstructural integrity; mean diffusivity (MD) as an inverse measure of membrane density sensitive to cellularity, edema, and necrosis; axial diffusivity (AD) as a measure of axonal injury; and radial diffusivity (RD) as an indirect measure of de- or dysmyelination and changes in the axonal diameter/density. Using DTI MRI, atrophy of the parietooccipital WM tracts was shown in DLB compared with AD, though this appears to be an early phenomenon, as AD demonstrated a greater longitudinal increase in MD in parietal and temporal regions, with no evidence of longitudinal changes in MD or FA in DLB relative to controls (Watson et al., 2012). However, DLB was differentiable from AD given that it was associated with reduced FA in the pons and left thalamus, highlighting that, despite similar levels of dementia severity, patterns of DTI changes in DLB and AD varied (Watson et al., 2012).

Iron quantification MRI The association between iron and Lewy bodies has long been established. Although the exact mechanisms linking these two phenomena have not been elucidated yet, several studies have demonstrated a strong link between pathological a-synuclein and MRI iron levels (Lewis et al., 2018) as well as brain iron and MRI iron levels (Langkammer et al., 2010). Iron load in the brain can be investigated by means of MRI in essentially two ways: first, by indirectly quantifying the amount of ferritin, the main storage protein of iron, which appears as hyperintense in structural T1, and hypointense in T2, and gradient echo sequences, or T2*, and by calculating relaxation rates, or relaxometry (R2, R2*, and R20 ), or by calculating the intrinsic differences of tissue magnetic susceptibility to iron, by means of sequences such as phase imaging, susceptibility-weighted imaging (SWI), or quantitative susceptibility mapping (QSM) (Orad & Shiner, 2022). Nigrosomes are small clusters of dopaminergic cells within the substantia nigra pars compacta. Of these, nigrosome-1 is the largest dopamine-containing cluster; it is located in the caudal and posterolateral part of the substantia nigra pars compacta and is hyperintense on iron-sensitive SWI sequences in HCs. When intact, it resembles the tail of a swallow on axial imaging (Schwarz et al., 2014). Compared with HCs and people with AD, the nigrosome-1 signal is decreased in PDD and DLB, possibly reflecting these pathologic findings (Balazova et al., 2019; Rizzo et al., 2019). Lack of the swallow tail sign can be used to differentiate PD patients from HCs with high sensitivity and specificity (80%e100%) (Kamagata et al., 2017).

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The swallow tail sign may be applicable in differentiating parkinsonian disorders such as DLB and PDD from AD. A hypointense nigrosome 1 (“loss of the swallow tail sign”) is consistently seen in parkinsonian disorders (Prange et al., 2019). A recent study demonstrated that MRI of the swallow tail sign may have diagnostic potential in DLB (Shams et al., 2017), as the hypointense nigrosome 1 visualized on iron SWI was more common in people with DLB compared with AD, frontotemporal dementia, and HCs. This corroborated a previous study which reported that measuring nigrosome 1 hypointensity with SWI achieved 90% diagnostic accuracy (93% sensitivity and 87% specificity) in DLB (Kamagata et al., 2017). A direct comparison of the swallow tail sign in DLB and PDD has, to our knowledge, not been made to date (Chen et al., 2021; Zhao et al., 2022).

Functional MRI studies Functional MRI (fMRI) is a technique that exploits the sensitivity of the MR signal to detect local changes in perfusion and metabolism using the so-called blood oxygenation levele dependent (BOLD) effect. The BOLD effect has been the basis for most fMRI studies today to map the regional activity of the human brain. BOLD can be measured in two conditions: at rest (resting state fMRI, rs-fMRI) or while performing a task (task-specific fMRI). Several studies at rest or employing different activating paradigms have been performed in people with Lewy body diseases to understand the pathophysiology of cellular activation and alterations in connectivity in this disorder. Although only a few fMRI studies have examined BOLD signal in DLB, differential patterns of functional connectivity in DLB compared with AD have been reported. Using the precuneus as the seed region, a study reported that DLB patients exhibit increased connectivity in the inferior parietal cortex and putamen, and decreased connectivity in the frontoparietal operculum, medial prefrontal cortex, and the primary visual cortex compared with AD, while a reversal of connectivity was observed in the right hippocampus (Galvin et al., 2011). Independent component analysis (ICA) has demonstrated that DLB displays greater connectivity in the default mode network compared with AD (Franciotti et al., 2013), which contrasts with previously reported connectivity dysfunctions between anterior and posterior segments of the default mode network in AD, when compared with HCs (Greicius et al., 2004). Furthermore, increased connectivity between the putamen and frontal, temporal, and parietal regions has been illustrated by DLB patients in comparison with AD patients, with the authors suggesting that this may be related to the prominent parkinsonian features in DLB (Franciotti et al., 2013). Consistent with the moderate preservation of memory function observed in DLB as opposed to AD, hippocampal connectivity has not been shown to differ in DLB compared with HCs, though the left hippocampal connectivity was identified to be higher in AD compared with controls, potentially reflecting a compensatory mechanism (Kenny et al., 2013). A recent study investigated within- and between-network connectivity in a range of resting state networks, being the first to investigate how DLB affects connectivity between these resting state networks (Kenny et al., 2012; Schumacher et al., 2018). DLB patients displayed more decreases in within-network connectivity compared with controls, primarily

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in temporal, motor, and frontal networks. In contrast, long-range functional connectivity appeared to be intact in DLB, with increased connectivity only identified between a frontal and a temporal network (Kenny et al., 2012; Schumacher et al., 2018). Interestingly, a meta-analysis of metabolic changes in DLB also demonstrated a decreased activity of the frontoparietal network when comparing DLB with HCs. The observed hypometabolism of parietal and occipital lobes also hints toward the visual hallucinations occurring in DLB. In accordance with this hypothesis, the authors found the lingual gyrus metabolism to be lowered in DLB when compared with AD, potentially relating to the difference in visual hallucinations (Ma et al., 2022). The lack of obvious differences when AD and DLB were compared using functional MRI suggests that various methods should be considered when studying differences between DLB and AD. Given the prominence of visuoperceptual impairments in DLB, a task-based fMRI study employed visual presentations of motion, color, and face paradigms to explore the functional integrity of the visual system in DLB. They discovered that DLB patients exhibited greater activation in the superior temporal sulcus compared with AD, specifically during the motion task (Sauer et al., 2006). However, these findings were not replicated by a more recent study, who reported that DLB patients did not exhibit any significant differences in functional response to objects, motion stimuli, or checkerboard in V1 and V2/V3 compared with controls (Taylor et al., 2012), proposing that function in the lower visual areas is relatively preserved. Interestingly, however, ROI analysis demonstrated that the DLB group had a reduction in V5/MT (middle temporal) activation when responding to motion stimuli (Taylor et al., 2012). Overall, these results imply that, in DLB, functional abnormalities affect the visual association areas, as opposed to the primary visual cortex, though it is difficult to decipher whether deviations at higher levels of the visual system contribute to the hallmark visuoperceptual impairments and visual hallucinations seen in DLB. Although studies have demonstrated alterations in functional connectivity in PDD (Rektorova et al., 2012; Seibert et al., 2012) and DLB (Galvin et al., 2011; Kenny et al., 2013), these were reported when comparing these disease groups against HCs. One study has compared DLB and PDD directly with the aim of identifying disease-specific functional connectivity patterns. This study reported that, for seeds situated within the frontoparietal network, DLB patients exhibited greater alterations in functional connectivity than PDD when compared with HCs, predominately at the precentral and postcentral gyri, cerebellar, occipital, and temporal regions, while in PDD, changes in functional connectivity were limited to the frontal cortices and precuneus (Peraza et al., 2015). Interestingly, although a supplementary motor area seed revealed similar regional functional connectivity alterations in the pre- and postcentral gyri, cerebellar, temporal, precuneal, and occipital regions, these alterations were more apparent in PDD than in DLB, potentially reflecting the prominent parkinsonism and motor dysfunction in PDD compared with DLB (Peraza et al., 2015). However, the study showed no significant differences in DLB versus PDD groups. Overall, these results suggest that differences between both diseases are subtle, which may be driven by their distinct pathological trajectories, thus potentially reflecting the chronological manifestation of cardinal symptoms in the Lewy body dementias.

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Conclusions This chapter summarizes the current MRI imaging literature of Lewy body dementias in the context of its differentiation from other causes of dementia, discusses the increasingly important role of biomarkers in differential diagnosis, and outlines promising areas for future research. Overall, MRI studies have yielded important insights into the underlying pathophysiology of DLB, PD-MCI, and PDD while showing promise in improving clinical differentiation of DLB from other types of dementias. While the field has made substantial progress in delineating the imaging characteristics associated with dementia subtypes, the ability to detect patterns that enable accurate prediction of diagnosis for specific individuals ultimately determines the clinical value of MRI and the measurements obtained from it. The reliable application of these methods in routine radiological practice may be facilitated by noneexpert-dependent, automated methods of analysis. There are several shortcomings in the current state of MRI research for DLB, PD-MCI, and PDD, including that presently the overwhelming majority of neuroimaging studies in DLB are cross-sectional, relatively small in size, and in participants in advanced stages of the disease. Therefore, larger prospective longitudinal studies are warranted to confirm the utility of many imaging techniques and monitor disease progression in early disease stages as well as at risk individuals and people with PD-MCI. Furthermore, studies involving multimodal neuroimaging data and larger cohorts are likely to make novel contributions in evaluating the utility of combined biomarkers in Lewy body dementias.

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C H A P T E R

12 Neuroimaging in multiple system atrophy Giacomo Tondo1, 2, Cristoforo Comi3, Andrea Naldi4, Edoardo Rosario de Natale5 and Marios Politis5 1

Neurodegeneration Imaging Group, Department of Clinical and Biomedical Sciences, Faculty of Health and Life Sciences, University of Exeter, United Kingdom; 2School of Psychology, VitaSalute San Raffaele University, Milan, Italy; 3Parkinson’s Disease and Movement Disorders Centre, Neurology Unit, University of Piemonte Orientale, Novara, Italy; 4Department of Neuroscience “Rita Levi Montalcini”, University of Turin, Turin, Italy; 5Neurodegeneration Imaging Group and Mireille Gillings Neuroimaging Centre, University of Exeter, London, United Kingdom

Introduction Multiple system atrophy (MSA) is an adult-onset, sporadic, neurodegenerative disorder characterized by a variable combination of parkinsonism poorly responsive to levodopa, cerebellar ataxia, autonomic dysfunction, and pyramidal signs (Gilman et al., 2008). MSA can be further classified in a parkinsonian-type MSA (MSA-P), if parkinsonism is the uppermost feature, and in a cerebellar-type MSA (MSA-C) if the cerebellar syndrome prevails (Roncevic et al., 2014). Clinical dysfunction is related to neurodegenerative changes, which primarily affect the basal ganglia, cerebellum, pons, inferior olivary nuclei, and spinal cord (Jellinger, 2014). The pathological hallmark of the disease is the accumulation of fibrillar a-synuclein (a-syn), which is normally present in neuronal axons and synapses, and in MSA forms abnormal deposits in glial cells named glial cytoplasmic inclusions (GCIs), which lead to a neuronal multisystem degeneration (Wakabayashi & Takahashi, 2006). This pathological evidence allows to classify MSA as an a-synucleinopathy, together with Parkinson’s disease (PD) and dementia with Lewy bodies (DLB) (McCann et al., 2014). Compared with those neurodegenerative disorders, MSA presents a worse prognosis (Savica et al., 2017). MSA clinical manifestations may be very heterogenous. Autonomic failure is an early clinical hallmark of MSA and usually involves the urogenital and cardiovascular systems (Fanciulli &

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Wenning, 2015). Orthostatic hypotension, together with bladder and sexual dysfunction, represents the most frequent autonomic symptom in MSA patients, often anticipating the emergence of motor signs (Kaufmann et al., 2017; Roncevic et al., 2014). A rigid-akinetic parkinsonism with bradykinesia and tendency to fall identifies the MSA-P subtype, while cerebellar dysfunction, including broad-based ataxic gait, limb ataxia, action tremor, dysarthria, and oculomotor abnormalities, characterizes the MSA-C subtype (Fanciulli & Wenning, 2015). While disease progresses, virtually all patients with MSA develop parkinsonism, and up to 60% will develop cerebellar dysfunction regardless of the clinical onset (Köllensperger et al., 2010). Other motor symptoms include pyramidal signs, which may be present in up to 50% of cases, anomalous neck and spine postures, hand and foot deformities, and, in advanced stages, dysphonia, drooling, and dysphagia (Fanciulli & Wenning, 2015). Frequent nonmotor symptoms include rapid eye movement sleep behavior disorder, which may precede the onset of motor symptoms (Palma et al., 2015), and cognitive and psychological impairment, which can emerge later in the disease course with depression or a dysexecutive syndrome suggesting frontal lobe impairment (Sambati et al., 2020; Stankovic et al., 2014). The predominant clinical manifestations reflect the distribution of neuronal degeneration. Neuronal loss involves mainly the putamen in MSA-P and the cerebellum, middle cerebellar peduncles, and pontine basis in MSA-C (Ahmed et al., 2012). In detail, neurodegeneration primarily affects the dorsolateral putamen, the caudate nucleus, globus pallidus, subthalamic nucleus, and substantia nigra in MSA-P (Jellinger, 2014). In the MSA-C subtype, neurodegeneration involves the cerebellar vermis and hemispheres, the dentate nucleus, inferior olive, cerebellopontine tracts, pontine basis, and, to a lesser extent, the substantia nigra, the locus coeruleus, and, barely, the striatum (Jellinger, 2014). In addition, in both subtypes of MSA, neuronal loss and reactive gliosis affect the central autonomic nervous system, including the brainstem nuclei, the sympathetic preganglionic neurons of the intermediolateral column of spinal cord, the hypothalamus, and the cardiac postganglionic sympathetic fibers (Shy & Drager, 1960; Tada et al., 2007). In the advanced stage of the disease, neurodegeneration may spread to cortical regions, especially frontal and temporal areas (Armstrong et al., 2005). The frequent clinical overlap among atypical parkinsonisms, especially at disease onset, makes the diagnostic workup challenging. In a large cohort of patients with a clinical diagnosis of MSA, only 62% had postmortem confirmation (Koga et al., 2015). On the other hand, in patients with autopsy-confirmed MSA, a clinical erroneous diagnosis had been formulated in 13% of cases (Koga et al., 2021). The relevant rate of misdiagnosis subsequently leads to mistakes in patient management, erroneous subject inclusion in clinical trials, imprecise prognostic prediction, and increased distress for patients and caregivers. MSA-P may be commonly misdiagnosed, in particular at the early stage, with PD (Palma et al., 2018). Furthermore, the presence of a poor levodopa-responsive parkinsonism makes this disorder similar to atypical parkinsonian syndromes, namely progressive supranuclear palsy (PSP) and corticobasal syndrome (CBS), neuropathologically characterized by tau deposition (Greene, 2019). PSP usually presents with symmetric akinetic-rigid parkinsonism with vertical supranuclear palsy, backward falls and cognitive and behavioral impairment due to frontal lobe dysfunction (Boxer et al., 2017). CBS, which is the typical clinical presentation of corticobasal degeneration (CBD), is characterized by limb and axial rigidity, bradykinesia, postural instability and falls, limb dystonia and myoclonus, associated with cognitive impairment, limb apraxia, and behavioral changes (Armstrong et al., 2013). Lastly, a severe cognitive impairment can be a late feature of MSA, and the simultaneous presence of IV. Clinical applications in atypical parkinsonian disorders

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parkinsonism, autonomic dysfunction, and cognitive decline may complicate differential diagnosis with DLB (Kao et al., 2009). Due to the variability of clinical manifestations, MSA-C symptoms may be erroneously ascribed to toxic, inflammatory, metabolic, or genetic ataxia, especially spinocerebellar ataxia (SCA) type 6 or late-onset Friedreich ataxia (Lin et al., 2014). MSA current diagnostic consensus criteria, which revised the original diagnostic criteria elaborated in 1999 (Gilman et al., 1999a), include three degrees of certainty, namely definite, probable, and possible MSA (Gilman et al., 2008). Definite MSA diagnosis requires neuropathological confirmation of the presence of GCIs in the cytoplasm of oligodendrocytes associated with olivopontocerebellar or striatonigral degeneration. A diagnosis of probable MSA relies on typical clinical manifestations and can be reached in the presence of a rigorously defined autonomic dysfunction associated with poorly levodopa-responsive parkinsonism or cerebellar ataxia. A diagnosis of possible MSA requires the presence of parkinsonism or cerebellar ataxia associated with at least one sign of autonomic dysfunction and one additional feature, including typical neuroimaging features with evidence of (1) atrophy on magnetic resonance imaging (MRI) in the putamen, middle cerebellar peduncle, pons, or cerebellum for MSA-P and atrophy of the putamen, middle cerebellar peduncle, or pons for MSA-C; (2) brain hypometabolism as revealed by 18-fluorodeoxyglucose positron emission tomography ([18F]FDG-PET) in the putamen, brainstem, or cerebellum for MSA-P and in the putamen for MSA-C; (3) presynaptic nigrostriatal dopaminergic denervation on single photon emission computed tomography (SPECT) or dopaminergic PET (Gilman et al., 2008). The revised consensus criteria, adding neuroimaging findings as supportive features, almost doubled sensitivity in diagnosing possible MSA, increasing from 22% to 41% at the first clinic visit (Osaki et al., 2009). Several clinical and research studies proved the importance of neuroimaging in aiding MSA and parkinsonian disorder differential diagnosis (Kim et al., 2017a; Niccolini & Politis, 2016). Along with the diagnostic utility, neuroimaging can add crucial insight in understanding of MSA pathogenesis and may represent a useful tool in detecting disease-specific changes and monitoring the effectiveness of novel therapeutic interventions. The purpose of this chapter is to provide an overview of the cardinal studies in the field of neuroimaging in MSA, aiming at identifying markers of disease and progression, characterizing disease-specific clinical subtypes, and supporting differential diagnosis from PD and other parkinsonian disorders.

Magnetic resonance imaging Brain MRI represents a crucial tool in supporting the clinical evaluation of parkinsonism, and several modalities are available to provide a complete assessment. Conventional MRI has been already included as supportive element in the diagnosis of possible MSA (Gilman et al., 2008). In addition, structural MRI may be useful in ruling out alternative diagnoses. Quantitative measurement and volumetric analysis add accuracy to the diagnostic workup. Measures of diffusivity changes of water molecules along white matter tracts and subcortical structures may provide an instrument for monitoring disease progression, together with iron-sensitive MRI sequences. Functional and perfusion MRI studies reported interesting findings revealing possible pathogenic mechanisms underlying neurodegeneration in MSA.

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The main results obtained in MRI studies using different approaches are summarized in Table 12.1.

Conventional MRI Structural imaging, including T1-weighted, T2-weighted, and fluid-attenuated inversion recovery (FLAIR) sequences, represents a widely available tool, easily interpretable, commonly carried out in most centers. For these reasons, conventional MRI sequences are routinely used for diagnostic purposes, allowing to identify specific atrophy patterns for MSA patients (Massey et al., 2012). Putaminal atrophy is the main recognizable sign in MSA-P, while infratentorial atrophy is almost invariably present in MSA-C (Lee et al., 2004; Schrag et al., 2000). Atrophy of the cerebellum, middle cerebellar peduncles, pons, medulla oblongata, inferior olives, and dilatation of the fourth ventricle represent common findings in both subtypes of MSA (Brooks & Seppi, 2009). These findings have been proposed to discriminate MSA from PD, where standard MRI is typically normal and other atypical parkinsonisms (Kraft et al., 1999; Bhattacharya et al., 2002; Naka et al., 2002a). Overall, the presence of atrophy in the putamen, in the middle cerebellar peduncle and the brainstem, show very high specificity in discriminating MSA from healthy controls and PD, but lower sensitivity, especially at the early stages of the disease (Bhattacharya et al., 2002; Savoiardo, 2003; Schrag et al., 2000; Watanabe et al., 2002). Alongside atrophy, characteristic signal changes can be recognized in MSA. Putaminal hypointensity in T2-weighted sequences is often present in MSA-P, although it should be interpreted in the light of the clinical picture, as it may also represent a normal finding in the elderly (Savoiardo, 2003). T2 hyperintensity of the cerebellum and of the middle cerebellar peduncle has been commonly reported in MSA-C (Bürk et al., 2005; Lee et al., 2004). The two most typical MRI signal intensity abnormalities include the hot cross bun and the putaminal slit sign. Although these signs are not pathognomonic, they help differentiate between MSA and other disorders with very high specificity (Brooks & Seppi, 2009). The hot cross bun, linked to the degeneration of the pontine and pontocerebellar fibers with concomitant preservation of the corticospinal tract, is a cruciform hyperintensity in T2-weighted sequences in the pons that resembles the Easter pastry. The appearance of this sign becomes more prominent with disease progression (Watanabe et al., 2002). It has been associated with MSA-C, and patients with a hot cross bunn sign are most likely to develop disability due to the disease severity (Zhu et al., 2021). The hot cross bun has high specificity (97%) but low sensitivity (50%) in differentiating MSA from PSP (Schrag et al., 2000), and it can also be observed in other diseases, such as some forms of SCA (Lee et al., 2009; Way et al., 2019). The putaminal slit sign is a hyperintense rim in the dorsolateral side of the putamen on T2-weighted sequences. Patients showing the putaminal slit are usually classified as MSA-P (Horimoto et al., 2002). The putaminal slit sign has high specificity for differentiating MSA-P from PD (90%), but lower sensitivity (72%) (Lee et al., 2004), and it can be detected as a normal finding in healthy subjects on MRI with high magnetic field strength (3 T) (Lee et al., 2005). In summary, on conventional MRI, atrophy and signal changes in the putamen, cerebellum, middle cerebellar peduncles, pons, midbrain, and medulla oblongata represent common findings in MSA, aiding differential diagnosis.

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Magnetic resonance imaging

TABLE 12.1

315

Common magnetic resonance imaging findings in multiple system atrophy.

MRI technique

Findings

Conventional MRI

Atrophy of the putamen, middle cerebellar peduncle or pons

Main references (Gilman et al., 2008)

The hot cross bun, a cruciform hyperintensity in T2-weighted images in the (Horimoto pons, is associated with MSA-C and with the development of cerebellar et al., 2002) dysfunction, while the putaminal slit, a hyperintense rim in the dorsolateral side of the putamen, is more frequently observed in MSA-P patients. Magnetic resonance volumetry

Atrophy in the putamen characterizes MSA-P patients; pons, cerebellum, and middle cerebellar peduncles are the main atrophic areas in MSA-C when compared with controls.

(Huppertz et al., 2016)

VBM

Atrophy in the striatum, midbrain, and prefrontal cortices in MSA-P patients when compared with controls.

(Brenneis et al., 2003)

Atrophy involving brainstem, cerebellum, and middle cerebellar peduncles in MSA-C patients when compared with controls.

(Specht et al., 2003)

Atrophy in the neocortex in MSA patients which progresses over time and including frontal, temporal, and parietal regions.

(Brenneis et al., 2007)

Diffusivity abnormalities in the putamen in MSA-P patients when compared with controls and PD patients, correlating with disease severity.

(Seppi et al., 2006a)

DWI and DTI

Abnormal diffusivity changes in the middle cerebellar peduncles and in the (Wang et al., cerebellum can be present in both MSA-P and MSA-C clinical subtypes 2011) when compared with controls. White matter tract degeneration in MSA involves especially the corticospinal tract and the cerebellar peduncles.

(Worker et al., 2014)

SWI

Low putaminal signal, suggesting increased iron deposition, characterizes MSA-P patients when compared with controls and PD patients.

(Wang et al., 2012a)

QSM

Higher QSM values in putamen, indicating iron deposition, in MSA patients when compared with controls and PD patients.

(Sjöström et al., 2017)

fMRI

Brain network disruption mainly affecting the default mode network, but also involving cerebellar connectivity, can be observed in MSA patients.

(Rosskopf et al., 2018)

Abnormal fMRI activation in several brain areas involved in motor control, (Planetta et al., including basal ganglia, cerebellum, and prefrontal cortices, during the 2015) execution of a motor task marks MSA-P patients when compared with PD. MRS

Reduced N-acetyl aspartate/creatine and/or N-acetyl aspartate/choline ratios can be underlined in the striatum of patients with MSA (but also in patients with other parkinsonism).

(Federico et al., 1999)

MTI

Low magnetization transfer ratio, indicating abnormalities in myelination and axonal density, can be shown in MSA patients when compared with controls in putamen, middle cerebellar peduncle, pons, and substantia nigra, matching the underlying pathological changes.

(Eckert et al., 2004)

(Continued)

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12. Neuroimaging in multiple system atrophy

Common magnetic resonance imaging findings in multiple system atrophy.dcont’d Main references

MRI technique

Findings

ALS

Several cerebellar perfusion alterations distinguish MSA-C patients when compared with controls and are correlated with changes in resting-state functional network organization.

(Wang et al., 2019)

Neuromelaninsensitive imaging

Reduced signal in substantia nigra and locus coeruleus in MSA patients when compared with controls.

(Matsuura et al., 2013)

In bold: one of the additional features included in the second consensus statement for the diagnosis of “possible” multiple system atrophy. ASL, arterial spin labeling; DTI, diffusion tensor imaging; DWI, diffusion-weighted imaging; fMRI, functional magnetic resonance imaging; MSA, multiple system atrophy; MSA-C, MSA cerebellar type; MSA-P, MSA parkinsonian type; MRI, magnetic resonance imaging; MRS, magnetic resonance spectroscopy; MTI, magnetization transfer imaging; PD, Parkinson’s disease; QSM, quantitative susceptibility mapping; SWI, susceptibility-weighted imaging; VBM, voxel-based morphometry.

Magnetic resonance volumetry Quantitative volumetric measure analyses to conventional MRI techniques yield higher levels of accuracy in the diagnostic workup of MSA patients. MRI volumetry allows quantitative assessment of brain volumes, resulting useful in improving the detection of focal brain pathology (Giorgio & De Stefano, 2013). Using an approach based on the segmentation of brain in selected regions of interest (ROIs), MRI volumetry studies showed different atrophy patterns in the two clinical MSA subtypes. MSA-P shows atrophy in the putamen, brainstem, cerebellum, pallidum, thalamus, and hippocampus (Huppertz et al., 2016; Lee et al., 2013; Messina et al., 2011; Sako et al., 2014; Schulz et al., 1999). In MSA-C, atrophy is primary located in pons, cerebellum, middle cerebellar peduncles, brainstem, putamen, and caudate (Huppertz et al., 2016; Schulz et al., 1999). Recent advances in imaging analysis, including the use of structural MRI-based software for automated compartmentalization of the brain and volume estimation of predefined ROIs, and the use of support vector machine, have allowed to reach great accuracy in the differential diagnosis of parkinsonian syndromes (Huppertz et al., 2016; Krismer et al., 2019). Scherfler and colleagues tested the accuracy of a classification algorithm compared to diagnosis based on clinical consensus criteria in a large cohort of parkinsonian patients, including 40 PD patients, 40 MSA-P, and 30 PSP. The classification algorithm included the volumes of midbrain, putamen, and cerebellar gray matter as potential discriminants and used z-thresholds obtained by standardizing data with sex-adjusted healthy control cohorts. The authors reported that the algorithm reached a total classification accuracy of 97% in the diagnosis of MSA-P and PSP at initial evaluation, while diagnosis based on clinical consensus criteria showed an accuracy of 62.9% (Scherfler et al., 2016). The measurement of diameters and areas of different structures known to be affected by atrophy in MSA may further improve differential diagnosis among parkinsonian syndromes (Möller et al., 2017; Oba et al., 2005). The MR Parkinsonism Index, which relates the volumes of the pons, midbrain, and cerebellar peduncles by the formula: MR Parkinsonism Index ¼ (pons/midbrain) * (middle cerebellar peduncle/superior cerebellar peduncle), has shown high sensitivity and specificity in distinguishing MSA-P from PSP and PD, considering

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that midbrain and superior cerebellar peduncles are significantly smaller in PSP, while middle cerebellar peduncle atrophy is higher in MSA (Hussl et al., 2010; Quattrone et al., 2008). Voxel-based morphometry (VBM) is an operator-independent automated method, involving a voxel-wise comparison of the local concentration of gray matter between two groups of subjects, representing an instrument to detect focal neuroanatomical differences between groups using T1-weighted imaging (Ashburner & Friston, 2000). Studies using VBM to characterize MSA patients reported significant atrophy affecting the putamen, caudate, midbrain, thalamus, and cerebellum in MSA-P (Brenneis et al., 2007; Tzarouchi et al., 2010; Yu et al., 2015) and the brainstem, cerebellum, and middle cerebellar peduncles in MSA-C (Specht et al., 2003, 2005). A number of studies have sought to investigate the presence of structural atrophy in cortical regions of MSA patients. MSA-P patients display significant atrophy in the primary sensorimotor, supplementary motor area, prefrontal cortices, and insula, increasing over time, when compared with both controls and PD patients (Brenneis et al., 2003, 2007; Tir et al., 2009). By contrast, frontotemporal and insular regions showed significant atrophy in MSA-C patients (Brenneis et al., 2006). Cortical and subcortical atrophy has been related to the development of cognitive impairment (Cao et al., 2021; Fiorenzato et al., 2017). A study showed that significantly lower performances in memory and executive functions tests can be detected in nondemented MSA-P patients compared with controls and that cognitive disfunction directly correlates with atrophy in the neocortex, cerebellum, and striatum (Kim et al., 2015). Complementary information to VBM can be provided by voxel-based relaxometry (VBR), a morphometric method measuring the relation rate, an intrinsic property of the tissue, where decreased relaxation rate indicates increased water content, thus reflecting tissue atrophy. In MSA, reduced relaxation rate has been reported in the putamen, midbrain, cerebellum, middle cerebellar peduncles, brainstem, and cortex in regions corresponding to VBM reduction of gray and white matter (Minnerop et al., 2007; Schulz et al., 1999; Specht et al., 2003, 2005; Tzarouchi et al., 2010).

Diffusion tensor imaging and diffusion-weighted imaging Diffusion-weighted imaging (DWI) provides image contrast based on the measurement of the random motion of water molecules (Pierpaoli et al., 1996). DWI signal can be calculated by apparent diffusion coefficient (ADC), which represents an absolute quantification of translational water motion, and it can be used to model ADC maps reflecting the integrity of brain tissue (Huisman, 2010). Diffusion tensor imaging (DTI) calculates the three-dimensional shape of the diffusion in the space and its orientation, becoming particularly helpful in tissues with highly organized fiber structure, such as the brain, adding information about the microstructure of the tissue (Baliyan et al., 2016). DTI is crucial in detecting white matter structural changes allowing the reconstruction of white mater tracts, based on the direction and magnitude of the anisotropic diffusion (Huisman, 2010). The voxel-based analysis of white matter diffusion data improves using methods avoiding the selection of ROIs, such as the tract-based spatial statistics (TBSS) analysis, which increases sensitivity in multisubject diffusion imaging studies (Smith et al., 2006). Two are the most studied measures of diffusion: mean diffusivity (MD) and fractional anisotropy (FA). MD is a parameter indicating the bulk diffusivity regardless of the direction

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of the molecules; FA indicates the degree of anisotropic diffusion within a region, showing higher values in highly organized areas, such as undamaged white matter tracts, and lower values in tissue where diffusion lacks a specific orientation, such as gray matter (Beaulieu, 2014). The diffusion of water molecules into intact fiber tracts is typically restricted, while, in the presence of damage altering fiber microstructure, the mobility of water molecules is increased (Alexander et al., 2007). Neurodegenerative disorders are associated with neuronal loss and progressive damage to the barriers restricting the motion of water molecules, thus generally, but not always, resulting in increased MD and reduced FA (Goveas et al., 2015). The most consistent finding reported in MSA has been an increased MD in the putamen, and especially in posterior putamen, which differentiate MSA from PD and healthy controls with high sensitivity and specificity (Barbagallo et al., 2016; Ito et al., 2007; Köllensperger et al., 2007; Meijer et al., 2015a; Pellecchia et al., 2009; Schocke et al., 2004, 2002; Seppi et al., 2004, 2006b; Umemura et al., 2013). In addition, both MSA-P and MSA-C subtypes recurrently showed increased ADC and reduced FA in the middle cerebellar peduncles (Nicoletti et al., 2006; Tsukamoto et al., 2012; Wang et al., 2011). Cerebellar and brainstem DWI abnormalities represent other common findings in MSA (Ito et al., 2007; Nicoletti et al., 2006; Tsukamoto et al., 2012; Wang et al., 2011). Diffusivity changes in the cerebellum, middle cerebellar peduncles, and basal ganglia can aid differential diagnosis among parkinsonian disorders (Paviour et al., 2007). Increased MD in the cerebellar hemisphere could discriminate patients with MSA-C and those with MSA-P from patients with PD and PSP with a positive predictive value of 100% (Nicoletti et al., 2013). Nair and colleagues combined structural MRI with volumetric analysis and DTI MRI in a cohort of 13 MSA patients (both clinical variants) and 26 PD patients. They found that a combination of increased diffusivity and volumetric atrophy in middle cerebellar peduncle, pons, putamen, cerebellum, and substantia nigra differentiated MSA patients from PD patients with a sensitivity and specificity of 92% and 96%, respectively (Nair et al., 2013). DWI has also been proposed as a tool for staging disease, since diffusivity changes in the putamen, pons, midbrain, middle cerebellar peduncles, and cerebellar white matter have been correlated in both MSA clinical subtypes with disease severity and disease duration (Blain et al., 2006; Kanazawa et al., 2004; Pellecchia et al., 2011; Schocke et al., 2002, 2004; Tha et al., 2010). By performing a serial diffusion-weighted imaging study in a cohort including 10 MSA-P and 10 PD patients, Seppi and colleagues showed a significantly increased diffusivity over time in the putamen of MSA-P patients, which correlated with motor impairment as assessed by the Unified Parkinson’s Disease Rating Scale (Seppi et al., 2006a). Coherently with findings cited before, DTI studies reported that changes of diffusivity in the midbrain, thalamus, putamen, caudate, substantia nigra, and cerebellum may be useful in distinguishing MSA from PD (Beliveau et al., 2021; Du et al., 2017; Ofori et al., 2017; Planetta et al., 2015) and MSA from PSP (Prodoehl et al., 2013). Worker and colleagues used a wholebrain TBSS approach in a study involving MSA (both clinical variants), PD, PSP patients, and control subjects. Reduced FA and increased MD have been found in MSA patients in the middle and inferior cerebellar peduncle, in the corticospinal tract, and in the medial lemniscus. The increased MD in the middle cerebellar peduncles, correlating with disease severity, was relevant in MSA and not in PSP and PD patients (where the white matter was found to be intact) (Worker et al., 2014). In a cohort including MSA-C, adult-onset ataxia, and

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healthy controls, DTI tract-based spatial statistics revealed major microstructural white matter changes in MSA-C that were not present in adult-onset ataxia, and in particular involving the pons, the cerebellum, and the corticospinal tract (Faber et al., 2020). Other studies using tract-based analysis consistently reported the involvement of corticospinal tract and pons transverse and longitudinal fibers in MSA patients (Ito et al., 2008; Ji et al., 2015; Makino et al., 2011; Yang et al., 2015). Taken together, DWI-DTI studies reported disease-specific abnormalities coherently with the anatomopathological distribution of damage in MSA, which may support diagnostic workup in combination with structural imaging.

Magnetic resonance imaging of iron deposition Brain iron plays an essential role in development and normal neuronal functions, but its abnormal deposition has been related to aging and neurodegeneration, contributing to oxidative stress and neurotoxicity (Brass et al., 2006). MRI can provide in vivo images of brain iron deposits, due to the shortening effect of the relaxion time of water protons attributable to ferritin deposits and hemosiderin, which results in hypointense signal in T2-weighted and T2*-weighted sequences (Schenck & Zimmerman, 2004). Postmortem evaluation of MSA-P patients has reported iron accumulation in putamen, especially in its posterolateral section, as one of the main neuropathological findings of this disease (Dickson, 2012). In agreement with this, hypointensity of the posterior putamen is already visible in T2-weighted images in MSAP patients (Vymazal et al., 1999). However, T2*-weighted imaging has shown to be more sensitive than T2 sequences in identifying putaminal hypointensity in MSA patients compared with PD and controls (Kraft et al., 2002). Susceptibility-weighted imaging (SWI) is a technique providing image-enhancing contrast of tissue having a different susceptibility than its surrounding structures, thus allowing to visualize compounds such as deoxygenated blood, blood derivates including hemosiderin and ferritin, and calcium (Haacke et al., 2009). It is advantageous to detect vascular diseases in the brain, such as hemorrhages, vascular malformations, and cerebral venous sinus thrombosis (Halefoglu & Yousem, 2018). Due to its high sensitivity to detect the amount of iron in the brain, SWI has also been employed in many neurodegenerative conditions (Adachi et al., 2015; Kirsch et al., 2009; Sheelakumari et al., 2017), including degenerative parkinsonisms, to study topographical alterations in iron deposition (Lee & Lee, 2019; Wang et al., 2016). Putaminal hypointensity in SWI has been consistently reported in MSA-P patients, and it can be detected even in the early stage of the disease (Lee et al., 2010; Lee & Baik, 2011; Han et al., 2013; Hwang et al., 2015; Ren et al., 2020; Wang et al., 2012b). Furthermore, it improves diagnostic accuracy of conventional MRI to identify MSA-P patients among different parkinsonian syndromes (Meijer et al., 2015b). Quantitative susceptibility mapping (QSM) is an MRI method based on detecting changes in local magnetic susceptibility, which allows computations of the underlying susceptibility of each voxel, providing a quantitative measure of magnetic susceptibility (Liu et al., 2015). Tissue magnetic susceptibility is highly related to the paramagnetic contribution of iron (Deistung et al., 2017), and QSM has shown, with postmortem validation, to be a sensitive tool to quantify, in vivo, iron tissue levels especially in gray matter (Langkammer et al., 2012). In pathology-confirmed parkinsonian disorder patients, including one patient with

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autoptic diagnosis of MSA, QSM showed a significant correlation with substantia nigra Perl’s stain for iron and not with other pathological measurements such as tau and glial cells count (Lewis et al., 2018). A few studies have investigated whether QSM findings could differentiate MSA patients from other similar clinical entities. Higher QSM values in putamen have been revealed in MSA patients (both clinical subtypes) when compared with controls, PD, and PSP patients (Mazzucchi et al., 2019; Sjöström et al., 2017). In addition, higher QSM values in the cerebellar dentate nucleus, correlating with disease duration, have been reported in MSA-C patients compared with SCA type 6, helping in the differential diagnosis of progressive ataxias (Sugiyama et al., 2019). A large prospective study including 70 patients with a parkinsonian disorder (PD, MSA-P, MSA-C, and PSP) combined an advanced DTI technique, the diffusion kurtosis imaging, with QSM, aiming at differentiating patients with early-stage parkinsonism. While changes in brainstem diffusion accurately differentiated between PD, PSP, and MSA-C, QSM discriminated the MSA-P from the PD group. The combination of diffusion and magnetic susceptibility data provided 83%e100% sensitivity and 81%e100% specificity in differentiating among the disease groups (Ito et al., 2017). A recent metaanalysis tested the diagnostic performances of putaminal abnormalities in T2*-weighted imaging, SWI, and QSM in distinguishing MSA-P and PD. A pooled sensitivity of 65% and specificity of 90% has been derived, confirming the potential of brain iron imaging techniques in aiding the diagnostic workup of MSA-P patients (Lim et al., 2021).

Functional magnetic resonance imaging Functional brain MRI (fMRI) provides a measurement of brain activity by detecting changes in blood oxygenation concentration and exploiting the blood-oxygenation levele dependent (BOLD) signal, which depends on paramagnetic properties of deoxygenate hemoglobin (Biswal et al., 1995). fMRI imaging can be assessed at rest or during the execution of a task. Resting-state fMRI studies aim to assess the correlation between spontaneous activation patterns of brain regions. This result can be obtained by measuring the spontaneous BOLD fMRI signal fluctuations while the subject is at rest, lying in the scanner with closed eyes, relaxed without falling asleep (Van Den Heuvel & Pol, 2010). The registered activity constitutes the resting-state functional connectivity, representing the relationship between the synchronized activity of anatomically separated brain regions at rest. Multiple brain networks have been identified at rest, such as the default mode network, the most well-defined resting-state network, and the others including the auditory, visual, sensorimotor, executive control, frontoparietal attention, salience, basal ganglia, and limbic and precuneus network (Beckmann et al., 2005; Seeley et al., 2007; Shirer et al., 2012; Thomas Yeo et al., 2011). Resting-state networks can be modulated during specific cognitive processes, and several connectivity alterations may develop in neurodegenerative conditions. Thus, resting-state fMRI represents a potential tool in identifying early brain activity alterations and in monitoring changes related to disease progression (Hohenfeld et al., 2018; Lv et al., 2018). In addition, changes in neuronal activity leading to a reduction in local blood oxygenation can be measured by exploiting the BOLD signal related to the execution of a specific task performed by the subject during the scan. Task-related fMRI may involve several stimuli (e.g., visual, auditory, motor, sensory, cognitive) and can be used to visualize brain areas whose increased

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activity depends on the task the subject is performing, obtaining different maps of brain activation (Glover, 2011). Few fMRI studies have been conducted on MSA patients. The disruption of the default mode network and sensorimotor circuits is the main reported finding in MSA-P and MSAC patients compared with controls (Rosskopf et al., 2018; You et al., 2011). Cerebellar connectivity may be also altered, and pontocerebellar hyperconnectivity, likely related to an adaptative compensatory response, has been reported (Rosskopf et al., 2018). A disruption of functional brain connectivity of cerebellocortical circuits, correlating with clinical performances as measured by the Unified Multiple System Atrophy Rating Scale, was evident in a study involving only MSA-C patients compared with controls (Ren et al., 2019). Functional network abnormalities were related to cognitive impairment in a recent study involving a large cohort of MSA patients (N ¼ 61), including patients with normal cognition and cognitive impairment. The results showed a correlation between clinical cognitive severity and the alterations in prefrontal connectivity, suggesting that cognitive impairment in MSA may be related to the disruption of the dorsolateral prefrontal cortex network (Yang et al., 2020). Resting-state fMRI studies aiming at differentiating MSA-P and PD patients reported inconclusive results. Again, the default mode network seems to be more affected in MSA-P patients than PD, alongside with other networks, including the striatothalamocortical loop and the cerebellum connectivity (Franciotti et al., 2015; Yao et al., 2017). The similar pattern of functional connectivity alterations reflects the clinical overlap between the two disorders, while the more severe alterations in MSA-P may explain the worse prognosis compared with PD (Wang et al., 2017). Functional brain network organization can be modulated, offering the chance to test potential therapeutic effects. Using repetitive transcranial magnetic stimulation, a noninvasive neuromodulation method, a resting-state connectivity study in MSA-P patients demonstrated a remodulation of the default mode, cerebellar, and limbic network, leading to motor symptoms improvement (Chou et al., 2015). Task-related fMRI was used to investigate the pattern of functional activation changes in MSA-P, PD, and healthy controls during the execution of a grip force task. Both MSA-P and PD patients compared with controls showed abnormal fMRI activation in several areas involved in motor control, including cerebellum, basal ganglia, thalamus, insula, primary sensorimotor, and prefrontal cortices, alongside with overall reduced volume, as revealed by the VBM analysis, in MSA-P patients compared with PD (Planetta et al., 2015). In summary, the available data on fMRI in MSA do not allow clear-cut conclusions and require further replication. While the data are not enough to justify fMRI application in clinical routine, they are promising and fMRI might be a valuable tool in providing further information to identify different MSA phenotypes, early alterations in functional brain network organization, which can precede the development of atrophy, and for monitoring disease progression. This technique may be potentially useful as a marker of disease progression and an outcome measure in clinical studies.

Magnetic resonance spectroscopy Magnetic resonance spectroscopy (MRS) is a valuable tool for mapping metabolic abnormalities in different human tissue, including the brain, by acquiring proton signals and discriminating molecules based on their chemical characteristics (Posse et al., 2013). Within

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the brain, the most prominent resonances detected by MRS are N-acetyl aspartate (NAA), creatine (Cr), choline (Cho), myoinositol (mI), glutamine and glutamate, lactate, and lipids (Currie et al., 2013). NAA reflects neurons’ integrity, and its reduction is related to neuronal and axonal damage (Moffett et al., 2007). mI is considered a glial marker, an increase in its resonance peak indicating gliosis and myelin damage (Kruse et al., 1993; Soares & Law, 2009). Cho is a constituent of the cell membrane, and it may increase in processes causing cell proliferation or membrane disruption (Miller, 1991; Ross & Bluml, 2001). The concentration of Cr is related to energy metabolism (Miller, 1991). Several MRS studies have shown heterogenous results of reduced NAA/Cho and NAA/ Cr ratios in the striatum of patients with parkinsonian disorders, including MSA, PD, PSA, and CBS (Abe et al., 2000; Clarke & Lowry, 2000; Davie et al., 1995; Federico et al., 1997, 1999). A metaanalysis of these works, however, concluded that MRS of the striatum was not conclusive in the differential diagnosis of these conditions (Clarke & Lowry, 2001). Subsequent studies revealed molecular changes in other brain structures, potentially aiding differential diagnosis. Reduced NAA values in the pallidum, putamen, and pontine basis have been helpful in the differential diagnosis between MSA-P and PD (Guevara et al., 2010; Watanabe et al., 2004). Significantly reduced cerebellar NAA/Cr, NAA/Cho, and NAA/mI ratios have been shown in MSA-C compared with MSA-P, PD, PSP, and some subtypes of SCA (Lirng et al., 2012; Zanigni et al., 2015). In addition, the pontine mI/Cr ratio has been reported to increase in MSA-C patients, positively correlating with clinical disability defined by the sum score of unified multiple system atrophy rating scale (Takado et al., 2011). In summary, MRS in association with conventional MRI can be used to track pathology changes, and metabolites may serve as biomarkers of clinical severity, but further evidence is needed to confirm its role in the differential diagnosis of MSA.

Magnetization transfer imaging Magnetization transfer imaging (MTI) is an MRI protocol assessing the magnetization interaction between hydrogen protons bound to water and protons contained in macromolecules, aiming at investigating tissue microstructure in the brain. The degree of interaction between protons changes substantially among different tissues with different macromolecular compositions, thus allowing the acquisition of brain imaging with very high tissue contrast (Wolff & Balaban, 1994). These differences can be quantified by calculating the magnetization transfer ratio, whose reduction has been associated with microstructural changes of white and gray matter in several conditions, including neurodegenerative diseases (Tambasco et al., 2015). When comparing MSA patients with controls, significantly lower magnetization transfer has been reported in the pons, middle cerebellar peduncle, putamen, substantia nigra, and the precentral gyrus white matter (Eckert et al., 2004; Naka et al., 2002b). However, the identification of MSA patients among other parkinsonian disorders seems not accurate enough (Eckert et al., 2004).

Arterial spin labeling Arterial spin labeling (ASL) is an advanced MRI technique using magnetically labeled water protons in the blood as an endogenous tracer for measuring cerebral blood flow

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(Petcharunpaisan et al., 2010). ASL represents a crucial tool in providing complementary information to the conventional MRI sequences, allowing a noninvasive reconstruction of the brain cerebral perfusion maps. It has been employed in several neurological disorders, including vascular disorders, neuropsychiatric diseases, cancer, and neurodegenerative conditions (Haller et al., 2016). The investigation of ASL MRI in MSA is still at the beginning. The first study investigating ASL MRI in MSA combined perfusion data and fMRI findings in a cohort of MSA-C patients compared with controls, revealing significant decreased perfusion in the cerebellum in MSA patients than in controls. In addition, using the vermis, the less perfused region in comparison with controls, as a marker to differentiate MSA-C patients and controls, the authors reported a sensitivity and specificity of 95.8% and 100%, respectively, thus suggesting ASL imaging data as a potential marker for the early diagnosis of MSA-C (Wang et al., 2019b). Another study explored the role of ALS MRI in differentiating MSA and PD. The study involved a large cohort of subjects (including 30 PD, 30 MSA, and 34 controls). It showed lower perfusion values in the cerebellum, caudate, and thalamus in both MSA and PD than controls associated with higher subcortical atrophy (specifically in the left amygdala, caudate nucleus, putamen, thalamus, cerebellum) only in MSA patients, demonstrating a dissociation pattern of atrophy and hypoperfusion in these disorders (Erro et al., 2020). When considering these initial findings, the results are promising and suggest using ASL MRI as a method for reaching early diagnosis and characterization of MSA.

Neuromelanin-sensitive magnetic resonance imaging Neuromelanin, a black pigment generated during catecholamine synthesis, is ordinarily present in dopaminergic and noradrenergic nuclei in the substantia nigra pars compacta and the locus coeruleus. Neuromelanin provides neuronal protection from oxidative stress binding metal ions, especially iron (Zucca et al., 2017). Neuromelanin binding to iron makes this molecule paramagnetic, and it can be visualized, as a hyperintense signal, in neuromelanin-sensitive T1-weighted fast spin echo images by 3 T scan (Sasaki et al., 2006, 2008). Consequently, neuromelanin-sensitive images can be used to measure dopamine function (Cassidy et al., 2019). In PD and neurodegenerative disorders involving dopamine function, neuromelanin is lost when dopaminergic neurons die. Few studies investigated the role of neuromelanin-sensitive imaging in the diagnostic workup of parkinsonian syndromes, and the results are controversial (Sulzer et al., 2018). The volume of the neuromelanin-positive region of substantia nigra is reduced in MSA, PSP, CBD, and PD patients compared with controls (Kashihara et al., 2011). However, only small-scale differences among different parkinsonian disorders have been underlined. The signal in lateral substantia nigra pars compacta is lower in MSA-P and PD patients when compared with PSP patients and healthy controls (Ohtsuka et al., 2014). Greater signal attenuation in the locus coeruleus in MSA (both subtypes) compared with PD has been reported, with signal reduction directly correlating with the severity of neuroradiological alterations typically seen in MSA, such as the hot cross bun sign (Matsuura et al., 2013). These interesting findings, suggesting a potential diagnostic role of neuromelanin-sensitive MRI, need to be replicated in further studies on larger cohorts of patients.

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Molecular imaging techniques PET and SPECT are critical tools able to reveal, in vivo, several molecular, biochemical, and pathobiological events. These include brain metabolism changes, blow flow alterations, synaptic abnormalities, neuroreceptor transmission modifications, misfolded protein deposition, and neuroinflammatory responses. For this reason, PET and SPECT have been largely used in clinical neuroscience, and especially in neurodegenerative disease, representing irreplaceable methods to detect neuropathological processes leading to neurodegeneration (Chandra et al., 2019; Perani et al., 2020; Strafella et al., 2017; Wilson et al., 2019; Wilson & Politis, 2018; Yousaf et al., 2019). PET and SPECT imaging studies have provided crucial information regarding pathology, natural history, and the differential diagnosis in parkinsonian disorders (Brumberg & Isaias, 2018; Pagano et al., 2016; Politis et al., 2017; Wang et al., 2012a; Xu et al., 2018). Molecular imaging plays a pivotal role in early differentiation between diseases having different pathological substrates, i.e., a-syn or tau deposition, or different pattern of functional abnormalities. In addition, neurochemical information provided by molecular imaging may aid in monitoring disease progression and assessing responses to potential therapeutic approaches (Politis, 2014). Table 12.2 summarizes the main molecular imaging findings regarding MSA clinical and research studies.

Striatal dopaminergic imaging Regardless of the clinical entity, parkinsonian disorders are characterized by a severe impairment of the nigrostriatal dopaminergic pathway, as highlighted by a great deal of PET molecular imaging studies conducted over the past 30 years (de Natale et al., 2018). Available tracers for investigating dopaminergic function include the PET tracers [18F] Dopa, [11C]raclopride, [11C]dihydrotetrabenazine (DTBZ), and the SPECT ligands [123I] b-CIT, [123I]FP-CIT, and [123I]iodobenzamide (IBZM). The integrity of presynaptic nigrostriatal projection can be assessed by [18F]Dopa-PET, measuring dopamine storage capacity through the quantification of the presynaptic enzyme L-amino acid decarboxylase (AADC), and [123I]b-CIT, [123I]-FP-CIT, and [99mTc]TRODAT-1 SPECT. [11C]DTBZ is used to visualize the presynaptic monoaminergic system, targeting the vesicular transporters of monoamine type 2 (VMAT2). [11C]raclopride, [123I]IBZM, and [18F]fallypride, binding dopamine D2/D3 receptors, investigate postsynaptic dopaminergic function (Kanthan et al., 2017; Vandehey et al., 2010; Varrone & Halldin, 2010). [18F]Dopa-PET studies reported a reduced uptake in MSA patients compared with healthy controls in the putamen, caudate, ventral striatum, globus pallidus, and in the red nucleus, reflecting loss of dopaminergic projections from the substantia nigra (Burn et al., 1994; Lewis et al., 2012; Rinne et al., 1995). Reduction of dopaminergic presynaptic terminals correlates with clinical symptoms, including both motor and autonomic dysfunction (Brooks, Ibanez, et al., 1990a; Brooks, Salmon, et al., 1990b; Lewis et al., 2012). MSA patients may show greater caudate involvement than PD, as revealed by the caudateeputamen index, calculated by a formula based on the difference in the uptakes in the caudate and putamen divided by the caudate uptake, which was significantly lower in MSA patients (Otsuka et al., 1997). However, the presynaptic dopaminergic function may be impaired in several basal ganglia

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Molecular imaging techniques

TABLE 12.2

Common molecular imaging findings in multiple system atrophy.

Molecular investigation Presynaptic dopaminergic imaging

Neuroimaging technique Findings 18

Main references

[ F]Dopa-PET Putaminal uptake reduction in MSA patients when compared with controls, correlating with motor disability

(Brooks, Ibanez, et al., 1990a)

[123I]b-CITSPECT

Striatal binding reduction around 50% of normal levels in MSA patients

(Pirker et al., 2000)

[123I]-FP-CITSPECT

Abnormal striatal DAT binding in MSA-P (but also in PD (Jin et al., 2013) atypical parkinsonism) (Nicastro et al., Semiquantitative SPECT signal analysis may show 2018) different patterns in MSA-P (higher asymmetry) and MSAC (borderline uptake values)

[99mTc] TRODAT-1SPECT

Overall striatal binding significantly reduced in MSA-P (60%) and MSA-C (30%) compared with normal controls

(Lu et al., 2004)

[11C]DTBZ-PET Reduced binding in the striatum in both MSA-C and MSA- (Gilman et al., P when compared with controls, indicating a reduced 1996) VMAT2 striatal density Postsynaptic dopaminergic imaging

Reduced binding in the striatum of MSA patients when [11C] Raclopride-PET compared with controls and PD patients, suggesting a prominent postsynaptic D2 deficit in MSA predicting measures of disease severity

(Antonini et al., 1997)

[18F]Fallypride Significantly reduced striatal uptake ratio in MSA patients (Schreckenberger (and PSP patients) compared with healthy controls and PD et al., 2004) patients, showing an overall accuracy of 86% in differentiating atypical parkinsonisms and PD

Brain metabolism

Brain perfusion

[123I]IBZM

Significant loss of striatal dopamine receptors in 63% of MSA patients without difference between patients with MSA-P and those with MSA-C

(Schulz et al., 1994)

[18F]FDG-PET

Hypometabolism in putamen, brainstem, or cerebellum

(Gilman et al., 2008)

Predominant putaminal hypometabolism in MSA-P, prominent cerebellar involvement in MSA-C

(Zhao et al., 2012)

Brain hypometabolism involving cortical areas, primarily frontal regions, and spreading to other cortices while disease progressing

(Lyoo et al., 2008)

99m

Tc-ECDSPECT

Reduced cerebral blood flow in the striatum, brainstem, and cerebellum in MSA patients when compared with both controls and PD patients

(Cilia et al., 2005)

[123I]MIBGSPECT

Myocardial MIBG uptake is generally normal in MSA (Nagayama et al., patients, but an abnormal tracer uptake reduction has been 2010) (Continued)

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12. Neuroimaging in multiple system atrophy

Common molecular imaging findings in multiple system atrophy.dcont’d Neuroimaging technique Findings

Main references

Cardiac sympathetic imaging

also reported, indicating myocardial postganglionic sympathetic dysfunction, which can involve up to 30% of cases

Cholinergic activity [123I]IBVMSPECT

Dysfunctional pontothalamic cholinergic pathway in MSA (Mazere et al., patients when compared with controls 2013)

Opioid system

[11C]PMP-PET Reduced subcortical acetylcholinesterase activity in MSA patient when compared with controls

(Gilman et al., 2010)

Mean putaminal tracer binding reduction, suggesting [11C] diprenorphine- dysfunction in the opioid basal ganglia signaling, in MSA PET patients when compared with controls

(Burn et al., 1995)

Neuroinflammation [11C]PK11195PET

Tau deposition

Amyloid burden

Microglia activation has been detected in regions targeted by neurodegenerative changes, and including putamen, caudate, substantia nigra, pons, and cortical areas

(Gerhard et al., 2003)

[11C]PBR28PET

TSPO overexpression was evident in the lentiform nucleus (Jucaite et al., and cerebellar white matter of MSA patients compared 2021) with PD patients; visual rating showed 100% specificity and 83% sensitivity in discriminating MSA form PD

[18F]AV1451PET

Increased posterior putaminal binding in MSA patients compared with controls, likely due to off-target binding

[18F]THK5351

Elevated uptake in the pons and cerebellum of MSA-C and (Schönecker in the lentiform nucleus in MSA-P patients, likely et al., 2019) indicating astrogliosis, and correlating with the severity of parkinsonian and cerebellar symptoms

[11C]PiB-PET

Absent cortical amyloid deposition in MSA patients

(Cho et al., 2017)

(Claassen et al., 2011)

In bold: one of the additional features included in the second consensus statement for the diagnosis of “possible” multiple system atrophy. AV1451, 7-(6-fluoropyridin-3-yl)-5H-pyrido[4,3-b]indole; b-CIT, 2b-carbomethoxy-3b-(4-iodophenyl)tropane; DAT, dopamine transporter; DTBZ, dihydrotetrabenazine; ECD, technetium-99m-ethyl cysteine dimer-SPECT; FDG, fluoro-2-deoxyglucose; FLB457, (S)-N-((1-ethyl-2-pyrrolidinyl)methyl)-5-bromo-2,3-dimethoxybenzamide; FP-CIT, N-u-fluoropropyl-2b-carbomethoxy-3b-(4-iodophenyl) nortropane; IBVM, iodobenzovesamicol; IBZM, iodobenzamide; MIBG, metaiodobenzylguanidine; MSA, multiple system atrophy; MSA-C, MSA cerebellar type; MSA-P, MSA parkinsonian type; PBR28, N-acetyl-N-(2-[11C]methoxybenzyl)-2-phenoxy-5-pyridinamine; PD, Parkinson’s disease; PET, positron emission tomography; PiB, 2-(4-N-11C-methylaminophenyl)-6-hydroxybenzothiazole; PMP, N-methylpiperidin-4-yl propionate; PK11195, -1-(2-chlorophenyl-N-methylpropyl)-3-isoquinolinecarboxamide; PSP, progressive supranuclear palsy; SPECT, single photon emission tomography; THK5351, (S)-6-[(3-fluoro-2-hydroxy)propoxy]-2-(2-methylaminopyrid5-yl)-quinoline; TRODAT-1, technetium-99m-labeled tropane derivative

disorders, reducing the value of presynaptic dopaminergic studies in providing differential diagnosis among degenerative parkinsonism (Antonini et al., 1997; Brooks & Seppei, 2009; Burn et al., 1994; Cilia et al., 2005). [123I]b-CIT, [123I]FP-CIT, and [99mTc]TRODAT-1 are the most used SPECT ligands for imaging the density of the dopamine transporter (DAT), expressed on dopaminergic terminals in the striatum. Their uptake is reduced in neurodegenerative conditions due to the loss of

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presynaptic dopamine terminals (Seibyl et al., 1998). The semiquantitative evaluation of DAT binding may be referred to the striatum in toto or to striatal subregions, namely caudate and putamen, or evaluated by a ROIs-based approach (Brumberg & Isaias, 2018). DAT-SPECT is usually abnormal in both MSA-P and MSA-C when compared with healthy controls, and in MSA-C, striatal dopaminergic loss may be evident also in patients without parkinsonism, limiting the possibility to differentiate the two clinical subtypes (Antonini et al., 2003; Kim et al., 2000; Muñoz et al., 2011; Perju-Dumbrava et al., 2012; Pirker et al., 2000; Plotkin et al., 2005). Subtle differences may be underlined using multimodal imaging techniques and indexes involving the striatum, which is expected to be most compromised in MSA-P than MSA-C patients (Kim et al., 2016; Nicastro et al., 2018). However, normal DATSPECT has been also reported, more frequently in MSA-C (Jeong et al., 2017; Jin et al., 2013; Muñoz et al., 2011; Vergnet et al., 2019). The use of subreference semiquantitative and multivariate pattern recognition analyses has been useful in discriminating among parkinsonisms (Badoud et al., 2016; Oh et al., 2012). In addition, DAT SPECT signal reduction is more symmetrical in MSA than PD (Lu et al., 2004) and shows a greater longitudinal progression, in line with a clinical faster deterioration (Nocker et al., 2012; Pirker et al., 2002). Nevertheless, DAT SPECT may be abnormal in MSA, in PD patients, and other parkinsonian disorders, thus complicating differential diagnosis (Brücke et al., 1997; Knudsen et al., 2004; Varrone et al., 2001). Other studies proposed extrastriatal reduction of DAT signal, e.g., in the midbrain (Scherfler et al., 2005) and in the hypothalamic region (Joling et al., 2017), in aiding differential diagnosis, but further confirmation is needed, considering the consistent overlap in findings in different neurodegenerative parkinsonian disorders. Lastly, DAT-SPECT may be useful to distinguish MSAC from other adult-onset cerebellar ataxias (Gebus et al., 2017). [11C]DTBZ-PET studies, for the quantification of the density of the presynaptic VMAT2 enzyme, are consistent with a decline in striatal dopamine innervation in MSA patients compared with controls (Gilman et al., 1996, 1999b; Gilman, Koeppe, et al., 2003b). VMAT2 density showed a higher reduction in the striatum of MSA-P compared with MSA-C patients (Gilman et al., 1996) that, conversely, showed reduced VMAT2 density in the cerebellum (Gilman et al., 1999a). In addition, VMAT2 expression negatively correlated, in the striatum, with the severity of parkinsonism and REM sleep behavioral disorder, and in the cerebellum, with cerebellar dysfunction (Gilman et al., 1999a; Gilman, Koeppe, et al., 2003b). Studies employing postsynaptic D2 receptor radiotracers have shown striatal reduced binding in MSA patients when compared with controls and PD patients (Antonini et al., 1997; Kim et al., 2002; Schreckenberger et al., 2004; Schulz et al., 1994; Schwarz et al., 1992; Van Laere et al., 2010; van Royen et al., 1993) that, when at the onset and untreated, conversely may show an increase in striatal D2 expression (Kaasinen et al., 2000). However, the reduction of postsynaptic D2 receptors does not seem sufficient to differentiate between MSA and PD (Knudsen et al., 2004), and normal findings cannot exclude a diagnosis of atypical parkinsonism (Plotkin et al., 2005).

Brain glucose metabolism Glucose is the primary energy source in the human brain, providing the ATP required to carry out physiological brain functions (Mergenthaler et al., 2013). [18F]FDG, the most widely IV. Clinical applications in atypical parkinsonian disorders

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used PET tracer, represents a glucose analog that accumulates in brain tissue, providing a measure of regional metabolic activity of neuronal cells (Varrone et al., 2009). Glucose metabolism is tightly related to local neural integrity; thus, decreased brain [18F]FDG-PET signal, indicating hypometabolism, reveals altered synaptic activity, neuronal dysfunction, or neuronal loss (Kato et al., 2016; Perani, 2014). The reliability of [18F]FDG-PET findings depends on the method used to analyze the images. In both clinical and research settings, visual evaluation of the images has been overcome by appropriate quantification methods, which increase the PET diagnostic and prognostic value (Perani et al., 2020). The most used approaches are univariate methods, employing the software package statistical parametric mapping (SPM) and analyzing voxel-by-voxel differences between patients and controls. At the same time, multivariate analysis can be applied to investigate network activity related to metabolic changes across functionally connected regions (Carli et al., 2021). Hypometabolism in the putamen, cerebellum, and midbrain is currently a supportive criterion for a possible MSA diagnosis (Gilman et al., 2008). Brain hypometabolism involves mainly the striatum in MSA-P (De Volder et al., 1989; Eidelberg et al., 1993) and the brainstem and cerebellum in MSA-C (Gilman et al., 1988, 1994; Grimaldi et al., 2019; Zhao et al., 2012). Regional brain metabolism reduction directly correlates with the severity of clinical impairment, namely brainstem hypometabolism correlates with autonomic dysfunction (Taniwaki et al., 2002) and striatal and cerebellar hypometabolism correlates with the severity of parkinsonism and ataxia, respectively (Perani et al., 1995). Cortical areas may also be affected by hypometabolic changes in both clinical subtypes of MSA, including the frontal cortex (Kim et al., 2017b; Lee et al., 2008; Otsuka et al., 1996), spreading to temporoparietal regions when disease progresses (Lyoo et al., 2008). The spread of hypometabolism to cortical regions, especially in the frontal lobes, has been related to the development of cognitive impairment in MSA patients (Park et al., 2020; Shen et al., 2021). In addition, the evidence of insular hypometabolism has been associated with a poor prognosis (Grimaldi et al., 2021). Cerebral glucose metabolism has been investigated to differentiate MSA from PD and other parkinsonian disorders (Akdemir et al., 2014; Antonini et al., 1998; Ghaemi et al., 2002; Juh et al., 2005; Kwon et al., 2008; Tripathi et al., 2013; Zhao et al., 2012), reaching high values of accuracy (Brajkovic et al., 2017; Hellwig et al., 2012; Tang et al., 2010; Tripathi et al., 2016). In a study involving 135 parkinsonian patients, Eckert and colleagues assessed the utility of [18F]FDG-PET in the differential diagnosis by using both visual assessment of scans and an SPM computer-assisted supported diagnosis. The SPM method overcame visual assessment agreeing with clinical diagnosis in 96% of MSA cases, reaching a sensitivity of 96% and a specificity of 99% (Eckert et al., 2005). Kwon and colleagues showed similar results reported 95.5% sensitivity and 100% specificity for SPM analysis in distinguishing MSA (both subtypes) and PD patients, performing better than MRI in the differential diagnosis (Kwon et al., 2007). Caminiti and colleagues used an optimized SPM single-subject procedure (Perani et al., 2014) to produce t-maps of brain hypometabolism able to classify different atypical parkinsonian disorders, including MSA, PSP, CBD, and DLB patients, showing 99% accuracy in predicting the clinical diagnosis at follow-up (Caminiti et al., 2017). Spatial covariance analysis has allowed to identify a disease-specific MSA-related pattern, showing metabolic connectivity reduction in the putamen, cerebellum, and brainstem (Eckert et al., 2008). The expression of this pattern has been suggested as a potential biomarker of

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disease and, correlating with clinical symptoms and disease duration, could be used to monitor disease progression (Ko et al., 2017; Poston et al., 2012).

Perfusional SPECT imaging Perfusional SPECT using 99mTc-ethyl cysteine dimer (ECD-SPECT) provides imaging of regional cerebral blood reflecting the integrity of neuronal activity (Herholz, 2011). In line with [18F]FDG-PET findings, perfusional SPECT showed a reduction in cerebral blood flow involving the striatum, thalamus, brainstem, and cerebellum in MSA patients when compared with controls and PD patients (Bosman et al., 2003; Cilia et al., 2005; Van Laere et al., 2004, 2006). Perfusion reduction was more evident in the posterodorsal putamen than in the caudate nucleus (Bosman et al., 2003; Feigin et al., 2002). Cortical and cerebellar perfusion inversely correlated with the severity of motor and cognitive symptoms (Van Laere et al., 2004). However, due to possible overlap in findings among parkinsonian groups, perfusional SPECT role in the differential diagnosis of parkinsonism remains uncertain (Feigin et al., 2002).

Sympathetic imaging In patients with PD, signs of impaired autonomic cardiovascular regulation include the loss of myocardial noradrenergic, postganglionic innervation, which can be detected by cardiac sympathetic imaging (Jain & Goldstein, 2012). [123I]metaiodobenzylguanidine (MIBG) SPECT and [18F]fluorodopamine PET, both indicating post-¼ganglionic innervation of the heart, have shown decreased cardiac uptake in PD patients (Goldstein, 2014; Kaufmann & Goldstein, 2013). Conversely, in MSA, where autonomic failure seems to be related to preganglionic dysfunction, MIBG-SPECT and [18F]fluorodopamine PET are usually normal, suggesting preserved postganglionic myocardial innervation (Braune, 2001; Fontanarosa et al., 2000; Goldstein et al., 2000; Orimo et al., 2002). In line with these findings, cardiac sympathetic imaging has been used to distinguish PD from atypical parkinsonism and especially from MSA (Chung et al., 2009; Orimo et al., 2012; Treglia et al., 2011; Umemura et al., 2013). However, cardiac sympathetic denervation may be present also in MSA (Nagayama et al., 2005, 2008; Orimo et al., 2007) and may involve up to 30% of patients (Nagayama et al., 2010). Therefore, in the differential diagnosis, neuroimaging evidence of spared cardiac sympathetic innervation excludes PD with a high degree of certainty, but neuroimaging evidence of cardiac postganglionic sympathetic impairment does not exclude MSA.

Opioid system Opioidergic signaling is involved in several functions, including modulation of basal ganglia activity, where high levels of endogenous opioid peptides and opioid receptors are present. Specifically, m, k, and d opioid receptors are abundant in the caudate and putamen, being also distributed in the thalamostriatal afferents and the nigrostriatal terminals (Sgroi & Tonini, 2018). The opioid system strictly interacts with the dopaminergic system by modulating neurotransmission and synaptic plasticity in the dorsal striatum, as shown in animal models (Atwood et al., 2014), thus representing a critical circuit in neurodegenerative

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disorders affecting basal ganglia. [11C]diprenorphine is a PET ligand binding with equal affinity to the three opioid receptors, allowing the study of the opioid system in the brain (Cunningham et al., 1991). Opioid signaling deficit has been related to the development of dyskinesia in PD (Piccini et al., 1997). Mean putaminal [11C]diprenorphine binding is decreased in MSA patients compared with controls, while PD patients showed no differences with controls (Burn et al., 1995). In addition to the low putaminal [11C]diprenorphine signal, reduced caudate binding in MSA-C patients has been reported (Rinne et al., 1995). However, there is still not enough evidence that opioid system imaging can be helpful in the diagnosis of MSA and in discriminating MSA from PD.

Neuroinflammation imaging Accumulating evidence suggests that chronic neuroinflammation plays a role in the pathogenesis of neurodegenerative diseases (Stephenson et al., 2018). Microglia and astrocytes are the leading players in modulating neuroinflammatory responses to several types of insults, including neurodegenerative processes. Thus, monitoring and modulating neuroinflammatory activities may have crucial clinical and therapeutic repercussions (Guzman-Martinez et al., 2019). Microglia, the brain’s resident macrophages are involved in several protective activities, including the phagocytosis of protein deposits and cellular debris, antigen presentation, and cytokine signaling (Comi & Tondo, 2017). Conversely, chronic microglial activation induces a detrimental proinflammatory environment, leading to neuronal damage and neurodegeneration (Stephenson et al., 2018). Activated microglia and reactive astrocytes overexpress the 18 kDa translocator protein (TSPO), an intracellular protein situated in the outer mitochondrial membrane (Gatliff & Campanella, 2012). PET imaging represents a unique tool allowing to in vivo detect TSPO overexpression and providing the opportunity to visualize brain neuroinflammatory responses (Werry et al., 2019). [11C]PK11195 is the progenitor, and also the most widely used, of a series of PET radiotracers that allow the in vivo measure of TSPO levels (Turkheimer et al., 2015), and it has been used to underline neuroinflammation in several neurodegenerative disorders, crucially showing correlations with neuronal dysfunction, brain metabolism, and clinical impairment (Turner et al., 2004; Pavese et al., 2006; Edison et al., 2008; Politis et al., 2012; Passamonti et al., 2018; Tondo et al., 2020, 2021). The first in vivo study on neuroinflammation in atypical parkinsonism dated to 2003 and was conducted on five patients with MSA who underwent a [11C]PK11195-PET. Increased uptake in the putamen, caudate, substantia nigra, pons, and the dorsolateral prefrontal cortex was detected in patients compared with controls, with areas of microglia activation corresponding to those of pathology distribution in MSA (Gerhard et al., 2003). In a subsequent study using [11C]PK11195 in MSA patients enrolled in a minocycline trial, treatment with this antiinflammatory drug was associated with reductions in [11C]PK11195 binding, but without any clinical benefit during the overall study (Dodel et al., 2010). A recent study confirmed significant microglia activation in regions targeted by neurodegenerative changes in MSA-P, including the striatum, pallidum, frontal, and parietal regions (Kübler et al., 2019). Despite [11C]PK11195-PET providing a great deal of visual information on glial activation, its use may be problematic due to its low specific-to-background signal ratio and inability to distinguish microglia and astrocytes activities. Many second-generation tracers have been

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developed, generally with much greater specific binding than the prototype, but implying other methodological caveats, including the presence of genetic variability in affinity binding (Fujita et al., 2017; Turkheimer et al., 2015). A second-generation TSPO tracer, the [11C]PBR28, has been used in a large multicenter study, including 30 MSA-P, 36 MSA-C, and 24 PD patients, and the results suggested a promising role for TSPO imaging in the diagnostic workup of parkinsonism. MSA patients showed a peculiar increased tracer binding in the lentiform nucleus and cerebellum, able to discriminate these patients from PD with a specificity of 100% and high sensitivity, and with both visual and machine learning approaches (Jucaite et al., 2021). In summary, the role of neuroinflammatory responses in MSA has not been fully elucidated yet. However, neuroinflammation imaging is a promising tool offering the opportunity to understand better pathogenetic mechanisms involved in neurodegeneration, possibly aiding the diagnostic workup of parkinsonism.

Cholinergic activity A significant loss of cholinergic neurons in the peduncleepontine and laterodorsal tegmental nuclei has been reported in postmortem evaluation of neuropathologically confirmed MSA (Schmeichel et al., 2008). Cholinergic activity can be investigated by PET imaging using the radiotracer [11C]N-methylpiperidin-4-yl propionate ([11C]PMP), which is hydrolyzed by the acetylcholinesterase enzyme (Shinotoh et al., 2004). MSA-P, PD, and PSP patients showed reduced acetylcholinesterase activity compared with controls in several cortical and subcortical regions, with cerebellar and brainstem [11C]PMP binding inversely correlating with the severity of gait disturbance (Gilman et al., 2010). [11C]PMP-PET has also been used to investigated acetylcholinesterase activity in eight MSA-C patients, showing a significant reduction in the thalamus and cerebellum of MSA-C patients compared with controls, and suggesting a possible role for the pharmacological modulation of the cholinergic system in the treatment of ataxia (Hirano et al., 2008). The integrity of the cholinergic pathways can be further examined by SPECT employing the [123I]iodobenzovesamicol (IBVM), a radiotracer of the vesicular acetylcholine transporter (Kuhl et al., 1994). Gilman and colleagues investigated the neurochemical basis of obstructive sleep apnea in MSA by combining [123I] IBVM-SPECT and [123I]DTBZ-PET imaging, to explore the density of pontothalamic cholinergic terminals and the striatal monoaminergic terminals, respectively. They found reduced [123I]IBVM binding in the thalamus and reduced [123I]DTBZ binding in the striatum of MSA patients compared with controls, with only the thalamic cholinergic deficit related to the severity of obstructive sleep apnea (Gilman, Chervin, et al., 2003a). The involvement of the pontothalamic cholinergic pathway has been confirmed in another study showing an inverse correlation between thalamic [123I]IBVM binding potential and MSA symptoms severity as assessed by the Unified Multiple System Atrophy Rating Scale (Mazere et al., 2013).

Tau, amyloid, and a-synuclein imaging Abnormal accumulation of misfolded proteins represents the pathological hallmark of several neurodegenerative diseases, including MSA. Although the exact sequence of pathological processes underlying proteinopathies is still not completely understood, it is clear

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that the early identification of the pathological deposits could potentially lead to the development of effective therapeutic strategies for neurodegeneration (Golde et al., 2013). In this regard, PET represents a unique tool for in vivo detecting brain neuropathology, marking different neurodegenerative conditions, allowing to detect misfolded proteins deposition. Several amyloid and tau PET tracers have been developed and extensively used in the past decade, while the development of tracers for a-syn pathology remains challenging (Perani et al., 2020). Since the pathological hallmark of MSA is the deposition of a-syn into GCIs, tau and amyloid imaging should be unremarkable in MSA patients, potentially aiding the differential diagnosis from parkinsonian disorders with different pathology. However, posterior putaminal binding of [18F]AV1451 binding, a PET tracer for detecting tau pathology, has been reported in four MSA-P patients compared with controls (Cho et al., 2017). Subcortical increased uptake of [18F]AV1451 may be due to off-target binding, given the suggested binding of this tracer in the basal ganglia, associated with age-related increases in iron accumulation (Choi et al., 2018), and the autoradiography evidence of binding to neuromelanin, melanin, and blood components (Marquié et al., 2017). Using the [11C]PBB3 tau ligand, Perez-Soriano and colleagues reported increased cortical and subcortical binding in one MSA patient (Perez-Soriano et al., 2017). However, binding to GCIs has been reported for [11C]PBB3 in an autoradiographic study (Koga et al., 2017). The [18F]THK5351 is a tau-PET tracer also binding to monoamine oxidase B (MAO-B), whose increase is common in the astrogliosis reaction accompanying neurodegenerative changes in parkinsonian disorders (Tong et al., 2017). Schönecker and colleagues reported elevated [18F]THK5351 uptake in the pons and cerebellum of MSA-C and in the lentiform nucleus in MSA-P patients compared with controls, PD, and PSP patients. Furthermore, in MSA patients, tracer uptake directly correlated with the presence of parkinsonian and cerebellar symptoms (Schönecker et al., 2019), suggesting a potential role of this PET imaging technique in the diagnostic workup and in monitoring disease progression. However, these results are initial, and further studies are needed to clarify the findings related to the tau-PET signal in MSA. Amyloid-PET can be useful in distinguishing MSA, where amyloid pathology should lack, from other parkinsonian disorders where an abnormal cortical amyloid deposition may be present, despite not being the main pathological alteration, such as DLB (Claassen et al., 2011). Recently, the research in molecular imaging in parkinsonian disorders has been actively focused on the development of PET tracers for the in vivo assessment of a-syn deposition (Korat et al., 2021). [11C]BF-227 is a PET tracer that showed a high affinity for pathological forms of a-syn (Fodero-Tavoletti et al., 2009). To measure a-syn in the brain of eight MSA patients, a study using [11C]BF-227 showed increased binding in subcortical white matter, putamen, globus pallidus, substantia nigra, primary motor cortex, and cingulate cortex (Kikuchi et al., 2010). However, the selectivity of [11C]BF-227 for a-syn is low, it also binds the amyloid b peptide (Kudo et al., 2007), and an autoradiographic study reported absent binding to GCIs in MSA brain tissue for this tracer (Verdurand et al., 2018). Since the development of in vivo imaging of a-syn deposition could be a crucial biomarker for diagnostic and prognostic purposes, as well as a precious pharmacological tool for drug development, the Michael J. Fox Foundation offered a $2 million prize for the development of a selective a-syn PET tracer (https://www.michaeljfox.org/news/alpha-synuclein-imaging-prize). Unfortunately, the

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development of a-syn PET tracer for detecting MSA pathology is complicated by the low amount of a-syn aggregates in MSA compared with other proteinopathies, by its intracellular localization, and by the different protein conformations, but imaging for synucleinopathy remains an up-and-coming candidate for disease diagnosis and monitoring (Korat et al., 2021).

Conclusions and future directions Neurodegenerative changes are associated with multiple cellular and biomolecular alterations, and neuronal damage derives from a combination of physiopathological mechanisms. Multimodal neuroimaging protocols provide integrated multiple information regarding structural, molecular, metabolic, and functional aspects. Thus, they may improve diagnostic and prognostic accuracy and allow the identification of novel markers of disease and progression for MSA. A study investigated diagnostic performances of an integrated model including volumetric MRI, [18F]Dopa-PET, [18F]FDG-PET, and [11C]raclopride-PET, in patients with MSA-P and PD, showing that the two conditions could be differentiated based on atrophy, metabolism, and D2 receptor density (Ghaemi et al., 2002). Multimodal imaging may help in identifying the relevance of changes revealed by MRI. DWI abnormalities, including increased putaminal MD, directly correlated with brain hypometabolism in MSA patients, confirming to reveal neuronal damage (Baudrexel et al., 2014). Similarly, the putaminal SWI signal showed a moderate positive correlation with brain metabolism, suggesting that reduced signal, corresponding with iron deposition, may be associated with neurodegeneration (Yoon et al., 2015). The hybrid PET-MRI approach has been developed in the past decade, fully integrating the two imaging techniques. This method is advantageous in studying neurodegenerative disease and movement disorders, where the simultaneous investigation of structural, functional, and molecular abnormalities may clarify the underlying pathological process, reducing the study duration and patient distress (Tondo et al., 2019). Ruan and colleagues performed hybrid PET-MRI including structural, perfusional (ASL), and metabolic ([18F]FDG) in 20 patients divided in PD and MSA groups: both metabolism and perfusion showed to be significantly reduced in the caudate nucleus, pons, and cerebellum in MSA patients and to differentiate this group from the PD one (Ruan et al., 2019). The hybrid PET-MRI technique is still in the early stages in the field of movement disorders, but the potential of this method in characterizing disease-related changes is undeniable, promoting early diagnosis and adequate disease monitoring, along with better prognostic stratification and clinical trial planning. MSA still lacks a specific treatment. Ongoing clinical trials on MSA involving neuroimaging techniques should work toward the definition of early reliable biomarkers for disease identification, which would allow testing novel neuroprotective and disease-modifying drugs. Currently, a recruiting clinical trial involving MSA patients aims to integrate structural, functional, and molecular data employing MRI, [18]FDG-PET, and the novel [11C] UCB-J PET tracer, able to reveal synaptic integrity at the earliest stage of neurodegeneration (NCT: 05121012). Identifying early signs of synaptic dysfunction might allow to stop or modify the neurodegenerative process. Imaging biomarkers can thus provide evidence aiding the diagnostic process, especially at the disease onset, and may reveal neuroanatomical and

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molecular changes associated with neuronal damage. Further studies in large and homogeneous cohorts are needed to clarify the role of the specific changes in the disease progression and standardize procedures and methods. Only a conclusive understanding of the pathological processes underlying MSA and the in vivo characterization of mechanisms leading to neurodegeneration would provide the basis to search for novel therapeutic strategies.

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13 Neuroimaging in progressive supranuclear palsy Edoardo Rosario de Natale1, Heather Wilson1, Marios Politis1 and Flavia Niccolini2, 3 1

Neurodegeneration Imaging Group and Mireille Gillings Neuroimaging Centre, University of Exeter, London, United Kingdom; 2Department of Neurology, King’s College Hospital NHS Foundation Trust, London, United Kingdom; 3Department of Neurology, Queen Elizabeth Hospital, Lewisham and Greenwich NHS Foundation Trust, London, United Kingdom

Introduction Progressive supranuclear palsy (PSP) is an adult-onset, neurodegenerative disease clinically characterized by the presence of symmetrical akineticerigid parkinsonian syndrome, supranuclear gaze palsy, early postural instability with falls backward, subcortical dementia, dysarthria, and dysphagia. PSP is characterized neuropathologically by accumulation of abnormal 4-repeat tau protein in subcortical nuclei, motor, and premotor cortices (Spillantini & Goedert, 2013). There are several PSP phenotypes including PSP Richardson’s syndrome (PSP-RS), PSP with predominant parkinsonism (PSP-P), PSP with progressive gait freezing (PSP-PGF), PSP with frontal presentation (PSP-F), PSP with speech/language disorder (PSP-SL), and PSP corticobasal syndrome (PSP-CBS) (Hoglinger et al., 2017). Early diagnosis of PSP is challenging due to its high heterogeneity and overlapping features with other neurodegenerative disorders. Neuroimaging biomarkers may aid in the early and differential diagnosis as well as in assessing progression and response to disease-modifying treatments.

Structural magnetic resonance imaging The typical structural MRI features observed in PSP are midbrain and superior cerebellar peduncles atrophy giving typical morphological markers such as “hummingbird’s” sign

Neuroimaging in Parkinson’s Disease and Related Disorders https://doi.org/10.1016/B978-0-12-821651-4.00004-X

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(Kato et al., 2003), “Mickey Mouse” sign (Massey et al., 2012), and “morning glory” sign (Adachi et al., 2004). Planimetric MRI measurements can assist in the differential diagnosis between PSP and its subforms to Parkinson’s disease (PD) and other atypical parkinsonisms (Mangesius et al., 2018) (Table 13.1). Several studies have assessed quantitative measures of midbrain anteroposterior diameter and volume (Barsottini et al., 2007; Choi et al., 2011; Kim et al., 2015a,b; Messina et al., 2011; Owens et al., 2016; Paviour et al., 2006; Righini et al., 2004; Slowinski et al., 2008; Warmuth-Metz et al., 2001; Yagishita & Oda, 1996). These studies found that PSP-RS patients usually show significant midbrain atrophy in comparison with healthy controls and patients with multiple system atrophy (MSA) and PD patients. It has been suggested that a ratio of the midbrain area/pons area (M/P ratio) may improve the specificity in distinguishing between PSP and MSA-P patients (Cosottini et al., 2007). PSP patients showed greater midbrain atrophy with relatively sparing of the pons, whereas MSA-P patients had an opposite pattern (Cosottini et al., 2007). Midbrain atrophy can be spotted in MRI scans of PSP patients before a clinical diagnosis is made and has been correlated to deficits in executive function, language, and fluency (Agosta et al., 2010; Ahn et al., 2019; Luca et al., 2021). In a longitudinal study conducted on 16 PSP patients scanned 12 months apart, the rate of midbrain atrophy was correlated with changes in the PSP rating scale score but not with measures of tau retention using the PET tracer [18F]AV1451 (Whitwell et al., 2019). Other studies have found that the M/P ratio was able to differentiate with high sensitivity and specificity PSP, PD, and MSA patients (Cosottini et al., 2007; Longoni et al., 2011; Massey et al., 2013; Morelli et al., 2011; Oba et al., 2005; Quattrone et al., 2008; Sankhla et al., 2016; Zanigni et al., 2016). Recent studies have assessed the potential of the M/P ratio over time. In a longitudinal study in PD and PSP patients, it was found that the P/M ratio showed stronger decline in PSP patients; however, the diagnostic accuracy of this measure versus PD did not improve over time (Kannenberg et al., 2021). Having a decreased P/M ratio at diagnosis could also, in combination with older age at onset, predict worse prognosis, in PSP patients (Cui et al., 2020). On the other side, other studies have shown that sensitivity of the M/P ratio was low (Hussl et al., 2010; Kim, Ma, & Kim, 2015; Owens et al., 2016; Picillo et al., 2020), but could be increased by coupling this structural MRI measure with other imaging values, such as cardiac MIBG SPECT to differentiate PSP from MSA (Sakuramoto et al., 2020). Another morphological feature of PSP is atrophy of the superior cerebellar peduncle with sparing of the middle cerebellar peduncle (Constantinides, Paraskevas, Stamboulis, & Kapaki, 2018; Paviour et al., 2005). In 2008, Quattrone and colleagues developed the Magnetic Resonance Parkinsonism Index (MRPI), which takes into account both the midbrainepons area ratio and the ratio of the middle to superior cerebellar peduncles width (Quattrone et al., 2008). The MRPI has shown high sensitivity and specificity in differentiating PSP-RS from MSA-P, PD, and vascular parkinsonism (Archer et al., 2020; Hussl et al., 2010; Kim, Ma, & Kim, 2015; Longoni et al., 2011; Morelli et al., 2011; Mostile et al., 2016; Nigro, Arabia, et al., 2017; Zanigni et al., 2016; Zhang et al., 2019) with an index value of 12.6 discriminating PSP from other causes of Parkinsonism with a 91% sensitivity and 95% specificity (Constantinides, Paraskevas, Velonakis, et al., 2018), and it appears to be less affected by age than the midbrainepons ratio (Morelli et al., 2014). In a recent study comparing MRPI and striatal binding ratio (SBR) of [123I]FP-CIT SPECT as index of nigrostriatal dopaminergic denervation in 53 PSP patients, MRPI showed 85.0% sensitivity, 100% specificity, and 94.3% accuracy, whereas SBR showed 95.0% sensitivity, 36.4% specificity, and 58.5% accuracy, suggesting

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that these two methods could be complementary in the individuation of PSP cases (Sakamoto et al., 2020). Recently, an automated procedure to assess MRPI has been developed and tested in large cohort of PSP patients (Nigro et al., 2017a,b, 2020). In a retrospective international multicenter study assessing 173 PSP patients and 483 non-PSP participants, automated MRPI was able to differentiate PSP patients from non-PSP subjects with 82.3% sensitivity and 93.8% specificity (Nigro et al., 2020). Sensitivity and specificity increased up to 88.9% and 94.7%, respectively, when only PSP-RS participants were compared with non-PSP participants (Nigro et al., 2020). MRPI values showed lower sensitivity and specificity in discriminating PSP-P patients from non-PSP patients (Nigro et al., 2020). More recently, the MRPI has been coupled with the measurement of the third ventricle width (MRPI 2.0) (Quattrone et al., 2018). In a validation study of this new index, the MRPI 2.0 has showed 100% sensitivity and 94.3% specificity in differentiating PSP-P and PD groups, with higher performance compared with MRPI in distinguishing those PSP patients with absence of vertical supranuclear gaze palsy (Quattrone et al., 2018). Several studies have also found atrophy of other subcortical structures including the caudate nucleus, putamen, globus pallidus, subthalamus, and thalamus in PSP patients (Albrecht et al., 2019; Cordato et al., 2005; Josephs et al., 2008; Looi et al., 2011; Massey et al., 2012; Messina et al., 2011; Saini et al., 2013; Schulz et al., 1999). Saini and colleagues showed nonspecific atrophy of the thalamus (Saini et al., 2013), whereas another group reported mediodorsal thalamic atrophy in PSP patients (Cordato et al., 2005). Hippocampal atrophy has also been showed in patients with PSP compared with healthy controls (Messina et al., 2011; Saini et al., 2013). Atrophy of the basal ganglia has been observed in different phenotypes of PSP such as patients with PSP-P (Agosta et al., 2010), PSP-CBS (Whitwell et al., 2010), and PSP-SL (Josephs et al., 2006), and thalamic atrophy has been reported in PSP-PGF (Hong et al., 2015). Atrophy of the caudate, putamen, and globus pallidus has been reported in PSP patients (Agosta et al., 2010; Canu et al., 2011; Josephs et al., 2011; Price et al., 2004; Saini et al., 2013; Whitwell et al., 2013). Caudate atrophy correlated with worse language and fluency test scores and atrophy of the putamen was significantly associated to apathy and behavioral disturbance in PSP patients (Agosta et al., 2010; Josephs et al., 2008). Cortical atrophy has also been reported in PSP involving mainly the middle frontal gyrus, superior frontal gyrus, primary motor cortex, premotor cortex, supplementary motor cortex, dorsolateral prefrontal cortex, and frontal operculum (Boxer et al., 2006; Brenneis et al., 2004; Cordato et al., 2002, 2005; Ghosh et al., 2012; Giordano et al., 2013; Josephs et al., 2008, 2011; Lagarde et al., 2013; Padovani et al., 2006; Sandhya et al., 2014; Taki et al., 2004; Wang et al., 2015; Whitwell, Avula, et al., 2011; Worker et al., 2014a). Patients with PSP-RS had greater whole brain and frontal atrophy than those with PD (Cordato et al., 2002; Giordano et al., 2013; Hassan et al., 2012; Worker et al., 2014a) and MSA-P (Worker et al., 2014a). Other studies have found frontal atrophy in other PSP phenotypes such as PSP-F (Hassan et al., 2012), PSP-SL (Josephs et al., 2006; Rohrer et al., 2010; Santos-Santos et al., 2016), and PSPCBS (Lee et al., 2011; Whitwell et al., 2010), which appears to be greater than that observed in PSP-RS suggesting that frontal atrophy could help differentiating different phenotypes of PSP. Volume loss in the frontal areas is similar in PSP-PGF (Hong et al., 2015) and PSP-P (Agosta et al., 2010) compared with PSP-RS. Posteriorelateral frontal atrophy was found to correlate with frontal behavioral inventory (FBI) scores in PSP patients (Josephs et al.,

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2011), whereas another study found that FBI scores correlated with atrophy of the orbitofrontal cortex (Cordato et al., 2002). These studies show evidence of frontal lobe dysfunction in PSP patients. Atrophy of cortical structures has also been correlated with presence of speech and language impairments in PSP patients. In a study on 13 PSP and 19 CBS patients, who underwent a composite language examination tool, presence of language impairment was associated with atrophy of left-lateralized frontotemporal cortex, amygdala, hippocampus, putamen, and caudate (Peterson et al., 2021). Additionally, reduced superior parietal volumes have been correlated, in PSP patients with presence of dysphagia (Clark et al., 2021). By contrast, a longitudinal study in PSP-RS patients did not show any correlation between cortical thinning and midbrain atrophy with disease progression and behavioral dysfunction (Agosta et al., 2018). Iron deposition, as measured with T2*, susceptibility-weighted imaging (SWI), and quantitative susceptibility mapping (QSM), has been extensively studied in PSP patients as opposed to other atypical parkinsonisms. Excess iron deposition has been observed in the putamen, globus pallidus, substantia nigra, red nucleus, subthalamic nucleus, and thalamus of PSP-RS patients (Boelmans et al., 2012; Fedeli et al., 2020; Gupta et al., 2010; Han et al., 2013; Lee et al., 2013; Miyata et al., 2019; Sjostrom et al., 2019). PSP patients show higher iron concentrations in the anterior and medial aspects of the globus pallidus and thalamus, whereas MSA-P patients have greater iron deposition in the posterolateral putamen (Han et al., 2013; Sjostrom et al., 2019), with increased c values in the red nucleus, subthalamic nucleus, and medial substantia nigra in PSP yielding the highest discriminating values compared with MSA patients (Mazzucchi et al., 2019). Signal changes of superior cerebellar peduncle on fluid-attenuated inversion recovery (FLAIR) MRI sequence were found in PSP patients but not in MSA-P and PD patients (Kataoka et al., 2008). Microstructural changes as assessed by diffusion tensor imaging (DTI) and diffusionweighted imaging (DWI) have been found in PSP patients (Table 13. 2). In a large international study, DWI showed an area under the curve (AUC) of 0.955 in differentiating PD patients versus atypical parkinsonism that, if coupled with the Movement Disorders Society Unified Parkinson’s disease Rating Scale (MDS-UPDRS) III scores, increased to 0.962 (Archer et al., 2019). PSP-RS patients had higher diffusion coefficient (ADC) measurements from DWI in the putamen, caudate nucleus, globus pallidus, superior cerebellar peduncle, inferior frontooccipital fasciculus, and midbrain compared with PD (Baudrexel et al., 2014; Kvickstrom et al., 2011; Nicoletti et al., 2006, 2008; Ohshita et al., 2000; Paviour et al., 2007; Seppi et al., 2003; Tsukamoto et al., 2012), but lower values were found in the middle cerebellar peduncle, cerebellum, and putamen compared with MSA-P (Baudrexel et al., 2014; Nicoletti et al., 2013; Tsukamoto et al., 2012; Wadia et al., 2013). Nicoletti and colleagues found that high ADC values in the superior cerebellar peduncle have sensitivity of 96.4% and specificity of 93.3% in differentiating PSP-RS from MSA-P patients (Nicoletti et al., 2008). Degeneration of the white matter tracts has been reported in DTI studies. The most common subcortical areas involved are the midbrain, pons, corpus callosum, cerebellum, superior cerebellar peduncle, thalamus, locus coeruleus, basal forebrain, and pedunculopontine nucleus (Canu et al., 2011; Coon et al., 2012; Erbetta et al., 2009; Ito et al., 2008; Knake et al., 2010; Meijer et al., 2015; Piattella, Upadhyay, et al., 2015; Pyatigorskaya et al., 2020; Saini et al., 2012; Spotorno et al., 2019; Surova et al., 2015; Tessitore et al., 2014; Wang et al., 2010; Whitwell, Master, et al., 2011, 2012; Worker et al., 2014b). Fractional anisotropy (FA)

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and mean diffusivity (MD) changes in selective regions such as the midbrain and pons (Meijer et al., 2015; Wang et al., 2010), dentate nucleus (Seki et al., 2018), corpus callosum and callosal radiations from the frontal lobe (Rosskopf et al., 2014; Surova et al., 2013), and the thalamic radiations (Piattella, Upadhyay, et al., 2015; Tessitore et al., 2014; Worker et al., 2014b) have been reported in PSP patients and appeared to be correlated with the disease grading and duration, respectively. Regional decreased FA and increased MD also correlated with increased tau retention as detected with [18F]AV1451 PET imaging (Sintini et al., 2019). A few studies have reported white matter damage in cortical regions such as the prefrontal cortex, orbitofrontal cortex, premotor cortex, motor cortex precentral gyrus, postcentral gyrus, and supplementary motor area, as well as limbic areas such as the amygdala and the hippocampus (Erbetta et al., 2009; Saini et al., 2013; Wang et al., 2019). A few studies have tested the potential of diffusion MRI to distinguish between PSP and other parkinsonisms and, within PSP, across its variants. PSP-RS display significantly reduced FA and increased MD, radial diffusivity (RD), and axial diffusivity (AD) values in nearly all brain structures compared with PD subjects (Talai et al., 2018). In a study on 25 PSP-RS patients, 9 MSA-C, 9 MSA-P, and 47 PD patients, tractography analysis of the corticospinal tract within the corona radiata showed significantly higher MD in PSP-RS as opposed to PD and healthy controls, whereas the MSA-C showed higher involvement of the middle cerebellar peduncles (Zanigni et al., 2017). In a multimodal MRI study employing different MRI sequences in a group of 18 PSP and 16 MSA-P patients and principal component analysis (PCA), this method revealed two distinctive components, the first being the mean MD value of the thalamus and the mean MD and FA values of the dentatorubrothalamic tract and the corpus callosum, and the second representing the mean MD and FA values of the middle cerebellar peduncle. By this means, 92% of PSP patients were differentiated correctly from MSA-P and PD and 80% of MSA-P patients could be distinguished from PD, suggesting DTI could be useful in discriminating between PSP and other atypical Parkinsonisms (Seki et al., 2019). Diffuse subcortical white matter alterations as detected with neurite orientation dispersion and density imaging (NODDI) and free-water DTI also showed high accuracy and high AUC (0.945 and 0.969, respectively) in distinguishing PSP from PD and MSA-P cases (Mitchell et al., 2019). Attempts to use DTI also to differentiate between PSP phenotypes have been made by a few neuroimaging groups. In one study, increased MD and decreased FA of the dentatorubrothalamic tract in PSP-RS was significantly worse that detected in PSP-P patients and correlated significantly with severity of gait and balance symptoms (Seki et al., 2018). Changes in AD have been reported in PSP-RS as opposed to other PSP variants in the putamen and globus pallidus (Wang et al., 2010), as well as in the frontal cortices and corpus callosum (Saini et al., 2012). A study using automated tractography showed that white matter alterations in the dentatorubrothalamic tract and the anterior thalamic radiation would discriminate between PSP-RS and PSP-P patients (Potrusil et al., 2020). However, other studies did not detect any distinctive features across the PSP variants PSP-RS and PSP-P (Agosta et al., 2012). A brain region frequently found to be affected by white matter alterations is the superior cerebellar peduncle. Canu and colleagues showed decreases in FA and increases in MD in the superior cerebellar peduncle with relatively sparing of the middle cerebella peduncle (Canu et al., 2011). Tessitore and colleagues found that postural instability and gait disturbance

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UPDRS subscores were associated to worse diffusivity parameters in the cerebellum and superior cerebellar peduncle (Tessitore et al., 2014), whereas another study showed correlation between FBI, PSP rating score, mini mental state examination (MMSE) and UPDRS scores, and FA value of superior cerebellar peduncle (Whitwell, Master, et al., 2011). Selective degeneration of this region also could separate among clinical PSP subgroups. Quattrone and colleagues, using tract-based spatial statistics (TBSS) whole-brain analysis, found that lower FA in the superior cerebellar peduncle could distinguish PSP-RS from PSP-P patients (Quattrone et al., 2019). More recently, Whitwell and colleagues demonstrated that PSP-RS showed greater degeneration of this region compared with PSP-P and PSP-SL patients, these latter showing greater white matter degeneration in the corpus callosum, internal and external capsule, and superior longitudinal fasciculus compared with the other variants (Whitwell et al., 2021). These findings suggest that cerebellum and superior cerebellar peduncle are involved in the pathogenesis of motor and cognitive symptoms in PSP and could have distinctive, differentiating features across PSP variants. A few recent studies have studied the whole-brain structural connectome in PSP patients. Abos and colleagues observed in a group of PSP patients, a reduced structural connectivity that was preponderant in frontal lobe-deep gray matter connections, which correlated with disease severity scores (Abos et al., 2019). These results were replicated in a cohort of 16 PSP-RS patients, which showed significantly abnormal deep gray matter connections within the basal ganglia, with lower path length, and abnormal degree, strength, local efficiency, betweenness, and participation coefficient, strengthening the hypothesis of PSP as a network-based disorder resulting by a complex disruption of interconnected regions, rather than by an isolated alteration (Prasad et al., 2021). Two longitudinal studies have assessed microstructural white matter alterations as a marker of disease progression in PSP. A significant progression of white matter damage was detected in cohorts of PSP-RS and PSP-P patients, which correlated with disease progression and behavioral dysfunction, which suggest that DTI could represent a marker of disease progression in PSP (Agosta et al., 2018; Caso et al., 2018). The analysis of DTI images can be sometimes hindered by the presence of extracellular water molecules that display random (i.e., nondirectional) movement in the extracellular space (free water). Free water can bias FA and MD indices, providing false-positives (MetzlerBaddeley et al., 2012). To overcome this issue, methods to separate the diffusion properties of brain tissue from free water have been developed (Pasternak et al., 2009). In one study, Planetta and colleagues examined 18 patients with PSP, 18 MSA, 18 PD, and 18 controls using free water correction analysis to DTI images. The PSP cohort showed increased free water values in the caudate, putamen, thalamus, and vermis and decreased in the superior cerebellar peduncle and the corpus callosum compared with controls, confirming a severe and widespread white matter pathology in this disease, as opposed to more selective regional abnormalities found in PD and MSA (Planetta et al., 2016). Another study investigated the free water diffusion in the posterior substantia nigra of a large cohort consisting of PSP, PD, and MSA patients (Ofori et al., 2017). They found that the degree of free water in this region was greatest in PSP patients, followed by MSA, PD, and healthy controls. Recent studies have used automated machine learning techniques to volumetric and diffusion MRI sequences to develop prediction models to distinguish PSP patients from other parkinsonisms (Bocchetta et al., 2020; Cherubini et al., 2014; Duchesne et al., 2009; Focke et al.,

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2011; Groschel et al., 2004; Huppertz et al., 2016; Kiryu et al., 2019; Marquand et al., 2013; Salvatore et al., 2014; Scherfler et al., 2016; Sjostrom et al., 2020; Talai et al., 2018, 2021). These studies found that assessment of brainstem (Bocchetta et al., 2020; Sjostrom et al., 2020), midbrain, basal ganglia, cerebellum, and thalamus was able to differentiate PSP-RS from those with PD and MSA-P (Duchesne et al., 2009; Huppertz et al., 2016; Marquand et al., 2013; Salvatore et al., 2014; Scherfler et al., 2016). Scherfler and colleagues developed a prediction model, including midbrain, putamen, and cerebellar gray matter volumes, which was able to differentiate PSP-RS from MSA and PD with 90% sensitivity and 100% specificity at the early stage of the disease (Scherfler et al., 2016). Groeschel and colleagues developed a mathematical model using MRI volumetry based on pattern of atrophic changes in 33 patients with probable, possible, or definite PSP and 18 with CBS (Groschel et al., 2004). They found significant reduction in average brain, brainstem, midbrain, and frontal gray matter volumes in PSP patients, whereas patients with CBS showed atrophy of parietal cortex and corpus callosum. When assessing only autopsy-confirmed PSP and CBS cases, midbrain, parietal white matter, temporal gray matter, brainstem, frontal white matter, and pons volumes were able to discriminate between PSP and CBS (Groschel et al., 2004). Talai and colleagues used support vector machine on DTI images of PSP-RS patients that showed widespread diffusion alterations compared with PD to differentiate between these two conditions, obtaining an accuracy of 87.7% (Talai et al., 2018). In another study from the same research group, the use of machine learning on DTI features of PSP and PD patients attained a 95.1% accuracy in this discrimination, significantly higher than the model using quantitative T2 features (Talai et al., 2021). In another recent study, support vector machine was applied to diffusion imaging in a group of 20 PSP-RS, 21 PD, and 23 healthy controls, with PSP-RS showing decreases in track density in brainstem, cerebellum, thalamus, corpus callosum, and corticospinal tract, as opposed to no changes in the other two groups (Nigro et al., 2019). Taken together, these studies show the potential of machine learning in the discrimination of PSP-related structural abnormalities. Further studies are needed in the future to assess its potential in predicting disease progression. The paramagnetic neuromelanineiron complex, as visualized with specific T1 weighted fast spin echo MRI sequences, represents a critical biomarker of depletion of catecholaminergic neurons of the substantia nigra and locus coeruleus, and has gained increasing attention in the research of parkinsonisms and of PSP in particular (Matsuura et al., 2021; Pyatigorskaya et al., 2020; Sasaki et al., 2006; Taniguchi et al., 2018). The area and volume of the substantia nigra could have discriminating properties between PSP and PD patients (Pyatigorskaya et al., 2020). Taniguchi and colleagues studied 11 PSP patients, 24 PD patients, and 10 controls analyzing the neuromelanin-positive substantia nigra area and the midbrain volume on volumetric MRI (Taniguchi et al., 2018). They found that the area corresponding to the substantia nigra pars compacta was significantly smaller in PSP as opposed to PD patients, however, without detecting any correlation between this MRI finding and the degree of motor deficits. Additionally, the contrast ratio of the locus coeruleus in PSP was found to be significantly higher compared with PD patients, with an AUC of 0.85, indicating good diagnostic discrimination efficacy (Matsuura et al., 2021). Further studies are needed to validate this as a potential differentiating marker of PSP versus PD, and to clarify its potential as progression marker in PSP.

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Functional magnetic resonance imaging Functional MRI (fMRI) studies in resting state or task specific have investigated neuroimaging correlates of clinical symptoms and functional pathology (Bharti et al., 2017; Brown et al., 2017; Burciu et al., 2015, 2016; Lemos et al., 2017; Piattella, Tona, et al., 2015; Rosskopf et al., 2017; Upadhyay et al., 2017; Whitwell, Avula, et al., 2011; Yang et al., 2021; Yu et al., 2018; Zwergal et al., 2011). Task-specific fMRI studies have concentrated in motor tasks relative to their main motor alterations. PSP patients during a grip force task showed reduced basal ganglia and motor and premotor cortical activity, with concomitant increased activation, suggesting compensatory mechanisms, in the parietal and occipital cortices (Burciu et al., 2015); at 1-year longitudinal observation of 19 PSP patients, a decline in fMRI signal in putamen, primary motor cortex, supplementary motor area, and superior motor regions of the cerebellum was observed, providing evidence for motor task-specific changes in the basal ganglia and cerebellothalamocortical networks (Burciu et al., 2016). Using an eye-tracking task in a group of eight PSP patients, patients showed decreased frontostriatal activation during prosaccades and antisaccades, compared with controls, together with a general alteration of the default mode network (DMN) degree of activation during all eye movement tasks. These findings point out to an alteration of the DMN and of frontostriatal functional connections in the generation of ocular alterations in PSP (Lemos et al., 2017). Another study using mental imagery of lying, standing, walking, or running found reduced activation in the mesencephalic brainstem tegmentum, thalamus, midline cerebellum, caudate, inferior and superior parietal lobule, and left postcentral gyrus in PSP patients compared with controls (Zwergal et al., 2011). Reduced activation in these brain regions was associated with increased postural instability and falls (Zwergal et al., 2011). Resting-state fMRI studies have shown disrupted or decreased functional connectivity in several brain networks, including the midbrain, the thalamus, the caudate, prefrontal, temporal and occipital cortex, the supplemental motor area, the amygdala, and the cerebellum in patients with PSP (Brown et al., 2017; Upadhyay et al., 2017; Yang et al., 2021; Yu et al., 2018) (114e117), with significant differences in cellular activity compared with PD patients (Yu et al., 2018). Two studies (Bharti et al., 2017; Upadhyay et al., 2017) have assessed the differences in resting functional connectivity in PSP and CBS patients. In both disorders, an increased connectivity of the DMN (Bharti et al., 2017) and of the thalamus was found (Upadhyay et al., 2017), but these two conditions differed in the functional connectivity of the cerebellar dentate nucleus, as it was decreased in subcortical and prefrontal cortical areas in PSP, but increased asymmetrically in the frontal cortex in CBS patients (Upadhyay et al., 2017). Within the cerebellar, a reduction in its functional connectivity in PSP was also associated with lower scores on the MMSE indicating therefore cerebellar dysfunction as distinctive and critical in the pathophysiology of PSP (Bharti et al., 2017). Another group studied 22 patients with PSP-RS and 12 with PSP-P phenotype (Rosskopf et al., 2017). PSP patients showed increased functional connectivity with the thalamus, and reduced connectivity within the midbrain, which was correlated with vertical gaze impairment. Reduced functional connectivity in the prefrontal cortex was instead associated to

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worse cognitive function (Rosskopf et al., 2017). PSP-RS had significantly increased functional motor network connectivity with the medial prefrontal gyrus (Rosskopf et al., 2017). Brown and colleagues studied functional connectivity deficits longitudinally in 12 PSP patients evaluated 6 months apart (Brown et al., 2017). They found a progressive decreasing connectivity between the subcorticalebrainstem and parietal cortex, which was associated with a progressive atrophy of the same brainstem regions and the posterior frontal cortex, suggesting an anterioreposterior temporal gradient of subcortical structural and functional degeneration in PSP (Brown et al., 2017).

Magnetic resonance spectroscopy Magnetic resonance spectroscopy (MRS) exploits the peculiar resonance properties of different ions (protons 1H, 13C, and 31P) in response to an external magnetic fields, and the measure of each resonance is an indicator of their concentration (Mlynarik, 2017). A few studies have sought to quantify the regional changes of metabolites in PSP as a marker of cellular suffering (Abe et al., 2000; Barbagallo et al., 2019; Federico et al., 1997). Federico and colleagues measured the concentration of N-acetyl-aspartate (NAA), creatine and phosphocreatine (Cr), and choline (Cho) in the putamen and the globus pallidus of PSP patients and found a significant reduction of the NAA/Cho and NAA/Cr ratios in PSP, suggesting neuronal loss in these basal ganglia regions (Federico et al., 1997). The putaminal NAA/Cr ratio was also significantly decreased in PSP patients compared with MSA patients (Abe et al., 2000). More recently, Barbagallo and colleagues found a significant decrease of scylloinositol and scylloinositol/Cr ratio in the supplementary motor area of PSP patients, which correlated with cognitive measures of attention and working memory (Barbagallo et al., 2019). Scylloinositol is an isomer of inositol that has been hypothesized to act as a stabilizer of Ab1-42, and its concentrations may reflect changes in the metabolism of these proteins (McLaurin et al., 2000). Further studies may shed more light on the possible role of this molecule and on its potential in PSP and other neurodegenerative diseases.

Molecular imaging studies in progressive supranuclear palsy Single-photon emission computed tomography Brain perfusion and striatal presynaptic dopaminergic system have been assessed using single-photon emission computed tomography (SPECT) in PSP patients (Alster et al., 2019; Antonini et al., 2003; Badoud et al., 2016; Brucke et al., 1997; Filippi et al., 2006; Goebel et al., 2011; Im et al., 2006; Kim et al., 2002; Messa et al., 1998; Oh et al., 2012; Pirker et al., 2000; Sakamoto et al., 2020; Seppi et al., 2006; Takaya et al., 2018; Van Laere et al., 2006) (Table 13.3). Regional blood flow and perfusion, using radiotracers such as [99mTc]HMPAO, [99mTc] ECD, and [123I]IMP, have been studied to investigate atypical parkinsonian syndromes in a few studies. These studies demonstrated a significant hypoperfusion in cortical structures pertaining to the frontal, parietal, occipital cortex, and cingulate gyri as well as in the basal ganglia, and in the thalamus (Habert et al., 1991; Johnson et al., 1992; Kimura et al., 2011;

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Slawek et al., 2001; Varrone et al., 2007). More recently, a study on a larger cohort of 21 PSP patients, with 10 MSA-P and 14 CBS, showed that significant differences in [99mTc]HMPAO uptake between MSA-P and PSP in the cerebellum and thalamus, whereas the study did not find any difference of tracer uptake between the PSP and CBS groups (Alster et al., 2019). Loss of dopamine transporter (DAT) binding, using DAT-specific SPECT radiotracers such as [123I]b-CIT, [123I]FP-CIT, [99mTc]TRODAT-1, and [123I]IPT, has been found in PSP patients as well as those with PD, MSA-P, and CBS (Badoud et al., 2016; Goebel et al., 2011; Im et al., 2006; Joling et al., 2017; Kim et al., 2002; Oh et al., 2012; Pirker et al., 2000). However, PSP patients showed a different pattern of DAT loss. The caudate appears to be more affected in PSP-RS than PD patients (Im et al., 2006). Im and colleagues showed lower DAT binding in the caudate head and caudate/putamen transitional region relative to the putamen, with smaller posterior putamen-to-caudate binding ratios in PSP patients compared with those with PD (Im et al., 2006). Other studies showed regional patterns of DAT binding consisting of a lower caudate to ventral striatum ratio (Oh et al., 2012), caudate to putamen ratio (Messa et al., 1998), or anterioreposterior putamen ratio (Van Laere et al., 2006) in PSP-RS patients. PSP patients showed a more symmetric pattern of DAT loss than that observed in PD and MSA-P patients (Antonini et al., 2003; Brucke et al., 1997; Filippi et al., 2006), with the highest asymmetry index observed in PD patients (Filippi et al., 2006). PSP patients showed lower striatal-to-occipital, but higher putamen-to-caudate binding ratios than PD patients (Antonini et al., 2003; Filippi et al., 2006). Two studies have shown decreased DAT binding in the midbrain of PSP-RS patients compared with PD patients, but a similar pattern of reductions was observed in MSA patients (Goebel et al., 2011; Seppi et al., 2006). Seppi and colleagues reported that brainstem DAT loss could differentiate PSP-RS and MSA from PD with 89.7% sensitivity and 94.1% specificity (Seppi et al., 2006). However, patterns of striatal DAT loss have limited value in differentiating across Parkinsonian disorders, especially when taking into consideration cohorts of PSP patients and of MSA-P, which may share common patterns of presynaptic dopaminergic degeneration in both striatal and extrastriatal areas on SPECT imaging (Badoud et al., 2016; Joling et al., 2017). A few studies have investigated patterns of DAT loss in different PSP phenotypes (Fasano et al., 2012; Lin et al., 2010). They found that both PSP-PGF and PSP-P phenotypes have similar patterns of presynaptic dopaminergic loss as those observed in PSP-RS (Fasano et al., 2012; Lin et al., 2010). One study on a total of 79 participants with either an atypical parkinsonism or a Lewy body disease, sought to investigate whether the combination of SPECT imaging biomarkers could improve the diagnostic accuracy in PSP patients. In this study, Takaya and colleagues showed that an image-based automated classification using a combination of DAT imaging with [123I]FP-CIT SPECT and perfusion imaging using [123I]IMP SPECT yielded high AUC values in the differentiation between PD from atypical parkinsonisms and, within these latter, in the differentiation between PSP, MSA, and CBS (Takaya et al., 2018). Postsynaptic dopamine D2 receptors integrity has been studied in PSP patients using [123I]-IZBM and [123I]IBP SPECT (Arnold et al., 2002a,b; Hellwig et al., 2012; Lin et al., 2010; Oyanagi et al., 2002; Plotkin et al., 2005). The majority of the studies have found decreased dopamine D2 receptors binding in PSP patients (Arnold, Schwarz, et al., 2002; Lin et al., 2010; Oyanagi et al., 2002; Plotkin et al., 2005). Kim and colleagues showed that dopamine D2 binding ratio of posterior putamen to caudate binding was more than one in almost all drug-naïve PD, levodopa-treated PD, and PSP patients, whereas this ratio

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was less than one in the majority of MSA patients suggesting different patterns of dopamine D2 loss in PD and atypical parkinsonism (Kim et al., 2002). However, when compared with PET imaging using [18F]FDG, this latter attained significantly higher degrees of discrimination between PSP and other atypical parkinsonisms and with Lewy body diseases (Hellwig et al., 2012). Further studies are needed to better understand the role of dopamine D2 receptor binding in the differential diagnosis of PSP. A few imaging studies have used [123I]MIBG and cardiac SPECT, an analog of norepinephrine to study the functioning of sympathetic neurons across PSP and other atypical parkinsonisms, and PD patients (Kashihara et al., 2006; Sakuramoto et al., 2020; Yoshita, 1998). Patients with PSP, similar to other atypical parkinsonisms and healthy controls, show higher values of the heart-to-mediastinum ratio compared with PD patients (Kashihara et al., 2006; Yoshita, 1998). Recently, Sakuramoto studied 10 PSP patients as opposed to 70 patients with PD together with [123I]MIBG SPECT and MRI imaging and calculated the diagnostic sensitivity of the midbrain-to-pons ratio and cardiac MIBG scintigraphy to be of 78.6% and 67.1%, respectively, with having at least one positive tests changed sensitivity to 100% and specificity to 70% (Sakuramoto et al., 2020). The use of cardiac SPECT may have potential, if used in combination with more established imaging tools, to improve the diagnostic accuracy between PSP and PD; however, further studies are needed to understand its value, especially against other atypical parkinsonisms.

Positron emission tomography Brain metabolism [18F]FDG PET has been used to assess regional cerebral glucose metabolism as a marker of neuronal activity and neurodegeneration in PSP (Table 13.4). [18F]FDG PET studies have shown significant hypometabolism in the midbrain, basal ganglia, thalamus, bilateral medial frontal cortex involving mainly the anterior and mid cingulate gyri, the supplementary motor area (SMA), the ventro- and dorsolateral premotor areas, and the prefrontal areas (Eckert et al., 2005; Hellwig et al., 2012; Juh et al., 2005; Nagahama et al., 1997; Nakagawa et al., 2005; Otsuka et al., 1989; Tang et al., 2010; Zhao et al., 2020). Autopsy-proven PSP-RS cases showed reduced glucose metabolism in the thalamus (100%), caudate (86%), midbrain (86%), and frontal lobes (71%) (Zalewski et al., 2014). In comparison with PD patients, PSP patients showed greater frontal hypometabolism (Klein et al., 2005). Specific voxel-based metabolic covariance patterns were employed to differentiate between PSP, MSA, and CBS patients to healthy controls (Eckert et al., 2008; Niethammer et al., 2014). Specific PSP patterns of hypometabolism included brainstem, medial thalamus, caudate nuclei, and medial frontal cortex (Eckert et al., 2008). Differential diagnosis with CBS is more challenging due to the overlapping pathophysiological features, but it has been suggested a greater glucose reduction in the midbrain and thalamus of PSP patients (Juh et al., 2005). Asymmetric presentation of clinical symptoms in PSP patients could also be a challenge in differentiating between PSP and CBS patients. Amtage and colleagues assessed differences in glucose metabolism between PSP with asymmetric clinical symptoms, PSP with symmetric

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13. Neuroimaging in progressive supranuclear palsy

presentation, and CBS patients (Amtage, Hellwig, et al., 2014). They found that PSP patients with asymmetrical clinical symptoms had greater asymmetrical hypometabolism contralateral to the clinically most affected side in ventrolateral thalamus, middle cingulate gyrus, and sensorimotor cortex compared with PSP patients with symmetrical symptoms (Amtage, Hellwig, et al., 2014). CBS patients showed asymmetric parietal hypometabolism spreading to the premotor cortex contralateral to the clinically most affected side (Amtage, Hellwig, et al., 2014). [18F]FDG PET has also been used to differentiate between PSP phenotypes. PSP-RS patients showed greater thalamic and frontal cortex hypometabolism, whereas PSP-P patients displayed significant putaminal hypometabolism (Eckert et al., 2005). The putamen/thalamus [18F]FDG uptake ratio enabled differentiating PSP-P from PSP-RS patients (Eckert et al., 2005). PSP-PGF patients had significant frontal hypometabolism, whereas midbrain hypometabolism was observed only in one-fourth of the cases (Park et al., 2009). PSP-SL showed reduced glucose metabolism in the frontal cortex, basal ganglia, and midbrain (Cerami et al., 2017; Roh et al., 2010). [18F]FDG PET studies have also investigated regional patterns of hypometabolism underlying PSP symptoms (Amtage, Maurer, et al., 2014; Takahashi et al., 2011; Zwergal et al., 2011, 2013). Postural imbalance and falls were associated to reduced glucose metabolism in the thalamus, and increases in the precentral gyrus were associated with severity of postural instability and frequency of falls in a cohort of 16 PSP patients (Zwergal et al., 2011). The same group assessed neuronal correlates of locomotor function in PSP by performing [18F] FDG PET at rest and during walking (Zwergal et al., 2013). They found that worse locomotor function was associated with hypometabolism of the prefrontal cortex and subthalamic nucleus. During walking, PSP patients showed reduced glucose metabolism in the prefrontal cortexesubthalamic nucleusepedunculopontine/cuneiform nucleus loop (Zwergal et al., 2013). A reduction of [18F]FDG uptake in the caudate, as well as in some areas belonging to the supraspinal locomotor network such as the supplementary motor area, the cingulate cortex, the thalamus, and the midbrain, was also associated, in PSP patients, with alterations of the anticipatory postural adjustments on gait initiation, suggesting a functional alteration in these areas to be critical for the onset of gait initiation alterations in PSP (Palmisano et al., 2020). Oculomotor dysfunction was associated to hypometabolism in the anterior cingulate gyrus in PSP patients (Amtage, Maurer, et al., 2014). Hypometabolism of the rostral cerebellum correlated with reduced velocity of horizontal saccades and peak velocity of the optokinetic reflex was associated to hypometabolism of the oculomotor vermis (Amtage, Maurer, et al., 2014). Hypometabolism in the inferior parietal and temporal regions and frontal eye field was associated to smooth pursuit eye movement dysfunction (Amtage, Maurer, et al., 2014). There is scarce literature on the possible correlation of [18F]FDG PET findings and cognitive disturbances in PSP; however, in one study on 16 PSP patients, worse cognitive function as assessed by MMSE correlated to hypometabolism of the frontal cortex (Takahashi et al., 2011). Recently, it has been reported that PSP patients with language deficits showed reduced brain metabolism in the frontal regions (Dodich et al., 2019). Pre- and posttreatment [18F]FDG PET scans have been employed to assess the effects of physostigmine, a cholinesterase inhibitor, on clinical symptoms in six PSP patients (Blin et al., 1995). Intravenous infusion of physostigmine was able to improve by 8%e32% regional

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glucose metabolism, ocular movements, and attention at the cognitive test in PSP patients (Blin et al., 1995). Dopaminergic system PET studies using [18F]DOPA and [18F]FP-CIT have assessed presynaptic dopaminergic dysfunction in PSP patients. [18F]DOPA uptake was significantly decreased in both caudate and putamen in PSP patients (Brooks et al., 1990). Differently from PD, PSP patients did not show a rostrocaudal putamen gradient of dopaminergic loss, but [18F]DOPA uptake was reduced at the same degree in the caudate, anterior, and posterior putamen (Brooks et al., 1990). PSP patients showed greater and earlier presynaptic dopaminergic dysfunction in the anterior caudate and ventral putamen compared with PD patients (Oh et al., 2012). However, dopaminergic PET has limited value in discriminating between parkinsonian syndromes. Tau deposition Recently, PET with specific radioligands binding to aggregated tau has provided a unique opportunity to assess tau pathology in living humans (Villemagne & Okamura, 2016). Firstgeneration and, more recently, second-generation tau tracers have been introduced to allow the molecular study of tau deposition in 4-repeat tauopathies, such as PSP (Lebouvier et al., 2017). The vast majority of tau PET studies in PSP have been carried out using the firstgeneration tau tracer [18F]AV-1451, with some studies also employing [18F]THK5351. Autoradiography studies with postmortem human tissue have shown that [18F]AV1451 selectively binds to hyperphosphorylated tau over amyloid-b plaques (Marquie et al., 2015). PET studies using [18F]AV1451 have shown increased tau deposition in the globus pallidus, putamen, caudate nucleus, thalamus, subthalamic nucleus, midbrain, and dentate nucleus of the cerebellum of PSP-RS patients compared with controls (Cope et al., 2018; Nicastro et al., 2020; Sintini et al., 2019; Vasilevskaya et al., 2020; Whitwell et al., 2018, 2020) (160e164). [18F]AV1451 uptake in the globus pallidus showed sensitivity and specificity of 93% in differentiating PSPRS from healthy controls and 93% sensitivity and 100% specificity in differentiating PSP-RS and PD patients (Cho et al., 2017). Smith and colleagues found increased [18F]AV1451 uptake in the basal ganglia of PSP-RS patients, although there were age-dependent increases in both PSP-RS and healthy control groups (Smith et al., 2017). [18F]AV1451 can also disclose different regional patterns of tau accumulation according to the PSP variant. In one study, 12 PSP-P showed increased tracer uptake in the putamen, coupled with increased midbrain volume, as opposed to a group of 53 PSP-RS (Whitwell et al., 2020). In comparison with AD patients, PSP-RS patients showed a greater [18F]AV1451 retention in cerebellar dentate and pallidum, whereas signal across the cortex was elevated in AD patients (Whitwell et al., 2017). Tau PET imaging studies have sought to understand the pathophysiology underlying the abnormal buildup and dissemination pattern of this protein in this disease, as well as its relationship with structural and microstructural regional alteration and its causative relationship with disease severity and progression. In one multimodal study coupling [18F]AV1451 PET, structural, and microstructural MRI, Sintini and colleagues found an association between increased tracer uptake in the cerebellar dentate, the red, and subthalamic nuclei,

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decreased volume in these regions, and altered FA and MD in superior cerebellar peduncle, sagittal striatum, and posterior corona radiata, suggesting that tau retention could be related to regional structural and microstructural alterations in PSP (Sintini et al., 2019). Cortical retention of [18F]AV1451 also correlated with gray matter volume loss in frontal regions, thinning of the parietooccipital area, and white matter alterations of the motor tracts (Nicastro et al., 2020). One important study conducted on a group of PSP and AD patients has tried to understand the differences in the pattern of tau spread between these two tauopathies (Cope et al., 2018). Differently from AD, where the spread of tau seems to follow spatial patterns of strong connectivity, in PSP, the deposition of tau appears to follow areas of increased metabolic demand and a lack of trophic support, which may explain the different progression of tau pathology in these two diseases (Cope et al., 2018). [18F]AV1451 regional brain uptake has also been investigated with concomitant retention of amyloid-b. In a study on 30 PSP patients, of which 40% showed amyloid-b deposition in an age-dependent manner as seen with [11C]PiB, the authors did not find any correlation between amyloid and tau deposition (Whitwell et al., 2018). Studies employing [18F]THK-5351 in PSP-RS patients confirmed high tau retention rates in the globus pallidus and midbrain (Ishiki et al., 2017). Patients with PSP and oculomotor dysfunction also showed significantly higher [18F]THK-5351 SUVRs in the midbrain, red nucleus, and raphe nucleus than those without (Hsu et al., 2020). First-generation tau tracers, however, carry significant drawbacks. [18F]AV1451 shows in the basal ganglia, the substantia nigra, the venous sinuses, and the choroid plexus (Lowe et al., 2016; Marquie et al., 2015). In particular, [18F]AV1451 and [18F]THK-5351 exhibit significant affinity for neuromelanin (Coakeley et al., 2018; Tago et al., 2019), and MAO-B (Ng et al., 2019; Vermeiren et al., 2018). For this reason, a whole new army of secondgeneration of tau tracers [18F]MK-6240, [18F]RO-948, [18F]PI-2620, [18F]GTP1, [18F]PM-PBB3, [18F]APN-1607, [18F]JNJ64349311, and [18F]CBD-2115 has been developed to visualize tau with higher affinity and less off-target binding (Leuzy et al., 2019; Lindberg et al., 2021; Weng et al., 2020). Of note, these tracers also show particular affinity for 4R-repeat tau, which make them particularly suitable for the study of PSP. First reports of studies employing these tau tracers in PSP patients describe increased uptake in several PSP-related subcortical regions (Brendel et al., 2020; Li et al., 2021). In one study, disease severity was correlated with the extent of [18F]APN-1607 uptake in the subthalamic nucleus, midbrain, substantia nigra, red nucleus, pontine base, and raphe nuclei (Li et al., 2021). Furthermore, [18F]PI2620 uptake strongly correlates with areas of reduced glucose metabolism as seen with [18F]FDG-PET imaging, suggesting this radiotracer could represent a surrogate marker of neuronal injury (Beyer et al., 2020). It is expected that further studies will come out using these promising new tracers that could shed light on the many unanswered questions about the pathophysiology of tau accumulation in PSP.

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TABLE 13.1

Structural MRI measures for the differential diagnosis of PSP.

Measure

References

Midbrain atrophy visual (Schrag et al., 2000) assessment (Adachi et al., 2004)

Midbrain area loss

Reduced midbrain diameter

Midbrain-pons ratio

MRPI

Patient populations 35 PSP-RS versus 54 MSA 5 PSP-RS versus 23 PD 14 MSA

(Righini et al., 2004)

25 PSP-RS versus 27 PD

(Massey et al., 2012)

22 PSP versus 13 MSA

(Oba et al., 2005)

21 PSP-RS versus 23 PD, 25 MSA-P, 31 HC

(Cosottini et al., 2007)

15 PSP-RS versus 7 MSA-P, 14 HC

(Zanigni et al., 2016)

23 PSP-RS versus 42 PD

(Ahn et al., 2019)

27 PSP-RS versus 27 PD 27 HC

(Asato et al., 2000)

8 PSP-RS versus 21 MSA-C, 9 MSA-P

(Cosottini et al., 2007)

17 PSP-RS versus 7 MSA-P, 4 HC

(Massey et al., 2012)

12 PSP-RS versus 7 MSA

(Kim, Ma, & Kim, 2015)

29 PSP-RS versus 82 PD

(Owens et al., 2016)

25 PSP-RS versus 25 MSA, 25 PD

(Oba et al., 2005)

22 PSP-RS versus 23 PD, 25 MSA-P, 31 HC

(Cosottini et al., 2007)

16 PSP-RS versus 7 MSA-P, 14 HC

(Quattrone et al., 2008)

33 PSP-RS versus 19 MSA-P, 108 PD

(Hussl et al., 2010)

22 PSP-RS versus 26 MSA-P, 75 PD

(Morelli et al., 2011)

42 PSP-RS versus 170 PD

(Longoni et al., 2011)

10 PSP-RS versus 25 PD

(Massey et al., 2013)

13 PSP-RS versus 7 MSA

(Kim, Ma, & Kim, 2015)

30 PSP-RS versus 82 PD

(Owens et al., 2016)

25 PSP-RS versus 25 MSA, 25 PD

(Sankhla et al., 2016)

20 PSP-RS versus 13 PD

(Mangesius et al., 2018)

55 PSP versus 194 PD, 63 MSA

(Sakuramoto et al., 2020)

10 PSP versus 70 PD, 16 MSA

(Cui et al., 2020)

59 PSP

(Kannenberg et al., 2021)

15 PSP versus 15 PD, 15 HC

(Quattrone et al., 2008)

33 PSP-RS versus 19 MSA-P, 108 PD

(Hussl et al., 2010)

22 PSP-RS versus 26 MSA-P, 75 PD

(Morelli et al., 2011)

42 PSP-RS versus 170 PD (Continued)

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13. Neuroimaging in progressive supranuclear palsy

Structural MRI measures for the differential diagnosis of PSP.dcont’d

Measure

References

Patient populations

(Longoni et al., 2011)

10 PSP-RS versus 25 PD

(Kim, Ma, & Kim, 2015)

30 PSP-RS versus 82 PD

(Sankhla et al., 2016)

20 PSP-RS versus 13 PD

(Nigro, Arabia, et al., 2017)

88 PSP-RS versus 234 PD

(Mostile et al., 2016)

12 PSP-RS versus 17 vPa

(Quattrone et al., 2018)

46 PSP-RS versus 34 PSP-P, 53 PD, 53 HC

(Constantinides, Paraskevas, Velonakis, et al., 2018)

24 PSP versus 9 CBS, 9 MSA, 18 PD, 15 HC

(Sakamoto et al., 2020)

53 PSP

(Picillo et al., 2020)

38 PSP-RS versus 21 PSP-P, 9 PSP-CBS, 6 PSP-PGF, 4 PSP-F, 35 PD, 38 HC

(Archer et al., 2020)

16 PSP versus 39 PD, 17 MSA-P

CBS, corticobasal syndrome; HC, healthy controls; MRPI, MR Parkinsonism Index; MSA-C, cerebellar variant of multiple system atrophy; MSA-P, parkinsonian variant of multiple system atrophy; PD, Parkinson’s disease; PSP, progressive supranuclear palsy; PSP-CBS, progressive supranuclear palsy with corticobasal syndrome; PSP-F, progressive supranuclear palsy with frontal predominance; PSP-P, progressive supranuclear palsy with parkinsonism; PSP-PGF, progressive supranuclear palsy with progressive gait freezing; PSP-RS, progressive supranuclear palsy with Richardson’s syndrome; vPa, vascular parkinsonism.

Other systems The availability of PET molecular tracers to study virtually every metabolic system has allowed in the recent years, the study of other metabolic system that could help in the comprehension of the pathophysiology of PSP (Table 13.1). Passamonti and colleagues have studied a group of 16 PSP-RS, 16 AD patients, and 13 healthy controls, with the translocator protein-specific tracer [11C]PK11195, which represents a marker of microglial activation and, therefore, of neuroinflammation (Passamonti et al., 2018). They found that PSP patients had increased binding in the pallidum, midbrain, and pons, which correlated with disease severity (Passamonti et al., 2018). A finding of an increased pallidal uptake in PSP patients was confirmed by a more recent study using the second-generation TSPO tracer [18F]GE-180 (Palleis et al., 2021) (Table 13.2). Two recent studies have investigated the association between inflammation and tau deposition in PSP patients. Malpetti and colleagues demonstrated that areas of regional [11C] PK11195 and [18F]AV1451 binding colocalized in both subcortical and cortical areas, and both correlated with clinical severity (Malpetti et al., 2021). These patients were followed up for up to 4 years, and it was shown that the PCA components reflecting neuroinflammation and tau burden in the brainstem and cerebellum correlated with the subsequent annual rate of change in the PSP rating scale, and with regional brain volume loss, indicating a relationship between these three pathophysiological alterations over time, and clinical progression in PSP (Malpetti et al., 2021) (Table 13.3).

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TABLE 13.2

Diffusion tensor and diffusion-weighted imaging studies in PSP.

Study

Number of patients

Main findings

(Seppi et al., 2003) 10 PSP 13 PD 12 MSA-P

PSP showed increased rADC in putamen, globus pallidus, and caudate nucleus. Putaminal rADC had discriminating power with 90% sensitivity and 100% positive predictive value. No difference between PSP and MSA-P.

(Paviour et al., 2007)

20 PSP 12 PD 11 MSA-P 7 HC

rADCs in the middle cerebellar peduncle and rostral pons significantly smaller in PSP compared with MSA-P, with discriminating potential with sensitivity of 91% and a specificity of 84%. Increased rADC of the brainstem rADCs associated with severity of motor symptoms.

(Nicoletti et al., 2008)

15 14 13 16

PSP patients had higher rADC of the superior cerebellar peduncle compared with PD, MSA-P, and HC and distinguished PSP from HC with 100% sensitivity and specificity and from patients with MSA-P with 96.4% sensitivity and 93.3% specificity.

(Erbetta et al., 2009)

9 PSP 11 CBS 7 HC

PSP PD MSA-P HC

PSP showed increased ADC in the thalamus, cingulum, motor areas, and supplementary motor areas; decreased FA in the frontoorbital white matter, anterior cingulum, and motor area.

(Wang et al., 2010) 17 PSP 17 HC

Decrease of FA and increase of MD in the midbrain, which correlated with disease progression. Motor scores correlated with FA of the caudate and MD of the pons. PSP-P and PSP-RS differ on AD values in the putamen and pallidus, as well as in intervoxel diffusion coherence values in the middle cerebellar peduncle.

(Whitwell, Master, 20 PSP et al., 2011) 20 HC

White matter abnormalities in the cerebellum, thalamus, and superior longitudinal fasciculus.

(Canu et al., 2011)

5 PSP 12 HC

PSP showed white matter alterations in superior cerebellar peduncle, corpus callosum, and cingulum bilaterally. Decreased AD in the left middle cerebellar peduncle.

(Kvickstrom et al., 2011)

13 PSP 12 HC

PSP showed decreased FA and increased ADC in the frontal part of the inferior frontooccipital fasciculus compared with the medial and occipital parts of the inferior frontooccipital fasciculus, more pronounced in patients with cognitive impairment. Correlation between FA and ADC values in the corticospinal tract and disease stage.

(Saini et al., 2012)

24 PSP 26 HC

PSP had diffusivity abnormalities in the frontoparietal cerebral white matter, thalamus, midbrain tectum, superior cerebellar peduncle, and cerebellar white matter. PSP-RS had more severe abnormalities in the frontal white matter compared with PSP-P. No correlation with clinical scales.

(Coon et al., 2012)

10 PSP 3 PLS 20 HC

PSP and PLS show reduced FA in superior cerebellar peduncle and body of the corpus callosum. (Continued)

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13. Neuroimaging in progressive supranuclear palsy

Diffusion tensor and diffusion-weighted imaging studies in PSP.dcont’d

Study

Number of patients

Main findings

(Agosta et al., 2012)

21PSP-RS 16 PSP-P 42 HC

PSP showed diffusivity abnormalities in the corpus callosum, frontoparietal, and frontotemporooccipital tracts. PSP-RS showed white matter alterations in the infratentorial white matter and thalamic radiations, compared with PSP-P.

(Nicoletti et al., 2013)

17 PSP 9 MSA-P 7 MSA-C 10 PD 10 HC

Median MD values from cerebellar hemispheres distinguish MSA-C and MSA-P from PSP-RS with 100% sensitivity, specificity, and positive predictive value. PSP-RS had significantly higher MD values in the vermis compared with HC.

(Surova et al., 2013)

16 10 10 16

PSP MSA-P PD HC

PSP had increased MD in thalamus, superior cerebellar peduncle, and midbrain. Increased MD of the dentatorubrothalamic tract compared with PD and MSA. Increased MD in thalamus and the dentatorubrothalamic tract correlated with disease stage and motor function in PSP.

(Worker et al., 2014b)

16 16 14 18

PSP MSA PD HC

PSP showed white matter abnormalities in corpus callosum, corona radiata, corticospinal tract, superior longitudinal fasciculus, anterior thalamic radiation, superior cerebellar peduncle, medial lemniscus, retrolenticular and anterior limb of the internal capsule, cerebral peduncle and external capsule bilaterally, left posterior limb of the internal capsule, and right posterior thalamic radiation.

(Tessitore et al., 2014)

18 PSP 18 HC

PSP showed white matter alterations in corpus callosum, fornix, midbrain, inferior frontooccipital fasciculus, anterior thalamic radiation, superior cerebellar peduncle, superior longitudinal fasciculus, uncinate fasciculus, cingulate gyrus, and corticospinal tract bilaterally. Correlation between frontocerebellar white matter loss and executive cognitive impairment.

(Rosskopf et al., 2014)

8 PSP-P 7 PSP-RS 15 PD 18 HC

PSP showed diffuse decrease of FA in frontal areas, the area II of the corpus callosum, and callosal radiations compared with HC and PD. FA values correlated with frontal lobe volumes.

(Piattella, Upadhyay, et al., 2015)

16 PSP 16 HC

PSP showed white matter abnormalities in cerebellar peduncles, thalamic radiations, corticospinal tracts, corpus callosum, and longitudinal fasciculi, with correlation with disease severity and cognitive impairment. No correlation between volumetric gray matter abnormalities and white matter abnormalities.

(Meijer et al., 2015) 60 patients with earlystage parkinsonism

PSP showed increased MD of midbrain and superior cerebellar peduncles.

(Planetta et al., 2016)

PSP, PD, and MSA showed increased free water in the substantia nigra compared with HC. Additional increased free water in basal ganglia, midbrain,

18 PSP 18 MSA

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TABLE 13.2

Diffusion tensor and diffusion-weighted imaging studies in PSP.dcont’d

Study

Number of patients

Main findings

18 PD 18 HC

thalamus, dentate nucleus, cerebellar peduncles, cerebellar vermis and lobules V and VI, and corpus callosum of PSP. PSP showed increased FA in putamen, caudate, thalamus, and vermis and decreased FA in the superior cerebellar peduncle and corpus callosum. Higher MD of the corticospinal tract in PSP-RS versus PD and HC, limited to the portion within the corona radiata.

(Zanigni et al., 2017)

25 PSP-RS 9 MSA-P 9 MSA-C 47 PD 27 HC

(Ofori et al., 2017)

71 PSP 63 MSA 184 PD 107 HC

PSP showed increased free water in the posterior substantia nigra compared with MSA and PD. Free waterecorrected FAis greater for PSP patients than HC and PD. Single-tensor FA decreased in PSP versus HC.

(Seki et al., 2018)

12 12 20 23

PSP-RS and PSP-P showed alteration of MD and FA in mesencephalic tegmentum, superior cerebellar peduncle, decussation of superior cerebellar peduncle, and dentate nucleus which correlated with regional volume loss. PSP-RS had more altered microstructural integrity of the dentatorubrothalamic tract compared with PSP-P and correlated significantly with the gait and postural stability sum score.

(Talai et al., 2018)

21 PSP-RS 52 PD

PSP-RS showed reduced FA and increased MD, RD, and AD in nearly all brain structures compared with PD. The classifier differentiated PD and PSP-RS with an accuracy of 87.7%.

(Caso et al., 2018)

10 PSP-P

During the 1.6-year follow-up, PSP-P showed progression of white matter abnormalities in supratentorial tracts compared with baseline.

(Agosta et al., 2018)

21 PSP-RS 36 HC

PSP-RS showed white matter abnormalities in main infratentorial and supratentorial tracts, which progressed over time, differently from cortical thinning. Corpus callosum and frontal white matter tract changes correlated with the progression of both disease severity and behavioral dysfunction.

(Spotorno et al., 2019)

16 PSP 34 LBD 44 HC Validation cohort of 34 PSP 25 LBD 32 HC

FA score distinguished between PSP and LBD with AUC of 0.97, specificity of 91%, and sensitivity of 94%. In the validation cohort, FA score distinguished between PSP and LBD with AUC 0.96, specificity 96%, and sensitivity 85%.

(Wang et al., 2019) 17 16 35 37

PSP-RS PSP-P PD HC

PSP MSA PD HC

PSP patients had higher amygdala and hippocampus MD and lower hippocampus FA compared with HC.

(Continued)

IV. Clinical applications in atypical parkinsonian disorders

374 TABLE 13.2

13. Neuroimaging in progressive supranuclear palsy

Diffusion tensor and diffusion-weighted imaging studies in PSP.dcont’d

Study

Number of patients

Main findings

(Seki et al., 2019)

18 16 16 21

PSP MSA-P PD HC

PCA revealed in PSP two components: mean MD value of the thalamus and the mean MD and FA values of the dentatorubrothalamic tract and the corpus callosum, and mean MD and FA values of the middle cerebellar peduncle. ROC analysis showed 92% of PSP patients were differentiated correctly from MSA-P and PD.

(Mitchell et al., 2019)

26 21 44 24

PSP MSA-P PD HC

Elevated Viso in posterior substantia nigra compared with HC. Viso, Vic, and ODI were altered in MSA-P and PSP in striatum, globus pallidus, midbrain, thalamus, cerebellum, and corpus callosum relative to HC. NODDI discriminated between PD and APS with AUC: 0.945, and free water with AUC 0.969.

(Quattrone et al., 2019)

48 30 37 38

PSP-RS PSP-P PD HC

PSP-RS showed low FA in superior cerebellar peduncles compared with PSP-P. PSP with supranuclear vertical gaze palsy had lower FA in the midbrain compared with PSP patients with slower saccades.

(Abos et al., 2019)

19 PSP 20 HC

PSP showed reduced numbers of streamlines in connections between frontal areas and deep gray matter structures, which correlated with clinical features. PSP showed abnormal small-world architecture in frontal lobe and deep gray matter structures in PSP patients.

(Nigro et al., 2019) 20 PSP-RS 21 PD 23 HC

PSP-RS showed decreases in track density in brainstem, cerebellum, thalamus, corpus callosum, and corticospinal tract compared with PD and HC.

(Pyatigorskaya et al., 2020)

11 PSP 51 PD 26 HC

PSP showed and diffusion changes in the midbrain, substantia nigra, subthalamic nucleus, globus pallidus, basal forebrain, locus coeruleus, pedunculopontine nucleus, and dentate nucleus.

(Potrusil et al., 2020)

15 13 18 20

PSP-RS and PSP-P showed altered diffusion of anterior thalamic radiation, corticospinal tract, superior longitudinal fasciculus, bundles of the corpus callosum and cingulate, and dentatorubrothalamic tract. Diffusion of the dentatorubrothalamic tract and the anterior thalamic radiation as well as the volume of the dorsal midbrain classified correctly 91.3% of PSP-RS, PSP-P, and PD patients.

(Whitwell et al., 2021)

28 PSP-RS 12 PSP-P 9 PSP-SL

PSP-RS PSP-P PD HC

PSP-RS showed greater degeneration of superior cerebellar peduncle compared with PSP-P and PSP-SL, whereas PSP-SL showed greater degeneration of body and genu of the corpus callosum, internal capsule, external capsule, and superior longitudinal fasciculus compared with the other variants. FA of the body of the corpus callosum differentiated PSP-SL from PSP-RS and PSP-P. FA of superior frontooccipital fasciculus and MD of the superior cerebellar peduncle differentiated PSP-RS from PSP-P.

IV. Clinical applications in atypical parkinsonian disorders

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TABLE 13.2

Diffusion tensor and diffusion-weighted imaging studies in PSP.dcont’d

Study

Number of patients

Main findings

(Prasad et al., 2021) 16 PSP-RS 12 HC

PSP-RS showed abnormal connectivity in a network comprised of predominantly cortical, and within this, predominant frontal connections. Abnormal deep gray matter connections were predominantly comprised of connections between structures of the basal ganglia. PSP-RS had lower path length, and abnormal degree, strength, local efficiency, betweenness centrality, and participation coefficient in several nodes.

(Talai et al., 2021)

Machine learning model distinguished PSP-RS, PD, and HC with classification accuracy of 65%. Using DTI features alone, the machine learning model achieved 95.1% accuracy (98/103).

20 PSP-RS 45 PD 38 HC

AD, axial diffusivity; ADC, apparent diffusion coefficient; AUC, area under the curve; CBS, corticobasal syndrome; DTI, diffusion tensor imaging; FA, fractional anisotropy; HC, healthy controls; LBD, Lewy body dementias; MD, mean diffusivity; MSA, multiple system atrophy; MSA-P, multiple system atrophy, parkinsonian type; MSA-C, multiple system atrophy, cerebellar type; NODDI, neurite orientation dispersion and density imaging; ODI, Orientation Dispersion Index; PD, Parkinson’s disease; PLS, primary lateral sclerosis; PSP, progressive supranuclear palsy; PSP-P, progressive supranuclear palsy, parkinsonian type; PSP-RS, progressive supranuclear palsy, Richardson’s syndrome; PSP-SL, progressive supranuclear palsy, speech and language phenotype; RD, radial diffusivity; ROC, receiver operating characteristics.

TABLE 13.3

Single-photon emission computerized tomography studies in PSP patients.

Study

Number of patients

(Habert et al., 1991)

Radioligand

5 PSP 19 AD 5 Pick’s disease 15 PDD 13 HC

[

99m

Tc]HMPAO

Main findings PSP patients showed a mild, significant hypoperfusion in the superior frontal cortex.

(Johnson et al., 11 PSP 1992) 10 HC

[123I]IMP

Reduction in PSP in basal ganglia, superior frontal, anterior parietal, and inferior frontal regions.

(Brucke et al., 1997)

4 PSP 9 MSA 113 PD

[123I]b-CIT

Striatal loss of tracer uptake with differences between caudate and putamen less marked compared to PD patients

(Messa et al., 1998)

5 PSP 13 PD 6 HC

[123I]b-CIT

PSP showed different uptake levels in the caudate compared with PD, with diagnostic differentiation in 10/13 PD patients.

[123I]MIBG

Heart-to-mediastinium ratio lower in PD compared with PSP, SND, and HC. Heart-to-mediastinum values in PSP in treatment with amitriptyline lower than PSP in no treatment with amitriptyline.

(Yoshita, 1998) 14 PSP 15 PD 15 SND (striatonigral degeneration) 20 HC

(Continued)

IV. Clinical applications in atypical parkinsonian disorders

376

13. Neuroimaging in progressive supranuclear palsy

TABLE 13.3

Single-photon emission computerized tomography studies in PSP patients.dcont’d

Study

Number of patients

Radioligand 123

I]b-CIT

Main findings PSP showed 60% reduction of striatal binding with no distinctive features compared with PD, MSA, or CBD. Putamen/caudate nucleus ratios reduced in PSP, MSA, and PD, but not CBD.

(Pirker et al., 2000)

8 PSP 18 MSA 4 CBD 48 PD 14 HC

[

(Slawek et al., 2001)

2 PSP 2 CBD

[99mTc]HMPAO

Diffuse frontal perfusion deficit in PSP patients, with striatal and occipital concomitant hypoperfusion.

(Kim et al., 2002)

6 PSP 18 PD 7 MSA 29 HC

[123I]b-CIT [123I]IBF

Reduction of DAT binding in all patients. PSP and PD showed reduction of putamen to caudate D2 ratio. MSA showed increase of putamen to caudate ratio.

(Arnold, 32 PSP Schwarz, et al., 2002)

[123I]IBZM

Reduction of specific striatal binding.

(Arnold, Tatsch, et al., 2002)

[123I]IBZM

Reduction of striatal binding in 10 patients and normal in 3 patients. Correlation with midbrain atrophy.

(Oyanagi et al., 6 PSP 2002) 7 PD 8 HC

[123I]IBF

PSP showed lower binding potential in the striatum compared with PD and HC on voxel-by-voxel analysis.

(Antonini et al., 10 2003) 10 70 12

[123I]FP-CIT

Greater striatal reduction in PSP compared with MSA-P and PD. Greater putamen/caudate ratio in PSP than in PD.

[123I]IBZM

Reduction of D2 striatal binding in six/eight patients with PSP.

(Im et al., 2006) 9 PSP 20 PD 10 HC

[123I]IPT

Striatal caudate/putamen ratio significantly reduced in PSP and PD but with different regional patterns of uptake. Ratio in the transitional region between head of caudate and putamen significantly reduced in PSP compared with PD.

(Van Laere et al., 2006)

12 PSP 24 MSA 8 LBD 27 ET 58 PD

[123I]b-CIT-FP

PSP, MSA, and LBD could be differentiated in 100% of cases compared with HC. When including PD, differentiation rate was 82.4%. Differentiation between ET and degenerative parkinsonism: 93%.

(Seppi et al., 2006)

14 17 15 13

[123I]b-CIT

All significant decrease of striatal binding. PSP and MSA-P additional brainstem reduction compared with HC and PD.

(Plotkin et al., 2005)

13 PSP

PSP MSA-P PD HC

8 PSP 25 PD 6 DLB 13 MSA 11 ET

PSP PD MSA-P HC

IV. Clinical applications in atypical parkinsonian disorders

Molecular imaging studies in progressive supranuclear palsy

TABLE 13.3

Single-photon emission computerized tomography studies in PSP patients.dcont’d

Study

Number of patients

Radioligand

Main findings

377

(Filippi et al., 2006)

15 PSP 21 PD 20 HC

[123I]FP-CIT

Striatal binding and striatal ratio to caudate, putamen, and occipital lobe reduced in PSP and HC, and significantly reduced in PSP compared with PD. Asymmetry index higher in PD compared with PSP.

(Kashihara et al., 2006)

11 PSP 11 MSA 9 CBD 130 PD 21 DLB 6 PAF

[123I]MIBG

Heart-to-mediastinum ratio lower in PAF, PD and DLB compared with PSP, CBD, and MSA.

(Varrone et al., 16 PSP 2007) 9 PD 10 HC

[99mTc]ECD

PSP showed hypoperfusion in the anterior cingulate and medial frontal cortex compared with PD. PSP showed additional hypoperfusion in the presupplementary motor area and prefrontal cortex.

(Lin et al., 2010)

6 PSP-RS 4 PSP-P 10 PD 7 HC

[99mTc]TRODAT-1 PSP-RS showed non-significant reduced striatal uptake [123I]IBZM compared with PSP-P PSP-RS showed slightly reduced D2 striatal binding; PSP-P showed slightly increased D2 striatal binding.

(Goebel et al., 2011)

15 15 15 15

PSP MSA PD HC

[123I]b-CIT

Predictive diagnostic accuracy of 83.3%, which increased to 93.3% when merging PSP and MSA-P groups.

PSP PD MSA-P HC

[99mTc]ECD

PSP showed reduced regional cerebral blood flow in the cingulate gyrus and thalamus. Regional blood flow in the thalamus discriminated PSP from other disorders.

(Hellwig et al., 23 PSP 2012) 13 MSA 8 CBD 23 PD 11 PDD/DLB

[123I]IBZM [18F]FDG PET

[18F]FDG PET showed larger ROC curve for discrimination between APS and LBD compared with [123I]IBZM SPECT. Sensitivity and specificity of [18F]FDG PET were 74% and 95% for PSP, respectively.

(Badoud et al., 32 PSP 2016) 24 MSA 30 CBD 306 PD

[123I]FP-CIT

PSP and MSA showed lower tracer uptake in the head of caudate compared with PD and CBD. No difference between MSA and PSP. 84%e90% specificity of PD versus APS.

(Joling et al., 2017)

[123I]FP-CIT

PSP showed lower striatal binding ratio in the caudate compared with PD and MSA-C. PSP, PD, and MSA-P showed lower striatal binding ratio in the posterior putamen compared with MSA-C. PSP and MSA-P showed lower extrastriatal uptake in the hypothalamus compared with MSA-C.

(Kimura et al., 19 2011) 28 12 17

13 PSP 9 MSA-P 7 MSA-C 30 PD

(Continued)

IV. Clinical applications in atypical parkinsonian disorders

378

13. Neuroimaging in progressive supranuclear palsy

TABLE 13.3

Single-photon emission computerized tomography studies in PSP patients.dcont’d

Study

Number of patients

Main findings

Radioligand 123

123

(Takaya et al., 2018)

46 LBD 33 APS

[ I]FP-CIT [ I] PSP versus MSA: aarea under the ROC curve was 0.920 Iodoamphetamine and total diagnostic accuracy was 88% PSP versus CBS: area under the ROC curve was 0.875 and total diagnostic accuracy was 77.8%.

(Sakuramoto et al., 2020)

10 PSP 16 MSA 70 PD

[123I]MIBG

PD versus PSP: diagnostic sensitivity of the midbrain-topons ratio and cardiac MIBG scintigraphy 78.6% and 67.1%. Having at least one positive test changed sensitivity to 100% and specificity to 70%. Having both positive tests changed sensitivity to 47.1% and specificity to 90%.

(Alster et al., 2019)

21 PSP 10 MSA-P 14 CBS

[99mTc]HMPAO

PSP showed different perfusion values in the cerebellum and thalamus versus MSA-P.

(Sakamoto et al., 2020)

53 PSP

[123I]FP-CIT

SBR had 95% sensitivity, 36.4% specificity, and 58.5% accuracy, as opposed to MR-Parkinsonism Index that showed 85% sensitivity, 100% specificity, and 94.3% accuracy.

AD, Alzheimer’s disease; APS, atypical parkinsonian syndromes; CBD, corticobasal degeneration; D2, dopamine receptor 2; DAT, dopamine transporter; DLB, dementia with Lewy bodies; ET, essential tremor; HC, healthy controls; LBD, Lewy body diseases; MSA, multiple system atrophy; MSA-C, multiple system atrophy, cerebellar type; MSA-P, multiple system atrophy, parkinsonian type; PAF, pure autonomic failure; PD, Parkinson’s disease; PDD, Parkinson’s disease dementia; PET, positron emission tomography; PSP, progressive supranuclear palsy; PSP-P, progressive supranuclear palsy, parkinsonian type; PSP-RS, progressive supranuclear palsy, Richardson’s syndrome; ROC, receiver operating characteristics; SBR, striatal binding ratio; SND, striatonigral degeneration.

TABLE 13.4

Positron emission tomography studies in PSP patients.

Study

Number of patients Radioligand Main findings

(Hosaka et al., 2002)

12 PSP 12 CBS 12 HC

[18F]FDG

PSP showed reduced cerebral glucose metabolism in the medial and lateral frontal gyri, basal ganglia, and midbrain compared with HC.

(Juh et al., 2005) 8 PSP 8 CBS 22 HC

[18F]FDG

In PSP, glucose metabolism was lower in the orbitofrontal, middle frontal, cingulate, thalamus, and midbrain compared with HC. PSP showed hypometabolism in the thalamus and the midbrain compared with CBS.

(Nakagawa et al., 2005)

9 3 9 3

[18F]FDG [11C] Raclopride

Caudate and putamen D2 receptor binding showed a positive correlation with cerebral metabolic rate for glucose. JPD group, but not in the MSA-PSP group. Putamen left/ right ratio of D2 receptor binding showed a positive correlation with that of cerebral metabolic rate for glucose in the MSA and PSP group.

(Eckert et al., 2005)

135 parkinsonian patients

[18F]FDG

Blinded computer assessment agreed with clinical diagnosis in 92.4% of all subjects. Concordance of visual inspection with clinical diagnosis was achieved in 85.4% of the patients scanned (88.4% early PD, 97.2% late PD, 76% MSA, 60% PSP, 90.9% CBS, 90.9% healthy control subjects).

PD JPD MSA PSP

IV. Clinical applications in atypical parkinsonian disorders

379

Molecular imaging studies in progressive supranuclear palsy

TABLE 13.4

Positron emission tomography studies in PSP patients.dcont’d

Study

Number of patients Radioligand Main findings

(Tang et al., 2010)

167 patients with parkinsonian features

[18F]FDG

Automated image-based classification procedure showed high specificity in distinguishing between parkinsonian disorders: PDd84% sensitivity, 97% specificity. MSAd85% sensitivity, 96% specificity. PSPd88% sensitivity, 94% specificity.

(Zalewski et al., 7 autopsy-proven 2014) PSP-RS

[18F]FDG

Autopsy-proven PSP-RS cases showed reduced glucose metabolism in the thalamus (100%), caudate (86%), midbrain (86%), and frontal lobes (71%).

(Klein et al., 2005)

10 PSP 9 PD

[18F]FDG

PSP patients showed greater frontal hypometabolism than PD patients.

(Eckert et al., 2008)

39 MSA 31 PSP

[18F]FDG

Specific PSP patterns of hypometabolism included brainstem, medial thalamus, caudate nuclei, and medial frontal cortex.

(Amtage, Hellwig, et al., 2014)

23 PSP 8 CBS

[18F]FDG

PSP patients with asymmetrical clinical symptoms had greater asymmetrical hypometabolism contralateral to the clinically most affected side in ventrolateral thalamus, middle cingulate gyrus, and sensorimotor cortex compared with PSP patients with symmetrical symptoms.

(Takahashi et al., 2011)

16 PSP

[18F]FDG

Worse cognitive function as assessed by MMSE correlated to hypometabolism of the frontal cortex.

(Zwergal et al., 16 PSP 2011)

[18F]FDG

Postural imbalance and falls were associated with reduced glucose metabolism in the thalamus, and increases in the precentral gyrus were associated with severity of postural instability and frequency of falls.

(Zwergal et al., 12 PSP 2013)

[18F]FDG

Worse locomotor function was associated with hypometabolism of the prefrontal cortex and subthalamic nucleus. During walking, PSP patients showed reduced glucose metabolism in the prefrontal cortexesubthalamic nucleusepedunculopontine/cuneiform nucleus loop.

(Dodich et al., 2019)

37 PSP 33 CBS

[18F]FDG

Patients with nonfluent primary progressive aphasia show metabolism decrease in the left frontoinsular and superior medial frontal cortex. PSP patients with language impairment show low frontal metabolism.

(Zhao et al., 2020)

19 PSP

[18F]FDG

Decreased glucose metabolism in the midbrain and frontal lobe couples with typical visual MRI signs.

(Palmisano et al., 2020)

26 PSP 14 HC

[18F]FDG

Reduction of uptake in the caudate and areas belonging to the supraspinal locomotor network correlates with alterations of the anticipatory postural adjustments on gait initiation.

(Brooks et al., 1990)

16 PD 18 MSA 10 PSP

[18F]DOPA

[18F]DOPA uptake was significantly decreased in both caudate and putamen in PSP patients. PSP patients did not show a rostrocaudal putamen gradient of dopaminergic loss. (Continued)

IV. Clinical applications in atypical parkinsonian disorders

380

13. Neuroimaging in progressive supranuclear palsy

TABLE 13.4

Positron emission tomography studies in PSP patients.dcont’d

Study

Number of patients Radioligand Main findings

(Oh et al., 2012) 49 19 24 21

PD PSP MSA HC

[18F]FP-CIT PSP patients showed greater and earlier presynaptic dopaminergic dysfunction in the anterior caudate and ventral putamen compared with PD patients.

(Cho et al., 2017)

14 PSP 15 PD 15 HC

[18F]AV1451 PSP patients showed greater [18F]AV1451 uptake in the putamen, globus pallidus, subthalamic nucleus, and dentate nucleus compared with HC. PD patients showed lower [18F] AV1451 binding in the substantia nigra than HC. In both PSP and PD, subcortical [18F]AV1451 binding did not correlate with the severity of motor dysfunction.

(Smith et al., 2017)

11 PSP 11 HC

[18F]AV1451 PSP patients showed increased [18F]AV1451 uptake in the basal ganglia compared with HC. [18F]AV1451 uptake in basal ganglia was correlated with age in both groups. [18F]AV1451 uptake in the globus pallidus correlated positively to PSP rating scale scores.

(Whitwell et al., 10 PSP 2017) 50 HC 10 AD

[18F]AV1451 PSP versus HCIncreased [18 F]AV1451 uptake in the globus pallidus, midbrain, dentate nucleus of the cerebellum, thalamus, caudate, and frontal regions. PSP versus ADIncreased [18 F]AV1451 uptake in the cerebellar dentate and globus pallidus. [18F]AV1451 uptake positively correlated with PSP rating scale.

(Passamonti et al., 2017)

19 PSP 15 AD 13 HC

[18F]AV1451 PSP versus AD and HCd[18F]AV1451 binding was increased in the midbrain, putamen, globus pallidus, and thalamus. AD versus HC and PSPd[18F]AV1451 binding was increased in the hippocampus and in occipital, parietal, temporal, and frontal cortices. Postmortem autoradiographic data showed that [18F]AV1451 strongly bound to AD related tau pathology, but less specifically in PSP.

(Ishiki et al., 2017)

3 PSP 13 AD 9 HC

[18F]THK5351

Autoradiography in the brain sections of patients with PSP demonstrated [3H]THK-5351 binding to tau deposits with a high selectivity. PSP patients showed increased [18F]THK-5351 uptake in the globus pallidus and midbrain.

(Coakeley et al., 12 PSP 2018) 10 HC

[18F]AV1451 Increased tracer uptake in the substantia nigra in healthy controls compared with PSP and PD patients.

(Cope et al., 2018)

[18F]AV1451 PSP patients show increased tau retention in areas associated with higher metabolic demand and lack of functional connectivity.

17 PSP 17 AD 12 HC

(Whitwell et al., 30 PSP 2018)

[18F]AV1451 Tau retention in AD-related regions correlated with tau [11C]PiB retention in PSP-related regions. 40% of PSP patients show amyloid retention, with no correlation.

IV. Clinical applications in atypical parkinsonian disorders

381

Molecular imaging studies in progressive supranuclear palsy

TABLE 13.4

Positron emission tomography studies in PSP patients.dcont’d

Study

Number of patients Radioligand Main findings

(Sintini et al., 2019)

34 PSP 29 HC

[18F]AV1451 Increased tau retention in the dentate nucleus, red nucleus, and subthalamic nucleus, which correlated with microstructural alterations in the superior cerebellar peduncle, sagittal striatum, and posterior corona radiata.

(Ng et al., 2019) 4 PSP 2 HC

[18F] THK5351

(Nicastro et al., 23 PSP-RS 2020) 23 HC

[18F]AV1451 Higher tau retention correlated with gray matter volume loss in frontal regions, microstructural changes in motor tracts, and cortical thinning in parietooccipital areas, and with cognitive impairment.

(Whitwell et al., 53 PSP-RS 2020) 23 PSP-SL 12 PSP-P 8 PSP-CBS 5 PSP-F 4 PSP-PGF 30 HC

[18F]AV1451 Increased tau uptake in striatum, pallidus, and thalamus, for all. PSP-RS, PSP-CBS, and PSP-F. PSP-SL patients show increased tau retention in the supplementary motor area and the motor cortex. PSP-P patients have increased tau retention in the putamen.

(Vasilevskaya et al., 2020)

11 PSP 6 CBS 6 CBS-AD 1 PSP-AD

[18F]AV1451 PSP-AD patients show increased tau retention in frontal and temporal regions, and in the inferior parietal region.

(Hsu et al., 2020)

17 PSP 28 HC

[18F] THK5351

Increased uptake in the globus pallidus and red nucleus. PSP patients with oculomotor dysfunction had increased uptake in the midbrain, red nucleus, and raphe nucleus. Patients with postural instability showed higher uptake in the frontal, parietal, precuneus, and sensorimotor cortex.

(Brendel et al., 2020)

60 10 10 10

[18F]PI2620

Increased tracer binding in various cortical and subcortical regions, with the highest difference in the globus pallidus. Sensitivity of PSP-RS diagnosis of 85% and specificity of 77%.

(Beyer et al., 2020)

13 PSP 7 AD

[18F]PI2620 [18F]FDG

Correlation between areas of high tau retention and hypometabolism in all cortical regions and cerebellum.

(Li et al., 2021)

20 PSP 7 MSA-P 10 PD 13 HC

[18F] APN1607

PSP patients show increased binding in striatum, putamen, pallidus, thalamus, subthalamic nucleus, midbrain, tegmentum, substantia nigra, pontine base, red nucleus, raphe nuclei, and locus coeruleus, with multiple correlations with disease severity.

(Passamonti et al., 2018)

16 PSP 16 AD

[11C] PK11195

Increased tracer binding in the thalamus, putamen, and pallidum of PSP compared with HC, with tracer binding in the pallidum, midbrain, and pons that correlated with disease severity.

PSP PD MSA HC

Uptake of [18F]THK5351 in PSP is affected by off-target binding to MAO-B binding sites.

(Continued)

IV. Clinical applications in atypical parkinsonian disorders

382

13. Neuroimaging in progressive supranuclear palsy

TABLE 13.4

Positron emission tomography studies in PSP patients.dcont’d

Study

Number of patients Radioligand Main findings

(Malpetti et al., 17 PSP-RS 2021)

Regional correlation of areas with increased inflammation [11C] PK11195 and tau retention in subcortical and cortical regions. [18F]AV1451 Correlation with clinical severity and subcortical areas of both inflammation and tau retention. Neuroinflammation and tau burden in the brainstem and cerebellum correlate with longitudinal annual rate of change of PSPRS scale.

(Palleis et al., 2021)

14 PSP 30 CBS 13 HC

[18F]GE-180 Increased tracer binding in several subcortical areas, with the highest difference in the globus pallidus internus in both PSP and CBS patients compared with controls.

(Holland et al., 2020)

14 PSP 15 CBS 15 HC

[11C]UCB-J [11C]PiB

Synaptic loss in the frontal, temporal, parietal, and occipital lobes, cingulate, hippocampus, insula, amygdala, and other subcortical structures.

AD, Alzheimer’s disease; CBD, corticobasal syndrome; CBS-AD, corticobasal syndrome, Alzheimer’s phenotype; D2, dopamine 2 receptor; HC, healthy controls; JPD, juvenile Parkinson’s disease; MAO-B, monoamine oxidase B; MMSE, mini mental state examination; MSA, multiple system atrophy; MSA-P, multiple system atrophy, parkinsonian variant; PD, Parkinson’s disease; PSP, progressive supranuclear palsy; PSP-AD, progressive supranuclear palsy, Alzheimer’s phenotype; PSP-CBS, progressive supranuclear palsy, corticobasal syndrome phenotype; PSP-F, progressive supranuclear palsy, frontal phenotype; PSP-FGF, progressive supranuclear palsy, progressive gait freezing phenotype; PSP-P, progressive supranuclear palsy, parkinsonian phenotype; PSP-RS, progressive supranuclear palsy, Richardson’s syndrome; PSP-SL, progressive supranuclear palsy, speech and language phenotype; PSPRS, Progressive Supranuclear Palsy Rating Scale.

Synaptic pathology can be visualized by means of PET imaging using the tracer [11C]UCBJ, which binds selectively to the synaptic vesicle glycoprotein 2A (SV2A) (Mansur et al., 2020). In a recent study of 14 PSP, 15 CBS, and 15 healthy controls, the binding of [11C]UCB-J was severely reduced in widespread areas of the frontal, temporal, parietal, and occipital lobes, cingulate, hippocampus, insula, amygdala, and subcortical structures, with a negative correlation with the PSP rating scale and a positive correlation with the revised Addenbrooke Cognitive examination (Holland et al., 2020) (Table 13.4).

Conclusion PSP is a relentlessly progressive neurodegenerative disease for which, differently from PD, even symptomatic therapy provides scarce relief. Additionally, the similarity of this condition to PD and other atypical Parkinsonisms is cause of significant diagnostic delays and of diagnostic revisions. Neuroimaging has played a significant role, in research and clinical setting, in the characterization of disease-specific signatures to assist in the correct distinction of this condition principally from MSA, CBS, and PD. In addition, molecular imaging is a decisive player in the study of PSP pathophysiology in vivo, in turn providing insights about the mechanisms and progression of tau deposition and improving targeting of treatment in future clinical trials. Molecular imaging is currently a dynamic field, and novel ligands are constantly being developed to characterize, in vivo, the pathophysiology of cellular alterations of PSP detected from preclinical or postmortem research, thus accelerating the translation of early research to clinical trials. It is envisaged that the application in human research of new PET readouts targeting newly identified cellular mechanisms of neuronal pathology, such as OIV. Clinical applications in atypical parkinsonian disorders

References

383

GlcNAc (Paul et al., 2019), will help in the leap forward needed to understand what determines this neurodegenerative disease and how to intervene to modify its progression.

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Wang, J., Wai, Y., Lin, W. Y., Ng, S., Wang, C. H., Hsieh, R., Hsieh, C., Chen, R. S., & Lu, C. S. (2010). Microstructural changes in patients with progressive supranuclear palsy: A diffusion tensor imaging study. Journal of Magnetic Resonance Imaging, 32, 69e75. Wang, G., Wang, J., Zhan, J., Nie, B., Li, P., Fan, L., Zhu, H., Feng, T., & Shan, B. (2015). Quantitative assessment of cerebral gray matter density change in progressive supranuclear palsy using voxel based morphometry analysis and cerebral MR T1-weighted FLAIR imaging. Journal of Neurologyogical Science, 359, 367e372. Warmuth-Metz, M., Naumann, M., Csoti, I., & Solymosi, L. (2001). Measurement of the midbrain diameter on routine magnetic resonance imaging: A simple and accurate method of differentiating between Parkinson disease and progressive supranuclear palsy. ArchNeurol, 58, 1076e1079. Weng, C. C., Hsiao, I. T., Yang, Q. F., Yao, C. H., Tai, C. Y., Wu, M. F., Yen, T. C., Jang, M. K., & Lin, K. J. (2020). Characterization of (18)F-PM-PBB3 ((18)F-APN-1607) uptake in the rTg4510 Mouse model of tauopathy. Molecules, 25. Whitwell, J. L., Ahlskog, J. E., Tosakulwong, N., Senjem, M. L., Spychalla, A. J., Petersen, R. C., Jack, C. R., Jr., Lowe, V. J., & Josephs, K. A. (2018). Pittsburgh Compound B and AV-1451 positron emission tomography assessment of molecular pathologies of Alzheimer’s disease in progressive supranuclear palsy. Parkinsonism Relative Disorders, 48, 3e9. Whitwell, J. L., Avula, R., Master, A., Vemuri, P., Senjem, M. L., Jones, D. T., Jack, C. R., Jr., & Josephs, K. A. (2011). Disrupted thalamocortical connectivity in PSP: A resting-state fMRI, DTI, and VBM study. Parkinsonism Relative Disorders, 17, 599e605. Whitwell, J. L., Duffy, J. R., Strand, E. A., Machulda, M. M., Senjem, M. L., Gunter, J. L., Kantarci, K., Eggers, S. D., Jack, C. R., Jr., & Josephs, K. A. (2013). Neuroimaging comparison of primary progressive apraxia of speech and progressive supranuclear palsy. European Journal of Neurology, 20, 629e637. Whitwell, J. L., Jack, C. R., Jr., Boeve, B. F., Parisi, J. E., Ahlskog, J. E., Drubach, D. A., Senjem, M. L., Knopman, D. S., Petersen, R. C., Dickson, D. W., & Josephs, K. A. (2010). Imaging correlates of pathology in corticobasal syndrome. Neurology, 75, 1879e1887. Whitwell, J. L., Lowe, V. J., Tosakulwong, N., Weigand, S. D., Senjem, M. L., Schwarz, C. G., Spychalla, A. J., Petersen, R. C., Jack, C. R., Jr., & Josephs, K. A. (2017). [(18) F]AV-1451 tau positron emission tomography in progressive supranuclear palsy. Movement Disorders, 32, 124e133. Whitwell, J. L., Master, A. V., Avula, R., Kantarci, K., Eggers, S. D., Edmonson, H. A., Jack, C. R., Jr., & Josephs, K. A. (2011). Clinical correlates of white matter tract degeneration in progressive supranuclear palsy. ArchNeurol, 68, 753e760. Whitwell, J. L., Tosakulwong, N., Botha, H., Ali, F., Clark, H. M., Duffy, J. R., Utianski, R. L., Stevens, C. A., Weigand, S. D., Schwarz, C. G., Senjem, M. L., Jack, C. R., Lowe, V. J., Ahlskog, J. E., Dickson, D. W., & Josephs, K. A. (2020). Brain volume and flortaucipir analysis of progressive supranuclear palsy clinical variants. Neuroimage Clinicals, 25, 102152. Whitwell, J. L., Tosakulwong, N., Clark, H. M., Ali, F., Botha, H., Weigand, S. D., Sintini, I., Machulda, M. M., Schwarz, C. G., Reid, R. I., Jack, C. R., Jr., Ahlskog, J. E., & Josephs, K. A. (2021). Diffusion tensor imaging analysis in three progressive supranuclear palsy variants. Journal of Neurology, 268, 3409e3420. Whitwell, J. L., Tosakulwong, N., Schwarz, C. G., Botha, H., Senjem, M. L., Spychalla, A. J., Ahlskog, J. E., Knopman, D. S., Petersen, R. C., Jack, C. R., Jr., Lowe, V. J., & Josephs, K. A. (2019). MRI outperforms [18F] AV-1451 PET as a longitudinal biomarker in progressive supranuclear palsy. Movement Disorders, 34, 105e113. Whitwell, J. L., Xu, J., Mandrekar, J., Gunter, J. L., Jack, C. R., Jr., & Josephs, K. A. (2012). Imaging measures predict progression in progressive supranuclear palsy. Movement Disorders, 27, 1801e1804. Worker, A., Blain, C., Jarosz, J., Chaudhuri, K. R., Barker, G. J., Williams, S. C., Brown, R. G., Leigh, P. N., Dell’Acqua, F., & Simmons, A. (2014b). Diffusion tensor imaging of Parkinson’s disease, multiple system atrophy and progressive supranuclear palsy: A tract-based spatial statistics study. PLoS One, 9, e112638. Worker, A., Blain, C., Jarosz, J., Chaudhuri, K. R., Barker, G. J., Williams, S. C., Brown, R., Leigh, P. N., & Simmons, A. (2014a). Cortical thickness, surface area and volume measures in Parkinson’s disease, multiple system atrophy and progressive supranuclear palsy. PLoS One, 9, e114167. Yagishita, A., & Oda, M. (1996). Progressive supranuclear palsy: MRI and pathological findings. Neuroradiology, 38(Suppl. 1), S60eS66.

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IV. Clinical applications in atypical parkinsonian disorders

C H A P T E R

14 Neuroimaging in corticobasal syndrome Heather Wilson1, Edoardo Rosario de Natale1, Marios Politis1 and Flavia Niccolini2, 3 1

Neurodegeneration Imaging Group and Mireille Gillings Neuroimaging Centre, University of Exeter, London, United Kingdom; 2Department of Neurology, King’s College Hospital NHS Foundation Trust, London, United Kingdom; 3Department of Neurology, Queen Elizabeth Hospital, Lewisham and Greenwich NHS Foundation Trust, London, United Kingdom

Introduction Corticobasal syndrome (CBS) is a rare progressive neurodegenerative disorder with onset in the mid-1950s and typical disease duration of 7 years (Alexander et al., 2014; Armstrong et al., 2013; Togasaki & Tanner, 2000). The pathology underlying CBS was initially characterized solely as corticobasal degeneration (CBS) by the presence of 4R tau protein deposition in cortical and striatal neurons and glia (Kouri et al., 2011). However, postmortem studies have highlighted the heterogenous neuropathology of CBS with reports of pathology associated with Alzheimer’s disease (AD), progressive supranuclear palsy (PSP), and frontotemporal lobar degeneration (FTLD) with transactivation response DNA-binding protein 43 kDa (TDP-43) inclusions (Boeve et al., 2003; Hu et al., 2009; Kouri et al., 2011; Lee et al., 2011; Ling et al., 2010). CBS-CBD remains the most common pathologic diagnosis, followed by CBS-PSP and CBS-AD (Lee et al., 2011). The clinical phenotype of CBS is characterized by parkinsonism and a combination of asymmetric rigidity, akinesia, dystonia, and myoclonus and cortical symptoms such as cortical sensory loss, alien limb behavior, and dementia (Gibb et al., 1989; Kertesz et al., 2000). Other clinical phenotypes include frontal behavioralespatial syndrome, nonfluent/ agrammatic variant of primary progressive aphasia, and CBS-PSP (Alexander et al., 2014; Armstrong et al., 2013). Early diagnosis is challenging due to the high rate of clinical overlap resulting in 24% of the patients being misdiagnosed (Meijer et al., 2012). Clinical features

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400

14. Neuroimaging in corticobasal syndrome

alone are unable to identify with certainty the underlying neuropathological substrate of CBS during life (Mathew et al., 2012). Molecular imaging tools, including positron emission tomography (PET) (Table 14.1) and single-photon emission computed tomography (SPECT), together with magnetic resonance imaging (MRI) (Table 14.2), have been employed to investigate disease pathways in CBS, as well as striving to disentangle the underlying pathologic substrate in vivo. This chapter provides an overview of the use of neuroimaging in CBS. TABLE 14.1

Positron emission tomography studies in CBS patients. Number of patients

Radioligand Key findings

Eidelberg et al., 1991

5 CBS

[18F]FDG

Parietal lobe asymmetry of 5% or more was evident in all CBS patients, whereas in PD patients and controls, all regional asymmetry measures were less than 5%.

Blin et al. (1992)

5 CBS

[18F]FDG

Hypometabolism in the frontal, temporal, sensorimotor, and parietal associative cortex, as well as in the caudate nucleus, lenticular nucleus, and thalamus contralateral to the most affected limbs. The most affected structure was the sensorimotor cortex.

Taniwaki et al. (1998)

6 CBS

[18F]FDG

Bilateral hypometabolism in the superior frontal cortex, medial frontal cortex, inferior frontal cortex, lateral frontal cortex, lateral posterior frontal cortex, inferior parietal cortex, putamen, and caudate.

Laureys et al. 6 CBS (1999)

[18F]FDG

Asymmetric pattern of cortical and subcortical hypometabolism.

Garraux et al. 22 CBS (2000)

[18F]FDG

Asymmetric hypometabolism in the putamen, thalamus, precentral, lateral premotor, and supplementary motor areas. Compared with PSP, CBS patients showed hypometabolism in the midbrain, anterior cingulate, and orbitofrontal regions.

Peigneux et al. (2001)

18 CBS

[18F]FDG

Distinct neural networks (parietofrontal) underlie distinct aspects of the upper limb apraxia in CBS.

Coulier et al. (2003)

7 CBS

[18F]FDG

Asymmetric patterns of hypometabolism. Most affected regions were the perirolandic cortex, the striatum, and the thalamus.

Niethammer et al. (2014)

10 CBS

[18F]FDG

Spatial covariance analysis showed CBS-related metabolic pattern. This pattern was characterized by bilateral, asymmetric metabolic reductions involving frontal and parietal cortex, thalamus, and caudate. This pattern accurately discriminated CBS from MSA and but not PSP.

Sha et al. (2015)

25 CBS

[18F]FDG [11C]PIB

FDG-PET showed approximately 90% sensitivity for predicting PIB + status and clinical criteria for CBS-temporoparietal variant had specificity of 71%. Combined clinical CBS-temporoparietal variant criteria (specific) with FDG-PET (sensitive) yielded the best overall discrimination of PIB + versus PIB-CBS patients.

29 CBS:

[18F]FDG

Study

IV. Clinical applications in atypical parkinsonian disorders

Introduction

TABLE 14.1 Study Pardini et al. (2019)

401

Positron emission tomography studies in CBS patients.dcont’d Number of patients

Radioligand Key findings

• 14 CBS-CBD • 10 CBS-AD • 5 CBS-PSP

CBS-CBD presented with asymmetric hypometabolism in the frontoparietal cortex, thalamus, and caudate in the more affected hemisphere, as well as the caudate and motor cortex in the less affected hemisphere. CBS-PSP presented with medialefrontal hypometabolic patterns with involvement of the caudate in the more affected hemisphere. CBS-AD presented with patterns of temporoparietal hypometabolism in the more affected hemispheres, as well as a small cluster in the parietal cortex in the less affected hemisphere. Hypometabolism in the posterior cingulate cortex and the precuneus in CBS-AD but not in CBSCBD or CBS-PSP. Overlapping cluster of hypometabolism was observed only in the precentral gyrus for all pathologic diagnoses.

Benvenutto et 30 CBS al. (2020)

Amyloid-negative CBS patients (n = 14) displayed more anterior [18F]FDG DAT SPECT cortical and brain stem hypometabolism. Amyloid-positive CBS patients (n = 16) showed more prominent posterior cortical abnormalities. DAT SEPCT was similar between amyloid-positive and amyloidnegative CBS cohorts.

Cerami et al. (2020)

21 CBS

[18F]FDG

Distinct metabolic signatures in CBS-AD (defined by the presence of AD-like CSF profile) compared with CBS-nonAD cases. CBSAD patients (n = 8) showed hypometabolism in posterior cingulate cortex, precuneus, and temporoparietal cortex, while CBS-non-AD patients (n = 13) showed hypometabolism in frontoinsular cortex and basal ganglia.

Parmera et al. 31 CBS (2021)

[18F]FDG [11C]PIB

Higher frequency of dysarthria in CBS Ab- cohort (n = 18) compared with CBS Ab+ (n = 13). Left-sided frontal hypometabolism, most prominent in inferior frontal and premotor cortex, in CBS patients with dysarthria. Positive correlation between FDG uptake in left inferior, middle, and superior temporal gyri with semantic verbal fluency, and between FDG uptake in frontal opercular gyrus, inferior, and middle temporal gyri with phonemic verbal fluency.

Sawle et al. (1991)

6 CBS

[18F]FDG Asymmetric reduction of FDOPA uptake into striatum, and [18F]FDOPA hypometabolism in superior temporal, inferior parietal cortex, and occipital association cortex.

Nagasawa et al. (1996)

6 CBS

[18F]FDOPA Asymmetric hypometabolism in the thalamus, the striatum, and [18F]FDG the parietal cortex contralateral to the more affected side. Asymmetric FDOPA reduction in the caudate and putamen contralateral to the more affected side.

Gerhard et al. 4 CBS (2004)

[11C] PK11195

Significantly increased mean [11C]PK11195 binding in the caudate nucleus, putamen, substantia nigra, pons, pre- and postcentral gyrus, and the frontal lobe. (Continued)

IV. Clinical applications in atypical parkinsonian disorders

402 TABLE 14.1

14. Neuroimaging in corticobasal syndrome

Positron emission tomography studies in CBS patients.dcont’d Number of patients

Radioligand Key findings

7 CBS

[11C]MP4A

Decreased acetylcholinesterase activity in the paracentral region, frontal, parietal, and occipital cortices, which correlated with cognitive function.

Kikuchi et al. 5 CBS (2016)

[18F] THK5351

Increased tau deposition in frontal and parietal cortices, and globus pallidus contralateral to the most affected limb.

Josephs et al. 1 CBD (2016)

[18F]AV1451 Increased tau binding in the putamen, pallidum, thalamus, [18F]FDG precentral cortex, Rolandic operculum, supplemental motor area, and left Broca's area. Correlation between tau-PET and autopsy [11C]PIB tau burden. Negative amyloid PET scan. Nonsignificant correlation between tau-PET and FDG-PET, gray matter volume, and rate of atrophy.

Cho et al. (2017)

6 CBS

[18F]AV1451 Increased tau binding in the putamen, globus pallidus, thalamus, and precentral gray and white matter in the hemisphere contralateral to the clinically most affected side.

Smith et al. (2017)

6 CBS

[18F]AV1451 Increased [18F]AV1451 uptake in the motor cortex, corticospinal tract, and basal ganglia in the hemisphere contralateral to the most affected body side.

Ali et al. (2018)

14 CBS

[18F]AV1451 Regional tau deposition was dependent from the presence of bamyloid as well as clinical presentation such as apraxia of speech.

Study Hirano et al. (2010)

Niccolini et al. 11 CBS (2018)

[18F]AV1451 Increased [18F]AV1451 uptake in parietal and frontal cortices [18F]AV45 contralateral to the clinically most affected side compared to healthy controls; and in precentral and postcentral gyri in the affected hemisphere compared with MCI patients. There was no cortical and subcortical amyloid-b depositions.

Ezura et al. (2019)

[18F] THK5351

Increased tau deposition by 6.53% in the superior parietal gyrus, 4.34% in the precentral gyrus, and 4.33% in the postcentral gyrus at 1 year follow-up.

[11C]UCB-J [11C]PIB

Greatest decrease in [11C]UCB-J binding in the medulla, hippocampus, amygdala, caudate, insula, and thalamus in CBSCBD versus controls. Global SV2A loss correlated with CBD rating scale and the revised Addenbrooke's Cognitive Examination.

10 CBS

Holland et al. 9 CBS-CBD (2020) 14 PSPRichardson's syndrome

Ab, amyloid-beta; AD, Alzheimer's disease; CBD, corticobasal degeneration; CBS, corticobasal syndrome; DAT, dopamine transporter, MCI, mild cognitive impairment; MSA, multiple system atrophy; PD, Parkinson's disease; PSP, progressive supranuclear palsy; SPECT, single-photon emission computed tomography.

Structural magnetic resonance imaging The structural MRI hallmark of CBS is an asymmetric frontoparietal cortical atrophy contralateral to the most clinically affected side (Grisoli et al., 1995; Hauser et al., 1996; Josephs et al., 2004; Meijer et al., 2012; Savoiardo, 2003; Soliveri et al., 1999). Using voxel-

IV. Clinical applications in atypical parkinsonian disorders

Structural magnetic resonance imaging

403

based morphometry (VBM), parietal cortex and corpus callosum gray matter loss was found in 18 CBS patients compared with 22 controls (Groschel et al., 2004). Another VBM study showed an asymmetric pattern of brain atrophy in the bilateral premotor cortex, superior parietal lobules, and striatum in 14 patients with CBS (Boxer et al., 2006). In pathologically confirmed CBS patients, Josephs et al. (2008) found a pattern of gray matter atrophy involving the posterior inferior, middle and superior frontal lobes, the superior premotor cortex, the posterior temporal and parietal lobes, insular cortex, and the supplemental motor area as well as subcortical nuclei such as globus pallidus, putamen, and the head of the caudate nucleus. Yu and colleagues performed a metaanalysis on 39 published VBM articles (Yu et al., 2015) using the modified anatomic likelihood estimation method, a statistical analysis technique to determine spatial concordance among VBM foci (Eickhoff et al., 2012). The most consistent finding in CBS was gray matter atrophy in left superior parietal lobule and thalamus (Yu et al., 2015). Another VBM study assessed different patterns of gray matter loss in 24 patients with postmortem-confirmed CBS (Whitwell et al., 2010). All CBS phenotypes showed atrophy in the premotor cortex, supplementary motor area, and insula (Whitwell et al., 2010). Five patients with frontotemporal lobar degeneration (FTLD) with TDP-43 immunoreactivity (CBS-TDP) had greater loss in lateral prefrontal cortex, medial prefrontal cortex, and posterior temporal lobe. Six patients with the AD variant of CBS (CBS-AD) showed greater loss in superior parietal lobe, precuneus, and occipital lobe (Whitwell et al., 2010). Similar patterns of gray matter loss were observed in seven patients with CBS-CBD and in six patients with PSP (CBS-PSP). They involved premotor cortex and supplementary motor area and were more severe in more severe in CBS-CBD (Whitwell et al., 2010). By using paired-pulse threshold tracking transcranial magnetic stimulation (TMS) and VBM analysis, Burrell and colleagues investigated neuroanatomical correlates of limb apraxia and cortical dysfunction (Burrell et al., 2014). They found that limb apraxia was correlated to atrophy of the precuneus/posterior cingulate within the posteromedial parietal lobe, and the premotor cortex, whereas cortical dysfunction was associated with atrophy of the primary motor and premotor cortices, and the thalamus (Burrell et al., 2014). Jutten and colleagues found more severe apraxia and cognitive deficits and a more widespread pattern of atrophy involving frontoparietal to orbitofrontal and temporal regions in CBS patients with initial left-sided impairment in comparison with those with initial right-sided impairment (Jutten et al., 2014). Freesurfer analysis showed decreased cortical thickness in the prefrontal cortex, precentral gyrus, supplementary motor area, insula, and temporal pole bilaterally (Upadhyay, Suppa, Piattella, Di Stasio, et al., 2016). The most severe atrophy was found in the frontoparietal regions contralateral to the clinically more affected side and correlated with the severity of upper motor limb symptoms (Upadhyay, Suppa, Piattella, Di Stasio, et al., 2016). Cortical thickness asymmetry in postcentral and paracentral gyri decreases as the disease progresses (Upadhyay, Suppa, Piattella, Di Stasio, et al., 2016). Another MRI study using Freesurfer showed different patterns of volume loss in CBS patients compared with those with PSP (Upadhyay, Suppa, Piattella, Bologna, et al., 2016). CBS patients showed more severe atrophy in perirolandic regions compared with PSP patients, whereas PSP had a smaller surface area and greater intracortical white matter (WM) loss in keeping with pathological data reporting a greater tau deposition in subcortical structures in PSP patients (Upadhyay, Suppa, Piattella, Bologna, et al., 2016). Microstructural abnormalities in the white matter have also been

IV. Clinical applications in atypical parkinsonian disorders

404 TABLE 14.2 Study

14. Neuroimaging in corticobasal syndrome

Magnetic resonance imaging studies in CBS patients. Number of Pathologically patients confirmed diagnosis Key findings

Hauser et al. 8 CBS (1996)

No

Asymmetrical cortical atrophy contralateral to the clinically more affected side

Soliveri et al. 16 CBS (1999)

No

Asymmetric frontoparietal cortical atrophy

Ukmar et al. 7 CBS (2003)

No

Decreased activation of the parietal lobe contralateral to the clinically most affected arm

Joseph at al. (2004)

6 CBD

Yes

Frontoparietal cortical and middle corpus callosum atrophy

Groschel et al. (2004)

18 CBS

No

Gray matter loss in midbrain, parietal white matter, brainstem, pons, temporal brain regions

Boxer et al. (2006)

14 CBS

No

Asymmetric pattern of brain atrophy in premotor and parietal cortex, superior parietal lobules, and striatum

Borroni et al. 20 CBS (2008)

Yes

Limb apraxia correlates with parietal atrophy and with FA reductions in the parietofrontal associative areas

Josephs et al. 21 CBD (2008)

Yes

Asymmetric frontoparietal gray and subcortical gray matter atrophy (visual assessment)

Whitwell et al. (2010)

7 CBD

Yes

Focal atrophy involving the premotor and supplemental motor area

Burrell et al. (2014)

17 CBS

No

Ten patients with asymmetric atrophy of the primary motor and premotor cortices and thalamus. Apraxia correlates with premotor and parietal atrophy

Jutten et al. (2014)

8 CBS

No

Asymmetric primary motor area atrophy in the hemisphere contralateral to the apraxic limb. Volumetric gray matter loss related to CBS pathology appears and progresses faster in l-CBS than in r-CBS

Tovar-Moll et al. (2014)

19 CBS

No

Damage to the midbody of the corpus callosum and perirolandic corona radiata

Whitwell et al. (2014)

9 CBS

No

Asymmetric degeneration of the splenium of the corpus callosum, premotor, and prefrontal white matter lobes

Yu et al. (2015)

165 CBS

No

Asymmetric gray matter atrophy in multiple cortical regions mainly involving the superior parietal lobe

Upadhyay et al. (2016)

11 CBS

No

Reduced CTh in the frontoparietal regions contralateral to the clinically more affected side

Upadhyay et al. (2016)

11 CBS

No

Reduced CTh in perirolandic brain regions

Bharti et al. (2017)

11 CBS

No

Increased within-network FC in the cerebellum, sensorimotor, executive-control, and insular networks

Upadhyay et al. (2017)

11 CBS

No

Increased FC between the dentate nucleus and the sensorimotor cortices contralateral to the clinically most affected side

CBS, corticobasal syndrome; CTh, cortical thickness; FC, functional connectivity; FA, fractional anisotropy.

IV. Clinical applications in atypical parkinsonian disorders

Functional magnetic resonance imaging

405

reported in CBS patients (Borroni et al., 2008; Tovar-Moll et al., 2014; Upadhyay, Suppa, Piattella, Bologna, et al., 2016; Whitwell et al., 2014; Zhang et al., 2016). Borroni and colleagues found fractional anisotropy decrease, an indicator of axonal suffering, in the long frontoparietal connecting tracts, the intraparietal associative fibers, the corpus callosum, and the sensorimotor projections of the cortical hand areas (Borroni et al., 2008). Moreover, limb apraxia was associated to fractional anisotropy reductions in the parietofrontal associative fibers (Borroni et al., 2008). CBS patients showed a similar pattern of bilateral decrease in white matter fibers in the anterior commissure, genu and body of the corpus callosum, corona radiata, and long intrahemispheric association pathways (Tovar-Moll et al., 2014). However, they also showed greater damage to the midbody of the corpus callosum and perirolandic corona radiate in comparison with those with the behavioral variant of FTLD (bv-FTLD), in keeping with the different clinical features of these two neurodegenerative diseases (Tovar-Moll et al., 2014). Whitwell and colleagues investigated white matter tract changes in a cohort of PSP and CBS patients (Whitwell et al., 2014). Reduced fractional anisotropy and increased mean diffusivity were found in the corpus callosum, middle cingulum bundle, and premotor and prefrontal white matter and superior cerebellar peduncles in both PSP and CBS patients (Whitwell et al., 2014). CBS had a more asymmetric supratentorial and posterior pattern of degeneration with greater involvement of the splenium of the corpus callosum, premotor, motor, and parietal lobes than PSP (Whitwell et al., 2014). These findings suggest an intraand interhemispheric structural dysfunction of white matter tract, which could represent the pathophysiological substrate of motor and higher cortical symptoms in CBS. A longitudinal study showed higher rates of fractional anisotropy reduction and mean diffusivity increase in basal ganglia and widespread white matter regions over a 6 months’ period in patients with CBS (Zhang et al., 2016). In CBS patients, axial diffusivity, which reflects axonal loss, was found to be more affected than radial diffusivity, a marker of myelin changes, suggesting that white matter changes may represent an axonal damage rather than demyelination in this neurodegenerative disorder (Upadhyay, Suppa, Piattella, Bologna, et al., 2016, Upadhyay, Suppa, Piattella, Di Stasio, et al., 2016).

Functional magnetic resonance imaging Resting-state functional MRI has been used to investigate functional connectivity in patients with CBS (Bharti et al., 2017; Ukmar et al., 2003; Upadhyay et al., 2017). Reduced activation of the motor areas and parietal lobe contralateral to the more affected arm during a simple and complex motor task was shown in CBS patients (Ukmar et al., 2003). CBS patients showed increased functional connectivity in the cerebellum, sensorimotor, executive control, and insular networks compared with healthy controls (Bharti et al., 2017). An explanation for this hyperconnectivity could be found in plasticity-related shift mechanisms from atrophic area of the brain to intact neurons or it could be the result of abnormal neuronal activity due to neurodegeneration. An increased functional connectivity was also found between the dentate nucleus and anterior cingulate cortex, supplementary motor area, primary motor cortex, prefrontal cortex, and the sensorimotor cortices contralateral to the most affected body

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406

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side (Upadhyay et al., 2017). This could represent a compensatory mechanism through which dentate nucleus neuronal reorganization helps to overcome motor and nonmotor symptoms. Upadhyay and colleagues showed significantly reduced thalamic functional connectivity in both PSP and CBS (Upadhyay et al., 2017). However, PSP patients had lower dentate nucleus functional connectivity with the basal ganglia, thalamus, and prefrontal cortex, which might be the results of dentate nucleus degeneration (Upadhyay et al., 2017).

Single-photon emission computed tomography SPECT studies have investigated presynaptic dopaminergic integrity in patients with CBS (Booth et al., 2015; Cilia et al., 2011; Hammesfahr et al., 2016; Kaasinen et al., 2013; Klaffke et al., 2006; O’Sullivan et al., 2008; Plotkin et al., 2005). Overall, these studies found a variable degree of dopamine transporter reductions in the striatum with greater hemispheric asymmetry contralateral to the more affected side compared with those patients with Parkinson’s disease (PD) (Booth et al., 2015; Cilia et al., 2011; Hammesfahr et al., 2016; Klaffke et al., 2006; Plotkin et al., 2005). Ten percent of CBS patients may show normal striatal dopamine transporter uptake (Booth et al., 2015; Cilia et al., 2011; Hammesfahr et al., 2016; Klaffke et al., 2006; Plotkin et al., 2005). A longitudinal study showed normal presynaptic dopaminergic function at the baseline scan performed after 2.3 years from onset of the disease and pathological FP-CIT uptake decreases at 10e15 months from baseline in CBS patients, suggesting that presynaptic dopaminergic dysfunction may occur at later stages of the disease (Ceravolo et al., 2013). Similarly, CBS patients showed postsynaptic dopaminergic dysfunction with a high degree of variability with the majority of CBS patients showing a preserved dopamine D2 receptor binding (Frisoni et al., 1995; Hammesfahr et al., 2016; Klaffke et al., 2006; Pirker et al., 2013). Hammesfahr and colleagues studied striatal presynaptic dopamine and postsynaptic D2 receptor functions in 23 early to midstage CBS patients whom one had postmortem confirmed diagnosis of definite CBS using FP-CIT-SPECT and IBZM-SPECT, respectively (Hammesfahr et al., 2016). They found that striatal presynaptic dopaminergic function was mildly decreased; however, 39% of the CBS patients showed no dopaminergic deficits; postsynaptic dopamine D2 receptors density was well preserved (Hammesfahr et al., 2016). This may explain the lack of response to dopaminergic therapy in CBS patients.

Positron emission tomography PET molecular imaging is a powerful in vivo tool for investigating brain function such as metabolism, receptor, and protein distributions, providing high sensitivity and temporal resolution (Phelps, 2000) (Table 14.1).

Brain metabolism Regional cerebral glucose has been assessed with [18F]FDG PET in atypical parkinsonism. In CBS patients, [18F]FDG PET has shown asymmetric cortical metabolic dysfunction more prominent in the parietal cortex, primary sensorimotor cortex, premotor areas, striatum,

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and thalamus (Blin et al., 1992; Coulier et al., 2003; Laureys et al., 1999; Niethammer et al., 2014; Taniwaki et al., 1998; Zhao et al., 2012). An association between cortical metabolic asymmetry and thalamic glucose metabolism dysfunction was found in CBS patients suggesting corticothalamic metabolic asymmetric network impairment in keeping with neuropathological findings (Eidelberg et al., 1991). Using [18F]FDG PET, Peigneux and colleagues studied neural substrate of upper limb apraxia in 18 CBS patients (Peigneux et al., 2001). They found that CBS patients who performed below the cutoff score at the upper limb apraxia assessment had significant decreases in [18F]FDG uptake in the anterior cingulate (Peigneux et al., 2001). CBS patients who were unable to correct their errors at the same rate as control subjects displayed hypometabolism in superior parietal lobule and supplementary motor area suggesting that different neural networks contribute to upper limb dyspraxia in CBS (Peigneux et al., 2001). Cerebral glucose metabolism voxel-based patterns have been employed in the differential diagnosis of atypical parkinsonism (Eckert et al., 2008; Niethammer et al., 2014). Using a spatial covariance mapping algorithm, patterns of hypometabolism in CBS involve asymmetrical decreases to the contralateral clinically most affected side in the cerebrum, lateral parietal, and frontal regions and thalamus, with relative bilateral increases in occipital regions (Niethammer et al., 2014). These hypometabolic patterns are able to distinguish CBS patients from those with multiple system atrophy (MSA) but not PSP (Niethammer et al., 2014). PSP-related hypometabolic patterns overlap by 24% with those seen in CBS (Niethammer et al., 2014). However, the degree of hemispheric asymmetry at the network level varies between these two diseases and enables differential diagnosis 92%e94% specificity (Niethammer et al., 2014). Differential diagnosis between PSP and CBS patients using [18F]FDG PET can be difficult. CBS patients showed a significantly asymmetrical hypometabolism of the parietal lobe and the primary sensorimotor cortex, while PSP patients usually showed a significantly lower bilateral glucose metabolism of the anterior cingulate gyrus and the upper brainstem (Eckert et al., 2005; Garraux et al., 2000; Hosaka et al., 2002; Nagasawa et al., 1996). Amtage and colleagues assessed differences in glucose metabolism between PSP with asymmetric clinical symptoms, PSP with symmetric presentation, and CBS patients (Amtage et al., 2014). CBS patients showed an asymmetric parietal hypometabolism in the premotor cortex contralateral to the clinically most affected side (Amtage et al., 2014). PSP patients with asymmetrical clinical symptoms showed significant asymmetrical hypometabolism contralateral to the clinically most affected side in ventrolateral thalamus, middle cingulate gyrus, and sensorimotor cortex compared with PSP patients with symmetrical symptoms (Amtage et al., 2014). In comparison with CBS patients, PSP patients with asymmetrical and symmetrical clinical symptoms showed similar bilateral medial frontal hypometabolism (Amtage et al., 2014). Eidelberg’s group developed an automated image-based classification procedure, which has high sensitivity and specificity in distinguishing patients with PD, MSA, and PSP (Tang et al., 2010). A recent study using this automated image-based classification procedure in 137 parkinsonian patients compared clinical diagnosis with algorithm-based diagnosis and showed that image-based algorithm had a high specificity in classifying the patients before final clinical diagnosis (Rus et al., 2020). have been reported in CBS patients who present with AD-like pathology compared with CBS patients with non-AD-like pathology, such as CBS-CBD and CBS-PSP, thus linking

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[18F]FDG PET patterns in CBS with different neuropathological substrates (Benvenutto et al., 2020; Cerami et al., 2020; Parmera et al., 2021). These findings could help to drive forward the potential inclusion of [18F]FDG PET, a tool to improve diagnostic accuracy, although further longitudinal studies are required. Recent work by Pardini and colleagues, combining [18F]FDG PET and postmortem neuropathologic examination, evaluated the presence of distinct and overlapping metabolic patterns driven by the distinctive neuropathologic substrate of clinical diagnosed CBS (Pardini et al., 2019). Twenty-nine CBS patients were stratified according to primary pathologic diagnosis, at postmortem, into CBS-CBD (n ¼ 14), CBS-AD (n ¼ 10), and CBS-PSP (n ¼ 5). In the combined cohort of clinically diagnosed CBS patients (n ¼ 29), asymmetric hypometabolism was present in frontal, temporal, and parietal, which is consistent with previous studies and distribution of gray matter atrophy and white matter changes (Lee et al., 2011; Tovar-Moll et al., 2014). Pathologically confirmed CBS-CBD patients presented with asymmetric hypometabolism in the frontoparietal cortex, thalamus, and caudate in the more affected hemisphere, as well as the caudate and motor cortex in the less affected hemisphere (Pardini et al., 2019). Metabolic patterns in CBS-CBD are in line with a previous case study reporting patterns of tau pathology, using [18F]AV1451 PET, and hypometabolism, using [18F]FDG PET, in a patient with autopsy-confirmed CBD (Josephs et al., 2016). Patients with pathologically confirmed CBS-AD presented with patterns of temporoparietal hypometabolism in the more affected hemispheres, as well as a small cluster in the parietal cortex in the less affected hemisphere (Pardini et al., 2019), consistent with metabolic patterns reported in AD (Bohnen et al., 2012). Hypometabolism was observed in the posterior cingulate cortex and the precuneus in CBS-AD but not in CBS-CBD or CBS-PSP. These findings confirm previous reports of posterior patterns of hypometabolism in CBS patients with positive amyloid PET scans (Sha et al., 2015). CBS-PSP patients presented with medialefrontal hypometabolic patterns with involvement of the caudate in the more affected hemisphere (Pardini et al., 2019). Interestingly, an overlapping cluster of hypometabolism was observed only in the precentral gyrus for all pathologic diagnoses. The identification of the precentral gyrus as the potential epicenter of a functional network, observed in functional MRI connectivity studies (Seeley et al., 2009; Zhou et al., 2012), could shed light on the overlap of symptomatology independently of the underlying pathology in CBS. These findings provide evidence that the underlying pathology significantly modulates metabolic patterns and supports hypotheses that different pathologic protein aggregates preferentially affect different brain regions, although the mechanisms underlying regional vulnerability remain to be elucidate. [18F]FDG PET has also been employed to investigate the metabolic signatures underlying language and motor speech impairment patterns in CBS (Parmera et al., 2021). CBS patients with dysarthria had left-sided hypometabolism and bilateral atrophy, detected using structural MRI VBM analysis, in opercular frontal regions, premotor cortex, and the supplementary motor area (Parmera et al., 2021). Furthermore, hypometabolism correlated with semantic verbal fluency in the left inferior, middle, and superior temporal gyri, and with phonemic verbal fluency in the left frontal opercular gyrus and the inferior and middle temporal gyri. These findings suggest the presence of different patterns of hypometabolism and structural atrophy in CBS patients with and without dysarthria. Parmera and colleagues also employed [11C]PIB PET, a marker of amyloid deposition, to explore speech and language deficits in CBS patients related to the presence or absence of amyloid deposition, as a marker of

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underlying AD pathology. The frequency of dysarthria was highest in CBS patients with amyloid-negative [11C]PIB PET scans compared with amyloid-positive CBS patients (Parmera et al., 2021). Together, these findings support the notion that amyloid-negative CBS patients, likely reflecting CBD and PSP pathologies, present with distinct motor speech features compared with amyloid-positive CBS patients and that dysarthria could distinguish CBS patients with non-AD-like pathology.

Dopaminergic system Brain monoaminergic function has been studied in patients with CBS using PET with [18F] DOPA, which measures the uptake of dopamine precursors to assess presynaptic dopaminergic integrity (Nagasawa et al., 1996; Sawle et al., 1991). These studies have found an asymmetric pattern of [18F]DOPA reductions in the putamen and caudate, contralateral to the most affected side, of patients with CBS (Nagasawa et al., 1996; Sawle et al., 1991). Most of the studies investigating presynaptic and postsynaptic dopaminergic integrity used SPECT and showed that nigrostriatal degeneration may occur at later stages of the disease and dopamine D2 receptors are usually well preserved in patients with CBS (Booth et al., 2015; Ceravolo et al., 2013; Cilia et al., 2011; Frisoni et al., 1995; Hammesfahr et al., 2016; Kaasinen et al., 2013; Klaffke et al., 2006; O’Sullivan et al., 2008; Pirker et al., 2013; Plotkin et al., 2005).

Microglial activation Microglia are resident immunosurveillant cells in the central nervous system; although microglia could be associated closely with tau pathology, its role in disease pathogenesis is still unknown. When microglia become activated, they overexpress the 18-kd translocator protein (TSPO), which can be detected in vivo with PET and selective radioligands (59, 60). Only one PET study using [11C]PK11195 PET has investigated microglial activation in four patients with CBS (Gerhard et al., 2004). [11C]PK11195 binding was increased in the caudate, putamen, substantia nigra, pons, pre- and postcentral gyrus, and the frontal lobe in keeping with the pathological changes observed in CBS (Gerhard et al., 2004). These data need to be interpreted cautiously since [11C]PK11195 has shown high level of nonspecific binding and a poor signal-to-noise ratio (Petit-Taboue et al., 1991), which complicates its quantification; moreover, testeretest data in control subjects showed only moderate intraindividual reproducibility (Jucaite et al., 2012). Further studies are warranted to investigate microglia activation, utilizing third-generation TSPP tracers such as [11C]ER176, and neuroinflammation beyond TSPO targets to elucidate inflammatory pathways in disease pathophysiology in CBS. An increasing number of PET tracers are available to investigate inflammatory pathways beyond TSPO and include astroglia activation, such as [11C] BU99008 for imidazoline 2 binding sites, cyclooxygenase-1 and cyclooxygenase-2 enzymes, such as [11C]PS13 and [11C]MC1, respectively, and the purinergic receptor P2X7, such as [11C]JN717 (Wilson et al., 2020).

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Cholinergic system Only one study investigated cholinergic functions in seven patients with CBS using [11C] MP4A PET. CBS patients showed decreases in acetylcholinesterase activity in the paracentral region, frontal, parietal, and occipital cortices, which correlated with cognitive function as measured by the mini mental status examination (MMSE) (Hirano et al., 2010). This may suggest that cognitive decline could be caused by cholinergic dysfunction and cholinesterase inhibitors may improve cognitive function in CBS.

Tau deposition PET with specific radioligands binding to aggregated tau has provided a unique opportunity to assess tau pathology in CBS patients (Ali et al., 2018; Cho et al., 2017; Ezura et al., 2019; Josephs et al., 2016; Kikuchi et al., 2016; McMillan et al., 2016; Niccolini et al., 2018; Smith et al., 2017). A [18F]AV1451 (also known as [18F]flortaucipir) PET study demonstrated increased tau binding in the putamen, globus pallidus, thalamus, and precentral gray and white matter in the hemisphere contralateral to the clinically most affected side in six CBS patients (Cho et al., 2017). Smith and colleagues (Smith et al., 2017) have shown increased [18F]AV1451 uptake in the motor cortex, corticospinal tract, and basal ganglia in the hemisphere contralateral to the most affected body side of six patients with CBS compared with healthy controls and patients with Alzheimer’s disease and PSP. Using [18F]THK5351 PET study, Kikuchi and colleagues showed increased tau deposition in frontal and parietal cortices, and globus pallidus contralateral to the most affected limb in five patients with CBS (Kikuchi et al., 2016). In a larger study, Ali and colleagues reported a variable regional [18F]AV1451 uptake in 14 CBS patients. Regional tau deposition was dependent from the presence of b-amyloid as well as clinical presentation such as apraxia of speech (Ali et al., 2018). Our group investigated tau aggregates with [18F]AV1451 PET and amyloid-b depositions with [18F]AV45 PET in a cohort of 11 CBS patients (Niccolini et al., 2018). CBS patients showed increases in [18F]AV1451 uptake in parietal and frontal cortices contralateral to the clinically most affected side compared to healthy controls and in precentral and postcentral gyri in the affected hemisphere compared with mild cognitive impairment (MCI) patients (Fig. 14.1) (Niccolini et al., 2018). Our data were confirmed at histopathological level in one CBS patient who underwent brain biopsy and showed sparse tau pathology in the parietal cortex colocalizing with increased [18F]AV1451 signal. We did not find any significant cortical and subcortical amyloid-b depositions in our cohort of CBS patients (Niccolini et al., 2018). A recent longitudinal study has assessed changes in [18F]THK5351 uptake at 1 year followup in 10 CBS patients (Ezura et al., 2019). CBS patients showed an increase in tau deposition by 6.53% in the superior parietal gyrus, 4.34% in the precentral gyrus and 4.33% in the postcentral gyrus compared with healthy controls, whereas [18F]THK5351 uptake did not increase in healthy controls (Ezura et al., 2019). Second-generation tau PET tracers, such as [18F]MK-6240, [18F]RO948, and [18F]PI-2620, have been developed to overcome limitation of first-generation tracers predominantly arising from off-target binding to monoamine oxidase and neuromelanin (Leuzy et al., 2019). [18F]PI-2620, which has high affinity to 3/4R tau in AD as well as to recombinant 4R tau fibrils, has recently

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FIGURE 14.1 Tau and amyloid-b deposition in anatomically defined brain regions of corticobasal syndrome patients. Axial summed [18F]AV1451 and [18F]AV45 PET images fused coregistered and fused with 3T MRI images for the cortex of a healthy control and four corticobasal syndrome patients. CBS, corticobasal syndrome. Color bar reflects range of [18F]AV1451 SUVR and [18F]AV45 SUVR intensity. *Contralateral to the clinically most affected body side in each patient (the images are in neurologic orientation). Figure edited from Niccolini, F., Wilson, H., Hirschbichler, S., Yousaf, T., Pagano, G., Whittington, A., Caminiti, S. P., Erro, R., Holton, J. L., Jaunmuktane, Z., Esposito, M., Martino, D., Abdul, A., Passchier, J., Rabiner, E. A., Gunn, R. N., Bhatia, K. P., Politis, M. & Alzheimer’s Disease Neuroimaging, I. (2018). Disease-related patterns of in vivo pathology in Corticobasal syndrome. European Journal of Nuclear Medicine and Molecular Imaging, 45, 2413e2425.

been applied in PSP and warrants further application in CBS patients with pathologically confirmed diagnosis (Brendel et al., 2020; Kroth et al., 2019; Mueller et al., 2020). Clinical diagnosis of CBS could be difficult due to the overlapping features with other neurodegenerative disorders, in vivo imaging of tau aggregates with PET has the potential to aid in the differential diagnosis of CBS and to monitor novel disease-modifying therapies aiming at the prevention and propagation of tau aggregation.

Synaptic pathology The recent development of PET tracers for synaptic vesicle protein 2A (SV2A), with [11C] UCB-J the most widely utilized to date, has enabled the in vivo investigation of synaptic

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pathology across neurodegenerative diseases (Wilson et al., 2020). Holland and colleagues recently reported loss of SV2A in CBD patients, classified with amyloid negative [11C]PIB PET scans, compared with controls with the greatest reductions in the medulla, hippocampus, amygdala, caudate, insula, and thalamus (Holland et al., 2020). Patterns of SV2A loss were more extensive than gray matter atrophy and maintained after partial volume correction suggesting that synaptic loss measured with [11C]UCB-J PET was not solely driven by atrophy. Furthermore, global SV2A loss correlated with worse scores on CBD rating scale as well as cognitive deficits, measured using the revised Addenbrooke’s Cognitive Examination. These findings highlight the potential value of synaptic PET as a tool to investigate disease mechanisms as well as to monitor response to novel synaptic therapies. Further studies are warranted to explore changes with disease progression in CBS patients with pathologically confirmed diagnosis.

Conclusion and future directions To date MRI, PET, and SPECTG have provided important insights into disease mechanisms and offer potential tools to aid differential diagnosis in CBS. There is a need for more imaging studies in pathologically confirmed CBS variants to help disentangle the relationship between pathologies, disease pathways, and clinical phenotypes. There are several novel PET tracers which warrant application in CBS, such as neuroinflammatory targets. Mitochondrial genomic variations have recently been highlighted in pathologically confirmed CBD cases (Valentino et al., 2020). The validation of the PET tracer [18F]BCPPEF for mitochondrial complex 1 could provide an opportunity to investigate mitochondrial pathology in CBS. More work is needed to develop a reliable neuroimaging biomarker, which can enable early diagnosis and monitor disease progression.

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C H A P T E R

15 Molecular imaging in Huntington’s disease Edoardo Rosario de Natale1, Heather Wilson1, Flavia Niccolini2, 3 and Marios Politis1 1

Neurodegeneration Imaging Group and Mireille Gillings Neuroimaging Centre, University of Exeter, London, United Kingdom; 2King’s College Hospital NHS Foundation Trust, London, United Kingdom; 3Lewisham and Greenwich NHS Foundation Trust, London, United Kingdom

Introduction Huntington’s disease (HD) is a fatal, neurodegenerative disorder with autosomal dominant inheritance, caused by a trinucleotide CAG repeat expansion in the exon 1 of the HTT gene, located in chromosome 14. It is characterized by a mixture of behavioral, cognitive, and motor disturbance, with subtle onset and inexorable progression to death within 15e20 years. Its prevalence in the United Kingdom is of around 12/100,000 (Evans et al., 2013), and therefore, HD is the most common genetic cause of dementia in the developed world. Many mechanisms, following the abnormal CAG repeat expansion, have been proposed to contribute to brain pathological damage, including toxicity of mutant Htt protein (mHtt), or of its toxic fragments and cell-to-cell spreading, formation of cellular inclusions, dysregulation of cellular transcriptional cascades, mitochondrial dysfunction, neuroinflammation, and others (Ghosh & Tabrizi, 2018). Compared with other neurodegenerative disorders, HD presents a number of interesting features. HD is caused by a single-gene mutation with 100% penetrance, and the size of the CAG expansion explains around 50%e60% of the variability of the age when the first visible clinical symptoms ensue (Wexler et al., 2004). Therefore, making it possible to estimate, in premanifest gene expansion carriers (HDGECs), the time to future phenoconversion (Langbehn et al., 2004). This offers the possibility to study HD in a temporal window unique in most neurodegenerative diseases, in which pathological events can be observed at a subclinical level and characterized in view of their estimated time to phenoconversion. Tracking prospectively these alterations would also unveil progression biomarkers of disease and better

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understand phenotypical variabilities that may be due to other cofactors (Bassi et al., 2017). Finally, it is hopeful that novel therapies devised to act directly at mHtt, in the premanifest stage, would make it possible to prevent the onset of clinical symptoms (Tabrizi, Ghosh, & Leavitt, 2019). The role of positron emission tomography (PET) in the comprehension of the pathophysiology of HD has been substantial (Wilson et al., 2017). PET molecular imaging makes use of radiolabeled tracers that bind to specific receptors, enzymes, or other molecular targets, giving quantitative information about minimal, subclinical changes in brain cellular homeostasis (Table 15.1). PET imaging therefore can represent an ideal tool for the in vivo study of early brain changes in premanifest HDGECs. PET imaging has been extensively used in crosssectional and longitudinal studies to measure brain metabolism, dopaminergic function, neuroinflammation, phosphodiesterases and other targets in HD (Pagano et al., 2016). In this chapter, we will present the main advances that the applications of PET imaging have provided in the study of HD pathology in premanifest and manifest stages. We will then discuss and compare the potential of PET imaging to track disease progression in longitudinal studies. Finally, we will mention the use of PET imaging as outcome measure in clinical trials for HD-related therapies.

Brain metabolism In physiological conditions, the metabolism of glucose in the brain is strictly correlated to regional neuronal and synaptic activity (Sokoloff, 1999). [18F]FDG is an analog of glucose that is captured in the brain and moved into the cells by glucose transporters where it is phosphorylated into FDG-6-phosphate. [18F]FDG has been extensively used in both clinical and research settings, for the indirect evaluation of regional brain activity, through the detection of areas of altered regional cerebral metabolic rate of glucose (rCMRglc). The first PET studies on patients with HD, conducted in the pregenetic era, have consistently demonstrated that HD patients have a significant decrease of rCMRglc in the caudate and putamen (Berent et al., 1988; Garnett et al., 1984; Kuhl et al., 1982; Mazziotta et al., 1987; Young et al., 1986) as well as in other cortical regions (Kuwert et al., 1990). For this reason, [18F]FDG has been widely used to study brain alterations in HDGECs throughout subsequent years. Manifest HDGECs show marked and consistent glucose metabolism loss in the striatum and in cortical regions, particularly in the frontotemporal cortex (Ciarmiello et al., 2006; Gaura et al., 2017; Kuwert et al., 1990; López-Mora et al., 2016; Sampedro et al., 2019). The degeneration of neurons in the caudate has been associated with presence of bradykinesia and rigidity and with poor cognitive performance on verbal learning and memory (Berent et al., 1988; Kuwert et al., 1990; Young et al., 1986). Putaminal [18F]FDG uptake loss inversely correlates with Unified Huntington’s Disease Rating Scale (UHDRS) scores for chorea, oculomotor dysfunction, and fine motor coordination (Young et al., 1986). Manifest HDGECs may also show areas of increased glucose metabolism, particularly in the thalamus, and the parietal and occipital cortex, as well as in the cerebellar nuclei (Gaura et al., 2017). Thalamic hypermetabolism has been associated with the presence of dystonia (Young et al., 1986). Hypermetabolism in thalamic, cerebellar, and cortical regions was associated with both hypokinetic and hyperkinetic motor alterations in HD patients (Gaura et al., 2017). The

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Brain metabolism

TABLE 15.1 PET radioligand

PET tracers applied in the research setting for Huntington’s disease. Main findings

References

Regional cerebral blood flow

According to the task, decrease of the blood flow in frontal and temporal regions.

(Bartenstein et al., 1997; Lawrence et al., 1998; Lepron et al., 2009; Weeks, CeballosBaumann et al., 1997)

[18F]FDG

Glucose uptake

Decrease of regional glucose metabolism in the striatum and in frontotemporal cortical regions, in both premanifest and symptomatic HDGECs. Correlation with motor and apathy scores.

(Antonini et al., 1996; Berent et al., 1988; Ciarmiello et al., 2006, 2012; Feigin et al., 2001, 2007; Garnett et al., 1984; Gaura et al., 2017; Grafton et al., 1992; Hayden et al., 1987; HerbenDekker et al., 2014; Kuhl et al., 1982; Kuwert et al., 1990; LópezMora et al., 2016; Martínez-Horta et al., 2018; Mazziotta et al., 1987; Sampedro et al., 2019; Tang et al., 2013; Young et al., 1986)

[18F]DOPA

AADC activity

Presynaptic Decreased or normal dopamine dopamine synthesis synthesis in HDGECs.

(Leenders et al., 1986; Martin & Hayden, 1987)

[11C]DTBZ

VMAT2

Presynaptic Decrease in VMAT2 activity in dopaminergic nerve HDGECs, with akinetic-rigid terminals motor form more affected.

(Bohnen et al., 2000)

[11C] SCH23390

D1 Postsynaptic direct receptor dopaminergic pathway

Decrease in up to half of premanifest HDGECs and, in manifest HDGECs, decrease ranging from 35% to 75% in striatal areas. Decrease in receptor availability in temporal, but not in frontal cortex.

(Andrews et al., 1999; Bäckman et al., 1997; Ginovart et al., 1997; Lawrence et al., 1998; Sedvall et al., 1994)

[11C] Raclopride

D2 Postsynaptic receptor indirect dopaminergic pathway

Decrease of D2 availability in striatal areas in up to half of premanifest HDGECs and in the totality of manifest HDGECs. Decrease in premanifest HDGECs, in frontotemporal and insular lobes. Decrease more pronounced in akinetic-rigid HD than choreic HD.

(Andrews et al., 1999; Bäckman et al., 1997; Lawrence et al., 1998; Pavese et al., 2010; Politis et al., 2008; Sánchez-Pernaute et al., 2000; Turjanski et al., 1995; van Oostrom et al., 2005, 2009)

[11C]FLB457

D2 Postsynaptic receptor indirect dopaminergic pathway

No alterations in HDGECs of D2 (Esmaeilzadeh et al., 2011) cortical density.

H15 2 O

Target

[11C]PK11195 TSPO

Pathway

Neuroinflammation- Increased binding in striatal and (Pavese et al., 2006; Politis et al., induced microglial cortical regions as well as in the 2008, 2011, 2015; Tai et al., 2007) activation hypothalamus, in both premanifest and manifest HDGECs. (Continued) V. Clinical applications in other movement disorders

424 TABLE 15.1 PET radioligand

15. Molecular imaging in Huntington’s disease

PET tracers applied in the research setting for Huntington’s disease.dcont’d Main findings

Target

Pathway

[ C]PBR28

TSPO

Neuroinflammation- Increased binding in pallidum (Lois et al., 2018) induced microglial and putamen of premanifest and activation manifest HDGECs.

[11C]ER176

TSPO

Neuroinflammation- Increased uptake in the bilateral (Rocha et al., 2021) induced microglial pallidum and putamen of activation manifest HDGECs, which correlated with disease severity.

[18F] JNJ42259152

PDE10A Intracellular Decrease of PDE10A activity in downstream the caudate of manifest signaling regulation HDGECs.

11

Decrease of PDE10A striatal [18F]MNI-659 PDE10A Intracellular downstream activity manifest HDGECs with signaling regulation fast and early progression in premanifest HDGECs. [11C]IMA107

References

(Ahmad et al., 2014)

(Fazio et al., 2020; Russell et al., 2014, 2016)

PDE10A Intracellular Decrease of PDE10A activity in (Niccolini et al., 2015; Wilson downstream striatal and cortical regions of et al., 2016) signaling regulation far-from-onset premanifest HDGECs, with increase in motor thalami. Decrease in insula and fusiform gyrus.

[18F]MK-9470 CB1 Cannabinoid receptor system

Decrease of cannabinoid receptor (Ceccarini et al., 2019; Van Laere density in manifest and et al., 2010) premanifest HDGECs. In the latter, correlation with apathy scores.

Opioid Opioid system [11C] Diprenorphine receptors

Decrease of opioid receptors (Weeks, Cunningham, et al., density in caudate, putamen, and 1997) cingulate cortex; increase in thalamus and prefrontal cortex.

[11C] Flumazenil

GABA GABAergic system Decrease in the caudate of (Holthoff et al., 1993; Künig receptor premanifest and manifest et al., 2000) HDGECs. Putamen levels decrease in premanifest HDGECs but increase in manifest HDGECs.

[18F]CPFPX

A1A Adenosine system receptor

Increase of A1A receptors in far from onset HDGECs which decreases steadily in near-onset premanifest HDGECs and falls below normal in manifest HDGECs.

(Matusch et al., 2014)

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Brain metabolism

TABLE 15.1 PET radioligand 11

[ C]UCB-J

PET tracers applied in the research setting for Huntington’s disease.dcont’d Main findings

Target

Pathway

References

SV2A

Synaptic pathology Loss of SV2A binding in the (Delva et al., 2022) putamen, caudate, pallidum, cerebellum, parietal, temporal, and frontal cortex of premanifest and manifest HDGECs.

A1A, adenosine type 1A; AADC, aromatic L-amino acid decarboxylase; CB, cannabinoid; D1, dopamine type 1; D2, dopamine type 2; HDGECs, Huntington’s disease gene expansion carriers; PDE10A, phosphodiesterase 10A; TSPO, translocator protein; PET, positron emission tomography; SV2A, synaptic vesicle 2A; VMAT2, vesicular monoamine transporter 2.

presence of hypermetabolic areas in HD has usually been interpreted as the effect of upregulatory mechanisms in response to striatocortical disconnections, as a possible result of pathological modifications of regional metabolism that could be detrimental to motor function (Gaura et al., 2017). The consistency of structural and functional brain dysfunction has been studied in HD patients by coupling [18F]FDG PET and structural MRI findings. Manifest HDGECs show consistent atrophy in precentral and parietooccipital regions, whereas reduction of [18F] FDG uptake was mostly seen in frontotemporal regions. Moreover, while structural volumetric loss correlated with motor scores, reduction of glucose metabolism correlated with apathy scores, revealing a mismatch between spatial structural and functional alterations in manifest HDGECs, which may be related to different aspects of disease (Sampedro et al., 2019). Premanifest HDGECs can have reduced levels of [18F]FDG PET uptake in both striatal and cortical regions (Grafton et al., 1992; Antonini et al., 1996; Ciarmiello et al., 2006, 2012, Herben-Dekker et al., 2014; López-Mora et al., 2016; Sampedro et al., 2019). Studies on early premanifest HDGECs have shown that [18F]FDG PET can detect regional hypometabolism of both striatal and cortical areas starting in the early premanifest disease stage. In a recent study, eight far-from-onset premanifest HDGECs showed a significant decrease of glucose metabolism in the striatum compared with controls, after correction for gray matter loss. Furthermore, the degree of striatal hypometabolism progressed in the passage to nearonset premanifest HDGECs and subsequently to manifest HDGECs patients (López-Mora et al., 2016). Premanifest HDGECs also consistently show hypometabolism of frontotemporal cortical regions, which correlates with predicted time to phenoconversion (Ciarmiello et al., 2006), and which can be detected since the early premanifest stage of disease. In a group of 19 far-from-onset premanifest HDGECs, more than 10.8 years from predicted phenoconversion, [18F]FDG uptake was significantly decreased in the frontotemporal cortex. Decreased [18F]FDG uptake was associated with high scores on apathy scales, thus giving a link between functional and clinical manifestations of disease activity even in early premanifest stages (Sampedro et al., 2019). Composite patterns of alterations of [18F]FDG PET uptake in distant regions can also be studied by spatial covariance analysis. Feigin and colleagues used scaled subprofile model and principal components analysis to study a group of premanifest and early manifest HDGECs, as opposed to a cohort of healthy controls (Feigin et al., 2001). They found an

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HD-related pattern of a relative hypometabolism of the caudate and lentiform nuclei and the mesial temporal cortex, which correlated with metabolic increases in the occipital cortex. This distinctive HD-related pattern was strong in early premanifest HDGECs and weakened when approaching phenoconversion. Furthermore, this HD-related pattern of glucose metabolism was present in absence of decreased density of striatal dopamine type 2 (D2) receptors, as seen by the specific D2 receptor ligand [11C]raclopride (Feigin et al., 2001). A different, independent group of premanifest HDGECs followed up for 4 years confirmed the presence of a similar network covariance pattern, with the addition of a relative metabolic increase in the ventrolateral and ventral posterolateral thalamus, and cerebellar vermis (Feigin et al., 2007). This HD-related pattern may represent the result of an initial disconnection between the striatum and some cortical areas, which, in turn, activates local compensatory upregulatory mechanisms, particularly in the mediodorsal thalamus and orbitofrontal cortex (Feigin et al., 2007). The presence of regional alterations of glucose metabolism has been associated, in some studies, with the presence of apathy. Martinez-Horta and colleagues tested 40 early manifest HDGECs with [18F]FDG PET and the Problem Behaviors Assessment scale and found that scores compatible with apathy were associated with a decrease of glucose metabolism in frontotemporal and parietal regions, which coupled with structural gray matter alterations in a network comprising the bilateral amygdala and the temporal cortex. These results highlight the role of the limbic system and of complex cortical structural and functional alterations in the genesis of this common symptom in HD (Martínez-Horta et al., 2018). A loss of frontotemporal glucose metabolism correlated with apathy scores also in a cohort of premanifest HDGECs (Sampedro et al., 2019). Neuronal activation can also be studied as a function of regional blood flow, using 15H2O PET imaging. The underlying principle is that an activation task causes increases in regional blood flow of activated brain regions. When regional activation fails, because of structural or functional alterations, a loss of regional blood flow can be associated with the corresponding motor or cognitive activation tasks. Weeks and colleagues studied seven manifest HDGECs at rest and during a motor task, which consisted of moving a joystick in freely selected directions paced by an auditory tone (Weeks, Ceballos-Baumann et al., 1997). This task is known to activate the dorsal prefrontal and the supplementary motor area, involved in decisionmaking, and the sensorimotor cortex, involved in execution of the movements (Playford et al., 1992). During movement, manifest HDGECs had impaired activation of the contralateral primary motor, medial premotor, bilateral parietal and bilateral prefrontal areas, and activation of the bilateral insulae (Weeks, Ceballos-Baumann et al., 1997). In another study, 13 manifest HDGECs were studied with a motor paradigm consisting in finger opposition task at 1.5Hz with their dominant hand. The patients showed reduced activity of the supplementary motor area, lateral premotor cortex, as well as the striatum (Bartenstein et al., 1997). These data demonstrate that, in manifest HDGECs, activation of areas involved in the programming of movement is impaired. This could be the effect of an impaired frontostriatal circuit, as part of a more general corticobasal ganglion-thalamocortical loop (Lawrence et al., 1998), which might be dopaminergic in origin (Bäckman et al., 1997). Moreover, this is consistent with the finding of a correlation between frontal volumetric loss and cognitive decline in HD (Bäckman et al., 1997). More recently, Lepron and colleagues used a word generation task in which manifest HDGECs listened to a noun and were instructed to respond with a semantically related

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Dopaminergic system

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noun. This task activated several cortical regions, such as the anterior cingulate, the left inferior frontal and temporal gyrus, and the left supramarginal gyrus (Lepron et al., 2009). The patients had an impaired activation of the left inferior temporal gyrus that inversely correlated with the increasing difficulty of the semantic task. Additionally, while in healthy controls the neuronal activity in the anterior cingulate and the inferior frontal gyri correlated with the difficulty of the task, this was not present in HD. These findings provide further evidence on the disruption of frontostriatal connections in HD, which might account for the increase in reaction time of these patients. The impaired activation of the left inferior temporal gyrus in manifest HDGECs was partially compensated by an activation of the left supramarginal gyrus, implicated in the phonological loop activity, and of the right inferior frontal gyrus, important in effortful retrieval processes (Lepron et al., 2009).

Dopaminergic system The striatal medium spiny neurons contain, in roughly equal proportion, postsynaptic D1 and D2 dopaminergic receptors, which mediate both the direct and indirect dopaminergic pathways (Gagnon et al., 2017). It has been postulated that degeneration of dopaminergic neurons in HD is pivotal regarding the onset of key motor and cognitive symptoms. For this reason, PET research in HD has concentrated mainly on the study of the postsynaptic dopaminergic system pathology, with fewer studies focusing on pre-synaptic dysfunction. The D1 receptor can be studied, with PET imaging, using the radiotracer [11C]SCH23390, a potent D1 receptor antagonist (DeJesus et al., 1989). [11C]Raclopride and [11C]FLB457 are two specific PET ligands for the D2/D3 receptors. [11C]FLB457 has high signal-to-noise ratio than [11C]raclopride for extrastriatal D2/D3 receptors and also shows low affinity for D4 receptors. On the other hand, [11C]FLB457 does not have suitable binding equilibrium in the striatum within the time frame of a PET measurement (Olsson & Farde, 2001). For this reason, [11C]raclopride has been more used for the study of striatal dopaminergic dysfunction, and [11C]FLB457 has found application specifically in the study of the postsynaptic dopaminergic system outside the striatum. Among presynaptic dopaminergic PET tracers, [11C]DTBZ is a PET radioligand that selectively binds to the vesicular monoamine transporter 2 (VMAT2), which transports dopamine into synaptic vesicles in the presynaptic terminal. Therefore, it provides useful information about the activity of the dopaminergic presynaptic terminal. Previous studies, employing [18F]DOPA PET, have aimed to test the level of activity of the aromatic L-amino acid decarboxylase (AADC) enzyme in a limited number of HD cases. These studies have provided inconclusive results showing either decreased levels (Martin & Hayden, 1987) or unaffected levels (Leenders et al., 1986) of dopaminergic synthesis and have since been discontinued. Sedvall and colleagues employed [11C]SCH23390 in a small group of five mild to moderate manifest HDGECs and one premanifest HDGECs. They found that the density of striatal D1 receptors was decreased by 75% in manifest HDGECs. The single premanifest HDGEC displayed values in the lower end of the healthy controls’ average (Sedvall et al., 1994). The decreased density of D1 receptors in the striatum was found, in another small study, to correlate with disease duration in manifest HDGECs (Ginovart et al., 1997). Interestingly, in the same study, a significant 23.9% decrease in D1 receptor levels was also found in the temporal

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cortex, which has been interpreted as relevant in view of the prominent cognitive alterations of HD patients. Successive to this finding, two works have sought to correlate D1 receptor pathology and cognitive disturbances in HD. In a first work, the degree of D1 receptor loss in the caudate of five manifest HDGECs correlated with alterations in verbal fluency, and the degree of D1 receptor loss in the temporal cortex correlated with poor performances in the tower of Hanoi test (Bäckman et al., 1997). In the second study, in which a cohort of 17 premanifest HDGECs was added, the levels of [11C]SCH23390 striatal binding, which decreased progressively relative to the expected time to phenoconversion, significantly correlated with performances on spatial span assessment. This finding lends further support about the central role that the disconnection between the striatum and other cortical and subcortical areas in HD exerts in the generation of cognitive decline also prior to phenoconversion (Lawrence et al., 1998). Studies using [11C]raclopride PET in HDGECs showed, similar to D1 deficits, a decrease of the D2 receptors availability in the caudate and putamen of premanifest and manifest HDGECs (Ginovart et al., 1997; Lawrence et al., 1998; Politis et al., 2008; Turjanski et al., 1995). Loss of D2 receptors in the striatum has also been shown to correlate with disease severity. [11C]Raclopride binding potential correlated with disease duration (Ginovart et al., 1997), as well as with poor performances on cognitive tests for letter fluency, pattern recognition, spatial span, and executive function (Lawrence et al., 1998). More recent studies have sought to investigate the importance of loss of D2 receptors in extrastriatal brain areas. Politis and colleagues employed [11C]raclopride in a group of 10 premanifest and 9 manifest HDGECs, who also underwent PET imaging for microglial activation, with the tracer [11C] PK11195 (Politis et al., 2008). They found that [11C]raclopride binding potential was significantly decreased, in both premanifest and manifest participants compared with healthy controls, in the hypothalamus, a critical area for the homeostasis of a number of physiological functions, such as sleep, circadian rhythms, and metabolism, thus extending the range of brain alterations in HD pathology (Politis et al., 2008). The same group published a further work on 16 manifest and 11 premanifest HDGECs. Here, 54.5% of premanifest and 62.5% of manifest HDGECs showed a significant reduction of [11C]raclopride in the cortex, with the highest involvement in the temporal and frontal areas (Pavese et al., 2010). Loss of D2 receptors in the cortex was associated, in participants with similar UHDRS and CAG repeats, with poor performances on attention and executive dysfunction cognitive tests, further spreading the possible pathogenic role of functional loss of postsynaptic dopaminergic D2 receptors in HD (Pavese et al., 2010). Following this, Esmaeilzadeh and colleagues used the extrastriatal specific D2 PET tracer [11C]FLB457 on a population of nine manifest HDGECs and nine healthy controls. Extrastriatal binding potential was not different in patients compared with controls; these results were confirmed after correction for partial volume effect. Moreover, no correlation was found in patients between tracer binding potential in extrastriatal areas and motor and cognitive HD symptoms (Esmaeilzadeh, Farde, et al., 2011). Since D2 receptor loss was a consistent finding in premanifest HDGECs, it was interesting to ascertain whether this event was temporally linked to other structural and functional known alterations of the premanifest stage. Van Oostrom and colleagues studied 27 premanifest HDGECs with [11C]raclopride and [18F]FDG PET imaging, and structural MRI (van Oostrom et al., 2005). They found that striatal decreases in D2 receptor density occurred consistently than reduced glucose metabolism or striatal atrophy in these premanifest

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Neuroinflammation

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HDGECs. Furthermore, decreased striatal D2 receptor density showed the strongest correlation with increased disease burden, therefore indicating that dopaminergic postsynaptic alterations could be a more sensitive indicator of early neuronal impairment in premanifest HDGECs. Besides a hyperkinetic movement disorder, HD can also be characterized by a more akinetic-rigid syndrome. This feature can be present either in advanced, severe cases of HD or in patients with the Westphal juvenile form, carrying very high CAG expansion sized genes. The pathophysiology underlying this parkinsonian syndrome in HD is unclear; however, this sign is generally associated with a presynaptic dopaminergic alteration. It has been hypothesized that advanced disease, or higher genetic burden, can cause a more global dopaminergic deterioration that includes also a presynaptic damage. Bohnen and colleagues have studied six akinetic-rigid and 13 choreic manifest HDGECs with the presynaptic PET tracer [11C]DTBZ. Akinetic-rigid patients had significantly lower binding in the caudate ( 33%), and in the anterior and posterior putamen ( 56% and 75%, respectively), compared with healthy controls (Bohnen et al., 2000). HDGECs with choreic phenotype also showed a 19% loss of VMAT2 concentration in the anterior putamen and 35% in the posterior putamen, compared with healthy controls. When comparing choreic and akinetic-rigid HDGECs, the latter had reduced [11C]DTBZ uptake in the dorsal and ventral putamen, which remained after correcting for atrophy. This finding suggests that HD patients have a disturbance of the nigrostriatal pathology, which is more severe in those with akinetic-rigid phenotype (Bohnen et al., 2000). Patients with akinetic-rigid versus choreic motor features may also differ in terms of postsynaptic dopaminergic dysfunction. In work on postsynaptic D1 and D2 receptors, Turjanski and colleagues noted that the HD patients with akinetic-rigid predominant phenotype showed slightly, nonsignificant, higher degrees of [11C]SCH23390 and [11C] raclopride striatal binding potential loss (Turjanski et al., 1995). These findings signify the presence of an overall more severe dopaminergic functional disease in patients with akinetic-rigid motor features. Additionally, in eight manifest HDGECs, UHDRS bradykinesia motor score was the best predictor of functional capacity and showed strong correlation with [11C]raclopride binding in the putamen, whereas UHDRS chorea scores showed significant correlations with [11C]raclopride binding in the striatum, albeit less strong than for bradykinesia. These data strengthen the concept that bradykinesia in HD may be a direct evidence of a severe pathological state in the striatum, which translates with worse motor and cognitive features, and can be visualized using PET imaging of the D2 receptors (Sánchez-Pernaute et al., 2000).

Neuroinflammation Microglia are the main neuroimmune cells of the brain and represent about 5%e15% of the whole brain cellular population. Microglia maintain the homeostasis of the brain cells by patrolling and intervening as first-line agents in case of local perturbations (Hickman et al., 2018). When this happens, microglia switch suddenly genic expression into production of receptors and proinflammatory cytokines, resulting in a dramatic shapeshifting into an activation state. It is thought that an overactivation of microglia can be neurotoxic to brain cells. In line with this hypothesis, microglia activation has been consistently detected in

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neurodegenerative diseases (Politis et al., 2012). Preclinical studies have demonstrated that mHtt can initiate a local microglial response, resulting in elevated numbers and activated morphological phenotypes (Yang et al., 2017). Moreover, microglia isolated from experimental HD mice models are more reactive to external stimulations than wild-type microglia (Björkqvist et al., 2008). All these points indicate a direct role of neuroinflammation in the pathogenesis of HD. Activated microglia express the surface, 18-kDa, translocator protein (TSPO) (Banati, 2002). TSPO has been used as target for a number of first- and secondgeneration PET tracers, which are able to assess microglia activation in vivo. Pavese and colleagues studied 11 manifest HDGECs with the TSPO tracer [11C]PK11195 and [11C]raclopride PET. They found that patients showed increased [11C]PK11195 uptake in the striatum, which inversely correlated with decreased [11C]raclopride uptake in the same region, as well as with higher UHDRS scores and CAG index scores. In addition to this, increased uptake of [11C]PK11195 was detected in the frontal cortex, anterior cingulate, and insulae (Pavese et al., 2006). The same group extended this design to study a group of 11 premanifest HDGECs and found that premanifest HDGECs were characterized by increased levels of microglia activation in both the striatum and the cerebral cortex (Tai et al., 2007). The comparison between the premanifest HDGECs group and the previously studied cohort of manifest HDGECs showed a nonsignificant trend toward increased [11C]PK11195 cerebral uptake in the manifest group. After removal of a possible outlier, the extent of microglia activation in the striatum correlated with the 5-year probability of phenoconversion (Tai et al., 2007). Further studies have concentrated on specific brain areas known to be critical in several nonmotor symptoms of HD such as sleep, circadian rhythms, and metabolism, such as the hypothalamus. Politis and colleagues found, in both manifest and premanifest HDGECs, a significant increase of [11C]PK11195 in the hypothalamus, which inversely correlated with [11C]raclopride uptake and, in the premanifest group, with increased 10-year probability of phenoconversion (Politis et al., 2008). The inverse relationship between neuroinflammation and loss of D2 receptors was further investigated in a successive study on premanifest HDGECs where an inverse correlation between increased microglial activation and reduced [11C]raclopride binding potential was seen in the sensorimotor and associative striatum, the bed nucleus of the stria terminalis, and the amygdala (Politis et al., 2011). The relationship between central and peripheral inflammation was the object of a further study by Politis and colleagues, where [11C]PK11195 binding, in premanifest HDGECs, was coupled with the quantification of peripheral cytokines (Politis et al., 2015). Increased [11C]PK11195 binding in the somatosensory striatum was associated with higher peripheral levels of Il-1b, IL-8, and TNF-a, molecules associated with increased microglial activity, neuroinflammatory response, and cellular brain damage in HD (Khoshnan et al., 2004). Despite the contribution given by [11C]PK11195 PET for the understanding of neuroinflammation in HD and other neurodegenerative diseaseserelated research, this tracer is not devoid of flaws, such as a significant off-target binding and low signal-to-noise ratio. For this reason, in recent years, a whole group of second-generation PET tracers for TSPO, which overcome the limitations of [11C]PK11195, have been developed. Recently, a first, pilot study was performed on seven manifest and one premanifest HDGECs with the PET tracer [11C]PBR28. Significantly increased TSPO binding was seen in the pallidum and putamen of both the premanifest and the manifest HDGECs, whereas a nonsignificant trend was detected for the thalamus and brainstem (Lois et al., 2018). More recently, Rocha and colleagues

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Phosphodiesterases

431

studied a cohort of six premanifest and six manifest HDGECs with the tracer [11C]ER176, detecting increased uptake in the bilateral pallidum and putamen, compared with controls, which correlated with disease severity (Rocha et al., 2021). While these results confirm the findings previously obtained with [11C]PK11195, further studies on larger sample sizes are warranted to confirm these results.

Phosphodiesterases The phosphodiesterases (PDEs) are dual-substrate intracellular enzymes that degrade phosphodiester bonds of the second messengers cyclic AMP and GMP (cAMP and cGMP), thus modulating signal transduction cascades. Phosphodiesterase 10A (PDE10A) is a striatal-specific isoform of PDE, highly expressed in the medium spiny neurons (Coskran et al., 2006). PDE10A regulates the function of both the direct and indirect dopaminergic output pathways, thus regulating molecular signaling in the corticostriatothalamic circuit and promoting neuronal survival (Nishi et al., 2008). Being closely connected to critical cellular processes of the medium spiny neurons, PDE10A has been regarded as potential marker of neuronal dysfunction. Transgenic HD mouse models showed decreased PDE10A striatal expression and decreased mRNA levels in premanifest stages (Hebb et al., 2004; Hu et al., 2004). PDE10A levels in the experimental mice model are also sensitive to the mHtt load (Häggkvist et al., 2017) and to mHtt changes after pharmacological intervention with zinc finger protein transcription factors (Zeitler et al., 2019). PDE10A inhibition in mice models also improves corticobasal function in HD models, partially reverting brain damage and preventing the emergence of symptoms (Beaumont et al., 2016). For these reasons, PDE10A has emerged as one of the most appealing potential markers of early HD disease process, possibly directly related to cellular dysfunction, as well as a potential therapeutic target for disease modification. Recently, several PET radioligands for PDE10A, namely [18F]JNJ42259152, [18F]MNI-659, and [11C]IMA107, have enabled the in vivo study of PDE10A in HDGECs. In a pilot study, Ahmad and colleagues employed [18F]JNJ42259152 on five manifest HDGECs who showed a 62.5% reduction of PDE10A density in the putamen, and a 70.7% reduction in the caudate, compared with healthy controls (Ahmad et al., 2014). A remarkable reduction of PDE10A density was also detected in another small study on eight manifest HDGECs, using [18F] MNI-659 PET. In this study, a significant correlation between striatal PDE10A loss and UHDRS scores was also found, providing evidence of the possible correlation between the loss of this enzyme and clinical severity in HD (Russell et al., 2014). PDE10A brain loss has been further characterized by two works from our group, using the tracer [11C] IMA107. In the first study, Niccolini and colleagues studied 12 far-from-onset premanifest HDGECs, 90% predicted time to onset of 25 years, and found that the levels of [11C] IMA107 in the caudate, putamen, and pallidum were significantly decreased by 26%e33% compared with healthy controls, and increased by 34.5% in the motor thalamic nuclei (Niccolini et al., 2015). In addition, after the functional parcellation of the striatum according to connectivity to the cortex, and to striatonigral/pallidal internal and pallidal external pathways, it was exhibited that decreases in PDE10A levels were profound in the sensorimotor striatum and in both the direct and indirect dopaminergic pathways (Niccolini et al., 2015).

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FIGURE 15.1

Reduction of (non displaceable) binding potential of the Phosphodiesterase 10A-sensitive PET tracer [11C]IMA107 in the striatum in a subject with far-from-onset, pre-manifest Huntington’s disease gene expansion carrier (right) as opposed to a healthy subject (left). HDGECs: Huntington’s disease Gene Expansion Carriers.

Moreover, correlation analysis revealed that a high uptake ratio between motor thalami and striatopallidal projections correlated with the probability of phenoconversion at 15 years (Niccolini et al., 2015), therefore providing evidence for PDE10A dysfunction as one of the earliest biomarkers of prediction of future phenoconversion in HD. In the second study, loss of extrastriatal PDE10A was seen in the insular cortex and the fusiform gyrus of 12 premanifest HDGECs compared with healthy controls (Wilson et al., 2016). In those participants, loss of [11C]IMA107 uptake in these regions correlated with signal loss in the basal ganglia. No correlations were found with performances on motor, cognitive, or behavioral tests; however, insular cortex and fusiform gyrus are associated with cognitive and behavioral functions; therefore, it can be hypothesized that loss of PDE10A in these regions, in far-from-onset HDGECs, could represent a very early molecular dysfunction, which precedes even subclinical onset of these symptoms (Wilson et al., 2016) (Fig. 15.1).

Opioidergic system Evidence for dysfunction of the opioidergic system in HD stems back for many decades. HD brains lack, in the caudate and putamen, of endogenous opioid substances such as met-enkephalin, substance P, dynorphin A1-8, dynorphin B, and a-neoendorphin (Dawbarn

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GABAergic system

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et al., 1986; Sandyk, 1985). Natural receptors of these substances are the opioid receptors, a class of widely represented G proteinecoupled receptors in the brain, which mediate a great deal of physiological responses, such as analgesia, respiratory depression, sedation, euphoria, anorexia, and pruritus (Nandhu et al., 2010). Opioid receptors are classified into four principal groups: m, d, s, and k receptors. There is evidence of subtle and heterogenous early decreases in the density of these receptors in the striatum of HD, in experimental mice models (Kennedy et al., 2005) and in premanifest HDGECs (Albin et al., 1992), which may be responsible for an active cellular response during the early stages of this disease. [11C]Diprenorphine, a nonspecific PET ligand that is a partial agonist of m, d, and k opioid receptors, has been employed by Weeks and colleagues to study a group of five manifest HDGECs and nine healthy controls (Weeks, Cunningham, et al., 1997). A significant reduction in tracer binding was observed in the caudate and putamen, and in the anterior cingulate of manifest HDGECs, which was confirmed using different analytical approaches. Voxel-wise statistical parametric mapping also detected an increase of thalamic and prefrontal cortex opioid receptor binding, which may be explained as an upregulatory measure. However, the small sample size of the group studied and the nonspecific nature of [11C]Diprenorphine cannot yield definitive conclusions about the biological significance of these findings in HD (Weeks, Cunningham, et al., 1997).

GABAergic system Striatal medium spiny neurons, the single cellular population mostly affected in HD, are GABAergic neurons. GABA (g-aminobutyric acid) is the main inhibitory neurotransmitter of the central nervous system. There are two principal subtypes of GABA receptors: GABAA and GABAB. GABAA receptors are ionotropic receptors that mediate and modulate, through its many isoforms, the vast majority of the postsynaptic inhibitory transmission across brain neurons, including the striatum. Postmortem studies in human HD brains have demonstrated a significant decrease of GABAA receptors in the caudate and putamen, and an increase in the cerebellum, frontal cortex, and external pallidus (Garret et al., 2018). The increase of GABAA receptors in the external pallidus and the frontal cortex can be explained by an upregulatory response to a net decrease of GABA neurotransmitter, found in postmortem HD brains (Perry et al., 1973; Storey & Beal, 1993). [11C]Flumazenil is a nonesubtype-selective antagonist of benzodiazepines that has been developed as PET tracer to quantify the density of GABA receptors in the brain, and as such has been used for the study of the pathology of GABAergic neurons in HD. Holthoff and colleagues studied six manifest HDGECs with [11C]flumazenil PET, and glucose metabolism with [18F]FDG PET, and found that the [11C]flumazenil K1 was significantly decreased by 26% in the caudate, while showing a nonsignificant decrease of 9% in the putamen and slight increases in other brain regions (Holthoff et al., 1993). The corresponding decrease of [18F]FDG in the caudate and putamen was higher than that of [11C]flumazenil, suggesting that GABA pathology and glucose metabolism do not follow similar time courses in HD pathology (Holthoff et al., 1993). A few years later, Kunig and colleagues studied a larger group of 13 premanifest and 10 manifest HDGECs with [11C]flumazenil, [18F]FDG, and [11C]raclopride (Künig et al., 2000). They detected a reduction of GABA receptors in the caudate, but not

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in the putamen of manifest HDGECs, compared with healthy controls despite observing a significant reduction of both glucose metabolism and D2 receptors in the caudate and in the putamen. In premanifest HDGECs, [11C]flumazenil caudate levels were normal in the absence of concomitant dopaminergic D2 pathology and only slightly decreased in presence of D2 pathology (Künig et al., 2000). These findings suggest ongoing compensatory mechanisms taking place in striatal GABAergic neurons in the premanifest stage, which are lacking in the manifest HD. Overall, these studies indicate that, despite the involvement of GABAergic medium spiny neurons in HD, functional loss is evident only in the manifest stage, when other metabolic systems, such as the dopaminergic system, have failed already. These results, however, need to be confirmed in larger studies with longitudinal observations.

Cannabinoid system The endocannabinoid system comprises the cannabinoid type 1 (CB1) and the cannabinoid type 2 (CB2) receptors. CB1 receptors belong to the G proteinecoupled family and are widely expressed in the central nervous system, and in the basal ganglia, where they overlook the process of inhibition of neurotransmission (Kendall & Yudowski, 2016). Here, CB1 receptors colocalize with D1 and D2 receptors in striatal medium spiny neurons and contribute toward the modulation of postsynaptic neurotransmission, in particular for dopaminergic- and glutamatergic-mediated signaling (Glass et al., 1997). Postmortem studies in HD have revealed that CB1 receptors are markedly loss early in HD pathophysiology (Glass et al., 2000). Furthermore, studies on experimental HD mice models have shown that striatal tissues lack CB1 receptors mRNA (Kuhn et al., 2007; Lastres-Becker et al., 2002), further highlighting the possible critical role of cannabinoid system dysfunction in the generation of HD-related pathology and clinical symptomatology. [18F]MK9470 is a high-selective, high-affinity PET ligand for the in vivo quantification of the regional concentration of CB1 receptors (Burns et al., 2007). Van Laere and collaborators studied 20 manifest HDGECs, using [18F]MK9470 PET, showing a 15%e25% significant and widespread decrease of CB1 receptors compared with healthy controls in all gray matter regions. They found a slightly significant inverse correlation between CB1 in the frontal cortex and CAG age product in HDGECs (Van Laere et al., 2010). The same group extended the use of [18F]MK9470 PET to a cohort of 15 premanifest HDGECs (Ceccarini et al., 2019). After partial volume correction, lower levels of CB1 receptors were found in the superior and middle prefrontal gyrus, superior parietal gyrus, precentral, and posterior gyrus of premanifest HDGECs. [18F]MK9470 uptake in the frontal and cingulate cortex areas correlated with Person Behavioral Assessment total, apathy, and depression scores, providing a possible molecular basis for the pathophysiology of these early and common neuropsychiatric features of HD (Ceccarini et al., 2019). Further studies investigating the cannabinoid system in premanifest HDGECs are warranted to further elucidate the role of CB1 receptors in disease pathophysiology and as a potential marker of disease progression.

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Adenosinergic system Adenosine plays a critical role in the modulation of dopaminergic and glutamatergic neurotransmission in the striatum, sparing precious energy for the homeostasis of the brain cell (Ferré et al., 2007; Fredholm, 2007). Adenosine exerts its modulatory function through four main receptors: A1, A2A, A2B, and A3. A1 receptors are the most represented in the brain, predominately found within the striatum, cortex, cerebellum, and hippocampus, at both the presynaptic and postsynaptic levels (Popoli et al., 2007). In the striatum, A1 receptors localize in the dynorphinergic spiny neurons, which also coexpress D1 receptors (Cybulska et al., 2020). Another important adenosinergic receptor is the A2A receptor. This receptor is found both presynaptically and postsynaptically predominately in enkephalinergic projecting spiny neurons coexpressing also D2 receptors and controls cellular activities involved in learning (Popoli et al., 2007). Preclinical studies have demonstrated that adenosinergic receptors are lost early in the HD disease course (Glass et al., 2000). Adenosinergic receptors can represent a potential intriguing target to modulate motor symptoms from basal ganglia degeneration and promote neuroprotection in neurodegenerative diseases. [18F]CPFPX is a potent xanthine-based antagonist of the adenosine A1 receptor, with strong affinity over A2A receptors (Bauer et al., 2003). There is only one PET study using this radioligand in HDGECs. In this cross-sectional study, Matusch and colleagues investigated the binding of [18F]CPFPX in a population of premanifest HDGECs, subdivided into far-from-onset, mean predicted time of onset of 17.6 years, and near-onset, mean predicted time of onset 8.5 years, as well as in manifest HDGECs (Matusch et al., 2014). The uptake of [18F]CPFPX in the whole brain was increased by 13% in the far-from-onset HDGECs compared with healthy controls. By contrast, uptake in near-onset HDGECs was almost identical to that of healthy controls ( 1%) and in manifest HDGECs was significantly decreased ( 19% in the striatum) in which the region with the lowest binding potential was the caudate, followed by the amygdala and the frontal cortex. Additionally, in premanifest HDGECs, here was a strong correlation between decreased A1 binding potential and years to predicted phenoconversion (Matusch et al., 2014). Although preliminary, and obtained with a small sample size, these results point toward a different expression of A1 receptors according to the stage of HD and may represent a useful tool to aid in the characterization of different preclinical stages of HD.

Synaptic pathology Synaptic vesicle glycoprotein 2A (SV2A) is an essential vesicle transmembrane protein widely expressed in all synapses in the brain (Bajjalieh et al., 1992, 1994). [11C]UCB-J is a selective PET radioligand for quantitative imaging of SV2A activity in the living brain (Mansur et al., 2020; Nabulsi et al., 2016), which has found wide applications in recent years for the study of aging and a number of other neurodegenerative conditions (Andersen et al., 2021; Chen et al., 2018; Holland et al., 2020; Wilson et al., 2020). In HD, a very recent cross-sectional PET study on 18 HDGECs (7 premanifest and 11 manifest) using [11C]UCB-J as a marker of synaptic pathology and [18F]FDG as a marker of brain metabolism demonstrated significant loss of SV2A binding in the putamen, caudate, pallidum, cerebellum, parietal, temporal, and frontal cortex, whereas reduced [18F]FDG uptake

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was restricted to caudate and putamen, with the premanifest cohort showing significant reductions in putamen and caudate only, and a correlation, in the total HD group, between SV2A loss in the putamen and motor impairment (Delva et al., 2021). SV2A molecular imaging and synaptic loss will constitute a novel and relevant molecular target to characterize early pathology in HD and to track disease progression in future studies.

Use of molecular imaging to track disease progression in Huntington’s disease Despite an increasing volume of evidence to suggest that multisystem dysfunction occurs in HD, which could be detected across all HD stages at multiple levels such as clinical, blood, CSF, and imaging, there is a compelling need of a reliable disease biomarker in both the clinical and research settings. In particular, the importance of a biomarker resides in its ability to track the progression through premanifest and manifest disease stages, so that it may be possible to predict, at any given point, the staging and the future outcome. Furthermore, it is important that a biomarker is strongly associated with a reliable endpoint so to represent a surrogate outcome measure for clinical trials on therapeutic agents targeting disease progression. PET molecular imaging has high potential to unveil a progression biomarker for its ability to spot subclinical changes, which are directly related to pathophysiological alterations and linked to disease activity. Therefore, PET imaging could offer a biomarker, which is liable to change following targeted therapeutic intervention. A number of PET imaging studies have been carried out in premanifest and manifest HDGECs aiming to identify disease progression biomarkers. Here, we will describe and compare the results from followup PET studies in HD using different molecular tracers. The brain regions that have been studied the most, to spot changes over time of HD pathology, are the striatal nuclei and the pallidus. In a first follow-up PET study, Grafton and colleagues tested 11 gene-positive and eight gene-negative at-risk individuals for HD (Grafton et al., 1992). At 42-month follow-up, they found that the gene-positive group had a significant 3.1% annual decrease of [18F]FDG uptake in the caudate and a near-significant 1.9% decrease in the putamen. They also found a discrepancy between functional PET and structural MRI measures of progressive degeneration, suggesting that these two phenomena could be independent (Grafton et al., 1992). A group of 10 premanifest HDGECs also underwent serial [18F]FDG PET scans, coupled with [11C]raclopride PET scans (A Antonini et al., 1996). It was found that the annualized rate of decrease in [11C]raclopride binding potential in the striatum was 6.3%, significantly higher than the 2.3% per year found with [18F]FDG, suggesting that, in the premanifest stage, D2 receptor degeneration could be a more sensitive marker of disease progression (Antonini et al., 1996). The degree of reduction in D2 receptor density is also in line with the insights coming from neuropathological studies (Furtado et al., 1996) and appears to be correlated with CAG repeat length (Antonini et al., 1998) and with neuropsychological tests for executive functions (Pavese et al., 2003). However, a more recent study on 18 premanifest HDGECs followed up with [11C]raclopride for 2 years showed a nonsignificant 2.6% annual rate of progression of putaminal D2 receptor loss, and the rate of decline did not correlate with the predicted distance to phenoconversion. Moreover, three HDGECs who developed clinical signs suggestive of phenoconversion did not show higher rates of D2 putaminal loss (van Oostrom et al., 2009). Loss of PDE10A has also been studied

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as a potential progression biomarker in HD. A first pilot study on six manifest and two premanifest HDGECs, with the PET tracer [18F]MNI659, showed a remarkable 16.6% annual loss of binding potential in the caudate and a significant loss of 6.9% and 5.8% in the putamen and pallidum, respectively (Russell et al., 2016). A subsequent multimodal study in which premanifest and manifest HDGECs performed two [18F]MNI659 and [11C]raclopride PET scans 18e28 months apart demonstrated annual rates of [18F]MNI659 signal loss in the caudate, putamen, and pallidum of 5.9%, 4.4%, and 4.3%, respectively, with only the caudate remaining significant after Bonferroni’s correction (Fazio et al., 2020). Additionally, the rate of progression for [18F]MNI659 between early and late premanifest, and between late premanifest and early manifest HDGECs, was higher than the rate of progression for [11C]raclopride. These findings suggest that the loss of striatal PDE10A enzyme could represent a more sensitive and earlier biomarker for progression in premanifest stages, compared with D2 receptor loss. A number of molecular imaging studies have demonstrated that progression of HD pathology is nonlinear. Antonini and colleagues found, in a group of 10 premanifest HDGECs and 8 manifest HDGECs who underwent [11C]raclopride PET, that the estimated percentage loss of D2 receptors in the striatum is a function of the CAG repeat length in the two populations of HDGECs (Antonini et al., 1998). However, the two populations follow two different trajectories. Premanifest HDGECs show a steeper trajectory, as a sign of faster progression, while manifest HDGECs show a milder trajectory, as sign of a slower progression after phenoconversion (Antonini et al., 1998). The following year, Andrews and colleagues found that the annual rate of striatal decrease in [11C]SCH23390 and [11C]raclopride binding in premanifest HDGECs followed two distinct paces: a slow one, where the rate of decrease for both D1 and D2 receptors was approximately 3% or less per year, and a fast one, where the rate of decrease was approximately 4.5% and 6.5% for D1 and D2 receptors, respectively, with individual peak of 22% (Andrews et al., 1999). It could be hypothesized that a faster progression may be a harbinger of HDGECs closer to phenoconversion or a worsening of motor symptoms. This hypothesis has been tested in a few studies using [18F]FDG PET as imaging marker. In one study, 43 premanifest HDGECs have been studied with two [18F]FDG PET scans 5 years apart (Ciarmiello et al., 2012). After 5 years, 26 of the HDGECs who were premanifest at baseline had started to show mild signs and symptoms suggestive of phenoconversion to manifest HD. All the phenoconverted HDGECs had [18F]FDG uptake values in the caudate, which were lower compared with HDGECs who did not show signs of phenoconversion. An ROC analysis established a threshold value of caudate glucose uptake of 1.0493 as distinctive between those who had phenoconverted and those who did not, with an area under the curve of 0.94, independently of the mutation size (Ciarmiello et al., 2012). A successive study sought to understand whether regional alterations in brain glucose metabolism could indicate a higher probability of future phenoconversion. Herben-Dekker and colleagues studied 22 premanifest HDGECs with longitudinal [18F]FDG PET imaging over 2 years (Herben-Dekker et al., 2014). They found that low values of [18F]FDG uptake in the putamen, together with poor clinical cognitive measures of psychomotor speed as predictive of future onset of clinical HD symptoms, accounted for about 66% of the variance in the 5year probability of motor onset. Seventeen participants were followed up clinically for an additional 8 years. At the end of the 8-year follow-up, 8 of the 17 premanifest HDGECs showed motor manifestations compatible with manifest HD. All those who phenoconverted had, 8 years before, [18F]FDG putaminal uptake values lower than healthy controls, whereas

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HDGECs who did not phenoconversion showed values of putaminal glucose uptake within normal range (Herben-Dekker et al., 2014). These findings underline the potential value of markers assessing striatal cellular functional loss, reflecting the loss of compensatory mechanisms, to predict phenoconversion in HD. Very recently, a large multimodal study using [18F]MNI659 and [11C]raclopride PET, and structural MRI, in 21 premanifest HDGECs divided into far onset and near onset, and 14 manifest HDGECs divided in stage 1 and stage 2, showed that loss of striatal PDE10A appeared earlier and progressed faster than both structural atrophy and D2 loss in the same area (Fazio et al., 2020). Furthermore, loss of PDE10A was more rapid between early and late premanifest stages, and between late premanifest and early manifest HD (Fazio et al., 2020). These findings indicate that striatal PDE10A loss may be a good biomarker candidate to track HD disease progression since the early premanifest stages and may be a candidate surrogate outcome measure for future clinical trials on HD. A single study has analyzed the potential of spatial covariance pattern analysis as a tool to predict disease progression in premanifest HDGECs. In 12 premanifest HDGECs, followed up for 7 years with serial [18F]FDG PET, Tang and colleagues found a linear increase of the HD-related spatial covariance network, not influenced by intercurrent phenoconversion (Tang et al., 2013). Compared with other imaging variables, such as structural MRI, [11C] raclopride D2 PET, and single region-of-interest [18F]FDG PET progression measures, the HD-related network demonstrated the fastest rates of progression and the highest expression at the time of development of clinical symptoms. The results of this study suggest therefore that [18F]FDG and spatial covariance network analysis may characterize premanifest HDGECs and providing an imaging signature to predict phenoconversion (Tang et al., 2013). Molecular markers of HD progression have also been investigated outside the basal ganglia. Ciarmiello and coworkers found that both premanifest and manifest HDGECs exhibited a progressive loss of glucose metabolism in the frontal, parietal, and temporal lobe and, in premanifest HDGECs, this preceded evidence of gray matter loss (Ciarmiello et al., 2006). Pavese and colleagues tested 12 manifest HDGECs with [11C]raclopride PET, 29 months apart, reporting a progressive loss of D2 receptors in the frontal and temporal cortex, and amygdala (Pavese et al., 2003). Future longitudinal studies, with robust study designs, are required to ascertain the potential of these brain regions as biomarkers of disease progression in HD.

Use of molecular imaging as outcome measure in Huntington’s disease trials Despite numerous efforts by the scientific community, there is still no disease-modifying therapy for HD. The past years have seen a high number of pharmacological and nonpharmacological clinical trials, including cellular transplantation, which have unfortunately shown only mixed success. PET imaging has been applied as an outcome measure in a few trials to assess the efficacy of the therapeutic agents employed. In a small trial using riluzole, a substance that reduces excitotoxicity, the 11 manifest HDGECs taking placebo showed significantly more pronounced reduction of [18F]FDG PET uptake in all cortical areas, compared with 12 manifest HDGECs taking the study drug (Squitieri et al., 2009). Moreover, in the placebo arm, the decrease of [18F]FDG uptake in the frontal, parietal, and occipital

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cortices correlated with UHDRS part I motor scores, whereas glucose metabolism in frontal and temporal cortices correlated with UHDRS part III behavioral scores. These findings indicate that [18F]FDG PET is a possible valid examination to test disease progression in HD clinical trials (Squitieri et al., 2009). In another small trial on eight manifest HDGECs who took pridopidine, a stabilizer of central dopaminergic activity, for 14 days, [18F]FDG PET was employed to assess alterations in neuronal activity that could precede a clinical response to the study drug (Esmaeilzadeh, Kullingsjö, et al., 2011). They found that the use of pridopidine was associated with an increase in metabolic activity in the precuneus, left superior temporal gyrus, and left middle frontal gyrus, as seen with statistical parametric mapping. In addition to this, principal component analysis revealed a strengthening of the correlation between imaging and clinical scores after treatment as a surrogate measure of the direct effect of pridopidine on the HD-related metabolic pattern (Esmaeilzadeh, Kullingsjö, et al., 2011). Finally, [18F]FDG PET was used to test the effect of memantine, a specific NMDA-receptor antagonist, on brain metabolism in a small open-label pilot study on four manifest HDGECs. Visual analysis indicated a relative improvement, in all four manifest HDGECs, in cortical metabolism compared with metabolism in the thalamus (Hjermind et al., 2011). Restorative therapy with stem cells has been hypothesized as a possible effective therapy in several neurodegenerative disorders, including HD (Lindvall et al., 2004). Implantation of fetal striatal fibroblasts in the murine model of HD has given promising results, showing a reconstitution of neural circuits as well as clinical improvements on motor (Kendall et al., 1998) and cognitive measures (Palfi et al., 1998). In HD, it is thought that stem cells obtained from the lateral and medial ganglionic eminence grafts are able to differentiate into medium spiny neuron cell lineage and, after transplantation, replace the function of the degenerated neurons (Precious et al., 2017). PET molecular imaging has been useful in determining the success of the transplantation procedure, as well as in monitoring over time the progress of the therapy (de Natale et al., 2018). Three studies have employed dopaminergic [11C]SCH23390 and [11C]raclopride PET imaging in small open-label trials on stem cell transplantation in HD. The limited data available do not allow conclusion about a beneficial or detrimental effect of stem cells transplantation in the postsynaptic dopaminergic function of advanced HDGECs. This is partly because in a small trial conducted on two patients, transplantation yielded opposite outcomes showing both increases and decreases in [11C]raclopride striatal binding (Reuter et al., 2008), and partly because of the design of some trials that gave priority to safety issues over the treatment efficacy (Barker et al., 2013; Rosser et al., 2002). In one trial, however, conducted on seven HDGECs followed up for 2 years after the surgical procedure, it was seen that the density of postsynaptic D1 receptors was stable at preoperative levels at both 12- and 24-month follow-ups, whereas striatal D2 receptor density declined by 14%e24%, despite the presence of stable global metabolic levels (Furtado et al., 2005; Hauser et al., 2002). These findings suggest that functional adaptation of the striatal dopaminergic system after transplantation may follow different timeframes according to D1 and D2 receptor systems. [18F]FDG PET has also been employed as an outcome measure for stem cell transplantation studies in HD. Five manifest HDGECs received bilateral caudate and putamen transplantation surgery in two sessions, 12 months apart, and were followed up for an additional 12 months after the final surgery. Three responder HDGECs showed an increase of brain metabolism in striatal areas consistent with MRI structural areas of grafting (Bachoud-Lévi

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et al., 2000). Increases in striatal metabolism were coupled with modifications of cortical metabolism, which was interpreted as the effect of the reconstruction of the physiological corticostriatothalamocortical loop, brought about by the graft tissue (Gaura et al., 2004). After a 6-year follow-up, [18F]FDG average uptake levels were found to have declined by only 7%, which is the average degree of metabolic decline that is normally expected over only 1 year in HD (Kremer et al., 1999). On the other hand, two nonresponders demonstrated a progressive reduction in [18F]FDG striatal uptake (Bachoud-Lévi et al., 2000). Similar successful results were obtained in another study on 10 HDGECs grafted bilaterally and followed up for 4 years. At 24 months, all patients showed an increase of glucose metabolism in cortical and subcortical areas, which remained stable at the end of the follow-up period (Paganini et al., 2014). By contrast, Krebs and colleagues did not detect, by means of [18F]FDG PET, any sign of survival or activity of the grafts of 10 transplanted manifest HDGECs (Krebs et al., 2011).

Future perspectives of molecular imaging in Huntington’s disease These are exciting times for HD research. There has probably never been so many promising therapies under investigation for this disease, many of which with the ambitious objective to modify the course of the disease (for an update, see Kumar et al., 2020). The development of many of these compounds has been possible thanks to recent advances in the comprehension of the pathobiology of HD, to which molecular imaging has played a central role. PET molecular imaging is a key facilitator for the translation from preclinical studies in animal models to clinical studies in patients. PET has the unique ability to provide multilayer information about the pathophysiology, the pharmacology, and the progression of biological processes leading to disease onset, as well as representing a potential outcome measure for therapeutic interventions (Hargreaves & Rabiner, 2014). It is envisaged, therefore, that PET imaging will provide critical information for target selection and drug development. Although PET imaging has promising potential, further research is required to improve its efficacy and reliability in HD. With this regard, future PET studies, employing selective radiotracers, may shed more light on the molecular pathophysiology of this disease (Table 15.2). Firstly, more longitudinal studies are needed to assess the behavior of some molecular pathways for which data are still not available. Secondly, and related to the former, PET imaging markers should be combined with other biological markers into statistical models that would increase the amount of variance explained for disease progression and age at phenoconversion in premanifest HDGECs. This would have beneficial consequences for the optimization of successful and cost-effective future clinical trials. Finally, novel PET probes should be used to study unexplored pathophysiological pathways in HD with potential to further explain disease pathophysiology and provide novel therapeutic targets (Table 15.2). For example, animal studies have highlighted the possible crucial role of the serotonergic (Yohrling et al., 2002), histaminergic (Goodchild et al., 1999), and glutamatergic (Bertoglio et al., 2020; Verhaeghe et al., 2018) systems, as well as novel targets for astroglial activity (Reynolds et al., 1996). Given the direct relationship between mHtt and disease activity, it is envisioned

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TABLE 15.2 Molecular pathway

441

Potential utility of novel PET tracers in future Huntington’s disease research. Potential radioligand(s) Target 11

Advantages/rationale

Histaminergic system

[ C]MK-8278 H3R [11C] GSK189254

H3R modulates intracellular signal cascades, in the ventral striatum influencing the activity of D1 and D2 receptors.

Serotonergic system

[11C] MDL100907

5-HT2AR

Animal models of HD show a degeneration of the serotonergic system at the early premanifest stage.

Adenosinergic system

[18F]MNI-444

A2AR

A2AR are expressed on the postsynaptic end of striatal MSNs and colocalize with D2R. Animal models show downregulation of A2AR before any significant neuronal loss has occurred.

Glutamatergic system

[11C]ABP688 mGluR5 [11C]AZD9272 [11C]FPEB

MSNs receive diffuse glutamatergic afferent inputs. Mutant huntingtin stimulates the mGluR5, resulting in toxic levels of intracellular calcium.

Microglial activation

[18F]DPA-714 [18F]GE180

Premanifest and manifest HDGECs show increased activation of microglia in striatal and extrastriatal areas.

Imidazoline 2binding sites

[11C]BU99008 I2BS

I2BSs are widely expressed in glial cells throughout the brain and regulate the expression of GFAP.

P2 purinoceptors

P2X7 [11C]JNJ717 [11C]SMW139

Mice models of HD show elevated levels of P2X7 in the synaptic terminals. Inhibition of P2X7 with Brilliant Blue G reduces motor symptoms and rate of neurodegeneration.

Mitochondrial function

[18F]BCPP-EF

MC1

mHtt affects directly mitochondrial function, by impairing respiration through multiple mechanisms. HD patients display a higher frequency of mtDNA mutations.

Tau deposition

[18F]PI-2620 [18F]MK-2640 [18F]RO-948

Tau

Neuropathological observation suggests that up to 60% of examined HD brains show neurofibrillary tangles at Braak stages IeIII.

Amyloid deposition

[18F] Florbetaben

b-Amyloid Advanced HD brains can show deposition of b-amyloid in the cortex.

TSPO

5-HT2AR, 5-hydroxytryptamine (serotonin) type 2A receptor; A2AR, adenosine type 2A receptor; D2, dopamine type 2 receptor; GFAP, glial fibrillary acidic protein; H3R, histamine type 3 receptor; HD, Huntington’s Disease; HDGECs, Huntington’s disease gene expansion carriers; I2BSs, imidazoline 2-binding sites; MC1, mitochondrial complex 1; MSNs, medium spiny neurons; PET, positron emission tomography; P2X7, purinoceptor 7; s-1, sigma 1; SV2A, synaptic vesicle glycoprotein 2A; TSPO, translocator protein.

that the ideal PET molecular tracer for HD would be a tracer targeting mHtt, which would become a key player in the definition of disease progression and as an outcome measure in trials targeting mHtt (Tabrizi, Ghosh, & Leavitt, 2019). Preclinical research has identified the novel PET tracer [11C]CHDI-626, as a potentially suitable tracer to bind mHtt aggregates in vivo, with fast kinetic and metabolism but early, and age- and load-associated increase of signal in HET mice (Bertoglio et al., 2021; Herrmann et al., 2021; Liu et al., 2021). Clinical studies are currently underway to test the suitability of this PET tracer as ground-breaking discovery for HD research.

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Verhaeghe, J., et al. (2018). Noninvasive relative quantification of [11C]ABP688 PET imaging in mice versus an input function measured over an arteriovenous shunt. Frontiers in Neurology, 9. https://doi.org/10.3389/ fneur.2018.00516 Weeks, R. A., Ceballos-Baumann, A., et al. (1997). Cortical control of movement in Huntington’s disease. A PET activation study. Brain: A Journal of Neurology, 120(Pt 9), 1569e1578. https://doi.org/10.1093/brain/120.9.1569 Weeks, R. A., Cunningham, V. J., et al. (1997). 11C-diprenorphine binding in huntington’s disease: A comparison of region of interest analysis with statistical parametric mapping. Journal of Cerebral Blood Flow and Metabolism: Official Journal of the International Society of Cerebral Blood Flow and Metabolism, 17(9), 943e949. https://doi.org/10.1097/ 00004647-199709000-00003 Wexler, N. S., et al. (2004). Venezuelan kindreds reveal that genetic and environmental factors modulate Huntington’s disease age of onset. Proceedings of the National Academy of Sciences of the United States of America, 101(10), 3498e3503. https://doi.org/10.1073/pnas.0308679101 Wilson, H., et al. (2016). Loss of extra-striatal phosphodiesterase 10A expression in early premanifest Huntington’s disease gene carriers. Journal of the Neurological Sciences, 368, 243e248. https://doi.org/10.1016/j.jns.2016.07.033 Wilson, H., et al. (2017). Molecular imaging markers to track Huntington’s disease pathology. Frontiers in Neurology. Frontiers Research Foundation. https://doi.org/10.3389/fneur.2017.00011 Wilson, H., et al. (2020). Mitochondrial complex 1, Sigma 1, and synaptic vesicle 2A in early drug-naive Parkinson’s disease. Movement Disorders. https://doi.org/10.1002/mds.28064 [Preprint]. Yang, H. M., et al. (2017). Microglial activation in the pathogenesis of Huntington’s Disease. Frontiers in Aging Neuroscience, 9(JUN). https://doi.org/10.3389/fnagi.2017.00193 Yohrling, G. J., IV, et al. (2002). Inhibition of tryptophan hydroxylase activity and decreased 5-HT1A receptor binding in a mouse model of Huntington’s disease. Journal of Neurochemistry, 82(6), 1416e1423. https://doi.org/10.1046/ j.1471-4159.2002.01084.x Young, A. B., et al. (1986). PET scan investigations of Huntington’s disease: Cerebral metabolic correlates of neurological features and functional decline. Annals of Neurology, 20(3), 296e303. https://doi.org/10.1002/ ana.410200305 Zeitler, B., et al. (2019). Allele-selective transcriptional repression of mutant HTT for the treatment of Huntington’s disease. Nature Medicine, 25(7), 1131e1142. https://doi.org/10.1038/s41591-019-0478-3

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C H A P T E R

16 Magnetic resonance imaging in Huntington’s disease Christina Belogianni1, a, Heather Wilson2, a, Edoardo Rosario de Natale2 and Marios Politis2 1

Prisma Health - Midland, Richland, University of South Carolina School of Medicine, Department of Neurology Residency Program, Columbia, United States; 2Neurodegeneration Imaging Group and Mireille Gillings Neuroimaging Centre, University of Exeter, London, United Kingdom

Introduction Since 1872, when Huntington’s disease (HD) was first described, significant advances have been made toward understanding the mechanisms underlying disease etiology and pathophysiology. HD, also referred to as Huntington’s chorea, is a dominantly inherited neurodegenerative disease of the central nervous system (CNS) caused by an expansion of a CAG triplet in the HTT gene on chromosome 4, which encodes a protein of unknown function called huntingtin. Patients with HD have 36 or more CAG repeats. Individuals with 36e39 CAG repeats may or may not develop symptoms of HD, while carriers of 40 or more CAG repeats will almost always develop symptoms within their lifetime (Penney et al., 1997). The gradual onset of deficits in behavior, cognition, and movement in HD gene expansion carriers (HDGECs) typically arises in the fourth and fifth decades of their life (Rosenblatt et al., 2012). In approximately 6% of cases, symptomatic onset occurs before 21 years of age predominantly with an akinetic-rigid syndrome, known as the Westphal variant. However, since with each generation the CAG repeat increases in length, symptomatic onset can manifest at a younger age through generations. The fact that genetic testing is available enables the identification of asymptomatic gene carriers, which provides a unique opportunity to study disease pathophysiology years before symptomatic onset. The time to

a

Authors contributed equally.

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16. Magnetic resonance imaging in Huntington’s disease

symptomatic onset can be predicted in premanifest HDGECs using a variant of the survival analysis formula, which combined the subject’s CAG repeat expansion length and age (Langbehn et al., 2004; Paulsen et al., 2008). Furthermore, the CAG age product (CAP) score, which provides an index of the duration and severity of an HDGEC’s exposure to the effects of mutant huntingtin, has been employed to stratify HDGECs into far-from and close-to predicted phenoconversion (Ross et al., 2014a). Subclinical changes are known to precede symptomatic conversion by as many as 15e20 years (Aylward et al., 2011; Paulsen et al., 2008, 2010; Tabrizi et al., 2011). Noninvasive neuroimaging techniques, such as magnetic resonance imaging (MRI), have been employed to study premanifest HDGECs to provide a more complete understanding of the disease course, explore neural changes, and predict the phenoconversion to manifest HD. At a cellular level, mutant huntingtin protein aggregates to form intranuclear inclusions, which subsequently leads to loss of GABAergic medium spiny neurons (MSNs) in the striatum (Han et al., 2010). There is marked neuronal loss in the dorsal striatum, with progressive neuronal loss in cortical regions as the disease advances (Han et al., 2010). However, mechanisms underlying neuronal loss and disease pathophysiology are not fully understood. Advances in neuroimaging techniques can be used as a tool to identify relevant biomarkers, to more accurately stage HDGECs and monitor disease progression, while providing insights into diseases pathophysiology. The role of positron emission tomography (PET) and MRI in foreseeing and monitoring the transition from the premanifest to manifest stage has significantly contributed to a better understanding of this disease and to the more accurate classification of subjects taking part in clinical studies (Pagano et al., 2016; Wilson et al., 2017, 2018; Wilson & Politis, 2018). MRI techniques are noninvasive and allow an objective assessment of changes in vivo that could provide new targets for disease-modifying treatment. MRI has shown sensitivity in detecting altered macro- and microstructural changes, network-connectivity patterns, and iron accumulation within the brains of premanifest and early manifest HDGECs. This chapter summarizes the main results from structural volumetric and diffusion imaging studies and discusses changes in network connectivity demonstrated by resting-state functional MR and arterial spin labeling techniques, magnetization transfer, and magnetic resonance spectroscopy as well as the role of iron concentration detected in the brain parenchyma. Finally, findings from MRI studies will be discussed in the context of biomarkers to track disease progression, and we will highlight the potential role of MRI variables as outcome measures in HD clinical trials.

Structural MRI markers of disease pathology in Huntington’s disease Structural MRI techniques have been employed to study macro- and microstructural changes in vivo in HD. The main categories of structural MRI techniques, which have been utilized in HD, include T1-weighted imaging, diffusion-weighted imaging (DWI), and magnetization transfer-weighted imaging. T1-weighted imaging can provide anatomical data with high resolution and high contrasts between tissue types, such as the gray and white matter, which is advantageous for the study of neuroanatomy and macrostructural changes in vivo. T1-weighted imaging, often referred to as structural volumetric MRI, can be used to

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Structural MRI markers of disease pathology in Huntington’s disease

451

extract quantitative parameters such as volumetric measures, using both regional-based and whole brain voxel-based approaches, and cortical thickness. Furthermore, T1-weighted images frequently aid the analysis of other MRI sequences, such as DWI and fMRI, as well as PET data playing a role in coregistration and normalization processes as well as the delineation and segmentation of regions of interest. DWI techniques measure the random movement of the water molecules in the brain (Johansen-Berg & Rushworth, 2009). Diffusion can be classified as isotropic where molecules can move without restrictions, as seen in cerebrospinal fluid (CSF), or anisotropic wherein the long axis of the fiber the molecules move relatively free compared with the perpendicular axis, as seen within the white matter fibers. DTI has been most prominently employed to date to study HD; however, with the development of novel techniques, such as neurite orientation dispersion and density imaging (NODDI), the investigation of the brain’s microstructural architecture and the relationship with disease pathophysiology is rapidly expanding field of research. Unlike water molecules, a great number of protons are not in liquid phase, rather they are bound inside macromolecules (Alexander et al., 2007). If a strong radiofrequency pulse, away from the resonance frequency of the liquid pool of protons, is applied, it will excite the bound protons, a concept that magnetization transfer-weighted (MT) imaging takes advantage of (Symms et al., 2004). Structural MRI has been employed in both cross-sectional and longitudinal studies revealing macro- and microstructural changes. In this section, we will focus on insights from structural MRI on the pathology underlying HD. Findings from longitudinal studies will be discussed in the context of biomarkers to track disease progression and as outcome measures in HD clinical trials in the last section of this chapter.

Volumetric MRI Some of the earliest volumetric studies, in the 1990s, demonstrated atrophy in the putamen of around 50% and in the caudate of around 30% in manifest HDGECs (Aylward et al., 1996; Harris et al., 1992, 1996). Structural imaging subsequently demonstrated that striatal volumetric loss starts in premanifest stages, long before the onset of motor symptoms in HD (Table 16.1). Volumetric loss in the putamen and caudate has been reported in premanifest HDGECs up to 9 and 11 years, respectively, from predicted symptomatic onset (Aylward et al., 1996, 2004; Paulsen et al., 2008). Furthermore, atrophy of the nucleus accumbens and globus pallidus is also present by the time a diagnosis of HD is made (Aylward et al., 2004). Extending beyond the basal ganglia, HD pathology affects the whole brain, and MRI studies suggest that the cortical thinning parallels with disease burden (Tabrizi et al., 2009a) and motor decline (Ruocco et al., 2006). Manifest HDGECs show cortical thinning predominantly in sensorimotor areas, total frontal lobe, and frontal white matter (WM) volume loss compared with healthy controls (Rosas et al., 2002, 2008) (Aylward et al., 1998). Manifest HDGECs with bradykinesia presentation show more pronounced atrophy in premotor and supplementary motor areas compared with manifest HDGECs with a choreic phenotype (Rosas et al., 2008). Neuroanatomical asymmetries, linked with domain-specific functional decline, have been reported in manifest HDGECs with leftward-biased gray matter (GM) atrophy and relative sparing, or a compensatory role, of the right hemisphere (Minkova et al., 2018), a concept supported by functional MRI studies (Kloppel et al., 2015). Volume loss in total brain tissue, WM, cortical GM and in the thalamus, caudate, and putamen has also

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452 TABLE 16.1

16. Magnetic resonance imaging in Huntington’s disease

Main volumetric MRI studies in Huntington’s disease over the past decade.

References

Subjects

Main findings

(Rosas et al., 2001)

27 symp-HDGECs 24 healthy controls

Reduced volume in the striatum correlated with higher CAG repeat number.

(Aylward, Sparks, et al., 2004)

19 pre-HDGECs 17 pre-HDGECs

Caudate volume reduction longitudinally was significant 11 years before phenoconversion. Putamen volume reduction longitudinally was significant 9 years phenoconversion.

(Ciarmiello et al., 2006)

24 pre-HDGECs 47 symp-HDGECs 54 healthy controls

Reduction in WM and GM volume and larger CSF volume in pre-HDGECs compared with healthy controls; association between decreased WM volume with closer predicted time to phenoconversion.

(Paulsen et al., 2008) PREDICT-HD

438 pre-HDGECS

Decreased striatal volume associated with earlier diagnosis.

(Biglan et al., 2009) PREDICT-HD

733 pre-HDGECs:

Higher motor scores at baseline were associated with increasing striatal volume loss closer to symptomatic onset; with worse e 277 far from onset motor scores accounting for 15% of variance in striatal volume. e 272 mid to onset Worse scores on oculomotor, bradykinesia, and chorea domains e 184 near to onset were associated with smaller striatal volumes.

196 healthy controls (Tabrizi et al., 2009) TRACK-HD

120 pre-HDGECs:

Decreased putaminal volume and signs of caudate atrophy in pre-HDGECs far from onset compared with controls. These e 62 far from onset findings became more prominent in pre-HDGECs near to onset. e 58 near to onset Widespread GM atrophy in the symp-HDGECs stage 1 and 123 symp-HDGECs: symp-HDGECs stage 2. Progressively WM loss among the HDGECs. e 77 stage 1 e 46 stage 2 123 healthy controls

(Paulsen et al., 2010) PREDICT-HD

491 pre-HDGECs:

Decreased volume in cerebral WM, cortical GM, thalamus, caudate, putamen, and total striatum were associated with e 180 far from onset estimated time to diagnosis after controlling for age, gender, and e 184 mid to onset intracranial volume. Total striatum atrophy was associated with e 127 near to onset UHDRS motor total scores; symbol digit total scores; Stroop 166 healthy controls Word, Color, and Interference scores; and verbal fluency scores; but not with UHDRS psychiatric total scores. WH atrophy was also associated with UHDRS motor total score, symbol digit total, Stroop Word, Color, and Interference test but not with verbal fluency or UHDRS psychiatric total scores.

(Hobs et al., 2010)

17 pre-HDGECs 26 symp-HDGECs 13 healthy controls

Increased GM atrophy in caudate, putamen, thalamus, nucleus accumbens in symp-HDGECs compared with controls. Increased WM, cerebral, cerebellar, corpus callosum (splenium) atrophy in early symp-HDGECs compared with controls. Pre-HDGECs compared with controls showed no significant changes.

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Structural MRI markers of disease pathology in Huntington’s disease

TABLE 16.1

Main volumetric MRI studies in Huntington’s disease over the past decade.dcont’d

References

Subjects

(Aylward et al., 2011) PREDICT-HD

211 pre-HDGECs:

Main findings

Near-to-onset and mid-to-onset pre-HDGECs demonstrated faster rate of atrophy in the total brain, striatum, and cerebral e 82 far from onset white matter (particularly in the frontal lobe). e 73 mid to onset e 56 near to onset

60 healthy controls (Tabrizi et al., 2011) TRACK-HD

116 pre-HDGECs 114 symp-HDGECs 115 healthy controls

Whole-brain atrophy rates were 0.2% per year higher in preHDGECs and 0.6% in symp-HDGECs. Caudate atrophy rates were 1.4% per year higher in pre-HDGECs and 2.9% in sympHDGECs.

(Tabrizi et al., 2012) TRACK-HD

117 pre-HDGECs 116 symp-HDGECs 116 healthy control

WM, GM, whole-brain atrophy in pre-HDGECs, and sympHDGECs compared with controls. Effect sizes for atrophy rates in the caudate and WM in sympHDGECs are bigger than controls.

(Tabrizi et al., 2013) TRACK-HD

Decrease putamen, striatum, caudate, GM, WM volume in preHDHECs far from and near to onset, as well as in sympe 58 far from onset HDGECs stage 1 and stage 2 compared with healthy controls. e 46 near to onset Whole-brain atrophy in pre-HDGECs near to onset, sympHDGECs stage 1 and stage 2 compared with healthy controls. 97 symp-HDGECs: 104 pre-HDGECs:

e 66 stage 1 e 31 stage 2 97 healthy controls (Long et al., 2015) PREDICT-HD

1078 pre-HDGECs

Baseline UHDRS total motor scores were the best predictor of motor symptom onset, followed by putamen volume loss, diagnostic confidence level, speeded tapping, paced tapping, caudate volume loss, and CAG expansion.

(Misiura et al., 2017) PREDICT-HD

984 pre-HDGECs

Cluster analysis revealed that reduced caudate and putamen volumes were related to motor symptom severity, cognitive control, and verbal leaning, after controlling for age and CAG repeat length.

(Baake et al., 2018) TRACK-HD

91 pre-HDGECs:

Thalamic atrophy was associated with higher probability of the presence of apathy in combined cohort of pre- and sympe 52 far from onset HDGECs at baseline. e 39 near to onset However, no association was found between subcortical atrophy and increasing apathy over a 2-year follow-up period. 80 symp-HDGECs: e 50 stage 1 e 30 stage 2

(Wijeratne et al., 2018)

120 pre-HDGECs from TRACK-HD 118 symp-HDGECs

Probability event-based modeling demonstrated regional volumetric loss occurred in the order: putamen, caudate, pallidum, insula white matter, nonventricular CSF, amygdala, optic chiasm, third ventricle, posterior insula, and basal (Continued)

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454 TABLE 16.1

16. Magnetic resonance imaging in Huntington’s disease

Main volumetric MRI studies in Huntington’s disease over the past decade.dcont’d

References

Subjects

Main findings

from TRACK-HD 119 healthy controls

forebrain. The temporal progression of volumetric loss was able to stratify premanifest HDGECs according to their predicted time to phenoconversion. Longitudinal analysis revealed cortical thinning in the superior frontal lobe and decreased subcortical volume in the caudate, putamen, and thalamus over 16 months. Progressive volumetric loss in the caudate correlated with motor decline and decreasing functional capacity. Progressive atrophy in the left frontal and right paracentral correlated with progressive cognitive decline.

(Ramirez-Garcia et al., 2020)

17 symp-HDGECs 17 healthy controls

(Wijeratne et al., 2020) IMAGE-HD, PREDICTHD, and TRACK-HD

332 pre-HDGECs 107 symp-HDGECs 185 healthy controls

Combining data sets, at 3 time points, from the PREDICT-HD, TRACK-HD, and IMAGE-HD studies, the caudate volume showed the highest power and lowest number of participants (n ¼ 661) to capture a treatment effect of 20%; followed by the pallidum (n ¼ 687), nonventricular CSF (n ¼ 939), insula white matter (n ¼ 1445), and the putamen (n ¼ 1650). Structural volumetric imaging markers provided greater power than standard clinical markers.

(Furlong et al., 2020) IMAGE-HD

34 pre-HDGECs 29 symp-HDGECs 26 healthy controls

Loss of thalamic volume in symp-HDGECs compared with preHDGEC and healthy controls. In symp-HDGECs, thalamic atrophy predicted neurocognitive and motor dysfunction.

(Bartlett et al., 2020)

27 pre-HDGECs:

Loss of hypothalamus GM volume was attenuated in 18 preHDGECs who undertook multidisciplinary rehabilitation therapy (intervention group) over 9 months compared with 11 preHDGECs receiving no intervention (control group). Serum BDNF levels were maintained in the intervention group, while the control group showed decreased BDNF levels.

e 18 intervention group e 11 control group

BDNF, brain-derived neurotrophic factor; GM, gray matter; pre-HDGECs, premanifest HDGECs; symp-HDGECs, symptomatic HDGECs; WM, white matter; HDGECs, Huntington’s disease gene expansion carriers; UHDRS, Unified Huntington’s Disease Rating Scale.

been reported in premanifest HDGECs, compared with healthy controls, with total striatal volume showing the largest differences (Aylward et al., 2011; Paulsen et al., 2006, 2008). Cortical atrophy in premanifest HDGECs (Nopoulos et al., 2010; Tabrizi et al., 2009a), and early manifest HDGECs (Tabrizi et al., 2009a), validates the concept that HD affects the entire brain and is in line with postmortem studies showing the prominent aggregation of Huntingtin protein aggregates in cortical neurons (Sapp et al., 1997). However, in premanifest HDGECs, cortical GM atrophy occurs after striatal atrophy and is often less severe. In 1996, volumetric loss of the basal ganglia was first associated with years to predicted symptomatic onset (Aylward et al., 1996). Subsequent studies identified that striatal volume loss was associated with CAG repeat length, predicted time to symptomatic onset, age of onset, and disease duration (Aylward et al., 1996; Hobbs, Barnes, et al., 2010; Rosas et al., 2001, 2003). These findings indicate that striatal atrophy likely starts many years before a diagnosis of HD is made and could represent a biomarker to predict symptomatic onset, and to track

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Structural MRI markers of disease pathology in Huntington’s disease

455

symptomatology in HDGECs. A number of studies, including PREDICT-HD (Aylward et al., 2011; Biglan et al., 2009; Paulsen et al., 2008, 2010) and TRACK-HD (Tabrizi et al., 2009a, 2012a), have investigated the link between structural volumetric changes and clinical symptoms including motor, cognitive, and neuropsychiatric symptoms. Within the striatum, volumetric loss in the putamen has been associated with motor impairments, assessed with the Unified HD Rating Scale (UHDRS) total motor scores in manifest HDGECs (Harris et al., 1992, 1996). Atrophy in the caudate has been associated with global cognitive impairment, assessed using the mini mental state examination (MMSE) (Harris et al., 1996), as well as deficits in executive dysfunction and emotion recognition (Henley et al., 2008; Hennenlotter et al., 2004; Kipps et al., 2007). In premanifest HDGECs, from the TRACK-HD study, volumetric loss in the thalamus has been associated with a higher probability of apathy; however, no association was found with the severity of apathy over a 2-year follow-up period (Baake et al., 2018). Recently, using cohorts from the IMAGE-HD study, thalamic volumetric loss in manifest HDGECs, compared with premanifest HDGECs and healthy controls, has been shown to predict neurocognitive and motor dysfunction (Furlong et al., 2020). In manifest HDGECs, worse cognitive performance and executive dysfunction have also been associated with volumetric loss in the frontal cortex, insula, and WM (Beglinger et al., 2005; Kassubek et al., 2005; Peinemann et al., 2005; Rosas et al., 2005, 2008), and atrophy of the frontal cortex has been associated with deficits in planning and emotion recognition (Backman et al., 1997; Bamford et al., 1995; Henley et al., 2008; Hennenlotter et al., 2004; Kipps et al., 2007). A large cohort of premanifest HDGECs, from the PREDICT-HD study, used cluster analysis to identify a relationship between clinical phenotypes with atrophy of the striatum (Misiura et al., 2019). They identified three primary clinical clusters, namely motor symptom severity, cognitive control, and verbal learning, which related to reduced caudate and putamen volumes, after controlling for age and CAG repeat length (Misiura et al., 2019). In conclusion, there is a wealth of literature to support the presence of atrophy in the pathophysiology of HD, starting from early premanifest stages, which have relevance to symptomatology (Table 16.1). Unraveling the relationship between clinical phenotypes of HD with structural changes of atrophy has the potential for the development of a standardized set of outcome measures, and stratification tools, for clinical trials.

Diffusion tensor imaging Early understandings of WM pathology suggested that WM alterations occur after GM volumetric loss due to Wallerian degeneration (de la Monte et al., 1988). However, evidence from T1weighted MRI studies suggested that WM alterations can occur independently from GM neuronal loss. For example, Tabrizi and colleagues reported the rate of WM atrophy was greater than GM atrophy, over 24 months, in premanifest and manifest HDGECs (Tabrizi et al., 2012b). The reduction in WM volume, observed on T1-weighted volumetric MRI, may suggest a decrease in the number of axons within a damaged region, a decrease in the amount of myelin surrounding the axons, or a combination of both. In HD, the demyelination hypothesis speculates that the presence of mutant huntingtin, accompanied by increased levels of microglia activation, leads to dysfunction of oligodendrocytes and axonal transport, which in turn could impair the restorative function of oligodendrocytes resulting in demyelination (Bartzokis et al., 2007). Therefore, DWI imaging techniques have been employed to provide in-depth information on microstructural integrity and architecture in HD (Table 16.2). V. Clinical applications in other movement disorders

456 TABLE 16.2

16. Magnetic resonance imaging in Huntington’s disease

Key findings from diffusion-weighted MRI studies in Huntington’s disease over the past decade.

References

Subjects

Main findings

(Mascalchi et a., 2004)

21 symp-HDGECs 21 healthy controls

Increased ADC in the caudate, putamen, cerebral periventricular WM, and whole brain, which correlated with increased disease severity, in symp-HDGECs.

(Seppi et al., 2006)

29 symp-HDGECS 27 healthy controls

Increased MD in the putamen, caudate, pallidum, and thalamus in sym-HDGECs compared with controls, which correlated with global functional impairment and CAG repeat length in the putamen and caudate.

(Rosas et al., 2006)

15 pre-HDGECs 17 symp-HDGECs 29 healthy controls

Decreased FA throughout the corpus callosum in symp-HDGECs, and in the body of the corpus callosum in pre-HDGECs, compared with controls; decreased FA correlated with performance on the Stroop Color, Word, and Interference task; increased FA in the anterior limb of internal capsule, and decreased FA in the posterior limb of the internal capsule, in both symp-HDGECs and preHDGEC compared with controls; increased FA in putamen in preHDGECs, with increased FA in the globus pallidum and putamen in symp-HDGECs, compared with controls; decreased FA in the frontal, sensorimotor, parietal subcortical WM in symp-HDGECs, and to a lesser extent in pre-HDGECs, compared with controls; whole brain increased ADC in symp-HDGECs compared with controls, with increased ADC in globus pallidum and putamen in pre-HDGECs and symp-HDGECS compared with controls.

(Kloppel et al., 2008) 25 pre-HDGECs 25 healthy controls

Decreased FA indicative of pre-HDGECs in anterior parts of corpus callosum (originate mostly from orbitofrontal cortex).

(Weaver et al., 2009) 4 pre-HDGECs 3 symp-HDGECs 7 healthy controls

Decreased FA and AD and increased RD in pre-HDGECs and symp-HDGECs at 12-month follow-up.

(Bohanna et al., 2011)

FA decreased, and MD increased significant in corpus callosum, fornix, externa/extreme capsules, and bilateral corona radiata in symp-HDGECs compared with controls.

17 symp-HDGECs 16 healthy controls

(Dumas et al., 2012) 27 pre-HDGECs 16 symp-HDGECs 28 healthy controls

(GeorgiouKaristianis et al., 2013a) IMAGE-HD

35 pre-HDGECs 36 symp-HDGECs 36 healthy controls

Pre-HDGECs demonstrated increased ADC in the corpus callosum and somatosensory cortex WM compared with controls. Symp-HDGECs demonstrated increased ADC in the corpus callosum and caudate compared with controls. Increased ADC in the corpus callosum, sensorimotor, and prefrontal cortex correlated with estimated time to phenoconversion, as well as motor and cognitive deficits. Increased FA in the caudate, putamen, and pallidum in preHDGECs compared with controls. Using quadratic discriminant analysis, pre-HDGECs could be differentiated from controls with higher accuracy when both volumetric and diffusion data from the basal ganglia were used up to 15 years before predicted phenoconversion.

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457

Structural MRI markers of disease pathology in Huntington’s disease

TABLE 16.2

Key findings from diffusion-weighted MRI studies in Huntington’s disease over the past decade.dcont’d

References

Subjects

Main findings

(Rosas et al., 2010)

19 pre-HDGECs 21 symp-HDGECs 19 healthy controls

Cross-sectional thinning in all segments of corpus callosum in symp-HDGECs compared with controls. Decreased FA, and increased RD and AD in all corpus callosum segments in sympHDGECs compared with controls. Decreased FA and increased AD in all corpus callosum segments in pre-HDGECs compared with controls.

(Sánchez-Castañeda 17 pre-HDGECs et al., 2013) 12 symp-HDGECs

Decreased volume of basal ganglia; increased iron content in globus pallidum; increased MD and FA in caudate, putamen, globus pallidum, hypothalamus; decreased FA thalamus and nucleus accumbens.

(Poudel et al., 2014) 35 pre-HDGECs 36 symp-HDGECs 35 healthy controls

Decreased tractography streamlines connecting the putamen with the prefrontal and motor cortex in pre-HDGECs; decreased streamlines in a network connecting the prefrontal, motor, and parietal cortices with both caudate and putamen in symp-HDGECs; correlation between RD in putameneprefrontal tract with Symbol Digit Modalities Test and Stroop performance in pre-HD, and between RD in the frontoparietal tract with UHDRS motor scores in symp-HD.

(Odish et al., 2015) TRACK-HD

Increased MD, AD, and RD in both WM and GM in sympHDGECS compared with pre-HDGECs and controls, increased AD in the WM in pre-HDGECs compared with controls; increased striatal AD in near-onset pre-HDGECs compared with pre-HDGECs far onset and controls; no longitudinal changes detected at 24month follow-up; correlation between diffusivity and cognitive impairment.

22 pre-HDGECs: e 11 far from onset e 11 near to onset 10 symp-HDGECs 24 healthy controls

(Matsui et al., 2014) 53 pre-HDGECs 34 healthy controls

Increased MD and RD in inferior and lateral prefrontal cortex regions in pre-HDGECs.

(Phillips et al., 2015) 25 pre-HDGECs 25 symp-HDGECs

Increased FA, decreased AD and RD in bilateral corticospinal tract in symp-HDGECs. Increased iron load in corticospinal tract in pre-HDGECs.

(Poudel et al., 2015) 28 pre-HDGECs IMAGE-HD 25 symp-HHDGECs 27 healthy controls

Voxel-based tract-based spatial statistical analysis showed decreased FA in the corpus callosum and cingulum WM at an 18-month follow-up in symp-HDGECs compared with controls, while no longitudinal changes were detected in pre-HDGECs. Symp-HDGECs showed increased RD in the corpus callosum and striatal projection pathways compared with pre-HDGECs. FA in the corpus callosum was associated with motor function in both pre-HDGECs and symp-HDGECs.

(Harrington et al., 2016)

Longitudinal changes over 24-month follow-up, in the superior frontooccipital fasciculus, most prominent in pe-HD close to symptomatic onset. Increased motor symptoms, over 24 month, were associated with changes in superior frontooccipital fasciculus diffusivity.

64 pre-HDGECs 37 healthy controls

(Shaffer et al., 2017) 191 pre-HDGECs: PREDICT-HD e 50 low CAP score

Alterations in WM tracts connecting the striatum to sensory and motor cortex in pre-HDGECs.

(Continued)

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458 TABLE 16.2

16. Magnetic resonance imaging in Huntington’s disease

Key findings from diffusion-weighted MRI studies in Huntington’s disease over the past decade.dcont’d

References

Subjects

Main findings

e 56 medium CAP score e 85 high CAP score

High CAP group had the greatest disease progression and also had the fastest rate of deterioration. Low CAP group showed greater MD and RD, compared with medium and high CAP groups, in the right premotor area eputamen tract compared with controls. Decreased FA in the corticospinal tract, corona radiate, corpus callosum, external capsule, thalamic radiations, superior and inferior longitudinal fasciculus, and inferior frontaleoccipital fasciculus in symp-HDGECs compared with the controls and pre-HDGECs; No statistically significant difference between the pre-HDGECs and controls.

70 healthy controls (Saba et al., 2017)

12 pre-HDGECs 11 symp-HDGECs 11 healthy controls

(Zhang et al., 2018) 38 pre-HDGECs TrackON-HD 45 healthy controls

Using NODDI MRI, pre-HDGECs showed widespread decrease in axonal density compared with controls, which correlated with disease progression in the body and genu of the corpus callosum. Pre-HDGECs showed increased coherence of axonal packing in the WM around the basal ganglia.

(Rosas et al., 2018)

31 pre-HDGECs 37 symp-HDGECs 38 healthy controls

Tractography showed changes in RD in pre-HDGECs correlated with neuropsychological dysfunction. Increases in AD in the sensorimotor, supramarginal, and fusiform gyrus correlated with regional cortical thinning in symp-HDGECs.

(Pflanz et al., 2020)

35 pre-HDGECs:

MD was decreased in the posterior basal ganglia GM in preHDGECs far from onset compared with a relative increase in MD in pre-HDGECs near to onset. In pre-HDGECs near to onset, MD was increased at 1-year follow-up compared with baseline in the posterior basal ganglia GM. In the corpus callosum WM, MD was decreased in the far from onset group compared with a relative increase in MD in the near to onset group. Probabilistic tractography showed on different between pre-HDGECs groups in FA or MD across tracts connecting the basal ganglia and the cortex.

e 11 far from onset e 11 mid to onset e 13 near to onset 19 healthy controls

(Sweidan et al., 2020)

11 symp-HDGECs 11 healthy controls

Reduced FA in the corpus callosum WM in symp-HDGECs at 7month follow-up compared with baseline, in the absence of detectable GM changes or motor decline.

(Gregory et al., 2020)

20 pre-HDGECs 40 symp-HDGECs 20 healthy controls

Decreased FA and increased MD, RD, and AD in HDGECs compared with controls; increased FA in the corpus callosum, while increased MD, RD, and AD across the whole brain, correlated with lower UHDRS total motor scores and disease burden score; increased ODI (from NODDI) across the whole brain correlated with higher disease burden score and with higher UPDRS total motor scores in the bilateral external capsule; increased levels of mutant huntingtin in the CSF correlated with decreased FA in the splenium and with increased MD in the midbody and splenium, RD in the splenium, and AD in the poster corona radiata (without correction of CAG and ageeCAG interaction). Patterns of higher neurofilament light in the CSF with decreased FA, higher MD, and ODI were nonsignificant.

AD, axial diffusivity; ADC, apparent diffusivity coefficient; CAP, CAG-age product score; CSF, cerebrospinal fluid; FA, fractional anisotropy; GM, gray matter; HDGECs, Huntington’s disease gene expansion carriers; MD, mean diffusivity; NODDI, neurite orientation dispersion and density imaging; ODI, orientation dispersion index; pre-HDGECs, premanifest HDGECs; RD, radial diffusivity; SMDT, Symbol Digit Modalities Test; symp-HDGECs, manifest HDGECs; UHDRS, Unified Huntington’s Disease Rating Scale; WM, white matter.

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459

The most widely used technique to study microstructural integrity is DTI. Quantitative measures of DTI, such as fraction anisotropy (FA), mean diffusivity (MD), the apparent diffusion coefficient (ADC), axial diffusivity (AD), and radial diffusivity (RD), provide valuable information regarding the integrity and the orientation of WM tracts and microstructural integrity within the subcortical GM (Alexander et al., 2007; Soares et al., 2013). The MD or ADC measures the rate of diffusion, with higher MD values indicating unrestricted diffusion; thus, increased MD typically reflects white matter degeneration. FA provides a measure of the degree to which fibers in each voxel are oriented in the same direction, while the AD and RD provide better characterization of specific microstructural changes such that increases in AD and RD represent axonal degeneration and demyelination, respectively (Song et al., 2002). The reduction in FA, suggesting WM degeneration, has been reported in premanifest and manifest HDGECs compared with healthy controls, particularly in the corpus callosum, fornix, and corona radiata, with increased FA in the GM of the basal ganglia (Liu, Yang, Burgunder, et al., 2016; Novak et al., 2014; Reading et al., 2005; Rosas et al., 2006b) (Mascalchi et al., 2004; Rosas et al., 2006b; Sanchez-Castaneda et al., 2013; Seppi et al., 2006). These results have been accompanied by alterations in MD, RD, and AD within the similar regions (Bohanna et al., 2011; Matsui et al., 2014; Odish et al., 2015; Weaver et al., 2009). Microstructural changes have also been associated with symptomatology, including motor and cognitive dysfunction, predicted time to phenoconversion as well as levels of mutant huntingtin protein in the CSF (Bohanna et al., 2008; Casella et al., 2020; Gregory et al., 2020; Liu, Yang, Burgunder, et al., 2016; Pflanz et al., 2020; Rosas et al., 2006b; Shaffer et al., 2017), therefore providing a link between microstructural pathology with the severity of clinical disease burden. Early studies by Kloppel and colleagues used DTI to investigate changes in microstructural architecture aiming to elucidate disease pathology and to help find new biomarkers for HD (Kloppel et al., 2008; Saba et al., 2017). By using probabilistic tractography, Kloppel and colleagues recorded selective damage of the frontostriatal WM tracts in premanifest HDGECs, which reflected early and selective damage of the eye fields of the frontal cortex (Kloppel et al., 2008). Dumas and colleagues explored if there is a connection between functional decline and structural WM changes across the different stages of HD (van den Bogaard, Dumas, & Roos, 2013). They studied premanifest HDGECs and early manifest HDGECs compared with two separate groups of healthy control, which were taken from the TRACK-HD study. In the premanifest group, white matter tracts of sensorimotor cortex were affected, while in the group of manifest HDGECs, the WM degeneration extended beyond the sensorimotor cortex, to the prefrontal cortex and the thalamus. Moreover, loss of integrity in the sensorimotor cortex was linked with a higher probability of motor diagnosis within the next 5 years and with a higher burden of pathology. These WM abnormalities suggest the utilization of DTI analysis as an early biomarker, which is further supported by Tabrizi and colleagues (Tabrizi et al., 2009a). The manifest HDGECs showed higher values of ADC in fibers crossing through corpus callosum, thalamus, sensorimotor cortex, and prefrontal cortex. The fact that the sensorimotor cortex is affected years before the motor manifestation may explain why the loss of fibers in the sensorimotor cortex correlates with low scores in specific domains of cognitive tests, such as visual array comparison task for visual short-term memory capacity (SPOT) and Stroop Word Reading (SWR) task, which require both motor coordination and intact networks for interpreting the perceptual inputs (Dumas, van den Bogaard, et al., 2012). Phillips and colleagues documented decreased FA and increased AD and RD in the corticospinal tracts

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of manifest HDGECs, which were correlated with CAG repeat length, age, and clinical variables (Phillips et al., 2015). Furthermore, Bohanna and colleagues, using tractography and tract-based spatial statistics (TBSS), demonstrated that motor symptoms correlated with higher MD in the corpus callosum body, a region filled with interhemispheric fibers connecting right and left premotor and supplementary motor cortices. They also found that impaired cognition was associated with increased RD in the corpus callosum genu, which connects the prefrontal cortices (Bohanna et al., 2011). These findings suggest that microstructural changes may be involved in the reduction of functional capacity of manifest HD (Bohanna et al., 2011), something that is further supported by other DTI studies (Rosas et al., 2006; Diana Rosas et al., 2010; Kloppel et al., 2008). While the interruption of DTI parameters, such as AD and RD reflecting axonal degeneration and demyelination, respectively, is widely utilized in the literature, and can provide important insights into disease pathophysiology, it is important to remember the assumptions upon which the diffusion tensor model is based and the potential limitation of these parameters as a direct reflection of biological processes. For example, between one- and two-thirds of each voxel of a brain image, there are multiple fiber orientations, including crossing fibers, which make the interruption of DTI parameter complex. Therefore, an increase in RD in a region of crossing fibers could be the result of factors other than, or in addition to, demyelination, such as a less coherent alignment of fibers, axonal loss, or a combination of factors (Jones et al., 2013; Wheeler-Kingshott & Cercignani, 2009). New DWI techniques have recently been developed, such as NODDI, and aim to provide a more comprehensive characterization of WM microstructural, which closely reflects biological changes (Alexander et al., 2010). Diffusion MRI studies are starting to employ these techniques in HD (Gregory et al., 2020; Lampinen et al., 2019; Zhang et al., 2018).

Magnetization transfer imaging Magnetization transfer imaging (MTI) is a relatively new technique for improving image contrast in MRI and is based on the application of off-resonance radiofrequency pulses and observing their effects on MR images, as well as measuring the signal intensity with and without application of the radiofrequency pulses (i.e., magnetization transfer ratio [MTR]) (Grossman et al., 1994). MTI exploits the contrast between tissues where protons are present in three states: bound to macromolecules, as free water and as water in the hydration layer between the macromolecules and the free water. The radiofrequency pulse is applied at a different frequency to the Larmor frequency of free-water protons, so this pulse saturates the protons of macromolecules and not those in free water. The saturated protons, then, partially transfer their magnetization to protons in the hydration layer and free water. And some of them in free water become saturated. When a radiofrequency at the Larmor frequency of the free-water protons is applied, the signal from the free water is reduced due to the presaturation. Molecules associated with myelin have been shown to dominate both these processes, as well as the macromolecular proton fraction (Koenig, 1991; Odrobina et al., 2005). Although MTRs have been more widely investigated in multiple sclerosis (Levesque et al., 2010), they demonstrate overall sensitivity in detecting myelin changes. Therefore, MTRs can be used to detect more subtle changes in the structural status of brain parenchyma (Grossman, 1994) (Table 16.3).

V. Clinical applications in other movement disorders

Magnetization transfer imaging

TABLE 16.3

461

Magnetization transfer imaging studies in Huntington’s disease over the past decade.

References

Subjects

Main findings

(Ginestroni et al., 2010)

15 pre-HDGECs 15 healthy controls

With the exception of the putamen, all subcortical regions and the cerebral cortex showed decreases in MTR in pre-HDGECs compared with controls.

(Jurgens et al., 2010)

16 pre-HDGECs 14 healthy controls

Lower MTR in the GM correlated with worse UHDRS motor scores; lower MTR in the whole-brain GM and WM was correlated with a higher CAG repeat length.

(Van den Bogaard 25 pre-HDGECs et al., 2012) 25 symp-HDGECs 28 healthy controls

No difference between pre-HD and HCs; decreased MTR in the cortical GM and WM in symp-HDGECs compared with controls; MTR correlated with disease burden, including motor and cognitive deficits.

(Van den Bogaard 21 pre-HDGECs et al., 2013) 21 symp-HDGECs 25 healthy controls

Increased MTR in the putamen in pre-HDGECs compared with controls; no longitudinal changes in symp-HDGECs over a 2-year follow-up period.

(Wiest et al., 2017) 10 pre-HDGECs 10 symp-HDGECs 10 healthy controls

Region of interest fuzzy clustering analysis demonstrated a distinct cluster for HDGECs with disease burden score 230.

GM, gray matter; HDGECs, Huntington’s disease gene expansion carriers; MTR, magnetization transfer ratio; pre-HDGECs, premanifest HDGECs; symp-HDGECs, manifest HDGECs; UHDRS, Unified Huntington’s disease Rating Scale; WM, white matter.

MTI studies typically report measures of mean MTR or the height of the MTR peak. MTI studies in HDGECs have shown some conflicting results (Table 16.3). Some studies have reported changes in MTR within the cortical and subcortical GM in premanifest HDGECs (Ginestroni et al., 2010; van den Bogaard, Dumas, Hart, et al., 2013; Wiest et al., 2017), while other studies report no significant changes (Mascalchi et al., 2004; van den Bogaard et al., 2012). In premanifest HDGECs, a decline in MTR in the GM has been shown to correlate with worse UHDRS motor scores, and lower MTR in the whole brain GM and WM correlated with higher CAG repeat length (Ginestroni et al., 2010; Jurgens, Bos, et al., 2010). In a recent multimodal study that explored the utilization of the macromolecular proton fraction as a marker for myelin breakdown in HD, reporting significant decreases of the macromolecular proton fraction across multiple WM pathways, including corpus callosum fibers, anterior thalamic radiation, prefrontal cortexecaudate, supplementary motor area/corticospinal tracteputamen, validating findings of previous studies (Matsui et al., 2014; Phillips et al., 2015). For a more complete understanding of myelin pathologies in neurodegenerative diseases, we need studies with more HDGECs in both premanifest and manifest states. To clarify if the measurements of MTI offer valid information about WM degeneration, further crosssectional and longitudinal multimodal studies are necessary. Van den Bogaard and colleagues did not find any longitudinal changes in MTR in manifest HDGECs indicating MTI may not be as sensitive marker to monitor disease progression (van den Bogaard, Dumas, Hart, et al., 2013). However, a recent study employed a binary spin-bath MT method, using clustering analysis, which demonstrated distinctive clusters for HDGECs depending on their disease burden score (Wiest et al., 2017). These findings suggest that binary spin-bath MT could offer a novel methodology to track disease burden disease and progression in HD.

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Functional MRI markers of disease pathology in Huntington’s disease Imaging modalities that examine the metabolic and functional discrepancies during the progression of HD are of great value, as they can detect abnormal functional patterns of activity before structural changes. Functional MR imaging (fMRI) measures changes in neurovascular blood flow, which is coupled with neural activity (Gore, 2003). While performing a task, such as finger tapping, the regional consumption of blood increases (Buxton, 2013). Oxyhemoglobin, which has diamagnetic properties, becomes deoxygenated and acquires paramagnetic properties, which can be measured using fMRI. Brain activity can be assessed either during rest, known as resting-state fMRI, or while performing a specific task, known as task-based fMRI. The signal in fMRI is blood oxygenated level dependent (BOLD), and the measured change is affected by only 5%e10% (Buxton, 2013). The deoxygenation is overcompensated by a greater regional cerebral blood flow (rCBF), so the concentration of deoxyhemoglobin inside the tissues decreases. This directly affects the MRI signal (Gore, 2003). Another valuable and relatively new technique is arterial spin labeling (ASL), which magnetically labels arterial blood, and it is used for mapping the CBF (Fan et al., 2016). Susceptibility-weighted imaging (SWI) and quantitative susceptibility mapping (QSM) sequences are sensitive to the distortion of the magnetic field, thus making them useful for measuring iron content (Haacke et al., 2005). Finally, in vivo magnetic resonance spectroscopy (MRS) uses proton signals to determine metabolic brain changes and follows metabolites such as N-acetyl aspartate (NAA) and myoinositol. In this section, we will focus on insights on the pathology underlying HD gained from functional MRI studies. Findings from longitudinal studies will be discussed in the context of biomarkers to track disease progression and as outcome measures in HD clinical trials in the last section of this chapter.

Resting-state fMRI Using rs-fMRI, the functional cohesiveness of resting-state networks in HD can be investigated, without the need for the patients to be cognitively capable to complete several behavioral tasks (Ross et al., 2014b). Resting-state fMRI studies have employed a variety of analysis methodologies including independent component analysis, seed-based connectivity as well as whole-brain connectomics and graph analysis approaches (Glover, 2011; Soares et al., 2016), to show functional connectivity changes in HD. Resting-state fMRI studies in HD have demonstrated mixed findings showing both increases and decreases in connectivity across networks, which are often associated with conflicting correlation with symptomatology (Table 16.4). A recent systematic review identified 23 resting-state fMRI studies showing connectivity changes in manifest HDGECs compared with healthy and/or premanifest HDGECs (Pini et al., 2020). Differences in measured BOLD signal enable researchers to detect changes in neuronal networks within the brain (Damoiseaux, 2006). The most widely studied resting-state networks in HD include the sensorimotor network, the executive network, the frontoparietal network, the default mode network, the dorsal attention network, the subcortical network focused on the striatum, and the visual and auditory networks (Pini et al., 2020). In manifest HDGECs,

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Resting-state fMRI

TABLE 16.4

Summary of the key findings from recent resting-state functional MRI studies in Huntington’s disease.

References

Subjects

Main findings

(Dumas et al., 2013) TRACK-HD

28 pre-HDGECs 20 symp-HDGECs 28 healthy controls

Decreased connectivity within the medial visual network in pre-HDGECs and symp-HDGECs, with additional decreased connectivity in the default mode network and executive network in symp-HDGECs.

(Koenig et al., 2014) PREDICT-HD

48 pre-HDGECs 16 healthy controls

Decreased connectivity in the primary motor cortex and somatosensory areas as disease burden increased and association with higher motor symptoms; functional connectivity was associated with worse performance in cognitive motor functioning; short- and long-range functional connectivity correlated with atrophy in the striatum.

(Wolf et al., 2014)

20 symp-HDGECs 20 healthy controls

Using ICA, decreased connectivity in the left fusiform gyrus associated with higher disease burden and cognitive deficits.

(Werner et al., 2014)

3 pre-HDGECs 14 symp-HDGECs 19 healthy controls

Using ICA, increased functional connectivity in the striatum, thalamus, and prefrontal, premotor parietal, precuneus, and anterior cingulate cortices in symp-HDGECs compared with controls. Functional connections between parietal and occipital networks were reduced at rest. Motor decline was linked with increased connectivity in the motor and parietal cortex. Deteriorating functional state was linked with increased connectivity in insula, thalamus, striatum, and frontal regions.

(Poudel et al., 2014) IMAGE-HD

25 pre-HDGECs 23 symp-HDGECs 18 healthy controls

Decreased connectivity in the dorsal attention network and sensorimotor network in pre-HDGECs. Decreased connectivity in the dorsal attention network and the executive functional network, with the dorsal attention network associated with cognitive deficits in symp-HDGECs.

(Klopel et al., 2015) Track-On

128 pre-HDGECs 11 healthy controls

Using dynamic causal modeling and seed-based connectivity, a combined resting-state and task-based study showed increased functional coupling between the right DLPFC, and the left cognitive network predicated better cognitive performance as neuronal loss increased. Increased performancerelated activity, consistent with compensation, was linked with increasing atrophy. Similar patterns were not detectable in the left hemisphere.

(Harrington et al., 2015)

48 pre-HDGECs 16 healthy controls

Using connectomics, decreased long-range frontostriatal and frontoparietal connectivity correlated with increased disease burden.

(Odish et al., 2015)

22 pre-HDGECs 17 healthy controls

Using ICA, no difference in the degree of connectivity change between the groups over 3 years.

(Liu et al., 2016)

10 symp-HDGECs 20 healthy controls

Symp-HDGECs showed decreased amplitude of low-frequency fluctuations in the right precuneus and angular gyrus, with decreases in the precuneus correlated with worse performance in the Stroop test and SDMT. Increased amplitude of low-frequency fluctuations in the right and left inferior temporal gyrus and left superior frontal gyrus, with increases in the inferior temporal gyrus, was correlated with worse performance in SDMT.

(Müller et al., 2016)

34 symp-HDGECs 32 healthy controls

Using seed-based connectivity analysis, within the motor network, connectivity of the left insula and the left agranular gyrus were reduced in symp-HDGECs compared with controls. In the basal gangliaethalamic (Continued)

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16. Magnetic resonance imaging in Huntington’s disease

TABLE 16.4

Summary of the key findings from recent resting-state functional MRI studies in Huntington’s disease.dcont’d

References

Subjects

Main findings

(Gargouri et al., 24 pre-HDGECs 2016) 18 symp-HDGECs TRACK-HD 18 healthy controls (McColgan et al., 2017)

64 pre-HDGECs 66 healthy controls

network, the connectivity of the caudate was lower in symp-HDGECs compared with controls, but there was no association with the UHDRS motor score. The lower the connectivity of the insula to the motor network, the higher the UHDRS motor score. No detectable link between thalamocortical functional and structural connectivity. Using connectomics, decreased global efficiency and network robustness in the somatosensory cortex were observed in symp-HDGECs. No changes in pre-HDGECS and no longitudinal changes over 2-year follow-up. Areas with strong structural connectivity (located anteriorly) demonstrated decreased functional connectivity in pre-HDGECs compared with controls. Posterior areas (e.g., caudate, precuneus) with weak structural connectivity showed increased functional connectivity.

(Gregory et al., 94 pre-HDGECs 2018) 16 symp-HDGECs Track-On

Using seed-based connectivity and dynamic causal modeling analysis, in a combined tasked-based and resting-state fMRI study, increased effectivity connectivity between the left and right DLPFC correlated with global cognition. Increased connectivity between the left and right premotor cortex correlated with motor performance. The authors propose an operational model for testing cross-sectional and longitudinal compensation in HD.

(Espinoza et al., 183 pre-HDGECs 2018) 73 healthy controls PREDICT-HD

Using ICA analysis, putaminal and putaminaleinsular connectivity was decreased, associated with worse motor and cognitive scores in preHDGECs. Local visual connectivity decreased while long-range frontooccipital connectivity increased.

(Coppen et al., 21 pre-HDGECs 2018) 20 symp-HDGECs 18 healthy controls

Symp-HDGECs had decreased connectivity in associative visual cortices. pre-HDGECs showed on differences compared with controls.

pre-HDGECs, premanifest HDGECs; SMDT, Symbol Digit Modalities Test; symp-HDGECs, manifest HDGECs; WM, white matter; HDGECs, Huntington’s disease gene expansion carriers; ICA, independent component analysis; UHDRS, Unified Huntington’s disease Rating Scale.

decreased connectivity within the visual network was associated with lower attention, visual scanning, and motor speed (Coppen et al., 2018; Dumas et al., 2013; Espinoza et al., 2018; Wolf et al., 2014). One study reported increased connectivity within the visual network in manifest HDGECs; however, no correlations were identified (Werner et al., 2014). Studies have reported no connectivity changes within the visual network in premanifest stages (Coppen et al., 2018; Odish et al., 2015; Poudel et al., 2014). Therefore, hypoconnectivity could play a role in visuomotor deficits in manifest HD, while the role in premanifest stages remains to be fully elucidated. To date, no changes have been identified in the auditory network across HDGECs at rest (Dumas et al., 2013; Odish et al., 2015; Poudel et al., 2014). Increased connectivity within the sensorimotor network in manifest HDGECs has been associated with worse motor symptoms and increasing cognitive dysfunction (SanchezCastaneda et al., 2017; Werner et al., 2014; Wolf et al., 2014). However, one study reports decreased sensorimotor connectivity in early manifest HDGECs with low UHDRS motor scores (Müller et al., 2016). In premanifest HDGECs, decreased functional connectivity within

V. Clinical applications in other movement disorders

Resting-state fMRI

465

the sensorimotor network has been associated with worse motor and cognitive symptoms (Koenig et al., 2014; Poudel et al., 2014; Unschuld et al., 2012). Therefore, increased or decreased sensorimotor connectivity could be characteristic of different disease stages with increases in manifest linked with cardinal motor symptoms of HD. Moreover, long- and short-range functional connectivity disturbances have been shown to correlate with striatal atrophy in premanifest HDGECs, suggesting that the functional connectivity changes could be linked with progressive striatal atrophy (Koenig et al., 2014). A 2016 study by Liu and colleagues, which utilized amplitude of low-frequency fluctuations to measure changes in innate neuronal networks, argued that innate neural activity changes observed in the precuneus and corticostriatal circuits may underly impaired cognition in early manifest HDGECs (Liu, Yang, Chen, et al., 2016). The temporal and frontal cortices demonstrated higher activity to compensate for lower activity in the striatum. These findings suggested that visualespatial and executive functions were the problematic cognitive domains in early manifest HDGECs (Liu, Yang, Chen, et al., 2016). Within the executive network, increased connectivity has been reported in frontal cortex, while decreased connectivity has been reported in parietal and subcortical structures (Dumas et al., 2013; Poudel et al., 2014; Werner et al., 2014; Wolf et al., 2014). Furthermore, decreases in the posterior executive network have been associated with higher disease burden and cognitive dysfunction (Wolf et al., 2014). McColgan and colleagues demonstrated an anterioreposterior dissociation of changes in functional connectivity in premanifest HDGECs (McColgan et al., 2017). A similar anterioreposterior shift in brain activation is seen in healthy aging using taskbased fMRI, which the authors interpret as a way the aging brain maintains cognitive integrity (Davis et al., 2008). Studies have investigated the relationship between structural and functional connectivity in HD showing a relationship between functional connectivity and underlying WM organization (Kloppel et al., 2015; McColgan et al., 2017; Müller et al., 2016). In premanifest HDGECs, patterns of dissociation were observed between anterior and posterior brain regions with decreased strength in posterior functional connectivity and increased strength in anterior functional connectivity (McColgan et al., 2017). The anteroposterior dissociation in functional connectivity supports the caudorostral gradient of striatal pathology in HD. According to a recent systematic review, 10 studies have reported a significant association between connectivity and CAG repeat length or disease burden scores in premanifest and manifest HDGECs, employing different analysis approaches, while four studies report no significant correlations (Pini et al., 2020). The significance of the changing patterns of neural architecture in HD is still poorly understood, and the interpretation of resting-state fMRI findings is complex. However, resting-state fMRI has the potential to provide insights on the pathophysiological mechanisms in HD. Specifically, it has been hypothesized that functional connectivity dysfunction, prior to structural changes, could reflect an intermediate phenotype between neuropathology and clinical symptomatology (Ross et al., 2014). A series of studies have employed both resting-state and task-based fMRI to investigate the compensatory effects observed in HD (Gregory et al., 2018; Kloppel et al., 2015). Regional compensation, referring to the ability to increase fMRI activity within specific networks, has been speculated to enable premanifest HDGECs to ameliorate neurodegenerative changes (Andrews et al., 2015; Gregory et al., 2017, 2018; Soloveva et al., 2020). Using 119 premanifest HDGECs from the Track-On cohort, Kloppel and colleagues demonstrated functional

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16. Magnetic resonance imaging in Huntington’s disease

compensatory processes, which increases in right hemisphere activation associated with better cognitive performance in premanifest stages with higher degree of neuronal volumetric loss (Kloppel et al., 2015). These compensatory increases in functional connectivity were not observed in the left hemisphere, supporting previous T1-weighted volumetric MRI studies indicating that the left hemisphere is more vulnerable to the effects of regional neuronal loss (Minkova et al., 2018). Overall, findings suggest that the increases in functional connectivity may represent a compensatory mechanism in premanifest HDGECs.

Task-based fMRI Various task-based fMRI studies have been used to investigate neural connectivity changes in HDGECs illustrating both increased and decreased activation based on the disease stage and the task being performed (Table 16.5). The finger-tapping task is commonly used to assess activity within the motor system. Increased activation in the caudal supplementary motor area and superior parietal regions has been reported in premanifest HDGECs, while decreased activation in the rostral supplementary motor area has been reported in premanifest HDGECs closer to disease onset (Kloppel, Draganski, et al., 2009). Increased signal in caudate and thalamus of premanifest HDGECs near to predicted phenoconversion and TABLE 16.5

Summary of the key findings from recent task-based functional MRI studies in Huntington’s disease.

References

Subjects

Main findings

(Clark et al., 2002)

3 symp-HDGECs 3 healthy controls

Reduced fMRI signal during the Porteus Maze task was noted in the symp-HDGECs compared with the controls in the occipital, parietal, and somatomotor cortices and in the caudate. Increased signal was found among the symp-HDGECs in the left postcentral and right middle frontal gyri.

(Kim et al., 2004)

8 pre-HDGECs 12 healthy controls

Among the pre-HDGECs, increased BOLD was noted in the right caudate, bilateral thalami, left middle temporal, right superior temporal, right superior frontal, right middle and inferior frontal, and right postcentral gyri. Less BOLD activity was noted in the right middle frontal, left precuneus, left middle occipital, and left middle frontal gyri among the pre-HDGECs compared with controls.

(Paulsen et al., 2004) 14 pre-HDGECs 7 healthy controls

On time task, the pre-HDGECs near to conversion performed poorly compared with controls. On pitch task, the pre-HDGECs near to conversion performed similar to controls. Pre-HDGECs far from conversion, no significant differences in accuracy were noted compared with control on either the time task or the pitch task. Reaction times were similar on both tasks among the groups.

(Georgiou et al., 2007)

Inferior parietal, precentral gyrus, and the SMA bilaterally were activated in both groups. Symp-HDGECs demonstrated additional activation in right and left AC and insula cortex, the right dorsal premotor cortex, middle frontal, right inferior frontal, and the left

20 symp-HDGECs 17 healthy controls

V. Clinical applications in other movement disorders

Task-based fMRI

TABLE 16.5

467

Summary of the key findings from recent task-based functional MRI studies in Huntington’s disease.dcont’d

References

(Wolf et al., 2008)

Main findings

Subjects

16 pre-HDGECs 16 healthy controls

superior parietal lobes. Control showed bilateral activation in the putamen. Increased signal in areas of premotor cortex of sympHDGECs was linked with higher UHDRS motor score. Reduced functional connectivity in the dorsolateral prefrontal cortex correlated with increased working memory load assessed using the verbal working memory task.

(Kloppel et al., 2009) 15 pre-HDGECs 12 healthy controls

Pre-HDGECs showed increased activation in supplementary motor area and superior parietal regions with decreased activity in rostral supplementary motor area in pre-HDGECs close to onset.

(Wolf et al., 2012)

18 pre-HDGECs 18 healthy controls

Deactivation of the frontostriatal and striatal regions in pre-HDGECs close to predicated phenoconversion, assess using the alertness task.

(GeorgiouKaristianis et al., 2013) IMAGE-HD

27 pre-HDGECs 17 symp-HDGECs 23 healthy controls

Increased activation over time in 1-BACK compared with 0-BACK in pre-HDGECs. Longitudinal increases in activation in dorsolateral prefrontal cortex compared with controls.

(Wolf et al., 2014)

13 pre-HDGECs 13 healthy controls

Anterior prefrontal connectivity correlated with disease burden scores at baseline and at 2-year follow-up; Pre-HDGECs showed higher connectivity in dorsal cingulate over time using the verbal working memory task over 24-month follow-up.

(Labuschagne et al., 119 pre-HDGECs Cross-sectionally among the three groups on map search task at 2016) 104 symp-HDGECs baseline and after 12 months. Higher signal on map search was linked 110 healthy controls with lower disease burden, and better motor functionality and more functional capacity. Cross-sectionally during mental rotation task, significant difference found when symp-HDGECs were compared with controls. Mental rotation responses were found slower only among the pre-HDGECs near to conversion compared with controls. Higher signal on mental rotation task was linked with better motor abilities and overall functional capacity. Longitudinally, signal on map search task was reduced over 12 months in symp-HDGECs, but no decline was found in the controls. Longitudinally, signal on mental rotation task did not show any change. Both tasks were linked with less volume and thickness in temporal and parietooccipital areas. (Dominguez et al., 2017)

35 pre-HDGECs 18 17 symp-HDGECs 29 healthy controls

(Garcia-Gorro et al., 10 pre-HDGECs 2018) 20 symp-HDGECs 29 healthy controls

Increased deactivation in the anterior cingulate, striatum, and default mode network in symp-HDGECs. Decreased activation in the right dorsolateral frontal cortex associated with behavioral and cognitive deficits. No longitudinal changes in pre-HDGECs over 30-month follow-up. The mean number of tapping was lower in the symp-HDGECs compared with controls in both right and left conditions. Pre-HDGECs did not demonstrate any difference compared with controls in finger tapping. Activation in contralateral primary sensorimotor, (Continued)

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468 TABLE 16.5 References

(Soloveva et al., 2020)

16. Magnetic resonance imaging in Huntington’s disease

Summary of the key findings from recent task-based functional MRI studies in Huntington’s disease.dcont’d Main findings

Subjects

15 pre-HDGECs 15 healthy controls

supplementary motor area, and ipsilateral cerebellum and visual areas was not reduced significant in the symp-HDGECs compared with controls after multiple comparisons correction; Reduced connectivity between putamen and primary sensorimotor in symp-HDGECs was linked with higher UHDRS motor scores. In the left putameneleft primary sensorimotor track, no link between diffusion measures and psychophysiological interaction was found in all groups. Diffusion measures of the interhemispheric tracts of corpus callosum connecting right and left primary sensorimotorless structural integrity linked with less lefteright primary sensorimotor functional activity among the two HD groups. Using compensation-related utilization of neural circuits hypothesis (CRUNCH) as a quantitative model of compensation, pre-HDGECs did not show compensatory increases in fMRI activity at lower levels of memory loads in left intraparietal sulcus.

pre-HDGECs, premanifest-HD; symp-HDGECs, manifest HDGECs; HDGECs, Huntington’s disease gene expansion carriers; fMRI, functional magnetic resonance imaging; UHDRS, Unified Huntington’s Disease Rating Scale.

increased signal in anterior cingulate and supplemental motor area in those further from disease onset suggested a probable mechanism of maintaining good cognitive function before subjects becoming symptomatic. Georgiou and colleagues studied 20 manifest HDGECs, and 17 controls using task-based fMRI performed a SIMON task (Georgiou-Karistianis et al., 2007). BOLD activity was higher in more cortical areas in manifest HDGECs compared with the cortical activation of controls specifically within anterior cingulate, insula, parietal, temporal, and frontal lobes. Moreover, higher levels of activation of anterior cingulate and premotor cortex were linked with the inhibition of unwanted movements. Clark et al. (2002) investigated networks of functional connectivity during the Porteus Maze task and found reduced signal in the caudate and the parietal, occipital, and somatomotor cortices, while activation was increased in the left postcentral and middle frontal gyri in premanifest HDGECs (Clark et al., 2002). The relationship between functional activation and structural connectivity has also been investigated. Garcia-Gorro and colleagues studied 10 premanifest HDGECs, 20 manifest HDGECs, and 29 controls using tasked-based fMRI performing a finger-tapping task and DTI (Garcia-Gorro et al., 2018). Among the manifest HDGECs, the researchers noticed that less connectivity between left and right primary sensorimotor, as well as between left putameneleft primary sensorimotor and left putameneright primary sensorimotor, was linked with higher UHDRS motor scores. Increased alterations in the left putameneleft primary sensorimotor track and in the interhemispheric track in the HDGECs were linked with increased disease burden and worse mobility (Garcia-Gorro et al., 2018). Diffusion measures of the interhemispheric tracts of corpus callosum connecting the right and left primary sensorimotor suggest that decreased structural integrity is linked with reduced lefteright primary sensorimotor functional activity in HDGECs. The observed reduction of connectivity was

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linked with worse motor capacity, providing a link between the functional activation and clinical symptoms. Using a verbal working memory task, a decreased activation has been reported in premanifest HDGECs within the dorsolateral prefrontal cortex, with increased activation in the left inferior parietal lob and right superior frontal gyrus in premanifest HDGECs close to predicted phenoconversion (Wolf et al., 2007). Increased working memory load has been shown to be associated with decreased functional connectivity in the dorsolateral prefrontal cortex in premanifest HDGECs (Wolf et al., 2008). However, a 2-year follow-up study did not show significant longitudinal decreases in activation of the dorsolateral prefrontal cortex (Wolf, Sambataro, et al., 2011). In 2016, an fMRI study followed up 119 premanifest HDGECs, 104 early manifest HDGECs, and 110 controls over 12 months (Labuschagne et al., 2016). The task-based fMRI data, while performing a map search and a mental rotation task, were compared cross-sectionally and longitudinally after 12 months. Among the two tasks, the most sensitive one appeared to be the map search, demonstrating cross-sectionally differences among all the three groups. The researchers also found a decrease in activation after 12 months in manifest HDGECs while there was no significant change in healthy controls. The reduced signal activation during map search task was linked with higher disease burden and lower functional capacity (Labuschagne et al., 2016). Overall, task-based fMRI has been employed across a number of studies to understand if HDGECs utilize the same or alternative networks to healthy controls as a mechanism by which the brain aims to maintain the performance of specific tasks. Measures of task-based fMRI could be most useful during premanifest stages to understand potential mechanisms, prior to significant decline in motor and cognitive symptoms, which could be targeted by novel interventions to slow and/or prevent progressive functional decline. Larger, multisite task-based fMRI studies across are required to overcome hurdles in the consistency of acquisition protocols, analysis methodologies, and interruption.

Arterial spin labeling ASL is a noninvasive MRI technique employed for quantitative mapping of cerebral blood flow (CBF) without the need for ionizing radiation. The ASL uses labeled arterial blood water protons, as an endogenous tracer, to follow differences in regional CBF reflecting the amount of activity and brain metabolism (Petcharunpaisan et al., 2010; Wang et al., 2005). In premanifest HDGECs, a perfusion imaging study showed regional resting-state CBF changes compared with healthy controls in both the striatum and the prefrontal cortex (Wolf, Grön, et al., 2011) (Table 16.6). The premanifest HDGECs were divided into two groups: one near and one far from clinical diagnosis. Both groups showed reduced perfusion in medial and lateral prefrontal cortex and increased perfusion in the right and left precuneus. The HDGECs near to predicted phenoconversion showed lower perfusion in the left putamen and higher values in the right hippocampus. The HDGECs far from predicted onset demonstrated important reduction in regional CBF in frontal cortex but not in the striatum, while premanifest HDGECs near the disease onset demonstrated reduced CBF in both areas (Wolf, Grön, et al., 2011). Furthermore, lower regional CBF in the left dorsolateral prefrontal cortex was associated with proximity to motor onset symptoms. Increased activation in the

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hippocampus and left precuneus could be a compensatory adaptation or the result of the reduced specialization that accompanies the progression of the disease as it is demonstrated in PET and fMRI studies on working and episodic memories in older population (Rajah & D’Esposito, 2005). Chen et al. (2012) used pulsed-ASL, together with T1-weighted volumetric MRI, to evaluate the degree of perfusion in 17 early manifest HDGECs and 41 controls. Perfusion was reduced mainly in the superior frontal, insular, lateral occipital, and posterior cingulate gyrus, while the inferior temporal and medial occipital cortical regions were adequately perfused. All regions of interest were hypoperfused among the early manifest group, and in many regions of interest, the degree of CBF reduction exceeded the degree of cortical thinning. The dorsal striatum demonstrated lower values of CBF, whereas pallidum showed increased perfusion. Larger longitudinal studies in HDGECs are required to assess the progression of altered perfusion in HD and the utility of ASL as a tool to monitor disease progression and symptomatology. In addition to CBF, cerebral blood volume (CBV) has been investigated in a study by Hua and colleagues using ultrahigh 7 T MRI in premanifest HDGECs to assess the potential of arteriolar CBV as a sensitive marker of homeostasis in the microvasculature (Hua et al., 2014). This pilot study demonstrated increased cortical arteriolar CBV in premanifest HDGECs without substantial brain atrophy. Arteriolar CBV in the frontal cortex correlated with the CAP score and estimated time to predicated phenoconversion. Further studies are warranted to investigate the relationship between CBV and CBF with HD pathology, as well as the potential use of CBV as a biomarker for clinical trials.

Iron In vivo and ex vivo findings support the excessive iron accumulation in mutant Huntington; however, the effects of iron accumulation on the pathophysiological events taking place in HD remain unclear. Iron-sensitive MRI, including T2* mapping, SWI, and QSM, has been employed to investigate the role of iron in the pathophysiology and progression of HD (Table 16.7). The high amount of iron found in oligodendrocytes appears to match the increased free radical theory put forth by Bartzokis and colleagues. In 1999 and later in 2007, Bartzokis and colleagues used field-dependent relaxation rate increase (FDRI) showing increased levels of iron in the caudate and lentiform body, and lower levels in frontal WM as well as in the genu of the corpus callosum (Bartzokis et al., 1999, 2007). Unchanged concentrations of iron were found in the splenium of the corpus callosum, hippocampus, and thalamus. When using T2 relaxometry, increased iron in the pallidum was identified to be associated with higher CAG- repeat length. T2 relaxation time shortening was noted in the globus pallidum of manifest HDGECs compared with controls, while the opposite was identified in the caudate nucleus, which is the key area in the pathology of HD (Vymazal et al., 2007; Jurgens, Jasinschi, et al., 2010; Syka et al., 2015). Manifest HDGECs with increased CAG repeat length may have more ferritin-bound iron in the pallidum and/or more toxic form of iron in the caudate, mediating the oxidative damage within the nucleus. Using symmetric spin echo, Dumas and colleagues did not find any significant changes in iron concentration in the pallidum of premanifest HDGECs (Dumas, Versluis, et al., 2012). However, higher

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Iron

TABLE 16.6

471

The main arterial spin labeling MRI studies in Huntington’s disease.

References

Subjects

Main findings

(Wolf et al., 2011)

18 pre-HDGECs 18 healthy controls

Decreased GM volume in medial frontal gyrus in pre-HDGECs. Total intracranial volume did not show significant difference between preHDGECs (far from clinical diagnosis) and controls, pre-HDGECs (near clinical diagnosis) and controls and both pre-HDGECs groups against each other. In both pre-HDGECs, far and near groups decreased regional CBF in medial and lateral regions of the prefrontal cortex and in left dorsolateral prefrontal cortex and increased regional CBF in bilateral precuneus. Pre-HDGECs near to diagnosis showed reduced CBF in left putamen and increased CBF in right hippocampus.

(Chen et al., 2012)

17 symp-HDGECs 41 healthy controls

Even after correcting for partial volume effect, the study showed significant reduced CBF in superior frontal, insular, paracentral, lateral temporal, and lateral occipital areas in symp-HDGECs compared with controls. SympHDGECs showed reduced CBF in caudate and putamen compared with controls. The thalamus showed no significant difference between the groups, and pallidum showed increased CBF compared with controls. Reduction of CBF and cortical thinning were directly associated in superior frontal, precentral, lateral occipital, and posterior cingulate gyrus. Postcentral gyrus and insula had reduction in CBF but not evidence of cortical thinning, while the lateral occipital lobe showed cortical thinning without significant reduction in perfusion.

(Steventon et al., 2020)

19 HDGECs 19 healthy controls

Cerebral perfusion was increased in the precentral gyrus, middle frontal gyrus, and hippocampus 40 minutes after exercise cessation in sympHDGECs, while cerebral perfusion was unchanged in healthy controls; In symp-HDGECs, CAG repeat length predicted the change in the precentral gyrus; intensity of exercise intervention predicted hippocampal perfusion change in HDGECs.

GM, gray matter; pre-HDGECs, premanifest HDGECs; SMDT, Symbol Digit Modalities Test; symp-HDGECs, manifest HDGECs; HDGECs, Huntington’s disease gene expansion carriers.

TABLE 16.7

The main iron sensitive MRI studies in Huntington’s disease.

References

Subjects

Main findings

(Bartzokis et al., 1999)

11 symp-HDGECs 27 healthy controls

Iron levels in the basal ganglia were significantly increased in sympHDGECs compared with controls.

(Bartzokis et al., 2007)

11 symp-HDGECs 27 healthy controls

Ferritin-iron levels were increased in the caudate, putamen, and globus pallidus symp-HDGECs, and decreased in the frontal lobe WM and the genu of corpus callosum; unchanged ferritin-iron levels in the hippocampus, thalamus, and splenium of corpus callosum.

(Jurgens et al., 2010)

17 pre-HDGECs 15 healthy controls

T2-weighted MRI showed a trend of increased hypointensities (thought to reflect iron deposition) in the basal ganglia in both hemispheres, and mainly in the globus pallidum in pre-HDGECs compared with controls. Increased number of hypointensities among the pre-HDGECs subjects correlated with higher UHDRS total motor scores and longer CAG repeat length. (Continued)

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16. Magnetic resonance imaging in Huntington’s disease

The main iron sensitive MRI studies in Huntington’s disease.dcont’d

References

Main findings

Subjects

(Rosas et al., 2012) 28 pre-HDGECs 34 symp-HDGECs 56 healthy controls

SWI field map values showed increased levels of iron in the pallidum, caudate, and putamen. SWI field map values were significantly correlated with CAG repeat expansion and increasing disease severity. Increased iron levels in the cortex were observed in more advanced disease HDGECs.

(Dumas et al., 2012)

Magnetic field correlation maps displayed increased iron load in the caudate and putamen in early symp-HDGECs compared with preHDGECs and controls and were a predictor of disease state for early symp-HDGECs; no difference in pre-HDGECs compared with controls; no correlation between magnetic field correlation value and volume in subcortical structures.

22 pre-HDGECs 27 early sympHDGECs 25 healthy controls

(Apple et al., 2014) 13 pre-HDGECs 13 healthy controls

Using 7-Tesla MRI, the local field shield was increased in preHDGECs compared with controls. Higher local field shield correlated with genetic disease burden.

(SánchezCastañeda et al., 2015)

19 pre-HDGECs 50 symp-HDGECs 73 healthy controls

Iron accumulated in the caudate in pre-HDGECs with the pathology progress iron increased more prominently bilaterally in the lentiform body in symp-HDGECs compared with controls. Compared with controls, the pre-HDGECs did not show any change, but the sympHDGECs showed increased iron content in the right premotor cortex, left frontal, and temporal areas and bilaterally in parietal and occipital regions. Among the HDGECs, the smaller iron content was demonstrated in the symp-HDGECs. The lower the volume of the lentiform body and anterior cingulate cortex, the greater the iron content. Iron content in caudate and lentiform body was linked with the CAG repeat length after correcting for the duration of HD.

(Syka et al., 2015)

14 symp-HDGECs 14 healthy controls

Significant T2 relaxation time shortening in the globus pallidum of symp-HDGECs compared with controls and T2 increase in the frontal WM of symp-HDGECs compared with controls. Symp-HDGECs with increased CAG repeat length have more ferritin-bound iron in the pallidum and/or more toxic form of iron in the caudate.

(Dominguez et al., 31 pre-HDGECs; 2016) 32 symp-HDGECs IMAGE-HD 30 healthy controls

Increased iron content in pallidum, putamen, and caudate in preHDGECs and symp-HDGEC compared with controls. Iron accumulation in both putamen and caudate was significantly associated with disease severity.

(van Bergen et al., 2016)

Higher susceptibility found in the caudate and lentiform body indicative of increased amount of iron. Decreased susceptibility found in the substantia nigra and hippocampus. Higher susceptibility values in the caudate and putamen correlated with decreased volumes and increased CAG repeat length.

15 pre-HDGECs 16 healthy controls

pre-HDGECs, premanifest HDGECs; symp-HDGECs, manifest HDGECs; HDGECs, Huntington’s disease gene expansion carriers; UHDRS, Unified Huntington’s disease Rating Scale.

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amount of iron was reported in the dorsal striatum of premanifest HDGECs, thus supporting the study by Bartzokis and colleagues (Bartzokis et al., 1999). In the study conducted by Sánchez-Castañeda and colleagues, progressive increases in iron levels were revealed, and simultaneously a decrease in volume, starting in the caudate and progressively affecting the putamen and globus pallidus, was identified in premanifest HDGECs (Sánchez-Castañeda et al., 2015). In cortical areas, such as the parietooccipital region, iron levels were not increased. SWI and QSM have also been employed to measure iron levels in the basal ganglia (Dominguez et al., 2016; Rosas et al., 2012). An increased concentration of iron was demonstrated in the putamen, pallidum, and caudate nucleus in both premanifest and manifest HDGECs from the IMAGE-HD. Moreover, increased iron concentrations in the lentiform body were associated with increased disease severity (Dominguez et al., 2016). Since 7 T MRI has become available, with better signal-to-noise ratio (SNR) signals, studies have further supported existing findings regarding increased iron deposition in the caudate among premanifest HDGECs individuals and also strengthened the association with worse motor scores (Apple et al., 2014; van Bergen et al., 2016). Future follow-up studies should take place to further validate iron concertation as a sensitive biomarker in HD.

Magnetic resonance spectroscopy MRS measures metabolites such as NAA and myo-inositol, which mark the neuronal cohesiveness and the immune system activation, respectively (Sturrock et al., 2010, 2015). As another promising way to explore early brain pathology in HD, MRS is used to measure alterations in cerebral metabolism (Table 16.8). Studies investigated the metabolic changes in subcortical deep GM, such as the putamen and thalamus (Ruocco et al., 2007; van Oostrom et al., 2007). Sturrock and colleagues studied 25 premanifest HDGECs, 29 early manifest HDGECs, and 30 controls from TRACK-HD, in an attempt to address the use of MRS TABLE 16.8

The main magnetic resonance spectroscopy studies in Huntington’s disease.

Reference

Subjects

Main findings

(Ruocco et al., 2007)

40 HDGECs 26 healthy controls

The thalamic NAA þ NAAG/total creatine ratio was decreased by 9% and glycerophosphocholine and phosphocholine/total creatine increased by 17% in symp-HDGECs compared with controls.

(van Oostrom et al., 2007)

19 pre-HDGECs 8 healthy controls

In putamen only a trend that NAA was decreased in pre-HDGECs compared with controls. putaminal NAA/thalamic NAA also demonstrated a trend of decrease; no significant correlation between NAA, choline, or creatine with the product of age and CAG repeat length was noted in putamen or thalamus. The decreased putaminal NAA/thalamic NAA ratio linked with higher disease burden.

Sturrock et al., 2010)

25 pre-HDGECs 29 symp-HDGECs 30 healthy controls

Total NAA and total creatine were reduced in symp-HDGECs compared with controls. Myo-inositol was increased in symp-HDGECs compared with pre-HDGEC subjects. The myo-inositol/total NAA and myo(Continued)

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TABLE 16.8 Reference

The main magnetic resonance spectroscopy studies in Huntington’s disease.dcont’d Main findings

Subjects

(van den Bogaard 10 pre-HDGECs et al., 2014) 3 symp-HDGECs 4 converted (Sturrock et al., 2015)

25 pre-HDGECs 29 symp-HDGECs 30 healthy controls

inositol in symp-HDGECs was linked with higher UHDRS scores, and the lower values of total NAA correlated to increased disease burden. Choline and total NAA were reduced in the putamen, and the myoinositol and creatine were reduced in the caudate among all the subjects. The four converters of the group showed a sharper decline in total NAA within the putamen compared with nonconverters. Total NAA was reduced at baseline, at 12 months and at 24 months cross-sectionally. No alteration was mentioned longitudinally.

pre-HDGECs, premanifest HDGECs; NAA, N-acetyl aspartate; NAAG, N-acetyl Aspartyl glutamate; symp-HDGECs, manifest HDGECs; WM, white matter; HDGECs, Huntington’s disease gene expansion carriers; GM, gray matter; UHDRS, Unified Huntington’s disease Rating Scale.

outcomes as a potential biomarker of HD progression (Sturrock et al., 2010). To make their findings robust, Sturrock and colleagues chose to normalize the MRS values to unsuppressed water and not to other metabolites such as total creatine, which also shifts in HD (Ruocco et al., 2007). In manifest HDGECs, the metabolites total NAA (tNAA), which demonstrates neuronal integrity, and total creatine both demonstrated lower values compared with controls. Myo-inositol, a glial cell marker, was significantly higher in manifest HDGECs compared with premanifest HDGECs. The ratio of myo-inositol to total NAA, and myoinositol, in manifest HDGECs was linked with higher UHDRS scores and the lower values of total NAA correlated to increased disease burden (Sturrock et al., 2010). Therefore, the ability to elucidate disease pathophysiology at a biochemical levels makes MRS a potentially unique tool to monitor response to therapeutic interventions.

The use of MRI to track disease progression in Huntington’s disease Macrostructural MRI measures Findings that striatal atrophy can be detected using volumetric T1-weighted MRI up to 23 years before predicted motor onset led to a number of studies aiming to validate striatal atrophy as a marker to track disease progression. Two of the largest longitudinal studies regarding HD, known as TRACK-HD and PREDICT-HD, validated that neuron loss in the striatum can predict, with high accuracy, the phenoconversion from premanifest to manifest stages of HD (Aylward et al., 2011; Tabrizi et al., 2012a). The size of the striatum declines in a linear and rapid manner once atrophy starts, and predicts the transition from one stage to the next in both premanifest HDGECs (Aylward et al., 2004, 2011; Hobbs, Henley, et al., 2010; Kloppel, Henley, et al., 2009) and manifest HDGECs (Kloppel, Henley, et al., 2009; Paulsen et al., 2010; Tabrizi et al., 2009a). In addition to total striatal atrophy, strong indicators of disease progression are volumetric loss in the putamen and caudate nucleus (Aylward, 2014). PREDICT-HD followed a large cohort of 1078 premanifest HDHGECs conducting a battery of motor and cognitive tests over a follow-up period of 12 years (Long et al., 2015). The

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475

aim was to identify markers at baseline that could accurately predict the risk of motor diagnosis at follow-up. The striatal volumetric loss, and especially volume loss in the putamen, held the second highest predictive value, immediately after the clinical value of the UHDRS total motor score (Long et al., 2015). This suggests a novel way to classify subjects according to their progression level at baseline and supports the use of striatal volume as a sensitive biomarker to monitor disease progression in premanifest HDGECs. TRACK-HD is a large longitudinal multicenter study, following a cohort of 366 HDGECs, which aimed to identify sensitive and reliable biomarkers in premanifest HD gene carriers and early manifest HD patients (Tabrizi et al., 2009b, 2011, 2012b, 2013). Far-from-onset premanifest HDGECs, more than 10.8 years from predicted symptomatic onset, showed gray matter atrophy confined to the striatum, while mid- and near-to-onset HDGECs, with less the than 10.8 years from predicted symptomatic onset, and in manifest HD stage 1 (mild stage) and stage 2 (moderate stage), showed higher rates of whole brain and GM volumetric loss (Tabrizi et al., 2012b). Therefore, cortical GM atrophy likely occurs after striatal atrophy in premanifest HDGECs and is often less severe, making striatal atrophy an earliest biomarker to track disease progression. WM atrophy, especially around frontostriatal regions and the corpus callosum, can provide valuable complementary information regarding disease progression (Aylward et al., 2011). Both PREDICT-HD and TRACK-HD have demonstrated that WM atrophy can be identified in preclinical stages and can be followed up over 1e2 years showing correlations with clinical progression. TRACK-HD presented significantly higher rates of whole-brain GM and WM atrophy in premanifest HDGECs who had signs of overall motor and cognitive progression compared with those that were not progressing. The highest rates of progressive WM atrophy were present, compared with rate of GM atrophy, from the earliest disease stages in far-from-onset premanifest HDGECs (Tabrizi et al., 2012b). Therefore, WM atrophy could offer another valuable tool for tracking disease progression and for predicting symptomatic onset in the premanifest HDGECs (Aylward et al., 2011; Tabrizi et al., 2012a). Furthermore, the presence of strong associations between volumetric measures and clinical outcomes suggests measures of atrophy could be a potential biomarker for assessing neuroprotective therapies in clinical trials. Recent longitudinal studies have aimed to validate the use of noninvasive MRI measures as suitable outcome measures for clinical trials as a biomarker to track disease progression. Ramirez-Garcia and colleagues demonstrated progressive cortical thinning in the superior frotal lobe and decreased subcortical volume in the caudate, putamen, and thalamus in manifest HDGECs at 16 month follow-up compared with baseline (Ramirez-Garcia et al., 2020). Furthermore, progressive volumetric loss in the caudate correlated with motor decline and decreasing functional capacity, while progressive atrophy in the left frontal and right paracentral correlated with progressive cognitive decline. Therefore, supporting the rate of change of volumetric atrophy in the caudate, superior frontal, and paracentral cortices, which correlated with symptomatology, as suitable biomarkers to track disease progression in future clinical trials.

Microstructural MRI measures To date, there are a limited number of longitudinal studies to follow the progression of WM changes in HDGECs and validating WM changes as a reliable outcome measure for

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future clinical trials. Weaver and colleagues used DTI and tract-based spatial statistics (TBSS) to follow the progression of white matter changes in four premanifest HDGECs (13.2 years to predicted disease onset), three manifest HDGECs, and seven controls, over a period of 1 year (Weaver et al., 2009). They found a reduction of FA between baseline and follow-up in premanifest HDGECs compared with controls within subcortical and frontoparietal tracks, as well as tracks originating from corpus callosum. This was combined with an additional important decrease in AD (Weaver et al., 2009). The authors hypothesized that progressively reduced WM integrity could explain, in part, why the fetal cell transplants in the striatum of HDGECs failed (Bachoud-Levi et al., 2006; Hauser et al., 2002). In a multimodal longitudinal study by Sanchez-Castaneda and colleagues, progressive loss of caudate volume was the consistent finding across all three groups of HDGECs: far (>15 years), and close to predicted motor onset (