Pattern Analysis of the Human Connectome [1st ed. 2019] 978-981-32-9522-3, 978-981-32-9523-0

Table of contents :
Front Matter ....Pages i-viii
Introduction (Dewen Hu, Ling-Li Zeng)....Pages 1-16
Multivariate Pattern Analysis of Whole-Brain Functional Connectivity in Major Depression (Dewen Hu, Ling-Li Zeng)....Pages 17-33
Discriminative Analysis of Nonlinear Functional Connectivity in Schizophrenia (Dewen Hu, Ling-Li Zeng)....Pages 35-54
Predicting Individual Brain Maturity Using Window-Based Dynamic Functional Connectivity (Dewen Hu, Ling-Li Zeng)....Pages 55-81
Locally Linear Embedding of Functional Connectivity for Classification (Dewen Hu, Ling-Li Zeng)....Pages 83-102
Locally Linear Embedding of Anatomical Connectivity for Classification (Dewen Hu, Ling-Li Zeng)....Pages 103-122
Locality Preserving Projection of Functional Connectivity for Regression (Dewen Hu, Ling-Li Zeng)....Pages 123-147
Intrinsic Discriminant Analysis of Functional Connectivity for Multiclass Classification (Dewen Hu, Ling-Li Zeng)....Pages 149-168
Sparse Representation of Dynamic Functional Connectivity in Depression (Dewen Hu, Ling-Li Zeng)....Pages 169-181
Low-Rank Learning of Functional Connectivity Reveals Neural Traits of Individual Differences (Dewen Hu, Ling-Li Zeng)....Pages 183-203
Multi-task Learning of Structural MRI for Multi-site Classification (Dewen Hu, Ling-Li Zeng)....Pages 205-226
Deep Discriminant Autoencoder Network for Multi-site fMRI Classification (Dewen Hu, Ling-Li Zeng)....Pages 227-258

Citation preview

Dewen Hu Ling-Li Zeng

Pattern Analysis of the Human Connectome

Pattern Analysis of the Human Connectome

Dewen Hu • Ling-Li Zeng

Pattern Analysis of the Human Connectome

123

Dewen Hu College of Intelligence Science and Technology National University of Defense Technology Changsha, Hunan, China

Ling-Li Zeng College of Intelligence Science and Technology National University of Defense Technology Changsha, Hunan, China

ISBN 978-981-32-9522-3 ISBN 978-981-32-9523-0 (eBook) https://doi.org/10.1007/978-981-32-9523-0 © Springer Nature Singapore Pte Ltd. 2019 This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors, and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, express or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Springer imprint is published by the registered company Springer Nature Singapore Pte Ltd. The registered company address is: 152 Beach Road, #21-01/04 Gateway East, Singapore 189721, Singapore

Contents

1

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 Multimodal Brain Imaging. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 sMRI-Based Structural Connectivity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 DTI-Based Anatomical Connectivity. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 fMRI-Based Functional Connectivity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 Dynamic Functional Connectivity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 Multivariate Pattern Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 Feature Extraction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 Dimensionality Reduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 Classifier Design and Performance Evaluation . . . . . . . . . . . . . . . . . . . . . . . 10 The Content of the Book . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

2

Multivariate Pattern Analysis of Whole-Brain Functional Connectivity in Major Depression . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 Subjects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 Image Acquisition and Preprocessing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 Identification of Features with High Discriminative Power . . . . . . . . . . 5 Support Vector Classification and Performance Evaluation . . . . . . . . . . 6 Altered Resting-State Functional Connectivity in Major Depression . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

3

Discriminative Analysis of Nonlinear Functional Connectivity in Schizophrenia . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 Participants . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 Imaging Acquisition and Preprocessing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 MIC and eMIC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 High Discriminative Connectivity Features . . . . . . . . . . . . . . . . . . . . . . . . . . .

1 1 2 3 3 5 6 7 8 8 9 9 17 17 19 20 21 23 24 26 29 35 35 37 38 39 41 v

vi

Contents

6 Support Vector Classification and Performance Evaluation . . . . . . . . . . 7 Functional Connectivity Changes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4

Predicting Individual Brain Maturity Using Window-Based Dynamic Functional Connectivity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 Participants . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 Image Acquisition and Preprocessing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 ALFF-FC Map of Dynamic Functional Connectivity . . . . . . . . . . . . . . . . 5 Partial Least-Squares Analysis and Age Prediction . . . . . . . . . . . . . . . . . . 6 Age-Dependent Changes in the Variability of the Dynamic FC During Maturation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 Control Analysis. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 Reproducibility . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

42 43 45 50 55 55 57 58 59 61 63 65 70 71 78

5

Locally Linear Embedding of Functional Connectivity for Classification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 83 1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 83 2 Feature Selection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 85 3 LLE-Based Dimensionality Reduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 86 4 K-Means Clustering-Based Classifier . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 90 5 Evaluation of Classification Performance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 91 6 Comparison with Other Classification Methods . . . . . . . . . . . . . . . . . . . . . . 94 7 Most Discriminative Functional Connectivity Features . . . . . . . . . . . . . . 97 8 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 97 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 101

6

Locally Linear Embedding of Anatomical Connectivity for Classification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 Participants and Imaging Protocol . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 Region of Interest Segmentation and Fiber Tracking . . . . . . . . . . . . . . . . . 4 Feature Selection and Classification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

7

Locality Preserving Projection of Functional Connectivity for Regression . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 Data Acquisition and Preprocessing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 Parametric Curve Fitting and Age-Related Changes in Interregional Functional Connectivity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 Low-Dimensional Embeddings . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

103 103 105 106 109 111 117 123 124 125 127 129

Contents

vii

5

Locally Adjusted Support Vector Regression (LASVR) for Age Prediction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 133 6 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 136 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 143

8

9

10

11

Intrinsic Discriminant Analysis of Functional Connectivity for Multiclass Classification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 Participants . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 Data Acquisition and Preprocessing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 IDA Algorithm and Intrinsicconnectomes . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 Multiclass Classification Based on Intrinsic Discriminative Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 Identification of Features with High Discriminative Power . . . . . . . . . . 7 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Sparse Representation of Dynamic Functional Connectivity in Depression . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 Data Acquisition and Preprocessing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 Dynamic Functional Connectivity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 Sparse Representation and Classification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 Extraction of Specific Dynamic Functional Connectivity Patterns. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Low-Rank Learning of Functional Connectivity Reveals Neural Traits of Individual Differences . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 Participants and Data Acquisition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 fMRI and DTI Data Processing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 Background-Subtraction on Individual Functional Connectivity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 Relating Background Skeleton of Functional Connectivity to Anatomical Connectivity Architecture . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 Sparse Representation of Functional Connectivity Traits and the Relation to Cognitive Behaviors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 Control Analysis. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

149 149 151 152 153 156 157 158 164 169 169 170 171 172 176 177 179 183 183 185 186 187 190 190 195 197 201

Multi-task Learning of Structural MRI for Multi-site Classification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 205 1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 205 2 Participants . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 208

viii

Contents

3 Image Acquisition and Preprocessing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 Multi-site Classification Using Multi-task Learning . . . . . . . . . . . . . . . . . 5 Experiment. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 Result . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12

Deep Discriminant Autoencoder Network for Multi-site fMRI Classification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 Participants . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 Image Acquisition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 Data Preprocessing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 Control of Motion Artifact . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 Functional Connectivity Measure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 Discriminant Autoencoder Network with Sparsity Constraint (DANS) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 Multivariate Pattern Classification. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 Estimation of the Discriminative Power of Functional Connectivity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

209 209 211 213 216 218 227 227 228 229 230 231 233 233 237 243 247 253

Chapter 1

Introduction

Abstract Pattern analysis of brain connectome attracts increasing attention in recent years. In this chapter, we first gave a brief review of multimodal brain imaging and brain connectome, and then we discussed some basic concepts of multivariate pattern analysis of brain connectome and its application in neuropsychiatric disorders, including feature extraction, dimensionality reduction, classifier design, and performance evaluation. Finally, we introduced the content of this book. Keywords Multivariate pattern analysis · Brain connectome · fMRI · DTI

1 Multimodal Brain Imaging In the past decades, the great progress of the cognitive neuroscience benefits from the rapid development of brain imaging techniques, including magnetic resonance imaging (MRI), positron emission tomography (PET), electroencephalography (EEG), electrocorticography (ECoG), magnetoencephalography(MEG), etc. There are three popular MR imaging modalities in neuroscience research: structural MRI (sMRI), diffusion tensor imaging (DTI), and functional MRI (fMRI). As a noninvasive in vivo functional imaging technique, fMRI has become a most popular neuroimaging approach in the field of neuroscience due to its promising spatiotemporal resolution (Fig. 1.1) [1]. At an early stage, studies always used task-related brain imaging to explore brain function and dysfunction by detecting task-induced brain activation, termed as functional segregation [2]. However, there are around 100 billion neurons and 100 trillion synapses in the human brain; series of complex cognitive function cannot be fulfilled by a single brain region. Nowadays, there is a widespread argument that a complex task will involve in multiple regions or an entire brain participate, termed as functional integration [2]. Thus, brain function research could be done from a perspective of brain connectome, to systematically characterize neurobiological basis of cognitive and affective functions at a large-scale level. In fact, MRIbased brain connectome becomes a hot research topic in the field of cognitive

© Springer Nature Singapore Pte Ltd. 2019 D. Hu, L.-L. Zeng, Pattern Analysis of the Human Connectome, https://doi.org/10.1007/978-981-32-9523-0_1

1

2

1 Introduction

Fig. 1.1 Multimodal brain imaging

neuroscience, providing a new prospective for studying the neural activity of the brain and the pathogenesis of neuropsychiatric disorders [3]. In this book, we will focus on MRI-based connectome of the human brain.

2 sMRI-Based Structural Connectivity MRI was first introduced in the imaging of the human brain in 1979. Gray/white matter tissues can be segmented from the brain MRI scans, and gray matter density/volume changes could reveal the morphological characteristics such as location and size of a given lesion brain region in clinical research of neurological diseases. Actually, sMRI has been widely used in the research of various neuropsychiatric disorders, including Alzheimer’s disease (AD) [4], Parkinson’s disease (PD) [5, 6], epilepsy [7], schizophrenia [8], depression [9, 10], etc. sMRI has been used in brain evolution, development, and plasticity research [11–13]. He et al. firstly proposed a sMRI-based structural connectome analysis approach for the human brains [14]. They used sMRI to measure regional cortical thickness of a group of normal subjects from the ICBM database and then constructed structural connectivity network by calculating the correlation coefficients of cortical thickness between brain regions. Bassett and colleagues reported aberrant topological patterns of large-scale structural connectivity networks in schizophrenia [15]. In recent years,

4 fMRI-Based Functional Connectivity

3

multivariate pattern analysis (MVPA) attracts increasing attention in the field of brain imaging analysis. As a data-driven technique, MVPA can both find potential neuroimaging-based biomarkers to differentiate patients from healthy controls at the individual subject level and potentially detect exciting spatially distributed patterns to further highlight the neural mechanisms underlying the behavioral symptoms of neuropsychiatric disorders, providing a useful tool to detect subtle changes of brain structural networks. However, sMRI provides limited information of brain structures, which is not enough for the understanding of brain cognitive function.

3 DTI-Based Anatomical Connectivity Basser et al. developed the DTI technique in 1994 [16], which uses the diffusion of water molecules to generate contrast in MR images and can provide anatomical brain connectivity by fiber tracking. White matter fiber tracking based on DTI is the only technique able to localize the white matter pathways in the brain in vivo. To date, the white matter fiber tractography can be grouped into two types: one is deterministic tracking and the other is probabilistic tracking. In the deterministic tracking, each voxel has a single fiber direction of diffusion, and then the direction of white matter fibers in the brain can be obtained by connecting the voxels with consistent direction of diffusion, while the probabilistic tracking attempts to generate a probability distribution map of fiber connectivity between brain regions [17]. DTI has been widely used in the imaging studies of neurodevelopment, aging, and neuropsychiatric disorders. Westlye and colleagues reported lifespan changes of the human brain white matter anatomical connectivity using DTI [18], and Ingalhalikar et al. found gender differences of anatomical connectivity of the human brain based on a large DTI sample [19], while our group used DTI to examine the anatomical connectivity changes in major depression [20].

4 fMRI-Based Functional Connectivity In this book, fMRI just denotes blood oxygenation level dependent (BOLD)-fMRI. In 1991, BOLD-fMRI has been applied in the human brain [21]. Previous fMRI studies determined task-related activated regions by calculating the correlation between BOLD signals and specific external task stimuli [22]. But the brain BOLD signals exhibit spontaneous modulation without any external stimuli [22]. In particular, Biswal et al. observed high correlation of spontaneous neural activities between bilateral motor areas during resting state [23]. From then on, the cognitive neuroscience research entered a new era of functional connectivity network analysis. Resting-state fMRI (rs-fMRI) is a method of fMRI that is used in brain mapping to evaluate regional interactions that occur in a resting or task-free state, without any explicit tasks performed. Due to no complex experimental design, easy operation,

4

1 Introduction

and easy acceptation by patients with neuropsychiatric disorders, rs-fMRI has its unique advantages in the study of human brain function. Brain activity is intrinsic, so any brain region will have spontaneous fluctuations in BOLD signal. The restingstate functional connectivity MRI (rs-fcMRI) becomes a powerful tool to explore brain’s large-scale network and to examine if it is altered in neuropsychiatric disorders. Functional connectivity just denotes the temporal synchronicity of neural activity of spatially separated brain regions. Researchers have proposed a series of functional connectivity analysis approaches, including linear correlation analysis [24], independent component analysis (ICA) [25], principal component analysis (PCA) [26], coherence analysis [27], clustering analysis [28], etc. Seed-based analysis is the most common linear correlation analysis approach. First, regions of interest (ROIs) are selected as seed regions according to the priori background knowledge, and then the Pearson’s correlation coefficients, as functional connectivity measures, were calculated between the time course of a given seed region and those of the voxels in the entire brain (Fig. 1.2). In 2003, Greicius and colleagues extracted default mode network (DMN) by using seed-based analysis with rs-fcMRI for the first time [29], and Fox et al. discovered the anticorrelation between the DMN and dorsal attention network (dATN) [30]. By dividing the whole brain into a number of regions (or defining a number of ROIs), region-to-region functional connectivity can be calculated to construct a whole-brain functional connectivity network. Rs-fcMRI linear correlation analysis has been widely used in the studies of neuronal mechanisms of cognition and behaviors, neurodevelopment, aging, and neuropsychiatric disorders. McKeown et al. was the first to introduce the ICA in the fMRI data analysis in 1998 [25]. As a multivariate blind source separation approach, ICA can extract a group of temporally independent BOLD signals or spatially independent brain activity components. Then, the group ICA approach was proposed, and several well-established functional brain networks are extracted [31, 32]: DMN, dATN,

Fig. 1.2 The flowchart of seed-based functional connectivity analysis

5 Dynamic Functional Connectivity

5

Fig. 1.3 Group ICA functionally relevant intrinsic connectivity networks

salience network, frontoparietal control network (FPN), sensory-motor network (SMN), visual network, auditory network, etc. which are consistently found in healthy subjects, different stages of consciousness (Fig. 1.3). The clinical studies always use the components extracted by ICA for brain network analysis in clinical populations, such as depression, schizophrenia, AD, etc.Despite the significance of functional connectivity in the field of cognitive neuroscience, it cannot characterize connectivity direction. Thus, Friston et al. proposed the conception of effective connectivity [33] and analyzed the effective connectivity between V1 and V2 on the basis of fMRI [34]. Then the effective connectivity analysis approaches including dynamic causal modeling (DCM) [35], Granger causal modeling (GCM) [36], and structural equation modeling (SEM) [37] were developed.

5 Dynamic Functional Connectivity Until recently, most functional connectivity studies have implicitly assumed that the statistical interdependence of signals between distinct brain regions is constant throughout recording periods of resting-state experiments, so the resulting

6

1 Introduction

characterization ultimately represents an average across complex spatiotemporal phenomena. However, the brain dynamically integrates, coordinates, and responds to internal and external stimuli across multiple time scales, so does functional connectivity. Functional connectivity has been demonstrated to change with state switching of the task demands [38, 39], learning [40, 41], sleep [42, 43], and anesthesia [44], etc. In fact, functional connectivity changes at much faster time scales (from seconds to minutes) [45–49]. Due to that quantitative description of dynamic changes of functional connectivity metrics over time can provide more information for revealing the basic attributes of brain networks, the conception of dynamic functional connectivity was proposed [50]. Now, there are several methods for dynamic functional connectivity analysis, such as sliding-window correlation analysis, time-frequency coherence analysis, and single-volume coactivation pattern [50].

6 Multivariate Pattern Analysis Encoding and decoding are two keywords in the brain imaging analysis methodology. Encoding (conventional univariate brain mapping) refers to finding brain regions from images, whose signals are highly correlated with known experimental conditions (type of stimulation task, disease, etc.), trying to establish statistical dependencies between experimental variables and measured brain responses. The progress of the neuroscience benefits a lot from such univariate statistical analysis methods. However, the experimental variables are not always linearly encoded within single brain region (functional integration vs. functional segregation). Decoding, the reverse mapping from measured physiological signals to the features encoded, can complement univariate statistical analysis. Decoding uses multivariate pattern analyses of brain imaging data with machine learning to predict the experimental conditions such as the perceptual/cognitive/disease state of an individual subject, further revealing the relationship between brain activity and experimental variables. Due to the use of multivariate information of brain imaging data [51], MVPA outperforms traditional univariate statistical analysis in the detection of subtle changes of brain structural and functional networks. Furthermore, univariate statistical analysis just reveals the group-level differences, but MVPA can find potential neuroimaging-based biomarkers to differentiate populations at the individual subject level, showing the potential in the clinical diagnosis and treatment evaluation of neuropsychiatric disorders. This is why MVPA becomes one of the hottest topics in the research field of brain imaging analysis and cognitive neuroscience. A basic framework of MVPA of brain imaging data includes four steps: feature extraction, dimensionality reduction, classifier design, and performance evaluation (Fig. 1.4). In this book, we will focus on MVPA of brain MRI data.

7 Feature Extraction

7

Fig. 1.4 A basic MVPA framework of brain functional connectivity

7 Feature Extraction We can extract different types of classification features from different modal MRI images. For example, gray/white matter density/volume [52–54] and cortical thickness [55, 56] can be extracted from sMRI, but these features lose the spatial patterns of brain sMRI. Thus, recently some researchers introduced 3D descriptors from computer vision in the sMRI pattern classification, achieving promising performance [57]. Fractional anisotropy (FA) and mean diffusivity (MD) were previously considered as classification features for DTI [58], but the use of anatomical connectivity becomes a trend recently. Robinson et al. defined 83 ROIs according to the literature and then constructed whole-brain anatomical connectivity network based on DTI data to differentiate the young adults from older adults [58]. And we identified the depressed patients from healthy controls based on wholebrain anatomical connectivity [20]. For task-related fMRI, the BOLD signal strength is always considered as classification feature [59, 60], but functional connectivity can be also used to differentiate cognitive states for some specific paradigms [61]. In particular, functional connectivity is one of the most popular feature descriptors for resting-state fMRI. Dosenbach and colleagues used whole-brain functional connectivity to predict brain maturity (from 7 to 30 years old) [62], and Craddock et al. first defined several key ROIs and then calculated region-to-region functional connectivity to predict depression [63]. Our group proposed to use wholebrain functional connectivity pattern for differentiating schizophrenic patients from

8

1 Introduction

healthy controls [64]. In addition to functional connectivity, regional homogeneity (ReHo) [65], dynamic functional connectivity [66], and effective connectivity [67] have been also used as classification features.

8 Dimensionality Reduction Pattern classification of MRI images is a big challenge due to high dimensionality of classification features, low signal-to-noise ratio, and small sample size. To overcome these problems, dimension reduction is often necessary before classifier design, including feature selection and data dimension reduction [68]. There are two types of feature selection algorithms, i.e., filter methods and wrapper methods [64]. The filter methods include t-statistical tests [57], correlation coefficients [64], etc. And the wrapper methods include sparse representation [69, 70], recursive feature elimination (RFE) [71], etc. These two types of feature selection methods have their own advantages, and the former is simple, fast but not optimal, while the latter has superior classification performance but slow speed and poor robustness. PCA and ICA are the commonly used data dimension reduction methods, both of which are linear dimension reduction methods. However, due to considerable individual differences between subjects, the distribution of the sample in brain image feature space is often nonlinear, and it is sometimes difficult to achieve good results by using linear dimension reduction methods. Thus, manifold learning [64, 72] and kernel methods [73] were recently introduced in dimensionality reduction of MRI image data.

9 Classifier Design and Performance Evaluation Most commonly used classifiers include Fisher discriminant analysis (FDA) [58, 65, 74], support vector machine (SVM) [75], etc. It is worth mentioning that multimodal fusion classifiers have recently attracted increasing attention [76, 77]. Machine learning can be divided into two categories: supervised learning and unsupervised learning [78, 79]. Supervised learning methods such as FDA and SVM can map multiple observation samples to multiple categories based on class labels, while unsupervised learning methods such as K-means, spectral clustering [80], and maximal margin clustering (MMC) [81–83] can explore the intrinsic structure of data without priori class label information. As known, the current diagnosis of psychiatric disorders including major depression based largely on self-reported symptoms and clinical signs may be prone to patients behaviors and psychiatrists’ bias. Hence, it is important to develop an unsupervised classification framework that is independent of prior clinical diagnoses to escape the above biases [79].

References

9

Regarding to the evaluation of classification performance, cross-validation is essential, and accuracy, sensitivity, specificity, F-score, receiver operating characteristic curve (ROC) [84] are also used for performance evaluation. Recently, deep learning has attracted increasing attention in the field of machine learning and artificial intelligence and has been demonstrated to prodigiously improve learning performance, by introducing multilayer neural network structure and automatically abstracting low-dimensional features by end-to-end learning [85, 86]. Kim and colleagues used a deep auto-encoder neural network for wholebrain functional connectivity classification of schizophrenia with a small sample size (n = 100) [87], illuminating the potential of deep learning in automatic diagnosis of clinical populations [88–91]. Furthermore, deep learning is capable of learning subtle hidden patterns from high-dimensional neuroimaging data, perhaps providing cues for understanding the neural basis of psychiatric disorders [92–94]. So the potential of deep learning in brain imaging-based classification needs more attention.

10 The Content of the Book There are totally 12 chapters in this book: Chap. 1 is the Introduction; Chaps. 2, 3 and 4 refer to feature extraction, including linear functional connectivity, nonlinear functional connectivity, and dynamic functional connectivity; Chaps. 5, 6, 7 and 8 refer to manifold learning for dimensionality reduction, including locally linear embedding (LLE) of functional/anatomical connectivity, locality preserving projection (LPP) of functional connectivity, and intrinsic discriminant analysis (IDA) of functional connectivity; Chaps. 9 and 10 refer to sparse representation of functional connectivity, including sparse representation of dynamic functional connectivity and low-rank learning of functional connectivity; Chaps. 11 and 12 refer to multi-task learning and deep learning for multi-site classification.

References 1. Ogawa, S., Tank, W.L., Menon, R., Ellermann, M.L., Kim, G.L., Merkle, H., Ugurbil, K.: Intrinsic signal changes accompanying sensory stimulation: functional brain mapping with magnetic resonance imaging. Proc. Natl. Acad. Sci. 89(13), 5951–5955 (1992). arXiv:https:// www.pnas.org/content/89/13/5951.full.pdf, https://doi.org/10.1073/pnas.89.13.5951. https:// www.pnas.org/content/89/13/5951 2. Friston, J.L.: Modalities, modes, and models in functional neuroimaging. Science 326(5951), 399–403 (2009). arXiv:https://science.sciencemag.org/content/326/5951/399.full.pdf, https:// doi.org/10.1126/science.1174521. https://science.sciencemag.org/content/326/5951/399 3. Smith, M.L.: The future of FMRI connectivity. NeuroImage 62(2), 1257–1266 (2012), 20 years of fMRI. http://www.sciencedirect.com/science/article/pii/S1053811912000390. https://doi. org/10.1016/j.neuroimage.2012.01.022

10

1 Introduction

4. Karas, G., Burton, E., Rombouts, S., van Schijndel, R., O’Brien, J., Scheltens, P., McKeith, I., Williams, D., Ballard, C., Barkhof, F.: A comprehensive study of gray matter loss in patients with alzheimer’s disease using optimized voxel-based morphometry. NeuroImage 18(4), 895– 907 (2003). https://doi.org/10.1016/S1053-8119(03)00041-7. http://www.sciencedirect.com/ science/article/pii/S1053811903000417 5. Burton, J.L., McKeith, I.G., Burn, J.L., Williams, D.L., O’Brien, T.L.: Cerebral atrophy in Parkinson’s disease with and without dementia: a comparison with Alzheimer’s disease, dementia with Lewy bodies and controls. Brain 127(4), 791–800 (2004). https://doi. org/10.1093/brain/awh088. arXiv:http://oup.prod.sis.lan/brain/article-pdf/127/4/791/1116046/ awh088.pdf 6. Zeng, L.-L., Xie, L., Shen, H., Luo, Z., Fang, P., Hou, Y., Tang, B., Wu, T., Hu, D.: Differentiating patients with parkinson’s disease from normal controls using gray matter in the cerebellum. The Cerebellum 16(1), 151–157 (2017). https://doi.org/10.1007/s12311-0160781-1. 7. Bernasconi, N., Duchesne, S., Janke, A., Lerch, J., Collins, D., Bernasconi, A.: Whole-brain voxel-based statistical analysis of gray matter and white matter in temporal lobe epilepsy. NeuroImage 23(2), 717–723 (2004). https://doi.org/10.1016/j.neuroimage.2004.06.015. http:// www.sciencedirect.com/science/article/pii/S1053811904003246 8. Chen, S., Xia, W., Li, L., Liu, J., He, Z., Zhang, Z., Yan, L., Zhang, J., Hu, D.: Gray matter density reduction in the insula in fire survivors with posttraumatic stress disorder: a voxelbased morphometric study. Psychiatr. Res.: Neuroimaging 146(1), 65–72 (2006). https:// doi.org/10.1016/j.pscychresns.2005.09.006. http://www.sciencedirect.com/science/article/pii/ S0925492705001514 9. Kubicki, M., Shenton, M., Salisbury, D., Hirayasu, Y., Kasai, K., Kikinis, R., Jolesz, F., McCarley, R.: Voxel-based morphometric analysis of gray matter in first episode schizophrenia. NeuroImage 17(4), 1711–1719 (2002). https://doi.org/10.1006/nimg.2002.1296. http://www. sciencedirect.com/science/article/pii/S1053811902912966 10. Zeng, L.-L., Shen, H., Liu, L., Fang, P., Liu, Y., Hu, D.: State-dependent and trait-related gray matter changes in nonrefractory depression. NeuroReport 26(2), 57– 65 (2015). https://journals.lww.com/neuroreport/Fulltext/2015/01020/State_dependent_and_ trait_related_gray_matter.3.aspx 11. Hill, J., Inder, T., Neil, J., Dierker, D., Harwell, J., Van Essen, D.: Similar patterns of cortical expansion during human development and evolution. Proc. Natl. Acad. Sci. 107(29), 13135– 13140 (2010). arXiv:https://www.pnas.org/content/107/29/13135.full.pdf, https://doi.org/10. 1073/pnas.1001229107. https://www.pnas.org/content/107/29/13135 12. Wilke, M., Krägeloh-Mann, I., Holland, K.L.: Global and local development of gray and white matter volume in normal children and adolescents. Exp. Br. Res. 178(3), 296–307 (2007). https://doi.org/10.1007/s00221-006-0732-z 13. Maguire, A.L., Woollett, K., Spiers, J.L.: London taxi drivers and bus drivers: a structural MRI and neuropsychological analysis. Hippocampus 16(12), 1091–1101 (2019). https://doi.org/10. 1002/hipo.20233 14. Evans, C.L., He, Y., Chen, J.L.: Small-world anatomical networks in the human brain revealed by cortical thickness from MRI. Cereb. Cortex 17(10), 2407–2419 (2007). arXiv: http://oup.prod.sis.lan/cercor/article-pdf/17/10/2407/17296816/bhl149.pdf, https://doi.org/10. 1093/cercor/bhl149 15. Bassett, S.L., Bullmore, E., Verchinski, A.L., Mattay, S.L., Weinberger, R.L., MeyerLindenberg, A.: Hierarchical organization of human cortical networks in health and schizophrenia. J. Neurosci. 28(37), 9239–9248 (2008). arXiv:http://www.jneurosci.org/ content/28/37/9239.full.pdf, https://doi.org/10.1523/JNEUROSCI.1929-08.2008. http://www. jneurosci.org/content/28/37/9239 16. Basser, P., Mattiello, J., LeBihan D.: MR diffusion tensor spectroscopy and imaging. Biophys. J. 66(1), 259–267 (1994). https://doi.org/10.1016/S0006-3495(94)80775-1. http://www. sciencedirect.com/science/article/pii/S0006349594807751

References

11

17. Behrens, T., Berg, J.L., Jbabdi, S., Rushworth, M., Woolrich, M.: Probabilistic diffusion tractography with multiple fibre orientations: what can we gain?. NeuroImage 34(1), 144– 155 (2007). https://doi.org/10.1016/j.neuroimage.2006.09.018. http://www.sciencedirect.com/ science/article/pii/S1053811906009360 18. Fjell, M.L., Engvig, A., Tamnes, K.L., Grydeland, H., Walhovd, B.L., Westlye, T.L., Ostby, Y., Dale, M.L., Bjørnerud, A., Due-Tønnessen, P.: Life-span changes of the human brain white matter: diffusion tensor imaging (DTI) and volumetry. Cereb. Cortex 20(9), 2055– 2068 (2009). arXiv:http://oup.prod.sis.lan/cercor/article-pdf/20/9/2055/17303951/bhp280.pdf, https://doi.org/10.1093/cercor/bhp280 19. Ingalhalikar, M., Smith, A., Parker, D., Satterthwaite, D.L., Elliott, A.L., Ruparel, K., Hakonarson, H., Gur, E.L., Gur, C.L., Verma, R.: Sex differences in the structural connectome of the human brain. Proc. Natl. Acad. Sci. 111(2), 823–828 (2014). arXiv:https://www.pnas. org/content/111/2/823.full.pdf, https://doi.org/10.1073/pnas.1316909110. https://www.pnas. org/content/111/2/823 20. Fang, P., Zeng, L.-L., Shen, H., Wang, L., Li, B., Liu, L., Hu, D.: Increased cortical-limbic anatomical network connectivity in major depression revealed by Diffusion Tensor Imaging. PLoS ONE 7(9), 1–10 (2012). https://doi.org/10.1371/journal.pone.0045972 21. Raichle, E.L.: A brief history of human brain mapping. Trends Neurosci. 32(2), 118– 126 (2009). https://doi.org/10.1016/j.tins.2008.11.001. http://www.sciencedirect.com/science/ article/pii/S0166223608002658 22. Fox, D.L., Raichle, E.L.: Spontaneous fluctuations in brain activity observed with functional magnetic resonance imaging. Nat. Rev. Neurosci. 8, 700–711 (2007). https://doi.org/10.1038/ nrn2201 23. Biswal, B., Zerrin Yetkin, F., Haughton, M.L., Hyde, S.L.: Functional connectivity in the motor cortex of resting human brain using echo-planar MRI. Magn. Reson. Med. 34(4), 537–541 (2019). https://doi.org/10.1002/mrm.1910340409 24. Friston, J.L.: Functional and effective connectivity in neuroimaging: a synthesis. Hum. Brain Mapp. 2(1), 56–78 (2019). https://doi.org/10.1002/hbm.460020107 25. McKeown, M.J., Jung, T.-P., Makeig, S., Brown, G., Kindermann, S.L., Lee, T.-W., Sejnowski, J.L.: Spatially independent activity patterns in functional MRI data during the stroop color-naming task. Proc. Natl. Acad. Sci. 95(3), 803–810 (1998). arXiv:https:// www.pnas.org/content/95/3/803.full.pdf, https://doi.org/10.1073/pnas.95.3.803. https://www. pnas.org/content/95/3/803 26. Friston, J.L., Frith, D.L., Liddle, F.L., Frackowiak, R.S.J.: Functional connectivity: the principal-component analysis of large (PET) data sets. J. Cereb. Blood Flow Metab. 13(1), 5–14 (1993), pMID: 8417010. https://doi.org/10.1038/jcbfm.1993.4 27. Sun, T.L., Miller, M.L., D’Esposito, M.: Measuring interregional functional connectivity using coherence and partial coherence analyses of fMRI data. NeuroImage 21(2), 647– 658 (2004). https://doi.org/10.1016/j.neuroimage.2003.09.056. http://www.sciencedirect.com/ science/article/pii/S1053811903006062 28. Cordes, D., Haughton, V., Carew, D.L., Arfanakis, K., Maravilla, K.: Hierarchical clustering to measure connectivity in fMRI resting-state data. Magn. Reson. Imaging 20(4), 305– 317 (2002). https://doi.org/10.1016/S0730-725X(02)00503-9. http://www.sciencedirect.com/ science/article/pii/S0730725X02005039 29. Greicius, D.L., Krasnow, B., Reiss, L.L., Menon, V.: Functional connectivity in the resting brain: a network analysis of the default mode hypothesis. Proc. Natl. Acad. Sci. 100(1), 253– 258 (2003). arXiv:https://www.pnas.org/content/100/1/253.full.pdf, https://doi.org/10.1073/ pnas.0135058100. https://www.pnas.org/content/100/1/253 30. Fox, D.L., Snyder, Z.L., Vincent, L.L., Corbetta, M., Van Essen, C.L., Raichle, E.L.: The human brain is intrinsically organized into dynamic, anticorrelated functional networks. Proc. Natl. Acad. Sci. 102(27), 9673–9678 (2005). arXiv:https://www.pnas.org/content/102/27/ 9673.full.pdf, https://doi.org/10.1073/pnas.0504136102. https://www.pnas.org/content/102/ 27/9673

12

1 Introduction

31. Beckmann Christian, F., Marilena, D., Devlin Joseph, T., Smith Stephen, M.: Investigations into resting-state connectivity using independent component analysis. Philos. Trans. R. Soc. B: Biol. Sci. 360(1457), 1001–1013 (2019). https://doi.org/10.1098/rstb.2005.1634 32. Damoiseaux, S.L., Rombouts, S.A.R.B., Barkhof, F., Scheltens, P., Stam, J.L., Smith, M.L., Beckmann, F.L.: Consistent resting-state networks across healthy subjects. Proc. Natl. Acad. Sci. 103(37), 13848–13853 (2006). arXiv:https://www.pnas.org/content/103/37/13848.full. pdf, https://doi.org/10.1073/pnas.0601417103. https://www.pnas.org/content/103/37/13848 33. Friston, J.L., Frith, D.L., Frackowiak, R.S.J.: Time-dependent changes in effective connectivity measured with PET. Hum. Brain Mapp. 1(1), 69–79 (2019). https://doi.org/10.1002/hbm. 460010108 34. Friston, J.L., Ungerleider, G.L., Jezzard, P., Turner, R.: Characterizing modulatory interactions between areas V1 and V2 in human cortex: a new treatment of functional MRI data. Hum. Brain Mapp. 2(4), 211–224 (2019). https://doi.org/10.1002/hbm.460020403 35. Friston, K., Harrison, L., Penny, W.: Dynamic causal modelling. NeuroImage 19(4), 1273– 1302 (2003). https://doi.org/10.1016/S1053-8119(03)00202-7. http://www.sciencedirect.com/ science/article/pii/S1053811903002027 36. Goebel, R., Roebroeck, A., Kim, D.-S., Formisano, E.: Investigating directed cortical interactions in time-resolved fMRI data using vector autoregressive modeling and granger causality mapping. Magn. Reson. Imaging 21(10), 1251–1261 (2003). https://doi.org/10.1016/j.mri. 2003.08.026. http://www.sciencedirect.com/science/article/pii/S0730725X03003370 37. Horwitz, B., Tagamets, M.-A., McIntosh, A.R. Neural modeling, functional brain imaging, and cognition. Trends Cogn. Sci. 3(3), 91–98 (1999). https://doi.org/10.1016/S13646613(99)01282-6. http://www.sciencedirect.com/science/article/pii/S1364661399012826 38. Esposito, F., Bertolino, A., Scarabino, T., Latorre, V., Blasi, G., Popolizio, T., Tedeschi, G., Cirillo, S., Goebel, R., Salle, D.L.: Independent component model of the default-mode brain function: assessing the impact of active thinking. Brain Res. Bull. 70(4), 263– 269 (2006). https://doi.org/10.1016/j.brainresbull.2006.06.012. http://www.sciencedirect.com/ science/article/pii/S0361923006002073 39. Fornito, A., Harrison, J.L., Zalesky, A., Simons, S.L.: Competitive and cooperative dynamics of large-scale brain functional networks supporting recollection. Proc. Natl. Acad. Sci. 109(31), 12788–12793 (2012). arXiv:https://www.pnas.org/content/109/31/12788.full.pdf, https://doi. org/10.1073/pnas.1204185109. https://www.pnas.org/content/109/31/12788 40. Rao, A.L., Sun, T.L., D’Esposito, M., Miller, M.L.: Functional connectivity of cortical networks involved in bimanual motor sequence learning. Cereb. Cortex 17(5), 1227–1234 (2006). arXiv:http://oup.prod.sis.lan/cercor/article-pdf/17/5/1227/991520/bhl033.pdf, https:// doi.org/10.1093/cercor/bhl033 41. Lewis, M.L., Baldassarre, A., Committeri, G., Romani, L.L., Corbetta, M.: Learning sculpts the spontaneous activity of the resting human brain. Proc. Natl. Acad. Sci. 106(41), 17558– 17563 (2009). arXiv:https://www.pnas.org/content/106/41/17558.full.pdf, https://doi.org/10. 1073/pnas.0902455106. https://www.pnas.org/content/106/41/17558 42. Horovitz, G.L., Fukunaga, M., de Zwart, A.L., van Gelderen, P., Fulton, C.L., Balkin, J.L., Duyn, H.L.: Low frequency bold fluctuations during resting wakefulness and light sleep: a simultaneous EEG-fMRI study. Hum. Brain Mapp. 29(6), 671–682 (2019). https://doi.org/10. 1002/hbm.20428 43. Horovitz, G.L., Braun, R.L., Carr, S.L., Picchioni, D., Balkin, J.L., Fukunaga, M., Duyn, H.L.: Decoupling of the brain’s default mode network during deep sleep. Proc. Natl. Acad. Sci. 106(27), 11376–11381 (2009). arXiv:https://www.pnas.org/content/106/27/11376.full. pdf, https://doi.org/10.1073/pnas.0901435106. https://www.pnas.org/content/106/27/11376 44. Boveroux, P., Vanhaudenhuyse, A., Bruno, M.-A., Noirhomme, Q., Lauwick, S., Luxen, A., Degueldre, C., Plenevaux, A., Schnakers, C., Phillips, C., Brichant, J.-F., Bonhomme, V., Maquet, P., Greicius, D.L., Laureys, S., Boly, M.: Breakdown of within- and between-network resting state functional magnetic resonance imaging connectivity during propofol-induced loss of consciousness. Anesthesiology 113(5), 1038–1053 (2010). https://doi.org/10.1097/aln. 0b013e3181f697f5

References

13

45. Allen, A.L., Damaraju, E., Plis, M.L., Erhardt, B.L., Eichele, T., Calhoun, D.L.: Tracking whole-brain connectivity dynamics in the resting state. Cereb. Cortex 24(3), 663– 676 (2012). arXiv:http://oup.prod.sis.lan/cercor/article-pdf/24/3/663/14099596/bhs352.pdf, https://doi.org/10.1093/cercor/bhs352 46. Kiviniemi, V., Vire, T., Remes, J., Elseoud, A.L., Starck, T., Tervonen, O., Nikkinen, J.: A sliding time-window ICA reveals spatial variability of the default mode network in time. Brain Connect. 1(4), 339–347 (2011), pMID: 22432423. https://doi.org/10.1089/brain.2011.0036 47. Chang, C., Glover, H.L.: Time-frequency dynamics of resting-state brain connectivity measured with fMRI. NeuroImage 50(1), 81–98 (2010). https://doi.org/10.1016/j.neuroimage. 2009.12.011. http://www.sciencedirect.com/science/article/pii/S1053811909012981 48. Hutchison, M.L., Womelsdorf, T., Gati, S.L., Everling, S., Menon, S.L.: Resting-state networks show dynamic functional connectivity in awake humans and anesthetized macaques. Hum. Brain Mapp. 34(9), 2154–2177 (2019). https://doi.org/10.1002/hbm.22058 49. Jones, T.L., Vemuri, P., Murphy, C.L., Gunter, L.L., Senjem, L.L., Machulda, M.L., Przybelski, A.L., Gregg, E.L., Kantarci, K., Knopman, S.L., Boeve, F.L., Petersen, C.L., Jack, R.L., Jr.: Non-stationarity in the resting brain’s modular architecture. PLoS ONE 7(6), 1–15 (2012). https://doi.org/10.1371/journal.pone.0039731 50. Hutchison, M.L., Womelsdorf, T., Allen, A.L., Bandettini, A.L., Calhoun, D.L., Corbetta, M., Penna, D.L., Duyn, H.L., Glover, H.L., Gonzalez-Castillo, J., Handwerker, A.L., Keilholz, S., Kiviniemi, V., Leopold, A.L., de Pasquale, F., Sporns, O., Walter, M., Chang, C.: Dynamic functional connectivity: promise, issues, and interpretations. NeuroImage 80:360–378 (2013), mapping the Connectome. https://doi.org/10.1016/j.neuroimage.2013.05.079. http://www. sciencedirect.com/science/article/pii/S105381191300579X 51. Haynes, J.-D., Rees, G.: Decoding mental states from brain activity in humans. Nat. Rev. Neurosci. 7, 523 (2006). https://doi.org/10.1038/nrn1931 52. Gong, Q., Wu, Q., Scarpazza, C., Lui, S., Jia, Z., Marquand, A., Huang, X., McGuire P, Mechelli, A.: Prognostic prediction of therapeutic response in depression using high-field MR imaging. NeuroImage 55(4), 1497–1503 (2011). https://doi.org/10.1016/j.neuroimage.2010. 11.079. http://www.sciencedirect.com/science/article/pii/S1053811910015570 53. Draganski, B., Chu, C., Jack, J., Clifford, R., Stonnington, M.L., Ashburner, J., Rohrer, D.L., Fox, C.L., Scahill, I.L., Frackowiak, R.S.J., Klöppel, S.: Automatic classification of MR scans in Alzheimer’s disease. Brain 131(3), 681–689 (2008). https://doi.org/10.1093/brain/awm319. arXiv:http://oup.prod.sis.lan/brain/article-pdf/131/3/681/898663/awm319.pdf 54. Uddin, Q.L., Menon, V., Young, B.L., Ryali, S., Chen, T., Khouzam, A., Minshew, J.L., Hardan, Y.L.: Multivariate searchlight classification of structural magnetic resonance imaging in children and adolescents with autism. Biol. Psychiatr. 70(9), 833–841 (2011), genetic and Environmental Contributors to Disturbed Cortical Development in Developmental Disorders. https://doi.org/10.1016/j.biopsych.2011.07.014. http://www.sciencedirect.com/science/article/ pii/S000632231100727X 55. Desikan, S.L., Cabral, J.L., Hess, P.L., Dillon, P.L., Glastonbury, M.L., Weiner, W.L., Schmansky, J.L., Greve, N.L., Salat, H.L., Buckner, L.L., Fischl, B.: Alzheimer’s Disease Neuroimaging Initiative. Automated MRI measures identify individuals with mild cognitive impairment and Alzheimer’s disease*. Brain 132(8), 2048–2057 (2009). arXiv:http://oup.prod.sis.lan/ brain/article-pdf/132/8/2048/743415/awp123.pdf, https://doi.org/10.1093/brain/awp123 56. Yoon, U., Lee, J.-M., Im, K., Shin, Y.-W., Cho, H.L., Kim, Y.L., Kwon, S.L., Kim, I.L.: Pattern classification using principal components of cortical thickness and its discriminative pattern in schizophrenia. NeuroImage 34(4), 1405–1415 (2007). https://doi.org/10.1016/j.neuroimage. 2006.11.021. http://www.sciencedirect.com/science/article/pii/S1053811906011232 57. Mwangi, B., Douglas Steele, J., Matthews, K., Ebmeier, P.L.: Multi-centre diagnostic classification of individual structural neuroimaging scans from patients with major depressive disorder. Brain 135(5), 1508–1521 (2012). arXiv:http://oup.prod.sis.lan/brain/article-pdf/135/ 5/1508/17865265/aws084.pdf, https://doi.org/10.1093/brain/aws084 58. Ardekani, A.L., Tabesh, A., Sevy, S., Robinson, G.L., Bilder, M.L., Szeszko, R.L.: Diffusion tensor imaging reliably differentiates patients with schizophrenia from healthy volunteers. Hum. Brain Mapp. 32(1), 1–9 (2019). https://doi.org/10.1002/hbm.20995

14

1 Introduction

59. Walther, B.L., Chai, B., Caddigan, E., Beck, M.L., Fei-Fei, L.: Simple line drawings suffice for functional MRI decoding of natural scene categories. Proc. Natl. Acad. Sci. 108(23), 9661–9666 (2011). arXiv:https://www.pnas.org/content/108/23/9661.full.pdf, https://doi.org/ 10.1073/pnas.1015666108. https://www.pnas.org/content/108/23/9661 60. Fu, H.L., Mourao-Miranda, J., Costafreda, G.L., Khanna, A., Marquand, F.L., Williams, C.L., Brammer, J.L.: Pattern classification of sad facial processing: toward the development of neurobiological markers in depression. Biol. Psychiatr. 63(7), 656–662 (2008), the Neurobiology and Therapeutics of Antidepressant-Resistant Depression. https://doi.org/10.1016/j.biopsych. 2007.08.020. http://www.sciencedirect.com/science/article/pii/S0006322307008773 61. Shirer, R.L., Greicius, D.L., Rykhlevskaia, E., Ryali, S., Menon, V.: Decoding subjectdriven cognitive states with whole-brain connectivity patterns. Cereb. Cortex 22(1), 158– 165 (2011). arXiv:http://oup.prod.sis.lan/cercor/article-pdf/22/1/158/14096754/bhr099.pdf, https://doi.org/10.1093/cercor/bhr099 62. Dosenbach, N.U.F., Nardos, B., Cohen, L.L., Fair, A.L., Power, D.L., Church, A.L., Nelson, M.L., Wig, S.L., Vogel, C.L., Lessov-Schlaggar, N.L., Barnes, A.L., Dubis, W.L., Feczko, E., Coalson, S.L., Pruett, R.L., Barch, M.L., Petersen, E.L., Schlaggar, L.L.: Prediction of individual brain maturity using fMRI. Science 329(5997), 1358–1361 (2010). arXiv:https://science.sciencemag.org/content/329/5997/1358.full.pdf, https://doi.org/10.1126/ science.1194144. https://science.sciencemag.org/content/329/5997/1358 63. Craddock, C.L., Holtzheimer, E.L., III, Hu, P.L., Mayberg, S.L.: Disease state prediction from resting state functional connectivity. Magn. Reson. Med. 62, 1619–1628 (2009) 64. Shen, H., Wang, L., Liu, Y., Hu, D.: Discriminative analysis of resting-state functional connectivity patterns of schizophrenia using low dimensional embedding of fMRI. NeuroImage 49(4), 3110–3121 (2010). https://doi.org/10.1016/j.neuroimage.2009.11.011. http://www. sciencedirect.com/science/article/pii/S1053811909011951 65. Zhu, C.-Z., Zang, Y.-F., Cao, Q.-J., Yan, C.-G., He, Y., Jiang, T.-Z., Sui, M.-Q., Wang, Y.-F.: Fisher discriminative analysis of resting-state brain function for attention-deficit/hyperactivity disorder. NeuroImage 40(1), 110–120 (2008). https://doi.org/10.1016/j.neuroimage.2007.11. 029. http://www.sciencedirect.com/science/article/pii/S1053811907010610 66. Li, X., Zhu, D., Jiang, X., Jin, C., Zhang, X., Guo, L., Zhang, J., Hu, X., Li, L., Liu, T.: Dynamic functional connectomics signatures for characterization and differentiation of PTSD patients. Hum. Brain Mapp. 35(4), 1761–1778 (2019). https://doi.org/10.1002/hbm.22290 67. Brodersen, H.L., Schofield, M.L., Leff, P.L., Ong, S.L., Lomakina, I.L., Buhmann, M.L., Stephan, E.L.: Generative embedding for model-based classification of fMRI data. PLoS Comput. Biol. 7(6), 1–19 (2011). https://doi.org/10.1371/journal.pcbi.1002079 68. Pereira, F., Mitchell, T., Botvinick, M.: Machine learning classifiers and fMRI: a tutorial overview. NeuroImage 45(1, Supplement 1), S199–S209 (2009), mathematics in Brain Imaging. https://doi.org/10.1016/j.neuroimage.2008.11.007. http://www.sciencedirect.com/science/ article/pii/S1053811908012263 69. Li, Y., Namburi, P., Yu, Z., Guan, C., Feng, J., Gu, Z.: Voxel selection in fMRI data analysis based on sparse representation. IEEE Trans. Biomed. Eng. 56(10), 2439–2451 (2009) 70. Ryali, S., Supekar, K., Abrams, A.L., Menon, V.: Sparse logistic regression for whole-brain classification of fMRI data. NeuroImage 51(2), 752–764 (2010). https:// doi.org/10.1016/j.neuroimage.2010.02.040. http://www.sciencedirect.com/science/article/pii/ S1053811910002089 71. Martino, D.L., Valente, G., Staeren, N., Ashburner, J., Goebel, R., Formisano, E.: Combining multivariate voxel selection and support vector machines for mapping and classification of fMRI spatial patterns. NeuroImage 43(1), 44–58 (2008). https://doi.org/10.1016/j.neuroimage. 2008.06.037. http://www.sciencedirect.com/science/article/pii/S1053811908007854 72. Shen, X., Meyer, G.L.: Low-dimensional embedding of fMRI datasets. NeuroImage 41(3), 886–902 (2008). https://doi.org/10.1016/j.neuroimage.2008.02.051. http://www.sciencedirect. com/science/article/pii/S1053811908001869

References

15

73. Hardoon, R.L., Mourão-Miranda, J., Brammer, M., Shawe-Taylor, J.: Unsupervised analysis of fMRI data using kernel canonical correlation. NeuroImage 37(4), 1250–1259 (2007). https:// doi.org/10.1016/j.neuroimage.2007.06.017. http://www.sciencedirect.com/science/article/pii/ S1053811907005708 74. Duda, O.L., Hart, E.L., Stork, G.L.: Pattern Classification. Wiley, New York (2012) 75. Vapnik, V.: The Nature of Statistical Learning Theory. Springer Science & Business Media, New York (2013) 76. Dai, Z., Yan, C., Wang, Z., Wang, J., Xia, M., Li, K., He, Y.: Discriminative analysis of early alzheimer’s disease using multi-modal imaging and multi-level characterization with multiclassifier (M3). NeuroImage 59(3), 2187–2195 (2012). https://doi.org/10.1016/j.neuroimage. 2011.10.003. http://www.sciencedirect.com/science/article/pii/S1053811911011645 77. Liu, F., Wee, C.-Y, Chen, H., Shen, D.: Inter-modality relationship constrained multi-modality multi-task feature selection for alzheimer’s disease and mild cognitive impairment identification. NeuroImage 84, 466–475 (2014). https://doi.org/10.1016/j.neuroimage.2013.09.015. http://www.sciencedirect.com/science/article/pii/S1053811913009518 78. Orrù, G., Pettersson-Yeo, W., Marquand, F.L., Sartori, G., Mechelli, A.: Using support vector machine to identify imaging biomarkers of neurological and psychiatric disease: a critical review. Neurosci. Biobehav. Rev. 36(4), 1140–1152 (2012). https://doi. org/10.1016/j.neubiorev.2012.01.004. http://www.sciencedirect.com/science/article/pii/ S0149763412000139 79. Bishop, M.L., Nasrabadi, M.L.: Pattern recognition and machine learning. J. Electron. Imaging 16(4), 049901–049902 (2007) 80. Shi, J., Malik, J.: Normalized cuts and image segmentation, Departmental Papers (CIS), pp. 888–905 (2000) 81. Wang, F., Zhao, B., Zhang, C.: Linear time maximum margin clustering. IEEE Trans. Neural Netw. 21(2), 319–332 (2010) 82. Li, Y.-F., Tsang, W.L., Kwok, J., Zhou, Z.-H.: Tighter and convex maximum margin clustering. In: van Dyk, D., Welling, M. (eds.) Artificial Intelligence and Statistics, pp. 344–51. PMLR (2009) 83. Xu, L., Neufeld, J., Larson, B., Schuurmans, D.: Maximum margin clustering. In: Advances in Neural Information Processing Systems, pp. 1537–44. Cambridge/London: MIT Press (2005) 84. Fawcett, T.: An introduction to ROC analysis. Pattern Recogn. Lett. 27(8), 861–874 (2006). https://doi.org/10.1016/j.patrec.2005.10.010. http://www.sciencedirect.com/science/ article/pii/S016786550500303X 85. Lecun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521(7553), 436 (2015) 86. Sun, Y., Wang, X., Tang, X.: Hybrid deep learning for face verification. IEEE Trans. Pattern Anal. Mach. Intell. 38(10), 1997–2009 (2016). https://doi.org/10.1109/TPAMI.2015.2505293 87. Kim, J., Calhoun, D.L., Shim, E., Lee, J.-H.: Deep neural network with weight sparsity control and pre-training extracts hierarchical features and enhances classification performance: evidence from whole-brain resting-state functional connectivity patterns of schizophrenia. NeuroImage 124, 127–146 (2016). https://doi.org/10.1016/j.neuroimage.2015.05.018. http:// www.sciencedirect.com/science/article/pii/S1053811915003985 88. Hazlett, C.L., Gu, H., Munsell, C.L., Kim, H.L., Styner, M., Wolff, J.L., Elison, T.L., Swanson, R.L., Zhu, H., Botteron, N.L., Collins, L.L., Constantino, N.L., Dager, R.L., Estes, M.L., Evans, C.L., Fonov, S.L., Gerig, G., Kostopoulos, P., McKinstry RC, Pandey, J., Paterson, S., Pruett, R.L., Schultz, T.L., Shaw, W.L., Zwaigenbaum, L., Piven, J., IBIS Network, Clinical Sites, Data Coordinating Center, Image Processing Core, Statistical Analysis.: Early brain development in infants at high risk for autism spectrum disorder. Nature 542(7641), 348–351 (2017). https://doi.org/10.1038/nature21369. http://europepmc.org/articles/PMC5336143 89. Suk, H.-I., Lee, S.-W., Shen, D., The Alzheimer’s Disease Neuroimaging Initiative.: Latent feature representation with stacked auto-encoder for ad/mci diagnosis. Brain Struct. Funct. 220(2), 841–859 (2015). https://doi.org/10.1007/s00429-013-0687-3

16

1 Introduction

90. Kawahara, J., Brown, J.L., Miller, P.L., Booth, G.L., Chau, V., Grunau, E.L., Zwicker, G.L., Hamarneh, G.: Brainnetcnn: convolutional neural networks for brain networks; towards predicting neurodevelopment. NeuroImage 146, 1038–1049 (2017). https:// doi.org/10.1016/j.neuroimage.2016.09.046. http://www.sciencedirect.com/science/article/pii/ S1053811916305237 91. Zhao, Y., Dong, Q., Chen, H., Iraji, A., Li, Y., Makkie, M., Kou, Z., Liu, T.: Constructing fine-granularity functional brain network atlases via deep convolutional autoencoder. Med. Image Anal. 42, 200–211 (2017). https://doi.org/10.1016/j.media.2017.08.005. http://www. sciencedirect.com/science/article/pii/S1361841517301287 92. Arbabshirani, R.L., Plis, S., Sui, J., Calhoun, D.L.: Single subject prediction of brain disorders in neuroimaging: promises and pitfalls. NeuroImage 145, 137–65 (2017), individual Subject Prediction. https://doi.org/10.1016/j.neuroimage.2016.02.079. http://www.sciencedirect.com/ science/article/pii/S105381191600210X 93. Guo, X., Dominick, C.L., Minai, A.L., Li, H., Erickson, A.L., Lu, J.L.: Diagnosing autism spectrum disorder from brain resting-state functional connectivity patterns using a deep neural network with a novel feature selection method. Front. Neurosci. 11, 460 (2017). https://doi. org/10.3389/fnins.2017.00460. https://www.frontiersin.org/article/10.3389/fnins.2017.00460 94. Vieira, S., Pinaya, H.L., Mechelli, A.: Using deep learning to investigate the neuroimaging correlates of psychiatric and neurological disorders: methods and applications. Neurosci. Biobehav. Rev. 74, 58–75 (2017). https://doi.org/10.1016/j.neubiorev.2017.01.002. http:// www.sciencedirect.com/science/article/pii/S0149763416305176

Chapter 2

Multivariate Pattern Analysis of Whole-Brain Functional Connectivity in Major Depression

Abstract Resting-state functional connectivity magnetic resonance imaging (rsfcMRI) studies have shown significant group differences in several regions and networks between depressed patients and healthy controls. This chapter conducted multivariate pattern analysis of whole-brain rs-fcMRI in major depression, which can be used to test the feasibility of identifying major depressive individuals from healthy controls. Twenty-four depressed patients and 29 demographically matched healthy volunteers were included in this study. Permutation tests were used to assess classifier performance. The experimental results demonstrate that 94.3% (P < 0.0001) of subjects were correctly classified via leave-one-out crossvalidation, including 100% identification of all patients. The majority of the most discriminating functional connections were located within or across the default mode network, affective network, visual cortical areas, and cerebellum, thereby indicating that the disease-related resting-state network alterations may give rise to a portion of the complex of emotional and cognitive disturbances in major depression. Moreover, the amygdala, anterior cingulate cortex, parahippocampal gyrus, and hippocampus, which exhibit high discriminative power in classification, may play important roles in the pathophysiology of this disorder. The current study may shed new light on the pathological mechanism of major depression and suggests that whole-brain rs-fcMRI may provide potential effective biomarkers for its clinical diagnosis. Keywords Multivariate pattern analysis · Major depression · fMRI · Functional connectivity · Resting state

1 Introduction Major depressive disorder is a common mental disorder characterized by a persistent, pervasive depressed mood or anhedonia, a sense of worthlessness, and cognitive impairments. Up to 10% of people with depressive episodes will become suicidal if untreated [1]. To date, the diagnosis of major depression has largely

© Springer Nature Singapore Pte Ltd. 2019 D. Hu, L.-L. Zeng, Pattern Analysis of the Human Connectome, https://doi.org/10.1007/978-981-32-9523-0_2

17

18

2 Multivariate Pattern Analysis of Whole-Brain Functional Connectivity in Major. . .

been based on self-reported symptoms and clinical signs. Understanding the pathophysiology of major depression is clearly an international imperative. It has been proposed that major depressive symptoms are associated with the dysregulation of a distributed neuronal network encompassing cortical and limbic regions rather than with the (functional) breakdown of a single discrete brain region [2]. Recently, resting-state functional connectivity magnetic resonance imaging (rsfcMRI) has attracted increasing attention for mapping large-scale neural network function and dysfunction. During rest, low-frequency (0.01–0.08 Hz) blood oxygen level-dependent fluctuations of the functional magnetic resonance imaging (fMRI) signals are thought to be related to spontaneous neuronal activity, and the correlation analysis method has proven effective for measuring functional connectivity network alterations in neuropsychiatric conditions, including depression [3]. The tonic nature of major depressive core symptoms indicates that rs-fcMRI may be helpful for improving our understanding of the pathophysiological mechanisms underlying affective and cognitive dysfunctions in major depression [3]. Based on rs-fcMRI, a growing body of studies has focused on the quantitative analysis of the brains of patients with neurologic and psychiatric disorders, including Alzheimer’s disease and dementia and schizophrenia. Using seed-based methods, rs-fcMRI studies have detected network alterations in depressed patients, especially abnormalities in the default mode network (DMN) and affective network. Similarly, Craddock et al. [4] employed multivoxel pattern analysis to predict major depressive state using resting-state functional connectivity limited to 15 predefined regions of interest (ROIs). Veer et al. [5] extracted resting-state networks of depressed patients using independent component analysis (ICA) and then used univariate statistical methods to investigate the identified components. These studies provide valuable insight into the pathological mechanism of major depression, but they also have some significant limitations. First, seed-based methods limit the obtained information to the selected ROIs and make it difficult to examine functional connectivity patterns on a wholebrain scale [6]. Second, traditional group-level statistical methods do not provide a mechanism for evaluating the discriminative power of the identified connections at the individual level [4, 7]. As a data-driven technique, multivariate pattern analysis (MVPA) based on whole-brain rs-fcMRI data can complement both seed-based and univariate statistical analyses. Whole-brain functional connectivity analysis, unlike those analyzing several predefined regions or networks of interest, can ensure the optimal use of the wealth of information present in the brain imaging data. In particular, MVPA methods can both find potential neuroimaging-based biomarkers to differentiate patients from healthy controls at the individual subject level and potentially detect exciting spatially distributed information to further highlight the neural mechanisms underlying the behavioral symptoms of major depression [8]. In recent years, there has been increasing interest in MVPA methods to categorize psychiatric patients from healthy controls using structural or functional brain images. If an MVPA-based classifier can label new samples with better-than-random accuracy,

2 Subjects

19

then the two populations are indeed likely to be different, and the classifier can capture the population differences [9]. In MVPA-based brain imaging analysis, the features for classification can be various structural characteristics or functional properties extracted from neuroimaging data. For rs-fcMRI, resting-state functional connectivity measured by the correlation of two fMRI time series has been used for the discrimination of psychiatric disorders [4, 10]. To date, it is unknown whether MVPA can capture whole-brain resting-state functional connectivity patterns to discriminate or identify depressed patients from healthy controls at the individual subject level with a high degree of accuracy. The purpose of this study was to explore significant disorder-related patterns using whole-brain rs-fcMRI in medication-free depressed patients without comorbidity and in carefully matched healthy controls and to discriminate patients from healthy subjects. The altered functional connections were expected to be observed in the resting-state networks that include areas known to be associated with affective and cognitive processing. Functional connectivity, measured by the correlation of two activity time series of anatomically separated brain regions, was used as a classification feature. This exploration will be helpful in further discovering the neural mechanisms underlying the behavioral symptoms of depression, which may offer additional information for advancing our understanding of the pathophysiology of this disorder.

2 Subjects The study’s participants included 32 patients diagnosed with major depressive disorder from the outpatient clinic at the First Affiliated Hospital of China Medical University and 33 demographically similar healthy volunteers recruited via advertisements. All of the subjects were right-handed native Chinese speakers. Three patients and two controls were removed from the sample, due to excessive head motion during scan acquisition (>2.5 mm translation and/or >2◦ rotation). Five additional patients and two additional control subjects were removed, due to head motions with acute fluctuations that caused a great deal of strong spurious correlation. The remaining 24 depressed patients and 29 healthy controls remained gender-, age-, education-, and weight-matched (see Table 2.1). Depressed patients met the criteria for a current episode of unipolar recurrent major depression based on the DSM-IV criteria [1]. Using the Structured Clinical Interview for DSM-IV [11], confirmation of the diagnosis was made by clinical psychiatrists. All patients were medication-naive at the time of the scan. Exclusion criteria included acute physical illness, substance abuse or dependence, a history of head injury resulting in loss of consciousness, and major psychiatric or neurological illness other than depression. Similar exclusion criteria were adopted for healthy control subjects. On the days of the scans, the depressive symptoms of patients

20

2 Multivariate Pattern Analysis of Whole-Brain Functional Connectivity in Major. . .

Table 2.1 Characteristics of the participants in this study Variable Sample size Gender (M/F) Age (years) Education (years) Weight (kg) Age of illness onset (years) Number of previous episodes Duration of current episode (months) HDRS HAMA CGI-S

Patient 24 8/16 31.83 ± 10.99 11.71 ± 3.13 60.5 ± 10.93 28.71 ± 10.90 1.63 ± 0.77 5.33 ± 6.29 26.42 ± 5.22 20.29 ± 5.25 5.92 ± 0.65

Control 29 9/20 33.62 ± 10.29 11.00 ± 3.12 62.55 ± 8.59

p-value 0.86a 0.54b 0.66b 0.45b

4.25 ± 1.02 3.55 ± 0.91

HDRS Hamilton depression rating scale, HAMA Hamilton anxiety rating scale, CGI-S clinical global impression scale-severity a Pearson Chi-square test b Two-sample t-test

were assessed with the 17-item Hamilton Depression Rating Scale (HDRS) [12], Hamilton Anxiety Rating Scale (HAMA) [13], and Clinical Global Impression Scale-Severity (CGI-S) [14] (see Table 2.1). Healthy volunteer subjects were studied under identical conditions. This study was approved by the Ethics Committee of the First Affiliated Hospital of China Medical University, and all participants gave written informed consent.

3 Image Acquisition and Preprocessing In the experiments, subjects were simply instructed to keep their eyes closed, relax, remain awake, and perform no specific cognitive exercise. After each session, subjects were asked whether they were awake and relaxed in the previous session, and all of the subjects confirmed that they were. Magnetic resonance images were acquired using a 1.5-T GE SIGNA scanner (GE Medical Systems). To reduce head movement, the subjects’ heads were fixed using foam pads with a standard birdcage head coil. All fMRI images were collected using a gradient-echo EPI sequence. The imaging parameters were as follows: repetition time/echo time (TR/TE) = 2000/50 ms, thickness/gap = 5/1.5 mm, field of view (FOV) = 240 × 240 mm, flip angle (FA) = 90◦ , matrix = 64 × 64, and slices = 20. Each functional resting-state session lasted about 8 min, and 245 volumes were obtained. Resting-state fMRI images were preprocessed using a statistical parametric mapping software package (SPM, http://www.fil.ion.ucl.ac.uk/spm). For each subject, the first five volumes of the scanned data were discarded for magnetic saturation.

4 Identification of Features with High Discriminative Power

21

The remaining 240 volumes were corrected by registering and reslicing for head motion. Next, the volumes were normalized to the standard EPI template in the Montreal Neurological Institute space. The resulting images were spatially smoothed with a Gaussian filter of 8 mm full-width half-maximum kernel, detrended to abandon linear trend, and then temporally filtered with a Chebyshev band-pass filter (0.01–0.08 Hz). The registered fMRI volumes with the Montreal Neurological Institute template were divided into 116 regions according to the automated anatomical labeling (AAL) atlas. The AAL atlas divides the cerebrum into 90 regions (45 in each hemisphere) and divides the cerebellum into 26 regions (9 in each cerebellar hemisphere and 8 in the vermis). All ROI masks were generated using the free software WFU_PickAtlas (Version 2.0, http://www.ansir.wfubmc. edu) [15]. Regional mean time series were obtained for each individual by averaging the fMRI time series over all voxels in each of the 116 regions. Note that aside from the band-pass filtering and correcting for movement, additional preprocessing steps, such as global signal regression, have recently been performed in functional connectivity analysis [16]. Global signal regression creates artifactual negative correlations, but this technique is suggested to improve the specificity of positive correlations and can remove specific confounds from the data to facilitate the evaluation of neurophysiological relationships [16], so the results with global signal regression are more readily or reliably interpreted. Therefore, each regional mean time series was further corrected by regressing out head motion and the global signals [17–20]. To further reduce spurious variance unlikely to reflect neuronal activity, we have included in regression the white matter and cerebrospinal fluid average signals, as well as the first-order derivative terms for the global, white matter, and cerebrospinal fluid average signals. The time courses of noise components extracted by using group ICA were also utilized for artifact removal for each subject [21]. The residuals of these regressions constituted the set of regional mean time series used for functional connectivity analyses. We evaluated functional connectivity between each pair of regions using Pearson correlation coefficient. Thus, for each subject, we obtained a resting-state functional network captured by a 116 × 116 symmetric matrix. Removing 116 diagonal elements, we extracted the upper triangle elements of the functional connectivity matrix as classification features, i.e., the feature space for classification was spanned by the (116 × 115)/2 = 6670 dimensional feature vectors.

4 Identification of Features with High Discriminative Power The abnormal resting-state functional connectivity patterns in depression are principally represented by the highly discriminating functional connections, and initially reducing the number of features accelerates computation and diminishes noise [8, 10, 18]. Therefore, feature selection was used to construct the feature space for classification by retaining the most discriminating functional connections and

22

2 Multivariate Pattern Analysis of Whole-Brain Functional Connectivity in Major. . .

eliminating the rest. The discriminative power of a feature can be quantitatively measured by its relevance to classification [22]. In this study, we used the Kendall tau rank correlation coefficient [23], which provides a distribution-free test of independence between two variables to measure the relevance of each feature to classification. Suppose that there are m samples in the patient group and n samples in the control group. Let xij denote the functional connectivity feature i of the jth sample and yj denote the class label of this sample (+1 for patients and −1 for controls). The Kendall tau correlation coefficient of the functional connectivity feature i can be defined as: τi =

nc − nd m×n

(2.1)

where nc and nd are the number of concordant and discordant pairs, respectively. Because the relationship between two samples that belong to the same group is not considered, the total number of sample pairs is m×n. For a pair of a two-observation datasets {xij ,yj } and {xik ,yk }, it is a concordant pair when sgn(xij − xik ) = sgn(yj − yk )

(2.2)

where sgn(·) is a signum function. Correspondingly, it is a discordant pair when sgn(xij − xik ) = −sgn(yj − yk )

(2.3)

Thus, a positive correlation coefficient τi indicates that the ith functional connectivity coefficient increases in the patient group compared to the control group. A negative τi indicates that the ith functional connectivity coefficient decreases in the patient group. The discriminative power was defined as the absolute value of the Kendall tau correlation coefficient. We subsequently ranked features according to their discriminative powers and selected those with coefficients over a threshold as the final feature set for classification. Because we used a leave-one-out cross-validation strategy to estimate the generalization ability of the classifiers (see below) and feature ranking is based on a slightly different training dataset in each iteration of the cross-validation, the final feature set differed slightly from iteration to iteration. Therefore, the contribution of different regions to classification was not evenly distributed, and some regions formed many highly discriminating functional connections with other regions, while some did not form any. Consensus functional connectivity was introduced here, which was defined as the functional connectivity feature appearing in the final feature set of each cross-validation iteration [18]. Region weight, representing

5 Support Vector Classification and Performance Evaluation

23

the relative contribution to identification of depressed patients, was denoted by its occurrence number in the consensus functional connections in this study. The consensus functional connectivity discriminative power was denoted by the average of its discriminative powers across all iterations of the cross-validation.

5 Support Vector Classification and Performance Evaluation When the dataset of features with high discriminative power were obtained, support vector machines (SVMs) with linear kernel function were employed to solve the classification problem [9]. The results were reported with the best parameter setting. Due to our limited number of samples, we used a leave-one-out cross-validation strategy to estimate the generalization ability of our classifier. The performance of a classifier can be quantified using the generalization rate, sensitivity, and specificity based on the results of cross-validation. Note that the sensitivity represents the proportion of patients correctly predicted, while the specificity represents the proportion of controls correctly predicted. The overall proportion of samples correctly predicted is evaluated by the generalization rate. Some researchers have explored a framework of permutation tests for assessing classifier performance [9]. Choosing the generalization rate as the statistic, permutation tests were employed to estimate the statistical significance of the observed classification accuracy. In permutation testing, the class labels of the training data were randomly permuted prior to training. Cross-validation was then performed on the permuted training set, and the permutation was repeated 10,000 times. It was assumed that a classifier learned reliably from the data when the generalization rate GR0 obtained by the classifier trained on the real class labels exceeded the 95% confidence interval of the classifier trained on randomly relabeled class labels. For any value of the estimated GR0 , the appropriate p-value p (GR0 ) represented the probability of observing a classification prediction rate no less than GR0 . We reject the null hypothesis that the classifier could not learn the relationship between the data and the labels reliably and declare that the classifier learned the relationship with a probability of being wrong of at most p (GR0 ). The classification results indicate that the final correct classification rate of the training dataset was 100% using the 550 most discriminating functional connections. Using leave-one-out cross-validation, the linear SVM classifier achieved an accuracy of 94.3% (100% for patients, 89.7% for healthy controls, p