Cognitive processes and brain: educational manual 9786010444966

The purpose of this textbook is to provide the students with basic knowledge in Cognitive Neuroscience. The material can

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Cognitive processes and brain: educational manual

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A.M. Kustubayeva


Almaty «Qazaq University» 2020

UDC 159.9(035.3) LBC 88.37 K 18 Recommended by the Academic Council of the Faculty of Philosophy and Political Science and Editorial and Publishing Council of KazNU al-Farabi

(Protocol №2 dated 15.01.2020)

Reviewers: PhD, postdoc A.T. Kamzanova PhD, Associate Professor A.A. Tolegenova PhD, Associate Professor M.V. Mun

Kustubayeva A.M. Cognition and brain: educational manual / A.M. Kustubayeva. K 18 – Almaty: Qazaq University, 2020. – 134 p. ISBN 978-601-04-4496-6 The purpose of this textbook is to provide the students with basic knowledge in Cognitive Neuroscience. The material can also be used as an introduction to Neuroscience for students in Psychology and other specialties. Students will be introduced to fMRI, EEG, and MEG techniques. Recent experimental studies and review articles on current questions in Neuroscience will be discussed along with existing theories about the mind and the brain. This course will familiarize students with the brain anatomy and function, as well as how to integrate diverse sources of information into a coherent view of how brain structure relates to and guides its functioning. Attention has been paid to most recent studies of the nervous system, with an extensive reference list. As a conclusion of the course, theories of Consciousness based on brain function will be discussed.

UDC 159.9(035.3) LBC 88.37 © Kustubayeva A.M., 2020 © Al-Farabi KazNU, 2020

ISBN 978-601-04-4496-6


FOREWORD To my parents, Kulsara and Mels Mombayevs

In my opinion the question of how our brain underlies our mind is the most complex and interesting problem debated for centuries. In spite of significant improvements in integrative science since the genius René Descartes’ ideas were developedin the 17th century, the brain-mind problem remains unsolved today. Understanding brain function underpinning cognitive processes requires integration of different scientific fields such as psychology, neurophysiology, philosophy, computer science, artificial intelligence… It was a very hot summer in Interlaken, Switzerland, one of the most beautiful places situated amidst lakes and mountains. Notwithstanding the absence of air conditioning in the historical Congress Center Kursall, the main hall was full of participants of the conference “The Science of Consciousness”. Participants were from all over the world with very different specialties who were brought together in order to share knowledge and help each other better understand the nature of consciousness. Exciting discussions at different sections made it very difficult to decide which one to attend. There were sections with intriguing topics, such as “Cognition in plants” (Paco Calvo, Chauncey Maher); “Transcendent Consciousness -Near-Death Versus Spiritual Contemplative Experiences” (Robert Hesse et al.); “Metaphysics of Temporal Consciousness” (Supriya Bajpai); “Quantum Consciousness” (James Tagg, Travis Craddock et al); “On the instability of language as a tool in the exploration of consciousness” (Sydney Lamb); “Inner speech and robot consciousness” (Melia E. Bonomo et al.) and others. I had the chance to attend fascinating speeches of the famous mathematician Roger Penrose, “Artificial Intelligence, Computation, Physical Law, and Consciousness”, and famous philosopher David Chalmers, “Zeno Goes to Copenhagen”. I mentioned these topics here to give examples of scientific ideas and ongoing research across the world related to the problem of consciousness. Certainly, my choice fell on the sections on the brain and mind, such as “Cortex is the organ 3

of mind” (Bernard J. Baars), “Neural Darwinism and Waking Consciousness: A Natural history of the brain in real time” (David B. Edelman et al)., “Connectomics and Consciousness: Integrating Information in Brain Networks” (Olaf Sporns) and others. This unforgettable conference brought inspiration to all participants who returned to their homes and continued to spread this scientific energy. What brought me to this conference? Origin and source of my energy to conduct research in this area were given to me by my parents, whom I unfortunately lost in recent years. My Dad, who always believed in my ability to think originally, helped me to develop analytical skills. My Mom, who shared with me her emotional intelligence and taught me how to deal with difficulties in life and how to be wise. My parents who inherited their wisdom from previous generations and passed it on my generation as an eternal flame. I believe that wisdom is the main value in our life which we have to reach independent of our career and personal achievements. Wisdom is the highest level of consciousness. I hope this textbook will help students to understand and love the “brain” and inspire them to learn more deeply about brain and cognitive processes. I hope this basic knowledge will help understand scientific literature in this field, the most recent discoveries and articles which are planned to be discussed during this course. The material can also be used as an introduction to Cognitive Neuroscience for students in other specialties. To facilitate understanding for students without a background in Neuroscience, this textbook will be started with a historical overview of the field and basic cellular and functional neuroanatomy, further exploring the processes occurring in the brain when we see, hear, talk, and act.


Lecture 1

INTRODUCTION TO COGNITIVE NEUROSCIENCE Outline: 1.1 Historical origins – Franz Joseph Gall – The ‘aggregate’ field view vs ‘localizationist’ view – Ivan Sechenov and Ivan Pavlov 1.2 Cognitive Neuroscience methods – Functional magnetic-resonance imaging (fMRI) – Diffusion tensor imaging (DTI) – Electroencephalography (EEG) – Magnetoencephalography (MEG) – Brain-computer interface (BCI) Questions Literature

1.1 Historical origins “Cognitive Neuroscience – With its concern about perception, action, memory, language and selective attention – will increasingly come to represent the central focus of all Neurosciences in the 21st century”. Eric R. Kandel, M.D. (Nobel Laureate)

History tells us cognitive psychologist George Miller and neuroscientist Michael Gazzaniga were the first to inculcate the term “Cognitive Neuroscience” in the 1970s at a meeting in the Algonquin Hotel in New York. The meeting was devoted to the problem of how brain function underlies the mind. Michael Gazzaniga was one of the founders of the Society and the Journal of Cognitive Neuroscience. Since that meeting, Cognitive Neuroscience began to integrate cognitive science and neuroscience, experimental psychology and neuropsychology. Today, Cognitive Neuroscience is an academic field integrating branches of psychology, neuroscience, and computational science into one field to study the biological substrate of mind and behavior. One of the central questions of Cognitive Neuroscience is how the brain produces cognition. 5

– Franz Joseph Gall Historically, the brain-mind problem was discussed even by ancient thinkers such as Plato and Aristotle. Later, it was thought that the most naturalistic approach to the question of how the brain influences cognitive abilities was made by phrenologists Franz Joseph Gall and J.G. Spurzheim in the 19th century. The book “The Anatomy and Physiology of the Nervous System in General, and of the Brain in Particular” by Gall became very famous and motivated many other studies to measure bumps on a human head and describe their function. Gall’s idea that human scalp bumps become larger with the frequency of use sheds light on some of the basic principles of how learning influences brain development. Although in the present-day phrenology it is considered a pseudoscientific approach, it attracted public interest in the brain-mind problem and attempts to relate the brain with the mind. Since the epoch of phrenology, the development of the brain-mind question moved in two directions: 1) the ‘localizationist’ view states that specific cognitive functions are localized in specific areas; 2) the ‘aggregate’ field view is opposite to the localizationist view and states that different brain areas participate in behavior depending on the context.

Figure 1. Brain structure and cognitive abilities. Franz Joseph Gall, 1848. (


– The ‘aggregate’ field view vs ‘localizationist’ view The ‘aggregate’ field view started with Jean Pierre Flourens, a French experimental psychologist. Seeing as phrenology was lacking scientific evidence, Flourens conducted experimental studies on living rabbits and pigeons by removing different areas of the brain. He concluded that the same brain areas may participate in different cognitive functions instead of precise localization. Yet, Flourens was one of the first who ascribed movement regulation to the cerebellum and vital functions to the medulla oblongata. The ‘localizationist’ view was developed later by John Hughlings Jackson who worked with patients with brain damage. He observed that in patients with epilepsy, seizures often induced the same muscles suggesting that they are ascribed to specific areas of the brain. Further, famous discoveries made by French neurologist Paul Broca (1861) and by German neurologist Carl Wernicke in understanding brain damage related to speech deficits strongly supported the localizationist field. An area in the left frontal lobe responsible for speech production was named after Broca. Brain lesions in Broca’s area cause an inability to speak but patients can still understand language, a condition termed Broca’s aphasia. Similarly, an area in the left parietal and temporal lobes responsible for understanding spoken and written language was named after Wernicke, and lesions leading to the inability to understand language yet being able to speak is termed Wernicke’s aphasia. However, recent studies questioned such localizationist views by finding that both brain areas have some integrated functions in language. We will discuss this matter in a subsequent lecture devoted to language. – Ivan Sechenov and Ivan Pavlov One of the most important contributions to understanding the brain-mind relationship was made by Russian physiologists Ivan Sechenov (1863) and Ivan Pavlov (1904). Their well-known Reflex theory is based on 3 principles: causality – every process has a cause; structure – every process is associated with the brain structure; analysis and synthesis – all mind processes induce the analysis and synthesis of the stimulus. Ivan Pavlov was awarded with the Nobel Prize in 7

Physiology in 1904 and his discoveries encompass a range of studies on reflexes and methodology known as classical conditioning, widely used in psychology to this day. Further, the development of the neuron doctrine by Santiago Ramon y Cajal and Camillo Golgi who shared the Nobel Prize in Physiology and Medicine in 1906 brought about new approaches and methods to investigate brain function on a cellular level and explore new ways of understanding neurophysiology. We will learn some further important neurophysiological investigations throughout the course, including insights into cognition from a neuronal level such as Vernon Mountcastle’s discovery of cortical column organization, Golgi’s discovery of biological electricity, Alexander Luria, referred to as the “Beethoven of Psychology” and the father of neuropsychology and psychophysiology, and his theory of three functional blocks. Additionally, the development of cognitive science was a premise for Cognitive Neuroscience. We have already mentioned George Miller, famous for his contribution to understanding working memory and works such as “The Magical Number Seven, Plus or Minus Two” (Miller, 1956). Noam Chomsky, Allen Newell and Herbert Simon, Ulric Neisser and David Marr – all of them made a significant contribution to cognitive science.

1.2 Cognitive Neuroscience methods Cognitive Neuroscience is an integrative scientific field employing methods from a wide range of fields. Neuropsychological and psychophysiological methods include TMS (1985) and fMRI (1991), EEG (1920) and MEG (1968), PET, SPECT, ECG, facial EMG, and microneurography. Cognitive psychology methods are based on cognitive experimental paradigms and related cognitive tasks. Inculcation of genetic methods brought clarification to cognitive function inheritance and its connection to brain function. Brain lesion studies contributed to cognitive neuroscience as well. Computational Neuroscience is a very fast-growing branch and its methods are improving our understanding of the brain and mind, such as computational modeling, 8

consolidation of data in databases, brain-computer interface, artificial intelligence and many more methods. – Functional magnetic-resonance imaging (fMRI) Magnetic Resonance Imaging (MRI) is one of the most widespread methods in Cognitive Neuroscience starting from the 1970s. This method is based on the physical principle of magnetic resonance. A strong magnetic field is applied which aligns nuclei in brain regions. A radiofrequency pulse (RF) transforms nuclei to a higher level of magnetization. After removing RF, nuclei return to their original state and emit energy which is received by a coil. Different tissues return to the equilibrium state at different rates and provide the contrast among tissues. Modification of sequence parameters repetition time (TR) and echo time (ET) help emphasize the contrast between gray matter and white matter (T1-weighted), or between brain tissue and cerebrospinal fluid (T2-weighted). T1 is characterized by short TR and short TE, while T2 is characterized by long TR and long TE. The strength of the magnet in use is measured in units of Tesla (from 0.2T to 7 T). Modern MRI techniques are classified into: 1) Structural MRI that provides anatomical images of the brain/organ; 2) Functional MRI (fMRI) that reflects dynamical changes of brain function during an experimental task, provides information about Blood Oxygen Level Dependent (BOLD) signals; 3) Diffusion-weighted/Diffusion tensor MRI that provides the information about white matter integrity and fiber tract tracing; 4) Magnetic Resonance Spectroscopy that provides the information about biochemistry of the brain/organ. Functional MRI detects changes in oxygenation. Ogawa et al. (1990) suggested that an increase in neuronal activity is accompanied by changes in oxygenation and the BOLD signal reflects the levels of neuronal activity. The fMRI method has advantages in spatial resolution but has low temporal resolution in comparison with the EEG method.


А. 14.5 S.


B. 18.5I


Figure 2. Example of significant changes in the BOLD signal when comparing healthy participants to the patients with major depressive disorder (MDD) during emotional face perception in specific brain regions. A) Right Insula. B) Left Rectal Gyrus BA 11/Left BA25, Subgenual Gyrus/Anterior Cingulate. Yellow color indicates an increased BOLD signal, blue color – a decreased BOLD signal in MDD patients. (Kustubayeva et al, 2019).

– Diffusion tensor imaging (DTI) Diffusion tensor imaging (DTI) allows measuring white matter tracts and is becoming one of the widely used imaging techniques. The diffusion anisotropy of water molecules facilitated by axons and myelin sheath provide information about white matter tracts. For each pixel, diffusion anisotropy is described by a diffusion tensor. DTI results are typically presented as tractograms and the main tracts have been described in the DTI Atlas (L. Hermoye, Newly developed DTI techniques such as diffusion kurtosis imaging (DKI), bi-tensor DTI, neurite orientation density and dispersion imaging (NODDI) can be used to identify biomarkers of neurodegenerative disorders such as Parkinson’s disease and Huntington’s disease (Andica et al., 2019). – Electroencephalography (EEG) Electroencephalography (EEG) is a technique which measures electrical activity changes at the scalp level. Changes in the surface electrical activity reflect internal electrical currents in the brain. It is thought that such currents reflect mainly extracellular postsynaptic potentials (EPSPs). EEG reflects thousands of simultaneously ongoing processes. First, Richard Caton (1842–1926) demonstrated electrical phenomena on rabbits and monkeys in 1875. Later on, Russian physiologist Pravdich-Neminsky published the first paper describing animal 10

EEG in dogs in 1912. The first human EEG experiment was performed by German physiologist and psychiatrist Hans Berger (1873–1941) in 1924. He described the dominant rhythm of EEG and called it the ‘alpha rhythm’. Since there was a discussion regarding compatibility of EEG data among individuals, the international system named as 1020% of electrode placement used in order to correct for inequality of brain sizes. The distance between electrodes consists of 10-20% of the total distance between ‘nasion’ and ‘inion’ or the right and left sides of the skull. Description of EEG rhythms and their function and origin is provided in Table 1.

Figure 3. The 10-20 % international system of electrode placement

Table 1 EEG rhythms and their function and origin Rhythm



Delta, IRDA

1-4 Hz, Limbic system Intermittent Rhythmical Delta Activation – bilateral, high amplitude, repetitive EEG activity at frequencies of 2-3 Hz.

Slow waves during sleep. HV is used to provoke absence seizures.


Cerebral blood flow (CBF) Decrease in CBF during IRDA in occipital and frontal lobes.




4-8 Hz, Hippocampus

Alpha Mu

8-12 Hz, Thalamus 8-13 Hz, central area


12-30 Hz, Cortex, beta activity reflects the influence of the Ascending Reticular formation on the cortex. 30-100 Hz, Smart neurons and neuronal assemblies within the cortex. (Munk, 2001, Holsher, 2001).


Function Drowsiness, early slow-wave sleep, meditation, relax creative state. Memory. Emotion. Relaxed wakefulness, most prominent over parietal and occipital sites. Alerting reaction. Index of cortical inactivity. Synchronous firing of motor neurons. Mu suppression – motor mirror neuron systems Mental activity, prominent in frontal areas. Increase in alerting reaction.

Cognitive rhythm. Precise synchronization by corticocortical forward/feedback connections (Matthias H.J. Munk, 2001).

Cerebral blood flow (CBF) Decrease in CBF in cortex, increase in thalamus.

Increase in the thalamus and insula; Decrease in the occipital, temporal, and frontal lobes.

Increase in visual cortex (LFP, Logothetis et al. 2001).

Spontaneous EEG measurements include:  Power Spectral Density (PSD) is a quantitative measurement of EEG waves in each frequency band. PSD is based on the Fourier transformation and describes the power of time series in Watts per Hertz or Volts per Hertz (W/Hz, V/Hz).  EEG coherence reflects a degree of synchronization between EEG electrodes. 12

 Nonlinear measurements: Entropy, Lyapunov’s exponent, Hurst parameters, etc. The event-related potential (ERP) is time-locked to an event and averaged throughout multiple trials within the same event category. Pauline and Hallowell Davis extracted the first ERPs in humans in 1939. ERP waves are characterized by amplitude and latency. ERP waves reflect different cognitive processes in the brain, with the main waves described below:  P100 occurs between 80-120 ms from initial stimulus. It is a first positive component and reflects the “cost of attention”;  N100 is observed between 80-120 ms mostly at frontal and central electrodes, it provides the information about auditory stimuli, considered as “preattentive”, and depends on unpredictability of stimulus;  P200 is found between 150-275 ms in the central-frontal, parieto-occipital areas. It is a “matching system” and is responsible for comparison of sensory stimuli stored in memory. The P200 wave reflects selective attention, feature detection, probability of stimulus, memory, language processes.  N200 is observed between 150-275 ms and reflects a cognitive control function, specifically an inhibitory response control mechanism.  P300 is considered an endogenous stimulus because it reflects personal reaction to a stimulus, evaluation and categorization of the stimulus (Sutton, 1969, Chapman and Bragdon, 1964). Further studies differentiated P300a and P300b waves:  P300a occurs between 250-280 ms after stimulus onset over the frontal-central electrode sites. P300a is associated with engagement of attention and processing of novelty, context update, top-down control (Donchin, 1981);  P300b is observed later between 300-500 ms over the temporal-parietal electrode sides and is related to probability of an event, cognitive workload, resource allocation, subsequent memory processsing, and bottom-up control (Kahneman, 1973, Polich, 2007). 13

Potential, mV

ERP -5 -4 -3 -2 -1 0 1 2 3 4 5 6 7 8




P2 P3


200 300 400 Time after stimulus, ms




Figure 4. Event Related Potential (ERP) waves

ERP studies became a very popular method due to availability of EEG techniques and informativeness regarding cognitive processes. As an example of their different applications, some interesting results from recent ERP studies include:  P300 can be a useful measurement of emotional reaction in stressful situations. One of popular implications is to use the P300 wave in a Lie Detector (see  Feedback-related negativity (FRN) is a negative wave observed between 200-350 ms in response to feedback stimulus. For instance, FRN peaks in high-risk decisions and low-risk decisions are different (Holroyd and Coles, 2002). The amplitude of the FRN is more negative after a negative feedback in comparison with positive feedback (Holroyd and Coles, 2002; B. Schuermann, T. Endrass, and N. Kathmann, 2012).  Parameteres of FRN are informative biomarkers of drug use due its theoretical application to motivation system. Results in FRN for a negative feedback indicate insensitivity to negative feedback and reduced cognitive control influences in MDMA users (Fig.5, Kustubayeva et al., 2015).


Figure 5. Feedback Related Negativity a) ERP waveform in HC, MDMA, and MJ for negative feedback. b) FRN values in 3 groups for positive and negative feedback. (Kustubayeva et al., 2015).

 Error-related negativity (ERN) represented mostly at frontal and central electrodes is a negative wave between 8-150 ms to errors. ERN is considered a conflict monitoring system and has emotional and motivational components as an error is made. It has been showed that ERN is generated in the Anterior cingulate cortex (ACC). ERN was first discovered by Natalia Behtereva and called an “error detector”


(published in the article “Neurophysiological aspects of human mental activity” in 1971). Later studies showed that ERN is found not only during execution of error, but also during observation of error with a lower amplitude (Hein et al., 2004). – Magnetoencephalography (MEG) Magnetoencephalography (MEG) is a magnetic field recording using magnetometers. MEG was first recorded by D. Cohen in 1968. The MEG signal, similar to the EEG signal, derives from synchronous electrical currents and is characterized by frequencies similar to EEG: delta, theta, alpha, beta, gamma. MEG has advantages in comparison to EEG in improved spatial resolution due to more stable magnetic fields, as well as higher sensitivity to superficial cortical activity. In comparison to fMRI, MEG has advantages in temporal resolution. Recent studies showed that MEG signal is a good biomarker for brain disorders such as multiple sclerosis, Alzheimer’s disease, alcoholism, etc. (Georgopoulos et al., 2007). Another study confirmed the effectiveness of action observation therapy in stroke patients predicted by MEG signals (Zhu et al., 2019). Questions: 1. How do you understand “Cognitive Neuroscience” as an academic field? 2. What is Phrenology? 3. Compare and contrast the ‘localizationist’ view and ‘aggregate’ field view. Further research into the literature is encouraged. 4. What kind of contributions to the development of Neuroscience as a field have been made by Paul Broca and Carl Wernicke? 5. What is Gazzaniga’s main contribution in the establishment of Cognitive Neuroscience? 6. Describe different techniques in Neuroscience. 7. Explain the basics of fMRI. What is a BOLD signal? 8. Explain the basics of EEG techniques and characteristics. Discuss ERP wave functions. 9. What is the DTI method and how is it applicable to neurodegenerative disorders? 10. Discuss advantages and disadvantages of MEG in comparison with EEG and fMRI. Literature: 1. Andica C., Kamagata K., Hatano T., Saito Y., Ogaki K., Hattori N. Aoki SMR biomarkers of degenerative brain disorders derived from diffusion imaging // J Magn Reson Imaging. – 2019. – doi: 10.1002/jmri.27019.


2. Cohen D., Cuffin B.N. Demonstration of useful differences between magnetoencephalogram and electroencephalogram // Electroencephalography and Clinical Neurophysiology. – 1983. – №56 (1). – p. 38-51. doi:10.1016/0013-4694(83)90005-6. 3. Donchin E., Ritter W., McCallum C. Cognitive psychophysiology: the endogenous components of the ERP. In: Callaway P.; Tueting P.; Koslow S., editors. Brain-event related potentials in man. – New York: Academic Press. – 1978. – p. 349-411. 4. Gazaniga M.S. The Cognitive Neurosciences. – Fourth edition., MIT Press, 2009. – p. 1358. 5. Georgopoulos A.P., Karageorgiou E., Leuthold A.C., Lewis S.M., Lynch J.K., Alonso A.A., Aslam Z., Carpenter A.F., Georgopoulos A., Hemmy L.S. Synchronous neural interactions assessed by magnetoencephalography: a functional biomarker for brain disorders // Journal of Neural Engineering. – 2007. – №4 (4). 6. Hein S., Rogier M., Coles M., Bekkering H. Modulation of activity in medial frontal and motor cortices during error observation // Nature neuroscience. – 2004. – №7,5. – p. 549-554. 7. Holroyd C.B., Coles M.G.H. The neural basis of error processing: reinforcement learning, dopamine, and the error-related negativity // Psychol. Rev. – 2002. – №109 (4). – p. 679-709. doi:10.1037/0033-295X.109.4.679. 8. Kahneman D. Attention and effort. Englewood-Cliffs: Prentice Hall. – 1973. 9. Kandel E.R., Schawtz J.H., Jessel T.M., Siegelbaum S.A., Hudspeth A.J. Principles of Neuronal Science. – 5th edition. – 2012. – p. 1760. 10. Ogawa S., Lee T.M., Kay A.R., et al. Brain magnetic resonance imaging with contrast dependent on blood oxygenation // Proc Natl Acad Sci. – USA. 1990. – №87:9868. 11. Polich J. Updating P300: An integrative theory of P3a and P3b // Clinical Neurophysiology. – 2007. – №118 (10): 2128–2148. doi:10.1016/j.clinph.2007.04.019. 12. Schuermann B., Endrass T., & Kathmann N. Neural correlates of feedback processing in decision‐making under risk // Frontiers in Human Neuroscience. – 2012. – №6. – р. 204. fnhum.2012.00204. 13. Zhu J.-D., Cheng C.-H., Tseng Y.-J., Chou C.-C., Chen C.-C., Hsieh Y.-W., Liao Y.-H. Modulation of Motor Cortical Activities by Action Observation and Execution in Patients with Stroke: An MEG Study // Neural Plast. – 2019. – doi:10.1155/2019/8481371.


Lecture 2

INTRODUCTION TO CELLULAR NEUROANATOMY Outline: 2.1 Neuron structure and classification. Membrane. Synapses – Main scientific discoveries in Cellular Neuroanatomy – Neurons. Types of neurons – Membrane – Resting potential and Action Potential – Synapses 2.2 Cortex layers. Neuroanatomy and a brief tour of the brain – Cortex layers – Columnar organization. From nerve cells to cognition – Neuroanatomy and a brief tour of the brain – Broadman Areas (BA) Questions Literature

2.1 Neuron structure and classification. Membrane. Synapses The brain is one of the most complicated structures known in the universe. As far as we know, there are some interesting statistics about brain structure:  20,000,000,000 neurons are contained in the adult human cerebral cortex (Pakkenberg and Gundersen, 1998, JCN, 384: 312 – 320);  164,000,000,000,000 synapses are contained in the cortex (Tang et al., 2001, Synapse 41(3):258-73);  100,000,000,000 is a total neuron number (estimated);  2,500,000,000,000 is a total number of non-neuronal cells (estimated);  40% of genes are thought to be neuron-specific, and the majority of the remainder are expressed in both the central and peripheral nervous systems. “76 % of human genes are expressed in at least one brain region…. 44% are differentially regulated and 28% are differentially alternatively spliced” (Dehay&Kennedy, 2009, p.455). 18

– Main scientific discoveries in Cellular Neuroanatomy A brief timeline describing famous scientists and their contributions to the development of Cellular Neuroanatomy is presented in Table 2. Table 2 Main scientific discoveries in Cellular Neuroanatomy Scientist and year of discovery



Luigi Galvani, 1771



Otto Deiters, 1860

Axon was distinguished from dendrite

Louis-Antoine Ranvier, 1878

First described the gaps or nodes found on axons

Santiago Ramón y Cajal, 1890

Microscopic structure of the brain. “Neuron doctrine”


K.S. Cole and H. Curtis, 1930

First intracellular recorder


Alan Hodgkin and Andrea Huxley, 1939-1952

Full quantitative description of the ionic basis of the Action Potential (H-H model, NP 1963)


Vernon Mountcastle

Columnar organization of the cortex


David Hubel and Torsten Wiesel, 1981

Cortical columns in vision

3 4

Classic neurohistological techniques include the method of staining tissues (Nissl stain, Golgi stain for pyramidal cells, Cresyl violet stain for motor neurons, etc.), electron and confocal microscopy, histochemical and immunohistochemical methods. There are constant improvements and new approaches being developed for brain imaging on a molecular level, such as CLARITY, developed by scientists at Stanford University, which allows replacing brain fats with a clear gel (Chung et al., 2013). Transformation of intact tissue into a nanoporous hydrogel-hybridized form helped researchers demonstrate intact-tissue imaging, which allowed seeing circuit wiring, neurotransmitters, proteins, and connections between cells (Chung et al., 2013). 19

– Neurons. Types of neurons Here we briefly present different types of neurons: 1) classified by function: sensory, motoneurons, and interneurons; 2) classified by structure: unipolar, bipolar, pseudounipolar, and multipolar. The different types of neurons show structural and functional similarities (Fig. 6). Neurons are clustered into nuclei, and nuclei are clustered into functional systems.

Dendrite Axon terminal Node of Ranvier

Myelin sheath


Schwann cell

Cell body Nucleus

Figure 6. Neurons. Types of neurons

Further, neurons are surrounded by non-neuronal cells of different forms, which serve as a supportive and nourishment system. Astrocytes are star-shaped cells, with an estimated number of astrocytes approximately 50 times more than neurons. One of the functions of astrocytes is to maintain and repair neurons, yet new emerging functions beyond support are being investigated. Plasticity of the nervous system depends on astrocytes. There are other cells, such as oligodendrocytes (small cells rich in granular reticulum and polyribosome), microglial cells, Schwann cells, and satellite cells, which all play important roles in supporting normal neuronal function and beyond it. – Membrane We concentrate now on the structure of the neuronal membrane and the synapse, which distribute the nerve impulse and conduct 20

nerve impulses along pathways. The cell membrane separates the interior of the cell from the surrounding environment and is selectively permeable. The membrane consists of a phospholipid bilayer with integral, peripheral, channel proteins. Transmembrane protein channels and permeases play important roles in the activation process by increasing or decreasing membrane permeability to different molecules. – Resting Potential and Action Potential During the resting state, the membrane is polarized, which means that internal and external layers have opposite electric potential. The membrane is permeable to potassium ions, chloride ions, and bicarbonate ions. The concentration of potassium is usually higher inside the cell than outside. The resting potential (RP) in most neurons varies between -60 and -70mV (Kandel E.R., 2012, p. 126). The RP in astroglia varies between -25 and -85 mV (Bolton et al., 2006), while the RP in hair cells (cochlea) varies between -45 and -60 mV (Purves et al., 2001). The action potential (AP) is characterized by a rapid depolarization and repolarization of the membrane. Depolarization is accompanied by increased membrane permeability to sodium ions, which causes a reverse in the polarity of the membrane until all available ion channels become inactivated. Activation of potassium channels returns the membrane to a polarized state again (repolarization). After repolarization, a negative shift may occur causing hyperpolarization. The AP is the basic mechanism of a nerve impulse. Changes in the membrane potential influence the ability to evoke another AP (excitability). With depolarization, excitability decreases until it achieves an absolute refractory period during the AP peak. With repolarization, the ability to evoke another AP increases, referred to as the relative refractory period. Such a mechanism serves for propagation of nerve impulses in one direction along the axon. – Synapses The synapse is a functional connection between two neurons, or a neuron and another effector cell. A synapse allows nerve impulses 21

to pass from the presynaptic membrane to the postsynaptic membrane through the synaptic cleft. The presynaptic site releases neurotransmitters, which trigger neurotransmitter receptors on the postsynaptic side. Depending on the type of neurotransmitter mainly used, neurons are classified as adrenergic, cholinergic, GABAergic, and glutamatergic. Regarding their function, there are two types of synapses: excitatory and inhibitory. Excitatory postsynaptic potentials (EPSPs) and Inhibitory presynaptic potentials (IPSPs) are two opposite phenomena. Long-term potentiation (LTP) is a long-lasting activation of transmission between neurons. LTP is one of the molecular mechanisms underlying learning and memory (Cooke S.F. et al., 2006; Bliss T.V. et al., 1993).

2.2 Cortex layers. Neuroanatomy and a brief tour of the brain – Cortex layers The first anatomical description of cortical layers was produced by Santiago Ramon y Cajal ( Below there is a table providing a summary of the six layers of the cerebral cortex and their structure and function. Table 3 The cerebral cortex mainly consists of six layers Lamina I

Cortical layer Molecular layer

Latin name

Types of cells

lamina molecularis

Horizontal cells of Cajal, the horizontal fibers of pyramidal cells, cells of Martinotti, stellate cells


Function Supragranular layers, Intracortical connections, associational, and commissural; communication between areas, developed over the

Lamina II

Cortical layer External granular

Latin name

Types of cells

lamina granularis externa

Densely packed stellate and small pyramidal cells Medium-sized pyramidal cells, stellate and basket cells Stellate and small granular cells, ascending and descending fibers, horizontal fibers Large pyramidal cells, horizontal fibers


External pyramidal

lamina pyramidalis externa


Internal granular

lamina granularis interna


Internal Pyramidal

lamina pyramidalis interna



lamina multiformis

Small polymorphic and fusiform cells

Function whole brain, regresses with age

Sensory input: receives thalamocortical connections, the most prominent in the primary sensory cortices Motor output: efferent projection to basal ganglia, brainstem, and spinal cord; connect the cerebral cortex with subcortical regions, the most developed in motor areas Projects to the thalamus

A study devoted to the evolution of the brain provides an interesting comparison of the human brain to the rat and mouse brains (see DeFelipe, 2011). Using the resource, you may estimate the differences in cortical thickness, number of neurons and synapses in each column, and the ratio of synapses to neurons. In phylogenesis, we see a gradual increase in thickness accompanied by an increase in synapses and a decrease in neurons (DeFelipe, 2011). With the evolution from the mouse to human brain, cortical thickness increases (1210 mm vs 2622 mm), the number of cortical layers increases (4 vs 6), the number 23

of neurons in one column decreases (364 vs 158), and the ratio of the number of synapses to the number of neurons (S/N) increases (21081 vs 29642) (DeFelipe, 2011). In ontogenesis, overproduction of neurons is observed at birth, followed by synaptic pruning. According to the authors, 700 synapses form every second during the first year of life and with age and experience connections are reduced (Chugani, 1997). The expression “use it or lose it” applies in this context in that if the connections between neurons are not used, they are pruned away. Experience shapes the brain architecture by defining which networks will continue to function and which networks should be eliminated. – Columnar organization. From nerve cells to cognition As mentioned earlier, Vernon Mountcastle was a researcher who suggested that horizontal neurons, which are located in more than 0.5 mm (500 µm) from each other, have sensory receptive fields that are not overlapped (Buxhoeveden & Casanova, 2002). In the human brain, around 80 neurons are combined into a mini-column. Approximately 50-100 mini-columns constitute a hypercolumn. To further develop the idea of columnar organization, David Hubel and Torsten Wiesel recorded electrophysiological signals from neurons of the visual system in cats. Their famous experiments allowed them to describe cortical columns in vision and the neighboring columns responsible for the orientation of lines (see Since their discovery, other cortical columns have been described, for instance the columns in the somatosensory cortex (Kaas et al., 1979, see Recent studies on the human brain evolution of horizontal layers and vertical columns in the cerebral cortex and neuronal migration are related to genetic triggers. Rakic’s study showed how specific genetic triggers influenced neuronal migration by visualizing each genetic marker (Rakic Lab, Nature, 2009, see – Neuroanatomy and a brief tour of the brain The brain is comprised of several main structures, including the cerebrum, cerebellum, and brainstem. 24

The cerebrum consists of the left and right hemispheres connected by the corpus callosum and associated with higher brain functions. The cerebral cortex includes the frontal, temporal, parietal, and occipital lobes. The frontal lobes are associated with executive control function, attention, memory, emotion regulation, decision making, self-regulation, speech production. The temporal lobes are involved in processing and recognition of auditory stimuli, speech comprehension and production, verbal memory, autobiographical memory. The parietal lobes are associated with vision, orientation, touch, sustaining attention, movement, integration of all information, language, manipulation of objects. The occipital lobes are involved in visual processing and recognition, body language, visual attention, spatial orientation. The cerebellum is associated with coordination of movements, balance, and posture. According to recent studies, the cerebellum also participates in higher brain function. The brainstem consists of the midbrain, pons, and medulla oblongata, and is responsible for vital life functions. The midbrain includes the thalamus (sensory relay) and hypothalamus (homeostasis), the red nucleus and substantia nigra participate in movement control, and are part of the dopaminergic pathway. The pons is a bridge between other parts of the brain and is associated with respiration, cardiac function, and sleep. The medulla oblongata regulates breathing, swallowing, vomiting, coughing. The limbic system is the emotional system and includes the thalamus, hypothalamus, hippocampus, amygdala, and pituitary gland. 25

The amygdala is related to emotions, especially to fear, emotional memory, and has opiate receptors. The hippocampus is associated with memory formation, connecting emotions, senses, and memory. The pituitary gland is the main part of the endocrine system and connects the nervous system with the endocrine system.

Cerebral cortex Corpus collosum


Hypothalamus Pituitary gland Pons


Hippocampus pus Medulla oblongata


Figure 7. Neuroanatomy of the brain

We will learn the anatomy of the brain in more detail during the course. Today, there are around 100 top structures in the whole brain 26

in the Harvard Brain Atlas ( To begin, I recommend learning brain structure in more detail by using fMRI images from the following websites: 1); 2); 3); 4); 5) There are several brain atlas templates used in fMRI studies: 1) Talairach and Tournoux atlas (Talairach and Tournoux, 1988) is one of the most influential atlases in brain imaging. It was created based on the individual brain of a 60-year-old female. 2) Montreal Neurological Institute (MNI) 305 was created by averaging 3D brain images from 305 right-handed participants. It is larger than the Talairach and Tournoux atlas (Evans et al, 1992, Collins et al, 1995). 3) Montreal Neurological Institute (MNI) 152 was created based on 152 healthy subjects using SPM and FSL software (Mazziotta et al., 2001). 4) ICBM-452 is created based on 452 young participants (2003). 5) Korean brain template was created based on 78 healthy righthanded subjects in 2005 (Lee eta l., 2005). 6) French brain template (45 subjects, Lalys et al., 2009). 7) Chinese brain template (56 right-handed subjects, Tang et al., 2010). – Broadman Areas (BA) German researcher Korbinian Brodmann defined brain areas based on his observations of the cerebral cortex using the Nissl stain. Brodmann published his cytoarchitectural map of cortical areas in humans, monkeys, and other species in 1909. Later on, Constantin von Economo and Georg Koskinas (1925) also provided similar cortical maps. However, 52 Brodmann areas (BA) are widely used nowadays 27

in brain research due to their convenience. You may find a cytoarchitectonic map suggested by Brodmann (see and learn about brain structures and their functions from Table 5. Table 4 A selection of Brodmann areas and their proposed functions Area numbers 1, 2, 3 4 5, 7 6

Name of brain structure Primary Somatosensory Cortex Primary Motor Cortex Somatosensory Association Cortex Premotor cortex and supplementary motor cortex


Frontal Eye Fields


Dorsolateral prefrontal cortex


Anterior prefrontal cortex

10, 11, 12, 47

Orbitofrontal cortex, superior frontal gyrus

13, 14, 16

Insular cortex


Anterior Temporal Lobe


Primary visual cortex (V1)


Function somatic sensory information processing motor control somatic sensation perception planning of complex movement, coordinating two hands movements saccadic eye movements, awareness, voluntary eye movements executive control, motor planning, working memory, cognitive flexibility, abstract reasoning executive control, goal-directed behavior, planning, reasoning, integration of outcomes, complex problem-solving, emotion, reward, decisionmaking, sensory integration, affective value of rewards emotion, empathy, metacognitive emotional feelings, regulation of the homeostasis semantic memory, social knowledge, personally relevant information conscious processing of visual stimuli

Area numbers 18

Name of brain structure Secondary visual cortex (V2)


Associative visual cortex (V3,V4,V5)


Inferior Temporal gyrus


Middle Temporal Gyrus

22, 41, 42

Superior temporal gyrus (STG)

23, 31, 29, 30

Posterior cingulate cortex

24, 32, 33

Anterior cingulate cortex


Subgenual area, part of Ventromedial prefrontal cortex Ventromedial prefrontal cortex Entorhinal cortex

10, 14, 25, 32, 11, 12, 13 28, 34 27, 28, 36

35, 36

Parahippocampal gyrus, Olfactory cortex Perirhinal cortex


Function orientation, spatial frequency, size, color, and shape, object recognition memory, conversion from short term memory to long-term memories receive impulses from V1, V2 and project them to parietal cortex, processing global motion, coherent motion complex object features, recognition of numbers, face perception word meaning in reading, writing, recognition of know faces auditory processing, perception of emotional faces, language, insight default mode network, awareness, episodic memory, activated during self-related thinking, deactivated during mediation emotion, error detection, attention allocation, reward anticipating, conflict monitoring, decision making, morality, and impulse control serotonin transporter, mood, anxiety, self-esteem, sadness emotion regulation, decision making, self-control declarative memory, spatial memory, memory consolidation emotion, visual background, visual hallucinations, social context memory, visual perception, item memory, object-recognition

Area numbers 37


39, 40

41, 42 43 44, 45 46 47


Name of brain structure Fusiform gyrus (FFA)

Superior and middle temporal gyri Angular and supramarginal gyri, parts of Wernike’s area Auditory cortex Primary gustatory cortex Inferior Frontal gyrus, Broca’s area Dorsolateral prefrontal cortex (DLPFC) Pars orbitalis, orbital part of inferior frontal gyrus, Broca’s area Parainsular area

Function high level vision, face and body recognition, word recognition, processing color, category identification high level semantic representation, emotional processing language perception and processing, spatial cognition, memory retrieval, attention identification of physical properties of the sound intensity of taste stimulus, taste familiarity language processing and speech production motor planning, organization, and regulation processing of syntax, semantic aspects of language connection between the insula and temporal lobe

Interesting facts: 1. The BRAIN (Brain Research through Advancing Innovative Neurotechnologies) Initiative project of the NIH (USA) provided 550 awards ($950 million) for brain research (started in 2013). 2. The Human Brain Project (HBP) is one of the FET (Future and Emerging Technology) Flagships, and is the largest scientific project of the European Union (started in 2013). Questions: 1. What discovery was made by Luigi Galvani? 2. Describe the contributions made by Hodgkin & Huxley. 3. What contribution was made by Hubel & Wiesel? 4. Describe the structure of neurons. What do neurons have in common with other brain cells? How are neurons different from other brain cells? 5. What are Ion channels and Membrane potential? Describe the Resting Potential and Action Potential. 6. Explain synaptic transmission. What kind of neurotransmitters do you know?


7. 8. 9. 10. 11. 12. 13. 14.

What are glia and their main functions? Describe cortical layers and their differentiation. What was the discovery made by V. Mountcastle? Explain columnar organization of the cortex. Describe Brodmann areas and their functions. Explain phylogenetic and ontogenetic principles of cortical changes. Describe the main anatomical regions of the brain. What are the Brodmann area (BA) numbers for the Primary somatosensory cortex? 15. Which BA number comprises Broca’s areas? Literature: 1. Bliss T.V., Collingridge G.L. A synaptic model of memory: long-term potentiation in the hippocampus // Nature. – 1993. – №361 (6407). – p. 31-9. – doi:10.1038/361031a0. PMID 8421494. 2. Bolton S., Greenwood K., Hamilton N., Butt A.M. Regulation of the astrocyte resting membrane potential by cyclic AMP and protein kinase A. GLIA, – 2006. 54, 4. – p. 316-328. 3. Buxhoeveden D.P., Casanova M.F. The minicolumn hypothesis in neuroscience // Brain. – 2002. – №125(5). – p. 935-951. 4. Chugani H.T. Synaptic Density. [Drawing]. In R. Shore, Rethinking the Brain: New Insights into Early Development. – New York: Families and Work Institute. – 1997. – p. 20. 5. Chung, K., Wallace, J., Kim, S. et al. Structural and molecular interrogation of intact biological systems // Nature. – 2013. – №497. – p. 332–337. doi:10.1038/nature12107 6. Collins D.L., Holmes C.J., Peters T.M., Evans A.C. Automatic 3-D modelbased neuroanatomical segmentation // Hum Brain Mapp. – 1995. – №3. – p. 190–208. 7. Cooke S.F., Bliss T.V. Plasticity in the human central nervous system // Brain. – 2006. – №129 (Pt 7). – р. 1659-1673. – doi:10.1093/brain/awl082. PMID 16672292. 8. Courtesy Rakic lab, Nature. – 2009. – 9. DeFelipe J. The Evolution of the Brain, the Human Nature of Cortical Circuits, and Intellectual Creativity. Biology, Medicine // Neuroanat. – 2011. – DOI:10.3389/fnana.2011.00029. 10. Dehay C., Kennedy H. Transcriptional Regulation and Alternative Splicing Make for Better Brains // Neuron. – 2009. – №62. – p. 455-457. 11. Evans A.C., Collins D.L., Milner B. An MRI-based stereotactic atlas from 250 young normal subjects // Soc Neurosci Abstr. – 1992. – №18. – p. 408–492. 12. ICBM 452 T1 Atlas. – 2003. – 13. Kaas J.H., Nelson R.J., Sur M., Lin C.S., et al. Multiple representations of the body within the primary somatosensory cortex of primates // Science. – 1979. – p. 521–523.


14. Kandel E.R., Schawtz J.H., Jessel T.M., Siegelbaum S.A., Hudspeth A.J. Principles of Neuronal Science. – 5th edition. – 2012. – p. 1760. 15. Lalys F., Haegelen C., Ferre J.C., El-Ganaoui O., Jannin P. Construction and assessment of a 3-T MRI brain template // Neuroimage. – 2010. – №49(1). – p. 345-54. 16. Lee J.S., Lee D.S., Kim J., Kim Y.K., Kang E., Kang H., Kang K.W., Lee J.M., Kim J.J., Park H.J., Kwon J.S., Kim S.I., Yoo T.W., Chang K.H., Lee M.C. Development of Korean standard brain templates // J Korean Med Sci. – 2005. – №20. – p. 483-488. 17. Loukas M., Pennell C., Groat C., Tubbs R.S., Cohen-Gadol A., Korbinian Brodmann (1868-1918) and His Contributions to Mapping the Cerebral Cortex // Neurosurgery. – 2011. – №68. – Issue 1. – p. 6-11. 18. Mazziotta J., Toga A., Evans A., Fox P., Lancaster J., Zilles K., Woods R., Paus T., Simpson G., Pike B., Holmes C., Collins L., Thompson P., MacDonald D., Iacoboni M., Schormann T., Amunts K., Palomero-Gallagher N., Geyer S., Parsons L., Narr K., Kabani N., Le Goualher G., Feidler J., Smith K., Boomsma D., Hulshoff Pol H., Cannon T., Kawashima R., Mazoyer B. A four-dimensional probabilistic atlas of the human brain // J Am Med Inform Assoc. – 2001. – №8 (5). – p. 401-30. 19. Pakkenberg B., Gundersen H.J.G. Neocortical neuron number in humans: effect of sex and age // J Comp Neurol. – 1997. – №384. – p. 312-320. 20. Purves D., Augustine G.J., Fitzpatrick D., et al., Sunderland (MA): Sinauer Associates // Neuroscience. 2nd edition. – 2001. 21. Tang Y., Hojatkashani C., Dinov I.D., Sun B., Fan L., Lin X., Qi H., Hua X., Liu S., Toga A.W. The construction of a Chinese MRI brain atlas: a morphometric comparison study between Chinese and Caucasian cohorts // Neuroimage. – 2010. – №51 (1). – p. 33-41. 22. Tang Y., Nyengaard J.R., De Groot D.M., Gundersen H.J. Total regional and global number of synapses in the human brain neocortex // Synapse. – N.Y. – 2001. – №41 (3). – p. 258-273. – 10.1002/syn.1083.


Lecture 3

INTRODUCTION TO SYSTEM NEUROANATOMY Outline: 3.1 Principles of functional systems – Approach 1. FS principles (Kandel et al., 2012) – Approach 2. Functional System Theory developed by P.K. Anokhin 3.2 Modulator Systems in the Brain – Reticular formation – Noradrenaline system – Dopamine system – Serotonin system 3.3 Sensory systems. Sensation and Perception – General structure of the sensory system – “Labeled line theory” – Threshold – Adaptation – Receptive field (RF) – Transduction Questions Literature

How do parts of the brain from a lower structural level generate cognition and emotions at a higher level?

3.1 Principles of functional systems In order to understand our mind from the brain perspective, it is necessary to know some physiological principles of higher level of brain organization. I would like to introduce two approaches developped by neuroscientists, outlined below. – Approach 1. Functional System (FS) principles (from “Principles of Neural Science” by Eric Kandel, 2012). A. FS includes several brain and nervous system regions with different types of information processing.


B. Each FS has identifiable pathways, which connect all parts of FS. C. FS creates topographical maps by projecting from older levels of nervous system to the next. D. FS has a hierarchical organization. Example: the lateral geniculate nucleus is responsive to a spot of light in a particular region. Primary visual cortex: a bar of light with a particular orientation. Association cortex: a bar of light moving in a certain direction (highly complex information). E. FS on one side of the brain controls the opposite side of the body. Example: Left hand movement is controlled by the right side of the brain; visual information from the nasal part of the right retina goes to the left side, and vice versa. F. Regulation of each FS occurs through using feedback information. G. FS pathways may include the same brain structures. Example: the thalamus is a relay for all sensory inputs. H. Each FS has the same output and input. Activation may be increased or decreased at one stage of the FS. I. Principal neurons are more likely to activate (excitation) the next stage of FS; local interneurons influence only cells around them and more often inhibit action (for instance, lateral inhibition). J. Hierarchical circuits: 1. Ascending from environment to the nervous system: “up” 2. Descending hierarchy out of the nervous system: “down” 3. Convergence 4. Divergence – Approach 2. Functional System Theory developed by Petr Kuzmich Anokhin (Anokhin, 1975; Anokhin, 1974). Anokhin described behavior in terms of a hierarchy of functional systems. The central part of FS is decision-making. Afferent synthesis includes afferent perception of the initial stimuli and dominant motivation and memory, and guides the following decision-making process. The decision-making process consists of generating both an Action Program and an Acceptor of the action results (anticipation of future results) (Fig. 8). FS establishment depends on a comparison between the Acceptor of action results and the final results of the action. Correspondence of anticipated results to the real results establishes 34

this FS and is reinforced by induction of positive emotions, whereas a mismatch between them induces negative emotions and destroys the old FS along and increases behavioral activity to create a new FS to achieve positive results in collation between the Acceptor of the action results and results parameters.

Figure 8. Functional system (Anokhin, 1974).

Homeostasis is a stable state with regulated variables of internal conditions and is characterized by relatively constant parameters (for example, temperature, blood pH, number of blood cells, etc., Bernard, 1865; Cannon, 1926). The relation between brain activation and performance was described by the Yerkes-Dodson Law: at the beginning, this relation is linear until at some point it is reversed (Yerkes R.M., Dodson J.D. 1908). Accordingly, an optimal level of activation is the most favorable condition or state.

3.2 Modulator Systems in the Brain Modulator systems in the human brain are separated into the following ones:  Reticular formation  Noradrenaline system  Dopamine system  Serotonin system 35

– Reticular formation Nuclei of the reticular formation and their functions:  The precerebellar reticular nuclei – coordination of muscle contractions;  The raphe nuclei (include serotonergic neurons) – sleep, pain sensation;  The parvocellular, parabrachial nuclei – regulation of nutrition and the respiratory systems;  The gigantocellular nucleus (include serotonergic neurons) – sleep and consciousness;  The pedunculopontine and lateral dorsal tegmental nucleus (cholinergic neurons) – locomotion, sleep and consciousness. – Noradrenaline system Noradrenaline is a neurotransmitter and hormone, also called norepinephrine. Noradrenaline is transported and stored in the synaptic vesicles, it influences the brain and body activation, its level is increased during stress reaction, especially in the ‘fight-or-flight’ response. Noradrenergic cell groups are found in the caudal part of the medulla, solitary nucleus, and locus coeruleus (part of the reticular formation). Main functions include alertness and preparing for actions. – Dopamine system Dopamine is a neurotransmitter in the synapses (also a hormone). Dopamine pathways, areas rich in dopamine, are divided into the nigrostriatal pathway (substantia nigra) with motor function control and the mesolimbic pathway (ventral tegmental area, nucleus accumbens, amygdala, cingulate gyrus, hippocampus, and olfactory bulb). The dopamine system participates in reward and motivation behavior. – Serotonin system Serotonin is a neurotransmitter, known as 5-HT (hydroxitryptamine), which provides the feeling of well-being and happiness. Low concentrations of 5-HT were found in patients with depression. The raphe nuclei are the brain structure providing serotonin to the entire brain, cerebellum, and spinal cord.



Frontal Cortex

Substantia nigra

Nucleus accumbens

VTA Raphe nuclei


Figure 9. Dopamine and Serotonin Pathways. Dopamine Pathway in blue; Serotonin Pathway in red

3.3 Sensory systems. Sensation and Perception – General structure of a sensory system Before describing each specific sensory system, we learn common principles of all sensory systems. Each sensory system consists of three parts and has a corresponding function, summarized in Table 5. Table 5 General structure of a sensory system Parts



A stimulus-detector unit represented by sensory receptors specific for each sensory system.

Transduction from specific mode (for instancelight or sound) into an electric stimulus.


Sensory pathways which are transferring information from detector units to specific initial receiving center. Secondary receiving and integrating centers where information is analyzed and integrated with other modalities.

Relays. All signals are nerve impulse and they transferring to the brain center.


Neurons, “labeled” by the source of information, representation of a signal in the brain


– “Labeled line theory” Further, the principles of the “labeled line theory” or the “special nervous energy doctrine” reflect correspondence between an external stimulus and representation of the stimulus in the brain. To summarize, the theory states:  Each special receptor reacts to corresponding stimuli with a receptor potential (see Table 6).  Each sensory system has its own special center in the brain.  There are specific connections between special receptors and brain centers. Table 6 Sensory systems Sensory system







Photoreceptors: rods and cones




Mechanoreceptors: hair cells in the cochlea




Chemoreceptors: olfactory neurons


Touch Pain




Mechanoreceptors: hair cells in the vestibular labyrinth

Human sensory systems include five components of sensation: 1. Modality; 2. Intensity; 3. Duration; 4. Localization; 5. Affect. – Threshold The sensory threshold is a minimum and maximum level of stimulus intensity, which elicits a sensation. There are different thresholds: 1. The absolute threshold is the lowest level of stimulus intensity at which it is detectable. 2. The differential threshold is the level of intensity at which minimal differences between two very close stimuli can be detected. 3. The terminal threshold is the highest level of stimulus intensity beyond which it is no longer detected.


4. The recognition threshold is the level at which a stimulus can be recognized as a particular object or cause, not only detected. – Adaptation Adaptation in sensory systems is a fundamental component of transduction. Adaptation is a phenomenon whereby the sensation is diminished if a stimulus is persistent, sensory receptors become less sensible after exposure to a stimulus for a long time. Therefore, adaptation reduces our awareness of long-term stimuli. Adaptation is a defensive reaction of our nervous system to avoid over-excitability of sensory cells. Examples of such adaptation are as follows: when one hears repetitive noise outside, his reaction is diminished, or when one enters a dark room, he will be able to see after several seconds. There are two types of adaptation: tonic adaptation is slow (example: olfactory system), while the phasic adaptation is fast (example: auditory system). – Receptive field (RF) The term “receptive field” was suggested by Sherrrington (1906). It is an area that is able to excite a specific neuron. Somatosensory and visual systems of the RF define the stimulus spatial resolution. The RF of the ganglion cell has a center with excitation or inhibition, which is surrounded by an antagonistic process. Auditory RF are spectro-temporal RF. Hubel&Wisel (1962) proposed the RF for visual system in cats. – Transduction Transduction is a process whereby physical stimuli are converted into neuronal signals. A stimulus is characterized by a range of intensity, which is decoded in the nerve impulse by frequency. All receptors transform different kinds of stimulus energy (light, sound, mechanical, etc.) to the nerve impulse. For instance, in the visual system, the wavelength of light is converted to the receptor potential and then to the action potential. Questions: 1. How are organization principles reflected on different levels: from cells to systems? 2. Does the system represent a collective property of the cells?


3. To what degree can mental processes be understood in terms of the properties of specific nerve cells and systems? 4. Does the mind represent a collective and emergent property of the whole brain? 5. Describe modulatory systems in the brain. 6. Provide examples for each principle of the functional system. 7. Describe feedback and decision-making. 8. Give an example of recent studies on functional system properties. 9. Describe general structure of a sensory system. 10. What are thresholds? 11. Adaptation. 12. Transduction. Literature: 1. Anokhin P.K. The functional system as a basis of the physiological architecture of the biological act. In P.K. Anokhin (Ed.). Biology and neurophysiology of the conditioned reflex and its role in adaptive behavior. – New York: Pergamon Press. – 1974. – p. 190-254. 2. Hubel D.H., Wiesel T.N. Receptive fields, binocular interaction and functional architecture in the cat's visual cortex // The Journal of Physiology. – 1962. – №160 (45). – p. 106-154. 3. Kandel E.R., Schwartz J.H., Jessel T.M., Siegelbaum S.A., Hudspeth A.J. Principles of Neuronal Science. 5th edition. – 2012. – p. 1760. 4. Sherrington C.S. Observations on the scratch-reflex in the spinal dog // Journal of Physiology. – 1906. – №34 (1-2). – p. 1-50. 5. Torre V., Ashmore J.F., Lamb T.D., Menini A. Transduction and adaptation in sensory receptor cells // The Journal of Neuroscience. – 1995. – №75 (12). – p. 7757-7768. 6. Yerkes R.M., Dodson J.D. The relation of strength of stimulus to rapidity of habit-formation // Journal of Comparative Neurology and Psychology. – 1908. – №18 (5). – p. 459-482.


Lecture 4

VISION. VISUAL NEUROSCIENCE Outline: 4.1 Peripheral part of the visual system – Eye – Retina. Visual coding 4.2 Visual pathways. Visual cortex – Optic chiasm – The lateral geniculate nucleus (LGN) – Primary visual cortex 4.3 Color perception. Distance and depth. Motion – Color perception. – Motion perception. – Depth and distance perception. – Modern studies on vision Questions Literature

4.1 Peripheral part of the visual system Visual Neuroscience is a branch in Cognitive Neuroscience, which has the purpose to find a relationship between neural activity and conscious visual experience. As it was mentioned previously, the visual cortex is one of the well-studied areas starting from famous research of visual perception on cats by David Hubel and Tornsten Wiesel. Today, neuroscientists have identified around 30 visual areas in the primate brain (Shimojo et al., 2001). – Eye The eye is a peripheral part of the visual system consisting of the iris, the cornea, the pupil, the sclera, the conjunctiva, the retina, and the optic nerve. The eye acts almost like a camera where the parts before the retina function as a focus in a camera. Changes in the pupil size and shapeplay an important role in visual accommodation. Accommodation is a process that allows us to see clearly at different distances: for nearby objects, ciliary muscles supporting the pupil contract and the 41

lens becomes more rounded; for distant objects, ciliary muscles relax and the lens flattens. Disturbances to ciliary muscles or the pupil influence the accommodation process and can lead to the development of myopia or hyperopia. – Retina. Visual coding The retina is the most important part of peripheral visual system and is responsible for transferring the energy of light to a nerve impulse. Visual receptor cells named cones and rods (according to their shape) are composed of photoreceptors and outer nuclear layer of the retina. Nowadays, new technology such as optical coherence tomography allowed identifying 18 layers in the retina. Besides visual receptor cells, the retina consists of other cells supporting receptor nourishment such as ganglion cells, amacrine cells, cuboidal cells, etc.. Cones and rods have a specific localization in the retina. The fovea is the part of the retina in the center of the macula and consists of a maximum concentration of cones. This area is responsible for the acuity of vision. There is an opposite area with the absence of visual receptors called the ‘blind spot’. Information in the optic nerve 50 % consists of the information provided by the fovea, and 50% – by the rest of the retina. Rods and cones have light-sensitive pigments, which are activated by light and induce a cascade of chemical reactions concluded by producing a receptor potential.

4.2 Visual pathways. Visual cortex – Optic chiasm The visual pathway starts from the optic nerve, which goes through the optic chiasm, optic tract, lateral geniculate nucleus, optic radiations and visual cortex. The optic chiasm is located at the bottom of the brain in front of the hypothalamus and serves as a place for crossing optic nerves to convey information from the nasal parts of the left and right eye to the opposite hemispheres. Information from the temporal hemiretina is transferred to the same side of the visual cortex. Crossing of information through the optic chiasm provides binocular and stereoscopic vision (depth perception). 42

– The lateral geniculate nucleus (LGN) The lateral geniculate nucleus (LGN) is a relay nucleus of the thalamus responsible for transferring visual information from subcortical areas to the primary visual cortex. The LGN consists of layers with magnocellular cells and parvocellular cells. Additionally, koniocellular layers are found in the LGN. The visual system encodes the following characteristics of the visual stimulus: shape, movement, color, and depth. Magnocellular cells are connected mostly to rods and are responsible for perception of movement, depth, and brightness. Parvocellular cells are connected to cones and are responsible for the perception of color and form. – Primary visual cortex The primary visual cortex is located in the occipital area known as V1 or Brodmann area 17, striate cortex. It is a highly specialized and most studied brain area, which receives information from the lateral geniculate nucleus (LGN). The primary visual cortex is wellmapped for spatial characteristics of signals. V1 cells have good tuning properties, which are important for orientation and color perception. It is responsible for edge detection. Hubel&Wiesel (1962) suggested an ice-cube model with two tuning properties, but the mechanism is still under question.







Figure 10. Visual images at different levels of the visual pathway

The secondary visual cortex V2 (BA 18), also called the peristriate cortex, is the first visual association area, and receives stimulus information from V1 and sends that information to V3, V4, V5, and back to V1. Neurons of V2 are also tuned like V1 cells to orientation, color, and spatial frequency of the signal. Additionally, neurons of V2 are sensitive to illusory contours (visual illusions, edges which are not visible), binocular disparity (differences in images created by two eyes). V2 participates in object recognition memory. 43

The third visual association cortex V3 participates in processing coherent motion, global motion (Braddick et al., 2001). V4 is called the extrastriate cortex and receives information from V1, V2, and encodes stimuli salience, important for long-term plasticity. V5 or middle temporal visual area is responsible for perception of motion and eye movements.

4.3 Color perception. Distance and depth. Motion – Color perception There are two theories of color vision: a) The Thrichromatic Theory, suggested by T. Young and H. Helmholtz, indicates that three types of cones are more sensitive to blue (450-495 nm, 610-670 THz), red (625-740 nm, 405-480 THz), and green (495-570 nm, 530-600 THz). All the other colors are a combination of variations in three cone types activation, the same as each color is possible to achieve by mixing the three main colors. Three types of cones are called S cells, M cells, and L cells. b) The Opponent Theory, suggested by E. Hering (1872), states that each color is analyzed in an antagonistic way such as white vs black. Nowadays, it has been shown that both theories are correct, but at different levels: the first one at the level of receptors, and the second one at the level of retinal ganglion cells. The lateral geniculate nucleus (LGN) has three zones (P-, M-laminae, and koniocellular laminae), which receive information from balanced L- and M- cones. In the primary visual cortex, the separation of three colors is shaped up by color tuning, “double-opponent” cells clustered to V1 blobs, from V1 blobs information goes to V2 thin stripes – then to V4 color module blobs. Color perception is subjective, and may have cultural differences. For instance, Humba people have absolutely different color categoryzation. Color perception depends on psychophysiological state and individual differentiation of color. Therefore, color tests (such as the Lusher color test) may be used to analyze internal emotional and motivational states, and individual differences as well. 44

– Motion perception Motion perception is based not only on visual perception, but also on vestibular and proprioceptive receptors. “Beta movement” (Muybridge, 1877) demonstration of series of stationary balls in consequent order is perceived as a movement of one ball. The “Phi phenomenon” is observed at a higher frequency of movement, as it was demonstrated with a tachistoscope by Werthmeier in 1912. Hassenstein-Reichardt detectors, motion-energy sensors, measure changes in luminance at one point and neighboring points of retina and are suggested as a model of motion perception. Hubel& Wiesel (1959) described how stimuli from the eye are processed in visual pathways and neocortex and detection of motion. Borst& Euler (2011) review new models of motion detection in their article “Seeing things in motion: models, circuits, and mechanisms” (2011). Authors discussed the models of motion detection such as the gradient model, the Barlow&Levick model, motion energy model (Adelson&Bergen, 1985), and other models. – Depth and distance perception Depth perception is based on depth cues perception. There are monocular cues (when you see with one eye) and binocular cues (when you see with both eyes). Monocular cues are motion parallax (differences in object positions, when you move you are able to analyze the velocity and direction of movements); perspective cues (when parallel lines converge ahead at a distance); aerial perspective cues ( when color saturation is different depending on the source of light); optical expansion cues (changing size of moving objects); kinetic depth cues (analyses of rotating objects), size cues (relative, absolute, familiar); accommodation (kinesthetic sensation of ciliary muscles); lighting/shading cues; occultation (overlapping surfaces); texture gradient cues; curvilinear perspective (parallel lines are curved); defocus blur (relative to horizon). Binocular cues are stereopsis (binocular disparity), convergence, shadow stereopsis (shadows influence depth perception). According to studies, processing depth and distance occurs in humans by disparity-tuned neurons, which are mostly in areas V3A and V4, V7 (Backus et al., 2001, Tsao et al., 2003). Distance cues are represented in dorsal visual areas (V3A, V3d, V6) (Berryhill&Olson, 2009). Monocular and binocular cues are integrated in inferior area V3A/B. 45

– Modern studies on vision To learn how visual perception studies have been improved with new technology (such as fMRI, PET, and others) in normal and abnormal conditions, I suggest reading the following review articles: 1) Kashou. Current Trends of fMRI in Vision Science: A Review. Functional Magnetic Resonance Imaging – Advanced Neuroimaging; 2) Farah&Aquirre. Imaging visual recognition: PET and fMRI studies of the functional anatomy of human visual recognition; 3) Shinsuke Shimojo, Michael Paradiso, and Ichiro Fujita. What visual perception tells us about mind and brain; 4) Zhang et al. Properties of cross-modal occipital responses in early blindness: An ALE meta-analysis (2019); 5) Pearson. The human imagination: the cognitive neuroscience of visual mental imagery (2019). Questions: 1. What does the peripheral part of the visual system consist of? 2. Define the term “photopic vision”. 3. Define the term “scotopic vision”. 4. Describe visual pathways and their influence on what we see. 5. What kind of information is analyzed by the dorsal “where” pathway processes? 6. What kind of information is analyzed by the ventral “what” pathway processes? 7. Name the areas of the visual system. 8. What kinds of receptors are responsible for color perception? 9. Describe differences between M cells and P cells. 10. Define the LGN layers. 11. Describe the ‘where’ (magno-) and ‘what’ (parvo-) cortical systems and their functions. 12. Visual cortex: how do we analyze visual objects? 13. How do we differentiate shapes and forms? 14. Describe the mechanism of color perception, the perception of lightness and darkness. 15. Describe different visual illusions. 16. Explain the mechanism of depth perception. 17. Explain the mechanism of motion perception. 18. Explain the concepts of vision. Does visual perception adequately illustrate the real external world? Literature: 1. Backus B.T., Fleet D.J., Parker A.J., Heeger D.J. Human cortical activity correlates with stereoscopic depth perception // J. Neurophysiol. – 2001. – №86. – p. 2054-2068.


2. Berryhill M.E., Olson I.R. The representation of object distance: evidence from neuroimaging and neuropsychology // Front Hum Neurosci. – 2009. – №3. – p. 43. – doi:10.3389/neuro.09.043.2009 3. Borst A., Euler T. Seeing Things in Motion: Models, Circuits, and Mechanisms // Neuron. – 2011. – №71.6(6). – p. 974-994. 4. Braddick O.J., O'Brien J.M., et al. Brain areas sensitive to coherent visual motion // Perception. – 2001. – №30 (1). – p. 61-72. 5. Conway B.R., Tsao D.Y. Color-tuned neurons are spatially clustered according to color preference within alert macaque posterior inferior temporal cortex // Proc Natl Acad Sci USA. – 2009. – №106 (42). – p. 18035-18039. 6. Farah M.J., Aguirre G.K. Imaging visual recognition: PET and fMRI studies of the functional anatomy of human visual recognition // Trends Cogn Sci. – 1999. – №3 (5). – p. 179-186. 7. Hubel D.H., Wiesel T.N. Receptive fields, binocular interaction and functional architecture in the cat's visual cortex // The Journal of Physiology. – 1962. – №160 (45). – p. 106-154. 8. Kandel E.R., Schawtz J.H., Jessel T.M., Siegelbaum S.A., Hudspeth A.J. Principles of Neuronal Science. – 5th edition. – 2012. – p. 1760. 9. Nasser H. Kashou. Current Trends of fMRI in Vision Science: A Review. Functional Magnetic Resonance Imaging – Advanced Neuroimaging. – 2012. – DOI: 10.5772/30862 10. Pearson J. The human imagination: the cognitive neuroscience of visual mental imagery // Nat Rev Neurosci. – 2019. – №20 (10). – Р. 624-634. – doi: 10.1038/s41583-019-0202-9. 11. Shimojo S., Paradiso M., Fujita I. What visual perception tells us about mind and brain // PNAS. – 2001. – №98 (22). –12340-12341. – 12. Stewart H.C. Hendry, Clay R. The Koniocellular Pathway in Primate Vision // Annual Reviews Neuroscience. – 2000. – vol. 23. – p. 127-53. 13. Tsao D.Y., Vanduffel W., Sasaki Y., Fize D., Knutsen T.A., Mandeville J.B., Wald L.L., Dale A.M., Rosen B.R., Van Essen D.C., Livingstone M.S., Orban G.A., Tootell R.B. Stereopsis activates V3A and caudal intraparietal areas in macaques and humans // Neuron. – 2003. – №39. – p. 555-568.


Lecture 5

AUDITORY SYSTEM. HEARING Outline: 5.1 Auditory pathways. Hierarchy and tonotopy – Auditory pathways – Signal Transduction in hearing cells – Theory of place – Superior Olivary Complex (SOC) – Medial Geniculate Nucleus (MGN) Auditory cortex Questions Literature

5.1 Auditory pathways. Hierarchy and tonotopy The auditory system (AS) consists of the auricle, meatus, tympanum, malleus, incus, stapes, cochlea, auditory pathways, and the auditory cortex. All parts of the system before the cochlea are necessary for transformation of sound stimuli to mechanical signals and conduction of such signals to the cochlea. – Auditory pathways The steps of signal processing through the auditory pathway involve: Step 1. Signal Transduction in the Cochlea. Step 2. Cochlear nucleus synapse. Step 3. Superior Olivary Complex. Step 4. Inferior Colliculus synapse. Step 5. Medial Geniculate Nucleus in the Thalamus. Step 6. Cortex. – Signal Transduction in hearing cells Step 1. Signal Transduction in the Cochlea. The tympanic membrane is comprised of the round window, oval window, basilar membrane, and helicotrema. Hair cells are auditory receptors located on the basilar membrane. Transformation of mechanical stimuli to nerve impulse starts from hair cell activation and generation of receptor potential; then it is followed by neurotransmitter diffusion, which induces an action potential in the nerve terminal. 48

Helicotrema Scala vestibuli Oval Window

Malleus Uncus


Scala media

Scala tympani Tympanum

Round window

Basilar membrane

Figure 11. Cochlear structure. Helicotrema

– Theory of place The “Place theory” (H. Helmholtz, 1895) explains how we perceive differences in the frequency of sound. A lower frequency of sound is perceived by a place closer to the helicotrema and a higher frequency of sound is perceived by a place closer to the round window. The “Temporal Theory” or timing theory is linked to volley principles suggested by Wever and Charles (1930): action potentials in auditory neurons in response to sound will have slightly different phases, and summation of firing of different action potentials from a group of neurons determines the final frequency. Later on, these principles were determined only for sounds with a frequency between 500Hz-5000Hz. FREQUENCE

50 Hz

Low 1500 Hz Medium 10 000 Hz High

Figure 12. The “Place theory” (H. Helmholtz, 1895)


Step 2. Cochlear Nuclei are organized tonotopically and consist of: 1. Stellate cells that are responsible for regular firing at particular frequencies; 2. Bushy cells that are responsible for single action potentials at sound onset and transmitting timing information, and conveying information about horizontal localization; 3. Fusiform cells that are responsible for wide frequency responses, functionally they may be both excitatory and inhibitory, and convey information for vertical localization; 4. Giant cells are responsible for connections between molecular and deep layers. – Superior Olivary Complex (SOC) Step 3. Superior Olivary Complex (SOC) – the brainstem nuclei are organized tonotopically. The SOC consists of: 1. Medial Superior Olive that calculates interaural time delays (ITD). Delay in time between two ears is 700 microseconds (μs) approximately. Minimum detectable delay is 0.000010 (10 micro) sec. The ITD code is the pattern of which neurons are active and which are not. 2. Lateral Superior Olive (LSO) that calculates interaural intensity differences (ILD). The basis for intensity sensitivity are excitatory inputs from the same side ear and inhibitory inputs from the opposite ear, the inputs, which are analyzed by LSO neurons. Step 4. Inferior colliculus (IC) that is organized tonotopically in several layers. Neurons within each layer are sensitive to both time and intensity differences between the ears. The IC localizes sound based on inter-aural time and intensity differences from earlier stages. Organized in a two-dimensional map of all combinations of delay and intensity differences. Provides the map of sound location in vertical and horizontal dimensions. – Medial Geniculate Nucleus (MGN) Step 5. Medial Geniculate Nucleus (MGN) is a relay nucleus of the thalamus, organized tonotopically. The MGN is divided into the following parts: 50

1. Ventral subnuclei are responsible for relaying intensity and frequency from two ears to the cortex; 2. Dorsal subnuclei are multimodal and convey complex information. 3. Medial subnuclei are responsible for detection of duration of sound and its intensity. 5.2 Auditory cortex Step 6. Primary Auditory Cortex (A1). Secondary Auditory Cortex (A2). Tertiary Auditory Cortex (A3).  Brodmann’s areas 41 and 42.  Superior Temporal Gyrus (STG, resides on Heschl’s gyrus and lateral sulcus).  Tonotopical organization.  Most neurons are sensitive to sounds from either ear, but not identically.  Zones of summation and suppression exist.  Summation columns are excited by stimulation to either ear.  Suppression columns are excited by stimulation of one side and suppressed by stimulation of the opposite ear. A 2-D map of columns that are responsive to every audible frequency and to each type of inter-aural interaction. Questions: 1. Explain the tonotopic organization of auditory system. 2. Describe cochlear structure. Hearing cells. 3. Explain the mechanism of spatial orientation. 4. How do we differentiate low, medium, and high frequency sounds? 5. Explain the Theory of “place”. 6. Explain the Temporal theory. 7. What is the olivary complex and its function? 8. Explain the integration of somatosensory, visual, and auditory association areas. 9. Describe information channeling: Steps 1-4. 10. Name parts of the auditory system. 11. Indicate the structure of basal membrane. 12. Draw the auditory pathway. 13. Characterize the primary auditory cortex. 14. Explain encoding the interaural time difference. 15. Explain encoding of intensity in the auditory system.


Literature: 1. Kandel E.R., Schawtz J.H., Jessel T.M., Siegelbaum S.A., Hudspeth A.J. Principles of Neuronal Science. 5th edition. – 2012. – p. 1760. 2. Moore B.C.J. An introduction to the psychology of hearing / Emeraldn Group Publishing. – 2003. 3. Walker K.M.M., Bizley J.K., King A.J., Schnupp J.W.H. Multiplexed and Robust Representations of Sound Features in Auditory Cortex // The Journal of Neuroscience. – 2011. – №31 (41). – р. 14565-14576. 4. Wever E., Charles B. The perception of low tones and the resonance-volley theory // Journal of Psychology: Interdisciplinary and Applied. – 1937. – №3 (1). – p. 101-114.


Lecture 6

MOTOR CORTEX. MOTOR UNITS AND MUSCLE ACTION. CONTROL OF MOVEMENT. CEREBELLUM. BASAL GANGLIA. VOLUNTARY MOVEMENT. MOTOR SYSTEMS – HIERARCHICAL ORGANIZATION Outline: 6.1 Motor system as a functional system (FS). Involuntary movements – Motor units. Alpha motor neurons. Spinal cord. Dorsal and ventral horns – Unit of movement: Sacromeres. Actin and Myosin – Involuntary movements: Reflexes. Repetitive rhythmic motor patterns 6.2 Voluntary movements – Motor control – Motor function representations in the motor cortex (Penfield) – Direction of movement Questions Literature

To start this topic, I would like to introduce a relatively recent fMRI study showing the difference in brain activity between motor execution and motor imagery by Hanakawa et al. (2008). There are the brain areas that are supposed to be motor execution-specific and imagery-specific. This study helps you understand the power of imagination of motor activity.

6.1 Motor system as a functional system (FS). Involuntary movements The motor system follows the principles of a major functional system as defined previously:  The motor system involves motor brain areas and the spinal cord to maintain posture and balance, to plan and coordinate different types of movement in our body. The main difference from sensory systems is that the motor system transforms internal neural signals to activate muscles for movement, whereas sensory systems transfer external signals to internal neural activity. 53

 Motor pathways link all parts of the functional system, e.g. Medial and lateral brain stem pathways, Lateral and Medial corticospinal tracts.  The motor system is organized topographically and hierarchically.  The motor system of the left side of the brain controls movements on the right side of the body, and the right side of brain control left side of body.  The motor system regulates its own activity by feedback feedforward control.  Distinct modalities of the motor system are different movements: reflexive, rhythmic, and voluntary. – Motor units. Alpha motor neurons. Dorsal and ventral horns. Charles Scott Sherrington (1906) proposed the concept of motor units. A motor unit consists of one neuron and muscle fiber innervated by this neuron. Groups of motor units (motor pool) are combined into a single muscle. The number of motor units engaged in contraction determines the force of muscle movement. The spinal cord is a nervous structure connected between the brain and body, it is about 40-50 cm in length, and has 31 pairs of spinal nerves, divided into 4 regions in ascending order: coccygeal nerve (1), sacral (5), lumbar (5), thoracic (12), and cervical (8). Grey matter forms 3 horns: ventral (motor neurons), dorsal (sensory neurons), and lateral (visceral neurons) horn. Motor neurons are projected to skeletal and internal smooth muscles and provide a mechanism for reflexes and voluntary movements. Sensory neurons convey information from the periphery of the sensory system to the central nervous system. – Unit of movement: Sacromeres. Actin and Myosin. A sarcomere is a unit of muscle tissue between two Z lines, including actin (thin) and myosin (thick) filaments. Myosin has a head with ATP (Adenosine triphosphate), which is the source of energy for contraction. Relaxed and contracted sacromeres are associated with muscle contractions during movement. A sacromere is divided into the following parts:  I band is the zone of thin filaments without thick filaments;  A band is the zone of thick filaments; 54

 H zone is the zone of thick filaments without actin within the A band;  M line is the middle of the sacromere. Contraction of muscles happens when I band and H zone are shortened, Z lines come closer, and the sarcomere is shortened as well. Tropomyosin moves actin relatively to myosin. – Involuntary movements: Reflexes. Repetitive rhythmic motor patterns. Reflexes and rhythmic movements are involuntary and engage stereotyped patterns of specific muscles, not controlled by our conscious mind. A reflex is an automatic involvement of neural pathways in response to a stimulus. To describe reflexes shortly, we mention the origin of the term and reflex theory suggested by I.P. Pavlov, who received a Nobel Prize (1904) and was named the “Father of Behaviorism”. First, it was Rene Descartes, a French philosopher and mathematician who introduced the term “reflex”. The well-known “Reflex theory” by Pavlov explains the mechanism of reflexes with his famous experiments with dogs. Pavlov determined two types of reflexes: conditioned and unconditioned. Reflex movements we discuss here are unconditioned stereotyped reactions to specific triggers in specific muscle patterns. A reflex arc is a pathway starting from stimulus – the afferent way – neuron – the efferent – reaction as a muscle construction. Spinal reflexes serve as clinical tools for diagnosis of a number of movement diseases and provide information about the lesion localization. Examples of repetitive rhythmic movements are swallowing, chewing, contractions of flexors and extensors. The spinal cord along with the brainstem are the structures responsible for repetitive rhythmic motor patterns. 6.2 Voluntary movements Voluntary movements are controlled movements toward a specific goal. Motor skills are a result of voluntary learning and improving with experience. Voluntary movements follow a principle opposite to the sensory systems principle: movements start with the internal representation of future results, whether the sensation starts with processing an external stimulus or with the purpose to produce an internal representation. 55

Voluntary movements are characterized by the following rules (Kandel et al, 2004): 1. Voluntary movements are regulated by a motor program; 2. A motor program determines the spatial characteristics of the movement; including joint angles (kinematics); 3. A motor program regulates forces to achieve a movement goal (dynamics); 4. Spatiotemporal features are movement schemes/primitives (biomechanics); 5. Representation of the movement outcome in the brain independent of the used effector and the way of action; 6. Motor equivalence: motor actions accomplished in distinct ways have the same features (Donald Hebb, 1950s). Example: writing the same text can be performed by the right hand, or left hand, or with a pen between teeth, or by the toes. 7. Reaction time (RT) to stimulus determined by the amount of information necessary to accomplish the action. RT to voluntary movements is longer than for reflexes. RT elevates with the increase in choices. RT decreases with a certain choice. RT diminishes with learning and experience; 8. Trade-off rule for relations between the speed and accuracy of the movement (Robert Woodworth,1899); 9. Feed-forward and feedback control circuits. – Motor control Nikolai Bernstein is a famous Russian neurophysiologist who developed biomechanics and was a pioneer in the field of motor control and learning. Table 7 Bernstein's Levels of Construction of Movements (Bernstein, 1946) Levels A B C D

Name the rubro-spinal level of paleokinetic regulation the level of synergy and “punch” (“shtamp”) or the thalamo-pallido level pyramidal-striatal level of the spatial field the parietal premotor level of action


Brain level Subcortical Subcortical Cortical Cortical

The ‘degrees of freedom problem’, or Bernstein’s problem, states that the motor system has an infinite number of degrees of freedom to perform one task. The higher the degree of freedom, the less efficient the motor performance. The main problem is to decrease the number of degrees of freedom. The central nervous system can “freeze degrees of freedom” and increase efficiency. Table 8 Three levels of motor control (Kandel et al., 2012) Levels 1 (highest) 2 3 (lowest)

Structure dorsolateral frontal cortex posterior lobe and premotor areas spinal cord

Function Goal of the movement Motor plan Spatiotemporal features of the muscle activity

– Motor function representations in the motor cortex (Penfield). Wilder Penfield is a neurosurgeon who used electrical stimulation to map the sensory and motor cortex. The somatotopic map later became famous as the cortical “homunculus”, which means “little man” in Latin. The sensory homunculus is a representation of sensory functions in the primary sensory cortex. The motor homunculus is a representation of motor functions in the primary motor cortex (see figres With current studies using fMRI techniques, scientists created a somatotopic map of the preterm human brain (31-36 weeks) (Dall’Orso et al., 2018). Conclusions made by the authors are that “functional activity within the sensorimotor cortex is already somatotopically organized in a pattern similar to the classic mature “homunculus” representations” (Dall’Orso et al., 2018, p. 2513). Only the responses for wrists, ankles, and mouth are different in infants. – Direction of movement. Direction of movement is encoded by a neural population vector (Georgopoulos et al., 1986). Direction of movement corresponds to the vector resulting from a sum of all cells, which are called a population vector. It is possible to trace the population vector in different 57

tasks. Neural population activity was measured by extracellular recording from the monkey motor cortex during movements in different directions. Weighted vector sum of neural population activity determines the direction of the movement during behavior.

Figure 13. Example of weighted vector

Motor learning, as the term implies, involves the improvement of movements through practice over time and encompasses the acquisition, consolidation and retention of a wide range of motor skills, from movements essential for survival and day-to-day activities to highly specialised functions, such as those involved in the fine finger movements of musicians (Willingham, 1998). Human, primate and rodent studies show that motor learning is driven by adaptive brain plasticity mechanisms and has the propensity to result in functional and structural changes in the motor areas, including the primary motor cortex, supplementary motor area, cerebellum, and parietal regions (Dayan and Cohen, 2011). Questions: 1. Describe the motor system hierarchy. 2. What are motor units? What are alpha motor neurons? 3. What are dorsal and ventral horns? 4. What are sarcomeres, actin and myosin? 5. Demonstrate reflexes. Give the description of repetitive rhythmic motor patterns. 6. Distinguish voluntary movements from involuntary ones.


7. 8. 9. 10. 11. 12. 13.

Explain feedback feed-forward control. Elucidate encoding of the Direction of Movement. Describe Penfield’s maps. Distinguish Bernstein's Levels of Construction of Movements. Explain what is motor equivalence. Describe three levels of motor system control. How do voluntary movements obey psychophysical principles?

Reference: 1. Bernstein N.A. The co-ordination and regulation of movements. – Oxford: Pergamon Press. – 1967. 2. Dall’Orso S., Steinweg J., Allievi A.G., Edwards A.D., Burdet E., Arichi T. Somatotopic Mapping of the Developing Sensorimotor Cortex in the Preterm Human Brain // Cereb. Cortex. – 2018. – №28 (7). – р. 2507-2515. 3. Dayan E., Cohen L.G. Neuroplasticity subserving motor skill learning // Neuron. – 2011. – №72 (3). – p. 434-454. 4.  Hanakawa T., Dimyan M.A., Hallett M. Motor planning, imagery, and execution in the distributed motor network: a time-course study with functional MRI Cerebr // Cortex. – 2008. – №18. – р. 2775-2788. 5. Georgopoulos A.P., Schwartz A.B., Kettner R.E. Neuronal population coding of movement direction // Science. – 1986. – Vol. 233. – Issue 4771. – р. 1416-1419. 6. Kandel E.R., Schawtz J.H., Jessel T.M., Siegelbaum S.A., Hudspeth A.J. Principles of Neuronal Science. 5th edition. – 2012. – р.1760. 7. Woodworth R.S. The accuracy of voluntary movement. Psychological Monographs. – 1899. – p. 1-114. 8, Sherrington Ch.S. The integrative action of the nervous system (1st ed.). – Oxford University Press: H. Milford. – 1906. – р. 411. 9. Willingham D.B. A neuropsychological theory of motor skill learning // Psychol. Rev. – 1998. – №105 (3). – p. 558-584.


Lecture 7

EMOTIONAL NETWORK: LIMBIC SYSTEM. AMYGDALA. HYPOTHALAMUS. THALAMUS. EMOTION AND COGNITION INTERACTION Outline: 7.1 A brief history of emotion and brain studies. Hypothalamus, Thalamus, Amygdala, Limbic system – Charles Darwin about the expression of the emotions. – William James and Carl Lange about emotion. Sensory and motor systems. – Cannon&Bard. Hypothalamus and thalamus. – James Papez. Lymbic system – Heinrich Klüver and Paul Bucy. Amygdala – MacLean. Lymbic system. – Two-factor theory of emotion. – A biological theory of emotion. – Informational theory of emotion. 7.2 Modern aprroaches to study emotion – LeDoux’s High and Low roads. – Paul Ekman and facial muscles. – Imaging studies of emotions Questions Literature

Before we start this lecture, I would like you to think over the following questions during the reading and try to answer at the end: 1. Does the brain possess a special system devoted to emotion? 2. Can we separate cognitive processes from emotional processes?

7.1 A brief history of emotion and brain studies. Hypothalamus, Thalamus, Amygdala, Limbic system – Charles Darwin about the expression of the emotions. Charles Darwin was the first who described in his famous book “Expression of the emotions in Man and Animals” (1872) that emotional facial expressions (fearful, angry, and happy) are universal and


have significance for adaptation to the social environment and successful communication. Darwin suggested three principles of emotion expression for man and animals: “I. The principle of serviceable associated Habits.- Certain complex actions are of direct or indirect service under certain states of the mind, in order to relieve or gratify certain sensations, desires,…”; ”II. The principle of Antithesis. Certain states of the mind lead to certain habitual actions, which are of service, as under our first principle…”; “III. The principle of actions due to the constitution of the Nervous System, independently from the first of the Will, and independently to a certain extent of Habit. – When the sensorium is strongly excited, nerve-force is generated in excess, and is transmitted in certain definite directions, depending on the connection of the nerve-cells, and partly on habit: or the supply of nerveforce may, as it appears, be interrupted” (pp 28-29, Charles Darwin, 1872). This evolutionary approach has led us to understanding emotion as a function of the nervous system. – William James and Carl Lange about emotion. Sensory and motor systems. William James and Carl Lange defined emotion as a physiological reaction to a stimulus. Famous James’s expressions are: “Do we run from bear because we are afraid or are we afraid because we run?”; “We feel sorry because we cry, angry because we strike, afraid because we tremble and not that we cry, strike, afraid because we are sorry, angry or fearful as the case may be” (William James, “What is an Emotion?”, Mind, vol. 9, 1884, pp. 188-205). Therefore, emotions are considered a function of the sensory and motor brain systems. – Cannon&Bard. Hypothalamus and thalamus. Cannon&Bard (1929) criticized that the conscious experience of emotion may occur independently from signals about changes in our physiological state and based on their experimental studies with cats proved the importance of the integrity of the hypothalamus and thalamus for emotional experience. If the neocortex is removed, everything above the forebrain, the animal shows sham rage. If the posterior hypothalamus is removed, a sham rage will also be expressed in some isolated elements. Authors concluded that integration of the hypotalamus and thalamus is key for “mediating emotions, including 61

regulating the peripheral signs of emotion and providing the cortex with the information required for the cognitive processing of emotion” (Cannon&Bard, 1929). – James Papez. Lymbic system James Papez (1937) suggested the circuit theory of emotion starting and ending with the hippocampal formation. Brain circuit: “hippocampal formation – fornix – mammilary bodies – mammilothalamic tract – anterior thalamic nucleus – cingulum – entorhinal cortex – hippocampal formation” (Shah et al., 2012). According to Papez, the experience of emotion is primarily associated with the cingulate cortex and, secondly, with other cortical areas, and the hypothalamus controls emotional expression. However, there is controversy in facts. Some facts state it was James Papez who proposed that feeling involves the limbic lobe, a brain area identified by Paul Broca, and includes an area around the brain stem and the cingulate gyrus, the parahippocampal gyrus, and the hippocampal formation (see figure Papez circle We will discuss later a current view of lymbic system structure. – Heinrich Klüver and Paul Bucy. Amygdala Additional brain structures related to emotional states and their dysregulation were found by Heinrich Klüver and Paul Bucy in1939. They described emotional changes resulting from bilateral lesions of the anterior temporal lobe (including the amygdala) in monkeys. Later on, it was named Klüver-Bucy syndrome. Animals with Klüver-Bucy syndrome may exibit addotional symptoms such as hyperphagia, hypersexuality, hyperorality, or visual agnosia. – MacLean. Lymbic system. MacLean (1949, 1952) extended the emotional circuit originally proposed by Papez by adding the amygdala, associative cortex, and prefrontal cortex. MacLean’s Tripartite Model separates the mammalian brain into three parts, organized in an evolutionary and developmental order: 1. Reptilian brain – responsible for basic needs, such as breathing and circadian rhythm; 2. Mammalian, or emotional brain (limbic system); 3. Neomammalian brain – the thinking brain. 62

– Two-factor theory of emotion. The two-factor theory of emotion, or the Schachter-Singer theory, underlines the significance of a cognitive component in emotional experience (1962). As a result of his famous experiments with epinephrine injection to volunteers, Schachter emphasized that emotions in the cortex are induced by ambiguous signals. Interpretation of ambiguous or nonspecific signals by volunteers in the experiment were dependent on their individual expectations and previous experience. This idea was explored futher by Magda Arnold, who suggested the “appraisal” theory (1960). According to this theory, implicit appraisal of a stimulus is transferred through action tendencies to consious feelings. She underlined three ways of experiencing emotions: precieving external triggers, remembering the emotions, imagining the emotion. She emphasized that affective memory is a dynamical processes in the brain (1984). – A biological theory of emotion. A biological theory of emotion was developed by P.K. Anochin based on his Functional System (FS) Theory (described in previous lectures). The essense of the theory is that positive emotions appear when the “Action program” and “Acceptors ofaction results” correspond to each other. In an opposite way, the system will produce a negative emotion and destroy the current FS to generate a new FS which will satisfy expectations. The positive emotion supports the current FS as a successful system to use it the next time. Thus, anatomical brain structures involved in a specific functional system are created depending on current dominant motivation and action programm, and are the basis for the current emotional state. – Informational theory of emotion. Futher advancement to understanding emotion was the Informational theory of emotion suggested by P.V. Simonov (1981). Emotion is a reflection of needs and the probability of their satisfaction in the human brain, appraised in the brain based on genetic and individual experience. Emotions may be described by the formula: E=f[N,(InIc)], where E is emotion, N is needs, In is the information about required resources and Ic is the information about existing resources to satisfy needs. Therefore, (In-Ic) is the probability of expectation to satisfy needs based on phylogenetic and ontogenetic experience. 63

7.2 Modern aprroaches to study emotion – LeDoux’s High and Low roads. According to famous LeDoux’s (1994) studies, “the brain does not possess a special system devoted to emotional functions” and emotion is “a function of sensory and motor areas of neocortex” (LeDoux&Phelps, 2008). In his experiments, rats were conditioned to fear a specific noise. Rats were exposed to a tone and a mild electric shock, and with strong signal the rats were threatened and emitted screams with a high frequency. The scream sound activates the amygdala directly. LeDoux concluded that there are two different pathways, “High” and “Low”, to induce an emotional state.

Figure 14. LeDoux’s High and Low roads (LeDoux&Phelps, 2008). HR – the High Road, LR – Low Road, 1 – Frontal cortex, 3 – Sensory Thalamus, 4 – Amygdala, 5 – Sensory Cortex.

LeDoux said: “The limbic system is a hypothetical construct of pathways in the forebrain, which contains the hippocampus, amygdala and a few other tiny structures, that supposedly gets all sorts of sensory input from the external world – sight, smell, hearing, touch and taste – as well as from the viscera. When these sensations are integrated in the limbic system, emotional experiences occur”. – Paul Ekman and facial muscles. Certainly, we should mention Paul Ekman, who in the 1990s proposed an expanded list of basic emotions and how they are encoded in facial 64

muscles. Fourty three muscle groups were described to maintain different emotions and inspired the development of the Facial Action Coding System. An important contribution to Affective Neuroscience was a welldesigned methodology to study emotional brain mechanisms by using modern technology such as fMRI. Ekaman faces became popular stimuli in emotional perception studies. Additional popular stimuli for emotional perception studies were developed by R.C. Gur (Gur et al., 2009). – Imaging studies of emotions For example, a voxel-based meta-analysis of emotional perception studies provided by Fusar-Poli et al (2009) were based on 105 fMRI studies with Ekman and Gur emotional faces (chosen among 551 potential studies based on the criteria). The authors created a functional map of emotions separately for different valences of emotion (Fusar-Poli et al, 2009). According to this map, fear induction is related to amygdala activation, sadness – to subcallosal cingulate, happiness – to the basal ganglia, disgust – to the basal ganglia. Table 9 Comparison of the brain activation (BOLD signal) during perception of different emotional faces (happy, sad, angry, fearful, disgust) and neutral faces (Fusar-Poli et al, 2009) Emotion valence happiness

sadness fear angry disgust

Brain structures bilateral amygdala, left fusiform gyrus, right anterior cingulate cortex the left lingual gyrus, the right amygdala bilateral amygdala the fusiform, medial frontal gyri left insula, right inferior occipital gyrus the insula bilaterally, right thalamus, left fusiform gyrus

The authors concluded, “the wide neurofunctional network underlying human face processing includes a number of visual, limbic, temporo-parietal, prefrontal and subcortical areas as well as the cerebellum. 65

Whereas occipital areas and the cerebellum were commonly activated across different emotions, a discrete response to valence has been reported for the limbic system and insular cortex. Although the basic emotions are not represented by entirely distinct neural circuits, they are at least partially separable. Sex, age and consciousness modulate the neurophysiological response to human emotional faces” (FusarPoli et al, 2009). One of the interesting studies by researchers from Finland tried to answer the question: “How do emotions such as happiness, nervoussness and anger feel in the body?” (Nummenmaa et al., 2014). The authors explained, “emotions adjust not only our mental, but also our bodily states. This way they prepare us to react swiftly to the dangers… Awareness of the corresponding bodily changes may subsequently trigger the conscious emotional sensations, such as the feeling of happiness” (Nummenmaa et al., 2014, see figure According to the authors, emotions are associated with increased activation in the chest, probably related to heart rate, in the head area, probably related to the face, in the legs, arms, and entire body. Scientists at Aalto University and Turku PET Centre measured brain activation during emotional movies and revealed strong synchronization between participants in emotional, attentional, and visual networks. “People tend to experience emotions in a similar manner, so it seems logical that brain activity of people experiencing emotions would be similar” (Nummenmaa et al., 2014). Linquist et al. (2012) compared locationists approach to psychological constructionist approach to understand emotions. The locationist approach is based on the assumption that emotion and different valence of emotions are strongly localized in the brain and inherited, and cannot be “broken down to basic psychological components”. In the opposite view, constructionists assume that “psychological events emerge out of more basic psychological operations that are not specific to emotion”. The authors concluded, “we found evidence that is consistent with a psychological constructionist approach to the mind: a set of interacting brain regions commonly involved in basic psychological operations of both an emotional and non-emotional nature” (Linquist et al., 2012). 66

However, it is a difficult question, and studies supporting both approaches are continued. Besides investigating basic emotions, some researchers develop explanations of more complex emotions based on brain activation. Ortigue et al. (2011) examined the neural correlates of such complex emotion such as “love” by using fMRI during viewing faces of long-term romantic partners. BOLD activation to faces of partners in pairs who are intensely in love for a long period is characterized by specific brain areas: 1) the ventral tegmental area (VTA) and dorsal striatum that are related to high dopamine and reward system; 2) the globus pallidus (GP), substantia nigra, raphe nucleus, thalamus, insular cortex, anterior cingulate, and posterior cingulate that are related to maternal attachment. Questions: 1. Interpret the history of emotion studies: from Charles Darwin to Cannon&Bard. 2. What is an emotion? Describe Papez’ circle. 3. Describe emotional brain. What is the limbic system? 4. Explain the two-factor theory of emotion. Differentiate emotion and cognition. 5. Explain the biological and informational theories of emotion. 6. What are High and Low roads? Are there two different ways for emotional processing? 7. Describe facial muscles and their application. 8. Elucidate modern views on emotional system. 9. What are complex emotions? Literature: 1. Anokhin P.K. Biology and Neurophysiology of the conditioned reflex. – M.: Medicine, Russia. – 1968. – р. 548. 2. Arnold M.B. Emotion and personality. – New York: Columbia University Press. – 1960. 3. Arnold M.B. Memory and the brain. Hillsdale. – NJ.: Erlbaum. – 1984. 4. Darwin Ch. Expression of the emotions. Man and Animals. – 1872. 5. LeDoux J.E. The emotional brain (Simon & Schuster, New York, 1996) // Neuroimage. – 2002. – p. 651-662. 6. Ekman P., Davidson R.J., Friesen W.V. Emotional expression and brain physiology II: The Duchenne smile // Journal of Personality and Social Psychology. – 1990. – №58. – p. 342-353. 7. Fusar-Poli P., Carletti F., Placentino A., Landi P. Functional atlas of emotional faces processing: a voxel-based meta-analysis of 105 functional magnetic resonance imaging studies // Journal of psychiatry & neuroscience. – 2009. – №34 (6). – p. 418-432.


8. Gur R.C., Schroeder L., Turner T., McGrath C., Chan R.M., Turetsky B.I., Alsop D., Maldjian J., Gur R.E. Brain activation during facial emotion processing // Neuroimage. – 2002. – №16 (3 Pt 1). – p. 651-62. 9. Iversen S., Kupfermann I., Kandel E.R. Emotional States and Feelings. – 2000. 10. James W. What is an Emotion? Mind. – 1884. – vol. 9. – p. 188-205. 11. Linquist K.A., Wager T.D., Kober H., Bliss-Moreau E., Barrett L.F. The brain basis of emotion: A meta-analytic review // Behav Brain Sci. – 2012. – №35 (3). – р. 121-143. 12. Nummenmaa L., Glerean E., Hari R., Hietanen J.K. Bodily maps of emotions // Proceedings of the National Academy of Sciences. 2014. – №111 (2). – p. 646-651. 13. Papez J.W. A proposed mechanism of emotion. // J Neuropsychiatry Clin Neurosci. – 1995. – №7 (1). – р. 103-12. – PMID 7711480. 14. Shah A., Jhawar S.S., Goel A. Analysis of the anatomy of the Papez circuit and adjoining limbic system by fiber dissection techniques // Journal of Clinical Neuroscience. – 2012. – №19 (2). – p. 289-298. – doi:10.1016/j.jocn.2011.04.039. 15. Simonov P.V. The emotional brain. – M.: Science, Russia. – 1981.


Lecture 8

EMOTION REGULATION. THEORIES OF EMOTIONAL INTELLIGENCE Outline: 8.1 Cognition and emotion interaction – Gross’ emotion regulation theory – FMI and EEG studies of emotion regulation strategies 8.2 Emotional intelligence theories – Ability-based model – Trait-based model – Mixed model – Emotional intelligence and decision making Questions Literature

8.1 Cognition and emotion interaction As you understand from the current and previous chapters, the terms “cognition” and “emotion” were traditionally investigated separately. Indeed, cognitive processes such as attention, memory, problem solving were studied without considering emotional states, and emotion was investigated as a different category from cognitive processes. The most recent view is that emotion and cognition conjointly and equally contribute to the control of thought and behavior (Gray et al., 2002), and it is impossible to separate them. As it was noticed by Velichkovsky, “it is an absurd idea about two separate evolutions – one for affective and futher cognitive mechanisms” (Velichkovsky, 2006, p. 367). Relatively recent fMRI studies are aimed at understand the integration of cognition and emotion in the brain. For instance, it has been shown that the anterior cingulate cortex, ACC, participates in affective and cognitive processing (Bush et al., 2008, see figures Two Counting Stroop Tasks, one with cognitive and another with emotional interference, involve two ACC subdivisions contrary activated by each task: activation of affective subdivision suppresess cognitive subdivision, and vice versa. Emotions may be considered as a modulatory system: 69

experiencing high emotional states modulates synchronization of cognitive and emotional networks. Futhermore, emotion is considered a basic level of consiousness in Damassio’s theory and Aleksandrov’s theory of consiousness, which will be discussed in subsequent lectures. – Gross’ emotion regulation theory Gross proposed a prototype concept of emotion and determined the following core features: 1. Emotions are whole-body phenomena. Emotions influence physiology, experience, and behavior. 2. Relevance. Emotions are induced depending on whether personal experience is relevant to goals. 3. Optional changes. Emotion has a response tendency regulated in different ways. Gross formulated a modal model of emotion: “situation>attention>appraisal>response>situation” (Gross, 2011, p. 5). Strategies for emotion regulation can be classified into antecedent focused (prior to emotional response) and response focused (after emotional response has been generated) (Gross&Munoz, 1995). Antecedent focused strategies may be at different levels of modal model of emotion: situation selection, situation modification, attentional deployment, cognitive change (Gross&Munoz, 1995). The second strategy may be at the level of response modulation. Gross expected that the first types of strategies are more effective than the second ones. Therefore, Gross with colleagues compared cognitive reappraisal strategy (antecedent) with expressive suppression strategy (response modulation). Studies confirmed the hypothesis that reappraisal strategy was more effective (Nolen-Hoeksema, 2012). – FMRI and EEG studies of emotion regulation strategies FMRI studies showed increased prefrontal activation and decreesed activation in the amygdala during down-regulation of negative emotion (Ochsner et al., 2004). Reappraisal induced activation in PFC earlier, and decreased amygdala and insula activation with negative experience (Goldin et al., 2008). In opposite, suppression showed late activation in PFC, and increased activation in amygdala and insula. 70

Activity in the right amygdala negatively and activity in the medial frontal gyrus positively correlated with reappraisal (Goldin et al., 2008). We used Gross’s theory of emotion-regulation in our study because “theory specifies different regulative strategies in terms of information-processing model. Reappraisal is a strategy that modifies the encoding of an emotive stimulus, typically towards constructing a more positive meaning. By contrast, suppression operates later in processing, following extraction of meaning, such that the person attempts to inhibit behavioral expressions of emotion”. The aim of the research was to find out if emotional intelligence, measured by Trait Meta-Mood Scale (TMMS, Salovey et al., 1995) predicted frontal EEG response to reappraisal and suppression strategies (Tolegenova, Kustubayeva, Matthews, 2014). Mood repair scale positively correlated with theta and gamma in reappraisal conditions only, attention to emotion scale also positively correlated with theta in reappraisal conditions only. Authors concluded that TMMS predicted higher power in theta and gamma bands, a pattern of response that may represent directed attention to emotional processing.

8.2 Emotional intelligence theories Emotional intelligence (EI) is the widely spread popular theory that started since Leuner (Leuner, 1966) used it in terms of non-cognitive types of intelligence. The root of EI is in Darwin’s theory of emotion and adaptation. Gardner (1983) suggested Multiple Intelligence model that includes inter- and intra-personal intelligence. – Ability-based model Mayer & Salovey (1998) developed the Ability-based model as a type of intelligence. Their initial definition states that EI is “the ability to perceive emotions, to access and generate emotions so as to assist thought, to understand emotions and emotional knowledge, and to reflectively regulate emotions so as to promote emotional and intellectual growth” (Mayer & Salovey, 1998, p. 5). The Mayer-Salovey71

Caruso Emotional Intelligence Test (MSCEIT) measures four branches (emotion perception, assimilating emotion, understanding emotion, managing emotion) and 8 subtests (two for each branch). The concept that EI is based on different abilities was used in other measurements: emotion perception – Ekman tests for basic emotions, JACBART (photos) and METT (micro-expressions), and Scherer tests for face and voice; multimedia stimuli; emotion language – richness of descriptions of feelings, LEAS (Lane, 2000); analysis of emotions from emotion words, pronunciation and cognitive processes in the Linguistic Inquiry and Word Count (LIWC: Pennebaker); Chronometric measures – Inspection time for emotion stimuli (Austin, 2005). – Trait-based model Distinct from the ability-based model, the Trait-based model was suggested by Petrides and Furnham (2000a). There are many measures of EI from the prospective of the trait-based model: the Swinburne University Emotional Intelligence Test (SUEIT); the Six Seconds Emotional Intelligence Assessment (SEI), the Schutte Self-Report Emotional Intelligence Test (SSEIT), a test by Tett, Fox, and Wang, and others. One of the most popular is the Trait Emotional Intelligence Questionnaire (TEIQue) that is available in 15 languages. Aims of TEIQue are to sample EI traits comprehensively and encompasses 15 subscales organized under 4 factors: well-being, self-control, emotionnality, and sociability. In our ongoing electroencephalography (EEG) research on emotion regulation, we used the Trait Meta-Mood Scale (TMMS, Salovey et al., 1995) that consists of 3 subscales: Attention to feelings (13 items), Clarity of feelings (11 items), and Mood repair (6 items). The reason why we used TMMS from a host of presented questionnaires is a selfreport measure of the ability to regulate and manage emotions and the subscales scores associated with prediction in mood regulation and anxiety. It has been found that the Clarity subscale has predictive ability for recovery from negative mood and rumination following induced negative mood (Salovey et al., 1995). Attention and Repair subscales Furthermore, higher scores on the Attention and Repair subscales correlated with lower levels of reported symptoms of social anxiety 72

and depression (Salovey, Stroud, Woolery, & Epel, in press; Williams, 2003), whereas other research (Extremia & Fernandez-Berrocal, 2006; Scime & Norvilitis, 2006) found that higher scores on the Clarity and Repair subscales correlated with lower levels of anxiety and depresssion and higher Role Physical, Social Functioning. Mental Health, Vitality and general Health, are quite the contrary for Attention subscale score. Furthermore, the Clarity and Repair subscale was associated with individual satisfaction, interpersonal relationships and self-esteem, whereas Palmer’s (Palmer et al., 2002) research revealed that only the Clarity subscale predicted life satisfaction not accounted for by positive and negative affect, and no prediction for well-being outcomes were found for all TMMS subscales (Donaldso-feilder&Bond, 2004). – Mixed model Mixed-based models were developed within the Emotional Competence Model by Daniel Goleman (1995), in the model of EmotionalSocial Intelligence by Reuven Bar-On. Goleman published “Emotional Intelligence” (1995) as a best-seller according to Time Magazine that brought wide popularity to the EI concept. He defined EI on twodimensional conceptualization: “being able to motivate oneself and persist in the face of frustrations ..., to control impulse and delay gratifications ..., to regulate one's moods and keep distress from swamping the ability to think”(Goleman, 1995, p. 34). The Bar-On Emotion Quotient Inventory (EQ-i) is based on the attitude that “emotional and social intelligence is a multi-factorial array of interrelated emotional, personal, and social abilities that influence our overall ability to actively and effectively cope with daily demands and pressures” (BarOn, 2000). – Emotional intelligence and decision making From brain lesion studies, it was suggested that patients with ventromedial prefrontal cortex and amygdala and insular damage have “defective somatic markers” and tend to exercise poor judgment in decision-making, make disadvantageous choices in their personal life and relations with others, and show low emotional intelligence (Baron R. et al., 1997, 2003). The study revealed significantly low 73

emotional intelligence and poor judgment in decision-making (the Gambling task) in spite of normal levels of cognitive intelligence (IQ). By using fMRI, Killgore & Yurgelun-Todd (2007) found that higher emotional intelligence negatively correlated with activity in the somatic marker circuitry in adolescents. Questions: 1. Explain the mechanism of emotion and cognition interaction. 2. Describe emotional regulation strategies. 3. Is reappraisal strategy more efficient? 4. Give examples of fMRI studies of reappraisal and suppression. 5. Provide examples of EEG study of reappraisal and suppression. 6. What is emotional intelligence? 7. Explain ability-based model of emotional intelligence. 8. Describe the Trait-based model of emotional intelligence. 9. Describe Mixed-based models of emotional intelligence. Literature 1. Bar-On R. Bar-On Emotional Quotient Inventory: User's manual . – Toronto: Multi-Health Systems. – 1997. 2. Bush G., Spencer T.J., Holmes J., Shin L.M., Valera E.M., Seidman L.J., Makris N., Surman C., Aleardi M., Mick E., Biederman J. Functional magnetic resonance imaging of methylphenidate and placebo in attention-deficit/hyperactivity disorder during the multi-source interference task // Arch Gen Psychiatry. – 2008. – №65 (1). – р. 102-114. 3. Gardner H. Frames of mind. – New York: Basic Books. – 1983. 4. Goldin P.R., Manber-Ball T., Werner K., Heimberg R., & Gross J.J. Neural mechanisms of cognitive reappraisal of negative self-beliefs in social anxiety disorder // Biological Psychiatry. – 2009. – №66 (12). – р. 1091-1099. 5. Goldin P.R., McRae K., Ramel W., Gross J.J. The neural bases of emotion regulation: Reappraisal and suppression of negative emotion // Biological Psychiatry. – 2008. – №63 (6). – р. 577-586. 5. Goleman D. Emotional intelligence. – New York: Bantam. – 1995. 6. Gross J.J. Antecedent- and response-focused emotion regulation: Divergent consequences for experience, expression, and physiology // Journal of Personality and Social Psychology. – 1998. – №74 (1). – р. 224-237. 7. Gross J.J. Emotion regulation: Affective, cognitive, and social consequences // Psychophysiology. – 2002. – №39 (3). – р. 281-291. 8. Tolegenova A., Kustubayeva A.M., Matthews G. Trait meta-mood, gender and EEG response during emotion-regulation // Personality and Individual Differences. – 2014. – Vol. 65. – р. 75-80. 8. Leuner B. Emotional intelligence and emancipation. A psychodynamic study on women // Prax Kinderpsychol Kinderpsychiatr. – 1966. – №15 (6). – р. 196-203.


9. Matthews G., Zeidner M., Roberts R.D. Emotional intelligence: science & myth. – London. England. – 2002. – 697 p. 10. Mayer J.D., Salovey P., & Caruso D. Competing models of emotional intelligence. In R. J. Sternberg (Ed.), Handbook of human intelligence (2nd ed.). – New York: Cambridge University Press. – 1998. 11. Ochsner K.N., Bunge S.A., Gross J.J., Gabrieli J.D. Rethinking feelings: An FMRI study of the cognitive regulation of emotion // Journal of Cognitive Neuroscience. – 2002. – №14. – p. 1215-1229. 12. Ochsner K.N., Ray R.D., Cooper J.C., et al. For better or for worse: Neural systems supporting the cognitive down- and up-regulation of negative emotion // Neuroimage. – 2004. – №23 (2). – p. 483-99. 13. Pertrides K.V., Furnham A. Trait Emotional intelligence: Behavioral validation in two studies of emotion recognition and reactivity to mood induction// European Journal of Personality. – 2003. – №17. – p. 39-57. 14. Velichkovsky B.M. Cognitive science: foundations of epistemic psychology. – Moscow. – 2006. – Vol. 2. – 432 p.


Lecture 9

MOTIVATION AND REWARD. LEARNING. REINFORCEMENT Outline: 9.1 Psychological theories of motivation – a brief overview 9.2 Neuroscience studies of motivation – Cannon. Homeostasis. Central Motive State (CMS) theory – Stellar discovery. Hypothalamus and drive. – James Olds and Peter Milner’s discovery. – Dopamine pathways and their function. – Nucleus Accumbens and their function. – Hierarchical organization of approach and avoidance systems. Questions Literature

9.1 Psychological theories of motivation – a brief overview In psychology, motivation concepts were improved over time, beginning with mechanistic models (Clark L. Hull, 1943, Kenneth W. Spence, 1956), followed by cognitive models (Atkinson, 1957, Heckhausen, 1991, Bandura, 1997), and now self-regulation models. A review article on the psychology of motivation (Gollwitzer&Oettingen, 2015) proposed to separate two groups of motivational studies: motivation as a basic need and motivation as action control (see Table 10). Table 10 Psychological theories of motivation Year Basic Needs 1920

Author Sigmund Freud

1938 1954

Henry Murray Abraham H. Maslow

Basic needs Two basic needs which are the life and death instincts 20 basic needs Levels of needs: physiological, safety, love/belonging, esteem, selfactualization


Year 1985 1988, 1995

Author McClelland Baumeister and Leary, Tesser Pyszzynski Emmons Gollwitzer and Kirchhof

Basic needs Needs for achievement Interpersonal relationships

Action Control 1957 1991 1992 1997

Atkinson Heinz Heckhausen Weiner Bandura

Expectancy-value theory “Situation – outcome expectancies” Attribution theory Self-efficacy theory



Goal concepts

1997 1996 1998

Need for high self-esteem Personal strivings, projects, life tasks Needs related to cognitive capabilities such as the need for competence and autonomy

9.2 Neuroscience studies of motivation A Neuroscience approach to motivation answers the following questions:  Which brain structures are responsible for motivation?  How and when are motivational brain structures activated?  How can brain motivational mechanisms explain psychological motivation theories? – Cannon. Homeostasis. Central Motive State (CMS) theory A neurobiological approach to study motivation started with W.B. Cannon. According to W.B. Cannon, motivation encompasses internal stimuli in the body resulting from homeostatic imbalance (Cannon, 1932). For instance, sensation of hunger can be correlated with stomach obstructions (Cannon, 1932). Certainly, it is a simplification of motivation to consider it as a subjective sensation of drive and such an approach provides no sufficient explanation for motivational states. Later studies on motivation have turned to investigation of motivation as the brain function. Lashley noted that motivation is an integration of neural, endocrine, and sensory systems. Close to 77

Lashley’s position was Morgan’s view on understanding motivation. He proposed the Central Motive State (CMS) theory: a “humoral motive factor” influences the brain (Morgan, 1943). – Stellar discovery. Hypothalamus and drive. His student, E. Stellar, continued his research and later suggested a hypothalamic theory of motivation. According to Stellar, the power of motivation depends on the level of activity of excitatory hypothalamic centers. He determined four classes of factors important for motivation:  excitatory and inhibitory centers in the hypothalamus;  sensory stimuli from receptors regulate the excitability of hypothalamus centers;  the internal environment chemical changes influence the hypothalamus;  connection of hypothalamus to cortical and thalamic centers (Stellar, 1954). We learn motivation today as a brain functional system. As a FS, motivation can be characterized by intensity, direction, and persistence. In Anokhin’s FS (Anokhin, 1974), motivation is an important part of afferent synthesis which influences decision-making processes. P.V. Simonov in his book “Motivated brain” (1991) described brain structures which are responsible for motivation function for biological, social, and intellectual needs. We start with the simplest motivational mechanism for basic needs, or drive states. FS for drive states is described as a servocontrol system. It is an automatic system with feedback regulation (like a car’s cruise control). Usually, negative feedback results from errors detected by encoders and leads to automatically corrected behavior to keep a homeostatic balance. A servomechanism is characterized by maintaining variables such as feedback/or error detector, error signal, or desired value. For instance, temperature regulation is an autonomic system with warm/cold-sensitive neurons acting as encoders. FS for temperature regulation integrates the autonomic nervous system, endocrine system, skeletomotor systems, and the osmolarity system. The hypothalamus includes a temperature center, as well as thirst and hunger centers. 78

The hypothalamus is an extremely important brain structure that controls the autonomic nervous system and endocrine system through the pituitary gland, and is responsible for homeostasis. The hypothalamus secretes hypothalamic releasing hormones, antidiuretic hormone (vasopressin) and oxytocin, which influence the hypophysis (pituitary gland). The hypophysis is located at the base of the skull (the sella turcica) and is divided into two parts: the adenohypophysis (anterior part) and the neurohypophysis (posterior part). The adenohypophysis produces hormones such as adrenocorticotropin (stress hormone), thyroid-stimulating hormones, prolactin, growth hormone, follicle stimulating, luteinizing hormones. Therefore, adenohypophysis hormones influence all target glands, and negative feedback from target glands regulates hormone production. The neurohypophysis is connected via nerve projections to the hypothalamus and stores hypothalamic hormones. – James Olds and Peter Milner’s discovery. Homeostasis provides anticipatory mechanisms: the suprachiasmatic nucleus is responsible for circadian rhythms (24 hours), regulates rhythms of drinking, feeding, and sleeping. James Olds and Peter Milner (1954) studied the mechanism of positive reinforcement learning. In their experiments, intracranial electrical stimulation of the septal area acted as a reward. Brain stimulation has become an experimental tool to investigate reward circuits. Such studies showed that brain structures, which participate in reward processing (one of them is called as a mesolimbic pathway) are characterized as being rich in dopaminergic neurons. – Dopamine pathways and their function. Dopamine is a neurotransmitter and neuromodulator, which enables signal transmission and increases connectivity among brain areas, especially those important for reinforcement learning. In the brain, there are approximately only 400,000 dopaminergic neurons. Dopaminergic brain areas were identified by A. Dahlstrom and K. Fuxe in the following brain structures: 1) the posterior hypothalamus; 2) the ventral tegmental area (VTA); 3) the periventricular nucleus; 79

4) the substantia nigra (basal ganglia); 5) the arcuate nucleus of the hypothalamus; 6) the zona incerta (subthalamus). There are four major dopaminergic pathways: 1) The mesolimbic pathway includes connections between the VTA and the nucleus accumbens (NA) in the striatum, and these structures are also part of limbic emotional areas. Therefore, this pathway is involved in emotional/motivational behavior by inducing pleasurable feelings related to reward and desire. An excess of dopamine in the mesolimbic pathway is associated with “positive symptoms” of schizophrenia. Suppressing dopamine with antipsychotic medication in these areas is successful for treatment of such symptoms. Additionally, these structures function in the neuromechanisms of addition, though mainly as incentive salience areas; 2) The mesocortical pathway includes connections between the VTA and the prefrontal cortex, parts of emotional and higher cognitive brain areas. This pathway is involved in emotion, attention, decisionmaking, planning, working memory and other functions. Moreover, this pathway is associated with “negative symptoms” of schizophrenia (decrease in emotional response). Mesocortical and mesolimbic pathways are interconnected and counterbalanced with each other. Damage to the mesocortical pathway may be a cause of insufficient dopamine transport to cortical areas, which goes to the mesolimbic pathway instead. Therefore, frontal areas in the normal brain regulate dopamine access to the limbic system; 3) The nigrostriatal pathway includes connections between the substantia nigra and the corpus striatum (caudate and putamen), and these areas are also part of the extrapyramidal motor system. Therefore, their function is synchronized with movement production and is involved in motor planning and motor control. It has been found that a decrease in dopamine by more than 80 % is one of the main causes of Parkinson’s disease; 4) The tuberoinfundibular pathway includes connections of dopamine cells of the arcuate nucleus to the median eminence. In this pathway, dopamine inhibits the release of the hormone prolactin.


– Nucleus Accumbens and its function. There are also cortico-striatal projections (not a pathway), which modulate dopamine in the basal ganglia from premotor/motor and orbito-frontal areas. Therefore, these projections regulate involuntary movements and inhibitory control of thoughts. In the last decades, many studies have been devoted to the nucleus accumbens (NAcc), which have two parts: a core and a shell. The shell is thought to be involved in reward processing, positive reinforcement, pleasurable/hedonic stimuli processing, and drug addiction. The core is engaged in motor action associated with reward and reinforcement learning, and sleep regulation. Both structures are activated in Pavlovian instrumental transfer processes: the shell – in specific learning, whereas the core – in general learning. According to several studies (Berridge, 2004, Niv et al., 2007, Salamone et al., 2009), anticipated reward determines the efforts to receive a reward. Two brain areas – the NAcc and the anterior insular cortex (aINS) are both involved in reward processing (Delgado, 2007, Graig, 2009, Nagvi&Bechara, 2009). It is a practical and economical question: How much effort do people wish to put forth for a better reward? An experimental study on food-deprived rats (Salamone et al., 1991) showed that NAcc activation accompanied a better reward. Clithero et al. (2011) conducted an fMRI study of reward anticipation in two modalities (monetary and candy), and showed that the NAcc is a critical area responsible for weighting rewards. Reward efficacy processing activated the NAcc even in an experiment without an explicit choice. The authors underline that heterogeneity in individual differences and in real-word multiple modalities, as well as different levels of motivation to access a reward are decoded in the NAcc and aINS. It is important to mention that the reward network and dopaminergic system are involved in drug abuse behavior. Drugs abuse elevates dopamine levels in the brain. Drugs such as cocaine, opiates, amphetamine, and nicotine function as positive reinforces. Cocaine and amphetamine influence the NAcc and block dopamine transporters. Nicotine influences presynaptic cholinergic receptors and activates the mesocorticolimbic pathway.


– Hierarchical organization of approach and avoidance systems. According to many scientists (Lang et al., 1998; Elliot, 2006; Scholer and Higgins, 2008, Spielberg et al, 2013, Corr, 2013), motivational systems are comprised of hierarchical levels to support approach and avoidance behavior where lower levels are subordinated by higher levels. Three classical neuropsychological systems were suggested by J. Grey (Gray & McNaughton, 2000): behavioral approach system (BAS), fight-flight-freeze system (FFFS), and behavioral inhibition system (BIS). Corr (2010, 2013) proposed a neuropsychological model of consciousness by taking into account complex motivational behavior. Scholer and Higgins (2008) suggested three hierarchical levels. Spielberg et al. (2013) added an additional fourth level (system, strategic, temperamental, and tactical) and implied neuroscience data to this point of view: “The present paper integrates research using more spatially specific methodologies (e.g., functional magnetic resonance imaging) with the rich psychological literature on approach/avoidance to propose an integrative network model that takes advantage of the strengths of each of these literatures” (Spielberg et al., 2013). Questions: 1. What psychological theories can provide for neuroscience of motivation? 2. Explain the Central Motive State (CMS) theory. 3. What is homeostasis and how it relates to motivation? 4. Hypothalamus function and its role in motivation? 5. Describe James Olds and Peter Milner’s discovery. 6. What is dopamine? Dopamine pathways. 7. What is Nucleus Accumbens and their function. 8. What is approach and avoidance systems? 9. How can brain motivational mechanisms explain psychological motivation theories? Literature: 1. Atkinson J.W. Motivational determinants of risk-taking behavior // Psychological Review. – 1957. – №64. – p. 359-372. 2. Bandura A. Self-efficacy: the exercise of control. – New York: W.H. Freeman and Company, 1997. 3. Berridge K.C. Motivation concepts in behavioral neuroscience // Physiol Behav Physiology & Behavior. – 2004. – №81 (2). – р. 179-209. 4. Cannon W.B. The Wisdom of the Body. – New York: W.W. Norton & Company. – 1932.


5. Hull C.L. Principles of Behavior: An Introduction to Behavior Theory. – New York: Appleton-Century-Crofts, 1943. 6. Corr P.J. Approach and Avoidance Behaviour: Multiple Systems and their Interactions // Emotion Review. – 2013. – Vol. 5. – №3. – p. 285-290. – DOI: 10.1177/1754073913477507. 7. Dahlström A., Fuxe K. Localization of monoamines in the lower brain stem // Experientia. – 1964. – №20. – р. 398-399 8. Dai D.Y., Robert J. Sternberg. Motivation, emotion, and cognition: integrative perspectives on intellectual development and functioning. – London. – 2004. – 473 p. 9. Delgado M.R. Reward-related responses in the human striatum // Ann NY Acad Sci. – 2007. – №1104. – р. 70-88. 10. Elliot A.J., Gable S.L., Mapes R.R. Approach and Avoidance Motivation in the Social Domain // Personality and Social Psychology Bulletin. – 2006. – №32 (3). – р. 378-91. 11. Heckhausen H. Motivation and Action. – Berlin: Springer-Verlag, 1991. 12. Freud S. Beyond the Pleasure Principle / in On Metapsychology. – Middlesex 1987. – p. 316. 13. Gollwitzer P.M., Oettingen G., Motivation: History of the Concept / In: James D. Wright (editor-in-chief), International Encyclopedia of the Social & Behavioral Sciences, 2nd edition. – Oxford: Elsevier. – 2015. – Vol. 15. – p. 936-939. 14. Gray J.A., McNaughton N. The neuropsychology of anxiety: An enquiry into the functions of the septo-hippocampal system (2nd ed.). – Oxford, UK: Oxford University Press. – 2000. 15. Spence K.W. Behavior Theory and Conditioning. – New Haven: Yale University Press, 1956. 16. Lang P.J., Bradley M.M., Cuthbert B.N. Emotion, motivation, and anxiety: Brain mechanisms and psychophysiology // Biological Psychiatry. – 1998. – №44. – р. 1248-1263. 17. Maslow A. Motivation and personality. – NY: Harper. – 1954. 18. Morgan C.T. Physiological Psychology. 3d ed. – New York: McGraw-Hill, 1943. 19. Olds J., Milner P. Positive reinforcement produced by electrical stimulation of septal area and other regions of rat brain // Journal of Comparative and Physiological Psychology. – 1954. – №47 (6). – р. 419-27. 20. Naqvi N.H., Bechara A. The hidden island of addiction: the insula // Trends Neurosci. – 2009. – №32 (1). – р. 56-67. 21. Niv Y., Daw N.D., Joel D., Dayan P. Tonic dopamine: opportunity costs and the control of response vigor // Psychopharmacology (Berl). – 2007. – №191. – р. 507-520. 22. Salamone J.D., Correa M., Farrar A.M., Nunes E.J., Pardo M. Dopamine, behavioral economics, and effort // Front Behav Neurosci. – 2009. – №3. – р. 13. 23. Scholer A.A., Higgins E.T. Distinguishing levels of approach and avoidance: An analysis using regulatory focus theory. In A.J. Elliot (Ed.), Handbook of Approach and Avoidance Motivation. – New York: Psychology Press, 2008. – р. 489-504.


24. Simonov P.V. Motivated brain: Neuro-Physiol (Monographs in Psychobiology). – Imprint of Taylor & Francis. – 1991. – p. 280. 25. Spielberg J.M., Heller W., Miller G.A. Hierarchical Brain Networks Active in Approach and Avoidance Goal Pursuit // Frontiers in Human Neuroscience. – 2013. – №7 (284). 26. Stellar E. The physiology of motivation // Psychological Review. – 1954. – №61 (1). – р. 5-22. 27. Weiner B. Human Motivation. Sage. – Newbury Park, CA. – 1992.


Lecture 10

EXECUTIVE CONTROL. FRONTAL CORTEX. CINGULATE CORTEX. ATTENTION NETWORKS AND ORIENTING. PARIETAL LOBE Outline: 10.1 History of executive control definitions – What is executive control (EC)? The Phineas Gage case. – Executive control definitions. 10.2 Two theories of cognitive control – Prefrontal Cortex. Luria’s Integrative model of brain functioning. – Posner’s theory of Attentional Networks 10.3 Cognitive Tasks and Executive control failure – Cognitive tasks to study executive control. – Examples of failure of executive control – Attention Deficit Disorder. – Mind wandering Questions Literature

10.1 History of executive control definitions A definition of executive functions was suggested by Royall et al. (2002): “The “executive functions” broadly encompass a set of cognitive skills that are responsible for the planning, initiation, sequencing, and monitoring of complex goal directed behavior” (Royall et al., 2002). – What is executive control (EC)? The Phineas Gage case. We will start with a famous medical case of Phineas Gage. Gage is probably one of the most famous patients in Neuroscience studied by neuroscientists starting from Henry Jacob Bigelow in Boston to nowadays H. Damasio et al. (1994). Gage survived an accident when a big iron rod was propelled through his head. The iron rod damaged an entire left frontal area and while all vital functions were saved for another 12 years, Gage’s personality had changed drastically. He lost the ability for goal-directed behavior, planning the future, and social 85

communication. Later he was able to recover to some extent his social ability. His skull is in the Museum of the Medical College of Harvard University. Psychologist Malcolm Macmillan published a book “An odd kind of fame” (2002). He described everything about Gage starting with his family background, the accident, and subsequent treatment by Dr. J.M. Harlow. Hanna Damasio et al. (1994) concluded that the damage of the prefrontal cortex caused abnormal and irrational decision-making and emotion processing, therefore Gage had problems with planning and social interactions. Antonio Damasio, in his book “Deacrtes’ Error: Emotion, Reason, and the Human Brain” uses Gage’s case to explain the rationality for emotions. We mention Phineas Gage because it was the first case in history, which showed a relation between executive functions and the prefrontal cortex. Today, there exist many theories to determine what is executive function. Table 11 provides some definitions of executive control with useful Literature recommended for reading. – Executive control definitions. Table 11 Definitions of executive control (EC) and corresponding literature Authors Broadbent

Luria, 1966

Definition of executive control Controlled processes are different from automatic processes. Executive functioning: anticipation, planning, execution, and selfmonitoring (Luria, 1966). Role of prefrontal cortex in EC.



A selective filter theory of attention

Broadbent D. Selective and control processes. Cognition. 10, 1-3, 1981, 53-58. The Frontal Lobes (1966) Functions of the frontal lobes (1982) Luria A.R. (1966) Human Brain and Psychological Processes. New York, NY: Harper and Row.

Integrative model of brain functioning


Authors Baddelay, 1974

Norman D.A. & Shallice T. (1980, 1986, 2002)

Definition of executive control “…adequate model of the central executive must have a range of other sub-processes if it is to be capable of serving the role of attentional controller, organizer of learning and retrieval planner…. One important function must be that of selective attention… Another is presumably in volved in the capacity to switch attention from one source to another … A very important executive demand on working memory is provided by the need to access and manipulate information in longterm memory...” (Baddelay, 1996, p. 1442) Local inhibition of competing schemas. “The functions we assume for the supervisory attentional control are those that require “deliberate attention” correspond closely to those ascribed by Luria (1966) to prefrontal regions of the brain, thought by Luria to be required for the programming, regulation, and verification of activity”. (Norman&Shallice,



Model of working memory

Baddeley A, Della Sala S (October 1996). “Working memory and executive control” (PDF). Philosophical Transactions of the Royal Society B. 351 (1346): 1397-403. Baddeley A.D. 1996 Exploring the central executive. Q.J. Exptl. Psychol. 49A(1), 5-28.

Model of executive control. Supervisory attentional system (SAS)

Norman D.A., & Shallice T. (1986). Attention to action: Willed and automatic control of behavior. In R.J. Davidson & G.E. Schwartz & D. Shapiro (Eds.), Consciousness and self-regulation: Advances in research, Vol. IV (Vol. IV). New York: Plenum Press. Shallice, Tim; Burgess, Paul; Robertson I. (1996). “The domain of



Russel Barkley (1997)

Definition of executive control 1986, retyped 2002, p. 7)

“The present theory holds that the satisfactory development of inhibition is essential for the normal performance of five other neuropsychological abilities: working memory, internalization of speech, selfregulation of affectmotivation-arousal, reconstitution, and motor control-fluencysyntax. The first four of these are considered executive in nature because they permit self-regulation, the control of behavior by internally represented information, and the cross-temporal organization of behavior. Such selfregulation gives rise to the direction and persistence of behavior toward future goals and the ability to re-engage that behavior if disrupted” (Barkley, 1997, p. 86).


Selfregulatory model


Articles/books supervisory processes and temporal behavior ion of behavior”. Philosophical Transactions of the Royal Society B. 351 (1346): 1405-1412. Barkley R.A. (1997). “Behavioral inhibition, sustained attention, and executive functions: Constructing a unifying theory of ADHD”. Psychological Bulletin. 121 (1): 65-94.

Authors Lezak, 1982

Posner (1994)

Duncan (1998, 2005, 2010)

Definition of executive control “The capacity for formulating goals, planning, and carrying out plans effectively – the executive functions – are essential for independent, creative, and socially constructive behavior” (Lezak, 1982) “executive control is required when tasks involve planning, error detection, novelty, difficult processing, or conflict” (Posner, 1994, p. 75). “…human behaviour resembles the sequential activity of conventional computer programs, assembling a series of operations that together achieves the final goal” (Duncan, 2010, p. 172). “The MD system is defined by common brain activity in tasks of many different kinds. In all tasks, the goal is achieved by a series of focused stages or subtasks. In lateral prefrontal cortex, neural properties are well adapted for construction and control of such sub-tasks, with focus on the specific content of a current cognitive operation, rapid reorganization with changing context,



Executive function theory

Lezak M.D. (1982). The problem of assessing executive functions. Int.J.Psychol. 17, 281-297.

Attentional Networks: orienting, alerting, executive control

Posner M.I., and Dehaene S. (1994). Attentional networks. Trends in Neurosci.17:75-79.

The multiple demand (MD) system

Random generation and the executive control of working memory. Baddeley A., Emslie H., Kolodny J., Duncan J. Q J Exp Psychol A. 1998 Nov; 51(4): 819-52. Duncan J. (2005). Prefrontal cortex and Spearman's g-factor, in Measuring the Mind: Speed, Control, and Age, eds Duncan J., Phillips L.H., McLeod P., editors. (Oxford: Oxford University Press;), 249-272. Duncan J. (2010). The multiple-demand (MD) system of the primate brain: mental programs for



Miller&C ohen, 2001

Miyake A. & Friedman N.P. (2000)

Definition of executive control and robust separation of successive task stages” (Duncan, 2010, p. 177). “…we propose that cognitive control stems from the active maintenance of patterns of activity in the prefrontal cortex that represent goals and the means to achieve them. They provide bias signals to other brain structures whose net effect is to guide the flow of activity along neural pathways that establish the proper mappings between inputs, internal states, and outputs needed to perform a given task”. (Miller&Cohen, 2001, p. 167). “...three often postulated executive functions – mental set shifting (“Shifting”), information updating and monitoring (“Updating”), and inhibition of prepotent responses (“Inhibition”) – and their roles in complex “frontal lobe” or “executive” tasks”.


An integrative theory of prefrontal cortex function

Articles/books intelligent behaviour. Trends Cogn. Sci. 14, 172-179. Miller E.R., Cohen J.D. (2001). An integrative theory of prefrontal cortex function. Annu Rev Neurosci. 24 (1), p. 167-202.

Miyake A., Friedman N.P., Emerson M.J., Witzki A.H., Howerter, A., Wager T.D. (2000). “The unity and diversity of executive functions and their contributions to complex 'frontal lobe' tasks: A latent variable analysis”. Cognitive Psychology. 41(1): 49-100.


Authors Royall et al., 2002

Banich (2009)

Definition of executive control “executive functions” broadly encompass a set of cognitive skills that are responsible for the planning, initiation, sequencing, and monitoring of complex goal- directed behavior“ (Royall et al, 2002, p. 378). “Executive control functions” can be separated from the specific cognitive domains (memory, language, and praxis) that are traditionally used to assess patients. (Royall et al., 2002, р. 396). “We are considering the nature of executive function at three distinct levels: the neurobiological (at the level of both neurotransmitters and brain systems), the psychological, and the computational. Our goal is to consider how information at each of these levels can be linked, and thereby lead to a theory of executive function that can better account for the many disparate pieces of knowledge currently available”. (Banich, 2009, p. 92)


Articles/books Royall D.R., Lauterbach E.C., Cummings J.L., Reeve A., Rummans T.A., Kaufer D.I., LaFrance W.C., Coffey C.E. Executive Control Function: A Review of Its Promise and Challenges for Clinical Research. J Neuropsychiatry Clin Neurosci 14:4, Fall 2002.

The cascadeof-control model of executive function


Banich M.T. (2009). Executive function: The search for an integrated account. Current Directions in Psychological Science. 18(2): 89-94.

We understand executive control (EC) as set of processes that control all aspects of behavior to achieve a goal. Therefore, EC includes cognitive processes: attention, working memory, cognitive inhibition and flexibility, planning, decision-making, and problem-solving. The basic neuroanatomy of EC involves the prefrontal cortex. As a reminder, the Frontal Lobes account for about 1/3 of the entire human brain and consists of three subdivisions: 1. Primary motor cortex 2. Premotor cortex 3. Prefrontal cortex The prefrontal cortex comprises 1/2 of the frontal lobes and is divided into the orbitofrontal cortex (OFC), dorsolateral prefrontal cortex (DLPFC), and anterior cingulate cortex (ACC). Frontal lesions cause deficits such as:  Personality disorders  Affect dysfunction  Motor control impairment  Language impairment  Problem-solving impairment  Memory impairment Frontal Lobe functions:  Planning  Problem-solving  Short-term memory  Meta-memory (knowledge of one’s own memory capabilities and strategies)  Temporal memory (memory for the order and sources/episodes of knowledge acquisition) What are Executive Functions?  Goal-directed action: Based on internal planning. Selection of task-relevant information.  Cognitive Control: Inhibit irrelevant information processing. Prevent interference. Manage processes ongoing in other brain regions. Memory management. 92

 Monitoring and Evaluation: Response Conflict. Compare goals with actual performance. Working memory and evaluation. 10.2 Two theories of cognitive control – Prefrontal Cortex. Luria’s Integrative model of brain functionning. Aleksander Luria made a genius in-depth analysis of brain functions based on neuropsychological tests on patients during World War II. He wrote widely known books, which have been translated into many languages: “Higher cortical functions in Man” (1962), “The Frontal Lobes” (1966), “The Working brain” (1973). Based on his clinical experience and experimental research, Luria suggested the Integrative model of brain functioning, which consists of three blocks: 1. The first block is comprised of the brainstem, Reticular Activation system, thalamus, medial and basal cortex, and is responsible for state of alertness, keeping tonus for normal brain functioning. 2. The second block is comprised of the parietal, occipital, and temporal lobes, and is responsible for perceiving, processing, and storing sensory information. 3. The third block is comprised of the frontal lobe, which is responsible for regulation of planning, execution, and evaluation of outcomes with the initial plan. In his book “The Working brain” (1973), Luria proposed three laws of brain functional organization:  Hierarchical organization: primary (specific sensory), secondary (associative integration), tertiary (high cognitive level);  Diminishing specificity: primary (one function), secondary (bimodal functions), tertiary (many non-specific functions);  Progressive Lateralization: the left hemisphere is responsible for detailed learning; the right hemisphere is responsible for global learning. – Posner’s theory of Attentional Networks One of the most popular theories of executive control is the Attention Network theory provided by Michael Posner (Posner, 1980). Based on experimental studies using modern techniques such as fMRI and genetic methods, Posner suggested three networks in the brain: 93

 The orienting system determines priority of a sensory stimulus and its location and modality. The orienting system has been associated with the pulvinar and superior colliculus, parietal and frontal lobes (frontal eye field), and the neurotransmitter acetylcholine (Posner, 1980, Posner &Rothbart, 2007).  The alerting system defines the arousal level of brain functioning and its sensitivity to a stimulus. The alerting system involves the reticular formation and thalamus. Tonic alerting is mostly associated with frontal and parietal areas of the right hemisphere. This is thought to be due to cortical distribution of the brain’s norepinephrine system (Coull, Frith, Frackowiak, & Grasby, 1996; Marrocco, Witte, & Davidson, 1994). Genetic polymorphisms in the MAOA gene were associated with the alerting system (Sommer et al., 2002).  Executive control is a monitoring and conflict resolving system in responses, thoughts, and feelings. EC has been associated with the anterior cingulate cortex, ventrolateral prefrontal cortex, basal ganglia, and the neurotransmitter dopamine. Genetic studies showed relation of the DRD4 gene to EC scores (Sommer et al., 2002).

10.3 Cognitive Tasks and Executive control failure – Cognitive tasks to study executive control. How are executive functions studied? The most popular cognitive tasks to measure executive control include the following:  Wisconsin Card Sorting Test (WCST, Grant&Berg, 1948): participants must find out the rule of how cards have been sorted. WCST helps to measure abstract reasoning, cognitive flexibility measured by the ability to change task strategies. It has deterministic outcomes.  Iowa Gambling Task (IGT, Bechara et al., 1994, also named as Bechara’s Gambling task): stimulation of real decision-making processes with “bad decks” and “good decks”, some of them with rewards. FMRI studies showed activation of orbitofrontal cortex (OFC) and ventromedial prefrontal cortex (vmPFC) (Bechara et al., 1994). It has probabilistic outcomes and can be stochastically manipulated. 94

 Stroop Task (Stroop, 1935) – measures cognitive interference, mismatch in color and written word meaning (for example, when the word “red” is typed in green colored letters – incongruent trials) meaning increased reaction time because written verbal system and color perception systems are in conflict. Congruent stimuli (for instance, the word “red” typed in red color) are perceived faster because both systems are in agreement. FMRI studies revealed that the anterior cingulate and dorsolateral prefrontal cortex are activated in participants during Stroop task performance (Bush, Luu, & Posner, 2000). The authors showed that the dorsolateral cingulate cortex has more connections to frontal and parietal cortex and is responsible for cognitive control. The ventral part of the cingulate cortex has more connections with limbic areas and is responsible for emotional control.  Flanker Task (B.A. Eriksen& Ch. W. Erikson, 1974) – measures the ability to inhibit a response in some context. Congruent flankers is when target and non-target are in the same direction, incongruent flankers – in opposite direction, neutral flankers – not the same and not the opposite direction. FMRI studies showed activation in the ACC.  Attentional Network task (ANT task, Fan et al., 2002) – measures efficiency of attentional networks: alerting, orienting, and executive control (Posner, 1980). You may try to run the mentioned above tasks from the website You may try to run the ANT task from the website: .fan/. – Examples of failure of executive control. Attention Deficit Disorder. Attention-Deficit/Hyperactivity Disorder (ADHD) is a neurodevelopmental disorder associated with inattentiveness, hyperactivity, and impulsivity. Below is a summary regarding ADHD: 1. may be a heterogeneous disorder; 2. affects / diagnosed in boys more often than in girls (~ 3 to 9:1 ratio); 3. diagnosed withby family history, interviews of family / educators, and symptoms checklist; 95

4. age of onset is variable; can be noticed ~ 3 to 5 years of age; typically diagnosed at school age; 5. ~ 2 to 9.5 % of all school age children; 6. Symptoms persist in ~ 40% of adults; 7. having siblings with ADHD is associated with increased incidence by 5 to 7 times; concordance rate of twins = 55% to 92%; 8. positive symptoms of hyperactivity: lack of inhibition and behavioral control; 9. negative symptoms of inattention: unable to sustain attention or maintain course of what one is doing; 10. treatment: Ritalin and other stimulants ~ amphetamines; act by prolonging action of dopamine; 11. genetics studies show dopamine transporter gene may be involved; dopamine is abundant in the prefrontal cortex and deficient in patients with Parkinson’s disease; 12. a recent imaging study shows a link between the gene and brain activity while performing a Stroop task; 13. putative brain structures involved include broad areas of the prefrontal cortex, (including ACC), the cerebellum, and the basal ganglia. Attentional Neglect is another example of problems with executive control:  A patient shows specific problems with visual spatial attention while other cognitive processes seem mostly intact.  Lesions are usually associated with PPC, especially in the right hemisphere, but also in some parts with frontal or left parietal damage.  Symptom presentation: a patient seems to be unable to attend to one region of space. – Mind-wandering Mind-wandering is a ubiquitous, yet understudied, phenomenon and has been estimated to take up around 30-50% of our waking hours (Killingsworth and Gilbert, 2010). In research terms, mind-wandering is defined as the interruption of focus and decoupling of attention from present activity due to unrelated thoughts and feelings (Smallwood and Schooler, 2015). The thought content is not necessarily random 96

however, tends to be related to subjective goals and concerns (Andrews-Hanna et al., 2014; Fox and Christoff, 2014). While the ability to engage in thoughts unrelated to surrounding stimuli is a distinct characteristic of human cognition and may be adaptive in particular contexts such as future planning, the phenomenon has also been associated with impairments in working memory capacity and performance on a range of cognitive tasks, as well as maladaptive conditions such as attention deficit hyperactivity disorder (ADHD). Questions: 1. What is executive control? 2. Give three definitions of the executive control. 3. Explain the theories of executive control based on working memory. 4. Describe the theories of executive control based on inhibition processes. 5. Interpret Luria’s theory of cognitive control. 6. Clarify Posner’s theory of Attentional Networks. 7. Discuss and form your opinion on whether mind-wandering is useful or detrimental in terms of executive control. Literature: 1. Andrews-Hanna J.R., Smallwood J., Spreng R.N. The default network and self- generated thought: Component processes, dynamic control, and clinical relevance // Ann N Y Acad Sci. – 2014. – №13 (16). – p. 29-52. 2. Baddeley A., Della Sala S. Working memory and executive control // Philos Trans R Soc Lond B Biol Sci. – 1996. – №351 (1346). – p. 1397-1403. 3. Barkley R.A. Executive functions-what they are, how they work, and why they evolved. – Guilford Press. – 2012. 4. Bechara A., Damasio A.R., Damasio H., Anderson S.W. Insensitivity to future consequences following damage to human prefrontal cortex // Cognition. – 1994. – №50 (1–3). – p. 7-15. 5. Broadbent D. Selective and control processes // Cognition. – 1981. – №10. – р. 1-3; 53-58. 6. Buckner R.L., Andrews-Hanna J.R., Schacter D.L. The brain’s default network: Anatomy, function, and relevance to disease // Ann N Y Acad Sci. – 2008. – №1124. – р. 1-38. 7. Coull J.T., Frith C.D., Frackowiak R.S., Grasby P.M. A fronto-parietal network for rapid visual information processing: a PET study of sustained attention and working memory // Neuropsychologia. – 1996. – №34 (11). – р. 1085-95. 8. Damasio A. Descartes' Error: Emotion, Reason, and the Human Brain. – Putnam Publishing. – 1994. 9. Damasio H., Grabowski T., Frank R., Galaburda A.M., Damasio A.R. The return of Phineas Gage: clues about the brain from the skull of a famous patient // Science. 1994. – Vol. 264, Issue 5162. – p. 1102-1105.


10. Fox K.C.R., Christoff K. Metacognitive facilitation of spontaneous thought processes: When metacognition helps the wandering mind find its way / In: The Cognitive Neuroscience of Metacognition. – 2014. – p. 293-319. 11. Killingsworth M.A., Gilbert D.T. A wandering mind is an unhappy mind // Science. – 2010. – №80. – р. 330:932. 12. Lezak M.D. The problem of assessing exective functions // Int. J. Psychol. – 1982. – №17. – p. 281-297. 13. Luria A.R. Human Brain and Psychological Processes. – New York, NY: Harper and Row. – 1966. 14. Macmillan M. An Odd Kind of Fame: Stories of Phineas Gage. – MIT Press. – 2000. – p. 118-119; 331-332. 15. Macmillan M. Phineas Gage – Unravelling the myth // The Psychologist. – 2008. – №21. – p. 828-831. 16. Marrocco R.T., Witte E.A., Davidson M.C. Arousal systems // Curr Opin Neurobiol. – 1994. – №4 (2). – р. 166-70. 17. Peterseen S.E., Posner M.I. The Attention System of the Human Brain: 20 Years After // Annu Rev Neurosci. – 2012. – №35. – p. 73-89. 18. Posner M.I. Orienting of Attention // Quarterly Journal of Experimental Psychology. – 1980. – №32 (1). – р. 3-25. 19. Posner M.I., Petersen S.E. The attention system of the human brain // Annu Rev Neurosci. – 1990. – №13 (1). – p. 25-42. 20. Posner M.I., Rothbart M.K. Research on attention networks as a model for the integration of psychological science // Annu Rev Psychol. – 2007. – №58. – р. 1-23. 21. Royall D.R., Lauterbach E.C., Cummings J.L., Reeve A., Rummans T.A., Kaufer D.I., LaFrance W.C., Coffey C.E. Executive control function: a review of its promise and challenges for clinical research. A report from the Committee on Research of the American Neuropsychiatric Association // J Neuropsychiatry Clin Neurosci. – 2002. – №14. – р. 4. 22. Smallwood J., Schooler J.W. The Science of Mind Wandering: Empirically Navigating the Stream of Consciousness // Annu Rev Psychol. – 2015. – №66. – p. 487-518. 23. Sommer M., Koch M.A., Paulus W., Weiller C., Büchel C. Disconnection of speech-relevant brain areas in persistent developmental stuttering // Lancet. – 2002. – №360 (9330). – р. 380-3. 24. Stawarczyk D., Majerus S., Maj M., Van der Linden M., D’Argembeau A. Mind-wandering: Phenomenology and function as assessed with a novel experience sampling method // Acta Psychol (Amst). – 2011. – №136. – p. 370-381.


Lecture 11

NEUROANATOMY OF MEMORY SYSTEMS. MEMORY THEORIES. TEMPORAL LOBE. HIPPOCAMPUS Outline: 11.1 Learning and memory – Types of Memory. – Amnesia. Types of Amnesia. – Studies with famous patients with memory impairments 11.2 Memory theories in Neuroscience – Big names in memory and brain research and their contributions. – A model of distributed representations Questions Literature

11.1 Learning and memory Learning can be defined as the acquisition of new information or knowledge that can change behavior. Memory is the retention of learned information. Endel Tulving, a famous psychologist in the field of memory, stated that “Memory is a gift of nature, the ability of living organisms to retain and to utilize acquired information. The term is closely related to learning, in that memory in biological systems always entails learning (the acquisition of information) and in that learning implies retention (memory) of such information” (Endel Tulving, 1972). Memory formation processes are encoding, storage, and retrieval. Memory consolidation is a process of memory trace stabilization, which divided to synaptic consolidation and systems consolidation (based on hippocampus, weeks after learning). Memory reconsolidation is a process of reactivation of memory. Synaptic consolidation occurs during a few hours after learning and based on long-term potentiation (LTP, lecture 2). Synaptic consolidation involves gene expression (Anokhin, 1991), which results in synaptic growth, and 99

protein synthesis (Gold, 2008). System consolidation is based on hippocampus and cortical processes of formation of representations of information (Squire&Alvarez, 1995). Engram – a memory trace stored in the brain. Types of Memory.  Declarative or Explicit.  Facts and Events.  Non-declarative or Implicit.  Procedural, perceptual, associative, non-associative. Declarative or Explicit a) Semantic memory for facts. b) Episodic memory for events.  Definitions by Endel Tulving Types of memory distinguished by time: 1. Working (seconds). a. Information management process. b. Part of the cognitive control system. c. Baddeley’s articulatory loop. 2. Short-term (minutes-hours-days). Examples:  What did I have for breakfast?  What did I wear yesterday? 3. Long-term (months-years-lifetime). – Amnesia. Types of Amnesia. Amnesia is loss of memory and the ability to learn. Types of Amnesia include: a. Retrograde amnesia – forgetting things one already knew before. b. Anterograde amnesia – inability to form new memories. c. Transient global amnesia – sudden short-term memory disruption, temporary episodic loss, deeply encoded facts. d. Post-traumatic amnesia – transient amnesia due to head injury. 100

e. Dissociative amnesia – that is a psychologically caused memory loss and may be in the following forms: repressed – results of inability to recall stressful or traumatic events; dissociative fugue – fugue state caused by psychological trauma; posthypnotic amnesia – that may happen during or after hypnosis, inability to recall events. f. Korsakoff’s syndrome – memory loss as a result of alcoholism or malnutrition due to vitamin B1 deficiency. – Studies with famous patients with memory impairments Patient H.M. As mentioned earlier, Henry Molaison (H.M., 1926-2008) was one of the most famous memory disorder patients. Patient H.M. was treated for epilepsy with surgical removal of the hippocampus, parahippocampal gyrus, and the amygdala. He was studied by several scientists: Brenda Milner, Clive Wearing, Daniel Schacter, Endel Tulving, and Larry Squire. H.M. could work with researchers for hours, but if they left and came back after several minutes they had to re-introduce themselves again and again. H.M. had no explicit memory, had a short-term memory deficit, and was not able to form long-term memory. Surprisingly, he was able to learn new motor skills like mirror-writing (procedural, implicit learning). At the same time, he learned every time as if it was the first time without remembering his past sessions. Patient R.B. Patient R.B. had a localized stroke and hypoxic damage to the hippocampus CA1 area. This region has connections with the third layer of the entorhinal cortex and subiculum. R.B. lost the ability to form declarative memory, thus having anterograde amnesia. The patient was described first in an article by Zola-Morgan, Squire, and Amaral “Human amnesia and the medial temporal region: enduring memory impairment following a bilateral lesion limited to field CA1 of the hippocampus” (1986). Patient N.A. Patient N.A. had a small fencing foil (toy) enter his cranium through the nose, which led to the damage to the dorsolateral thalamus, mammillothalamic tract and the postcommissural fornix. This led to retrograde and anterograde amnesia for verbal memories. He also was studied by L. Squire. 101

11.2 Memory theories in Neuroscience – Big names in memory and brain research and their contributions. Karl Lashley (1890-1958) conducted one of the first experimental studies on rats trained with a reward learning task. He lesioned different brain areas in order to find a brain structure responsible for the engram and was unsuccessful in determining any specific structure. In his work, “In search of the engram” (1950), he concluded that he was not able to demonstrate a memory trace and proposed a brain principle called “Law of mass action”: memory is represented in multiple cortical areas and removing one part of the brain can be compensated by other parts of the brain, a concept termed ‘cortical equivalence’. Wilder Penfield (1891-1976) is a famous neurosurgeon who performed surgery on epileptic patients and removed the source of epileptic activity. Before surgery, he used electrical stimulation and observed the patient’s reaction to define more precisely the source of epileptic activity. This method helped him map the sensory and motor cortex, which later became famous as the cortical homunculus (we have mentioned this in Lecture 6 on motor function). When he stimulated the temporal lobes, patients reported recall of memories. They were very precise and very vivid (with smells, sounds, and colors, with some subjects experiencing déjà vu). He concluded that the temporal lobes are responsible for memory, and déjà vu is an abnormal function of long-term and short-term memory. Alexander Luria (1902-1977) wrote several books on memory. His book “The Mind of a Mnemonist” (1968) described Solomon Shereshevskii, who had seemingly unlimited memory capacity and multimodal synesthesia. He was able to remember himself since he was 1 year old. At the same time, he had a deficit in cognitive function – an inability for abstract thinking and impaired face recognition. Fornazzari et al. (2018) proposed that Shereshevskii’s symptoms could nowadays be diagnosed as autistic spectrum disorder (ASD) (Fornazzari et al., 2018). Later on, Luria published a two-volume book “The 102

Neuropsychology of memory”: the first volume is “Memory dysfunctions caused by local brain damage” (1974) and the second is “Memory dysfunctions caused by damage to deep cerebral structures” (1976). Donald Hebb (1904-1985) is a neuropsychologist known for his theory of learning. His book “The Organization of Behavior” (1949) explains the neuronal mechanisms of learning. Hebb is one of the most cited scientists of 20th century. “Neurons that fire together, wire together” is the most popular expression in the learning and memory field. Activation of two neurons is followed by an increase in the strength of synaptic connections between two cells. Hebbian engrams are widely distributed among linked cells in the assembly. Reciprocally intercomnected cells are characterized by reverberation, while representation of the object is kept in short-term memory (Hebb, 1949). Hebb encouraged brain network computer models, which are very popular nowadays. Brenda Milner (1918) was a pioneer in memory research. She worked with the famous patient H.M. who had bilateral lobectomy along with hippocampus removal. H.M. had impaired memory and was not able to memorize new events, but was able to do motor learning. He was able to learn motor skills, but he never remembered that he had done it before. Milner explained this case with the concept of multiple memory systems. Short-term and long-term memory storage are separate. Temporal lobe impairment caused an inability to transfer information from short-term memory into long-term memory. The hippocampus is necessary for remembering facts and experience. Long-term memory is not a function of the hippocampus. Short-term memory is not a function of the temporal lobes. Larry Squire (1941) is a behavioral neuroscientist, psychiatrist, and author of famous books “Memory and Brain” and “Memory: From Mind to Molecules” with Eric Kandel, based on his research with animal models and human patients with memory problems. Larry Squire separated declarative and procedural memory systems, which operate in parallel in the brain. He also worked with patient H.M. and concluded that he had an impairment of declarative memory only. 103

Nondeclarative memory (implicit) is exhibited through performance, whereas declarative memory is exhibited through recollection (Squire, 2009). Damage to the medial temporal lobe influenced recent memory mainly (in animals, tested on information received 30 days before) (Squire, 2009). Endel Tulving (1927) is a big name in the memory field. A psychologist and neuroscientist, Tulving determined differences between semantic and episodic memory and supported their brain systems (Tulving, 1972). Human ability to move mentally in time from the past to the future, backward and forward, is provided by episodic memory. Tulving proposed the encoding specificity principle, which states that a memory trace should be retrieved by effective retrieval cues. Retrieval cues should be complementing to the memory trace, and be specific to only this unique memory trace. Forgetting may be related to absence of specific retrieval cues (Tilving, 1973). Eric Kandel (1929) received the Nobel Prize in Physiology or Medicine in 2000 (shared with Arvid Carlsson and Paul Greengard) for his contribution to neuronal mechanisms of memory. He used Aplysia californica, a famous organism for studying learning and memory processes because it has a simple nervous system (around 20000 neurons) and demonstrates reaction to external stimuli (a well-known gill-withdrawal reflex). Kandel’s experiments with Aplysia demonstrated habituation, dishabituation, sensitization processes and supported the Hebbian learning mechanism. John O’Keefe (1939) discovered “place cells” in the hippocampus, which are responsible for temporal coding as theta phase precession. He shared the Nobel Prize in Physiology or Medicine (2014) with May-Britt and Edvard Moser for place cells discovery. Theta phase precession is a synchronous firing of neurons within the theta frequency (4-7 Hz). This phenomenon is possible to measure by local field potential (LFP) and electroencephalography (EEG). O’Keefe published a book with Lynn Nadel titled “The Hippocampus as a Cognitive Map” (1978). Another contribution made by O’Keefe is the description of boundary vector cells, which are located in the medial entorhinal cortex and subiculum (Lever et al., 2009). 104

Eleanor Maguire (1970) is a neuroscientist who became famous for her research on London taxi drivers who demonstrated a larger posterior hippocampus. Hippocampal volume was correlated to the years of experience of working as taxi drivers. From the short history above you may understand the tendency of how the question of relation of the brain to memory has been developed, what brain structures participate in memory, what types of memory exist, and that they have different mechanisms. Concepts of memory storage:  An engram is a memory trace/storage (Lashley, Penfield).  Cell assembly is a group of simultaneously active cells (Hebb).  Content addressable memory: content defines what brain structure to engage to memorize this content (Hebb).  Distributed representations. – A model of distributed representations Desimone et al (1984) discovered a population of cells in the inferior temporal cortex in the monkey brain that responded selectively to faces (Desimone et al., 1984). The authors suggested that was a mechanism of encoding memories of faces. According to Hebb’s suggestion, the cortex can be used for sensory processing and for memory storage. Distributed representations are a simultaneous activation that strengthens a response through the mechanism of long-term potentiation (LTP). LTP is the persistent activation in the synapse, a cellular mechanism of learning and memory. The authors simplified the nervous system and proposed that three faces (Erik’s, Kyle’s, Kenny’s) activate neuron A, B, C correspondingly. Before learning, each cell has the same activation for all faces. After learning, if you follow the non-distributed memory model, each neuron will have strong activation to a corresponding face only and zero activation to other faces.


In the distributed memory model, maximum activation will be observed for a corresponding face, and weighted activation for the rest of the faces. Human recognition of faces is not based only on an individual neuron, it is based on distributed weighted activity of many neurons. Memory is stored as the relative weight connection strengths among the nerve cells. Only combined information across all three cells discriminates the different individuals. Activity at a single cell is not discriminatory for the whole set of stimuli (see figure in Neuroscience: Exploring the Brain, 3rd Ed, Bear, Connors, and Paradiso. 2007 Lippincott Williams, p. 736). FMRI studies provided interesting results with the human brain. Rissman & Wagner in their article “Distributed representation in memory: Insights from functional brain imaging” (2012) deliver a good review on fMRI studies with a new analytical tool called MVPA (multi-voxel pattern analysis) which allows us “to decode the information represented within distributed fMRI activity patterns” (Rissman&Wagner, 2012, p. 101). Questions: 1. Describe the types of memory and brain structures underlying different types of memory. 2. Describe the types of amnesia. Brain damage in different types of amnesia. 3. Explicate learning and memory time frames. 4. Describe Kandel’s experiments with Aplysia californica. 5. Clarify memory consolidation processes. 6. Describe Maguire’s study on London taxi drivers. 7. Explain Luria’s study of memory. 8. Explicate the Multi-source model of memory (Intraub & Richardson). 9. Describe John O’Keefe’s discovery on memory. Literature: 1. Anokhin K.V., Mileusnic R., Shamakina I.Y., Rose S.P. Effects of early experience on c-fos gene expression in the chick forebrain // Brain Res. – 1991. – №544 (1). – p. 101-107. 2. Bear M.F., Connors B.W., Paradiso M.A. Neuroscience: Exploring the Brain, 3rd Ed, Lippincott Williams. – 2007. 3. Fornazzari L., Leggieri M., Schweizer T.A., Arizaga R.L., Allegri R.F., Fischer C.E. Hyper memory, synaesthesia, savants Luria and Borges revisited // Dementia & neuropsychologia. – 2018. – №12 (2). – p. 101-104. – doi:10.1590/1980-57642018.


4. Gold P.E. Protein synthesis inhibition and memory: formation vs amnesia // Neurobiology of Learning and Memory. – 2008. – №89 (3). – p. 201-211. – doi:10.1016/j.nlm.2007.10.006. 5. Kandel E.R. In Search of Memory: The Emergence of a New Science of Mind. – New York: W.W. Norton & Company. – 2007. 6. Lashley K.S. In search of the engram. In Society for Experimental Biology // Physiological mechanisms in animal behavior. (Society's Symposium IV.): Academic Press. – 1950. – p. 454-482. 7. Lever C., Burton S., Jeewajee A., O'Keefe J., Burgess N. Boundary Vector Cells in the Subiculum of the Hippocampal Formation // Journal of Neuroscience. – 2009. – №29 (31). – p. 9771-9777. 8. Maguire E.A., Gadian D.G., Johnsrude I.S., Good C.D., Ashburner J., Frackowiak R.S.J., Frith C.D. Navigation-related structural change in the hippocampi of taxi drivers // Proceedings of the National Academy of Sciences. – 2000. – №97 (8). – p. 4398–4403. 9. Moser E., Kropff E., Moser M. Place cells, grid cells, and the brain's spatial representation system // Annual Review of Neuroscience. – 2008. – №31. – р. 69-89. – doi:10.1146/annurev.neuro.31.061307.090723. 10. O'Keefe J. A review of the hippocampal place cells // Progress in Neurobiology. – 1979. – №13 (4). – р. 419-39. – doi:10.1016/0301-0082(79)90005-4. 11. O'Keefe J. Place units in the hippocampus of the freely moving rat // Experimental Neurology. – 1976. – №51 (1). – p. 78–109. – doi:10.1016/0014-4886(76)90055-8. 12. O'Keefe J., Nadel L. The Hippocampus as a Cognitive Map. – Oxford, United Kingdom: Oxford University Press. – 1978. 13. O'Keefe J., Nadel L. The Hippocampus as a Cognitive Map. – Oxford, United Kingdom: Oxford University Press. – 1978. 14. Squire L.R. Memory and Brain. – New York: Oxford: Oxford University Press, 1987. – Р. 315. 15. Squire L.R. Memory: from Mind to Molecules, 2nd Edition, with Eric Kandel. Greenwood Village: Roberts & Co // The Journal of Neuroscience. – 2009. – №29. – р. 12711-12716. 16. Squire L.R., Alvarez P. Retrograde amnesia and memory consolidation: A neurobiological perspective // Current Opinion in Neurobiology. – 1995. – №5 (2). – p. 169-177. – doi:10.1016/0959-4388(95)80023-9. 17. Squire L.R., Amaral D.G., Zola-Morgan S., Kritchevsky M., Press G. Description of brain injury in the amnesic patient N.A. based on magnetic resonance imaging // Experimental Neurology. – 1989. – Vol. 105, Issue 1. – р. 23-35. 18. Tulving E. Episodic and semantic memory / In Tulving E., Donaldson W. (eds.). Organization of Memory. – New York: Academic Press. – 1972. – p. 381-402. 19. Zola-Morgan S., Squire L.R., and Amaral D.G. Human amnesia and the medial temporal region: enduring memory impairment following a bilaterial lesion limited to field CA1 of the hippocampus // J Neurosci. – 1986. – №6. – р. 2950-2967.


Lecture 12

NEUROSCIENCE OF LANGUAGE Outline: 12.1 Language. Thought – Language acquisition theories. – Broca’s and Wernicke’s areas. Brain Functional asymmetry. – Broca’s area function revision. New neurobiological model of language. 12.2 Language impairments – Aphasia. Types of aphasia. – Williams Syndrome – Dyslexia 12.3 FMRI studies of bilingualism Questions Literature

12.1 Language. Thought Language is the ability to encode thoughts into external sound via a speech system. Thinking is the ability to produce thoughts and to transfer one thought to another person. Thinking may occur without language known as a non-linguistic form of thought. Human language is a system of communication and has its development and acquisition periods in life. More than 6,000 languages exist today. All languages have a universal design. Aphasia is a language impairment due to damage to brain areas responsible for language function. Terminology of Linguistics:  Phonology: rules of how sounds go together. Phonemes are the smallest units of sound-bearing elements.  Morphology: rules of how morphemes go together. Morphemes are the smallest units of meaning-bearing elements.  Syntax: rules of how words go together.  Semantics: meaning of words. – Language acquisition theories.


There are three approaches to language acquisition: 1. Noam Chomsky suggested the nativist theory. All children have language acquisition device (LAD), which is a brain area with a predisposition (set of rules) to language acquisition. All children have the ability to learn language without hard efforts. 2. Empiric theory proposed that language acquisition occurs from a sensory perception without an innate LAD system (Willliam James). 3. Behaviorist theory: language acquisition is learning by operant conditioning and reinforcement learning (Skinner). – Broca’s and Wernicke’s areas. Brain Functional asymmetry. Pierre Paul Broca (1824-1880) and Karl Wernicke (1848-1905) are pioneers in providing ground-breaking evidence toward brain function localization. Broca’s patient who had a problem with speech production was L.V. Leborgne. He was able to pronounce “Tan…” for what he called as “Tan” patient (1864). The second patient with an analogical problem was L. Lelong. Their brain autopsies showed the damage to the left inferior frontal lobe. The next 12 patients confirmed the idea that this specific brain area is related to speech production. Broca’s area includes left BA 44, 45. An interesting fact is that in women, Broca's area is about 20% larger than in men. Damage to this area causes Broca’s aphasia (motor aphasia), an inability to produce speech, while speech comprehension is preserved. Inspired by Broca’s results, Karl Wernicke studied the relationship between language impairment and brain dysfunction localization. He was able to discover a sensory aphasia different from motor aphasia: symptoms were in impaired understanding of speech, with preserved ability to speak, but in a meaningless way. This was related to the damage to the left posterior superior temporal gyrus (STG). Wernicke’s area includes left BA 39, 40. Damage to this area causes Wernicke’s aphasia (motor aphasia): preserved but meaningless speech, and an inability to comprehend. Broca’s and Wernicke’s discoveries brought Hunglings Jackson to the idea of functional asymmetry of the brain and existence of the leading hemisphere (1874). He was neurologist and researcher on epilepsy. However, Roger Sperry is a pioneer in the study of the brain functional asymmetry. He received the Nobel Prize in 1981 for his 109

contribution in “split-brain research” (the brain after the surgery with corpus callosotomy as a treatment of epileptic patients). Roger Sperry with later joined Michael Gazzaniga conducted famous experiments with “split-brain” patients to examine the changes in perception and thinking, their personality and intelligence after performing the surgery (Gazzaniga, 1967). – Broca’s area function revision. New neurobiological model of language. Modern studies showed that the relationship between Broca’s area and Broca’s aphasia is not consistent. Additionally, some studies revealed that Broca’s area is engaged in syntax processing, phonological segmentation, articulatory and phonemic representations, arm gestures, and language comprehension (Caplan, 2006), specifically sentence comprehension (Rogalsky&Hickok, 2011). On the other hand, patients with Broca’s aphasia showed problems with working memory (Rogalsky&Hickok, 2011) and executive control, since to be able to say several sentences we need to keep in mind all of them, and put them in correct order, and then perform speech production. Wernicke’s brain area damage also influences speech production. Therefore, the classical neurobiological model of language called Wernicke-Lichtheim-Geschwind (WLG) model was revised. Haggort suggested a Memory, Unification, and Control (MUC) model (Hagoort, 2014) as an alternative model, which takes into account new achievements based on fMRI studies of human language. According to this model, language involves: 1) memory storage for phonological, morphological, and syntactic information – representtations in the angular gyrus and supramarginal gyrus; 2) unification – in the inferior frontal cortex, semantic unification – in the BA 47, BA 45, syntactic unification – in the BA 45 and BA 44, phonological – in the BA 44 and BA 6; 3) executive control – dorsolateral prefrontal cortex, anterior cingulate cortex (ACC), parietal cortex (Hagoort, 2014).


12.2 Language impairments – Aphasia. Types of aphasia. Types of aphasia: – Wernicke's Aphasia: lesion in posterior STG, fluent speech, poor in comprehension, poor in repeating, poor in naming; – Broca's Aphasia: lesion in IFG, non-fluent speech, “good” in comprehension, poor in repeat, poor in naming; – Conduction aphasia: damage to white matter fiber tracts connecting W to B, lesion in Arcuate Fasciculus, fluent speech, good in comprehension, poor in repeat, good in naming; – Anomic aphasia: lesion in Temporal / Parietal Cortices, fluent speech, good in comprehension, mild in repeating, moderate in naming; – Global aphasia: lesion in posterior STG and IFG, non-fluent speech, poor in comprehension, poor in repeating, poor in naming; – Transcortical motor aphasia: infarct in perisylvian area, nonfluent speech, good in comprehension, good in repeating, mild in naming; – Transcortical sensory aphasia: infarct in the left posterior cerebral area, fluent speech, poor in comprehension, good in repeating, moderate in naming; – Progressive aphasia: gradual loss of language abilities as a result of dementia, Alzheimer’s disease; – Deaf aphasia: language disabilities in deaf individuals. Sign language as a compensatory language. Pure word deafness: missing out an auditory input & repeating. – Williams Syndrome Williams Syndrome (WS) provides evidence that language requires a neural system independent of other cognitive abilities. WS is a developmental genetic disease affecting a wide variety of cognitive functions including visual-spatial abilities, poor reading and writing, preserved in spoken language functioning, fluent speech, performed better in grammatical comprehension and production, larger vocabulary size, often good musical ability, especially expressiveness; preserved phonological working memory, and social expressiveness. 111

– Dyslexia Dyslexia is a disorder that affects normal cognitive function (missing out a visual input & output). Dysfunction in language areas, more functional than anatomical, specifically in interconnection between the cerebellum and cortex. There are two basic forms of dyslexia: peripheral and central. – Peripheral dyslexias are neglect dyslexia (misreading half of word); attentional dyslexia; letter-by-letter reader (also called word form dyslexia). – Central dyslexias are reading without understanding the meaning (word meaning blindness), surface dyslexia (deterioration of whole-word reading but retaining grapheme-to-phoneme (phonetic) conversion reading); phonological dyslexia (whole-word reading is good; graphemic-phonemic conversion is poor); deep dyslexia (semantic errors).

12.3 FMRI studies of bilingualism Bilingualism is an ability to express oneself fluently in two languages. According to latest studies, bilinguals have better cortical plasticity, better executive control (Noort et al., 2019), and greater density of grey matter in the inferior parietal cortex (Mechelli et al., 2004). According to fMRI studies, for the second language acquisition at an early age (“early bilinguals”), two languages are represented in common frontal areas, whereas “late bilinguals” showed separate regions for the native language and the second language in Broca’s area (Kim et al., 1997). In Wernicke’s area, there was no separation between the native and second language. FMRI studies by Lukasik et al. (2018) compared early and late bilinguals with monolinguals during working memory task performance, and how language switching frequency was influenced by working memory scores. The sample size was 485 participants. The authors failed to support the bilingual executive advantage (BEA) hypothesis. Kuhl et al. (2016) used the DTI method to investigate white matter structural differences between monolinguals (American English) and 112

bilinguals (American English and Spanish), and found that bilinguals have an “induced neuroplasticity, and the degree of alteration is proportional to language experience, and the modes of immersive language experience have more robust effects on different brain regions and on different structural features” (Kuhl et al., 2016). A systematic review provided by Noort et al. (2019) based on 46 original studies on the BEA hypothesis reported that 54,3% supported a beneficial effect, 28,3% showed mixed results, and the rest (17,4%) did not support the hypothesis. Questions: 1. What is the difference between definitions of “language” and “thinking”? 2. Explain the Language acquisition theory suggested by Noam Chomsky. 3. Elucidate Empiric theory of language acquisition. 4. Clarify Behaviorist theory of language acquisition. 5. What are Broca’s area and Broca’s aphasia? 6. What are Wernicke’s area and Wernicke’s aphasia? 7. Clarify Broca's area function revision. 8. Explain Wernicke-Lichtheim-Geschwind (WLG) model. 9. Explain Memory, Unification, and Control (MUC) model. 10. What is Aphasia? What types of aphasia do you know? 11. What is Williams Syndrome (WS)? 12. What is Dyslexia? Describe the types of dyslexia. 13. What is bilingualism? 14. Give examples of FMRI studies of bilingualism. 15. Explain the Bilingual executive advantage (BEA) hypothesis. Literature: 1. Caplan D. Why Is Broca'S Area Involved in Syntax? Cortex. – 2006, Volume 42. – Issue 4. – р. 469-471. 2. Gazzaniga M. The Split Brain in Man // Scientific American. – 1967. – №217(2). – p. 24-29. 3. Gazzaniga M.S. The split brain revisited // Scientific American. – 1998. – p. 51-55. 4. Hagoort P., Indefrey P. The neurobiology of language beyond single words // Annu Rev Neurosci. – 2014. – №37. – р. 347-362. 5. Hughlings J.J. On the nature of the duality of the brain. In J. Taylor (Ed.), Selected writings of John Hughlings Jackson. – London, United Kingdom: Hodder & Stoughton. – 1874a/1932. – Vol. 2. – p. 129-145. 6. Kim K.H.S., Relkin N., Lee K.M., Hirsch J. Distinct cortical areas associated with native and second languages // Nature. – 1997. – №388(6638). – р. 171-174. 7. Kuhl P.K., Stevenson J., Corrigan N.M., van den Bosch J.J.F., Can D.D., Richards T. Neuroimaging of the bilingual brain: Structural brain correlates of listening and speaking in a second language // Brain Lang. – 2016. – №162. – р. 1-9.


8. Lukasik K.M., Lehtonen M., Soveri A., Waris O., Jylkkä J., Laine M. Bilingualism and working memory performance: Evidence from a large-scale online study // PLoS ONE. – 2018. – №13(11). 9. Mechelli A., Crinion J.T., Noppeney U., O'Doherty J., Ashburner J., Frackowiak R.S., Price C.J. Neurolinguistics: structural plasticity in the bilingual brain // Nature. – 2004. – №431(7010). – p. 757. 10. Rogalsky C., Hickok G. The role of broca’s area in sentence comprehension // J. Cognitive Neuroscience. – 2011. – №23. – p. 1664-1680. DOI: 11. Van den Noort M., Vermeire K., Bosch P., Staudte H., Krajenbrink T., Jaswetz L., Struys E., Yeo S., Barisch P., Perriard B., Lee S.H., Lim S. A Systematic Review on the Possible Relationship Between Bilingualism, Cognitive Decline, and the Onset of Dementia // Behav Sci (Basel). – 2019. – №9 (7). – pii: E81. doi: 10.3390/bs9070081.


Lecture 13

FUNCTIONAL SEGREGATION AND INTEGRATION. BRAIN CONNECTIVITY. FUNCTIONAL CONNECTIVITY. DEFAULT-MODE AND INTRINSIC CONNECTIVITY NETWORKS Outline 13.1 Functional segregation and integration – Brain Connectivity. – Anatomical connectivity – Functional connectivity, – Effective connectivity – Measurements of brain networks 13.2 Default mode network (DMN) – Definition of DMN – Anatomy of DMN Questions Literature

13.1 Functional segregation and integration At the beginning of the course, we discussed two directions in brain research: 1) localizationism, historically started with phrenologist Joseph Gall; and 2) aggregate field view started by French scientist Jean Pierre Flourens. Nowadays, functional segregation is understood as a specialization of different cortical areas to specific functions. Segregated specialized brain areas are integrated for a more complex behaviour. Therefore, segregation and integration are complementary processes. As mentioned by K. Friston, “Imaging neuroscience has firmly established functional segregation as a principle of brain organization in humans. The integration of segregated areas has proven more difficult to assess. One approach to characterize integration is in terms of functional connectivity, which is usually inferred on the basis of correlations among measurements of neuronal activity. Functional connectivity is defined as statistical dependencies among remote neurophysiological events” (K. Friston). 115

   

Brain Connectivity. Anatomical connectivity Functional connectivity, Effective connectivity

There are three types of connectivity: 1) Anatomical/or structural connectivity represented by anatomical links, white matter tracts spatially interconnected between clusters of coupled cortical areas; 2) Functional connectivity is temporal connectivity (coherence or correlations) between clusters, which may not be connected anatomically; 3) Effective connectivity reflects causal interactions, measurement of influence of one network on another one (Friston, 1994, 2003). fMRI and EEG time-series can be analyzed using Granger causality to identify interactions between networks/neural activity during performance of cognitive tasks. In order to understand brain connectivity, it is necessary to learn the terminology related to this approach. According to Sporns et al., (2004), analysis of brain connectivity can be based on graph theory. Graph theory is a mathematical concept of graphs as a mathematical structure for modeling relations between objects. In graph theory, nodes (vertices) are connected by edges (links). In terms of brain connectivity, nodes are large-scale networks. Edges represent structural, functional, or effective connections. Links may be measured by weights. In anatomical networks, weights inform about size, density, coherence of tracts; in functional networks, weights tell us about magnitude of correlations; in effective networks – about magnitude of causal interactions (Rubinov&Spons, 2010). Links may be characterized by directionality. – Measurements of brain networks Measurements of brain networks: 1) degree is the number of links; 2) degree distribution encompasses degrees of all nodes; 3) density is a mean of the network degree. Clustering coefficient is the fraction of triangles around an individual node used for measuring functional 116

segregation. Global efficiency is “the average inverse shortest path length” used for measuring functional integration (Rubinov&Spons, 2010). Network hubs are highly connected and central brain region networks.

Structural connectivity

Functional connectivity

Effective connectivity

Figure 15. Structural, Functional, Effective connectivity (based on Sporns, 2007,

13.2 Default mode network (DMN) – Definition of DMN The default mode network (DMN) refers to a set of regions in the brain that comprise a highly correlated network activated during resting state (day-dreaming, mind-wandering) and deactivated during cognitive tasks. Some studies showed that the DMN is active also when participants think about themselves, their experience, autobiographical information (Fox et al., 2005), remembering the past and imagining the future, thinking about others, understanding their emotions and during social cognition (Padmanabhan et al., 2017), and moral reasoning (Chiong et al., 2013). The DMN is negatively correlated with attention networks (Spreng et al., 2016). Aberrant activity of the DMN has been shown for a range of pathologies, such as autism (Padmanabhan et al., 2017), depression (Liu et al. 2018), schizophrenia (Hu et al., 2017), and posttraumatic stress disorder (Miller, 2017). The DMN can be separated into hubs and subsections: the functional hubs (posterior cingulate cortex, medial prefrontal cortex, and angular gyrus), the dorsal medial subsystem, and the medial temporal subsystem (Buckner et al., 2008). Interestingly, fMRI studies investigating mind-wandering have commonly identified the DMN as showing activity during the generation of spontaneous thought. – Anatomy of DMN 117

Table 12 Anatomy of the default mode network (3 subsystems, Andrews-Hanna, 2014)


Brain Areas Dorsal medial subsystem Dorsal medial prefrontal cortex (dmPFC) Tempoparietal junction (TPJ) Lateral temporal cortex


Anterior temporal pole Functional hubs Posterior cingulate cortex, precuneus Medial prefrontal cortex (mPFC) Angular gyrus


Function Thinking about others Determination of purpose of others action Beliefs about others Retrieval of semantic knowledge Abstract information Thinking about self Decisions, autobiographical information, planning future Attention, spatial cognition, episodic memory

Medial temporal subsystem Questions: 1. Describe functional segregation and integration. 2. Describe structural connectivity. 3. Describe functional connectivity. 4. Describe effective connectivity. Give examples of studies of effective connectivity. 5. What do you know about fMRI studies of functional connectivity? 6. What are the default mode network (DMN) and intrinsic connectivity networks? 7. What can you tell about modern studies on connectivity? 8. Describe functions of DMN. 9. Describe the structure of DMN. Literature: 1. Andrews-Hanna J.R. The brain's default network and its adaptive role in internal mentation // The Neuroscientist: A Review Journal Bringing Neurobiology, Neurology and Psychiatry. – 2012. – №18(3). – p. 251-270. 2. Andrews-Hanna J.R., Smallwood J., Spreng R.N. The default network and self-generated thought: component processes, dynamic control, and clinical relevance // Annals of the New York Academy of Sciences. – 2014. – №1316 (1). – p. 29-52. – doi:10.1111/nyas.12360. 3. Buckner R.L., Andrews-Hanna J.R., Schacter D.L. The brain’s default network: Anatomy, function, and relevance to disease. – Ann NY.: Acad Sci, 2008. – №1124. – р. 1-38.


4. Bullmore E., Sporns O. Complex brain networks: graph theoretical analysis of structural and functional systems // Nature reviews neuroscience. – 2009. 5. Chiong W., Wilson S.M., D'Esposito M., Kayser A.S., Grossman S.N., Poorzand P., Seeley W.W., Miller B.L., Rankin K.P. The salience network causally influences default mode network activity during moral reasoning // Brain. – 2013. – №136 (Pt 6). – p. 1929-1941. – doi: 10.1093/brain/awt066. 6. Fox M.D., Snyder A.Z., Vincent J.L., Corbetta M., Van E., David C., Raichle M.E. The human brain is intrinsically organized into dynamic, anticorrelated functional networks // Proceedings of the National Academy of Sciences of the United States of America. – 2005. – №102 (27). – p. 9673-9678. 7. Friston K.J. Functional and Effective Connectivity: A Review. Brain connectivity. – 2011. – №1, Vol. 1. – DOI: 10.1089/brain.2011.0008. 8. Hong S.B., Zalesky A., Cocchi L., Fornito A., Choi E.J., Kim H.H., Suh J.E., Kim C.D., Kim J.W., Yi S.H. Decreased functional brain connectivity in adolescents with internet addiction. – 2013. 9. Hu M.-L., Zong X.-F., Mann J.J., Zheng J.-J., Liao Y.-H., Li Z.-Ch., He Y., Chen X.-G., Tang J.-S. A Review of the Functional and Anatomical Default Mode Network in Schizophrenia // Neurosci Bull. – 2017. – №33 (1). – p. 73-84. – doi: 10.1007/s12264-016-0090-1. 10. Liu X., Jiang W., Yuan Y.G. Aberrant Default Mode Network Underlying the Cognitive Deficits in the Patients With Late-Onset Depression // Frontiers in Aging Neuroscience. – 2018. – №10. – DOI: 10.3389 /fnagi.2018.00310 11. Miller D.R., Hayes S.M., Hayes J.P., Spielberg J.M., Lafleche G., Verfaellie M. Default Mode Network Subsystems are Differentially Disrupted in Posttraumatic Stress Disorder // Biol Psychiatry Cogn Neurosci Neuroimaging. – 2017. – №2 (4). – p. 363-371. – doi: 10.1016/j.bpsc.2016.12.006. 12. Padmanabhan A., Lynch Ch.J., Schaer M., Menon V. The Default Mode Network in Autism // Biol Psychiatry Cogn Neurosci Neuroimaging. – 2017. – №2 (6). – p. 476-486. – doi: 10.1016/j.bpsc.2017.04.004. 13. Raichle M.E. The brain's default mode network // Annu Rev Neurosci. – 2015. – №38. – р. 433-47. 14. Rubinov M., Sporns O. Complex network measures of brain connectivity: uses and interpretations // Neuroimage. – 2010. 15. Sporns O. Contributions and challenges for network models in cognitive neuroscience // Nature Neuroscience. – 2014. – №17 (5). – p. 929. – DOI: 10.1038/nn.3690 16. Spreng N.R., Stevens D.W., Viviano J.D., Schacter D.L. Attenuated anticorrelation between the default and dorsal attention networks with aging: Evidence from task and rest // Neurobiol Aging. – 2016. – №45. – p. 149160. doi: 10.1016/j.neurobiolaging.2016.05.020. 17. Tononi G., Edelman G.M., Sporns O. Complexity and coherency: integrating information in the brain // Trends in cognitive sciences. – 1998.


Lecture 14

THEORIES OF CONSCIOUSNESS Outline: 14.1 A Biological Theory of Consciousness by Gerald Edelman 14.2 Francis Crick and Christof Koch: Dynamic coalitions theory of consciousness Questions Literature

14.1 A Biological Theory of Consciousness by Gerald Edelman Gerald Edelman is an American biologist who was awarded with the Nobel Prize in Physiology or Medicine in 1972 together with Rodney Richard Porter for their discovery of the structure of antibody molecules and significant contribution to understanding the immune system. Edelman devoted his later career to the theory of consciousness. In his book “The Mindful Brain” (1978), Edelman proposed the theory of Neural Darwinism based on development of brain plasticity. The book “Topobiology” (1988) was devoted to the developmental theory of neuronal networks. “The Remembered Present: A Biological Theory of Consciousness” (1990) presented his final theory of consciousness. Later, a new book with co-author G. Tononi “A Universe of Consciousness: How Matter Becomes Imagination” (2001) extended Edelman’s original theory of consciousness. Gerald Edelman suggested a biological theory of consciousness based on evolutionary and developmental principles of the brain. According to Edelman, consciousness is “a process that emerges from interactions of the brain, the body, and the environment, a result of dynamic interactions among widely distributed groups of neurons” (Edelman, 2003, p. 5520). He supposed that modern neuroscience will allow constructing a neuronal framework for properties of consciousness. He assumes that the thalamocortical system with rich connections with other networks is the most significant brain area for conscious activity.


The theoretical basis of Edelman’s view on consciousness is the theory of neural group selection (TNGS) or non-genetic Darwinism. TNGS was initially proposed for the human immune system, and later Edelman tried to apply this concept to brain development. He specified that “the brain is a selectional system, one in which large numbers of variant circuits are generated epigenetically, following which particular variants are selected over others during experience… Such repertoires of variant circuits are degenerate, i.e., structurally different circuit variants within this selectional system can carry out the same function or produce the same output. Subsequent to their incorporation into anatomical repertoires during development, circuit variants that match novel signals are differentially selected through changes in synaptic efficacy. Differential amplification of selected synaptic populations in groups of neurons increases the likelihood that, in the future, adaptive responses of these groups will occur following exposure to similar signals” (Edelman, 2003, p. 5521). Such diversity of selectional neural systems is one of the fundamental properties of consciousness. In order to fill an explanatory gap between neural activity and individual experience, Edelman suggested the term “qualia”. Qualia are high-level discriminations created by neural activity from multidimensional signals such as “complex scenes, memories, images, emotions”, which comprise consciousness (Edelman, 2003, p. 5521). Another important property of consciousness is “reentry”. Edelman explained the term “reentry” as a selectional process of recursive activation happening in parallel between brain maps, which binds neuronal groups spatially and temporally for specific function. He divides consciousness into primary consciousness (lower level animals) and higher-order consciousness (animals with semantic capabilities). Primary consciousness is characterized by an ability to integrate sensory and motor events, form a multimodal scene and adaptive behavior in current time only. Higher-order consciousness is characterized by an ability to access past experience and plan future events. The reentrant dynamic core is a functional interaction among neural circuits activated by external signals and involves synchronization among large brain areas. This network integration is metastable and 121

changeable to new integrative circuits depending on events in periods of less than 500 ms. The reentrant dynamic core comprises higherorder discrimination, current qualia. “Qualia thus reflect the causal sequences of the underlying metastable neural states of the complex dynamic core…. Given the hyperastronomical functional connectivity patterns of the dynamic core, however, no two subjects can have identical core activity. This is consistent with the TNGS, which views the brain as a selectional system in which myriad neural states provide degenerate repertoires for matching a rich array of signals. Degenerate patterns (24) in the reentrant dynamic core provide an adaptive system for dealing with the enormously complex combinations of such signals” (Edelman, 2003, p. 5524).

14.2 Francis Crick and Christof Koch: Dynamic coalitions theory of consciousness Francis Crick and Christof Koch summarize their view on consciousness in their article “A framework for consciousness” (2003). They tried to outline the neuronal correlates of consciousness (NCC). Authors apply their framework to the visual system since it is the best studied sensory system. Authors suggested two modes of activity: “slower, all-purpose conscious mode” and fast, stereotyped “zombie modes”. Two modes interfere with each other, slow and fast modes work together. Zombie mode has a feed-forward information flow, conscious mode has a flow in two directions. Authors proposed the term “coalitions” meaning the same as Hebb’s “assemblies”. Dynamics of coalition changes and the most dominated coalition is a current conscioussness. Coalitions may be different: widespread and smaller, two or several coalitions may be combined or be in reciprocal relations. For me, this definition of “coalition” reminds the term “dominant” suggested by Ukhtomskii (1921). He understood “dominant” as an area of increased excitability (“winning coalition”). However, it is a century back. Further, Crick&Koch discussed explicit representations and prefered using the term “nodes” with columnar property instead of 122

neurons that may have explicit representations. New visual stimulus for the first time rapidly goes to the highest level, and then moves down in the hierarchical levels. The way from parietal areas to the frontal areas may be derived additionally by driving inputs, and the way from frontal to parietal areas may be modulated by modulating inputs. Authors suggested that conscious awareness is “a series of static snapshots, with motion ‘painted’ on them” (Crick& Koch, 2002, p. 122). They also used the term penumbra assuming changes in firing not only coalition neurons, but also many other neurons as an effect of NCC as a whole system. Authors also compare their ideas with related ideas (Baars, Dennett, Grossberg and others). Questions: 1. Explain the basis of the Edelman’s theory of consiousness. 2. Describe the Theory of consciousness suggested by Crick& Koch. 3. Underline all similarities and dissimilarities between two theories of consiousness. Literature: 1. Chalmers D.J. The Conscious Mind: in Search of a Fundamental Theory. – New York, Oxford Univ. Press. – 1995. 2. Crick F.C., Koch C. A framework for consciousness // Nat. Neurosci. – 2003. – №6 (2). – p. 119-126. 3. Crick F.C., Koch C. Why neuroscience may be able to explain consciousness // Scientific American. – 1995. – №273 (6). – p. 84-85. 4. Crick F.C., Koch C. Consciousness and neuroscience // Cereb. Cortex. – 1998. – №8. – p. 97-107. 5. Crick F.C., Koch C. The unconscious homunculus / in The Neural Correlates of Consciousness (ed. Metzinger, T.). – Cambridge, Massachusetts, MIT Press. – 2000. – p. 103-110. 6. Crick F.C., Koch C. Towards a neurobiological theory of consciousness // Sem. Neurosci. – 1990. – №2. – p. 263-275. 7. Crick F.C., Koch C. What are the neural correlates of consciousness? / Problems in Systems Neuroscience (eds. van Hemmen, L. & Sejnowski, T.J.). – New York, Oxford Univ. Press. – 2003. 8. Edelman G.M. & Mountcastle V.B. The Mindful Brain: Cortical Organization and the Group-Selective Theory of Higher Brain Function. – Cambridge, MIT Press, MA. – 1978. 9. Edelman G.M. Bright Air, Brilliant Fire: On the Matter of the Mind. – NY: Basic Books. – 1992. 10. Edelman G.M. Naturalizing consciousness: A theoretical framework // PNAS. – 2003. – №100 (9). – р. 5520-5524.


11. Edelman G.M. Neural Darwinism: selection and reentrant signaling in higher brain function // Neuron. – 1993. – №10 (2). – p. 115-125. 12. Edelman G.M. Neural Darwinism: The Theory of Neuronal Group Selection. – New York, Basic Books. – 1987. 13. Edelman G.M. The Remembered Present: A Biological Theory of Consciousness. – New York: Basic Books. – 1989. 14. Edelman G.M., Giulio Tononi. A Universe of Consciousness. How matter becomes imagination. – Basic Books. 2000. 15. Hebb D. The Organization of Behavior: a Neuropsychological Theory. – New York, John Wiley. – 1949. 16. Koch C. Is Consciousness Universal? // Scientific American Mind. – 2014. – № 1. – Vol. 25. 17. Koch C., Ullman S. Shifts in selective visual attention: towards the underlying neural circuitry // Human Neurobiology. – 1985. – Vol. 4. – р. 219-227. 18. Tononi G., Edelman G.M. Consciousness and complexity // Science. – 1998. – № 282(5395). – p. 1846-1851. 19. Tononi G., Koch C. Consciousness: here, there and everywhere? // Philosophical Transactions of the Royal Society B. – 2015. – №1668, Vol. 370.


Lecture 15

DAMASIO’S THEORY OF CONSCIOUSNESS. ALEKSANDROV’S THEORY OF CONSCIOUSNESS. ARTIFICIAL INTELLIGENCE. BRAIN COMPUTER INTERFACE Outline: 15.1 Damasio’s theory of consciousness 15.2 Aleksandrov’s theory of consciousness 15.3 Anochin’s theory of consciousness 15.4 Artificial intelligence 15.5 Questions 15.6 Literature

Historically, studies of cognition and emotion were conducted separately methodologically. However, last decades underlined the inefficiency of such an approach. Nowadays, it has been changed to a view that emotion and cognition are integrated in the brain and conjointly contribute to complex behavior. 15.1 Damasio’s theory of consciousness Antonio Damasio, a famous Portuguese-American neuroscientist is director of The Brain and Creativity Institute. He is an author of popular books, including “Descartes’ Error: Emotion, Reason, and the Human Brain” (1994); “The Feeling of What Happens: Body and Emotion in the Making of Consciousness” (1999); “Self Comes to Mind: Constructing the Conscious Brain” (2010). Damasio is one of the most cited authors according to statistics of the Institute for Scientific Information. Damasio suggested the somatic marker hypothesis, which became popular and induced new studies on decision-making, emotion, and consciousness. The meaning of “somatic markers” is that feelings in the body are related to our emotions. Damasio said, “We suggest that the neural substrate of feeling states is to be found first subcortically and then secondarily repeated at cortical level. The subcortical level 125

would ensure basic feeling states while the cortical level would largely relate feeling states to cognitive processes such as decision-making and imagination” (Bechara et al.,1994). Somatic markers are related to physiological changes in the body (heart rate, posture, facial expression and others) when a person experiences emotions in specific situations. Therefore, somatic markers in future behavior influence decision-making implicitly or explicitly. There are two famous simple experiments to support Damasio’s ideas: The first experiment is called David’s case or “bad/good guy” experiment designed by Daniel Tranel. Patient David had an extensive damage of the temporal lobes, including the hippocampus and the amygdala. As a consequence, he had impairment in learning and memory and was not able to remember new people. During the experiment, two guys came to him every morning and one of them was always good to him and supportive, while the second one was bad. David could not recognize them every morning, he was not able to remember any experience with them, and they were always as new persons for him. However, after some days when David was asked with whom he would prefer to work, he always chose the “good guy”. The conclusion was that “the brain knows more than the conscious mind reveals” (Damasio, 1999). The second experiment was on the impairment of one emotion: fear. At a first glance, patient S. had normal cognitive function and her mood was always positive. It was noticed that she could not determine fear expression in other faces. She could recognize all other emotions at the same time. She was good at drawing but never drew faces with expression of fear. Examination showed that she had bilateral damage of the amygdala in her brain. Damasio underlined the importance of emotion in complex behavior (see He suggested two pathways for somatic marker responses: “body loop” and “as-if-body loop”. The second pathway is related to anticipation in changes of bodily sensations. Damasio’s studies with the Iowa Gambling task (known also as Bechara’s Gambling Task, 126

Damasio, 1994, Bechara et al.,1994) confirmed that decision-making processes are influenced by somatic markers. Brain structures associated with somatic markers are the ventromedial prefrontal cortex (VMPFC) and the amygdala. Extensive studies support that the VMPFC and the amygdala are both activated during the gambling task and modulate the outcome of the decision-making processes. The Iowa Gambling task later became a very popular task used in more than 400 studies. 15.2 Aleksandrov’s theory of consciousness According to Yuri Aleksandrov (Aleksandrov&Sams, 2005), emotion was considered as a lower level of a unified system of consciousness. His theory is based on Anokhin’s Functional System theory (Lecture 3) in terms of the theoretical framework “systemic psychophysiology”. Subjective experience is based on dynamic interaction and integration of a group of neurons within a functional system. To remind from Lecture 3, there are specific concepts in the functional system theory: 1) a system-creating factor is a result of the behavior; 2) systemogenesis is a development of functional systems in ontogenesis. From Aleksandrov’s point of view, “development, and hence this increase in differentiation, is based on the specialization of neurons in relation to newly formed more differentiated systems” (Aleksandrov&Sams, 2005, p.392). Each developmental and differential level is related to unique combination of emotion (E) and consciousness (C). Lower differentiation is maximally presented by E and minimally by C. With growth in differentiation, this combination changes in the opposite way. The highest differentiation is associated with maximal prevalence of C and lowest prevalence of E. This combination corresponds to more complex cognitive functions and behavior. 15.3 Anochin’s theory of consciousness Konstantin Anokhin is a Russian neurobiologist known for his contribution to gene activation in the brain during learning and memorizing 127

(Anokhin, 1991). According to Anokhin, memory is a process and the result has a structure. Subjective experience storage is a trace of consciousness. Anokhin suggested that consciousness is a hypernetwork of a thousand brain networks. His new term “cognitom”/ “cog” is a critical transition between two states in evolution. “Cog is a dual…like a pyramid … the base of pyramid… cognitive group equally specialized neurons… and the apex of the pyramid is an unit of cognitive experience and knowledge”, explained Anokhin (Anokhin, 2019, On the international conference “The Science of consciousness” (Interlaken, Swiss, 2019) Konstantin Anokhin suggested the Cog_relational Information Theory (CRIT): “Cog stands for cognizance – an element of individual knowledge. As a cognitive element each cog is characterized by a purview – the distribution of past and future states of the world, that correspond to its activation…At the neural level, cog means neuronal Co-Operative Group. The key postulate of CRIT is “no cog – no bit” principle (Anokhin, 2019, abstract in the Proceedings of the conference “The Science of consciousness”, Interlaken, 2019, P. 192).

15.4 Artificial intelligence Artificial intelligence (AI) is associated with human intelligence/ human mind machine, which is able to mimic human cognitive functions such as perception, learning, memory, knowledge representtation, attention, reasoning, decision-making, language, and coping human emotions. Certainly, cognitive functions modeling in AI is a very simplified prototype of human cognition. However, it is extremely fast-growing area of research. One of the interesting areas is machine learning (ML) which is inculcated in many aspects of Neuroscience. Machine learning is based on computer algorithms, which are able to learn by experience automatically. There are two types of learning: supervised learning with inputs labeling by human and unsupervised learning without human inputs labeling. Supervised learning is divided into classification (categorization) and regression (established relationships between inputs and outputs predicts future changes of these relationships). 128

One of the examples of using machine learning algorithms in Neuroscience is the analysis of fMRI data (BOLD signal, see lecture 1). Multi-voxel pattern analysis (MVPA) becomes a leading method, which is sensitive to multidimensional representations. MVPA uses the classification approach. MVPA classification analysis allows defining statistically discriminable activity patterns in different conditions. Linear Support Vector Machines are supervised learning models with learning algorithms. Analysis includes the next steps: preprocessing data, estimating single-subject activity pattern, selecting voxels, splitting data, training the classifiers, testing the classifiers, statistical inference. Another algorithm is Fisher linear discriminant, which allows determining class features of a linear combination. The Decoding Toolbox is a software package for multivariate analyses (Hebart M.N., Görgen K., Haynes J.-D., 2016, MVPA results allowed showing that default mode network (DMN) is actively engaged in focused task switches (Smith et al., 2018) in spite previous findings of decreased activation of DMN during cognitive tasks. MVPA analyses helped to define how categorical discrimination modulated by behavioral and “multiple demand” (MD) system is responsible for top-down regulation by coding of taskrelevant discriminations (Erez&Duncan, 2015). Artificial neural networks (ANN) are a growing area of research. ANN is based on neural mechanism of excitation and concepts of neural networks that was described by the fFamous Hebb’s expression “fire together, wire together”. Deep learning is ANN, which is able to learn enough long causal links (Dechter, 1986). – Brain-computer interface (BCI) Brain-computer interface (BCI) is a most recent and growing area of inculcation which provides the ability to relate brain activation of specific cognitive functions to a computer device bypassing the normal physiological pathway. First BCIs provided the possibility for patients with movement disabilities to move a body part using their own brain activity induced by mental imagination of a specific function. An example of BCI is Neurochat (, a device which allows to write out


one’s thoughts. BCI may also be used for healthy subjects for various applications. See an example of brain teleoperation between the BCI laboratory at the University of Zaragoza (Spain) and the University of Vilanova (Spain), at a distance of 260 km ( Questions: 1. Explain the somatic marker hypothesis. 2. What is the experiment with “bad/good guy”? 3. Why did S. patient not feel negative emotions? 4. How different is Aleksandrov’s theory of consciousness from Damasio’s theory? 5. What is systemogenesis? 6. What is differentiation? 4. Explain the main idea in Cog_relational Information Theory. 7. What is Artificial Intelligence? 8. What is Machine Learning? 9. What is MVPA? 10. Give examples of using AI and ML in Neuroscience. Literature: 1. Alexandrov Yu.I., Sams M.E. Emotion and consciousness: Ends of a continuum // Cognitive Brain Research. – 2005. – Vol. 25. – р. 392. 2. Anokhin K.V., Mileusnic R., Shamakina I.Y., Rose S.P. Effects of early experience on c-fos gene expression in the chick forebrain // Brain Res. 1991. – №544 (1). – p. 101-107. 3. Anokhin K.V. Towards a Cognitive Foundation for Quantum Mechanics.The proceedings of the conference “The Science of consciousness”, Interlaken. – 2019. – p. 192 4. Damasio A. The Feeling of What Happens: Body and Emotion in the Making of Consciousness. – San Diego, New York, London: “A harvest book”. – 1999. – p. 386. 5. Dechter R. Learning while searching in constraint-satisfaction problems. University of California. Computer Science Department, Cognitive Systems Laboratory. – 6. Erez Y., Duncan J., Discrimination of visual categories based on behavioral relevance in wide spread regions of frontoparietal cortex // The Journal of Neuroscience. – 2015. – №35 (36). – p. 12383-12393. 7. Hebart M.N., Görgen K., Haynes J.-D. The Decoding Toolbox (TDT): A versatile software package for multivariate analyses of functional imaging data. Front. Neuroinform. – 2015. – №8:88. – doi: 10.3389/fninf.2014.00088. 8. Smith V., Mitchell D.J., Duncan J. Role of the Default Mode Network in Cognitive Transitions. Cerebral Cortex. – 2018. – p. 1-12.


CONTENT FOREWORD .................................................................................................... 3 Lecture 1. INTRODUCTION TO COGNITIVE NEUROSCIENCE ............................................................................................ 5 1.1 Historical origins .......................................................................................... 5 1.2 Cognitive Neuroscience methods ................................................................. 8 Lecture 2. INTRODUCTION TO CELLULAR NEUROANATOMY......................................................................................... 18 2.1 Neuron structure and classification. Membrane. Synapses ........................... 18 2.2 Cortex layers. Neuroanatomy and a brief tour of the brain ........................... 22 Lecture 3. INTRODUCTION TO SYSTEM NEUROANATOMY......................................................................................... 33 3.1 Principles of functional systems ................................................................... 33 3.2 Modulator Systems in the Brain ................................................................... 35 3.3 Sensory systems. Sensation and Perception .................................................. 37 Lecture 4. VISION. VISUAL NEUROSCIENCE .......................................... 41 4.1 Peripheral part of the visual system .............................................................. 41 4.2 Visual pathways. Visual cortex .................................................................... 42 4.3 Color perception. Distance and depth. Motion ............................................. 44 Lecture 5. AUDITORY SYSTEM. HEARING .............................................. 48 5.1 Auditory pathways. Hierarchy and tonotopy ................................................ 48 5.2 Auditory cortex. ........................................................................................... 51 Lecture 6. MOTOR CORTEX. MOTOR UNITS AND MUSCLE ACTION. CONTROL OF MOVEMENT. CEREBELLUM. BASAL GANGLIA. VOLUNTARY MOVEMENT. MOTOR SYSTEMS – HIERARCHICAL ORGANIZATION .................... 53 6.1 Motor system as a functional system (FS). Involuntary movement........................................................................................ 53 6.2 Voluntary movement .................................................................................... 55


Lecture 7. EMOTIONAL NETWORK: LIMBIC SYSTEM. AMYGDALA. HYPOTHALAMUS. THALAMUS. EMOTION AND COGNITION INTERACTION ......................................... 60 7.1 A brief history of emotion and brain studies. Hypothalamus, Thalamus, Amygdala, Limbic system ....................................... 60 7.2 Modern approaches to study emotion ........................................................... 64 Lecture 8. EMOTION REGULATION. THEORIES OF EMOTIONAL INTELLIGENCE ....................................... 69 8.1 Cognition and emotion interaction ............................................................... 69 8.2 Emotional intelligence theories .................................................................... 71 Lecture 9. MOTIVATION AND REWARD. LEARNING. REINFORCEMENT ................................................................. 76 9.1 Psychological theories of motivation – a brief overview .............................. 76 9.2 Neuroscience studies of motivation .............................................................. 77 Lecture 10. EXECUTIVE CONTROL. FRONTAL CORTEX. CINGULATE CORTEX. ATTENTION NETWORKS AND ORIENTING. PARIETAL LOBE ........................................................................................... 85 10.1 History of executive control definitions ..................................................... 85 10.2 Two theories of cognitive control ............................................................... 93 10.3 Cognitive Tasks and Executive control failure ........................................... 94 Lecture 11. NEUROANATOMY OF MEMORY SYSTEMS. MEMORY THEORIES. TEMPORAL LOBE. HIPPOCAMPUS ............... 99 11.1 Learning and memory ................................................................................ 99 11.2 Memory theories in Neuroscience .............................................................. 102 Lecture 12. NEUROSCIENCE OF LANGUAGE.......................................... 108 12.1 Language. Thought. .................................................................................... 108 12.2 Language impairments ............................................................................... 111 12.3 FMRI studies of bilingualism ..................................................................... 112 Lecture 13. FUNCTIONAL SEGREGATION AND INTEGRATION. BRAIN CONNECTIVITY. FUNCTIONAL CONNECTIVITY. DEFAULT-MODE AND INTRINSIC CONNECTIVITY NETWORKS ..................................... 115


13.1 Functional segregation and integration ....................................................... 115 13.2 Default mode network (DMN) ................................................................... 117 Lecture 14. THEORIES OF CONSCIOUSNESS .......................................... 120 14.1 A Biological Theory of Consciousness by Gerald Edelman ....................... 120 14.2 Francis Crick and Christof Koch: Dynamic coalitions theory of consciousness ...................................................... 122 Lecture 15. DAMASIO’S THEORY OF CONSCIOUSNESS. ALEKSANDROV’S THEORY OF CONSCIOUSNESS. ARTIFICIAL INTELLIGENCE. BRAIN COMPUTER INTERFACE ............................................................... 125 15.1 Damasio’s theory of сonsciousness ............................................................ 125 15.2 Aleksandrov’s theory of сonsciousness ...................................................... 127 15.3 Anochin’s theory of сonsciousness............................................................. 127 15.4 Artificial intelligence ................................................................................. 128


Еducational issue

Almira Melsovna Kustubayeva


Editor V. Popova Typesetting U. Moldasheva Cover design Ya. Gorbunov Acknowledgment The author wants to express her profound gratitude to Manzura Zholdassova for help with edition references, and Altyngyl Kamzanova for help with figures.

IB №13587 Signed for publishing 01.05.2020. Format 60x84 1/16. Offset paper. Digital printing. Volume 8,37 printer’s sheet. 80 copies. Order №4035. Publishing house «Qazaq University» Al-Farabi Kazakh National University KazNU, 71 Al-Farabi, 050040, Almaty Printed in the printing office of the «Qazaq University» publishing house.

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