end sem report+ final1 documents

8/13/2019 End Sem Report+ Final1 documents

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Introduction to Biological Neurons

1.1IntroductionAfter a century of research, our knowledge of the human brain is still very much incomplete.

Hundreds of different brain areas have been mapped out in various species. Neurons in these regions havebeen classified, sub-classified, and reclassified based on anatomical details, connectivity, responseproperties, and the channels, neuropeptides, and other markers they express. Hundreds of channels havebeen quantitatively characterized, and the regulation and gating mechanisms are beginning to beunderstood. Multi-electrode recordings reveal how hundreds of neurons in various brain areas respond to

stimuli. Despite this wealth of descriptive data, we still do not have a grasp on exactly how thesethousands of neurons are supposed to accomplish computation. To understand the minimal knowledgeabout how brain is doing this enormous computation and signal processing we should have some basicknowledge of biology, neuroscience, biochemistry, biophysics, signal& systems, networks andinformation theory

A vast majority of neurons respond to sensory or synaptic inputs by generating a train ofstereotypical responses called action potentials or spikes. Deciphering the encoding process which

transforms continuous, analog signals (photon fluxes, acoustic vibrations, chemical concentrations and soon) or outputs from other neurons into discrete, fixed-amplitude spike trains is essential to understandneural information processing and computation, since often the nature of representation determines the

nature of computation possible. Researchers, however, remain divided on the issue of the neural codeused by neurons to represent and transmit information. Although there are many open problem in this area

but to model a problem analytically is still very much challenging.

The brain is a sophisticated and complex organ that nature has devised. In order to understandbrain function fairly, we must begin by learning how brain cells work individually and then see how they

are assembled to work together. There are mainly two types of cell in central nervous system: Neuron andGlia. Although there are many neurons in the human brain (about 100 billion), glia outnumber neurons by

tenfold. However, neurons are more important cells for the major functions of the brain. It is the neuronsthat sense changes in the environment, communicate these changes to other neurons, and command the

bodys responses to these sensations. Glia, or glial cells, are thought to contribute to brain function mainlyby insulating, supporting, and nourishing neighboring neurons.

1.2 Relevant Physiological AspectsA typical neuron has four parts: (a) cell body or soma, (b) axon, (c) dendrites and (d) neuronal

membrane.

Fig1: Schematic of a neuron structure [1]

(a) Cell body or SOMA 20m diameter, watery fluid inside called cytosol and contains organelles like nucleus, rough ER,

smooth ER, mitochondria and golgi apparatus [1]. When signal from different dendrites propagate


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towards axon hillock cellbody assumed to act as a conducting and it also performs the local energy

balancing.

Fig2: Cell Body [2]

(b) AxonThe axon, a structure found only in neurons that is highly specialized for the transfer of

information over distances in the nervous system. The axon begins with a region called the axon hillock,

which tapers to form the initial segment of the axon proper. It serves as the telegraph wire that sendsinformation over great distances. The end is called the axon terminal or terminalbutton. The terminal is a

site where the axon comes in contact with other neurons (or other cells) and passes information on to

them. This point of contact is called the synapse [1].

(c) Dendr ite

It acts like a receiver for a neuron. The term dendrite is derived from the Greek for tree, as

these dendrites resemble the branches of a tree extended from the soma. The dendrites of a single neuron

are collectively called a dendritic tree. The branches are covered with thousands of synapses. The

dendritic membrane under the synapse (the postsynaptic membrane) has many specialized protein

molecules called receptors that detect the neurotransmitters in the synaptic cleft [1].

(d) Neuronal Membrane

The neuronal membrane serves as a barrier to enclose the cytoplasm inside the neuron and to

exclude certain substances that float in the fluid that bathes the neuron. The membrane is about 5 nm

thick and is studded with proteins. The protein composition of the membrane varies depending on

whether it is in the soma, the dendrites, or the axon. Neuronal membrane gives a neuron the remarkable

ability to transfer electrical signals throughout the brain and body [1].

1.3 Action PotentialThe Action potential is an electrical signal that conveys information over distances in the nervous

system. The cytosol in the neuron at rest is negatively charged with respect to the extra-cellular fluid. The

action potential is a rapid reversal of this situation such that, for an instant, the inside of the membrane

becomes positively charged with respect to the outside. The action potential is also often called a spike, anerve impulse, or a discharge. The action potentials generated by a cell are all similar in size and duration,

and they do not diminish as they are conducted down the axon. The frequency and pattern of action

potentials constitute the code used by neurons to transfer information from one location to another. Action

potential has certain identifiable parts, called the rising phase, overshoot, falling phase, undershoot, after-

hyper-polarization [1].


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Fig 3: Action potential [1]

1.4 SynapseThe junction between two neurons is called a synapse. With respect to a synapse, we refer to the

sending neuron as the pre-synaptic cell and to the receiving neuron as the postsynaptic cell. Most

synapses occur on the dendrites but some occur on the somas or the axons of other neurons. The most

common type of synapse in the (vertebrate) brain is the chemical synapse. For this type of synapse, the

axon comes very close to the postsynaptic neuron, leaving only a small gap of about 20-40 nanometers

across between pre and postsynaptic cell membranes, called the synaptic cleft. The pre-synaptic signal is

transmitted across the synaptic cleft by transformation from electrical signal into a chemical one and then

back into electrical signal on the postsynaptic side. The chemical signal is sent in the form of

neurotransmitter molecules. About 5,000 of these molecules are packaged in small spheres called synaptic

vesicles which reside in the pre-synaptic terminal [2].

Fig 4: Synapse [1]


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Major Developments in Neuroscience and Neural

Information Theory

Review of recent literature [2, 3, 4] suggests that research on neuroscience may be classified in three

broad directions:

2.1 Experimental NeuroscienceExperimental neuroscience is an important discipline with an aim to understand the molecular,

cellular, physiological, structural and behavioral basis of normal function of the nervous system and its

diseases.

2.2 Theoretical NeuroscienceThe task of understanding the principles of information processing in the brain poses, apart from

numerous experimental questions, challenging theoretical problems on all levels from molecules to

behavior. This Theoretical Neuroscience concentrates on modeling approaches on the level of neurons

and small populations of neurons, since one think that this is an appropriate level to address fundamental

questions of neuronal coding, signal transmission, or synaptic plasticity. Neuron is a dynamic element

that emits output pulses whenever the excitation exceeds some threshold. The resulting sequence of

pulses or spikes contains all the information that is transmitted from one neuron to the next. Signal

transmission and signal processing in neuronal systems need to be understood with the help of distributed

network consists of single neurons [3].

I ntegrate-and-Fi re Model of the Neurons

The leaky integrate-and-fire model (LIF) [5] is one of the most elementary spiking

models and has been widely used to gain a better understanding of information processing in

neurons. In this model, the sub-threshold membrane potential of a neuron is governed by a first-

order linear differential equation


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( ) - v( )( ) = C + rest

in

v tdv ti t

dt R (1)

which corresponds to the circuit in Fig. 5 below [5]:

Fig 5: Equivalent circuit for leaky integrate-and-fire neuron [2]

In this model, the membrane time constant is = RCm

. The value ofm

is typically 8

20 ms. A solution of above equation is

0

0

1 1 (t-t ) (t- )

0

1( ) v + e ( ( ) v ) + e ( )m m

t

rest rest int

v t v t i d C

(2)

By redefining the voltage reference, we can instead consider ( ) vrestv t

. Replacing( ) v

restv t by ( )v t , we have for

0t t

0

0

1 1 (t-t ) (t- )

0

1( ) e ( ) + e ( )m m

t

int

v t v t i d C

0 0 0,

1( ) 1 ( ) + ( ) ( ) * h(t)

in ti t t v t t t

C

.(3)

Where 0

1 t

,h(t) e 1 ( )m

t t

and * denotes convolution. This is true as long as ( )v t is

less than the threshold. When a neuron has just output a spike at time 0t 0 , we will assume that

the membrane potential is reset to 0( ) 0v t . Then,

1= * h

inv i

C . (4)


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as long as < Tv where T is the threshold function. In the above notation, we assume thatin

i

is causal; that is, for. Of course, the function h defined above is also causal. Suppose we let

mt . Then,

0 ,h(t) 1 ( )

t t

. This is called the perfect / leakless integrator model. As a

generalization of the leaky integrate-and fire model, we will allow h to be any decaying causal

function and use (4) to define the membrane potential. Note that ini is a result of incoming spike

train [2, 3, 5].

Refractory Per iod

Output spikes from a neuron are usually well separated. Even with strong input, it is

virtually impossible to excite a second spike during or immediately after a first one. This

minimal distance between two spikes is defined as the absolute refractory period of the neuron.

The typical length of this period is about 24 ms. The absolute refractory period is followed by a

state of relative refractoriness during which it is difficult, but not impossible to generate an

action potential. The relative refractory period may last around 1020ms. Both of these

refractory phases can be modeled in the IF-model by using a decaying threshold function.

Immediately after a spike is generated, we may assume that the threshold is large (possible

infinite), and hence it is impossible for the membrane potential to built up and reach the value of

the threshold in a short amount of time. Using decaying threshold implies that as time passes, the

threshold will be at a lower value and hence it is easier to generate a spike [2].

Energy Consumption

Another important fact is that our nervous system consumes a lot of energy. Human

brains consume 20% of energy consumption for adults and 60% for infant .When we block the

neural signaling by anesthesia, the brains energy consumption is halved. This suggests thatabout 50% of the energy is used to drive signals along axons and across synapses. In 1996, Levy

and Baxter included the amount of energy expended by neuron in their study, initiating

theoretical studies of energy-efficient coding in nervous systems [6].

2.3 Neural Information Theory or Living Information Theory [8]

Information theory, the most rigorous way to quantify neural code reliability, is an aspect

of probability theory that was developed in the 1940s as a mathematical framework for

quantifying information transmission in man-made communication systems. The theorys rigor

comes from measuring information transfer precision by determining the exact probabilitydistribution of outputs given any particular signal or input. Moreover, because of its

mathematical completeness, information theory has fundamental theorems on the maximum

information transferrable in a particular communication channel. In engineering, information

theory has been highly successful in estimating the maximal capacity of communication channels

and in designing codes that take advantage of it. In neural coding, information theory can be used

to precisely quantify the reliability of stimulusresponse functions, and its usefulness in this


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context was recognized early. One can argue that this precise quantification is also crucial for

determining what is being encoded and how. In this respect, researchers have recently taken

greater advantage of information-theoretic tools in three ways-

First,the maximum information that could be transmitted as a function of firing rate has been

estimated and compared to actual information transfer as a measure of coding efficiency.

Second,actual information transfer has been measured directly, without any assumptions about

which stimulus parameters are encoded, and compared to the necessarily smaller estimate

obtained by assuming a particular stimulusresponse model. Such comparisons permit

quantitative evaluation of a models quality[10].

Third,researchers have determined the limiting spike timing precision used in encoding, that

is, the minimum time scale over which neural responses contain information.

I nformation Theory in L iving Systems

While applying information theory in living systems we should keep in mind that, basic

concepts and methods of classical information theory developed by Shanon and his fellowresearchers , which is very much effective for man-made communication systems may not be

always applicable to the long standing mysteries of nature. One must think critically before

applying information theoretic concept for analysis of information processing abilities of neurons

and hence our brain. One should always remember two things-

Judicious application of Shannons fundamental concepts of entropy, mutual information,

channel capacity is crucial to gaining an elevated understanding of how living systems handle

sensory information what is the capacity of a single neuron channel [8, 10].

Living systems have little if any need for the elegant block and convolutional coding theorems

and techniques of information theory because, organisms have found ways to perform theirinformation handling tasks in an effectively Shannon- optimum manner without having to

employ coding in the information-theoretic sense of the term [8, 10].

2.4. Comments on Outstanding Research Issues1. Proposed models in literature do not consider complete signal flow from axon to axon

terminal and axon terminal to synaptic cleft and synaptic cleft to dendrites or cell body

and dendrite or cell body to axon hillock.

2. All the signals that reach dendrite or cell body, do not cross threshold and generate actionpotential. What happen to these signals? Will they act as jitter for next action potential???How local energy balancing takes place???

3. Neocortex is known to exhibit memory and a functional element of neocortex is a neuron.It is not clear from the available literature if there is any memory trace in a single neuron.

Modeling of a single neuron is still an active area of research [1, 3].


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Top-Down and Bottom-Up Approach in Neuroscience

Computational neuroscience is used to bridge the gap between the mathematical

neuroscience and experimental neuroscience. Hodgkin and Huxley combined their experiments

with a mathematical description [5], which they used for simulations on one of the early

computers. One of the central tasks of computational neuroscience is to bridge the different

levels of description by simulation and mathematical theory. The bridge can be built in two

different ways - Bottom-up models and Top-down models. Bottom-up models integrate what is

known on a lower level to explain phenomena observed on a higher level. Top-down models, on

the other hand, start with known cognitive functions of the brain (e.g., working memory), and try

to predict how neurons or group of neurons should behave in order to achieve that function.

Some examples of the top-down approach are theories of associative memory, reinforcement

learning, and sparse coding [11].

3.1. Bottom-Up ApproachThe brain contains billions of neurons that generate short electrical pulses, called action

potentials or spikes to communicate with each other. Hodgkin and Huxleys description of

neuronal action potentials [5] is widely used framework for biophysical neuron models. In these

models, cell membrane of a neuron is described by a number of ion channels, with specific time

constants and gating dynamics that control the momentary state (open or closed) of a channel

(Fig. 6C). By a series of mathematical steps and approximations, theory has sketched a

systematic bottom-up path from such biophysical models of single neurons to macroscopic

models of neural activity [11].

In the f i rst step, biophysical models of spike generation are reduced to integrate-and-fire

models where spikes occur whenever the membrane potential reaches the threshold (Fig. 6B).

In the next step, the population activity A(t)defined as the total number of spikes

emitted by a population of interconnected neurons in a short time windowis predicted from the

properties of individual neurons using mean-field methods known from physics. Each neuron

receives input from many others, it is sensitive only to their average activity (mean field) but

not to the activity patterns of individual neurons [11].

Instead of the spike based interaction among thousands of neurons, network activity can

therefore be described macroscopically as an interaction between different populations Such

macroscopic descriptionsknown as population models, neural mass models, or, in the

continuum limit, neural field models (Fig. 6A)help researchers to gain an intuitive and more

analytical understanding of the principal activity patterns in large networks. Although the

transition from microscopic to macroscopic scales relies on purely mathematical arguments,

simulations are important to add aspects of biological realism (such as heterogeneity of neurons

and connectivity, adaptation on slower time scales, and variability of input and receptive fields)


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that are difficult to treat mathematically. However, the theoretical concepts and the essence of

the phenomena are often robust with respect to these aspects [11].

Fig6: Bottom up approach in nervous system [11]

3.2. Decision-Making: Combines Top-Down and Bottom-UpOften we have to take a decision between two alternatives (A or B), such as Should I do

it or not?. Psychometric measures of performance and reaction times for two alternative forced-

choice decision-making paradigms can be explained by a phenomenological drift-diffusion

model. This model consists of a diffusion equation describing a random variable that

accumulates noisy sensory data until it reaches one of two boundaries corresponding to a specific

choice (Fig. 7A). Although this model able to describe reaction time distribution, it suffers from

a crucial disadvantage, namely the difficulty in assigning a biological meaning to the model

parameters [11].

Recently, neurophysiological experiments have begun to reveal neuronal correlates of

decision making, in tasks involving visual patterns of moving random dots or vibrotactile or

auditory frequency comparison. Computational neuroscience offers a framework to bridge the

conceptual gap between the cellular and the behavioral level. Explicit simulations of microscopic

models based on local networks with large numbers of spiking neurons can reproduce and

explain both the neurophysiological and behavioral data. These models describe the interactions

between two groups of neurons coupled through mutually inhibitory connections (Fig. 7C).

Suitable parameters are inferred by studying the dynamical regimes of the system and choosing


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parameters consistent with the experimental observations of decision behavior. Thus, the pure

bottom-up model is complemented by the top-down insights of target functions that the network

needs to achieve [11].

Fig 7: Decision making in nervous system [11]

3.3. Large-scale brain networks and cognitionMuch of our current knowledge of cognitive brain function has come from the modular

paradigm, in which brain areas are postulated to act as independent processors for specific

complex cognitive functions [15]. Recent research shows that this paradigm has serious

limitations and might in fact be misleading. Even the functions of primary sensory areas of the

cerebral cortex, once thought to be pinnacles of modularity, are being redefined by recent

evidence of cross-modal interactions. A new paradigm is emerging in cognitive neuroscience

that suggests instead of working in a modular way for a cognitive function brain areas are

working conjointly as a large scale networks [12, 13, 15] .

Large-scale structural brain networksThe neuroanatomical structure of large-scale brain networks give us an idea of connected

brain areas that facilitates signaling along a particular pathway for the service of specific

cognitive functions. It is important to identify the brain areas that constitute structural network

nodes and the connecting paths that serve as structural network edges to know which

configurations of interacting areas are possible. In the past, large-scale structural brain networks


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were often schematized by two-dimensional wiring diagrams, with brain areas connected by

lines or arrows representing pathways. Currently, more sophisticated network visualization and

analysis schemes are being developed and used. Principal methods to define structural nodes and

edges in the brain is described first. Some of possible functional consequences of the structural

organization of large-scale brain networks is described then [15].

NodesThe nodes of large-scale structural brain networks are typically brain areas defined by: (i)

cytoarchitectonics; (ii) local circuit connectivity; (iii) output projection target commonality; and

(iv) input projection source commonality. A brain area can be described as a subnetwork of a

large-scale network; this subnetwork consists of neuron populations (nodes) and connecting

pathways (edges). Despite the complex internal structure of each node, it is often convenient,

particularly in network modeling research, to treat them as unitary neural masses that serve as

spatially undifferentiated (lumped) nodes in large-scale networks. The definition of nodes are

changing time to time as new methods are developed and understanding of structure functionrelations in the brain evolves .Techniques used in recent years to determine structural nodes from

neuroanatomical data include: (i) anatomical parcellation of the cerebral cortex using the

Brodmann atlas; (ii) parcellation in standardized Montreal Neurological Institute (MNI) space

using macroscopic landmarks in structural magnetic resonance imaging (sMRI) data; (iii)

subject-specific automated cortical parcellation based on gyral folding patterns; (iv) quantitative

cytoarchitectonic maps; and (v) neurochemical maps showing neurotransmitter profiles .Diverse

tradeoffs arise in the use of these techniques [15]. Classical, but still popular Brodmaan mapping

scheme is used for analysis. (Details are given in appendix - 1)

Problem in node selectionA major problem being that of anatomical specificity versus extent of coverage across the

brain. This problem is particularly acute for the cerebral cortex because the borders of most

cortical regions cannot be reliably detected using macroscopic features from sMRI. The choice

of spatial scale for nodal parcellation has important consequences for the determination of

network connectivity [15].

Although newer methods offer a tighter link with the functional architecture of the brain,

but still coverage exists for only a small set of cortical regions and a wide area of human

prefrontal and temporal cortices have not yet been adequately mapped. Most anatomical

parcellation studies have focused on the cerebral cortex. Less attention has been paid to

subcortical structures such as the basal ganglia and the thalamus, which have only been

demarcated at a coarse level using sMRI. Brainstem systems mediating motivation, autonomic

function and arousal have been poorly studied because they are very difficult to identify using in

vivo techniques. Nonetheless, it is important to identify these structures because they

significantly influence cortical signaling and thus affect cognitive function [15].


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Fig 8: Structural nodes of cerebral cortex [15]Edges

The edges connecting brain areas in large-scale structural networks are long-range axon

pathways. Network edges are directed because axon fiber pathways have direction from the

somata to the synapses, and can be bidirectional when axon pathways run in both directions

between particular brain areas. Each brain area has a unique connection set of other areas with

which it is interconnected. Network edges have variable weights based on the number and size of

axons in the pathways, and the number and strengths of functioning synapses at the axon

terminals [15].

Three main approaches are currently used to trace axon pathways, and thus determine

structural network edges.The first is autoradiographic tracing in experimental animals. In the macaque monkey,

this technique has provided a rudimentary map of anatomical links between major cortical areas

and more recently has successfully detailed rostrocaudal and dorsalventral connectivity

gradients between major prefrontal and parietal cortical areas [15].

The secondapproach uses diffusion-based magnetic resonance imaging methods, such as

diffusion tensor imaging (DTI) and diffusion spectrum imaging (DSI), to determine major fiber

tracts of the human brain in vivo by identifying the density of connections between brain areas

[15]. (Fig .9)

The thirdapproach to mapping of network edges uses anatomical features such as local

cortical thickness and volume to measure anatomical connectivity. In this approach, which has

evolved during the same recent time period as DTI technology, interregional covariation in

cortical thickness and volume across subjects is used to estimate connectivity [15].


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Problem in edge selection

In autoradiographic tracing however it is difficult, to extrapolate from macaque

connectional neuroanatomy to that of the human brain because the degree of pathway homology

between macaque and human brains is not well understood.

Diffusionbased tractography of the entire human brain is still in its early days, but rapidly

evolving techniques are providing reliable estimates of the anatomical connectivity of several

hundred cortical nodes [12, 13]. With additional anatomical constraints on seeds and targets

in diffusion- based tractography, it is increasingly possible to make closer links between

projection zones and cytoarchitectonic maps [15].

In the third method edges that are identified might not actually reflect axonal pathways

and precaution is required in interpreting the results. Nevertheless, networks identified using this

approach have revealed stable graph-theoretic properties [15].

Comments on Structural Nodes and Edges

Recent studies have combined both node and edge detection to identify structuralnetworks, either across the whole brain or within specific brain systems. At the whole-brain

level, network nodes are determined by one of the parcellation methods described above, and

then network edges are determined by DTI or DSI. If, however, structural network nodes are

inferred from DTI or DSI patterns of convergence and divergence, nodes and edges cannot be

independently identified. Within specific functional systems, such as for language or working

memory, the nodes are constrained to lie within the system and then the edges are identified by

diffusion-based tractography . The use of cytoarchitectonic boundaries to define the nodes allows

aspects of brain connectivity that are more closely linked to the underlying neuronal organization

to be uncovered in parallel [12-15].

Large-scale functional brain networksHuman brain has evolved to provide survival strategy to human in a way that one can

survive in a wide varity of ambience changes, act differently in different condition depending on

the situtiation. At each moment certain set of conditions must be analyzed by human brain with

the help of perception. The set of perception along with the learned concepts produce an

immediate solution to the immediate problem and act accordingly. It is reasonable to assume that

a set of interconnected brain areas act in tandem to provide these solutions, as well as

corresponding behavior and that they interact dynamically to achieve an action. A large-scale

functional network can therefore be defined as a collection of interconnected brain areas that

interact to perform circumscribed functions. The topological form of functional networks

changes throughout an individuals lifespan and is uniquely shaped by maturational and learning

processes within the large-scale neuroanatomical connectivity matrix for each individual [13-15].


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NodesThe characterization of functional networks in the brain requires identification of

functional nodes. However, there is no commonly agreed definition of what constitutes a

functional node in the brain. Since the advent of advanced functional electrophysiological and

neuroimaging methods, additional methodologies to define functional network nodes have

become available. A network node can be a circumscribed brain region displaying elevated

metabolism in positron emission tomography (PET) recordings, elevated blood perfusion in

functional magnetic resonance imaging (fMRI) recordings, or synchronized oscillatory activity

in local field potential (LFP) recordings. Participation of a brain area in a large-scale functional

network is commonly inferred from its activation or deactivation in relation to cognitive

function. A group of brain areas jointly and uniquely activated or deactivated during cognitive

function with respect to a baseline state can represent the nodes of a large-scale network for that

function [15].

Problem of selection of Functional nodesA major challenge is to determine how functional network nodes defined by different

recording modalities are related, and how they relate to structural network nodes. From the

network perspective, cognitive functions are carried out in real time by the operations of

functional networks comprised of unique sets of interacting network nodes. For a brain area to

qualify as a functional network node, it must be demonstrated that, in combination with a

particular set of other nodes, it is engaged in a particular class of cognitive functions. Although it

is not yet known how the various definitions of large-scale functional network nodes derived

from different recording modalities are related, a possible scenario is that the elevated

excitability of neurons within an area leads to elevated metabolic activity, which in turn causes

an increase in local blood oxygen availability. The elevated excitability could also causeincreased interactions between neurons within the area. Interactions between different

populations can produce oscillatory activity and can have important functional consequences if,

for example, the interactions lead to increased sensitivity of neurons within the area to the inputs

that they receive [15].

Much of the work in the field of functional neuroimaging uses the fMRI blood-oxygen-level-

dependent (BOLD) signal to identify the nodes of large-scale functional networks by relating the

joint activation of brain areas to different cognitive functions. fMRI BOLD activation has

revealed network nodes that are involved in such cognitive functions as attention [58], working

memory, language, emotion, motor control and time perception [15].

EdgesThe identification of functional network edges comes from different forms of functional

interdependence (or functional connectivity) analysis, which assesses functional interactions

among network nodes. The identification of network edges, like that of network nodes, is highly

dependent on the monitoring methodology. Functional interdependence analysis can identify


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network edges from time series data in the time (e.g. cross-correlation function) or frequency

(e.g., spectral coherence or phase synchrony measures) domain. In either domain, the analysis

can use a symmetric measure, in which case significant interdependences are represented as

undirected edges, or an asymmetric measure, in which case they are represented as directed

edges. Methods using directional measures include Granger causality analysis and dynamic

causal modeling. Functional interdependences must be statistically significant for them to

represent the edges of large-scale functional networks. Determination of thresholds for

significance testing of network edges is often fraught with difficulty, and the particular method

used for threshold determination can have an appreciable impact on the resulting large-scale

network. Certain graph-theoretic measures, however, do not suffer from this problem because

they take into account the full weight structure of the network. Fluctuations in neuronal

population activity at different time scales can control the time-dependent variation of

engagement and coordination of areas in large-scale functional networks. Network edges are

possibly best represented by the correlation of time series fluctuations at different time scales,

reflecting different functional network properties. The correlation of slow fluctuations at rest infMRI BOLD signals possibly reflects slow interactions necessary to maintain the structural and

functional integrity of networks, whereas the correlation of fast fluctuations could reflect fast

dynamic coupling required for information exchange within the network [12-15].

Functional interdependence has been observed across a range of time scales from

milliseconds to minutes. Recent evidence suggests that slow intracranial cortical potentials are

related to the fMRI BOLD signal. It is possible that functional networks are organized according

to a hierarchy of temporal scales, with structural edges constraining slow functional edges, which

in turn constrain progressively faster network edges. Studies in both monkeys and humans

support the existence of hierarchical functional organization across time scales [15].

Intrinsic functional brain networksFunctional interdependence analysis has often been used to investigate interactions

between brain areas during task performance. Although task-based analyses have enhanced our

understanding of dynamic context-dependent interactions, they often have not contributed to a

principled understanding of functional brain networks. By focusing on task-related interactions

between specific brain areas, they have tended to ignore the anatomical connectivity and

physiological processes that underlie these interactions. Intrinsic interdependence analysis of

fMRI data acquired from subjects at rest and unbiased by task demands has been used to identify

intrinsic connectivity networks (ICNs) in the brain. ICNs identified in the resting brain include

networks that are also active during specific cognitive operations, suggesting that the human

brain is intrinsically organized into distinct functional networks. One key method for identifying

ICNs in resting-state fMRI BOLD data is independent component analysis (ICA), which has

been used to identify ICNs involved in executive control, episodic memory, autobiographical

memory, self-related processing and detection of salient events. ICA has revealed a sensorimotor

ICN anchored in bilateral somatosensory and motor cortices, a visuospatial attention network

anchored in intra-parietal sulci and frontal eye fields, a higher-order visual network anchored in


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lateral occipital and inferior temporal cortices, and a lower-order visual network. This technique

has allowed intrinsic, as well as task-related, fMRI activation patterns to be used for

identification of distinct functionally coupled systems, including a central-executive network

(CEN) anchored in dorsolateral prefrontal cortex (DLPFC) and posterior parietal cortex (PPC),

and a salience network anchored in anterior insula (AI) and anterior cingulate cortex (ACC) [15].

A second major method of ICN identification is seedbased functional interdependence

analysis. Like ICA, this technique has been used to examine ICNs associated with specific

cognitive processes such as visual orienting attention, memory and emotion. First, a seed region

associated with a cognitive function is identified. Then, a map is constructed of brain voxels

showing significant functional connectivity with the seed region. This approach has

demonstrated that similar networks to those engaged during cognitive task performance are

identifiable at rest, including dorsal and ventral attention systems and hippocampal memory

systems. It has also revealed distinct functional circuits within adjacent brain regions: functional

connectivity maps of the human basolateral and centromedial amygdal [15].

Graph-theoretic studies of resting-state fMRI functional connectivity results havesuggested that human large-scale functional brain networks are usefully described as small-

world. Other graph-theoretic metrics such as hierarchy have been useful in characterizing

subnetwork topological properties, but a consistent view of hierarchical organization in large-

scale functional networks has yet to emerge [15]


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Problem Formulation and MethodologyIn our work we have hypothised human nervous system according the following tree.

Graphical representation of human nervous system is given below-

Fig 12: Human Nervous System(HNS), Graphical representation


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Problem#1

Model human nervous system(HNS) as a large distributed network and try to predict some of the

cognitive behavior from this network mathematically.


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Appendix-1(Brodmaan Area)

Areas Broad Parts Location in Brain Functions Remarks

Area- 3, 2, 1 Primary Somatosensory

Cortex

The lateral postcentral gyrus

is a prominent structure in

the parietal lobe of thehuman brain and an

important landmark. It is the

location of the primarysomatosensory cortex

The main sensory receptive area for

the sense of touch

Area- 4 Primary Motor Cortex It is located in the posteriorportion of the frontal lobe.

is about the same as the

precentral gyrus.

The borders of this area are:

theprecentral sulcus in front(anteriorly), themedial

longitudinal fissure at thetop (medially), thecentral

sulcus in back (posteriorly),

and thelateral sulcus alongthe bottom (laterally).

Plan and execute movements.

Area- 5 Somatosensory AssociationCortex

Part of the parietal cortex inthe human brain.

It is situated immediately

posterior to theprimary

somatosensory areas

(Brodmann areas 3, 1, and2), and anterior to

Brodmann area 7.

It is involved in somatosensoryprocessing and association.(The

somatosensory system is a diverse

sensory system comprising thereceptors and processing centres to

produce thesensory modalities such

as touch,temperature, proprioception(body position), andnociception

(pain). Thesensory receptors cover

theskin andepithelia,skeletalmuscles, bones andjoints,internal

organs,and thecardiovascular

system.)

Area- 6 Premotor cortex andSupplementary Motor Cortex

(Secondary Motor

Cortex)(Supplementary motorarea)

It is part of thefrontalcortex in thehuman brain.

Situated just anterior to the

primary motor cortex(BA4), it is composed of the

premotor cortex and,

medially, thesupplementarymotor area,or SMA.

This large area of the frontal cortex isbelieved to play a role in the planning

of complex, coordinated movements.

Brodmann area 6 isalso called agranular

frontal area 6 in

humans because itlacks an internal

granular cortical

layer (layer IV).

Area- 7 Somatosensory Association

Cortex

Part of theparietalcortex in

thehuman brain.Situated

posterior to theprimarysomatosensory cortex

(Brodmann areas 3, 1 and

2), and superior to theoccipital lobe

This region is believed to play a role

in visuo-motor coordination.

area 7 along witharea 5 has been

linked to a wide variety of high-levelprocessing tasks, including activation

in association with language use.This

function in language has beentheorized to stem from how these two

regions play a vital role in generating

conscious constructs of objects in theworld.
http://en.wikipedia.org/wiki/Precentral_gyrushttp://en.wikipedia.org/wiki/Precentral_sulcushttp://en.wikipedia.org/wiki/Anteriorhttp://en.wikipedia.org/wiki/Medial_longitudinal_fissurehttp://en.wikipedia.org/wiki/Medial_longitudinal_fissurehttp://en.wikipedia.org/wiki/Medial_%28anatomy%29http://en.wikipedia.org/wiki/Central_sulcushttp://en.wikipedia.org/wiki/Central_sulcushttp://en.wikipedia.org/wiki/Posterior_%28anatomy%29http://en.wikipedia.org/wiki/Lateral_sulcushttp://en.wikipedia.org/wiki/Human_anatomical_terms#Anatomical_directionshttp://en.wikipedia.org/wiki/Brodmann_area_5http://en.wikipedia.org/wiki/Brodmann_area_5http://en.wikipedia.org/wiki/Primary_somatosensory_areashttp://en.wikipedia.org/wiki/Primary_somatosensory_areashttp://en.wikipedia.org/wiki/Brodmann_area_7http://en.wikipedia.org/wiki/Sensory_systemhttp://en.wikipedia.org/wiki/Sensory_modalityhttp://en.wikipedia.org/wiki/Temperaturehttp://en.wikipedia.org/wiki/Proprioceptionhttp://en.wikipedia.org/wiki/Nociceptionhttp://en.wikipedia.org/wiki/Sensory_receptorshttp://en.wikipedia.org/wiki/Skinhttp://en.wikipedia.org/wiki/Epitheliahttp://en.wikipedia.org/wiki/Skeletal_musclehttp://en.wikipedia.org/wiki/Skeletal_musclehttp://en.wikipedia.org/wiki/Bonehttp://en.wikipedia.org/wiki/Jointhttp://en.wikipedia.org/wiki/Organ_%28anatomy%29http://en.wikipedia.org/wiki/Cardiovascular_systemhttp://en.wikipedia.org/wiki/Cardiovascular_systemhttp://en.wikipedia.org/wiki/Premotor_cortexhttp://en.wikipedia.org/wiki/Supplementary_motor_areahttp://en.wikipedia.org/wiki/Supplementary_motor_areahttp://en.wikipedia.org/wiki/Frontal_lobehttp://en.wikipedia.org/wiki/Cerebral_cortexhttp://en.wikipedia.org/wiki/Human_brainhttp://en.wikipedia.org/wiki/Primary_motor_cortexhttp://en.wikipedia.org/wiki/Brodmann_area_4http://en.wikipedia.org/wiki/Premotor_cortexhttp://en.wikipedia.org/wiki/Supplementary_motor_areahttp://en.wikipedia.org/wiki/Supplementary_motor_areahttp://en.wikipedia.org/wiki/Parietal_lobehttp://en.wikipedia.org/wiki/Cerebral_cortexhttp://en.wikipedia.org/wiki/Human_brainhttp://en.wikipedia.org/wiki/Primary_somatosensory_cortexhttp://en.wikipedia.org/wiki/Primary_somatosensory_cortexhttp://en.wikipedia.org/wiki/Postcentral_gyrushttp://en.wikipedia.org/wiki/Postcentral_gyrushttp://en.wikipedia.org/wiki/Occipital_lobehttp://en.wikipedia.org/wiki/Brodmann_area_5http://en.wikipedia.org/wiki/Conscioushttp://en.wikipedia.org/wiki/Conscioushttp://en.wikipedia.org/wiki/Brodmann_area_5http://en.wikipedia.org/wiki/Occipital_lobehttp://en.wikipedia.org/wiki/Postcentral_gyrushttp://en.wikipedia.org/wiki/Postcentral_gyrushttp://en.wikipedia.org/wiki/Primary_somatosensory_cortexhttp://en.wikipedia.org/wiki/Primary_somatosensory_cortexhttp://en.wikipedia.org/wiki/Human_brainhttp://en.wikipedia.org/wiki/Cerebral_cortexhttp://en.wikipedia.org/wiki/Parietal_lobehttp://en.wikipedia.org/wiki/Supplementary_motor_areahttp://en.wikipedia.org/wiki/Supplementary_motor_areahttp://en.wikipedia.org/wiki/Premotor_cortexhttp://en.wikipedia.org/wiki/Brodmann_area_4http://en.wikipedia.org/wiki/Primary_motor_cortexhttp://en.wikipedia.org/wiki/Human_brainhttp://en.wikipedia.org/wiki/Cerebral_cortexhttp://en.wikipedia.org/wiki/Frontal_lobehttp://en.wikipedia.org/wiki/Supplementary_motor_areahttp://en.wikipedia.org/wiki/Supplementary_motor_areahttp://en.wikipedia.org/wiki/Premotor_cortexhttp://en.wikipedia.org/wiki/Cardiovascular_systemhttp://en.wikipedia.org/wiki/Cardiovascular_systemhttp://en.wikipedia.org/wiki/Organ_%28anatomy%29http://en.wikipedia.org/wiki/Jointhttp://en.wikipedia.org/wiki/Bonehttp://en.wikipedia.org/wiki/Skeletal_musclehttp://en.wikipedia.org/wiki/Skeletal_musclehttp://en.wikipedia.org/wiki/Epitheliahttp://en.wikipedia.org/wiki/Skinhttp://en.wikipedia.org/wiki/Sensory_receptorshttp://en.wikipedia.org/wiki/Nociceptionhttp://en.wikipedia.org/wiki/Proprioceptionhttp://en.wikipedia.org/wiki/Temperaturehttp://en.wikipedia.org/wiki/Sensory_modalityhttp://en.wikipedia.org/wiki/Sensory_systemhttp://en.wikipedia.org/wiki/Brodmann_area_7http://en.wikipedia.org/wiki/Primary_somatosensory_areashttp://en.wikipedia.org/wiki/Primary_somatosensory_areashttp://en.wikipedia.org/wiki/Brodmann_area_5http://en.wikipedia.org/wiki/Brodmann_area_5http://en.wikipedia.org/wiki/Human_anatomical_terms#Anatomical_directionshttp://en.wikipedia.org/wiki/Lateral_sulcushttp://en.wikipedia.org/wiki/Posterior_%28anatomy%29http://en.wikipedia.org/wiki/Central_sulcushttp://en.wikipedia.org/wiki/Central_sulcushttp://en.wikipedia.org/wiki/Medial_%28anatomy%29http://en.wikipedia.org/wiki/Medial_longitudinal_fissurehttp://en.wikipedia.org/wiki/Medial_longitudinal_fissurehttp://en.wikipedia.org/wiki/Anteriorhttp://en.wikipedia.org/wiki/Precentral_sulcushttp://en.wikipedia.org/wiki/Precentral_gyrus


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Area- 8 Includes Frontal eye fields Part of the frontal cortex in

the human brain. Situatedjust anterior to the premotor

cortex (BA6)

Believed to play an important role in

the control of eye movements.

Area- 9 Dorsolateral prefrontal cortex Part of the frontal cortex in

the human brain.

DLPFC serves as the highest cortical

area responsible for motor planning,

organization, and regulation.

It plays an important role in theintegration of sensory and mnemonic

information and the regulation of

intellectual function and action,especially in relation toimpulse

control.

It is also involved inworking

memory.

However, DLPFC is not exclusivelyresponsible for the executive

functions. All complex mental activity

requires the additional cortical andsubcortical circuits with which the

DL-PFC is connected.

Area- 10 Anterior prefrontal cortex

(most rostral part of superiorand middle frontal gyri)

It is the anterior-most

portion of the Prefrontalcortex in the human brain.

One of the least well understood

regions of the human brain".

Present research suggests that it isinvolved instrategicprocesses in

memory recall and variousexecutive

functions.

Duringhuman

evolution,thefunctions in this

area resulted in its

expansion relativeto the rest of the

brain

Area- 11 Orbitofrontal area (orbital and

rectus gyri, plus part of the

rostral part of the superior

frontal gyrus)

It is a prefrontal cortex

region in the frontal lobes in

the brain

It is involved in the cognitive

processing of decision-making.

Area- 12 Orbitofrontal area (used to be

part of BA11, refers to thearea between the superior

frontal gyrus and the inferior

rostral sulcus)

It occupies the most rostral

portion of the frontal lobe.

Not known

Area- 13, 14 Insular cortex In each hemisphere of themammalian brain the insular

cortex (often called insula,

insulary cortex or insularlobe) is a portion of the

cerebral cortex folded deep

within the lateral sulcus (the

fissure separating the

temporal lobe from the

parietal and frontal lobes).

The insulae are believed to beinvolved in consciousness and play a

role in diverse functions usually

linked to emotion or the regulation ofthe body's homeostasis. These

functions include perception, motor

control, self-awareness, cognitive

functioning, and interpersonal

experience. In relation to these it is

involved in psychopathology.

Area -14 for non-humanprimates.

Area- 15 Anterior Temporal Lobe Subdivisions of the cerebral

cortex in the brain.

Area 15 was defined

by Brodmann in theguenon monkey, but

he found no

equivalent structure

in humans.

Area- 17 Primary visual cortex (V1) Part of thecerebral cortex It

is located in theoccipital

lobe, in the back of thebrain.

Responsible for processing visual

information.
http://en.wikipedia.org/wiki/Impulse_controlhttp://en.wikipedia.org/wiki/Impulse_controlhttp://en.wikipedia.org/wiki/Working_memoryhttp://en.wikipedia.org/wiki/Working_memoryhttp://en.wikipedia.org/wiki/Strategichttp://en.wikipedia.org/wiki/Recall_%28memory%29http://en.wikipedia.org/wiki/Executive_functionshttp://en.wikipedia.org/wiki/Executive_functionshttp://en.wikipedia.org/wiki/Human_evolutionhttp://en.wikipedia.org/wiki/Human_evolutionhttp://en.wikipedia.org/wiki/Primatehttp://en.wikipedia.org/wiki/Cerebral_cortexhttp://en.wikipedia.org/wiki/Occipital_lobehttp://en.wikipedia.org/wiki/Occipital_lobehttp://en.wikipedia.org/wiki/Occipital_lobehttp://en.wikipedia.org/wiki/Occipital_lobehttp://en.wikipedia.org/wiki/Cerebral_cortexhttp://en.wikipedia.org/wiki/Primatehttp://en.wikipedia.org/wiki/Human_evolutionhttp://en.wikipedia.org/wiki/Human_evolutionhttp://en.wikipedia.org/wiki/Executive_functionshttp://en.wikipedia.org/wiki/Executive_functionshttp://en.wikipedia.org/wiki/Recall_%28memory%29http://en.wikipedia.org/wiki/Strategichttp://en.wikipedia.org/wiki/Working_memoryhttp://en.wikipedia.org/wiki/Working_memoryhttp://en.wikipedia.org/wiki/Impulse_controlhttp://en.wikipedia.org/wiki/Impulse_control


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Area- 18 Secondary visual cortex (V2) It is part of the occipital

cortex in the human brain.

is the second major area inthe visual cortex, and the

first region within the visual

association area.

It receives strong feedforward

connections from V1 (direct and viathe pulvinar) and sends strong

connections to V3, V4, and V5. It also

sends strong feedback connections toV1.

Area- 19 Associative visual cortex(V3,V4,V5)

It is part of the occipitallobe cortex in the human

brain.

Area 19 has been noted to receiveinputs from the retina via the superiorcolliculus and pulvinar, and may

contribute to the phenomenon of

blindsight.

In patients blindfrom a young age,the area has been

found to be

activated bysomatosensory

stimuli.

Area- 20 Inferior temporal gyrus It is placed below the

middle temporal gyrus, andis connected behind with the

inferior occipital gyrus; it

also extends around theinfero-lateral border on to

the inferior surface of the

temporal lobe, where it islimited by the inferior

sulcus.

This region believed to play a part in

high-level visual processing andrecognition memory.

It may also be involved in face

perception, and in the recognition of

numbers.

Area- 21 Middle temporal gyrus Middle temporal gyrus is a

gyrus in the brain on the

Temporal lobe. It is locatedbetween the superior

temporal gyrus and inferior

temporal gyrus

Its exact function is unknown, but it

has been connected with processes as

different as contemplating distance,recognition of known faces, and

accessing word meaning while

reading.

Area- 22 Superior temporal gyrus, ofwhich the caudal part is

usually considered to contain

the Wernicke's area

The superior temporal gyrusis one of three (sometimes

two) gyri in the temporal

lobe of the human brain,which is located laterally to

the head, situated somewhat

above the external ear. The

superior temporal gyrus isbounded by: the lateral

sulcus above; the superiortemporal sulcus (not always

present or visible) below; an

imaginary line drawn fromthe preoccipital notch to the

lateral sulcus posteriorly.

On the left side of the brain this areahelps with generation and

understanding of individual words. On

the right side of the brain it helps todiscriminate pitch and sound intensity,

both of which are necessary to

perceive melody and prosody.

Researchers believe this part of thebrain is active in processing language.

Area- 23 Ventral posterior cingulate

cortex

The posterior cingulate

cortex is the backmost partof the cingulate cortex, lying

behind the anterior cingulate

cortex. This is the upper partof the "limbic lobe". The

cingulate cortex is made up

of an area around themidline of the brain.

Surrounding areas include

the retrosplenial cortex andthe precuneus.

The posterior cingulate cortex forms a

central node in the "default mode"network of the brain. It has been

shown to communicate with various

brain networks simultaneously and isinvolved in various functions. Along

with the precuneus, the posterior

cingulate cortex has been implicatedas a neural substrate for human

awareness in numerous studies of both

the anesthesized and vegetative(coma) state. Imaging studies indicate

a prominent role for the posterior

cingulate cortex in pain and episodic

memory retrieval. It has also been

revealed that increased size of

posterior ventral cingulate cortex isrelated to the working memory

performance decline. Furthermore, the


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posterior cingulate may be involved in

the capacity to understand what otherpeople believe.

Area- 24 Ventral anterior cingulatecortex.

The anterior cingulatecortex (ACC) is the frontal

part of the cingulate cortex,

surrounding the frontal part

of the corpus callosum.

It appears to play a role in a widevariety of autonomic functions, such

as regulating blood pressure and heart

rate, as well as rational cognitive

functions, such as reward anticipation,decision-making, empathy, impulse

control,and emotion.

Area- 25 Subgenual area (part of theVentromedial prefrontal

cortex

It is an area in the cerebralcortex of the brain

This region is extremely rich inserotonin transporters and is

considered as a governor for a vast

network involving areas likehypothalamus and brain stem, which

influences changes in appetite and

sleep; the amygdala and insula, which

affect the mood and anxiety; the

hippocampus, which plays an

important role in memory formation;and some parts of the frontal cortex

responsible for self-esteem

Area- 26 Ectosplenial portion of the

retrosplenial region of the

cerebral cortex

It is theretrosplenial region

of thecerebral cortex.It is a

narrow band located in theisthmus of cingulate gyrus

adjacent to thefasciolargyrus internally. It is

bounded externally by the

granular retrolimbic are

Not known

Area- 28 Ventral entorhinal cortex Located in the medial

temporal lobe

Functioning as a hub in a widespread

network for memory and

navigation.HeEC-hippocampus

systemplays an important role in

autobiographical/declarative/episodicmemories and in particularspatial

memories includingmemory

formation, memory consolidation,andmemory optimization insleep.The

EC is also responsible for the pre-

processing (familiarity) of the inputsignals in the reflexnictitating

membrane response of classical trace

conditioning, the association ofimpulses from theeye and theear

occurs in the entorhinal cortex.

The EC is the main

interface between

the hippocampus

and neocortex.

Area- 29 Retrosplenial cingulate cortex In the human it is a narrow

band located in theisthmusof cingulate gyrus.(The

cingulate cortex is a part of

thebrain situated in themedial aspect of thecerebral

cortex.It includes the cortexof the cingulate gyrus,which lies immediately

above thecorpus callosum,

and the continuation of thisin thecingulate sulcus.The

cingulate cortex is usually

considered part of thelimbic

lobe.)

It receives inputs from the thalamus

and the neocortex, and projects to theentorhinal cortex via the cingulum. It

is an integral part of the limbic

system, which is involved withemotion formation and processing,

learning, and memory. Thecombination of these three functionsmakes the cingulate gyrus highly

influential in linking behavioral

outcomes to motivation (e.g. a certainaction induced a positive emotional

response, which results in learning). It

also plays a role in executive function

and respiratory control.

cingulate cortex

highly important indisorders such as

depressionand

schizophrenia.

Area- 30 Part of cingulate cortex In the human it is a narrow

band located in the isthmus
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of cingulate gyrus.

Area- 31 Dorsal Posterior cingulate

cortex

In the human it occupies

portions of the posterior

cingulate gyrus and medialaspect of the parietal lobe.

Approximate boundaries are

the cingulate sulcus dorsally

and the parieto-occipitalsulcus caudally. It partially

surrounds the subparietalsulcus, the ventral

continuation of the cingulate

sulcus in the parietal lobe.Cytoarchitecturally it is

bounded rostrally by the

ventral anterior cingulate

area 24, ventrally by the

ventral posterior cingulate

area 23, dorsally by thegigantopyramidal area 4 and

preparietal area 5 and

caudally by the superior

parietal area 7

Area- 32 Dorsal anterior cingulate

cortex

In the human it forms an

outer arc around the anterior

cingulate gyrus. Thecingulate sulcus defines

approximately its inner

boundary and the superiorrostral sulcus (H) its ventral

boundary; rostrally it

extends almost to themargin of the frontal lobe.

Cytoarchitecturally it is

bounded internally by theventral anterior cingulate

area 24, externally by

medial margins of theagranular frontal area 6,

intermediate frontal area 8,granular frontal area 9,frontopolar area 10, and

prefrontal area 11

Area- 33 Part of anterior cingulate

cortex

It is a narrow band located

in the anterior cingulate

gyrus adjacent to the

supracallosal gyrus in the

depth of the callosal sulcus,near the genu of the corpus

callosum.

Cytoarchitecturally it isbounded by the ventral

anterior cingulate area 24

and the supracallosal gyrus

Area- 34 Dorsal entorhinal cortex (onthe Parahippocampal gyrus)

Area 28

Area- 35 Perirhinal cortex (in the rhinalsulcus)

Perirhinal cortex is acortical region in the medial

temporal lobe

It receives highly-processed sensoryinformation from all sensory regions,

and is generally accepted to be an

important region for memory.

Area- 36 Ectorhinal area, now part of

the perirhinal cortex (in the

It is located in temporal

region of cerebral cortex.

With its medial boundary


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rhinal sulcus) corresponding

approximately to the rhinalsulcus it is located primarily

in the fusiform gyrus.

Cytoarchitecturally it isbounded laterally and

caudally by the inferior

temporal area 20, medially

by the perirhinal area 35 androstrally by the

temporopolar area 38

Area- 37 Fusiform gyrus The fusiform gyrus is part of

the temporal lobe andoccipital lobe

There is still some dispute over the

functionalities of this area, but there isrelative consensus on the following:

processing of color information face

and body recognition (see Fusiform

face area) word recognition (see

Visual word form area)within-

category identification

Area- 38 Temporopolar area (mostrostral part of the superior and

middle temporal gyri)

It is part of thetemporalcortex in thehuman brain.

BA 38 is at the anterior end

of the temporal lobe, known

as the temporal pole.

The functional significance of thisarea TG is not known, but it may bind

complex, highly processed perceptual

inputs to visceral emotional responses

is unique to humans

Area- 39 Angular gyrus, considered bysome to be part of Wernicke's

area

The angular gyrus is aregion of the brain in the

parietal lobe, that lies nearthe superior edge of the

temporal lobe, and

immediately posterior to thesupramarginal gyrus

It is involved in a number of processesrelated to language, number

processing and spatial cognition,memory retrieval, attention, and

theory of mind.

Area- 40 Supramarginal gyrus

considered by some to be part

of Wernicke's area

It is part of the parietal

cortex in the human brain.

The inferior part of BA40 is

in the area of thesupramarginal gyrus, which

lies at the posterior end of

the lateral fissure, in theinferior lateral part of the

parietal lobe.

It is probably involved with language

perception and processing, and lesions

in it may cause Receptive aphasia or

transcortical sensory aphasia

Area- 41, 42 Auditory cortex The auditory cortex is the

part of the cerebral cortexthat processes auditory

information in humans and

other vertebrates. It islocated bilaterally, roughly

at the upper sides of the

temporal lobesin humanson the superior temporal

plane, within thelateral

fissure and comprising partsofHeschl's gyrus and the

superior temporal gyrus,including planum polare andplanum temporale

A part of the auditory system, it

performs basic and higher functions inhearing.

Area- 43 Primary gustatory cortex Defined in the postcentral

region ofcerebral cortex.It

occupies thepostcentralgyrus and theprecentral

gyrusbetween the

ventrolateral extreme of the

central sulcus and the depth

of thelateral sulcus at the

Not known
http://en.wikipedia.org/wiki/Temporal_lobehttp://en.wikipedia.org/wiki/Cerebral_cortexhttp://en.wikipedia.org/wiki/Human_brainhttp://en.wikipedia.org/wiki/Temporal_lobehttp://en.wikipedia.org/wiki/Lateral_fissurehttp://en.wikipedia.org/wiki/Lateral_fissurehttp://en.wikipedia.org/wiki/Heschl%27s_gyrushttp://en.wikipedia.org/wiki/Superior_temporal_gyrushttp://en.wikipedia.org/wiki/Planum_temporalehttp://en.wikipedia.org/wiki/Cerebral_cortexhttp://en.wikipedia.org/wiki/Postcentral_gyrushttp://en.wikipedia.org/wiki/Postcentral_gyrushttp://en.wikipedia.org/wiki/Precentral_gyrushttp://en.wikipedia.org/wiki/Precentral_gyrushttp://en.wikipedia.org/wiki/Central_sulcushttp://en.wikipedia.org/wiki/Lateral_sulcushttp://en.wikipedia.org/wiki/Lateral_sulcushttp://en.wikipedia.org/wiki/Central_sulcushttp://en.wikipedia.org/wiki/Precentral_gyrushttp://en.wikipedia.org/wiki/Precentral_gyrushttp://en.wikipedia.org/wiki/Postcentral_gyrushttp://en.wikipedia.org/wiki/Postcentral_gyrushttp://en.wikipedia.org/wiki/Cerebral_cortexhttp://en.wikipedia.org/wiki/Planum_temporalehttp://en.wikipedia.org/wiki/Superior_temporal_gyrushttp://en.wikipedia.org/wiki/Heschl%27s_gyrushttp://en.wikipedia.org/wiki/Lateral_fissurehttp://en.wikipedia.org/wiki/Lateral_fissurehttp://en.wikipedia.org/wiki/Temporal_lobehttp://en.wikipedia.org/wiki/Human_brainhttp://en.wikipedia.org/wiki/Cerebral_cortexhttp://en.wikipedia.org/wiki/Temporal_lobe


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insula.

Area- 44 Pars opercularis, part of

Broca's area

n the human, this region

occupies the triangular part

of the inferior frontal gyrusand, surrounding the

anterior horizontal limb of

the lateral sulcus, a portion

of the orbital part of theinferior frontal gyrus.

Bounded caudally by theanterior ascending limb of

the lateral sulcus, it borders

on the insula in the depth ofthe lateral sulcus.

Recent neuroimaging studies show

BA44 involvement in selective

response suppression in go/no- gotasks and is therefore believed to play

an important role in the suppression of

response tendencies.Neuroimaging

studies also demonstrate that area 44is related to hand movements.

Area- 45 Pars triangularis Broca's area Brodmann area 45 (BA45),

is part of the frontal cortex

in the human brain. Situated

on the lateral surface,

inferior to BA9 and adjacent

to BA46.

Together with BA 44, it comprises

Broca's area, a region that is active in

semantic tasks, such as semantic

decision tasks (determining whether a

word represents an abstract or a

concrete entity) and generation tasks(generating a verb associated with a

noun).

Area- 46 Dorsolateral prefrontal cortex Brodmann area 46, or

BA46, is part of thefrontal

cortex in thehuman brain.Itis betweenBA10 andBA45.

BA46 is known as middle

frontal area 46. In the

human brain it occupies

approximately the middle

third of themiddle frontalgyrus and the most rostral

portion of theinferior

frontal gyrus.Brodmann

area 46 roughly corresponds

with thedorsolateral

prefrontal cortex (DLPFC)

The DLPFC plays a role in sustaining

attention and working memory.

Lesions to the DLPFC impair short-term memory and cause difficulty

inhibiting responses. Lesions may alsoeliminate much of the ability to make

judgements about what's relevant and

what's not as well as causing problemsin organization. The DLPFC has

recently been found to be involved in

exhibiting self-control.

Area- 47 pars orbitalis, part of the

inferior frontal gyrus

Brodmann area 47, or

BA47, is part of the frontalcortex in the human brain.

Curving from the lateral

surface of the frontal lobeinto the ventral (orbital)

frontal cortex. It is below

areas BA10 and BA45, andbeside BA11.

This area is also known as

orbital area 47. In the

human, on the orbital

surface it surrounds the

caudal portion of theorbital

sulcus (H) from which itextends laterally into the

orbital part ofinferior

frontal gyrus

BA47 has been implicated in the

processing of syntax in oral and signlanguages, and more recently in

musical syntax.

Area- 48 Retrosubicular area (a smallpart of the medial surface of

the temporal lobe)

In the human it is located onthe medial surface of the

temporal lobe.

Not known

Area- 49 Parasubicular area in a rodent
http://en.wikipedia.org/wiki/Insular_cortexhttp://en.wikipedia.org/wiki/Frontal_lobehttp://en.wikipedia.org/wiki/Cerebral_cortexhttp://en.wikipedia.org/wiki/Human_brainhttp://en.wikipedia.org/wiki/Brodmann_area_10http://en.wikipedia.org/wiki/Brodmann_area_45http://en.wikipedia.org/wiki/Middle_frontal_gyrushttp://en.wikipedia.org/wiki/Middle_frontal_gyrushttp://en.wikipedia.org/wiki/Inferior_frontal_gyrushttp://en.wikipedia.org/wiki/Inferior_frontal_gyrushttp://en.wikipedia.org/wiki/Dorsolateral_prefrontal_cortexhttp://en.wikipedia.org/wiki/Dorsolateral_prefrontal_cortexhttp://en.wikipedia.org/wiki/Orbital_sulcushttp://en.wikipedia.org/wiki/Orbital_sulcushttp://en.wikipedia.org/wiki/Inferior_frontal_gyrushttp://en.wikipedia.org/wiki/Inferior_frontal_gyrushttp://en.wikipedia.org/wiki/Inferior_frontal_gyrushttp://en.wikipedia.org/wiki/Inferior_frontal_gyrushttp://en.wikipedia.org/wiki/Orbital_sulcushttp://en.wikipedia.org/wiki/Orbital_sulcushttp://en.wikipedia.org/wiki/Dorsolateral_prefrontal_cortexhttp://en.wikipedia.org/wiki/Dorsolateral_prefrontal_cortexhttp://en.wikipedia.org/wiki/Inferior_frontal_gyrushttp://en.wikipedia.org/wiki/Inferior_frontal_gyrushttp://en.wikipedia.org/wiki/Middle_frontal_gyrushttp://en.wikipedia.org/wiki/Middle_frontal_gyrushttp://en.wikipedia.org/wiki/Brodmann_area_45http://en.wikipedia.org/wiki/Brodmann_area_10http://en.wikipedia.org/wiki/Human_brainhttp://en.wikipedia.org/wiki/Cerebral_cortexhttp://en.wikipedia.org/wiki/Frontal_lobehttp://en.wikipedia.org/wiki/Insular_cortex


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Area- 52 Parainsular area (at the

junction of the temporal lobeand the insula)

It is located in the bank of

the lateral sulcus on thedorsal surface of the

temporal lobe. Its medial

boundary correspondsapproximately to the

junction between the

temporal lobe and the

insula.

Not known


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