short course book iii

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Analysis and Function ofLarge-Scale Brain Networks

Organized by Olaf Sporns, PhD


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Short Course III

Analysis and Funcion o Larg-Scal Brain NworksOla Sporns, PhD


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Please cite articles using the model:

[AUTHORS LAST NAME, AUTHORS FIRST & MIDDLE INITIALS] (2010)[CHAPTER TITLE] In: Analysis and Function o Large-Scale Brain Networks. (Sporns O, ed)

pp. [xx-xx]. Washington, DC: Society or Neuroscience.

All articles and their graphics are under the copyright o their respective authors.

Cover graphics and design 2010 Society or Neuroscience.


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Table o Contents

Introduction

Networks o the Brain: Quantitative Analysis and ModelingOlaf Sporns, PhD . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7

Moving Between Functional and Eective ConnectivityAnthony R. McIntosh, PhD . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15

Relating Functional Measures to Network Descriptions in the Study o Brain Systems

Steven E. Petersen, PhD, Steven M. Nelson, PhD,Kelly Anne Barnes, PhD, and Bradley L. Schlaggar, MD, PhD . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25

Relating Variations in Network Connectivity to Cognitive Function

Michelle Hampson, PhD . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35

Large-Scale Brain Networks in Cognition: Emerging Principles

Vinod Menon, PhD . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43

Clinical Applications o Complex Network AnalysisDanielle S. Bassett, PhD . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55


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Introduction

Neurons orm complex networks whose anatomical architecture and integrated physiological activityare essential or brain unction. Modern recording and imaging technology, combined with quantitative

approaches to network analysis, are beginning to reveal characteristic structural and unctionalpatterns in brain networks. At the large scale o brain regions and interregional pathways, recent

studies have begun to demonstrate how structural eatures o brain networks shape their unctionaldynamics, and how dierent classes o unctional networks become engaged in spontaneous and task-

evoked neural activity. Emerging evidence indicates a relationship between network organization andindividual cognitive perormance, as well as an important role or network disturbances in nervoussystem dysunction.

The architecture o large-scale brain networks thus oers new insights into how brain unction emerges

rom the activity o distributed brain systems. Which aspects o network architecture are critical orecient and coordinated neural processing? Which eatures o networks are most strongly associated

with disruptions o behavior and cognition?

This short course will provide neuroscientists with an overview o current concepts, methods, and

analysis tools in this emerging eld. Special emphasis will be placed on how the organization olarge-scale networks may inorm our understanding o behavior and cognition in the healthy and the

diseased brain.

Course organizer: Ola Sporns, PhD, Department o Psychological and Brain Sciences, IndianaUniversity. Faculty: Danielle S. Bassett, PhD, Department o Physics, University o Caliornia, SantaBarbara; Michelle Hampson, PhD, Department o Diagnostic Radiology, Yale University; Anthony R.

McIntosh, PhD, Rotman Research Institute, Baycrest Centre or Geriatric Care, University oToronto; Vinod Menon, PhD, Department o Psychiatry and Behavioral Sciences, Department o

Neurology and Neurological Sciences, Program in Neuroscience, Stanord University Medical School;

and Steven E. Petersen, PhD, Department o Neurology, Washington University in St. Louis Schoolo Medicine.


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Department o Psychological and Brain SciencesIndiana University

Bloomington, Indiana

Networks o the Brain:

Quantitative Analysis and ModelingOla Sporns, PhD


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experimental system employedin a given empirical study. A

ew basic steps are common tomost approaches (Fig. 1). First,

network nodes and edges mustbe dened. This is an extremely

important step in any graph-based analysis o a brainnetwork because all statistical

analyses depend on the way thebiological system is partitioned

into a set o nodes and edges.At the level o large-scale

brain systems, node denitioninvolves partitioning the braininto coherent regions on the

basis o histological or imagingdata. Objective, data-driven

parcellation methods are anactive area o investigation

and still ace a number oserious challenges. Signicantprogress has been made by

using clustering techniquesthat assess the similarity prole

o structural (Johansen-Berg etal., 2004) or unctional (Cohen

et al., 2008) connections toderive boundaries betweencoherent brain regions.

Once nodes are dened, the denition o edgestypically involves estimating pairwise associationsbetween nodes. Structural networks are constructed

on the basis o measured ber tracts or pathways,whereas unctional and eective edges are otenbased on statistical associations estimated rom time

series data. A wealth o possible measures is availableor representing unctional coupling. While most

studies o unctional connectivity still utilize simplemeasures such as correlation or coherence, more

complex strategies involving partial correlationsor estimates o directed (causal) interactions arebeginning to gain ground.

Once a brain network has been constructed, it can beanalyzed with quantitative tools rom graph theory.Many such tools and measures are available, and

at the time o writing only a small subset has beenadapted and applied in the context o neuroscience.

Beore graph-theoretical approaches become morewidely used, several important methodological issues

need to be addressed. For example, recent studieshave ocused on the impact parcellation schemesand spatial scales make on the robustness o graph

metrics (Zalesky et al., 2009) and on their test-retestreproducibility (Deuker et al., 2009). So ar, these

methodological studies suggest that graph metricsreport key eatures o network organization with highreliability and robustness.

In the remainder o the chapter, we will distinguish

three broad classes o graph metrics that capturedistinct aspects o brain network organization:

The existence of specialized communities ormodules (unctional segregation);

The pattern of global interactions between

communities (unctional integration); and The functional impact of individual network

elements (unctional infuence).

Functional Segregation: Clusteringand ModularityO particular importance or a neural nodes processingcharacteristics and unctional contribution are its

interactions with its immediate neighbors. Theseare dened as the collection o nodes to which it is

directly connected. Numerous studies o large-scalebrain networks have shown that neural regionsare arranged in clusters or communities, with

individual nodes communicating in densely andmutually interconnected neighborhoods.

Figur 1. Recording structural and unctional brain networks. The diagram illustratesour major steps: denition o network nodes (step 1), estimation o a suitable associa-

tion measure (step 2), generation o an association matrix (step 3), and graph theoretical

analysis o the resulting network (step 4). Modied with permission rom Bullmore and

Sporns (2009), their Figure 1.


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The clustering coecient (Watts and Strogatz,1998) is one o the most elementary measures or

capturing the degree to which nodes in a networkorm local communities. Clustering o a node is highi the nodes neighbors are also neighbors o each

other. In neural terms, a region has a high clusteringcoecient i the regions to which it is connected

are also connected to each other. Averaged overthe entire network, the clustering coecient reports

the degree to which the network as a whole consistso nodes that share local connectivity. Becauseclustering varies greatly depending on the size and

density o any given network, it is important toconduct statistical comparisons within populations

o appropriately constructed random networks.

In many (but not all) cases, high clustering indicatesthe existence o multiple segregated communities o

nodes. Such communities or modules can be identiedby using algorithms that search or partitioningschemes. These schemes optimally subdivide the

network, given a modularity measure: or example,one that is based on the relative density o within-

module to between-module connections (Newman,2006). Numerous studies o structural and unctionalbrain networks have identied modules in large-scale

brain networks whose placement and boundariesoten coincide with either known cognitive networks

(Dosenbach et al., 2008) or unctional subdivisionso the human brain. By extending analytic

approaches to modularity, investigators have recently

demonstrated that modules in brain networks arearranged hierarchically (Meunier et al., 2009). Thisarchitectural eature promotes economical physicalembedding (Bassett et al., 2010) and may have

signicant implications or brain dynamics (Kaiser etal., 2007).

Functional Integration: Path Lengthand EciencyWhile clustering and modularity provide inormationabout the networks local community structure, a

complementary set o measures captures the networks

capacity to engage in more global interactions thatbind together and integrate its dynamic activity.Several o these measures are based on paths:

specically, the lengths o the shortest paths linkingpairs o nodes. Generally, shorter paths are thoughtto be more eective in passing inormation. Thus,

the average path length or a network can providean indication o its capacity or global inormation

exchange. A related measure (essentially an inverseo the average path length but less disrupted by

the presence o disconnected nodes) is the globaleciency (Latora and Marchiori, 2001). As is the

case or clustering, path length should be quantiedin relation to a null population o random graphs,

controlling or the size and density o the network.

Because o the importance o communication and

inormation fow in large-scale brain networks,these measures o unctional integration have airly

straightorward neurobiological implications. In anetwork with high eciency, short communication

paths can be identied between most or all pairs onodes. Since clustering and path length are capturingcomplementary aspects o a networks unctional

organization, they are oten measured in conjunction.Also, these measures can be combined to assess the

degree to which the network balances the existenceo local and segregated communities with global,

systemwide integration. High clustering and ashort path length are the dening characteristics

o a universal class o network architectures oundin social, technological, and biological systems,including the brain (Sporns and Zwi, 2004). These

are reerred to as small-world networks (Watts andStrogatz, 1998). The modular small-world networks

encountered in the brain not only allow or ecientinormation processing but are economical with

respect to their wiring and metabolic cost (Bassettand Bullmore, 2006).

Functional Infuence: Centralityand Hubs

Real-world networks deviate rom randomness;in many cases, this entails specialization among

nodes. Dierent classes o network elements canbe distinguished by the way they participate in thenetwork, i.e., by the way they are connected to the rest

o the system. An important distinction can be madebased on their infuence: that is, their potential

impact on the system as a whole and their capacityto transer or process inormation. Highly infuential

nodes are oten reerred to as hubs, and identiyingsuch hubs in brain networks can help one to mapregions o the brain that are critical or coordinating

unctional interactions and or generating coherent

system states. Hubs can be identied either on thebasis o the number o interactions they engage inor by the degree to which they participate in short

paths across the network. The latter measure,called betweenness centrality (Freeman, 1977), isparticularly salient or structural networks, and it can

be computed or edges as well.

Once an optimal modularity partition has beenidentied (Fig. 2A), the diversity o a nodes

connections with respect to individual modules canbe assessed in the orm o a participation coecient


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(Guimer et al., 2007) (Fig. 2B). O particular

interest are highly connected nodes with a highparticipation coecient: the so-called connectorhubs. These maintain a diverse set o between-

module connections and thus acilitate globalintermodule communication. On the other hand,

high-degree nodes with ew or less diverse between-module connections have a low participation index.

Consequently, these so-called provincial hubsparticipate mostly in interactions within their

own module.

Hubs are o special interest in large-scale brain

networks. Their high degree o centrality and, inthe case o connectors, high level o participation

in multiple unctional communities predict thatthey will play a crucial role in integrative processes

and inormation fow. The association o at leastsome hubs in the human brain with regions thatengage in a high rate o metabolism (Hagmann et

al., 2008), as well as with neuropathological changesin degenerative brain disease (Buckner et al., 2009),

suggests intriguing hypotheses that may link brainnetwork topology to unction. Furthermore, the

assessment o centrality or infuence is a crucialcomponent or predicting unctional disturbancesthat will occur upon node or edge deletion. In a

neurobiological context, the loss o more highlycentral nodes or edges owing to trauma or disease

should result in more widespread disruptions o

inormation fow and dynamics in the remainingbrain (Alstott et al., 2009).

Future ApplicationsGraph methods and their application to large-scalenetworks have begun to provide signicant insights

into the organization and unction o the humanbrain. The remaining contributions to this short

course illuminate various approaches, ranging romanatomical networks to unctional connectivity inthe resting brain, task-evoked activity, individual

dierences, and clinical populations. As theapplications o graph theory continue to expand,

important methodological and interpretationalquestions will need to be addressed. For example,

objective methods or comparing networks withinindividual subjects or across subject populations willbe needed to acilitate longitudinal studies o brain

development and disease progression.

Many aspects o brain networks await utureinvestigation. Network approaches have alreadyrevealed signicant between-subject variability in

structural and unctional connectivity, so the roleo variations in networks or variable cognition

and behavior will likely be an intense area o utureresearch. Other promising avenues will lead to theareas o translational neuroscience and in discovering

relations between genetic and brain networks.

Figur 2. Modularity and classication o hubs. The schematic diagram A shows three modules (gray circles) linked by provincial

(green) and connector hubs (red). Provincial hubs link nodes within a single module, while connector hubs link modules to eachother. The diagram B shows a visualization o the community structure o the unctional connectivity estimated rom simulated

blood oxygenation leveldependent (BOLD) responses o 47 regions o the macaque cortex (Honey et al., 2007). Two modules

consisting mostly o visual and somatomotor regions are linked by multiple connector hubs located predominantly in parietal and

rontal cortex.

A B


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ReerencesAlstott J, Breakspear M, Hagmann P, Cammoun L,

Sporns O (2009) Modeling the impact o lesions inthe human brain. PLoS Comput Biol 5:e1000408.

Barabsi AL (2009) Scale-ree networks: a decade

and beyond. Science 325:412-413.

Barabsi AL, Oltvai ZN (2004) Network biology:understanding the cells unctional organization.

Nat Rev Genetics 5:101-111.

Bassett DS, Bullmore ET (2006) Small world brain

networks. Neuroscientist 12:512-523.

Bassett DS, Greeneld DL, Meyer-Lindenberg A,

Weinberger DR, Moore SW, Bullmore ET (2010)Ecient physical embedding o topographically

complex inormation processing networks inbrains and computer circuits. PLoS Comp Biol

6:e1000748.

Boccaletti S, Latora V, Moreno Y, Chavez M,

Hwang DU (2006) Complex networks: Structureand dynamics. Phys Reports 424:175-308.

Buckner RL, Sepulcre J, Talukdar T, Krienen FM,Liu H, Hedden T, Andrews-Hanna JR,Sperling RA, Johnson KA (2009) Cortical hubs

revealed by intrinsic unctional connectivity:mapping, assessment o stability, and relation to

Alzheimers disease. J Neurosci 29:1860-1873.

Bullmore E, Sporns O (2009) Complex brain

networks: graph theoretical analysis o structural

and unctional systems. Nat Rev Neurosci10:186-198.

Cohen AL, Fair DA, Dosenbach NUF, Miezin FM,

Dierker D, Van Essen DC, Schlaggar BL, PetersenSE (2008) Dening unctional areas in individual

human brains using resting state unctionalconnectivity MRI. Neuroimage 41:45-57.

Deuker L, Bullmore ET, Smith M, Christensen S,Nathan PJ, Rockstroh B, Bassett DS (2009)Reproducibility o graph metrics o human brain

unctional networks. Neuroimage 47:1460-1468.

Dosenbach NU, Fair DA, Cohen AL, Schlaggar BL,

Petersen SE (2008) A dual-networks architectureo top-down control. Trends Cogn Sci 12:99-105.

Freeman LC (1977) A set o measures o centralitybased on betweenness. Sociometry 40:35-41.

Guimer R, Sales-Pardo M, Amaral LA (2007)Classes o complex networks dened by role-to-

role connectivity proles. Nat Phys 3:63-69.

Hagmann P, Cammoun L, Gigandet X, Meuli R,

Honey CJ, Wedeen VJ, Sporns O (2008) Mappingthe structural core o human cerebral cortex. PLoS

Biol 6:e159.

Horwitz B (2003) The elusive concept o brainconnectivity. Neuroimage 19:466-470.

Jirsa VK, McIntosh AR (2007) Handbook o brainconnectivity. New York: Springer.

Johansen-Berg H, Behrens TE, Robson MD,

Drobnjak I, Rushworth MF, Brady JM, Smith SM,Higham DJ, Matthews PM (2004) Changes inconnectivity proles dene unctionally distinct

regions in human medial rontal cortex. Proc NatlAcad Sci USA 101:1333513340.

Kaiser M, Grner M, Hilgetag CC (2007) Criticalityo spreading dynamics in hierarchical clusternetworks without inhibition. New J Phys 9:110.

Latora V, Marchiori M (2001) Ecient behavior osmall-world networks. Phys Rev Lett 87:198701.

Meunier D, Lambiotte R, Fornito A, Ersche KD,

Bullmore ET (2009) Hierarchical modularityin human brain unctional networks. FrontNeuroinormatics 3:37.

Newman MEJ (2006) Modularity and communitystructure in networks. Proc Natl Acad Sci USA

103:8577-8582.

Reijneveld JC, Ponten SC, Berendse HW,

Stam CJ (2007) The application o graphtheoretical analysis to complex networks in the

brain. Clin Neurophysiol 118:2317-2331.

Rubinov M, Sporns O (2010) Complex network

measures o brain connectivity: uses and

interpretations. Neuroimage 52:1059-1069.

Sporns O (2010) Networks o the brain. Cambridge,MA: MIT Press.

Sporns O, Zwi J (2004) The small world o thecerebral cortex. Neuroinormatics 2:145-162.

Sporns O, Tononi G, Ktter R (2005) The humanconnectome: a structural description o the human

brain. PLoS Comput Biol 1:245-251.

Strogatz SH (2001) Exploring complex networks.

Nature 410:268-277.

Watts DJ (2004) The new science o networks.

Annu Rev Sociol 30:243-270.

Watts DJ, Strogatz SH (1998) Collective dynamics

o small-world networks. Nature 393:440-442.

Zalesky A, Fornito A, Harding IH, Cocchi L, Ycel M,

(2009) Whole-brain anatomical networks: Doesthe choice o nodes matter? Neuroimage 50:970-

983.


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Rotman Research InstituteBaycrest Centre or Geriatric Care

University o TorontoOntario, Canada

Moving Between Functional and

Eective ConnectivityAnhony R. McInosh, PhD


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IntroductionOne o the current challenges in neuroscience is to

understand how brain operations give rise to mentalphenomena ranging rom sensation and perception

to memory and attention. We are getting to know

a great deal about how the brain unctions in basicsensory and motor systems. For higher mentalunctions, however, a long scientic battle has beenraging as to whether such unctions are localizable.

A dominant assumption in neuroscience is thatcertain parts o the brain play unique roles in mental

unction. This idea o one region/one unction comesrom early studies that showed some remarkable

cognitive decits due to lesions in specic partso the brain. Up until the last 15 to 20 years, thetools available to neuroscientists have allowed them

to examine only small parts o the brain at a time;their ndings, although limited, have reinorced the

notion o discrete unctions in specic brain regions.

Modern neuroimaging tools allow us to measure howthe entire brain reacts as people perorm dierent

mental operations. We are nding that many morebrain areas light up when someone pays attention,thinks, and remembers than we would have expected

based on the results rom brain lesion studies.However, many researchers in the eld, who continue

to ocus on one or two critical brain regions, overlookthis new inormation.

The brain is made up o individual elements: rom cells

to neural ensembles. These elements are connected,so their individual actions can be combined throughtheir interactions. The combined responses o small

groups o cells give interacting brain areas a richresponse repertoire, ranging rom simple sensation

to consciousness and reason. When neuroimagingdata are examined in terms o brain interactions,it is observed that many regions cooperate in our

thought processes. Emerging neurobiological theoriesemphasize the combined actions o interacting brain

elements (cells to ensembles to regions) as the linkbetween the brain and human mental unction

(McIntosh, 2000a,b).

From a network perspective, anything that aects

the integrity o a specic brain region will necessarilyinfuence the operation o the entire network

or networks in which this region participates.Behavioral decits ollowing damage, or arising

rom disease processes, could thus refect either theabnormal operation o a damaged network, or theormation o a completely dierent network with

a new behavioral repertoire. Thus, much could belearned about brain dysunction (as well as normal

unction) by examining network operations in

subjects where mental unctions are compromised bydamage or disease.

I normal brain unction and dysunction result romthe action o distributed networks, then analytic

approaches tuned to such dynamics would bestcapture these actions. What ollows reviews some o

the basic methods that have used or network analysisand presents the underlying theory or applying and

developing a new perspective that serves to unitethe understanding o brain unction and dysunctionwithin one ramework.

Theoretical Basis and Tools orNetwork Analysis

Network analysis, as applied to neuroimaging, can

be considered a collection o analytic methods:e.g., interregional correlations/covariances or the

corresponding measure in the requency domain,such as coherence. These methods attempt tomeasure the interdependency among brain areas

during dierent cognitive states. The drivingassumption behind the use o these approaches is

that the correlations/covariances o activity measureneural interactions. Neural interactions reer, in a

general sense, to infuences that dierent elements inthe nervous system have on each other via synapticcommunication; the term elements reers to any

constituent o the nervous system, either a singleneuron or collections thereo.

Traditional approaches to understanding neural

interactions have ocused on studying systematicvariation in activity with some manipulatedparameter. However, activity changes in one neural

element usually result rom a change in the infuenceo other connected elements; thus, ocusing on

activity in one area will cause one to miss the changein aerent infuence. Furthermore, it is logically

possible or the infuences on an element to changewithout an appreciable change in measured activity.The simplest example is where an aerent infuence

switches rom one source to another, without a

change in the strength o the infuence. For example,in the eed-orward network depicted in Figure 1,region C may show similar activity patterns when

infuence rom either A or B is strong. Thereore,monitoring regional activity alone would not be ableto dierentiate the source o the eects, but measures

o the relation o activity between elements (e.g.,path v versus w) would be able to.

The measurement o neural interactions in

neuroimaging has developed under two generalapproaches. The rst emphasizes pairwise


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(ERP) studies. Within-subjects analysis assesses thedirect relation between regions, while across-subjects

analysis indicates the stability o that relation.These are complementary, not contradictory, pieces

o inormation.

For illustration, say we chose ten people o varyingheights and weight and asked them to pull on apotentiometer by fexing their arm (an arm curl).

I you measured muscle activity in the arm o eachsubject, say through blood fow, and correlated them,

you would probably nd a strong correlation withthe biceps and brachialis muscles. Although each

person would dier in the amount o blood fowto the muscles, rom the correlation based on thisvariance, you would conclude that the muscles on

the ventral surace o the arm have something todo with fexion. I, instead, you measured muscle

activity in a single subject with a progressive increasein the resistance to arm fexion, you would nd a

correlation between muscle activity in the ventralpart o the arm. Replicating the measurement byrunning dierent subjects would lead you to the

same conclusion you had reached by using thebetween-subjects covariance. The point here is that

computing covariances between or within subjectscan lead to complementary conclusions, so long as

there are adequate experimental controls and thestatistical analysis ensures the answers are reliable.

It should not be taken as a suggestion that all the

network analysis steps listed above must be carriedacross to every data set. Obviously, the choiceo analysis (unctional connectivity or eective

connectivity) depends on the particular questionone has to ask o the data. Functional connectivity

analyses are likely satisactory when the goal is inthe exploratory/explanatory mode. For example, i apeculiar activation pattern were noted in one group,

assessing the unctional connectivity o that regionwith the rest o the brain could help explain the

peculiarity in terms o a dierence in the pattern ointeractions in that group, relative to controls. On

the other hand, i the question were phrased in terms

o directed infuences, then analysis o eectiveconnectivity would be needed. For example, i

the question was whether top-down infuencesrom prerontal to temporal cortices vary between

groups, an analysis o eective connectivity mustbe perormed to distinguish top-down rom bottom-

up eects.

Taxonomy o TechniquesOne has only to casually fip through an issue o

NeuroImage or Human Brain Mappingto realize thatmethodological developments in the estimation o

unctional and eective connectivity are exploding.The sections below briefy characterize the major

methods used or estimating connectivity and listtheir advantages and disadvantages. This is by no

means an exhaustive list.

Funcional connciviyRegional correlation

This is perhaps the simplest and most oten used

method. Pairwise correlations o regions o interest, orvoxels, provide a snapshot o unctional connectivitypatterns (Horwitz et al., 1984, 1991). This method

has the advantage o simplicity and uses a minimalnumber o assumptions beyond linearity. Where the

technique becomes problematic is when the numbero correlations grows and one must correct or

multiple statistical tests (the same problem as withother univariate measures). Moreover, as the number

o correlations grows, easily summarizing the patternsbecomes dicult. It is at this point that multivariatemethods may be helpul (see below).

Psychophysiological interactions

Linear regression methods sometimes appear tolie in a gray area between unctional and eectiveconnectivity. For example, the method to estimate

psychophysiological interactions (PPIs) (Fristonet al., 1997) in the statistical parametric mapping

(SPM) package is used to assess task-dependentchanges in the degree that one region (Y) predicts or

explains the activity o another (X) (McIntosh and

Gonzalez-Lima, 1994). However, the PPI approachprovides the same statistical result as would be

obtained i the roles oX and Ywere reversed. Thus,the PPI method is most similar to an estimate o

unctional connectivity.

Principal component analysis

A tried-and-true method, principal componentanalysis (PCA) has been applied to a number o

neuroimaging data sets to summarize complexpatterns o interregional correlations. It is a helpul

means to ollow rom the calculation o pairwisecorrelations. The PCA solutions are always unique

or a given data set (compared with those oindependent component analysis [ICA]), and the

calculation o the principal components is relativelyast. The main drawbacks include: Orthogonality of components, which may impose

artiactual groupings within a component. Thiseect can be alleviated somewhat by orthogonal or

oblique rotation; and The decomposition depends on the rank of matrix.

I there are more regions and observations, thematrix will be rank-decient, which can obscurethe true grouping o regions.


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Independent component analysis

ICA is a newer method than PCA and has been

applied extensively to MRI and EEG data. It wasoriginally a denoising method but has since beenshown to be quite powerul or extracting resting state

networks in MRI data (using a variation o the usualICA: tensor ICA) (Beckmann and Smith, 2005).

ICA has the advantage over PCA o not assumingorthogonality but rather maximal independence. In

this case, it has the capacity to separate artiactualcomponents rom those o interest. This capacitydepends, however, on the favor o ICA used and the

nature o the artiact. The drawbacks o ICA include:Nonunique solutions without additional constraints;

and Computationally expensive for large data sets.

Partial least squares

The partial least squares (PLS) method has been usedin neuroimaging or more than a decade and has beenapplied to PET, MRI, and EEG (McIntosh et al.,

1996a; McIntosh and Lobaugh, 2004). It is relatedto canonical correlation analysis in that it relates

the neuroimaging data to the experimental design(e.g., design contrasts); perormance measures; or, or

unctional connectivity, one or more voxels. In thelatter case, it can be considered to be a multivariateextension o PPI. PLS has the fexibility to work on

combinations o design, behavior, and voxels and hasbeen extended to merge multiple imaging data sets

(Martinez-Montes et al., 2004). It has the advantage

o creating a fexible ramework or direct testingo statistical dependency in neuroimaging data. Itsmain drawbacks are as ollows: Orthogonal extraction of components like PCA

may obscure the true dependencies. To oset thiseect, the extraction can be done with ICA (Lin

et al., 2003); Interpretation can be complicated in complex

designs; and Statistical assessment through resampling is

computationally expensive.

eciv connciviyStructural equation modelingStructural equation modeling (SEM) is a multivariatelinear regression tool and has been used primarily or

PET and MRI data (McIntosh and Gonzalez-Lima,1994; Buchel and Friston, 1997), although its use

has been extended to EEG data (Astol et al., 2004,2005). Its primary use has been to identiy changesin eective connectivity between tasks or groups

within a dened anatomical network (Protzner andMcIntosh, 2006). It has also been used to identiy

likely patterns o eective connectivity in a given

data set (Bullmore et al., 2000). It has the advantageo allowing ast and robust computations and can be

used or rather complicated models (McIntosh et al.,1996b); more recently, it was validated or use withneuroimaging data based on large-scale simulations

(Kim and Horwitz, 2009; Marrelec et al., 2009).It has a long history and, thus, several sotware

packages and numerous algorithmic variations areavailable. For its application to neuroimaging, the

main drawbacks are as ollows: Absolute assessment of model t is very dependent

on sample size;

It needs to prespecify connection directions; and It cannot deal with fully reciprocal models.

Granger causality

Granger causality (GC) is a general methodologicalapproach or analyzing dependencies in time series.

Its most common implementation comes in the ormo autoregressive modeling (Goebel et al., 2003).There are also variations that operate in the spectral

domain (Kaminski et al., 2001), although they havenot been used in MRI. Methods that generally all

under this label have the advantage o workingdirectly with the time series, allowing inerences

on directionality without needing to prespeciy thedirection (c. SEM and DCM). Its main drawbacksare as ollows:

Most implementations are pairwise. Multivariateextensions are possible (Deshpande et al., 2009),

but with many regions, the solutions may become

unstable; For fMRI, GC requires relatively short repetition

time (TR) to get a robust time series; and There has been a recent observation that GC may

provide spurious estimates o directional eects inMRI data (David et al., 2008). However, a series o

papers that will appear in the journal NeuroImagewill address this observation (Roebroeck, et al.,

in press).

Dynamic causal modeling

Unlike SEM and GC, DCM was designed specicallyor neuroimaging data and has been applied to MRI

and EEG (Friston et al., 2003; Kiebel et al., 2009).Like SEM, DCM has also received some validation

through large-scale simulations (Lee et al., 2006).DCM uses a generative model o the measured signalto iner its neural sources. The eective connectivity

estimation then proceeds based on the neural sourceactivity rather than the measured signal (e.g., blood

oxygen leveldependent [BOLD] or EEG). The modelrst estimates the intrinsic connections between

sources and then the changes in the connections thatcome about through external perturbation (usuallythe experimental design). This can be thought o


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McIntosh AR, Gonzalez-Lima F (1994) Structuralequation modeling and its application to network

analysis in unctional brain imaging. Hum BrainMapp 2:2-22.

McIntosh AR, Grady CL, Ungerleider LG, Haxby JV,

Rapoport SI, Horwitz B (1994) Network analysiso cortical visual pathways mapped with PET. J

Neurosci 14:655-666.

McIntosh AR, Bookstein FL, Haxby JV, Grady CL(1996a) Spatial pattern analysis o unctional brain

images using Partial Least Squares. Neuroimage3:143-157.

McIntosh AR, Grady CL, Haxby JV, Ungerleider LG,Horwitz B (1996b) Changes in limbic and

prerontal unctional interactions in a workingmemory task or aces. Cereb Cortex 6:571-584.

McIntosh AR, Lobaugh NJ (2004) Partial least

squares analysis o neuroimaging data: applicationsand advances. Neuroimage 23:S250-S263.

Mechelli A, Price CJ, Noppeney U, Friston KJ (2003)A dynamic causal modeling study on category

eects: bottom-up or top-down mediation? J CognNeurosci 15:925-934.

Protzner AB, McIntosh AR (2006) Testing eectiveconnectivity changes with structural equation

modeling: what does a bad model tell us? HumBrain Mapp 27:935-947.

Roebroeck A, Formisano E, Goebel R. Reply to

Friston and David: Ater comments on: Theidentication o interacting networks in thebrain using MRI: Model selection, causality anddeconvolution. Neuroimage, in press.

Seminowicz DA, Mayberg HS, McIntosh AR,

Goldapple K, Kennedy S, Segal Z, Ra-Tari S(2004) Limbic-rontal circuitry in major depression:a path modeling metanalysis. Neuroimage

22:409-418.

Stam CJ, Jones BF, Manshanden I, van Cappellen van

Walsum AM, Montez T, Verbunt JP, de Munck JC,van Dijk BW, Berendse HW, Scheltens P (2006)

Magnetoencephalographic evaluation o resting-state unctional connectivity in Alzheimers

disease. Neuroimage 32:1335-1344.

Stephan KE (2004) On the role o general system

theory or unctional neuroimaging. J Anat205:443-470.

Strother SC, Anderson JR, Schaper KA, Sidtis JJ,Liow J-S, Woods RP, Rottenberg DA (1995a)

Principal components analysis and the scaledsubprole model compared to intersubject averaged

and statistical parametric mapping: I. Functionalconnectivity o the human motor system studied

with [15O]water PET. J Cereb Blood Flow Metab15:738-753.

Strother SC, Kanno I, Rottenberg DA (1995b)Principal components analysis, variancepartitioning, and unctional connectivity.

J Cereb Blood Flow Metab 15:353-360.

Welchew DE, Honey GD, Sharma T, Robbins TW,

Bullmore ET (2002) Multidimensional scalingo integrated neurocognitive unction and

schizophrenia as a disconnexion disorder.Neuroimage 17:1227-1239.


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Department o NeurologyWashington University in St. Louis School o Medicine

St. Louis, Missouri

Relating Functional Measures to NetworkDescriptions in the Study o Brain Systems

Svn e. Prsn, PhD, Svn M. Nlson, PhD,Klly Ann Barns, PhD, and Bradly L. Schlaggar, MD, PhD


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IntroductionUnlike in other networks, such as those seen in social

systems, the components o brain networks, i.e., theirnodes and edges, are not easily dened. As mentioned

in the chapter by Sporns, node and edge denition

is critical to understanding unctional networks inthe brain. The teaching points o this chapter relateto an integration o evoked unctional responses, asdetermined rom task-related unctional magnetic

resonance imaging (MRI), and resting correlationsbetween brain regions, as dened with resting state

unctional connectivity MRI (rs-cMRI). Byintegrating the two kinds o data across a number o

analyses, we make the ollowing arguments: That candidate node denitions are aided greatly

by an integrated approach; and

That the outcome of the more calibrated nodedenition can provide deeper insights into

unctional dierentiation and interpretation.

To this end, a set o analyses ocused on the letlateral parietal cortex (LLPC) will be presented.

These studies will include several analyses o task-evoked MRI activation studies and rs-cMRIstudies. This combined approach results in a sixold

parcellation o LLPC based on several actors: thepresence (or absence) o memory retrievalrelated

activity, dissociations in the prole o task-evokedtime courses, and membership in large-scale resting

brain networks. This parcellation strategy shouldserve as a roadmap or uture investigations aimed

at understanding LLPC unction. In addition, thisanalysis strategy can be applied to other extents othe cerebral cortex.

Why the LLPC?In humans, parietal cortex has traditionally been

linked to processing mechanisms involving attention(Corbetta et al., 1998; Rushworth et al., 2001;Corbetta and Shulman, 2002; Yantis et al., 2002;

Dosenbach et al., 2006, 2007). Other accounts oparietal cortex unction, particularly ocused on the

let hemisphere, have examined its role in reading

(Turkeltaub et al., 2002), as well as numerosityjudgments and arithmetic (Gbel and Rushworth,2004; Hubbard et al., 2005).

More recently, a surge in research has been devotedto understanding the contributions LLPC makes

to memory retrieval (Wagner et al., 2005). Inparticular, a great deal o research has been aimed

at understanding how humans distinguish betweenpreviously experienced inormation (old) and thatwhich is novel (new), a phenomenon known as

the retrieval success eect (Henson et al., 2000;

Konishi et al., 2000; McDermott et al., 2000; Wheelerand Buckner, 2003). The most common regions

showing retrieval success eects are ound in thelateral parietal cortex (Simons, 2008), and althoughthis dierential activation is typically bilateral, the

most robust eects include a large expanse o LLPC(McDermott et al., 2009). A secondary nding across

these studies is the presence o a dorsalventraldistinction in LLPC. This distinction appears to

dissociate dorsal regions near intraparietal sulcus(IPS) involved in amiliarity judgments rom moreventral regions near the angular gyrus (AG) that

are involved in recollection (Henson et al., 1999;Wheeler and Buckner, 2004).

Studies across domains have yielded a multitude

o processing descriptions, suggesting that distinctregions in parietal cortex might subserve unique

unctional contributions. The analyses presentedhere attempt to provide a parcellation schemebased on convergence across multiple data types.

In doing so, we highlight the utility o a large-scalenetwork perspective.

Preliminary Parsing o LLPC RegionUsing rs-cMRI Boundary MappingThe rst step in parsing the LLPC region is to use therecently developed technique o rs-cMRI boundary

mapping to identiy correlationally distinct regionsin LLPC. rs-cMRI boundary mapping is based

on the observation that rs-cMRI can dissociateregions within the cortex using edge-detection

algorithms (Cohen et al., 2008). The techniquedeveloped in Cohen et al. (2008) compares whole-brain correlation maps o adjacent cortical seeds and

searches or these abrupt changes in maps that depictboundaries between cortical regions.

For the purposes o this experiment, a 27 27

grid o small spherical oci (6 mm diameter) wasgenerated over the extent o LLPC (Fig. 1A)using Caret sotware (Van Essen Laboratory,

Saint Louis, MO) (Van Essen et al., 2001;

http://brainmap.wustl.edu/caret). The grid extendedoutside the traditional bounds o parietal cortex todecrease the chance that any unctional borders

near the anatomical boundaries o LLPC wouldgo undetected.

The resulting rs-cMRI boundary map depicts thelikely boundary at any given ocus in the patch

(Fig. 1B). Hot and cool colors indicate highand low probabilities, respectively, o the existence

o a boundary. The apparent centers o the boundedregions in LLPC were obtained by inverting the map


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so that hot colors indicate rs-cMRI map consistencybetween nearby seeds (Fig. 1C,D). Regions o interest

(ROIs) were dened as 10 mm diameter spheres(gray) at peak locations o consistency using two-

dimensional peak-nding algorithms. This resultedin 25 ROIs across the grid. Ten o the dened ROIswere outside o the parietal cortex and were excluded

rom urther analyses, leaving 15 LLPC mappingROIs to become targets o additional investigation.

Preliminary Examination o theFunctional Responses o Each ROIWe next applied the 15 mapping ROIs to a numbero task-related MRI studies that contained acontrast o old versus new items and perormed

a meta-analysis. Preliminary examination othe unctional responses o each ROI showed a

geographic distinction between retrieval-related and-unrelated regions. Only the seven more posterior

Figur 1. rs-cMRI data were used to generate probabilistic boundary maps in order to dene regions in

LLPC.A, A square patch o 729 spherical oci (6 mm diameter, 27 27 grid, spaced 6 mm apart) was created

using Caret sotware (Van Essen et al., 2001) and is shown here on an infated cortical surace rendering.

The surace is rotated to allow better visualization o LLPC. A (anterior), P (posterior), L (lateral), M (medial).

B, rs-cMRI boundary map generated using Canny method indicates the likelihood o a border at each seed.

Cooler colors represent stable rs-cMRI patterns, whereas hotter colors represent high border likeli-

hood, i.e., rapidly changing rs-cMRI patterns. C, Inverted rs-cMRI boundary map demonstrates peaks o

stability rom the previous map. Centers are shown as dark gray spheres (10 mm diameter) on the infated

surace. The blue circle indicates ROIs located within LLPC. D, Unprojected data rom previous panel C al-

lowing better visualization o borders. Gray dots represent ROIs, and those circled in blue indicate regions

located within LLPC. For orientation purposes, the grid contains anatomical labels that roughly correspond

to these locations on the cortical surace. aIPS, anterior intraparietal sulcus; SMG, supramarginal gyrus; SPL,

superior parietal lobule; vIPS, ventral intraparietal sulcus.


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Dosenbach NUF, Visscher KM, Palmer ED,Miezin FM, Wenger KK, Kang HC, Burgund ED,

Grimes AL, Schlaggar BL, Petersen SE (2006) Acore system or the implementation o task sets.

Neuron 50:799-812.

Dosenbach NUF, Fair DA, Miezin FM, Cohen AL,Wenger KK, Dosenbach RAT, Fox MD,

Snyder AZ, Vincent JL, Raichle ME, Schlaggar BL,Petersen SE (2007) Distinct brain networks or

adaptive and stable task control in humans. ProcNatl Acad. Sci. USA 104:11073-11078.

Fair DA, Dosenbach NUF, Church JA, Cohen AL,Brahmbhatt S, Miezin FM, Barch DM, Raichle ME,

Petersen SE, Schlaggar BL (2007) Developmento distinct control networks through segregation

and integration. Proc Natl Acad Sci USA104:13507-13512.

Fair DA, Cohen AL, Power JD, Dosenbach NUF,Church JA, Miezin FM, Schlaggar BL, Petersen SE(2009) Functional brain networks develop rom a

local to distributed organization. PLoS ComputBiol 5:e1000381.

Gbel SM, Rushworth MF (2004) Cognitiveneuroscience: acting on numbers. Curr Biol

14:R517-R519.

Henson RN, Rugg MD, Shallice T, Josephs O,

Dolan RJ (1999) Recollection and amiliarity inrecognition memory: an event-related unctional

magnetic resonance imaging study. J Neurosci

19:3962-3972.

Henson RN, Rugg MD, Shallice T, Dolan RJ (2000)Condence in recognition memory or words:dissociating right prerontal roles in episodic

retrieval. J Cogn Neurosci 12:913-923.

Hubbard EM, Piazza M, Pinel P, Dehaene S (2005)Interactions between number and space in parietalcortex. Nat Rev Neurosci 6:435-448.

Konishi S, Wheeler ME, Donaldson DI,Buckner RL (2000) Neural correlates o episodic

retrieval success. Neuroimage 12:276-286.

McDermott KB, Jones TC, Petersen SE, Lageman SK,Roediger III HL (2000) Retrieval success isaccompanied by enhanced activation in anterior

prerontal cortex during recognition memory:an event-related MRI study. J Cogn Neurosci

12:965-976.

McDermott KB, Szpunar KK, Christ SE (2009)

Laboratory-based and autobiographical retrievaltasks dier substantially in their neural substrates.

Neuropsychologia 47:2290-2298.

Nelson SM, Cohen AL, Power JD, Wig GS,Miezin FM, Wheeler ME, Velanova K,

Donaldson DI, Phillips JS, Schlaggar BL,Petersen SE (2010) A parcellation schemeor human let lateral parietal cortex. Neuron

67:156-170.Newman ME (2006) Modularity and community

structure in networks. Proc Natl Acad Sci USA103:8577-8582.

Rushworth MFS, Paus T, Sipila PK (2001) Attentionsystems and the organization o the human parietal

cortex. J Neurosci 21:5262-5271.

Simons JS, Peers PV, Hwang DY, Ally BA,

Fletcher PC, Budson AE (2008) Is the parietallobe necessary or recollection in humans?

Neuropsychologia 46:1185-1191.

Supekar K, Musen M, Menon V (2009) Development

o large-scale unctional brain networks in children.PLoS Biol 7:e1000157.

Turkeltaub PE, Eden GF, Jones KM, Zero TA (2002)Meta-analysis o the unctional neuroanatomy

o single-word reading: method and validation.Neuroimage 16:765-780.

Van Essen DC, Dickson J, Harwell J, Hanlon D,Anderson CH, Drury HA (2001) An integratedsotware suite or surace-based analyses o cerebral

cortex. J Am Med Inorm Assoc 41:1359-1378.See also http://brainmap.wustl.edu/caret.

Wagner AD, Shannon BJ, Kahn I, Buckner RL(2005) Parietal lobe contributions to episodic

memory retrieval. Trends Cogn Sci 9:445-453.

Wheeler ME, Buckner RL (2003) Functional

dissociation among components o remembering:control, perceived oldness, and content. J Neurosci

23:3869-3880.

Wheeler ME, Buckner RL (2004) Functional-

anatomic correlates o remembering and knowing.Neuroimage 21:1337-1349.

Yantis S, Schwarzbach J, Serences JT, Carlson RL,Steinmetz MA, Pekar JJ, Courtney SM (2002)

Transient neural activity in human parietal cortexduring spatial attention shits. Nat Neurosci5:995-1002.


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IntroductionCognitive science is evolving rom a ocus on discrete

brain areas towards an emphasis on distributedmodels o brain unction. In the latter, cognition

emerges rom the complex interaction o widespread

brain areas. To examine interregional interactions,early neuroimaging studies used positron emissiontomography (PET) to correlate activity patterns indierent brain areas across subjects (Clark et al.,

1984; Horwitz et al., 1984; Metter et al., 1984). Bycontrasting one group o subjects with another, group

dierences in brain unction could then be relatedto interactions between brain areas (Horwitz et al.,

1998). However, the study o individual dierencesin connectivity patterns with PET was typicallynot easible because limited temporal resolution

prevented the assessment o connectivity in eachsubject (although an exception can be ound in the

work o Glaubus et al., 2003).

The ability to assess network connectivity patternsor each individual subject was one o the exciting

advances made possible by the improved temporalresolution o unctional magnetic resonance imaging(MRI). This capability has enabled researchers

to examine relationships between individualscognitive unction and their network connectivity,

which has proven to be a powerul approach orstudying the neural basis o individual dierences.

For example, an early study we conducted revealedrobust correlations in a relatively limited sample size

between reading skills and connectivity betweenbrain areas in the reading circuitry (Hampson et al.,2006a). More recently, studies relating cognitive

variables to network connectivity have becomepopular in cognitive neuroscience. This research has

tended to highlight the role o two specic networksin human cognition: the cognitive control networkand the deault mode network, which are discussed

in more detail below.

Cognitive Control or Task-PositiveNetwork

Neuroimaging studies have identied a set orontal and parietal brain regions that appear to beimportant in human intelligence (Jung and Haier,

2007). In particular, activation-based unctionalimaging studies have reported increased activity in

many brain regions: dorsolateral and ventrolateralprerontal cortices, premotor areas, dorsomedial

prerontal cortex, anterior insula, the intraparietalsulcus, and portions o the inerior parietal lobule.

This activity is seen during a range o workingmemory and attention/executive unction tasks(Cabeza and Nyberg, 2000; Corbetta and Shulman,

2002; Owen et al., 2005). These cognitive controlareas have been shown to fuctuate together in the

resting state (Fox et al., 2005; Seeley et al., 2007),suggesting that they orm an intrinsic brain network.Adopting the terminology o Fox et al. (2005), we

reer to these cognitive control regions as task-positive areas.

The strength o unctional connectivity between

task-positive brain areas in the resting state hasproven relevant to cognitive unction. For example,Seeley and colleagues identied this network via

independent components analysis. They reportedthat the strength o the intraparietal sulcus within

the extracted component was correlated withperormance on the Trail Making Test, a measure o

executive unction (Seeley et al., 2007). Anotherstudy, by Song et al., identied many unctional

connections between the dorsolateral prerontalcortex and other task-positive brain areas that werecorrelated with intelligence scores on the Wexler

Adult Intelligence Scale (Song et al., 2008). Thus,unctional connectivity within the task-positive

network appears to play a role in individualdierences in cognitive ability.

Deault Mode or Task-NegativeNetworkAnother set o regions that appear to be relevantto cognitive unction are the deault-mode regions.

This set o brain areas includes the ventromedialprerontal cortex, a region o the posterior cingulate

cortex extending into the precuneus, and lateralparietal regions, all o which have been ound inmeta-analyses to have decreased blood fow during

a variety o tasks (Shulman et al., 1997; Mazoyer etal., 2001). These regions were also shown to fuctuate

together at rest, supporting the view that they orma well-integrated network (Greicius et al., 2003).

This network has been hypothesized to perorm thedeault mental processes subjects engage in whentheir attention is not ocused on a specic task

hence the term deault mode network (Raichle et

al., 2001).

According to the deault mode theory, task

engagement suspends deault mode processing,resulting in decreased activity in these regions.However, it is important to note that a decrease in

blood fow can be associated with an increase ininormation processing and engagement and does

not necessarily imply suspension o processing. Forexample, a region that shits rom random baseline

ring to phase-locking with other regions at a lowerring rate may decrease its net activity level (resulting


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et al., 2009). These new measures have greatpotential to summarize the complex patterns o

network connectivity that determine brain unction,and as such, to provide promising new tools or

cognitive neuroscientists.

As more is learned regarding the network propertiesunderlying individual dierences in cognition, acritical challenge will be to modulate these patterns

in order to improve cognitive unction. Along theselines, a recent study explored the relationship between

cardiovascular tness, unctional connectivity, andcognition (Voss et al., 2010). Its ndings point to

the importance o physical exercise as a potentialintervention or improving cognitive networks inolder adults and the need or more longitudinal

studies. Another study involving a paradigm orvisual perceptual learning reported signicant

changes in unctional connectivity associated withthe degree o learning (Lewis et al., 2009), illustrating

our capacity or modulating network dynamics andthereby improving cognitive unction. In our lab,a particularly interesting new orm o intervention

is bioeedback o real-time MRI, which has beenshown eective in modulating pain perception

(deCharms et al., 2005) and in acilitating linguisticprocessing (Rota et al., 2009). Studies examining the

relationship between changes in network propertiesduring such interventions, and coincident changesin cognitive unction, are likely to yield new insights

into the brain organization underlying cognition and

its plasticity.

ReerencesAchard S, Bullmore E (2007) Eciency and cost

o economical brain unctional networks. PLoSComput Biol 3:e17.

Andrews-Hanna JR, Snyder AZ, Vincent JL,Lustig C, Head D, Raichle ME, Buckner RL(2007) Disruption o large-scale brain systems in

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Alzheimers disease. J Neurosci 29:1860-1873.

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brain. Nat Rev Neurosci 3:201-215.

deCharms RC, Maeda F, Glover GH, Ludlow D,

Pauly JM, Soneji D, Gabrieli JDE, Mackey SC(2005) Control over brain activation and pain

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Gong G, Rosa-Neto P, Carbonell F, Chen ZJ,He Y, Evans AC (2009) Age- and gender-related

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Menon V (2004) Deault-mode network activitydistinguishes Alzheimers disease rom healthy

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Department o Psychiatry and Behavioral SciencesDepartment o Neurology and Neurological Sciences

Program in NeuroscienceStanord University Medical School

Stanord, Caliornia

Large-Scale Brain Networks in Cognition:

Emerging PrinciplesVinod Mnon, PhD


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A Network Perspectiveon CognitionFunctional brain imaging has ocused primarily

on localization o unction, revealing activationin specic brain regions during the perormance o

particular cognitive tasks. It is becoming increasinglyapparent that cognitive neuroscience needs to go

beyond this mapping o complex cognitive andpsychological constructs onto individual brain areas(Fuster, 2006). As a result, a network paradigm is

becoming increasingly useul or understanding theneural underpinnings o cognition (Bressler and

Menon, 2010). Furthermore, a consensus is emergingthat the key to understanding the unctions o any

specic brain region lies in understanding how itsconnectivity diers rom the pattern o connectionsin other unctionally related brain areas (Passingham

et al., 2002). In recent years, neuroscientists

interests have shited towards developing a deeperunderstanding o how intrinsic brain architectureinfuences cognitive and aective inormation

processing (Greicius et al., 2003; Fox and Raichle,2007; Dosenbach et al., 2008).

In the sections that ollow, we briefy reviewemerging methods or characterizing and identiying

major neurocognitive networks in the human brain.We then provide two specic examples o how such

networks can provide undamental new insights intothe brain bases o undamental cognitive processes.The rst example ocuses on the surprisingly crucial

role o the insular cortex in salience, attention, andcognitive control. The second example demonstrates

how intrinsic unctional and structural connectivityo the parietal cortex can inorm and constrain

inormation processing models across multiplecognitive domains.

Identiying Major CognitiveNetworksA ormal characterization o core brain networksanatomically distinct, large-scale brain systems

having distinct cognitive unctionswas rstenunciated by Mesulam (1990). In this view, the

human brain contains at least ve major coreunctional networks:1. A spatial attention network anchored in posterior

parietal cortex (PPC) and rontal eye elds;2. A language network anchored in Wernickes and

Brocas areas;3. An explicit memory network anchored in the

hippocampalentorhinal complex and ineriorparietal cortex;

4. A ace-object recognition network anchored in

midtemporal and temporopolar cortices; and

5. A working memoryexecutive function networkanchored in prefrontal and inferior parietal cortices.

The nodes o these core networks have been inerredrom the results o MRI studies, during tasks that

manipulate one or more o these cognitive unctions.A ull characterization o core unctional brain

networks, however, will require additional studiesto validate the nodes o these networks using other

criteria, to measure their edges, and to determinewhether other core networks exist.

In recent years, diusion tensor imaging (DTI)and resting state MRI have emerged as novel tools

or characterizing structural and unctional brainnetworks. They are able to do so independently o

cognitive domains, experimental manipulations, andbehavior. Recent work in systems neuroscience has

characterized several major brain networks that areidentiable in both the resting brain (Damoiseaux etal., 2006; Seeley et al., 2007b) and the active brain

(Toro et al., 2008). Importantly, major unctionalbrain networks (and their composite subnetworks)

show close correspondence in independent analyseso resting and task-related connectivity patterns(Smith et al., 2009), suggesting that unctional

networks coupled at rest are also systematicallyengaged during cognition. The analysis o resting

state unctional connectivity, using both model-based and model-ree approaches, has proved to be

a useul technique or investigating unctionally

coupled networks in the human brain. Although themethod relies on analysis o low-requency signalsin MRI data, electrophysiological studies point to areliable neurophysiological basis or these signals (He

et al., 2008; Nir et al., 2008).

The analysis o resting state MRI allows us todiscover the organization and connectivity o several

major brain networks that cannot be easily capturedwith the help o other techniques. Conceptualizingthe brain as comprising multiple distinct, interacting

networks provides a systematic ramework orunderstanding undamental aspects o human

brain unction.

Independent component analysis (ICA) has turnedout to be an important method or identiyingintrinsic connectivity networks (ICNs) rom

resting state MRI data (Damoiseaux et al., 2006;Seeley et al., 2007a). ICA has been used to identiy

ICNs involved in executive control, episodicmemory, autobiographical memory, sel-related

processing, and detection o salient events. ICAhas also revealed a sensorimotor ICN anchoredin bilateral somatosensory and motor cortices; a


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visuospatial attention network anchoredin intraparietal sulci and rontal eye elds;

a higher-order visual network anchoredin lateral occipital and inerior temporal

cortices; and a lower-order visual networkanchored in the striate and extrastriate

cortex (Damoiseaux et al., 2006). Thistechnique has also allowed intrinsic(Fig. 1) as well as task-related (Fig. 2)

MRI activation patterns to be used orthe identication o distinct unctionally

coupled systems. These systems includea central-executive network (CEN)

anchored in dorsolateral prerontal cortex(DLPFC) and PPC, and a salience network(SN) anchored in anterior insula (AI) and

anterior cingulate cortex (ACC) (Seeleyet al., 2007a; Sridharan et al., 2008).

These prominent networks can be readily

identied across a wide range o cognitivetasks, and their responses increase anddecrease proportionately with task

demands. The CEN and SN typicallyshow increases in activation, whereas

the deault-mode network (DMN) showsdecreases in activation (Raichle et al.,

2001; Greicius et al., 2003; Greicius andMenon, 2004). CEN nodes that showstrong intrinsic unctional coupling

also show strong coactivation during

cognitively challenging tasks. In particular,the CEN is critical or actively maintainingand manipulating inormation in working

memory, and or judgment and decision-making in the context o goal-directed

behavior (Miller and Cohen, 2001;Petrides, 2005; Muller and Knight, 2006;Koechlin and Summereld, 2007).

The DMN includes the medial temporal

lobes and the angular gyrus (AG), inaddition to the posterior cingulate cortex

(PCC) and the ventromedial prerontal

cortex (VMPFC). These areas perorm avariety o unctions: The PCC is activated

during tasks that involve autobiographicalmemory and sel-reerential processes

(Buckner and Carroll, 2007); the VMPFCis associated with social cognitive processes

related to sel and others (Amodio andFrith, 2006); the medial temporal lobe isengaged in episodic and autobiographical

memory (Cabeza et al., 2004); and the

Figur 1. Two core neurocognitive networks identied using intrinsic

physiological coupling in resting state MRI data. The SN (shown in red) isimportant or monitoring the saliency o external inputs and internal brain

events, and the CEN (shown in blue) is engaged in higher-order cognitive

and attentional control. The SN is anchored in AI and ACC and eatures

extensive connectivity with subcortical and limbic structures involved in

reward and motivation. The CEN links the dorsolateral prerontal and pos-

terior parietal cortices, and has subcortical coupling that is distinct rom

that o the SN. Seeley et al. (2007), their Fig. 2, reprinted with permission.

antTHAL, anterior thalamus; dACC, dorsal anterior cingulate cortex; dCN,

dorsal caudate nucleus; dmTHAL, dorsomedial thalamus; FI, ronto-insular

cortex; HT, hypothalamus; PAG, periaqueductal gray; Put, putamen; SLEA,

sublenticular extended amygdala; SN/VTA, substantia nigra/ventral teg-

mental area; TP, temporal pole.

Figur 2. Three major unctional networks in the human brain. Task-relat-ed activation patterns in the CEN and SN, and deactivation patterns in the

DMN, during an auditory event segmentation task. Activation and deacti-

vation patterns can be decomposed into distinct subpatterns. A, Analysis

with the general linear model (GLM) revealed regional activations (Let) in

the right anterior insula (rAI) and ACC (blue circles); DLPFC and PPC (green

circles) and deactivations (Right) in the VMPFC and PCC. B, ICA provided

converging evidence or spatially distinct networks. From let to right: SN

(rAI and ACC), CEN (rDLPFC and rPPC), and DMN (VMPFC and PCC). Srid-

haran et al. (2008), their Fig. 1, reprinted with permission.


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the AI in particular, is to rst identiy stimuli rom the

vast and continuous stream that impacts the senses.Once such a stimulus is detected, the AI acilitates

task-related inormation processing by initiatingappropriate transient control signals. These signals

engage brain areas that mediate attentional, workingmemory, and higher order cognitive processes whiledisengaging the DMN via mechanisms that have

been described in the previous section.These crucial switching mechanisms help ocus

attention on external stimuli; as a result, they takeon added signicance or saliency. The large-scalenetwork switching mechanisms we have described

here can be thought o as the culmination o ahierarchy o saliency lters. In these lters, each

successive stage helps to dierentially ampliy astimulus suciently to engage the AI. The precise

pathways and lters underlying the transormation o

external stimuli, and the manner in which the AIis activated, remain to be investigated. O critical

importance, our model suggests that, once a stimulusactivates the AI, it will have preerential access to the

brains attentional and working memory resources.

Although dynamical systems analysis o MRI datacan help capture aspects o causal interactionsbetween distributed brain areas, a more complete

characterization o bottom-up and top-downattentional control requires access to temporal

dynamics on the 30-70 ms time scale. Analysis o

combined EEG and MRI data provides additional

insights into how the SN plays an important rolein attentional control (Crottaz-Herbette and

Menon, 2006). Figure 4 is a schematic model obottom-up and top-down interactions that underlie

attentional control. This model was suggestedby the relative timing o responses in the AI andACC, versus other cortical regions, based on our

dynamic source-imaging study and by lesion studieso the P3a complex (Soltani and Knight, 2000). The

spatiotemporal dynamics underlying this processhave ve distinct stages: Stage 1: ~150 ms poststimulus, primary sensory

areas detect a deviant stimulus, as indexed by themismatch negativity (MMN) component o the

evoked potential; Stage 2: This bottom-up MMN signal is transmitted

to other brain regions, notably the AI and the

ACC; Stage 3: ~200-300 ms poststimulus, the AI and

ACC generate a top-down control signal, asindexed by the N2b/P3a component o the

evoked potential. This signal is simultaneouslytransmitted to primary sensory areas, as well as

other neocortical regions; Stage 4: ~300-400 ms poststimulus, neocortical

regions, notably the premotor cortex and

temporoparietal areas, respond to the attentionalshit with a signal that is indexed by the time-

averaged P3b evoked potential; and

Figur 4. Schematic model o dynamic bottom-up and top-down interactions underlying at-

tentional control. See text or description o stages. Crottaz-Herbette and Menon (2006), their

Fig. 6, adapted with permission.


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Furthermore, recent cytoarchitectonic analyses othe human IPL have suggested that the AG and IPS

can be parcellated into distinct subregions (Choiet al., 2006; Caspers et al., 2008). Using observer-

independent denitions o cytoarchitectonicborders, Caspers and colleagues have dened two

subdivisions within the AG: one anterior (PGa), andone posterior (PGp) (Caspers et al., 2006). Withinthe IPS, this research group has demonstrated at

least three cytoarchitectonically distinct humanintraparietal (hIP) areas, labeled hIP2, hIP1, and

hIP3 (Caspers et al., 2008). Resting state unctionalconnectivity analyses showed that PGa was more

strongly linked to basal ganglia, ventral premotorareas, and VLPFC, whereas PGp was more stronglyconnected with VMPFC, PCC, and hippocampus:

regions comprising the deault mode network. Theanterior-most IPS ROIs (hIP2 and hIP1) have

been linked with ventral premotor and middlerontal gyrus, whereas the posterior-most IPS ROI

(hIP3) showed connectivity with extrastriate visualareas. Tractography using DTI revealed structuralconnectivity between most o these unctionally

connected regions (Fig. 5).

These ndings provide evidence or unctionalheterogeneity o cytoarchitectonically dened

subdivisions within the IPL. They also oer anovel ramework or synthesizing and interpreting

the task-related activations and deactivations thatinvolve the IPL during cognition. Our connectivity

analyses o networks associated with the IPS suggesta general principle o organization; by means o it,posterior IPS regions that are closely linked to the

visual system transorm stimuli into motor action viaanterior IPS connections to the prerontal cortex.

Specically, unctional and structural connectivityresults point to strong connections between hIP1

and insula. Along with the ndings noted in theprevious section, this observation suggests that suchan interconnected system may help to mediate the

detection o visually salient stimuli. More broadly,such investigations provide new inormation about

the unctional and structural organization o thehuman parietal cortex. This understanding, in turn,

places constraints on inormation processing modelso parietal cortex unction, with broad implicationsacross multiple cognitive domains (Uddin et

al., 2010).

Figur 5. Dierential structural connectivity within the PPC. Structural connectivity o hIP subdivisions.

A, DTI tractography and density o bers between hIP2, hIP1, and hIP3 and target inerior rontal oper-

cular ROIs. Both hIP2 and hIP1 showed greater structural connectivity with inerior rontal opercular than

did hIP3 (*p < 0.05, **p < 0.01). B, DTI tractography and density o bers between hIP2, hIP1, and hIP3

and target insula ROIs. hIP1 showed greater structural connectivity than hIP2 and hIP3 with insula (*p

< 0.05, **p < 0.01). C, DTI tractography and density o bers between hIP2, hIP1, and hIP3 and target

superior occipital cortex ROIs. hIP3 showed greater structural connectivity with superior occipital cortex

than did hIP2 (**p < 0.01). Uddin et al. (2010), their Fig. 5, reprinted with permission.


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