The Economics of Brain Network

8/2/2019 The Economics of Brain Network

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Theeconomicsofbrainnetwork

EdBullmore

CABDyNComplexityCentre,SadBusinessSchoolUniversit o Ox ord

November8,2011


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An economical model of brain networks

Brainsmake

adaptive

value

at

some

physical

cost

Adaptivevalue:perceptions,cognitions,behaviours thathelptheorganismsurviveinachan in com etitiveenvironment

Physicalcost:volume,wiringandmetaboliccostsofnervoussystems ,

valueareoftenthemostcostly

Longdistanceconnectionsneededforintegrativeprocessing

Likeprofitablebusinesses,brainnetworksnegotiateaneconomictrade

o e weena ngva uean con ro ngpro uc oncos s


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Howdidwestartthinkingaboutbrainnetworks?

Macro Micro

Ramny Cajal

(1890)

Mayo,Meynert

(1827,1870)


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Explosionofhighqualityneuroimaging datanowmakes

connec omes aun ng yaccess e

About100,000,000,000neuronsinthe

humanbrain(100billion)

About100,000synapsesperneuron

1quadrillioncellularconnections(1015)

inhumanbrainconnectome

1000sofstructuralandsignalling

proteinspersynapticconnection

Sporns,Tononi &Ktter (2005)PLoS CompBiol


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Graphtheorypowerfullysimplifiesthetopologyofcomplex

sys ems

Efficiency

ClusteringBassett&Bullmore(2006)

Neuroscientist

Degree

Hubs

Topology:thestudyofpropertiesthatarepreservedundercontinuousdeformationofobjects


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Thesmallworldofthewormsbrain

Caenorhabditiselegans Smallworld

Highclusteringorcliquishnessofconnections

Anatomy(277neurons,7000synapses)

betweenneighboringnodes Shortpathlengthorhighefficiencyofcommunicationbetween anypairofnodes

Costefficient 46%maximumefficiencyofinformationtransferforabout

4%

maximum

connection

cost

Topology(277nodes,7000edges)

Watts&Strogatz (1998)Nature;Latora &Marchiori (2001)PhysRevLett


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Vertes etal(2011)YouTube(searchonneuro tweets)


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Fromneuroimaging tobraingraphs

1. Estimateanassociationmatrixfrom

thedata

Whatare

the

nodes?

Whatmetricofconnectivity?

.

associationmatrix

Whataretheedges?

3. Measuretopologicalpropertiesofeachgraph

4. Makecomparisonsbetweengraphs

Fornito etal(2010)FrontSysNeuro

Brain graphs are statistical models entailing assumptions and tradeoffs which influence parameter values

Brain graph parameters make sense relativistically, not absolutely; comparison between graphs is not trivial

Bassett&Bullmore(2010)Curr OpNeurol;Bullmore&Bassett(2011)Annu RevClin Psychol


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Manynetworkpropertiesareconservedacrossmanyscalesandkinds

Smallworldness

highclustering

shortpathlengthorhighefficiency

Costefficiency

highefficiencyofinformationtransferforrelativelylow

connectioncost

Hubnodes

fattaileddegreedistributions

nodesaremoredenselyconnectedtoothernodesinthe

samemodulethantonodesinothermodules

Bullmore&

Sporns (2009)

NatRevNeurosci

Sporns etal(2007)PLoS ONE;Yuetal(2008)Cereb Cortex;Meunier etal(2010)FrontNeurosci


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Humanbraingraphsandotherinformationprocessingnetworksare

Nodesinthesamemoduleareoften,butnotalways,anatomicalaswellastopologicalneighbours:sointramodular

edgeswillbeshorterdistancethanintermodularedges

Meunier etal(2010)FrontNeurosci;Bassettetal(2010)PLoS CompBiol;Chenetal(2008)Cereb Cortex

Braingraphstypicallyhavemoduleswithinmodules


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Whatsspecialandwhatsuniversalabouthumanbrains

compare

oo er

n orma on

ne wor s

HumanBrain Network

RestingstateFMRI

EconomicNetwork

NewYorkStockExchange

SocialNetwork

Twittergadaffi

Vertes etal(2011)FrontSysNeurosciBlue =BrainGreen =Market


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Backtoanatomy

Countin the material and metabolic costs of brain networks

Cajals economicalprinciple:

Werealizedthatallofthevariousconformationsoftheneuronanditsvariouscomponentsaresimplymorphologicaladaptationsgovernedbylawsofconservationfortime,space,andmaterial.Increasingawarenessalsoofthemetabolic

orenergycostsofthenervoussystemand

thebiologicaldrivetocontrolmetabolicas

wellasmaterialcostsofbrains

VanEssen(1997)Nature

Niven &Laughlin(2008)JExpBiolGarcia

Lopez

(2010)

FrontNeuroanatomy


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Brainnetworksareeconomicallywiredbut

Original Minimal

Macaque

C.elegansKaiser&Hilgetag (2006)PLoS CompBiol


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Tradeoffsbetweenconnectiondistanceand

topo ogy

n

uman

ra nnetwor s

AlexanderBlochetal(2011)inreview


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Costefficiencyanditsheritabilityinhumanbrainnetworks

competitivecriteriaofminimising costandmaximising efficiency

we

might

predict

that

costefficiencyisheritable

Tradeoffbetweentopologicalefficiencyandconnectioncost(Euclideandistance

betweenfunctionally

connected

regions)

was

measuredin16MZand13DZtwinpairs

Globalcostefficiencywasheritable~0.6and ~ .

symmetricalcortical

regions,

including

DMN

components

Fornito etal(2011)JNeurosci


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Expensive,longrangeintegrativeconnectionsmaybeworthit

forextracognitivecapacity

Greaterefficiency(orshorterpathlength)

ofhumanbrainnetworksiscorrelatedwith

higherIQ

VandenHeuvel etal(2009) Neurosci;Lietal(2009)PLoS CompBiol;Bassettetal(2010)PLos CompBiol;

o a neurona wor space eory

predicts integrativenetworkswillbe

requiredforconscious,effortful

processing

Dehaene etal (1998)ProcNatl Acad SciBaars (1993)Acognitivetheoryofconsciousness


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Workingmemoryloadbreaksmodularityanddrivesworkspace

Modules

Clustering

Longdistance

edges

Intermodular

recordedusing

MEG

in

healthy

volunteersperformingNback

workingmemorytask

edges

Kitzbichler etal(2011)JNeurosci


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Changesincognitiveloadareassociatedwithrapid reconfiguration

ofnetworktopologyandconnectiondistance

Kitzbichler etal(2011)JNeurosci


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Cartooninterpretationofeconomicalsmallworldarchitecturein

Integratedprocesses

Highefficiency

Shortpathlength

General egexecutive

Isotropic(IQ)

Distributed

Conscious ort u

Highclustering Segregatedprocesses

(Lowercost) Specialised

eg

face

visionEncapsulated

Localised

Unconscious

Automatic


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Normalbraindevelopmentisassociatedwithchangesinnetwork

FunctionalMRInetworksConnectiondistanceincreases

AnatomicalDTInetworksTopologicalefficiencyincreases

Fairetal(2007)ProcNatl Acad SciHagmann etal(2010)ProcNatl Acad Sci

withage withage


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Functionalnetworksandconnectorhubsareless

pars mon ous yconnecte

nsc zop ren a

AlexanderBlochetal(2011)inreview


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Whatcouldbegoodabouttheschizophrenia

connectome

Connectorhubsaddvaluebutarealso

pointsofvulnerabilityifthenetworkis

attackedor

lesioned

Schizophrenicbrainnetworkshavegreater

robustness,perhapsrepresentinggreater

resilienceagainstpathologicalattack

Achardet

al

(2006),

JNeurosci;Honey&Sporns(2008)HumBrainMapp;

Lynalletal(2010)JNeurosci;Vertesetal(2011),inreview


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Conclusions

Brains

make

adaptive

value

at

some

physical

cost

Inbrainnetworks,thetopologicalpropertiesthataddthemostadaptivevalueareoftenthemostcostly

Likeprofitablebusinesses,brainnetworksnegotiateaneconomictradeoffbetweenaddingvalueandcontrollingproductioncosts

Brainnetworkshavecomplextopologyembeddedinanatomicalspace

Longdistance

connections,

often

between

modules,

are

important

for

efficiency

of

information

transfer

and

formation

of

workspaces

Topologicalpropertiessuchasefficiencyandrobustnesshaveadaptivevalueintermsofsupportingeffortfulcognitiveprocessesandresiliencetoadverseperturbation

Brainscanrapidlyandslowlyreconfigurethemselvesintermsofconnectiondistanceandtopology

TradeoffsbetweenwiringcostandtopologicalefficiencyhavebeendemonstrateddirectlyinCelegans and,lessdirectly,inhumans

Itisplausiblethataneconomicaltradeoffbetweentopologicalandspatialpropertiesisanimportantcriterionfordevelopmentalandevolutionaryselectionofbrainnetworks

Brain isor ersimpactingoncognitive unction avea norma networ properties,spatia yan topo ogica y,suggestingt at

neurologicaland

psychiatric

symptoms

arise

especially

when

the

more

costly

components

of

networks

are

lesioned or

develop

abnormally

Aneconomicalmodelofbrainnetworkorganizationisnotyetrefutedandcouldbenefitfromfurthertesting

Moreprecisecharacterizationofwiringcostinhumannetworks

Morestudiesofbrainnetworksinexperimentallytractableanimalmodels

Morecomputationalmodelingofnetworkselectionbyeconomiccriteria

More

comprehensive

and

larger

sample

mapping

of

network

abnormalities

across

a

range

of

brain

disorders


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ManyThanks!

SophieAchard Aaron

Alexander

Bloch

Dani Bassett RichardCoppola

HumanBrain

Project,

NIBIB/NIMH

NIH/CambridgePhDProgram

AlexFornito JayGiedd Carsten Giessing Nitin Gogtay Rik Henson

,

MRC/Wellcome TrustBehavioural

&

Man re Kitz ic er Renaud Lambiotte Naaman Mammuz DavidMeunier AndreasMeyerLindenberg

ClinicalNeurosciencesInstitute

MRCCognition&BrainSciencesUnit

GlaxoSmithKlineR&D

u apopor Raymond

Salvador

OlafSporns PetraVertes

[email protected]

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The Economics of Brain Network