AlexFornito

BrainandMentalHealthLabMonashInstituteofCognitiveandClinicalNeurosciences

MonashUniversity

e:alex.fornito@monash.edu@AFornito

Anintroductiontobrainnetworks

Felleman &VanEssen,Cereb Cortex,1991

segregation integration

two fundamental principles of brain organization

Broca’s Leborgne (akaTan)

Scoville &Milner’sHM

Harlow’sPhineasGagePolyak, 1957

Huth etal.Nature,2015

Nature,1993

1990-1992discovery of BOLD

contrast

Ogawaetal.PNAS,1992

Conturo etal.PNAS,1999

1999 development of

diffusion tractography

nanoscalenm

microscalenmto𝜇m

meso𝜇m to mm

macroscalemmtocm

connectome:

a comprehensivestructuraldescriptionofthenetworkofelementsandconnections

formingthehumanbrain.

Sporns,Tononi,Kötter,PLoS CompBiol,2005

modellingbrainnetworksasgraphs

any network can be modelled as a graph of nodes connected by edgesnodes represent fundamental processing units

edges represent the interactions between nodes

nodeedge

LeonhardEuler(1707-1783)

a briefhistoryofgraphtheory

PaulErdős(1913-1996)

Alfréd Rényi(1921-1970)

a briefhistoryofgraphtheory

random scalefree

Barabasi &Albert,Science,1999;Barabasi &Oltvai,NatRevGenet,2004

realnetworkshavehubs realnetworksaresmall-world

WattsandStrogatz,Nature,1999

realnetworksaremodular

Girvan&Newman,PNAS,2004;Clauset,Moore&Newman,Nature,2008

definingnodes

Brainnodesshouldhave:1. Intrinsicconsistency2. Extrinsicdifferentiation3. Spatiallyconstrained

vandenHeuvel etal.NeuroImage,2009Hayasaka &Laurienti,NeuroImage,2010

voxel-based

Glasser &VanEssenJNeurosci 2011

myeloarchitecture

Dosenbach,etal.Science,2010Fornitoetal.PNAS,2012

functional

Hagmann,etal.PLoS One,2007Zalesky,etal.NeuroImage,2010

Fornitoetal.FontSysNeurosci,2010

random

Tzuorio-Mazoyer,etal.NeuroImage,2002Desikan,etal.NeuroImage,2006

anatomical

Eickhoff etal.NeuroImage,2007

chemoarchitecture

Brodmann,1909

cytoarchitecture

Eickhoff etal.NeuroImage,2006

probabilistic

Fornitoetal.NeuroImage,2013

Yeoetal.JNeurophysiol,2011Craddocketal.HumBrainMapp,2012

Poweretal.Neuron,2011

data-driven multimodal

Glasser etal.Nature,2016

definingedges

FunctionalconnectivityAstatisticaldependencebetweenspatiallydistinctneurophysiologicalsignals

EstimatedatlevelofmeasuredsignalsCanbedirectedorundirected

EffectiveconnectivityTheinfluencethatoneneuronalsystemexertsoveranother

Usuallymodel-basedDescribescausalinteractionsattheneuronallevel

Friston etal,1994,HumBrainMapping;Fornitoetal,2015,NatRevNeurosci

StructuralconnectivityThephysical(anatomical)connectionsbetweenbrainregions

IntrinsicallydirectedDirectionalitycannotberesolvedwithMRI

definingedges

Modha &SinghPNAS,2010Shanahanetal.Fron CompNeurosci,2013

http://cocomac.org

informatics

Bocketal.Nature,2011Whiteetal.PTRS,1986

electronmicroscopy

Ohetal.Nature,2014http://connectivity.brain-map.org

tract-tracing

Axer,etal.NeuroImage,2011

PLI

Hagmann etal.PLoS One,2007

DWI

Chiangetal.Curr Biol,2011www.flycircuit.tw

geneticlabelling

structural functional

electrophysiology

Yuetal.Cereb Cortex,2008

calciumimaging

Ahrensetal.NatMethods,2013

ECoG

Bastos etal.Neuron,2015Brovelli,etal.PNAS,2004

M/EEG fMRI

Engeletal.Neuron,2013 Smithetal.NeuroImage,2011

mappingstructuralconnectivityinhumanswithDWI

Johansen-Berg&Rushworth,AnnRevNeurosci,2014;Mori&Zhang,Neuron,2008;Tournier etal.Magn ResMed,2011;Isenberg,2011;Thomas,etal.PNAS,2014

measuringconnectionweightswithdiffusionMRI

connectionweightstypicallyestimatedas:numberofconnectingstreamlines

averagetractFAorotherdiffusivityindex

but:

emergingapproachesinclude:axondiameter&density(Assaf etal.NeuroImage,2013)

trackdensityimaging(Calamante,etal.NeuroImage,2011)myelincontent(Deoni etal.Magn ResMed,2008)

definingedges

Modha &SinghPNAS,2010Shanahanetal.Fron CompNeurosci,2013

http://cocomac.org

collated

Bocketal.Nature,2011Whiteetal.PTRS,1986

electronmicroscopy

Ohetal.Nature,2014http://connectivity.brain-map.org

tract-tracing

Axer,etal.NeuroImage,2011

PLI

Hagmann etal.PLoS One,2007

DWI

Chiangetal.Curr Biol,2011www.flycircuit.tw

geneticlabelling

structural functional

electrophysiology

Yuetal.Cereb Cortex,2008

calciumimaging

Ahrensetal.NatMethods,2013

ECoG

Bastos etal.Neuron,2015Brovelli,etal.PNAS,2004

M/EEG fMRI

Engeletal.Neuron,2013 Fornitoetal.NeuroImage,2013Smithetal.NeuroImage,2011

mappingfunctionalconnectivitywithfMRI

rest task

Fox&Raichle,NatRevNeurosci,2002Achard etal.JNeurosci,2005Smithetal.NeuroImage,2011

Fornitoetal.PNAS,2012Fornitoetal.Biol Psychiatry,2012Coleetal.NatNeurosci,2013

statisticaldependence

correlationpartialcorrelationmutualinformation

…etc

dynamicsZalesky etal.PNAS,2014

Hutchison,etal.NeuroImage,2013

Fornito, Zalesky & Bullmore, 2016, Fundamentals of Brain Network Analysis

topologicalbiasesofcommonfunctionalconnectivitymeasures

Zalesky, Fornito, Bullmore, NeuroImage, 2012

functionalconnectivity

structuralconnectivity

adjacencymatrix

braingraph

fromdatatograph

definenodes&edges buildgraph

Fornito, Zalesky, & Breakspear. 2015, Nat Rev Neurosci

matricesandgraphsareformallyequivalent

Fornito, Zalesky & Bullmore, 2016, Fundamentals of Brain Network Analysis

whichgraphbestrepresentsthebrain?

binary weighted

heterogeneousedgesheterogeneousnodes

directed

hybrid

Fornito, Zalesky, & Breakspear. 2013, NeuroImage; Fornito, Zalesky, Bullmore, 2016, Fundamentals of Brain Network Analysis

whichgraphbestrepresentsthebrain?

functionalconnectivity

structuralconnectivity

adjacencymatrix

braingraph

fromdatatograph

connectivity

topology

definenodes&edges networkanalysisbuildgraph

Fornito, Zalesky, & Breakspear. 2015, Nat Rev Neurosci

functionalconnectivity

structuralconnectivity

adjacencymatrix

braingraph

fromdatatograph

connectivity

topology

definenodes&edges networkanalysisbuildgraph

Fornito, Zalesky, & Breakspear. 2015, Nat Rev Neurosci

Networkthresholding&statisticsAndrewZalesky

Paths,diffusion&communicationBratislav Misic

Modularity(static&dynamic)SarahMuldoon

Centrality&hubsMartijn vandenHeuvel

Null&generativemodelsPetraVertes

Graphs provide useful models of brain networksA unified framework for representing multiscale organization

summary

There are different methods for defining brain nodes and edgesEach has pros and cons; constrains interpretation of findings

Brain networks can be analysed at the level of connectivity or topologyEffects can be mapped at individual edges, nodes, or globally

Brain networks can be directed/undirected and weighted/unweightedNode/edge heterogeneity and dynamics can also be accomodated

resources

Amazonhttp://amzn.to/1MLgMhU

Elsevierhttp://bit.ly/1Hp29OZ

graphtheory/networkscience

Newman(2010)Networks:Anintroduction.

Newman(2003)SIAMRev.

Albert&Barabasi (2002)RevModernPhysics

graphtheoryandthebrain

Sporns (2011)Networksofthebrain.

Sporns (2012)Discoveringthehumanconnectome.

Bullmore &Sporns (2009)NatRevNeurosci.

Bullmore &Bassett(2011)Annu RevClin Psychol.

Fornito,Zalesky &Breakspear (2013)NeuroImage.

software

Brainconnectivitytoolbox:https://sites.google.com/site/bctnet/

Graphanalysistoolbox:https://www.nitrc.org/projects/gat/

Network-basedstatistic:http://www.nitrc.org/projects/nbs/

Task-relatedfunctionalconnectivity(cPPI):http://www.nitrc.org/projects/cppi_toolbox/

Networkvisualizationhttp://immersive.erc.monash.edu.au/neuromarvl/

Anopen-accessjournalfornetworkneuroscienceatalllevels,

frommoleculartomacroscales

http://www.mitpressjournals.org/netn