NCNC 2010 - FAUbressler/NCNC10/Sporns_1-30-10.pdf · The Human Connectome Sporns et al. (2005) PLoS Comput. Biol. 1, e42. Diffusion imaging and computational tractography allow the

Modeling the Network Architecture of the Human Brain

Olaf SpornsDepartment of Psychological and Brain Sciences

Indiana University, Bloomington, IN 47405http://www.indiana.edu/~cortex , [email protected]

NCNC 2010 - FAU

Outline

Brain ConnectivityNetwork Science Approaches

Linking Structure to FunctionBuilding a Map of the Human BrainRelating Structure to Dynamics

Networks in Brain Injury and DiseaseVulnerability and Lesion Modeling

Brain Connectivity

Examples of Complex Networks

http://users.design.ucla.edu/~akoblin/work/faa/

Network Science

Networks as Models of Complex Systems

Vespignani (2009) Science 325, 425. Schweitzer (2009) Science 325, 422.Porter et al. (2009) Notices of the AMS 56, 1084.

US commuting pattern

US House of Representatives committees and subcommittees

A subset of the international financial network

Brain Connectivity

Examples of Complex Networks

Jeong et al. (2001) Nature 411, 41 Stelzl et al. (2005) Cell 122, 957

yeast interactome

human interactome

Microscopic: Single neurons and their synaptic connections.

Mesoscopic: Connections within and between microcolumns (minicolumns) or other types of local cell assemblies

Macroscopic: Anatomically segregated brain regions and inter-regional pathways.

Brain Connectivity

Multiple Scales – Cells, Circuits, Systems

Tamily A. Weissman (Harvard University)Patric Hagmann (EPFL/CHUV Lausanne)

Anatomical (Structural) Connectivity: Pattern of structural connections between neurons, neuronal populations, or brain regions.

Functional Connectivity: Pattern of statistical dependencies (e.g. temporal correlations) between distinct (often remote) neuronal elements.

Brain Connectivity

Multiple Modes – Structural, Functional, Effective

Van Wedeen (MGH/Harvard University)Achard et al. (2006) J. Neurosci. 26, 63 McIntosh et al. (1994) J. Neurosci. 14, 655

Effective Connectivity: Network of causal effects, combination of functional connectivity and structural model.

Brain Connectivity

Network Measures and their Interpretation

Rubinov and Sporns (2010) NeuroImage

Measures of functional segregation: Clustering – Motifs -- Modularity

Measures of functional integration:Path Length -- Efficiency

Functional Segregation – Functional Integration – Functional Influence

Measures of functional influence:Centrality

Brain Connectivity

Modern Network Science

Watts & Strogatz (1998) Nature 393, 440. Tononi et al. (1998) Trends Cogn Sci 2, 474.

Research in the social sciences has focused on the structure of specific social systems and how their network structure contributes to specific functional outcomes.

Research in statistical physics and complex systems has focused on identifying universal principles of network organization, on common patterns shared among very different networks – e.g. “small world.”

small world

Brain Connectivity

Anatomical Organization of Cerebral Cortex

Felleman and Van Essen (1991) Cerebral Cortex 1,1. Van Essen et al. (1992) Science 255, 419.

Principles of network architecture in cortex:

Specialization

Integration

Streams (modules)

Hierarchy

Networks of mammalian cerebral cortex form a small world (high clustering, short path length), and contain modules linked by hubs.

Brain Connectivity

Small-World Brain Networks

Sporns et al. (2000) Cereb Cortex 10, 127. Sporns & Zwi (2004) Neuroinformatics 2, 145.Sporns et al. (2007) PLoS ONE 2, e1049.

regions of macaque visual and sensorimotor cortex

Each functionally specialized cortical region has a unique connectional fingerprint – a unique set of inputs and outputs.

Brain Connectivity

Small-World Brain Networks

Passingham et al. (2002) Nature Rev Neurosci 3, 606. Hilgetag et al. (2000) Phil. Trans. Royal Soc. B 355, 91

Structural modules consist of nodes that have similar connections with other nodes.

Structural modules reflect functional relationships.

regions and interconnections of cat cerebral cortex

The connectome can be defined on multiple scales:Microscale (neurons, synapses)Macroscale (parcellated brain regions, voxels)Mesoscale (columns, minicolumns)

Most feasible in humans, with present-day technology: macroscale, diffusion imaging→ central aim of the NIH Human Connectome Project

Other methodologies and systems:Mesoscale mapping of mouse brain connectivity (Bohland et al.)Microscale mapping of neural circuitry (Lichtman et al.)Serial EM reconstruction (Briggman and Denk)

Brain Connectivity

The Human Connectome

Sporns et al. (2005) PLoS Comput. Biol. 1, e42

Diffusion imaging and computational tractography allow the noninvasive mapping of white matter fiber pathways.

Brain Connectivity

Mapping Human Brain Structural Connectivity

Hagmann et al. (2008) PLoS Biol. 6, e159

Brain Connectivity



A B

LH RH

Brain Connectivity



The Human BrainBrain Connectivity


Iturria-Medina et al. (2008) NeuroImage 40, 1064. Gong et al. (2009) Cereb Cortex 19, 524.

The core is comprised of precuneus and posterior cingulate cortex, plus adjacent regions.

Brain regions within the structural core share high degree, strength and betweenness centrality, and they constitute connector hubs that link all major structural modules.


The Structural Core – A Major Hub in the Brain


Network analysis reveals

• Exponential (not scale-free) degree distribution• Robust small-world attributes• Multiple modules interlinked by hub regions• Positive assortativity• A structural core in posterior medial cortex


Modules and Hubs in the Human Brain


centrality

core

centrality

PCC/Precuneus and Centrality

Network representation of default mode correlation strengths (Fransson and Marrelec, 2008)

High centrality of the precuneus in structural networks (Iturrina-Medina et at., 2008; Gong et al., 2009; Hagmann et al., 2008)

PCC has high rate of resting metabolism (Gusnard and Raichle, 2001)

Macaque PMC is highly connected (Parvizi et at., 2006)

Brain Connectivity

Posterior Cingulate Cortex / Precuneus – Converging Evidence

Gong et al. (2009) Cerebral Cortex 19, 524 Parvizi et al. (2006) PNAS 103, 1563Gusnard & Raichle (2001) NRN 2, 685 Fransson & Marrelec (2008) NeuroImage 42, 1178

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A significant proportion of brain activity is “intrinsic” or “spontaneous”, occurring even in the absence of explicit task or stimulus.

Spontaneous fluctuations in the fMRI BOLD signal exhibit consistent anatomical patterns, with functional connectivity linking several brain regions (PCC, MPF etc) into a “default mode network”.

Functional Connectivity

Endogenous Neural Activity in the Human Brain

Raichle et al. (2001) PNAS 98, 676. Greicius et al. (2003) PNAS 100, 253.Fox et al. (2005) PNAS 102, 9673. Movie: Vincent, Raichle et al. (Washington University)


Functional Networks – Modularity and Hubs

Achard et al. (2006) J. Neurosci. 26, 63

Resting-state functional networks have small-world topology, with a core of interconnected hub regions.


Functional Networks – Modularity and Hubs

Buckner et al. (2009) J. Neurosci. 29, 1860.

Network analysis of resting-state fMRIsignals identifies cortical hubs, and several of them are core components of the default mode network.

Relating Structural and Functional ConnectivityLinking Structure to Function

Direct Comparison of Structural and Functional Connections

Hagmann et al. (2008) PLoS Biol. 6, e159. Honey et al. (2009) PNAS 106, 2035.

Relation between SC and rsFC for empirical data (left) and computational model (right). Note that the fully deterministic (nonlinear and chaotic) model does not yield a “simple” linear SC-rsFC relationship.C

All Participants, All Areas

r2 = 0.62

rsF

C

SC

structural connectivity(SC)

Towards a Large-Scale Model of the Human Brain

Structural connections of the human brain predict much of the pattern seen in resting state functional connectivity.

Linking Structure to Function

Modeling Human Resting State Functional Connectivity

Honey et al. (2009) PNAS 106, 2035.

functional connectivity(rsFC) - empirical

functional connectivity(rsFC) – nonlinear model

seeds placed in PCC, MPFC

Networks in Brain Injury and Disease

Nonlocal Lesion Effects and Disconnection

The generally accepted theory according to which aphasia, agnosia, apraxia etc. are due to destruction of narrowly circumscribed appropriate praxia, gnosia, and phasia centres, must be finally discarded on the basis of more recent clinical and anatomical studies. It is just in the case of these focal symptoms that the concept of complicated dynamic disorders in the whole cortex becomes indispensable.

Constantin von Monakow (1914)

Von Monakow (1914) Die Lokalisation im Grosshirm und der Abbau der Funktion durch Kortikale HerdeCatani and Mesulam (2008) Cortex 44, 953

LichtheimAphasia

Modeled lesion locations

Effect of random or targeted node deletion

The structural network is vulnerable to deletion of highly central nodes, but more resilient to deletion of strong nodes, or randomly selected nodes.

Alstott et al. (2009) PLoS Comput Biol (in press)


Modeling the Impact of Lesions in the Human Brain

Effects of lesions in anterior cingulate (L194) or precuneus (L821)

Centrality of lesion site partially predicts lesion effects


Modeling the Impact of Lesions in the Human Brain

Alstott et al. (2009) PLoS Comput Biol (in press)


Network Hubs and Alzheimer’s Disease

Buckner et al. (2009) J. Neurosci. 29, 1860

Buckner et al. (2009)Identification of network hubs in resting state / task-evoked fcMRIMapping of Aβ deposition with PET imaging


Disturbed Connectivity in Schizophrenia

Bassett et al. (2008) J. Neurosci. 28, 9239Whitfield-Gabrieli et al. (2009) PNAS 106, 1279

Functional connectivity at rest correlates with psychopathology

Structural brain networks of people with schizophrenia show reduced hierarchy, longer connection distance, and abnormal hub distribution

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Summary

The Brain is a Complex Network Organized on Multiple ScalesFunctional states are the outcome of system-wide interactions

Brain Networks Form a Small WorldSmall-world architecture allows the brain to efficiently process information, promotes complexity

The Brain is Always Active – Even “at Rest”Endogenous processes vs. exogenous perturbations, multiple time scales

Human Brain Networks have Modules, Hubs and a Structural CoreCore located in medial parietal cortex Hubs may serve as integrators of cortico-cortical signal trafficIndividual variations – clinical disturbances

Computational Models Capture Large-Scale Human Brain ActivityPossibility of a global brain simulatorModels as tools for exploring substrates of human cognition

Funded by the JS McDonnell Foundation

Summary

Documents

NCNC 2010 - FAUbressler/NCNC10/Sporns_1-30-10.pdf · The Human Connectome Sporns et al. (2005) PLoS Comput. Biol. 1, e42. Diffusion imaging and computational tractography allow the