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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
Mapping Human Brain Structural Connectivity
Hagmann et al. (2008) PLoS Biol. 6, e159
A B
LH RH
Brain Connectivity
Mapping Human Brain Structural Connectivity
Hagmann et al. (2008) PLoS Biol. 6, e159
The Human BrainBrain Connectivity
Mapping Human Brain Structural 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 Human BrainBrain Connectivity
The Structural Core – A Major Hub in the Brain
Hagmann et al. (2008) PLoS Biol. 6, e159
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
The Human BrainBrain Connectivity
Modules and Hubs in the Human Brain
Hagmann et al. (2008) PLoS Biol. 6, e159
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 Connectivity
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 Connectivity
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)
Networks in Brain Injury and Disease
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
Networks in Brain Injury and Disease
Modeling the Impact of Lesions in the Human Brain
Alstott et al. (2009) PLoS Comput Biol (in press)
Networks in Brain Injury and Disease
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
Networks in Brain Injury and Disease
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