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Sporns kavli2008
- 1. Kavli Institute 2008 Brain Networks for Efficient Computation Olaf Sporns Department of Psychological and Brain Sciences Indiana University, Bloomington, IN 47405 http://www.indiana.edu/~cortex , osporns@indiana.edu Outline Brain Connectivity Network Science Approaches Brain Dynamics Structure, Function, Information, Complexity The Human Brain Building a Map of the Human Brain
- 2. Outline Brain Connectivity Brain Dynamics The Human Brain
- 3. Brain Connectivity The Brain is a Complex Network Organized on Multiple Scales 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. Sporns (2007) Brain Connectivity. www.scholarpedia.org
- 4. Brain Connectivity Structure and Function of the Brain are Intricately Linked 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. Effective Connectivity: Network of causal effects, combination of functional connectivity and structural model.
- 5. Brain Connectivity Brain Networks Form a Small World In highly evolved brains, structural brain connectivity forms a small-world (high clustering, short path length, low wiring cost, modules, hubs) Sporns and Zwi (2004) Highly clustered connection patterns at the large- scale reflect functional relations between sets of brain regions. These functional relations may be a result of clustered connectivity. Hilgetag et al., 2000 Kaiser and Hilgetag, 2006 Short path lengths indicate that all cortical areas can be linked in very few processing steps.
- 6. Outline Brain Connectivity Brain Dynamics The Human Brain
- 7. Brain Dynamics The Brainis Organized to Efficiently Extract and Coordinate Information Two major organizational principles of cortex: Segregation (anatomical/functional) clustering Integration (anatomical/functional) path length These principles are complementary and interdependent. Two major challenges for information processing in the brain: Rapid extraction of information (elimination of redundant dimensions, efficient coding, maximum information transfer) Coordination of distributed resources to create coherent states Both challenges must be solved simultaneously, within a common neural architecture.
- 8. Brain Dynamics Segregation + Integration = Complexity complexity: coexistence of complexity segregation and integration (local and global structure) C ( X ) = H ( X ) − ∑i H ( xi X − xi ). Movie courtesy of Vincent, Raichle, Snyder et al (Washington University) small-world structural network spontaneous activity in a neural model spontaneous activity in a human brain
- 9. Outline Brain Connectivity Brain Dynamics The Human Brain
- 10. The Human Brain The Brain is Always Active – Even “at Rest” Slow fluctuations in fMRI signal at rest may reflect neuronal baseline activity. Patterns of resting state BOLD signal change are consistent across subjects. Spontaneous fluctuations reveal the existence of two distributed and anti- correlated resting state networks. Damoiseaux et al., PNAS (2006) Fox et al., PNAS (2005) fMRI resting state functional networks of wavelet coefficients show small-world attributes. Small-world networks (in wavelet space) may be fractal across multiple frequency ranges. Achard et al., J Neurosci. (2006), Bassett et al., PNAS (2006)
- 11. The Human Brain Connectivity+ Dynamics = Endogenous Brain Activity Connection matrix of macaque cortex + Dynamic equations describing the physiology of a neural population = Spontaneous (endogenous) neural dynamics (chaoticity, metastability) Honey, Breakspear, Kötter, Sporns (2007) PNAS
- 12. The Human Brain Neural Dynamics Unfold on Multiple Time Scales Fast fluctuations in neural synchrony drive slower fluctuations in neural population activity. Functional brain networks reflect the small-world architecture of their underlying structural substrate (structural/functional modularity). simulated fMRI cross-correlations
- 13. The Human Brain FunctionalBrain Networks form a Variable Repertoire static pattern (anatomy) variable pattern (functional relations)
- 14. The Human Brain The Connectome is Necessary for Understanding Brain Function The human connectome represents a comprehensive structural description of the network of elements and connections forming the human brain. Proposed initial focus: thalamocortical system Possible scales of the human connectome: Microscale (neurons, synapses) Macroscale (parcellated brain regions, voxels) Mesoscale (columns, minicolumns) Most feasible approach: macroscale (first draft), followed by “filling-in” at the mesoscale. Sporns, O., Tononi, G., and Kötter, R. (2005) The human connectome: A structural description of the human brain. PLoS Comp. Biol.
- 15. The Human Brain FiberPathways of the Cerebral Cortex can be Mapped with MRI Diffusion Spectrum Imaging (DSI) and Computational Tractography Hagmann, Cammoun, Gigandet, Meuli, Honey, Wedeen, Sporns (2008) PLoS Biology
- 17. A B C RH LH
- 18. The Human Brain Human Brain Networks have a Structural Core We analyzed weighted human brain connection matrices from 5 individual subjects for a broad range of measures, including degrees/strength, small- world attributes, assortativity, motifs, centrality, efficiency. Network modularity was assessed with k-core decomposition, spectral community detection and nodal participation indices. All network analyses point to the existence of a structural core in human cortex, centered on posterior medial cortex, and comprised of cuneus/precuneus, superior parietal cortex and portions of cingulate cortex. 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 contains brain regions that form the posterior components of the human default network.
- 19. The Human Brain A scan 1 scan 2 subject A subject B subject C subject D subject E B subject A-E C
- 20. The Human Brain Human Brain Networks Have Numerous Hubs connector hub distribution centrality distribution
- 21. The Human Brain HumanBrain Networks Show Individual Variations
- 22. The Human Brain Structural and Functional Connections are Highly Correlated A B C all subjects, PCUN + PC all subjects, all areas r2 = 0.53 r2 = 0.62 C
- 23. RH LH
- 24. RH LH
- 25. The Human Brain Computational Models Capture Large-Scale Human Brain Activity Structural connections of the human brain shape functional activations and dynamic states. r = 0.85 rPC r = 0.76 r = 0.87 Honey et al. (PNAS, in revision) rsFC rsFC SC (empirical) (nonlinear model) empirical nonlinear model SC rsFC
- 26. Summary The Brain isa Complex Network Organized on Multiple Scales Structure-function relationship, plasticity, turnover, redundancy Brain Networks Form a Small World 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 a Structural Core and Hubs Core located in medial parietal cortex – a region central to self and consciousness Hubs may serve as integrators of cortico-cortical signal traffic Individual variations – clinical disturbances Computational Models Capture Large-Scale Human Brain Activity Possibility of a global brain simulator Models as tools for exploring mechanistic substrates of human cognition Funded by the JS McDonnell Foundation
- 27. Summary The Brain isa Complex Network Organized on Multiple Scales Cells to systems Scalable architecture – common principles? Structure and Function of the Brain are Intricately Linked Structure shapes function shapes structure … Reorganization and plasticity Brain Networks Form a Small World High clustering, short path length Reflects volume and processing constraints The Brain is Organized to Efficiently Extract and Coordinate Information A dual challenge addressed in a common architecture Small-world attributes map onto information processing requirements Segregation + Integration = Complexity Complexity is a mixture of randomness and regularity Complexity emerges from structural small-world networks
- 28. Summary The Brain isAlways Active – Even “at Rest” Endogenous processes vs. exogenous perturbations Connectivity + Dynamics = Endogenous Brain Activity Coupled dynamic models Metastability, itinerancy Neural Dynamics Unfold on Multiple Time Scales Milliseconds to seconds Fractal (self-similar) functional connectivity Long-term averages more stable than short-term averages Functional Brain Networks form a Variable Repertoire Cognitive microstates? Robustness versus flexibility
- 29. Summary Fiber Pathways ofthe Cerebral Cortex can be Mapped with MRI Noninvasive methodology Rapid technological development Increasingly refined maps Human Brain Networks have a Structural Core and Hubs Core located in medial parietal cortex – a region central to self and consciousness Hubs may serve as integrators of cortico-cortical signal traffic Human Brain Networks Show Individual Variations Relation to cognitive/behavioral variation unknown Network disturbances can help to diagnose brain disease Structural and Functional Connections are Highly Correlated Topological principles shared between anatomical and functional networks Endogenous brain activity – an expression of structural linkages Computational Models Capture Large-Scale Human Brain Activity Possibility of a global brain simulator Models as tools for exploring mechanistic substrates of human cognition Funded by the JS McDonnell Foundation
- 30. The Human Brain 1)High consistency of DSI tractography between hemispheres. 2) High consistency of DSI tractography in repeat scans. r2 = 0.78 scan 1 scan 2 RH r2 = 0.94 LH 3) Connection patterns are robust to degradation (simulation scanning and tractography noise). 4) Comparison between macaque DSI tractography and connection patterns derived by anatomical tract tracing shows significant overlap. 5) Comparison between structural and functional connections in human brain shows significant correlation.
- 31. Macaque Brain Imaging DSIacquisition from a single fixed m. fascicularis cortical hemisphere
- 32. Macaque Brain Imaging A Comparison of DSI tractography data with classical tract tracing neuroanatomical data B B DSI Cocomac fiber data density (symmetrized) known present unknown known absent