Modelling, Analysis and Visualization of Brain Connectivity

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Presentation transcript:

Modelling, Analysis and Visualization of Brain Connectivity Jos Roerdink Institute for Mathematics and Computing Science, University of Groningen roe@cs.rug.nl www.rug.nl/informatica/onderzoek/programmas/svcg

Outline Anatomical Connectivity Functional Connectivity Effective Connectivity

Brain Connectivity Special issue on Functional Connectivity E. Bullmore,L. Harrison, L. Lee, A. Mechelli, K. Friston (eds.) Vol. 2, No. 2, 2004

Central questions How is integrated human brain function constrained anatomically? How is it organized dynamically? How can it best be modeled mathematically?

Diffusion-tensor imaging

Themes Graph theoretical analysis of brain networks measured by fMRI, EEG, PET Statistical analysis of functional and effective connectivity Dynamic causal modeling: path analytic methods for effective connectivity analysis Brain dynamics (nonlinear dynamical systems, stochastic systems)

The CoCoMac (“Collations of Connectivity data on the Macaque brain”) Database literature database of tracing data on the macaque monkey brain full transparency from published data to final output integration of redundant or contradictory published data transformation of integrated primary data into coherent brain maps

On-line interface http://www.cocomac.org Keywords searches in three data categories: literature partitioning schemes (brain maps) e.g. - Felleman & Van Essen, 1991: visual cortex - Pandya and Seltzer, 1982: parietal cortex - Hackett et al., 1998: auditory cortex Connectivity Query construction and display of results (XML output) can be automated

Connectivity Viewer Data shown for light green selected areas Existing connections: light grey Absent connections: dark blue

Problems & Perspectives omissions in published data confusing number of brain maps and varying nomenclature optimization of interfaces needs software engineering cross-species comparisons link to ontology of macaque brain structures

Functional connectivity MRI (fcMRI) Does not rely on a comparison of experimental and baseline conditions as in usual fMRI But, it detects interregional temporal correlations of blood oxygen level-dependent (BOLD) signal fluctuations Greicius et al. , Functional connectivity in the resting brain: A network analysis of the default mode hypothesis PNAS, 2003, vol. 100, no. 1, 253–258

Approach Identify regions of interest (ROIs) with increased or decreased activity during a cognitive (working memory) task For each ROI, compute an averaged times series Use the resulting time series as covariate in whole-brain statistical parametric analysis

Results posterior cingulate cortex (PCC) connectivity patterns during visual task and resting-state

Conclusions deactivation : certain regions show greater activity during rest and passive visual processing than during cognitive tasks these regions support a default mode network of brain function precise mental processes supported by the default mode network remain to be elucidated

Whole brain connectivity no-task or “resting” state fMRI volumes parcellated using anatomical template regional mean time series for 90 regions (major cortical gyri and subcortical nuclei) Statistical correlation analysis, hierarchical clustering and multidimensional scaling Salvador, Suckling, Menon & Bullmore, Neurophysiological architecture of human brain images. Submission to Cerebral Cortex, July 2004

Multidimensional Scaling Regions color-coded by membership of 6 main systems identified by hierarchical cluster analysis Lines indicate statistically significant inter-regional partial correlation

Dynamical Connectivity dynamical measures of interdependence role of nonlinear (de)synchronization alternative to statistical approaches based on covariance data dynamical systems theory (differential equations, chaotic systems, control theory)