Modelling, Analysis and Visualization of Brain

Connectivity

Jos RoerdinkInstitute for Mathematics and Computing

Science, University of Groningen

roe@cs.rug.nlwww.rug.nl/informatica/onderzoek/programmas/svcg

OutlineOutline

Anatomical Connectivity Functional Connectivity Effective Connectivity

Brain ConnectivityBrain Connectivity

Special issue on

Functional Connectivity

E. Bullmore,L. Harrison,

L. Lee, A. Mechelli, K. Friston

(eds.)

Vol. 2, No. 2, 2004

Central questionsCentral questions

How is integrated human brain function constrained anatomically?

How is it organized dynamically?

How can it best be modeled mathematically?

Diffusion-tensor imagingDiffusion-tensor imaging

ThemesThemes

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

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 interfacehttp://www.cocomac.org

On-line interfacehttp://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

ConnectivityQuery construction and display of results (XML

output) can be automated

Connectivity ViewerConnectivity Viewer

Data shown for light

green selected areas

Existing connections:

light grey

Absent connections:

dark blue

Problems & PerspectivesProblems & 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)

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

ApproachApproach

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

ConclusionsConclusions

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 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 ScalingRegions color-coded by membership of 6 main systems identified by hierarchical cluster analysis

Lines indicate statistically significant inter-regional partial correlation

Dynamical ConnectivityDynamical 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)