Notice Board

Tittle: The Virtual Brain simulation platform: Inferring principles of network interactions underlying cognition

Abstract: The challenge in studying the brain as a complex adaptive system is that complexity arises from the interactions of structure and function at different spatiotemporal scales. Modern neuroimaging can provide exquisite measures of structure and function separately, but misses the fact that the brain complexity emerges from the intersection of the two. Here is where computational modelling of brain networks can help. Models that simulate different combinations of subordinate features of behaviour of a complex system that often can only be measured invasively (e.g. local population dynamics and long-range interactions) identify the combination of features that most likely give rise to emergent behaviour that often is observable noninvasively (e.g. EEG, MEG, fMRI). We can exploit the power of large-scale network models to integrate disparate neuroimaging data sources and evaluate the potential underlying biophysical network mechanisms. This approach is now feasible because of the developments in a whole-brain simulation platform, TheVirtualBrain (TVB). TVB integrates empirical neuroimaging data from different modalities to construct biologically plausible computational models of brain network dynamics. TVB is a generative model wherein biophysical parameters for the level of cell population activity and anatomical connectivity are optimized/fitted so that they generate an individual’s observed data in humans, macaques or rodents. The inferences about brain dynamics, complexity, and the relation to cognition are thus made at the level of the biophysical features (e.g., balance of excitation and inhibition in a cell population) that generated the observed data, rather than particular features of the data (e.g. FC).