BraiNavLab: Dynamics and Brain Connectivity

Modelling brain connectivity allows characterizing the interplay among different regions, either in the form of functional dependencies (functional and effective connectivity) or backbone of connections (structural connectivity). Besides this, understanding the relationship between such different forms of connectivity and disentangling the link between structural (physical) connections and functionality represents one of the current hot topics in the field. Functional measures are usually derived from functional MRI (BOLD and ASL) or electroencephalographic recordings (EEG) during task activities or while resting, enabling capturing patterns of statistical dependence among neural elements. Moreover, focusing on the temporal dynamics of functional connectivity allows to uncover how brain activity evolves across the brain regions and over time. These approaches result into spatial dynamic states and associated summary measures, in particular persistence, transition, frequency and dwell time that can represent novel indices to discriminate across pathological conditions. In case of effective connectivity, approaches such as dynamic causal modeling, Granger causality and adaptive directed transfer function can be applied to further inform on the directed causal effects. From a structural point of view, the connectivity is represented by the white matter streamlines linking the different brain regions. Such a link can be quantified by the normalized number of fibers or by summary statistics of microstructural indices collected along the fiber bundles (microstructure-informed connectome). Brain structure and function can be finally represented in the form of brain networks, comprising a set of nodes (neural elements) and edges (their mutual connections). These networks can be further examined with network science methods extracting measures of segregation, integration or influence.