Our main goal is to understand integrative aspects of brain structure and function, with an emphasis on how the connections and interactions among neural elements (neurons, populations, brain regions) give rise to brain dynamics, cognition and behavior. Our approach is to view the brain as a complex network that is embedded in a behaving organism and supports the processing and integration of information. To make sense of the brain as a complex system we employ a broad range of analysis and modeling techniques, particularly methods coming from computational neuroscience, graph theory, time series analysis, complexity and information theory. The lab is at the forefront of network models of neural systems, all modalities of brain connectivity, and the emerging field of connectomics. Many of our ongoing projects involve collaborations in brain mapping (with an emphasis on human brain data, but also inclusing non-human primate, rodent and insect brains), brain dynamics (as recorded with EEG, MEG or fMRI), development of new methods for network analysis (especially centrality and modularity), and individual differences in brain networks across healthy populations as well as disturbances in brain injury and disease (ADHD, schizophrenia).
Mika Rubinov (University of Cambridge), Olaf Sporns and a growing number of contributors worldwide maintain an open-source Matlab toolbox for brain network analysis, the Brain Connectivity Toolbox.