Network Neuroscience (Sep 2018)
Mapping hybrid functional-structural connectivity traits in the human connectome
Abstract
One of the crucial questions in neuroscience is how a rich functional repertoire of brain states relates to its underlying structural organization. How to study the associations between these structural and functional layers is an open problem that involves novel conceptual ways of tackling this question. We here propose an extension of the Connectivity Independent Component Analysis (connICA) framework to identify joint structural-functional connectivity traits. Here, we extend connICA to integrate structural and functional connectomes by merging them into common “hybrid” connectivity patterns that represent the connectivity fingerprint of a subject. We tested this extended approach on the 100 unrelated subjects from the Human Connectome Project. The method is able to extract main independent structural-functional connectivity patterns from the entire cohort that are sensitive to the realization of different tasks. The hybrid connICA extracts two main task-sensitive hybrid traits. The first trait encompasses the within and between connections of dorsal attentional and visual areas, as well as frontoparietal circuits. The second trait mainly encompasses the connectivity between visual, attentional, default mode network (DMN), and subcortical network. Overall, these findings confirm the potential of the hybrid connICA for the compression of structural/functional connectomes into integrated patterns from a set of individual brain networks. A crucial question in neuroscience is how a rich functional repertoire of brain states relates to its underlying structural organization. How to study the associations between structural and functional layers is an open problem that requires novel conceptual frameworks. We here propose an extension of our connectivity independent component analysis (connICA) framework to integrate structural and functional connectomes and obtain hybrid connectivity fingerprints. We use this method to extract two task-sensitive independent structural-functional connectivity patterns. The first encompasses the within and between connections of dorsal attentional and visual areas, as well as frontoparietal circuits. The second mainly encompasses the connectivity between visual, attentional, DMN, and subcortical networks. These findings confirm the potential of hybrid connICA for the compression of structural/functional connectomes into integrated patterns.
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