Mapping hybrid functional-structural connectivity traits in the human connectome

TL;DR

This paper extends the connICA framework to identify joint structural-functional brain connectivity traits, revealing task-sensitive hybrid patterns that enhance understanding of the brain's integrated network organization.

Contribution

The authors develop a hybrid connICA method that combines structural and functional connectomes into shared connectivity patterns, enabling more comprehensive brain network analysis.

Findings

Identified two main task-sensitive hybrid connectivity traits.

Traits involve dorsal attentional, visual, fronto-parietal, DMN, and subcortical networks.

Method successfully captures integrated brain connectivity patterns.

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 test 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 extracted two main task-sensitive hybrid traits. The first, encompassing the within and between connections of dorsal attentional and visual areas, as well as fronto-parietal circuits. The second, mainly encompassing the connectivity between visual, attentional, DMN and subcortical networks. Overall, these findings confirms the potential ofthe hybrid connICA for the compression of structural/functional connectomes into integrated patterns from a set of individual brain networks.