Modularity Reinforcement for Improving Brain Subnetwork Extraction.

Functional subnetwork extraction is commonly employed to study the brain’s modular structure. However, reliable extraction from functional magnetic resonance imaging (fMRI) data remains challenging. As representations of brain networks, brain graph estimates are typically noisy due to the pronounced noise in fMRI data. Also, confounds, such as region size bias, motion artifacts, and signal dropout, introduce region-specific bias in connectivity, e.g. a node in a signal dropout area tends to display lower connectivity. The traditional approach of global thresholding might thus remove relevant edges that have low connectivity due to confounds, resulting in erroneous subnetwork extraction. In this paper, we present a modularity reinforcement strategy that deals with the above two challenges. Specifically, we propose a local thresholding scheme that accounts for region-specific connectivity bias when pruning noisy edges. From the resulting thresholded graph, we derive a node similarity measure by comparing the adjacency structure of each node, i.e. its connection fingerprint, with that of other nodes. Drawing on the intuition that nodes belonging to the same subnetwork should have similar connection fingerprints, we refine the brain graph with this similarity measure to reinforce its modularity structure. On synthetic data, our strategy achieves higher accuracy in subnetwork extraction compared to using standard brain graph estimates. On real data, subnetworks extracted with our strategy attain higher overlaps with well-established brain systems and higher subnetwork reproducibility across a range of graph densities. Our results thus demonstrate that modularity reinforcement with our strategy provides a clear gain in subnetwork extraction.