Inter-individual alignment of multimodal brain networks with anatomical constraints.

When using a cortical parcellation, it is generally assumed that the regions correspond across subjects, meaning regions with the same label are expected to have the same structural or functional roles from one subject to any other. However, at a fine-grained scale, environmental and genetic factors shape both cortex and white matter differently, making it challenging to consistently label all parcels for every subject. Brain networks can be constructed with regions as nodes and their connectivity as edges. One critical step while constructing those networks is the definition of the nodes due to the spatial inter-individual variability, which can limit the reliability of group studies' results. In this work, we propose alignment criteria to register the brain nodes across subjects by allowing the permutation of tiny cortical regions. Those criteria account for multiple brain network perspectives built from different imaging modalities. Our across-subject multimodal alignment of brain networks includes constraints that restrict possible permutations to anatomically plausible ones. The identified permutations are finally applied to the brain networks not used for optimization, and also improve the alignment of these networks. This work has been validated on real magnetic resonance imaging data from the Human Connectome Project.