Brain Network Alignment Using Structural and Functional Connectivity With Anatomical Constraints

Brain research often segments the cortex into regions based on an atlas, assuming a perfect matching of the regions across different subjects. However, due to genetic and environmen-tal factors, inter-subject variability makes it challenging to produce a single brain atlas with a perfect correspondence of region labels across subjects, especially at a fine-grained scale. Previous work has proposed to use structural connec-tomes constructed from diffusion magnetic resonance imaging to permute brain regions across subjects and align the cortical regions from one subject to another, leading to an improved similarity of connectomes across subjects. In this work, we propose a multimodal approach to exploit simultaneously structural and functional connectivity information in the alignment process. Spatial constraints are included to prevent unlikely permutations between remote regions. Ex-perimental results show the validity of the approach and the effectiveness of the constraint.