He, Y.; Chan, Y. H.; Rajapakse, J. C.

Gender differences in terms of structural and functional organization of the human brain have been extensively studied, but existing works have mostly been limited to single modalities. In this paper, we propose a graph attention network architecture (BrainGAT) that uses informative subject-level features extracted from multimodal brain graphs to construct a population graph for gender classification. We show that while the extracted subject-level features can be directly used for classification, using these graph embeddings to construct a population graph further improves model performance. On the gender classification task, BrainGAT outperforms baseline models and existing multimodal modeling approaches, achieving an accuracy of 83.13% on the Human Connectome Project dataset. Salient connections highlighted by BrainGAT include connections between the inferior parietal and dorsolateral prefrontal areas of the cortex for females, while connections within the posterior cingulate cortex are highly salient for males. In sum, BrainGAT enables multimodal data to be modelled via population graphs in a parameter-efficient way.