  Graph convolutional networks (GCNs) allow to apply traditional convolution
operations in non-Euclidean domains, where data are commonly modelled as
irregular graphs. Medical imaging and, in particular, neuroscience studies
often rely on such graph representations, with brain connectivity networks
being a characteristic example, while ultimately seeking the locus of
phenotypic or disease-related differences in the brain. These regions of
interest (ROIs) are, then, considered to be closely associated with function
and/or behaviour. Driven by this, we explore GCNs for the task of ROI
identification and propose a visual attribution method based on class
activation mapping. By undertaking a sex classification task as proof of
concept, we show that this method can be used to identify salient nodes (brain
regions) without prior node labels. Based on experiments conducted on
neuroimaging data of more than 5000 participants from UK Biobank, we
demonstrate the robustness of the proposed method in highlighting reproducible
regions across individuals. We further evaluate the neurobiological relevance
of the identified regions based on evidence from large-scale UK Biobank
studies.
