  Schizophrenia (SZ) is a severe brain disorder marked by diverse cognitive
impairments, abnormalities in brain structure, function, and genetic factors.
Its complex symptoms and overlap with other psychiatric conditions challenge
traditional diagnostic methods, necessitating advanced systems to improve
precision. Existing research studies have mostly focused on imaging data, such
as structural and functional MRI, for SZ diagnosis. There has been less focus
on the integration of genomic features despite their potential in identifying
heritable SZ traits. In this study, we introduce a Multi-modal Imaging Genomics
Transformer (MIGTrans), that attentively integrates genomics with structural
and functional imaging data to capture SZ-related neuroanatomical and
connectome abnormalities. MIGTrans demonstrated improved SZ classification
performance with an accuracy of 86.05% (+/- 0.02), offering clear
interpretations and identifying significant genomic locations and brain
morphological/connectivity patterns associated with SZ.
