Multi-modal Imaging Genomics Transformer: Attentive Integration of Imaging with Genomic Biomarkers for Schizophrenia Classification

TL;DR

This paper introduces MIGTrans, a transformer-based model that effectively integrates imaging and genomic data to improve schizophrenia diagnosis and interpretability.

Contribution

It presents a novel multi-modal transformer architecture that combines genomic and neuroimaging data for enhanced schizophrenia classification.

Findings

Achieved 86.05% classification accuracy.

Identified significant genomic locations linked to SZ.

Revealed brain morphological and connectivity patterns associated with SZ.

Abstract

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.