Parsing disease heterogeneity in structural and functional MRI-derived measures using normative modeling and Generative Adversarial Networks (GANs).

Abstract

We present a preliminary analysis of a GAN-based normative modeling technique for capturing individual-level deviations in brain measures, addressing heterogeneity in neurological disorders. By leveraging self-supervised training on pseudo-synthetically simulated patient data, our method detects disease-related effects without the need for large, disease-specific datasets. We demonstrate the versatility of this approach by applying it to structural MRI and resting-state fMRI data, identifying neuroanatomical and functional connectivity deviations in Alzheimer's disease (AD) and Traumatic Brain Injury (TBI). This model's ability to accurately capture disease-related abnormalities in brain measures highlights its potential as a powerful tool for personalized diagnosis and the study of brain disorders, opening new avenues for research.