Work

Parsing disease heterogeneity in structural and functional MRI-derived measures using normative modeling and generative adversarial networks (GANs)

Year: 2025

Type: conference-paper

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.

Source: SPIE Medical Imaging: Computer-Aided Diagnosis

DOI: 10.1117/12.3040541

Authors: Sai Spandana Chintapalli, Sindhuja T. Govindarajan, Haochang Shou, Yong Fan, Hao Huang, Christos Davatzikos

Institutions: University of Pennsylvania, Children's Hospital of Philadelphia

Language: English

Topic: Functional Brain Connectivity Studies

Subfield: Cognitive Neuroscience

Field: Neuroscience

Domain: Life Sciences

SDG: Good health and well-being

Open Access status: green

Funders: National Institutes of Health, National Institute on Aging