ArticleProceedings of SPIE--the International Society for Optical Engineering2025
Parsing disease heterogeneity in structural and functional MRI-derived measures using normative modeling and Generative Adversarial Networks (GANs).
Article in Proceedings of SPIE--the International Society for Optical Engineering, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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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.
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