Paper 13407-114
Paper 13407-114
Parsing disease heterogeneity using normative modelling and generative adversarial networks (GANs)
19 February 2025 • 5:30 PM - 7:00 PM PST | Golden State Ballroom
Abstract
We introduce a novel Generative Adversarial Network (GAN)-based normative modeling technique for analyzing MRI derived brain measures and detecting heterogeneous effects of brain disorders. The proposed method learns to synthesize a patient-specific control by removing disease-related variations from brain measures while preserving disease-unrelated variations. The deviation of the patient from the synthesized control, acts as a personalized image-derived biomarker that is sensitive to disease effects and their severity. In the current study, we demonstrate the method's utility by applying it to detect deviations in brain measures derived from two different imaging modalities: 1) using structural MRI derived brain measures to detect neuroanatomical deviations in participants with Alzheimer's disease, and 2) using functional MRI derived brain measures to detect functional connectivity deviations in participants exposed to traumatic brain injury.
Presenter
Sai Spandana Chintapalli
Univ. of Pennsylvania (United States)
Spandana Chintapalli is a passionate and driven PhD student in bioengineering at the University of Pennsylvania. With an MSc in Biomedical Engineering, she has built a strong foundation in the principles and applications of engineering in the medical field. Her research focuses on the intersection of advanced computational models and healthcare, particularly in the development of innovative solutions for disease diagnosis and prognosis.
At the University of Pennsylvania, Spandana is engaged in exciting research that leverages machine learning and artificial intelligence to address complex medical challenges. She is particularly interested in the application of Generative Adversarial Networks (GANs) for mapping and analyzing neuroimaging data to understand disease progression and brain aging. Her work aims to develop models that can accurately detect disease effects and provide insights into individual-level variations, which are crucial for personalized medicine.