About the Levin Lecture Series:
During the Fall and Spring semesters, the Department of Biostatistics holds seminars, called the Levin Lecture Series, on a wide variety of topics which are of interest to both students and faculty. The speakers are occasionally departmental faculty members themselves but very often are invited guests who spend the day of their seminar discussing their research with Biostatistics faculty and students. Lectures are in-person only.
Zhengwu Zhang, PhD
Assistant Professor
University of North Carolina, Statistics & Operations Research
Generative Models for Brain Network Data Analysis: VAEs, GANs, and Diffusion Models
Abstract:
Generative models are transformative tools for analyzing complex brain network data, enabling the capture of intricate patterns and their relationships with human traits like cognition. In this talk, we introduce generative models—specifically Variational Auto-Encoders (VAEs), Generative Adversarial Networks (GANs), and diffusion models—and their applications in neuroimaging. We begin with Graph Auto-Encoding (GATE), a VAE-based model that characterizes the population distribution of brain graphs, improving cognitive trait prediction in large datasets like ABCD and HCP. Next, we address motion artifacts in structural connectomes using a motion-invariant VAE (inv-VAE), enhancing accuracy in brain network analyses. We then discuss an interpretable GAN framework, named Disentangled Adversarial Flow (DAF), which leverages multi-source datasets to improve predictive modeling in studies with limited samples. Finally, we explore a conditional latent diffusion model for unpaired volumetric harmonization of brain MRI (HCLD), enabling efficient harmonization across sites without paired data. These advances underscore the pivotal role of generative models in enhancing neuroimaging analyses and deepening our understanding of brain structure and function.
