Modern Gaussian Processes for Neuroimaging Data Analysis

Abstract: Recent advances in neuroimaging have produced massive and heterogeneous datasets, ranging from fMRI with high spatial resolution to EEG with high temporal resolution, characterized by complex spatiotemporal correlations and substantial inter-subject variability. Traditional regression models and Gaussian process (GP) approaches with fixed parametric kernels often fail to model such complex data effectively while maintaining scalability and interpretability. This talk introduces a family of modern Bayesian GP frameworks that integrate deep kernel learning, neural network priors, and geometric modeling for large-scale neuroimaging analysis. An example is the Deep Kernel Learning Process (DKLP), which embeds deep neural networks within GP priors to learn data-adaptive covariance structures directly from imaging data. DKLP provides a unified modeling foundation for image-on-scalar, scalar-on-image, and image-on-image regression, supported by theoretical guarantees and efficient posterior computation. Applications to fMRI data from the Adolescent Brain Cognitive Development (ABCD) study reveal reproducible cortical activation patterns associated with cognitive ability, while analyses of EEG-based brain–computer interface data demonstrate robust neural decoding under high noise. I will also discuss scalable heat-kernel GPs on manifolds and thresholded GP–based spatially varying neural network priors, which together expand the scope of Bayesian inference for complex neuroimaging data.
 

Bio: Dr. Jian Kang is Professor and Associate Chair for Research in the Department of Biostatistics at the University of Michigan. His research lies at the intersection of Bayesian statistics, machine learning, and artificial intelligence, with applications in neuroimaging, brain–computer interfaces, omics, and precision medicine. He has published more than 175 papers in leading statistics, machine learning, and biomedical journals. Dr. Kang has served as an Associate Editor for several premier statistical journals, including the Journal of the American Statistical Association (JASA), The Annals of Applied Statistics (AOAS) and Biometrics. He is a Fellow of both the Institute of Mathematical Statistics (IMS) and the American Statistical Association (ASA). He currently serves as Chair of the ASA Section on Statistics in Imaging.