Regression Analysis of Neuroimaging Data Leveraging Deep Neural Networks

About this event

The complex interplay between neuroimaging data and variables of interest poses significant challenges for conventional regression models. These challenges are due to the ultra-high dimensionality, varying levels of noise, and limited sample sizes inherent to this type of data. In this talk, I will introduce a series of regression models specifically designed for neuroimaging data analysis, utilizing Deep Neural Networks (DNN) to enable more accurate statistical inference. Unlike traditional approaches, our innovative methods offer enhanced flexibility in capturing intricate patterns in brain activity, accommodating the heterogeneity in noise levels and spatial dependencies across different brain regions. I will delve into parameter estimation, inference procedures, and the theoretical underpinnings of our advanced models, ultimately demonstrating their superior performance over existing methodologies through rigorous simulations and real-world neuroimaging case studies.