Title: Spatial temporal analysis of multi-subject fMRI data
Authors: Tingting Zhang - University of Virginia (United States) [presenting]
Abstract: Functional magnetic resonance imaging (fMRI) data analysis faces several challenges, including extensive computation and difficulty in obtaining statistically efficient estimates of the brain responses. We propose a new statistical model and computational algorithm to address these challenges. Specifically, we develop a new multi-subject, low-rank model within the general linear model framework for stimulus-evoked fMRI data. The new model assumes that the brain responses of different brain regions and subjects fall into a low-rank structure and can be represented by a few principal functional shapes. As such, the new model enables borrowing information across subjects and regions and increasing the ensuing estimation efficiency of brain responses, while accommodating the variation of brain activities across subjects, stimulus types, and regions. We propose two different optimization functions and a new fast-to-compute algorithm to address two research questions of broad interest in psychology studies: evaluating brain responses to different stimuli and identifying brain regions with different responses. Through both simulation and real data analysis, we show that the new method can outperform the existing methods by providing more efficient estimates of brain responses to designed stimuli.