Spatiotemporal Mixed Modeling of Multi-Subject fMRI via Method of Moments

• Activity Number: 420
• Type: Topic Contributed
• Date/Time: Tuesday, August 2, 2016 : 2:00 PM to 3:50 PM
• Sponsor: Section on Statistics in Imaging
• Author(s): Benjamin Risk * and David Matteson and R. Nathan Spreng and David Ruppert
• Companies: Statistical and Applied Mathematical Sciences Institute and Cornell University and Cornell University and Cornell University
• Keywords: Best linear unbiased prediction ; Covariogram ; Neuroimaging ; Smoothing ; task fMRI

Abstract

Estimating spatiotemporal models for multi-subject fMRI is computationally challenging. We propose a mixed model for localization studies with locally stationary spatial random effects and time series errors. We develop method-of-moment estimators that leverage population and spatial information and are scalable to massive datasets. In simulations, subject-specific estimates of activation are considerably more accurate than the standard voxel-wise general linear model. Our mixed model also allows for valid population inference. We apply our model to cortical data from a motor task from the Human Connectome Project. Whereas the subject-specific maps based on the univariate approach appear noisy, our approach includes model-based smoothing that results in clearly delineated regions of activation. Subject-specific maps of activation from task fMRI are increasingly used in pre-surgical planning for tumor removal and in locating targets for transcranial magnetic stimulation. Our findings suggest that using spatial and population information is a promising avenue for improving clinical neuroimaging.