Efficient modeling and inference for event-related fMRI data

Authors:
Chunming Zhang;Yuefeng Lu;Tom Johnstone;Terry Oakes;Richard J. Davidson
Affiliations:
Department of Statistics, University of Wisconsin, Madison, WI 53706, USA;Eli Lilly and Company, USA;Waisman Laboratory for Brain Imaging and Behavior, University of Wisconsin, Madison, WI 53705, USA;Waisman Laboratory for Brain Imaging and Behavior, University of Wisconsin, Madison, WI 53705, USA;Waisman Laboratory for Brain Imaging and Behavior, University of Wisconsin, Madison, WI 53705, USA
Venue:
Computational Statistics & Data Analysis
Year:
2008

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Abstract

Event-related functional magnetic resonance imaging (efMRI) has emerged as a powerful technique for detecting brains' responses to presented stimuli. A primary goal in efMRI data analysis is to estimate the Hemodynamic Response Function (HRF) and to locate activated regions in human brains when specific tasks are performed. This paper develops new methodologies that are important improvements not only to parametric but also to nonparametric estimation and hypothesis testing of the HRF. First, an effective and computationally fast scheme for estimating the error covariance matrix for efMRI is proposed. Second, methodologies for estimation and hypothesis testing of the HRF are developed. Simulations support the effectiveness of our proposed methods. When applied to an efMRI dataset from an emotional control study, our method reveals more meaningful findings than the popular methods offered by AFNI and FSL.