Spatial-Temporal Analysis of Multi-Subject Functional Magnetic Resonance Imaging Data

Tingting Zhang, Minh Pham, Guofen Yan, Yaotian Wang, Sara Medina-DeVilliers and James A. Coan

Econometrics and Statistics, 2024, vol. 31, issue C, 117-129

Abstract: Functional magnetic resonance imaging (fMRI) is one of the most popular neuroimaging technologies used in human brain studies. However, fMRI data analysis faces several challenges, including intensive computation due to the massive data size and large estimation errors due to a low signal-to-noise ratio of the data. A new statistical model and a computational algorithm are proposed to address these challenges. Specifically, a new multi-subject general linear model is built for stimulus-evoked fMRI data. The new model assumes that brain responses to stimuli at different brain regions of various subjects fall into a low-rank structure and can be represented by a few principal functions. Therefore, the new model enables combining data information across subjects and regions to evaluate subject-specific and region-specific brain activity. Two optimization functions and a new fast-to-compute algorithm are developed to analyze multi-subject stimulus-evoked fMRI data and address two research questions of a broad interest in psychology: evaluating every subject’s brain responses to different stimuli and identifying brain regions responsive to the stimuli. Both simulation and real data analysis are conducted to show that the new method can outperform existing methods by providing more efficient estimates of brain activity.

Keywords: fMRI Data; General linear model; Low-rank representation (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:eee:ecosta:v:31:y:2024:i:c:p:117-129

DOI: 10.1016/j.ecosta.2021.02.006

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