The human brain is hard to study and analysis, not because of the complexity of the brain structure, such as neurons and neurons connections, but also because of the complexity of the brain activities. Since in different scales, for example, different time series, different physical senses, the measurements of the human brain activities can be varied. Tring to measure the brain regions relationships in person, the Functional Magnetic Resonance Imaging (fMRI) is one of the methods. The focus of this paper is on analyzing human brain regions relationships in different time domains and different scans of fMRI by using low-rank multivariate general linear model (LRMGLM). The function of the model is to penalize optimization and characterize variation across different regions and stimulus in hemodynamic response functions (HRFs). After analyzing the fMRI data with LRMGLM model, we also analyzed data by methods of Cross Validation and Principal Components Analysis (PCA).
Library of Congress Subject Headings
Brain--Magnetic resonance imaging--Statistics; Brain--Physiology--Mathematical models; Brain mapping--Statistical methods
Applied Statistics (MS)
Song, Ge, "Low-Rank Multivariate General Linear Model with Relationship between Brain Regions and Time Points" (2019). Thesis. Rochester Institute of Technology. Accessed from
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