Low-Rank Multivariate General Linear Model and One-Way Random Effect Models for Brain Response Analysis
Human brain is the central organ of human nervous system, the activity in brain becomes a significant topic in neuroscience and medical science field. New techniques for detecting brain regions activity has been developed very fast in recent year, a basic method is functional MRIs which can measure brain activities based on oxygen level in bloodstream. This work will introduce a new approach to analyze brain region relationships through low-rank multivariate general linear model and one-way random effect model. By using fMRI and low-rank multivariate general liner model, this model contains a new penalized optimization function, which can lead to smooth HRF (Hemodynamic response functions) temporally and spatially. Also, this new model is flexible to characterize variation across different regions and stimulus types, moreover, it enables information across voxels and use fewer parameters. After analysis our fMRI data through low-rank multivariate general linear model, we apply one-way random effect model to analyze the brain regions connection via multiple subjects.
Library of Congress Subject Headings
Brain--Mathematical models; Multivariate analysis; Random data (Statistics)
Applied Statistics (MS)
Yang, Yidan, "Low-Rank Multivariate General Linear Model and One-Way Random Effect Models for Brain Response Analysis" (2019). Thesis. Rochester Institute of Technology. Accessed from
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