A low-rank multivariate general linear model for multi-subject fMRI data and a non-convex optimization algorithm for brain response comparison

The focus of this paper is on evaluating brain responses to different stimuli and identifying brain regions with different responses using multi-subject, stimulus-evoked functional magnetic resonance imaging (fMRI) data. To jointly model many brain voxels’ responses to designed stimuli, we present a...

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Vydané v:	NeuroImage (Orlando, Fla.) Ročník 173; s. 580 - 591
Hlavní autori:	Zhang, Tingting, Pham, Minh, Sun, Jianhui, Yan, Guofen, Li, Huazhang, Sun, Yinge, Gonzalez, Marlen Z., Coan, James A.
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	United States Elsevier Inc 01.06.2018 Elsevier Limited
Predmet:	Algorithms Brain Brain - physiology Brain mapping Brain Mapping - methods Computer Simulation Design Estimates Experiments fMRI Functional magnetic resonance imaging Hemodynamic response function Hemodynamics - physiology Humans Linear Models Magnetic Resonance Imaging - methods Matrix factorization Methods Models, Neurological Multivariate analysis Neuroimaging Optimization Optimization algorithms Regularization Spline Time series fMRI Matrix factorization Regularization Hemodynamic response function Optimization Spline
ISSN:	1053-8119, 1095-9572, 1095-9572
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