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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Veröffentlicht in:	NeuroImage (Orlando, Fla.) Jg. 173; S. 580 - 591
Hauptverfasser:	Zhang, Tingting, Pham, Minh, Sun, Jianhui, Yan, Guofen, Li, Huazhang, Sun, Yinge, Gonzalez, Marlen Z., Coan, James A.
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	United States Elsevier Inc 01.06.2018 Elsevier Limited
Schlagworte:	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
Online-Zugang:	Volltext
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