title: Locally regularized spatiotemporal modeling and model comparison for functional MRI.
abstract: In this work we treat fMRI data analysis as a spatiotemporal system identification problem and address issues of model formulation, estimation, and model comparison. We present a new model that includes a physiologically based hemodynamic response and an empirically derived low-frequency noise model. We introduce an estimation method employing spatial regularization that improves the precision of spatially varying noise estimates. We call the algorithm locally regularized spatiotemporal (LRST) modeling. We develop a new model selection criterion and compare our model to the SPM-GLM method. Our findings suggest that our method offers a better approach to identifying appropriate statistical models for fMRI studies.
authors.affiliation: Massachusetts General Hospital NMR Center, Charlestown, Massachusetts, USA.
authors.forename: P L
authors.initials: PL
authors.lastname: Purdon
authors.forename: V
authors.initials: V
authors.lastname: Solo
authors.forename: R M
authors.initials: RM
authors.lastname: Weisskoff
authors.forename: E N
authors.initials: EN
authors.lastname: Brown
mesh_terms: D001143:Arousal / Q000502:physiology*; D016477:Artifacts; D001921:Brain / Q000098:blood supply / Q000502:physiology*; D001931:Brain Mapping; D006801:Humans; D007091:Image Processing, Computer-Assisted; D008279:Magnetic Resonance Imaging; D008959:Models, Neurological; D015233:Models, Statistical; D010100:Oxygen / Q000097:blood*; D012039:Regional Blood Flow / Q000502:physiology
chemical_list: D010100:Oxygen