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.
journal_name
Neuroimagejournal_title
NeuroImageauthors
Purdon PL,Solo V,Weisskoff RM,Brown ENdoi
10.1006/nimg.2001.0870keywords:
subject
Has Abstractpub_date
2001-10-01 00:00:00pages
912-23issue
4eissn
1053-8119issn
1095-9572pii
S1053-8119(01)90870-5journal_volume
14pub_type
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