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.

journal_name

Neuroimage

journal_title

NeuroImage

authors

Purdon PL,Solo V,Weisskoff RM,Brown EN

doi

10.1006/nimg.2001.0870

keywords:

subject

Has Abstract

pub_date

2001-10-01 00:00:00

pages

912-23

issue

4

eissn

1053-8119

issn

1095-9572

pii

S1053-8119(01)90870-5

journal_volume

14

pub_type

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