Open AccessActivated region fitting: A robust high‐power method for fMRI analysis using parameterized regions of activation
Author(s) -
Weeda Wouter D.,
Waldorp Lourens J.,
Christoffels Ingrid,
Huizenga Hilde M.
Publication year - 2009
Publication title -
human brain mapping
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.005
H-Index - 191
eISSN - 1097-0193
pISSN - 1065-9471
DOI - 10.1002/hbm.20697
Subject(s) - voxel , smoothness , gaussian , computer science , power (physics) , parameterized complexity , statistical power , hum , signal (programming language) , pattern recognition (psychology) , interpretation (philosophy) , artificial intelligence , noise (video) , algorithm , mathematics , statistics , physics , art , mathematical analysis , quantum mechanics , performance art , programming language , image (mathematics) , art history
An important issue in the analysis of fMRI is how to account for the spatial smoothness of activated regions. In this article a method is proposed to accomplish this by modeling activated regions with Gaussian shapes. Hypothesis tests on the location, spatial extent, and amplitude of these regions are performed instead of hypothesis tests of individual voxels. This increases power and eases interpretation. Simulation studies show robust hypothesis tests under misspecification of the shape model, and increased power over standard techniques especially at low signal‐to‐noise ratios. An application to real single‐subject data also indicates that the method has increased power over standard methods. Hum Brain Mapp 2009. © 2009 Wiley‐Liss, Inc.