Open AccessApplying FSL to the FIAC data: Model‐based and model‐free analysis of voice and sentence repetition priming
Author(s) -
Beckmann Christian F.,
Jenkinson Mark,
Woolrich Mark W.,
Behrens Timothy E.J.,
Flitney David E.,
Devlin Joseph T.,
Smith Stephen M.
Publication year - 2006
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.20246
Subject(s) - computer science , functional magnetic resonance imaging , inference , artificial intelligence , sentence , speech recognition , general linear model , priming (agriculture) , repetition priming , psychology , linear model , machine learning , neuroscience , cognition , botany , germination , biology , lexical decision task
This article presents results obtained from applying various tools from FSL (FMRIB Software Library) to data from the repetition priming experiment used for the HBM'05 Functional Image Analysis Contest. We present analyses from the model‐based General Linear Model (GLM) tool (FEAT) and from the model‐free independent component analysis tool (MELODIC). We also discuss the application of tools for the correction of image distortions prior to the statistical analysis and the utility of recent advances in functional magnetic resonance imaging (FMRI) time series modeling and inference such as the use of optimal constrained HRF basis function modeling and mixture modeling inference. The combination of hemodynamic response function (HRF) and mixture modeling, in particular, revealed that both sentence content and speaker voice priming effects occurred bilaterally along the length of the superior temporal sulcus (STS). These results suggest that both are processed in a single underlying system without any significant asymmetries for content vs. voice processing. Hum Brain Mapp, 2006. © 2006 Wiley‐Liss, Inc.