TY - JOUR
T1 - A hierarchical model for simultaneous detection and estimation in multi-subject fMRI studies
AU - Degras, David
AU - Lindquist, Martin A.
N1 - Funding Information:
This research was supported by NIH grant R01EB016061.
PY - 2014/9
Y1 - 2014/9
N2 - In this paper we introduce a new hierarchical model for the simultaneous detection of brain activation and estimation of the shape of the hemodynamic response in multi-subject fMRI studies. The proposed approach circumvents a major stumbling block in standard multi-subject fMRI data analysis, in that it both allows the shape of the hemodynamic response function to vary across region and subjects, while still providing a straightforward way to estimate population-level activation. An efficient estimation algorithm is presented, as is an inferential framework that allows for not only tests of activation, but also tests for deviations from some canonical shape. The model is validated through simulations and application to a multi-subject fMRI study of thermal pain.
AB - In this paper we introduce a new hierarchical model for the simultaneous detection of brain activation and estimation of the shape of the hemodynamic response in multi-subject fMRI studies. The proposed approach circumvents a major stumbling block in standard multi-subject fMRI data analysis, in that it both allows the shape of the hemodynamic response function to vary across region and subjects, while still providing a straightforward way to estimate population-level activation. An efficient estimation algorithm is presented, as is an inferential framework that allows for not only tests of activation, but also tests for deviations from some canonical shape. The model is validated through simulations and application to a multi-subject fMRI study of thermal pain.
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U2 - 10.1016/j.neuroimage.2014.04.052
DO - 10.1016/j.neuroimage.2014.04.052
M3 - Article
C2 - 24793829
AN - SCOPUS:84904649303
SN - 1053-8119
VL - 98
SP - 61
EP - 72
JO - NeuroImage
JF - NeuroImage
ER -