Spatiotemporal group ICA applied to fMRI datasets.
Exploratory data analysis techniques such as independent component analysis (ICA) do not depend on a priori hypotheses and are able to detect unknown, yet structured spatiotemporal processes in neuroimaging data. We present fMRI data of two different subject-groups (young and old), which performed a modified Wisconsin Card Sorting Test (WCST). Spatiotemporal ICA and SPM-generated brain maps of the subject data are compared. For the group analysis a singular value decomposition approach was used. Spatiotemporal ICA reveals a frontoparietal network being activated while subjects performed different variants of the WCST. Contrary to the SPM analysis, ICA analysis revealed significant differences between young and old subjects as well as significant within-group differences.While young subjects showed with increasing task demands (A>>B>>C) increasing activation of the right lateral prefrontal cortex and of the medial orbito-frontal cortex, old subjects showed no such gradient in activation pattern and appeared to be more distributed.
Kohler
Ch
Ch
Institute for Biophysics, Computational Intelligence Group, University of Regensburg, D-93040, Germany.
Keck
I
I
Gruber
P
P
Lie
Ch H
ChH
Specht
K
K
Tome
A M
AM
Lang
E W
EW
D000465:Algorithms; D003627:Data Interpretation, Statistical; D016208:Databases, Factual; D001330:Electronic Data Processing; D006801:Humans; D008279:Magnetic Resonance Imaging / Q000379:methods*; D015233:Models, Statistical; D008962:Models, Theoretical; D009483:Neuropsychological Tests; D015203:Reproducibility of Results; D012984:Software; D013647:Task Performance and Analysis; D013997:Time Factors