Independent component analysis of fMRI group studies by self-organizing clustering.
Independent component analysis (ICA) is a valuable technique for the multivariate data-driven analysis of functional magnetic resonance imaging (fMRI) data sets. Applications of ICA have been developed mainly for single subject studies, although different solutions for group studies have been proposed. These approaches combine data sets from multiple subjects into a single aggregate data set before ICA estimation and, thus, require some additional assumptions about the separability across subjects of group independent components. Here, we exploit the application of similarity measures and a related visual tool to study the natural self-organizing clustering of many independent components from multiple individual data sets in the subject space. Our proposed framework flexibly enables multiple criteria for the generation of group independent components and their random-effects evaluation. We present real visual activation fMRI data from two experiments, with different spatiotemporal structures, and demonstrate the validity of this framework for a blind extraction and selection of meaningful activity and functional connectivity group patterns. Our approach is either alternative or complementary to the group ICA of aggregated data sets in that it exploits commonalities across multiple subject-specific patterns, while addressing as much as possible of the intersubject variability of the measured responses. This property is particularly of interest for a blind group and subgroup pattern extraction and selection.
Esposito
Fabrizio
F
Second Division of Neurology, Second University of Naples, Italy. faesposi@unina.it
Scarabino
Tommaso
T
Hyvarinen
Aapo
A
Himberg
Johan
J
Formisano
Elia
E
Comani
Silvia
S
Tedeschi
Gioacchino
G
Goebel
Rainer
R
Seifritz
Erich
E
Di Salle
Francesco
F
D000328:Adult; D001931:Brain Mapping; D016000:Cluster Analysis; D004292:Dominance, Cerebral / Q000502:physiology; D005260:Female; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000706:statistics & numerical data*; D021621:Imaging, Three-Dimensional / Q000706:statistics & numerical data*; D008279:Magnetic Resonance Imaging / Q000706:statistics & numerical data*; D008297:Male; D009716:Numerical Analysis, Computer-Assisted; D010364:Pattern Recognition, Visual / Q000502:physiology*; D010775:Photic Stimulation; D025341:Principal Component Analysis; D012016:Reference Values; D015203:Reproducibility of Results; D014793:Visual Cortex / Q000502:physiology*