Dave Langers1,2
1Otorhinolaryngology, University
Medical Center Groningen, Groningen, Netherlands; 2Eaton-Peabody Laboratory,
Massachusetts Eye and Ear Infirmary, Boston, MA, United States
Spatial
Independent Component Analysis (sICA) is increasingly being used for the
analysis of fMRI datasets with unpredictable response dynamics, like in
resting state experiments. However, group-level statistical assessments are
difficult, and proper statistical characterization and validation under the
null-hypothesis are so far lacking. In the current study, a novel method is
proposed that is based on retrospective matching of individual component maps
to aggregate group maps. Selection bias is analytically predicted and
explicitly corrected for. It is shown that valid outcomes are obtained, in
the sense that the achieved specificity does not violate the imposed
confidence levels, only if bias-correction is applied. Sensitivity and
discriminatory power remain acceptable, and only moderately smaller than
those of a biased method. Finally, it is shown that the method is able to
identify significant effects of interest in an actual dataset, proving its
applicability as a group-level sICA fMRI data analysis method.