Unbiased group-level statistical assessment of independent component maps by means of automated retrospective matching.
This report presents and validates a method for the group-level statistical assessment of independent component analysis (ICA) outcomes. The method is based on a matching of individual component maps to corresponding aggregate maps that are obtained from concatenated data. Group-level statistics are derived that include an explicit correction for selection bias. Outcomes were validated by means of calculations with artificial null data. Although statistical inferences were found to be incorrect if bias was neglected, the use of the proposed bias correction sufficed to obtain valid results. This was further confirmed by extensive calculations with artificial data that contained known effects of interest. While uncorrected statistical assessments systematically violated the imposed confidence level thresholds, the corrected method was never observed to exceed the allowed false positive rate. Yet, bias correction was found to result in a reduced sensitivity and a moderate decrease in discriminatory power. The method was also applied to analyze actual fMRI data. Various effects of interest that were detectable in the aggregate data were similarly revealed by the retrospective matching method. In particular, stimulus-related responses were extensive. Nevertheless, differences were observed regarding their spatial distribution. The presented findings indicate that the proposed method is suitable for neuroimaging analyses. Finally, a number of generalizations are discussed. It is concluded that the proposed method provides a framework that may supplement many of the currently available group ICA methods with validated unbiased group inferences.
Langers
Dave R M
DR
Department of Otorhinolaryngology/Head and Neck Surgery, University Medical Center Groningen, Groningen, The Netherlands. d.r.m.langers@med.umcg.nl
D000465:Algorithms; D001331:Automation; D001931:Brain Mapping / Q000379:methods*; D003198:Computer Simulation; D006801:Humans; D008279:Magnetic Resonance Imaging / Q000379:methods; D015233:Models, Statistical; D012815:Signal Processing, Computer-Assisted