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Abstract #1139

Unbiased Group-Level Statistical Assessment of Independent Component Maps by Means of Automated Retrospective Matching

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.