Journal
HUMAN BRAIN MAPPING
Volume 40, Issue 1, Pages 65-79Publisher
WILEY
DOI: 10.1002/hbm.24355
Keywords
functional magnetic resonance imaging data; penalized unsupervised learning; robustness; statistical parametric map
Funding
- National Institute of Mental Health [MH094172]
- Ministry of Education [2017R1D1A1B05028565]
- National Research Foundation of Korea [2017R1D1A1B05028565]
- National Science Foundation [NSF IIS-1607919]
- National Research Foundation of Korea [2017R1D1A1B05028565] Funding Source: Korea Institute of Science & Technology Information (KISTI), National Science & Technology Information Service (NTIS)
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Combining statistical parametric maps (SPM) from individual subjects is the goal in some types of group-level analyses of functional magnetic resonance imaging data. Brain maps are usually combined using a simple average across subjects, making them susceptible to subjects with outlying values. Furthermore, t tests are prone to false positives and false negatives when outlying values are observed. We propose a regularized unsupervised aggregation method for SPMs to find an optimal weight for aggregation, which aids in detecting and mitigating the effect of outlying subjects. We also present a bootstrap-based weighted t test using the optimal weights to construct an activation map robust to outlying subjects. We validate the performance of the proposed aggregation method and test using simulated and real data examples. Results show that the regularized aggregation approach can effectively detect outlying subjects, lower their weights, and produce robust SPMs.
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