Regularized aggregation of statistical parametric maps

Li Yu Wang, Jongik Chung, Cheolwoo Park, Hosik Choi, Amanda L. Rodrigue, Jordan E. Pierce, Brett A. Clementz, Jennifer E. McDowell

Research output: Contribution to journalArticlepeer-review

1 Scopus citations


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.

Original languageEnglish
Pages (from-to)65-79
Number of pages15
JournalHuman Brain Mapping
Issue number1
StatePublished - Jan 2019


  • functional magnetic resonance imaging data
  • penalized unsupervised learning
  • robustness
  • statistical parametric map


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