Regularized Aggregation of Statistical Parametric Maps

• Activity Number: 39 - Topics in Clustering
• Type: Contributed
• Date/Time: Sunday, July 29, 2018 : 2:00 PM to 3:50 PM
• Sponsor: Korean International Statistical Society
• Author(s): Cheolwoo Park* and Li-Yu Wang and Jongik Chung and Hosik Choi and Amanda Rodrigue and Jordan Pierce and Brett Clementz and Jennifer McDowell
• Companies: University of Georgia and University of Georgia and University of Georgia and University of Georiga and University of Georgia and University of Georgia and University of Georgia and University of Georgia
• Keywords: Functional magnetic resonance imaging data; Penalized unsupervised learning; Robustness; Statistical parametric map

Abstract

Combining statistical parametric maps (SPM) from individual subjects is the goal in some types of group-level analyses of functional magnetic resonance imaging (fMRI) 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.