Regularized aggregation of statistical parametric maps

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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.
Publisher
WILEY
Issue Date
2019-01
Language
English
Article Type
Article
Citation

HUMAN BRAIN MAPPING, v.40, no.1, pp.65 - 79

ISSN
1065-9471
DOI
10.1002/hbm.24355
URI
http://hdl.handle.net/10203/285772
Appears in Collection
MA-Journal Papers(저널논문)
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