Regularized aggregation of statistical parametric maps
| Autor: | Jordan E. Pierce, Cheolwoo Park, Li-Yu Wang, Amanda L. Rodrigue, Hosik Choi, Jongik Chung, Jennifer E. McDowell, Brett A. Clementz |
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| Rok vydání: | 2018 |
| Předmět: |
Computer science
False positives and false negatives 050105 experimental psychology 03 medical and health sciences 0302 clinical medicine Simple average Robustness (computer science) parasitic diseases Image Processing Computer-Assisted Humans 0501 psychology and cognitive sciences Radiology Nuclear Medicine and imaging Research Articles Parametric statistics Brain Mapping Radiological and Ultrasound Technology business.industry 05 social sciences Brain Pattern recognition Magnetic Resonance Imaging Neurology Data Interpretation Statistical Neurology (clinical) Artificial intelligence Anatomy business human activities 030217 neurology & neurosurgery Optimal weight Unsupervised Machine Learning |
| Zdroj: | Hum Brain Mapp |
| ISSN: | 1097-0193 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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