Regularized aggregation of statistical parametric maps.
Hum Brain Mapp
; 40(1): 65-79, 2019 01.
Article
em En
| MEDLINE
| ID: mdl-30184306
ABSTRACT
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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MEDLINE
Assunto principal:
Processamento de Imagem Assistida por Computador
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Encéfalo
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Mapeamento Encefálico
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Interpretação Estatística de Dados
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Aprendizado de Máquina não Supervisionado
Idioma:
En
Ano de publicação:
2019
Tipo de documento:
Article