Journal
INTERNATIONAL JOURNAL OF BIOSTATISTICS
Volume 12, Issue 1, Pages 31-44Publisher
WALTER DE GRUYTER GMBH
DOI: 10.1515/ijb-2015-0030
Keywords
Multivariate pattern analysis (MVPA); structural magnetic resonance imaging (MRI); confounding; inverse probability weighting; support vector machine (SVM); machine learning; predictive modeling
Funding
- NIH [R01 NS085211]
- Center for Biomedical Image Computing and Analytics at the University of Pennsylvania
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Understanding structural changes in the brain that are caused by a particular disease is a major goal of neuroimaging research. Multivariate pattern analysis (MVPA) comprises a collection of tools that can be used to understand complex disease efxcfects across the brain. We discuss several important issues that must be considered when analyzing data from neuroimaging studies using MVPA. In particular, we focus on the consequences of confounding by non-imaging variables such as age and sex on the results of MVPA. After reviewing current practice to address confounding in neuroimaging studies, we propose an alternative approach based on inverse probability weighting. Although the proposed method is motivated by neuroimaging applications, it is broadly applicable to many problems in machine learning and predictive modeling. We demonstrate the advantages of our approach on simulated and real data examples.
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