J A. Welge1,2, Richard A. Komoroski2
1Environmental Health, University of Cincinnati, Cincinnati, OH, United States; 2Center for Imaging Research, University of Cincinnati, Cincinnati, OH, United States
Using prior 31P NMR data for the composition of phospholipid (PL) and PL metabolites in postmortem schizophrenic and matched control brains, we searched for multivariate regression models to classify these samples. Because the number of measurements exceeded the number of samples, variable selection was required. We employed Akaikes Information Criterion in conjunction with repeated cross-validation using random splits of the data into model-building and validation subsets. This procedure addressed the risk of over-fitting the sample data and generated predictions from data not used to select the model. Certain metabolites that were not individually significant produced accurate classification when modeled jointly.