Data Driven Classification Using fMRI Network Measures: Application to Schizophrenia.

ABSTRACT: Using classification to identify biomarkers for various brain disorders has become a common practice among the functional MR imaging community. Typical classification pipeline includes taking the time series, extracting features from them, and using them to classify a set of patients and healthy controls. The most informative features are then presented as novel biomarkers. In this paper, we compared the results of single and double cross validation schemes on a cohort of 170 subjects with schizophrenia and healthy control subjects. We used graph theoretic measures as our features, comparing the use of functional and anatomical atlases to define nodes and the effect of prewhitening to remove autocorrelation trends. We found that double cross validation resulted in a 20% decrease in classif

SUBMITTER: Moghimi P 

PROVIDER: S-EPMC6218612 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature