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Alterations in brain network connectivity play an important role in the pathogenesis of schizophrenia. We investigate whether large-scale Kernelized Granger Causality (lsKGC) can capture such alterations using restingstate fMRI data. Our method utilizes dimension reduction combined with the augmentation of source timeseries in a predictive time-series model for estimating directed causal relationships among fMRI time-series. As a multivariate approach, lsKGC identifies the relationship of the underlying dynamic system in the presence of all other time-series. Here, we examine the ability of lsKGC to accurately identify schizophrenia patients from fMRI data using a subset of 31 subjects from the Centers of Biomedical Research Excellence (COBRE) data repository. We use brain connections estimated by lsKGC as features for classification. After feature extraction, we perform feature selection by Kendall’s tau rank correlation coefficient followed by classification using a support vector machine. For reference, we compare our results with cross-correlation, typically used in the literature as a standard measure of functional connectivity, and several other standard methods. Using 100 different training/test data splits with 10-fold cross-validation we obtain mean/std f1-scores of 84.87% ± 19.78% and mean Area Under the receiver operating characteristic Curve (AUC) values of 93.00% ± 16.61% across all tested numbers of features for lsKGC, which is significantly better than the results obtained with cross-correlation (AUC=53.25% ± 29.29%, f1-score=45.03% ± 30.82%), and multiple other competing methods, including partial correlation, tangent, precision, and covariance methods. Our results suggest the applicability of lsKGC as a potential imaging biomarker for schizophrenia.
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