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Changes in brain network connectivity can be observed in schizophrenia and other psychiatric diseases. We investigate whether large-scale Extended Granger Causality (lsXGC) can capture such alterations using resting-state 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, lsXGC identifies the relationship of the underlying dynamic system in the presence of all other time-series. Here, we examine the ability of lsXGC 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 lsXGC 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 87.40% ± 19.73% and mean Area Under the receiver operating characteristic Curve (AUC) values of 95.00% ± 13.69% across all tested numbers of features for lsXGC, which is significantly better than the results obtained with cross-correlation (AUC=54.75% ± 30.96%, f1-score=51.10% ± 27.54%), and multiple other competing methods, including partial correlation, tangent, precision, and covariance methods. Our results suggest the applicability of lsXGC as a potential imaging biomarker for schizophrenia.
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