  The literature manifests that schizophrenia is associated with alterations in
brain network connectivity. 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 time-series in a predictive time-series model for estimating directed
causal relationships among fMRI time-series. The lsXGC is a multivariate
approach since it identifies the relationship of the underlying dynamic system
in the presence of all other time-series. Here lsXGC serves as a biomarker for
classifying schizophrenia patients from typical controls using a subset of 62
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. As a reference method, we compare our results with
cross-correlation, typically used in the literature as a standard measure of
functional connectivity. We cross-validate 100 different training/test
(90%/10%) data split to obtain mean accuracy and a mean Area Under the receiver
operating characteristic Curve (AUC) across all tested numbers of features for
lsXGC. Our results demonstrate a mean accuracy range of [0.767, 0.940] and a
mean AUC range of [0.861, 0.983] for lsXGC. The result of lsXGC is
significantly higher than the results obtained with the cross-correlation,
namely mean accuracy of [0.721, 0.751] and mean AUC of [0.744, 0.860]. Our
results suggest the applicability of lsXGC as a potential biomarker for
schizophrenia.
