  It has been shown in the literature that marijuana use is associated with
changes in brain network connectivity. We propose large-scale Extended Granger
Causality (lsXGC) and investigate whether it can capture such changes using
resting-state fMRI. This method combines dimension reduction with source
time-series augmentation and uses predictive time-series modeling for
estimating directed causal relationships among fMRI time-series. It is a
multivariate approach, since it is capable of identifying the interdependence
of time-series in the presence of all other time-series of the underlying
dynamic system. Here, we investigate whether this model can serve as a
biomarker for classifying marijuana users from typical controls using 126 adult
subjects with a childhood diagnosis of ADHD from the Addiction Connectome
Preprocessed Initiative (ACPI) database. 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, which is typically used in the
literature as a standard measure of functional connectivity. Within a
cross-validation scheme of 100 different training/test (90%/10%) data splits,
we obtain a mean accuracy range of [0.714, 0.985] and a mean Area Under the
receiver operating characteristic Curve (AUC) range of [0.779, 0.999] across
all tested numbers of features for lsXGC, which is significantly better than
results obtained with cross-correlation, namely mean accuracy of [0.728, 0.912]
and mean AUC of [0.825, 0.969]. Our results suggest the applicability of lsXGC
as a potential biomarker for marijuana use.
