Large‑scale Granger causality analysis of cannabis‑related network changes in the resting state with informed priors

Functional magnetic resonance imaging (fMRI) in the resting state often involves analyzing thousands of potential connections with only a few hundred time points, presenting a significant challenge in causal modeling and topology inference of brain networks. This study evaluates whether large-scale Granger Causality (lsGC)—which uses principal component analysis and autoregressive modeling—can more accurately and efficiently detect network changes associated with cannabis use compared to the PCMCI causal discovery method and a non-causal correlation baseline. The study included forty young adults, half of whom were regular cannabis users and half non-users, analyzed using the Harvard–Oxford atlas that covers 118 regions of the brain. To minimize bias, connections were ranked based on a predefined scheme related to cannabinoid-1-receptor density and known addiction neurocircuitry. The Support-vector machines were then trained on subsets of 10 to 100 edges. The results of multiple Wilcoxon signed-rank tests showed that lsGC significantly outperformed both PCMCI and the correlation approach in the low-to moderate feature range (20-50 features), achieving optimal performance with evidence-based feature selection (AUC 0.803, accuracy 0.770, F1 0.756; p ⪅ 0.001). PCMCI exhibited lower performance (AUC 0.592) and required more computation time, while the correlation method was ineffective in all feature ranges (AUC 0.562). The most discriminative connections involved limbic reward structures and frontal executive regions, which aligns with previous findings in cannabis neuroimaging studies. These findings suggest that lsGC is a practical, interpretable, and computationally efficient causal biomarker to classify cannabis use and is promising for other high-dimensional connectome studies with limited scan durations.