Graph attention transformers and large-scale granger causality to classify marijuana consumption from functional MR images

In this study, we explore the potential of large-scale Granger Causality (lsGC) estimated brain network connectivity as a biomarker for classifying marijuana users from typical controls using resting-state functional Magnetic Resonance Imaging (fMRI). It is well-established in the literature that marijuana use is associated with alterations in brain network connectivity, and we investigate whether lsGC can effectively capture such changes. The lsGC method, a multivariate approach based on dimension reduction and predictive time-series modeling, allows for estimating directed causal relationships among fMRI time series, considering the interdependence of time series within the underlying dynamic system. We employ a dataset consisting of 60 adult subjects with a childhood diagnosis of ADHD from the Addiction Connectome Preprocessed Initiative (ACPI) database. Brain connections estimated using lsGC are extracted as features for classification. We utilize a Graph Attention Neural Network (GAT) to accomplish the classification task. The GAT model is specifically chosen for its ability to leverage graph-based data and capture complex interactions between brain regions, making it well-suited for handling fMRI-based brain connectivity data. To assess the performance of our approach, we employ a cross-validation scheme with five-fold cross-validation. The mean accuracy computed for the correlation coefficient method is approximately 53.78%, with a standard deviation of about 4.80, while the mean accuracy for our approach, lsGC, is approximately 64.89%, with a standard deviation of 1.10. The findings suggest that lsGC, in conjunction with a Graph Attention Neural Network, holds promise as a potential biomarker for identifying marijuana users, providing a more effective and reliable classification approach than conventional functional connectivity measures. The proposed methodology offers a valuable contribution to neuroimaging-based classification studies and highlights the importance of considering directed causal relationships in brain network connectivity analysis when investigating the impact of marijuana use on the brain.