  The excessive consumption of marijuana can induce substantial psychological
and social consequences. In this investigation, we propose an elucidative
framework termed high-order graph attention neural networks (HOGANN) for the
classification of Marijuana addiction, coupled with an analysis of localized
brain network communities exhibiting abnormal activities among chronic
marijuana users. HOGANN integrates dynamic intrinsic functional brain networks,
estimated from resting-state functional magnetic resonance imaging (rs-fMRI),
using long short-term memory (LSTM) to capture temporal network dynamics. We
employ a high-order attention module for information fusion and message passing
among neighboring nodes, enhancing the network community analysis. Our model is
validated across two distinct data cohorts, yielding substantially higher
classification accuracy than benchmark algorithms. Furthermore, we discern the
most pertinent subnetworks and cognitive regions affected by persistent
marijuana consumption, indicating adverse effects on functional brain networks,
particularly within the dorsal attention and frontoparietal networks.
Intriguingly, our model demonstrates superior performance in cohorts exhibiting
prolonged dependence, implying that prolonged marijuana usage induces more
pronounced alterations in brain networks. The model proficiently identifies
craving brain maps, thereby delineating critical brain regions for analysis.
