The sustained use of marijuana significantly impacts the lives and health of
people. In this study, we propose an interpretable novel framework called the
HOGAB (High-Order Attention Graph Attention Neural Networks) model to analyze
local abnormal brain activity in chronic marijuana users in two datasets. The
HOGAB integrates dynamic intrinsic functional networks with LSTM technology to
capture temporal patterns in fMRI time series of marijuana users. Moreover, we
use the high-order attention module in neighborhood nodes for information
fusion and message passing, enhancing community clustering analysis for
long-term marijuana users. Furthermore, we improve the overall learning ability
of the model by incorporating attention mechanisms, achieving an AUC of 85.1%
and an accuracy of 80.7% in multigraph classification. In addition, we compare
linear machine learning methods and evaluate the effectiveness of our proposed
HODAB model. Specifically, we identified the most relevant subnetworks and
cognitive regions that are negatively influenced by persistent marijuana use,
revealing that chronic marijuana use adversely affects cognitive control,
particularly within the Dorsal Attention and Frontoparietal networks, which are
essential for attentional, cognitive, and higher cognitive functions. The
results show that our proposed model is capable of accurately predicting
craving maps and identifying brain maps associated with long-term cravings, and
also pinpointing active areas that are important for analysis.

通过使用 HOGAB 模型，本研究发现长期使用大麻会对认知控制产生负面影响，特别是在注意力、认知和高级认知功能所需的脑网络中，以预测渴望图和识别与长期渴望相关的脑图，并确定对分析重要的活跃区域。