Analyzing Resting-State fMRI Data in Marijuana Users via High-Order Attention Brain Network

arXiv:2403.00033v1 Announce Type: cross
Abstract: 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 …

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