用改进的因果分析提升脑影像分类准确率，区分大麻使用者与健康人。
Enhancing Graph Attention Neural Network Performance for Marijuana Consumption Classification through Large-scale Augmented Granger Causality (lsAGC) Analysis of Functional MR Images
- 用大规模增强格兰杰因果分析提取脑区间的动态因果连接
- 结合图注意力网络，分类准确率达61.47%，优于传统相关性方法
- 适合神经影像、精神疾病研究者，尤其关注脑网络因果机制
本研究探讨了大规模增强格兰杰因果分析（lsAGC）在区分大麻使用者与对照组中的有效性，基于静息态功能磁共振成像（fMRI）数据。研究使用来自注意力缺陷多动障碍童年期诊断的60名成年人数据集（Addiction Connectome Preprocessed Initiative, ACPI），通过lsAGC提取脑网络之间的定向因果关系作为分类特征。该方法融合降维与源时间序列增强，建模时间序列预测以估计因果连接。采用图注意力神经网络（GAT）进行分类，五折交叉验证下，相关系数法平均准确率为52.98%（标准差1.65），而lsAGC方法达61.47%（标准差1.44）。结果表明，考虑脑网络的定向因果连接可显著提升分类性能，为神经影像分类提供了新思路。
原文摘要 · Abstract (English)
In the present research, the effectiveness of large-scale Augmented Granger Causality (lsAGC) as a tool for gauging brain network connectivity was examined to differentiate between marijuana users and typical controls by utilizing resting-state functional Magnetic Resonance Imaging (fMRI). The relationship between marijuana consumption and alterations in brain network connectivity is a recognized fact in scientific literature. This study probes how lsAGC can accurately discern these changes. The technique used integrates dimension reduction with the augmentation of source time-series in a model that predicts time-series, which helps in estimating the directed causal relationships among fMRI time-series. As a multivariate approach, lsAGC uncovers the connection of the inherent dynamic system while considering all other time-series. A dataset of 60 adults with an ADHD diagnosis during childhood, drawn from the Addiction Connectome Preprocessed Initiative (ACPI), was used in the study. The brain connections assessed by lsAGC were utilized as classification attributes. A Graph Attention Neural Network (GAT) was chosen to carry out the classification task, particularly for its ability to harness graph-based data and recognize intricate interactions between brain regions, making it appropriate for fMRI-based brain connectivity data. The performance was analyzed using a five-fold cross-validation system. The average accuracy achieved by the correlation coefficient method was roughly 52.98%, with a 1.65 standard deviation, whereas the lsAGC approach yielded an average accuracy of 61.47%, with a standard deviation of 1.44. The suggested method enhances the body of knowledge in the field of neuroimaging-based classification and emphasizes the necessity to consider directed causal connections in brain network connectivity analysis when studying marijuana's effects on the brain.
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