用机器学习分析脑部影像，区分阿片成瘾者与健康人。
Functional Brain Network Identification in Opioid Use Disorder Using Machine Learning Analysis of Resting-State fMRI BOLD Signals
- 基于脑区网络的时频特征，用机器学习区分患者与健康人。
- 默认模式网络和显著性网络分类准确率超70%，AUC超85%。
- 首次揭示时频分析对阿片成瘾研究的关键作用，适合神经影像研究者。
通过静息态功能磁共振成像（rs-fMRI）研究阿片类药物使用障碍（OUD）的神经机制，有助于制定更有效的治疗策略。现有研究多采用全时段平均的BOLD信号分析方法，而本研究首次利用数据驱动的机器学习技术，对关键功能网络（默认模式网络DMN、显著性网络SN、执行控制网络ECN）的局部神经活动进行时频分析，以区分OUD患者与健康对照（HC）。研究纳入31名OUD患者和45名HC，提取各网络的时频特征，并通过5折交叉验证分类实验评估其判别能力，同时考虑重要人口学特征。结果表明，DMN与SN具有最强判别力，平均F1分数分别为0.7097和0.7018，平均AUC分别为0.8378和0.8755，均显著优于随机水平（p < 0.05）。后续的Boruta机器学习分析显示，所有三组网络在小波系数上均有显著差异（p < 0.05），证实了机器学习与时频分析在OUD研究中的必要性。
原文摘要 · Abstract (English)
Understanding the neurobiology of opioid use disorder (OUD) using resting-state functional magnetic resonance imaging (rs-fMRI) may help inform treatment strategies to improve patient outcomes. Recent literature suggests time-frequency characteristics of rs-fMRI blood oxygenation level-dependent (BOLD) signals may offer complementary information to traditional analysis techniques. However, existing studies of OUD analyze BOLD signals using measures computed across all time points. This study, for the first time in the literature, employs data-driven machine learning (ML) for time-frequency analysis of local neural activity within key functional networks to differentiate OUD subjects from healthy controls (HC). We obtain time-frequency features based on rs-fMRI BOLD signals from the default mode network (DMN), salience network (SN), and executive control network (ECN) for 31 OUD and 45 HC subjects. Then, we perform 5-fold cross-validation classification (OUD vs. HC) experiments to study the discriminative power of functional network features while taking into consideration significant demographic features. The DMN and SN show the most discriminative power, significantly (p < 0.05) outperforming chance baselines with mean F1 scores of 0.7097 and 0.7018, respectively, and mean AUCs of 0.8378 and 0.8755, respectively. Follow-up Boruta ML analysis of selected time-frequency (wavelet) features reveals significant (p < 0.05) detail coefficients for all three functional networks, underscoring the need for ML and time-frequency analysis of rs-fMRI BOLD signals in the study of OUD.
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