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| 基于焦虑障碍患者及高危人群的脑电图研究 |
| 冯廷炜1,冯博1,侯依琳1,任垒1,毋琳1,李丹阳2,杨伟3,张鹏1,王步遥4,李红政5,王卉1,王秀超1*,刘旭峰1* |
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(1. 空军军医大学军事医学心理学系, 西安 710032;
2. 新疆师范大学教育科学学院, 乌鲁木齐 830054;
3. 西京学院心理咨询中心, 西安 710123;
4. 北京中医药大学东方学院心理咨询中心, 廊坊 061199;
5. 中国人民解放军联勤保障部队第九二三医院精神心理科, 南宁 530021
*通信作者) |
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| 摘要: |
| 目的 探讨焦虑障碍患者及高危人群脑电特征，为军队征兵心理选拔及多质融合理论提供客观支持。方法 招募焦虑障碍患者（焦虑障碍组，n＝38）、焦虑障碍高危者（焦虑高危组，n＝39）及健康人（正常组，n＝38）并收集其脑电数据，使用eeglab 软件对3组被试的多质融合指标［功率谱密度（PSD）、时频幅值、功能连接）进行分析，考察PSD、加权相位延迟指数（wPLI）能否用作评估焦虑障碍的脑异常指标；采用Python 2.0 Scikit-Learn包的支持向量机与K近邻分类器对3组被试进行二分类。结果 在δ、θ和α低频频段，3组被试PSD差异显著。PSD在δ频段组间主效应差异有统计学意义（F＝97.55，P＜0.001），焦虑障碍组（6.16±0.61）＞焦虑高危组（5.22±0.73）＞正常组（3.36±0.06）；PSD在θ频段组间主效应差异有统计学意义（F＝65.87，P＜0.001），焦虑障碍组（2.25±0.07）＞焦虑高危组（2.23±0.08）＞正常组（1.34±0.39）；PSD在α频段组间主效应差异有统计学意义（F＝178.73，P＜0.001），焦虑障碍组（2.02±0.45）＞焦虑高危组（1.94±0.57）＞正常组（0.98±0.02）。在β1、β2及γ高频频段，焦虑高危组前额叶（FP1、FP2）和颞叶（T3、T4）区PSD有上升波动。在β1频段焦虑障碍组与正常组的wPLI分别在TP7-FC3电极对（t＝2.45，P＜0.05）与T5-FC3电极对（t＝－3.01，P＜0.05）的差异有统计学意义。结合行为学、频域、时频和功能连接4种特征筛选指标应用于机器学习，多质融合指标较单纯行为学指标识别焦虑高危者与正常人的准确率从75.00%提高到82.61%。结论 利用机器学习对脑电指标进行分类，结合多质融合理论可作为提高区分焦虑障碍人群的潜在特征，在未来征兵心理选拔与临床评估中具有前瞻性意义。 |
| 关键词: 焦虑障碍 焦虑高危 脑电图 功能连接 机器学习 |
| DOI：10.16781/j.CN31-2187/R.20220573 |
| 投稿时间：2022-07-07修订日期：2022-12-06 |
| 基金项目:军队后勤重大项目（AKJWS221J001），空军装备综合研究重点项目（KJ2022A000415）. |
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| Electroencephalogram study on anxiety disorder patients and high-risk populations |
| FENG Tingwei1,FENG Bo1,HOU Yilin1,REN Lei1,WU Lin1,LI Danyang2,YANG Wei3,ZHANG Peng1,WANG Buyao4,LI Hongzheng5,WANG Hui1,WANG Xiuchao1*,LIU Xufeng1* |
(1. Department of Military Medical Psychology, Air Force Medical University, Xi'an 710032, Shaanxi, China;
2. College of Education Science, Xinjiang Normal University, Urumqi 830054, Xinjiang Uygur Autonomous Region, China;
3. Psychological Counselling Center, Xijing University, Xi'an 710123, Shaanxi, China;
4. Psychological Counseling Center, Oriental College, Beijing University of Chinese Medicine, Langfang 061199, Hebei, China;
5. Department of Psychiatry, No. 923 Hospital of Joint Logistics Support Force of PLA, Nanning 530021, Guangxi Zhuang Autonomous Region, China
*Corresponding authors) |
| Abstract: |
| Objective To investigate the electroencephalogram (EEG) characteristics of anxiety disorder patients and high-risk populations, and to provide Objective support for the military psychological selection and the theory of multi-quality integration. Methods The EEG data from anxiety disorder patients (anxiety disorder group, n＝38), individuals at high risk for anxiety disorders (anxiety high-risk group, n＝39), and healthy volunteers (normal group, n＝38) were collected and analyzed by the eeglab software to investigate the multi-quality integration indicators (power spectral density ［PSD］, time-frequency amplitude, and functional connectivity). Whether PSD and weighted phase lag index (wPLI) could serve as neurophysiological indicators for assessing anxiety disorder-related brain abnormalities were examined. Binary classification among the 3 groups was performed by support vector machine and K-nearest neighbors (KNN) classifiers from the Python 2.0 Scikit-Learn package. Results There were significant differences in the PSD among the 3 groups in the δ, θ, and α low-frequency bands. The main effect of the δ band between groups was significant (F＝97.55, P＜0.001), with the PSD of the anxiety disorder group (6.16±0.61)＞the high-risk anxiety group (5.22±0.73)＞the normal group (3.36±0.06). The main effect of the θ band between groups was significant (F＝65.87, P＜0.001), with the PSD of the anxiety disorder group (2.25±0.07)＞the high-risk anxiety group (2.23±0.08)＞the normal group (1.34±0.39). The main effect of the α band between groups was significant (F＝178.73, P＜0.001), with the PSD of the anxiety disorder group (2.02±0.45)＞the high-risk anxiety group (1.94±0.57)＞the normal group (0.98±0.02). In the β1, β2, and γ high-frequency bands, there were fluctuating increases in PSD in the prefrontal (FP1, FP2) and temporal (T3, T4) regions of the high-risk anxiety group. In the β1 band, the differences in wPLI between the anxiety disorder group and normal group were significant at the TP7-FC3 (t＝2.45, P＜0.05) and T5-FC3 electrode pairs (t＝－3.01, P＜0.05). By integrating behavioral, frequency domain, time-frequency, and functional connectivity features into machine learning, the multi-quality integration indicators improved the accuracy of identifying high-risk anxiety individuals from normal individuals from 75.00% to 82.61%, compared to using behavioral indicators alone. Conclusion Combination of a multi-quality integration approach with machine learning can identify distinguishing features of individuals at risk for anxiety disorders, which holds potential implications for military psychological selection and clinical assessments. |
| Key words: anxiety disorders high risk of anxiety electroencephalogram functional connectivity machine learning |
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