Key Laboratory of Cognition and Personality of the Ministry of Education, Southwest University, 400715, Chongqing, China.
Chongqing Tongnan Teacher Training College, 402600, Chongqing, China.
Brain Topogr. 2022 Jul;35(4):481-494. doi: 10.1007/s10548-022-00905-0. Epub 2022 Jul 5.
Previous studies on resting-state electroencephalographic responses in patients with depressive disorders have identified electroencephalogram (EEG) parameters as potential biomarkers for the early detection and diagnosis of depressive disorders. However, these studies did not investigate the relationship between resting-state EEG microstates and the early detection of depressive symptoms in preclinical individuals. To explore the possible association between resting-state EEG microstate temporal dynamics and depressive symptoms among college students, EEG microstate analysis was performed on eyes-closed resting-state EEG data for approximately 5 min from 34 undergraduates with high intensity of depressive symptoms and 34 age- and sex-matched controls with low intensity of depressive symptoms. Five microstate classes (A-E) were identified to best explain the datasets of both groups. Compared to controls, the mean duration, occurrence, and coverage of microstate class B increased significantly, whereas the occurrence and coverage of microstate classes D and E decreased significantly in individuals with high intensity of depressive symptoms. Additionally, the presence of microstate class B was positively correlated with participants' Beck Depression Inventory-II (BDI-II) scores, and the presence of microstate classes D and E were negatively correlated with their BDI-II scores. Further, individuals with high intensity of depressive symptoms had higher transition probabilities of A→B, B→A, B→C, B→D, and C→B, with lower transition probabilities of A→D, A→E, D→A, D→E, E→A, E→C, and E→D than controls. These results highlight resting-state EEG microstate temporal dynamics as potential biomarkers for the early detection and timely treatment of depression in college students.
先前关于抑郁症患者静息态脑电图反应的研究已经确定了脑电图（EEG）参数作为早期检测和诊断抑郁症的潜在生物标志物。然而，这些研究并没有调查静息态脑电图微状态与临床前个体中抑郁症状的早期检测之间的关系。为了探索静息态脑电图微状态时间动态与大学生抑郁症状之间的可能关联，对 34 名高强度抑郁症状的大学生和 34 名低强度抑郁症状的年龄和性别匹配对照者进行了大约 5 分钟闭眼静息态 EEG 数据的脑电图微状态分析。为了最好地解释两组数据集，确定了五个微状态类（A-E）。与对照组相比，高强度抑郁症状个体的微状态类 B 的平均持续时间、出现次数和覆盖度显著增加，而微状态类 D 和 E 的出现次数和覆盖度显著降低。此外，微状态类 B 的存在与参与者的贝克抑郁量表第二版（BDI-II）评分呈正相关，微状态类 D 和 E 的存在与他们的 BDI-II 评分呈负相关。此外，高强度抑郁症状个体的 A→B、B→A、B→C、B→D 和 C→B 的跃迁概率较高，而 A→D、A→E、D→A、D→E、E→A、E→C 和 E→D 的跃迁概率较低。这些结果强调了静息态脑电图微状态时间动态作为大学生早期检测和及时治疗抑郁的潜在生物标志物。
