Choi Kang-Min, Lee Taegyeong, Im Chang-Hwan, Lee Seung-Hwan
Clinical Emotion and Cognition Research Laboratory, Inje University, Goyang, Republic of Korea.
School of Electronic Engineering, Hanyang University, Seoul, Republic of Korea.
Front Psychiatry. 2024 Oct 17;15:1469645. doi: 10.3389/fpsyt.2024.1469645. eCollection 2024.
Recent resting-state electroencephalogram (EEG) studies have consistently reported an association between aberrant functional brain networks (FBNs) and treatment-resistant traits in patients with major depressive disorder (MDD). However, little is known about the changes in FBNs in response to external stimuli in these patients. This study investigates whether changes in the salience network (SN) could predict responsiveness to pharmacological treatment in resting-state and external stimuli conditions.
Thirty-one drug-naïve patients with MDD (aged 46.61 ± 10.05, female 28) and twenty-one healthy controls (aged 43.86 ± 14.14, female 19) participated in the study. After 8 weeks of pharmacological treatment, the patients were divided into non-remitted MDD (nrMDD, n = 14) and remitted-MDD (rMDD, n = 17) groups. EEG data under three conditions (resting-state, standard, and deviant) were analyzed. The SN was constructed with three cortical regions as nodes and weighted phase-lag index as edges, across alpha, low-beta, high-beta, and gamma bands. A repeated measures analysis of the variance model was used to examine the group-by-condition interaction. Machine learning-based classification analyses were also conducted between the nrMDD and rMDD groups.
A notable group-by-condition interaction was observed in the high-beta band between nrMDD and rMDD. Specifically, patients with nrMDD exhibited hypoconnectivity between the dorsal anterior cingulate cortex and right insula (p = 0.030). The classification analysis yielded a maximum classification accuracy of 80.65%.
Our study suggests that abnormal condition-dependent changes in the SN could serve as potential predictors of pharmacological treatment efficacy in patients with MDD.
近期的静息态脑电图（EEG）研究一致报道，重度抑郁症（MDD）患者的异常功能性脑网络（FBNs）与治疗抵抗特征之间存在关联。然而，对于这些患者在外部刺激下FBNs的变化知之甚少。本研究调查了突显网络（SN）的变化是否能够预测静息态和外部刺激条件下药物治疗的反应性。
31例未服用过药物的MDD患者（年龄46.61±10.05岁，女性28例）和21名健康对照者（年龄43.86±14.14岁，女性19例）参与了本研究。经过8周的药物治疗后，患者被分为未缓解的MDD（nrMDD，n = 14）和缓解的MDD（rMDD，n = 17）组。分析了三种条件（静息态、标准和偏差）下的EEG数据。以三个皮质区域为节点，加权相位滞后指数为边，在α、低β、高β和γ频段构建SN。采用方差模型的重复测量分析来检验组间条件交互作用。还在nrMDD组和rMDD组之间进行了基于机器学习的分类分析。
在nrMDD组和rMDD组之间的高β频段观察到显著的组间条件交互作用。具体而言，nrMDD患者的背侧前扣带回皮质和右侧岛叶之间表现出连接性降低（p = 0.030）。分类分析的最大分类准确率为80.65%。
我们的研究表明，SN中异常的条件依赖性变化可能作为MDD患者药物治疗疗效的潜在预测指标。
