School of Psychology, University of East London, London, UK.
School of Psychology, University of East London, London, UK.
J Affect Disord. 2025 Jan 15;369:576-587. doi: 10.1016/j.jad.2024.09.054. Epub 2024 Sep 16.
To investigate oscillatory networks in bipolar depression, effects of a home-based tDCS treatment protocol, and potential predictors of clinical response.
20 participants (14 women) with bipolar disorder, mean age 50.75 ± 10.46 years, in a depressive episode of severe severity (mean Montgomery-Åsberg Rating Scale (MADRS) score 24.60 ± 2.87) received home-based transcranial direct current stimulation (tDCS) treatment for 6 weeks. Clinical remission defined as MADRS score < 10. Resting-state EEG data were acquired at baseline, prior to the start of treatment, and at the end of treatment, using a portable 4-channel EEG device (electrode positions: AF7, AF8, TP9, TP10). EEG band power was extracted for each electrode and phase locking value (PLV) was computed as a functional connectivity measure of phase synchronization. Deep learning was applied to pre-treatment PLV features to examine potential predictors of clinical remission.
Following treatment, 11 participants (9 women) attained clinical remission. A significant positive correlation was observed with improvements in depressive symptoms and delta band PLV in frontal and temporoparietal regional channel pairs. An interaction effect in network synchronization was observed in beta band PLV in temporoparietal regions, in which participants who attained clinical remission showed increased synchronization following tDCS treatment, which was decreased in participants who did not achieve clinical remission. Main effects of clinical remission status were observed in several PLV bands: clinical remission following tDCS treatment was associated with increased PLV in frontal and temporal regions and in several frequency bands, including delta, theta, alpha and beta, as compared to participants who did not achieve clinical remission. The highest deep learning prediction accuracy 69.45 % (sensitivity 71.68 %, specificity 66.72 %) was obtained from PLV features combined from theta, beta, and gamma bands.
tDCS treatment enhances network synchronization, potentially increasing inhibitory control, which underscores improvement in depressive symptoms. Baseline EEG-based measures might aid predicting clinical response.
探讨双相抑郁中的振荡网络、家庭为基础的经颅直流电刺激（tDCS）治疗方案的效果，以及临床反应的潜在预测因子。
20 名（14 名女性）双相障碍患者，平均年龄 50.75±10.46 岁，处于严重程度的抑郁发作（平均蒙哥马利-阿斯伯格评定量表（MADRS）评分 24.60±2.87），接受家庭为基础的经颅直流电刺激（tDCS）治疗 6 周。临床缓解定义为 MADRS 评分<10。在基线、治疗开始前和治疗结束时，使用便携式 4 通道 EEG 设备（电极位置：AF7、AF8、TP9、TP10）采集静息态 EEG 数据。提取每个电极的脑电波段功率，并计算相位锁定值（PLV）作为相位同步的功能连接测量。将深度学习应用于预处理 PLV 特征，以检查临床缓解的潜在预测因子。
治疗后，11 名参与者（9 名女性）达到临床缓解。观察到抑郁症状改善与额部和颞顶叶区域通道对的 delta 波段 PLV 之间存在显著正相关。在 beta 波段 PLV 中观察到网络同步的交互效应，在颞顶叶区域，达到临床缓解的参与者在 tDCS 治疗后显示出同步性增加，而未达到临床缓解的参与者显示出同步性减少。在几个 PLV 波段中观察到临床缓解状态的主要效应：与未达到临床缓解的参与者相比，tDCS 治疗后临床缓解与额部和颞部以及几个频带（包括 delta、theta、alpha 和 beta）的 PLV 增加有关。最高的深度学习预测准确性为 69.45%（敏感性 71.68%，特异性 66.72%），来自 theta、beta 和 gamma 波段的 PLV 特征组合。
tDCS 治疗增强了网络同步性，可能增加了抑制性控制，从而强调了抑郁症状的改善。基于基线 EEG 的测量可能有助于预测临床反应。
