Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, California.
Wu Tsai Neuroscience Institute, Stanford University, Stanford, California.
JAMA Psychiatry. 2020 Apr 1;77(4):397-408. doi: 10.1001/jamapsychiatry.2019.3867.
Despite the widespread awareness of functional magnetic resonance imaging findings suggesting a role for cortical connectivity networks in treatment selection for major depressive disorder, its clinical utility remains limited. Recent methodological advances have revealed functional magnetic resonance imaging-like connectivity networks using electroencephalography (EEG), a tool more easily implemented in clinical practice.
To determine whether EEG connectivity could reveal neural moderators of antidepressant treatment.
DESIGN, SETTING, AND PARTICIPANTS: In this nonprespecified secondary analysis, data were analyzed from the Establishing Moderators and Biosignatures of Antidepressant Response in Clinic Care study, a placebo-controlled, double-blinded randomized clinical trial. Recruitment began July 29, 2011, and was completed December 15, 2015. A random sample of 221 outpatients with depression aged 18 to 65 years who were not taking medication for depression was recruited and assessed at 4 clinical sites. Analysis was performed on an intent-to-treat basis. Statistical analysis was performed from November 16, 2018, to May 23, 2019.
Patients received either the selective serotonin reuptake inhibitor sertraline hydrochloride or placebo for 8 weeks.
Electroencephalographic orthogonalized power envelope connectivity analyses were applied to resting-state EEG data. Intent-to-treat prediction linear mixed models were used to determine which pretreatment connectivity patterns were associated with response to sertraline vs placebo. The primary clinical outcome was the total score on the 17-item Hamilton Rating Scale for Depression, administered at each study visit.
Of the participants recruited, 9 withdrew after first dose owing to reported adverse effects, and 221 participants (150 women; mean [SD] age, 37.8 [12.7] years) underwent EEG recordings and had high-quality pretreatment EEG data. After correction for multiple comparisons, connectome-wide analyses revealed moderation by connections within and between widespread cortical regions-most prominently parietal-for both the antidepressant and placebo groups. Greater alpha-band and lower gamma-band connectivity predicted better placebo outcomes and worse antidepressant outcomes. Lower connectivity levels in these moderating connections were associated with higher levels of anhedonia. Connectivity features that moderate treatment response differentially by treatment group were distinct from connectivity features that change from baseline to 1 week into treatment. The group mean (SD) score on the 17-item Hamilton Rating Scale for Depression was 18.35 (4.58) at baseline and 26.14 (30.37) across all time points.
These findings establish the utility of EEG-based network functional connectivity analyses for differentiating between responses to an antidepressant vs placebo. A role emerged for parietal cortical regions in predicting placebo outcome. From a treatment perspective, capitalizing on the therapeutic components leading to placebo response differentially from antidepressant response should provide an alternative direction toward establishing a placebo signature in clinical trials, thereby enhancing the signal detection in randomized clinical trials.
ClinicalTrials.gov identifier: NCT01407094.
尽管功能磁共振成像发现表明皮质连接网络在治疗选择中对重度抑郁症有作用,但它的临床应用仍然有限。最近的方法学进展揭示了使用脑电图(EEG)的类似于功能磁共振成像的连接网络,这是一种在临床实践中更容易实施的工具。
确定 EEG 连接是否可以揭示抗抑郁治疗的神经调节因素。
设计、地点和参与者:在这项非预设的二次分析中,对来自于“在临床护理中确定抗抑郁反应的调节因子和生物标志物”的研究数据进行了分析,这是一项安慰剂对照、双盲随机临床试验。招募于 2011 年 7 月 29 日开始,于 2015 年 12 月 15 日完成。在四个临床地点招募了 221 名年龄在 18 至 65 岁之间、未服用抗抑郁药物的门诊抑郁症患者,并进行了评估。分析基于意向治疗进行。统计分析于 2018 年 11 月 16 日至 2019 年 5 月 23 日进行。
患者接受选择性 5-羟色胺再摄取抑制剂盐酸舍曲林或安慰剂治疗 8 周。
对静息状态 EEG 数据进行正交功率包络连接分析。意向治疗预测线性混合模型用于确定哪些治疗前连接模式与舍曲林与安慰剂的反应相关。主要临床结局是在每次研究访视时进行的 17 项汉密尔顿抑郁量表的总分。
在招募的参与者中,有 9 人在首次服药后因报告的不良反应而退出,221 名参与者(150 名女性;平均[标准差]年龄为 37.8[12.7]岁)进行了 EEG 记录,并具有高质量的治疗前 EEG 数据。经过多次比较校正后,连接组全分析显示,抗抑郁组和安慰剂组的连接都受到广泛皮质区域内和区域间的调节,其中最明显的是顶叶。alpha 波段和较低的 gamma 波段连接性越高,预示着安慰剂效果越好,抗抑郁效果越差。这些调节连接中连接水平越低,与快感缺失程度越高相关。调节治疗反应的连接特征因治疗组而异,与从基线到治疗第 1 周的变化特征不同。17 项汉密尔顿抑郁量表的平均(SD)基线得分是 18.35(4.58),所有时间点的得分均为 26.14(30.37)。
这些发现确立了基于脑电图的网络功能连接分析在区分抗抑郁药与安慰剂反应方面的效用。顶叶皮质区域在预测安慰剂反应方面发挥了作用。从治疗的角度来看,利用导致安慰剂反应与抗抑郁反应不同的治疗成分,应该为在临床试验中建立安慰剂特征提供一个替代方向,从而增强随机临床试验中的信号检测。
ClinicalTrials.gov 标识符:NCT01407094。