Jaworska Natalia, de la Salle Sara, Ibrahim Mohamed-Hamza, Blier Pierre, Knott Verner
Institute of Mental Health Research, University of Ottawa, Ottawa, ON, Canada.
Cellular & Molecular Medicine, Faculty of Medicine, University of Ottawa, Ottawa, ON, Canada.
Front Psychiatry. 2019 Jan 14;9:768. doi: 10.3389/fpsyt.2018.00768. eCollection 2018.
Individuals with major depressive disorder (MDD) vary in their response to antidepressants. However, identifying objective biomarkers, prior to or early in the course of treatment that can predict antidepressant efficacy, remains a challenge. Individuals with MDD participated in a 12-week antidepressant pharmacotherapy trial. Electroencephalographic (EEG) data was collected before and 1 week post-treatment initiation in 51 patients. Response status at week 12 was established with the Montgomery-Asberg Depression Scale (MADRS), with a ≥50% decrease characterizing responders ( = 27/24 responders/non-responders). We used a machine learning (ML)-approach for predicting response status. We focused on Random Forests, though other ML methods were compared. First, we used a tree-based estimator to select a relatively small number of significant features from: (a) demographic/clinical data (age, sex, individual item/total MADRS scores at baseline, week 1, change scores); (b) scalp-level EEG power; (c) source-localized current density (via exact low-resolution electromagnetic tomography [eLORETA] software). Second, we applied kernel principal component analysis to reduce and map important features. Third, a set of ML models were constructed to classify response outcome based on mapped features. For each dataset, predictive features were extracted, followed by a model of all predictive features, and finally by a model of the predictive features. Fifty eLORETA features were predictive of response (across bands, both time-points); alpha/theta eLORETA features showed the highest predictive value. Eighty-eight scalp EEG features were predictive of response (across bands, both time-points), with theta/alpha being most predictive. Clinical/demographic data consisted of 31 features, with the most important being week 1 "concentration difficulty" scores. When all features were included into one model, its predictive utility was high (88% accuracy). When the important features were extracted in the final model, 12 predictive features emerged (78% accuracy), including baseline scalp-EEG frontopolar theta, parietal alpha and frontopolar alpha. These findings suggest that ML models of pre- and early treatment-emergent EEG profiles and clinical features can serve as tools for predicting antidepressant response. While this must be replicated using large independent samples, it lays the groundwork for research on personalized, "biomarker"-based treatment approaches.
患有重度抑郁症（MDD）的个体对抗抑郁药的反应各不相同。然而，在治疗前或治疗过程早期识别能够预测抗抑郁药疗效的客观生物标志物仍然是一项挑战。患有MDD的个体参与了一项为期12周的抗抑郁药物治疗试验。在51名患者治疗开始前和治疗开始后1周收集了脑电图（EEG）数据。使用蒙哥马利-阿斯伯格抑郁量表（MADRS）确定第12周的反应状态，反应者的特征是降低≥50%（反应者/无反应者=27/24）。我们使用机器学习（ML）方法预测反应状态。尽管对其他ML方法进行了比较，但我们重点关注随机森林。首先，我们使用基于树的估计器从以下方面选择相对较少数量的显著特征：（a）人口统计学/临床数据（年龄、性别、基线、第1周的单项/总MADRS评分、变化评分）；（b）头皮水平的EEG功率；（c）源定位电流密度（通过精确低分辨率电磁断层扫描[eLORETA]软件）。其次，我们应用核主成分分析来减少和映射重要特征。第三，构建了一组ML模型，根据映射特征对反应结果进行分类。对于每个数据集，提取预测特征，然后是所有预测特征的模型，最后是预测特征的模型。50个eLORETA特征可预测反应（跨频段，两个时间点）；α/θ eLORETA特征显示出最高的预测价值。88个头皮EEG特征可预测反应（跨频段，两个时间点），其中θ/α最具预测性。临床/人口统计学数据包括31个特征，其中最重要的是第1周的“注意力不集中困难”评分。当将所有特征纳入一个模型时，其预测效用很高（准确率88%）。当在最终模型中提取重要特征时，出现了12个预测特征（准确率78%），包括基线头皮EEG额极θ、顶叶α和额极α。这些发现表明，治疗前和治疗早期出现的EEG特征和临床特征的ML模型可作为预测抗抑郁反应的工具。虽然这必须使用大型独立样本进行复制，但它为基于“生物标志物”的个性化治疗方法的研究奠定了基础。
