Al-Kaysi Alaa M, Al-Ani Ahmed, Loo Colleen K, Powell Tamara Y, Martin Donel M, Breakspear Michael, Boonstra Tjeerd W
Faculty of Engineering and Information Technology, University of Technology Sydney, Ultimo, Australia.
Faculty of Engineering and Information Technology, University of Technology Sydney, Ultimo, Australia.
J Affect Disord. 2017 Jan 15;208:597-603. doi: 10.1016/j.jad.2016.10.021. Epub 2016 Oct 24.
Transcranial direct current stimulation (tDCS) is a promising treatment for major depressive disorder (MDD). Standard tDCS treatment involves numerous sessions running over a few weeks. However, not all participants respond to this type of treatment. This study aims to investigate the feasibility of identifying MDD patients that respond to tDCS treatment based on resting-state electroencephalography (EEG) recorded prior to treatment commencing.
We used machine learning to predict improvement in mood and cognition during tDCS treatment from baseline EEG power spectra. Ten participants with a current diagnosis of MDD were included. Power spectral density was assessed in five frequency bands: delta (0.5-4Hz), theta (4-8Hz), alpha (8-12Hz), beta (13-30Hz) and gamma (30-100Hz). Improvements in mood and cognition were assessed using the Montgomery-Åsberg Depression Rating Scale and Symbol Digit Modalities Test, respectively. We trained the classifiers using three algorithms (support vector machine, extreme learning machine and linear discriminant analysis) and a leave-one-out cross-validation approach.
Mood labels were accurately predicted in 8 out of 10 participants using EEG channels FC4-AF8 (accuracy=76%, p=0.034). Cognition labels were accurately predicted in 10 out of 10 participants using channels pair CPz-CP2 (accuracy=92%, p=0.004).
Due to the limited number of participants (n=10), the presented results mainly aim to serve as a proof of concept.
These finding demonstrate the feasibility of using machine learning to identify patients that will respond to tDCS treatment. These promising results warrant a larger study to determine the clinical utility of this approach.
经颅直流电刺激（tDCS）是治疗重度抑郁症（MDD）的一种很有前景的方法。标准的tDCS治疗需要在几周内进行多次治疗。然而，并非所有参与者都对这种治疗有反应。本研究旨在探讨根据治疗开始前记录的静息态脑电图（EEG）来识别对tDCS治疗有反应的MDD患者的可行性。
我们使用机器学习从基线EEG功率谱预测tDCS治疗期间情绪和认知的改善情况。纳入了10名目前诊断为MDD的参与者。在五个频段评估功率谱密度：δ（0.5 - 4Hz）、θ（4 - 8Hz）、α（8 - 12Hz）、β（13 - 30Hz）和γ（30 - 100Hz）。分别使用蒙哥马利 - 阿斯伯格抑郁评定量表和符号数字模态测验评估情绪和认知的改善情况。我们使用三种算法（支持向量机、极限学习机和线性判别分析）和留一法交叉验证方法训练分类器。
使用EEG通道FC4 - AF8，10名参与者中有8名的情绪标签被准确预测（准确率 = 76%，p = 0.034）。使用通道对CPz - CP2，10名参与者中有10名的认知标签被准确预测（准确率 = 92%，p = 0.004）。
由于参与者数量有限（n = 10），所呈现的结果主要旨在作为概念验证。
这些发现证明了使用机器学习识别对tDCS治疗有反应的患者的可行性。这些有前景的结果值得进行更大规模的研究以确定这种方法的临床实用性。
