Electroencephalogram (EEG) provides noninvasive measures of brain activity and is found to be valuable for diagnosis of some chronic disorders. Specifically, pre-treatment EEG signals in alpha and theta frequency bands have demonstrated some association with anti-depressant response, which is well-known to have low response rate. We aim to design an integrated pipeline that improves the response rate of major depressive disorder patients by developing an individualized treatment policy guided by the resting state pre-treatment EEG recordings and other treatment effects modifiers. We first design an innovative automatic site-specific EEG preprocessing pipeline to extract features that possess stronger signals compared with raw data. We then estimate the conditional average treatment effect using causal forests, and use a doubly robust technique to improve the efficiency in the estimation of the average treatment effect. We present evidence of heterogeneity in the treatment effect and the modifying power of EEG features as well as a significant average treatment effect, a result that cannot be obtained by conventional methods. Finally, we employ an efficient policy learning algorithm to learn an optimal depth-2 treatment assignment decision tree and compare its performance with Q-Learning and outcome-weighted learning via simulation studies and an application to a large multi-site, double-blind randomized controlled clinical trial, EMBARC.

翻译：脑电图（EEG）提供非侵入性的脑活动测量方式，并被发现对某些慢性疾病的诊断具有重要价值。具体而言，治疗前的α和θ频段脑电图信号已显示出与抗抑郁药物反应（众所周知其反应率较低）存在一定关联。我们旨在设计一个集成流程，通过开发基于静息态治疗前脑电图记录及其他治疗效果修饰因子的个体化治疗方案，来提高重度抑郁症患者的反应率。我们首先设计了一种创新的自动化位点特异性脑电图预处理流程，以提取相比原始数据信号更强的特征。然后，我们使用因果森林估计条件平均处理效应，并采用双重稳健技术来提高平均处理效应估计的效率。我们展示了治疗效应的异质性证据以及脑电图特征的调节能力，同时观察到显著的平均处理效应——这是传统方法无法获得的结果。最后，我们采用高效策略学习算法学习最优深度为2的治疗分配决策树，并通过模拟研究以及一项大规模多中心双盲随机对照临床试验EMBARC的应用，将其性能与Q学习和结果加权学习进行比较。