Machine Learning Models for Identifying Neural Predictors of TMS Treatment Response in MDD

经颅磁刺激(TMS)是治疗抑郁症的一种有效且易耐受的治疗方法 精神障碍(MDD)。TMS成本高昂且时间密集，因此识别响应标记将减少经济和 心理负担。此外，治疗反应是高度可变的。临床和诊断的异质性 抑郁症对反应的神经标记物有显著的影响。关于神经标志物的文献 TMS强度和靶点的变异性使其复杂化，这可能会进一步改变反应。电场 模型通过同时考虑强度和结构来估计目标受到刺激的程度 每个参与者的信息，但目前还没有研究对这种联系进行调查 大脑电场和治疗反应之间的关系。此外，神经生物学上的相关性 背外侧(DlPFC)TMS治疗反应尚不清楚。机器学习也许能够帮助我们 了解这些复杂的特征及其与治疗反应的关系。从而适当地 个性化治疗，我将开发一个数据驱动的机器学习模型，它使用以下内容：(1)Pre- 反映电路失调的治疗静止态连通性；(2)电场建模以估计 个体大脑上的电场或电压，作为刺激充分性的标志；和(3) 预期的目标网络连接作为目标参与度的标志。我们之前已经演示过 机器学习预测MDD患者抗抑郁疗效的可行性。我们将优化和拓展一个模式。 根据多伦多大学档案数据开发的预测dlPFC TMS响应的数据。我们将对此进行验证 外部有三组数据：我们在匹兹堡大学收集的数据，布朗大学的档案数据， 和伪造的TMS数据。作为一个探索性目标，我们将确定预测dlPFC TMS的模型是否 治疗反应能够预测对dmPFC TMS刺激的反应。在我的博士学位期间 生物工程，我开发了基于核的机器学习模型来个性化神经网络标记 抗抑郁剂反应。考虑到抑郁症的临床和神经异质性，我将利用我的机器 通过接受高级优化方法和培训的学习和神经成像经验 抑郁症神经生物学确定稳定、可重复性的神经预测因子实现TMS治疗反应 临床可翻译的个性化治疗。这将使我能够开发优化的治疗模式 响应和促进我的长期职业目标，使用大规模开发个性化治疗算法 数据。我以前在机器学习、生物工程、神经成像以及 对抑郁症的初步了解使我能够最大限度地发挥培训目标的好处 在这项提案中概述了。 1

Transcranial magnetic stimulation (TMS) is an effective and easy-to-tolerate treatment for major depressive disorder (MDD). TMS is costly and time-intensive so identifying markers of response would reduce financial and psychological burden. Further, treatment response is highly variable. Clinical and diagnostic heterogeneity of depression contributes to significant variability in neural markers of response. The literature on neural markers is complicated by variability in TMS intensity and targets, which may further modify response. Electrical field models estimate the degree to which a target is stimulated by considering both the intensity and structural information of each participant but at this time there are no studies that have investigated the association between brain electrical fields and treatment response. Moreover, the neurobiological correlates of dorsolateral (dlPFC) TMS treatment response are not well understood. Machine learning may be able to help us understand these complex set of features and their association to treatment response. Thus to appropriately personalize treatments, I will develop a data-driven machine learning model that uses the following: (1) pre- treatment resting state connectivity that reflects circuit dysregulation; (2) electrical field modeling to estimate the electrical field or voltage on individual patient’s brain, as a marker of sufficiency of stimulation; and (3) expected target network connectivity as a marker of target engagement. We have previously demonstrated feasibility of machine learning to predict antidepressant response in MDD. We will optimize and expand a model developed on archival University of Toronto data that predicted dlPFC TMS response. We will validate this externally on three sets of data: data we collect at University of Pittsburgh, archival data from Brown University, and sham TMS data. As an exploratory aim, we will identify whether our model that predicts dlPFC TMS treatment response is capable of predicting response to dmPFC TMS stimulation. During my PhD in Bioengineering, I developed kernel-based machine learning models to personalize neural networks markers of antidepressant response. Given the clinical and neural heterogeneity of depression, I will leverage my machine learning and neuroimaging experience by receiving training in advanced optimization approaches and depression neurobiology to identify stable, reproducible neural predictors of TMS treatment response to achieve clinically translatable personalized treatments. This will allow me to develop optimized models of treatment response and facilitate my long-term career goal to develop personalized treatment algorithms using large-scale data. My previous experiences in machine learning, bioengineering, neuroimaging, as well as the preliminary understanding of depression uniquely position me to maximize the benefits of training aims outlined in this proposal. 1