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

用于识别 MDD 中 TMS 治疗反应神经预测因素的机器学习模型

• 批准号: 10322734
• 负责人: HELMET Talib KARIM
• 金额: $ 17.06万
• 依托单位: UNIVERSITY OF PITTSBURGH AT PITTSBURGH
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-01-01 至 2025-12-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10322734
• 关键词: Algorithms; Anterior; Antidepressive Agents; Archives; Biological; Biomedical Engineering; Brain; Clinical; Clinical Treatment; Complex; Corpus striatum structure; Data; Data Set; Development; Doctor of Philosophy; Goals; Heterogeneity; Individual; Left; Literature; Machine Learning; Major Depressive Disorder; Measures; Mental Depression; Modeling; Motor; Neurobiology; Neurosciences; Participant; Patients; Placebos; Positioning Attribute; Prediction of Response to Therapy; Reproducibility; Research; Rest; Rewards; Testing; Time; Training; Transcranial magnetic stimulation; Universities; archive data; archived data; base; career; clinical diagnosis; clinical diagnostics; clinically translatable; cohort; cost; design; electric field; executive function; experience; individual patient; large scale data; machine learning model; neural network; neuroimaging; personalized medicine; predicting response; predictive modeling; psychologic; random forest; relating to nervous system; response; response biomarker; support vector machine; translational scientist; treatment response; voltage

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

经颅磁刺激（TMS）是主要抑郁症的有效且易于解决的治疗 障碍（MDD）。 TMS是昂贵且耗时的，因此确定响应标记将减少财务和 心理负担。此外，治疗反应是高度可变的。临床和诊断异质性 抑郁导致反应神经标记的显着差异。关于神经标记的文献 TMS强度和靶标的变异性使其复杂化，这可能会进一步改变响应。电场 模型通过考虑强度和结构来估计刺激目标的程度 每个参与的信息，但目前尚无研究研究协会 在脑电场和治疗反应之间。此外，神经生物学的相关性 背层（DLPFC）TMS治疗反应尚不清楚。机器学习可能能够帮助我们 了解这些复杂的特征及其与治疗反应的关联。适当地 个性化处理，我将开发一个以数据驱动的机器学习模型，该模型使用以下内容：（1） 反映电路失调的治疗静止状态连通性； （2）估计电场建模 个体患者大脑上的电场或电压，是刺激充足的标志； （3） 预期目标网络连接是目标参与的标记。我们以前已经证明了 机器学习的可行性以预测MDD中的抗抑郁反应。我们将优化和扩展模型 开发了多伦多档案馆数据，该数据预测了DLPFC TMS响应。我们将验证这个 外部关于三组数据：我们在匹兹堡大学收集的数据，布朗大学的档案数据， 和假TMS数据。作为探索目的，我们将确定预测DLPFC TMS的模型是否是否 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，我开发了基于内核的机器学习模型，以个性化神经网络标记 抗抑郁反应。鉴于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的培训，学习和神经影像学经验 抑郁神经生物学，以识别TMS治疗反应的稳定，可重复可再现的神经膜片以实现 临床翻译的个性化治疗。这将使我能够开发优化的治疗模型 反应并促进我长期的职业目标，以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，神经影像以及 对抑郁症的初步理解独特地定位了我，以最大程度地提高培训的好处 在此提案中概述了。 1