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

Public Health Relevance

This is a K01 application designed to develop a data-driven machine learning model that will take into account several neural markers of response to Transcranial Magnetic Stimulation (TMS). The application proposes expanded training in machine learning optimization approaches and depression neurobiology for the applicant, who has a PhD in Bioengineering. The goal of the study is to identify stable, reproducible neural predictors of TMS treatment response to achieve clinically translatable personalized treatments, which matches the candidate?s long-term goal to conduct research on and develop models that personalize medicine for depression. 1

National Institute of Health (NIH)
National Institute of Mental Health (NIMH)
Research Scientist Development Award - Research & Training (K01)
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Adult Psychopathology and Disorders of Aging Study Section (APDA)
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Chavez, Mark
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University of Pittsburgh
Schools of Medicine
United States
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