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

> **NIH NIH K01** · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · 2022 · $170,607

## Abstract

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.
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## Key facts

- **NIH application ID:** 10322734
- **Project number:** 5K01MH122741-02
- **Recipient organization:** UNIVERSITY OF PITTSBURGH AT PITTSBURGH
- **Principal Investigator:** HELMET Talib KARIM
- **Activity code:** K01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $170,607
- **Award type:** 5
- **Project period:** 2021-01-01 → 2025-12-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10322734

## Citation

> US National Institutes of Health, RePORTER application 10322734, Machine Learning Models for Identifying Neural Predictors of TMS Treatment Response in MDD (5K01MH122741-02). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10322734. Licensed CC0.

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