# Scalable Biomarkers and Generative Digital Twins for Personalized Neurostimulation in Depression

> **NIH NIH R01** · WEILL MEDICAL COLL OF CORNELL UNIV · 2024 · $657,575

## Abstract

Project Summary/Abstract
More than 100 million people in the United States currently show signs of clinical depression, approximately
three times more than before the onset of the COVID-19 crisis. Currently, the main treatment options for such
depressed individuals include pharmacological and psychological interventions, the acute and long-term
effectiveness of which are significantly limited: up to one-third of patients develop treatment-resistant
depression. Noninvasive neurostimulation therapies such as repetitive Transcranial Magnetic Stimulation
(rTMS)–where a magnetic coil placed over the cortex is used to focally stimulate the brain–have recently
emerged as promising low-risk interventions for treatment-resistant depression. However, the mechanisms and
appropriate parameters for this treatment remain poorly understood. In the best cases, rTMS can have
dramatic effects, changing the course of a patient's life in hours. In many cases, however, it has little to no
measurable effect. This raises the obvious question: why do current rTMS protocols work well for some
individuals but not for others? Could we adapt protocols to work well for everyone, potentially providing reliable
personalized treatment or even a lasting cure? A growing literature suggests this is possible if we learn to tailor
treatment to individual differences in human neurophysiology. Here we propose an innovative and unique
approach towards precision psychiatric neurostimulation: personalized modeling of treatment using
“Generative Digital Twins”. To affordably build and scale Generative Digital Twins, we propose combining high
density electroencephalography (HD-EEG)–which is non-invasive, inexpensive, and easily deployable–to
measure longitudinal changes in brain connectivity during an accelerated rTMS treatment protocol for
depression. Then, using controllable generative neural networks that allow detailed predictive simulations of
individual response trajectories given rTMS treatment, we can begin to predict outcomes prior to treatment,
understand individual responses, and personalize treatment parameters.

## Key facts

- **NIH application ID:** 10906262
- **Project number:** 5R01MH131534-03
- **Recipient organization:** WEILL MEDICAL COLL OF CORNELL UNIV
- **Principal Investigator:** LOGAN GROSENICK
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $657,575
- **Award type:** 5
- **Project period:** 2022-09-15 → 2027-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10906262

## Citation

> US National Institutes of Health, RePORTER application 10906262, Scalable Biomarkers and Generative Digital Twins for Personalized Neurostimulation in Depression (5R01MH131534-03). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10906262. Licensed CC0.

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