# Towards real-time personalized brain state-dependent TMS to enhance poststroke hand rehabilitation

> **NIH NIH R03** · UNIVERSITY OF TEXAS AT AUSTIN · 2024 · $87,286

## Abstract

PROJECT SUMMARY
Stroke commonly disrupts the corticospinal tract (CST) and impairs hand function, but current rehabilitation
approaches incompletely restore pre-stroke hand motor control. Transcranial magnetic stimulation (TMS)
interventions that target the residual CST and strengthen its neural transmission are promising candidates for
enhancing paretic hand muscle activation during rehabilitation and promoting true hand motor recovery. To
maximize their therapeutic effects, poststroke TMS interventions must reliably activate the residual CST and
enhance residual CST transmission. We and others recently showed that neurotypical adults exhibit
spontaneous resting brain activity patterns during which TMS most strongly activates the CST (i.e., strong CST
states). When TMS interventions are delivered during these strong CST states, they preferentially enhance CST
transmission and enhance motor learning. However, virtually all poststroke TMS interventions are uncoupled
from strong CST states, such that only a fraction of TMS stimuli occur when the residual CST is best activated
by TMS and neural transmission within it is most likely to be enhanced. Instead, poststroke TMS interventions
should be delivered solely during strong CST states. Because each stroke survivor has a unique pattern of brain
damage, recovery-related brain reorganization, and motor impairment, these strong CST states must be fully
personalized. To address these issues, we developed a novel real-time EEG-informed TMS system that delivers
stimulation during personalized strong CST states. Our system uses personalized machine learning classifiers
and participant-specific datasets to identify multivariate EEG activity patterns that predict strong CST activation,
indexed by large motor-evoked potentials (MEPs). Once these EEG activity patterns are detected in real-time,
TMS is delivered. In this project, we will demonstrate the feasibility of real-time EEG-informed personalized brain
state-dependent TMS in chronic stroke survivors while also determining hand impairment inclusion criteria for
future clinical trials that investigate this promising personalized brain stimulation approach. In Aim 1, we will
establish feasibility of real-time personalized brain state-dependent TMS in the poststroke CST system by
comparing MEP amplitudes elicited during strong CST states to those elicited during random CST states. In Aim
2, we will correlate differences in MEP amplitudes elicited during strong versus random CST states with
poststroke hand impairment. We will then identify the hand impairment levels of stroke survivors who exhibit
larger MEPs during personalized strong than random CST states; previous literature suggests that these stroke
survivors are most likely to benefit from future personalized poststroke brain state-dependent TMS interventions.
Overall, this project will demonstrate feasibility of personalized poststroke brain state-dependent TMS and
establish hand impairment inclusion ...

## Key facts

- **NIH application ID:** 10816855
- **Project number:** 1R03HD114188-01
- **Recipient organization:** UNIVERSITY OF TEXAS AT AUSTIN
- **Principal Investigator:** Sara J Hussain
- **Activity code:** R03 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $87,286
- **Award type:** 1
- **Project period:** 2024-08-01 → 2025-08-15

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10816855

## Citation

> US National Institutes of Health, RePORTER application 10816855, Towards real-time personalized brain state-dependent TMS to enhance poststroke hand rehabilitation (1R03HD114188-01). Retrieved via AI Analytics 2026-06-13 from https://api.ai-analytics.org/grant/nih/10816855. Licensed CC0.

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