# Developing real-time personalized TMS to target residual corticospinal connections after stroke

> **NIH NIH R21** · UNIVERSITY OF TEXAS AT AUSTIN · 2024 · $205,217

## Abstract

PROJECT SUMMARY
Stroke commonly disrupts the corticospinal tract (CST) and impairs hand function. Transcranial magnetic
stimulation (TMS) interventions that target and strengthen residual CST connections are promising candidates
for improving poststroke hand function. To maximize their therapeutic effects, such interventions must repeatedly
activate the residual CST and enhance its neural transmission. We and others recently showed in neurotypical
adults that resting brain activity spontaneously alternates between EEG activity patterns (brain states) that
predict strong and weak CST activation. TMS interventions also preferentially enhance CST transmission when
delivered during strong CST states but instead diminish CST transmission when delivered during weak CST
states. However, virtually all poststroke TMS interventions are uncoupled from the current brain state, such that
only a fraction of TMS stimuli coincide with brain states during which the beneficial effects of TMS are likely to
be strongest. To resolve this issue, poststroke TMS interventions should be delivered solely during brain states
reflecting strong CST responses. Given that each stroke survivor has a unique pattern of brain damage and
recovery-related brain reorganization, these brain states must be fully personalized. We recently developed a
personalized machine learning framework that successfully identifies electroencephalography (EEG) activity
patterns that predict strong and weak CST states in neurotypical adults. Our framework is fully personalized and
is therefore unaffected by lesion-related changes in brain structure and/or function, making it ideal for application
in the poststroke brain. In this project, we will use this framework to establish the mechanistic rationale and
methodological foundation for future personalized brain state-dependent TMS interventions that target and
strengthen the residual CST after stroke. In Aim 1, we will use our machine learning framework to identify
personalized brain states that predict strong and weak residual CST activation in chronic stroke survivors; we
will also evaluate relationships between our framework’s performance and functional and structural metrics of
poststroke CST pathway integrity. Results of Aim 1 will establish poststroke brain state-dependency of residual
CST output and the relationship of this state-dependency to CST integrity. In Aim 2, we will develop and validate
a real-time EEG algorithm that accurately delivers TMS during personalized brain states reflecting strong and
weak CST activation in neurotypical adults. Results from Aim 2 will demonstrate the technical feasibility of
personalized, real-time brain state-dependent TMS. Overall, this project fits the scope of the NIMH/NINDS R21
mechanism because it will develop a novel neuroengineering approach that can in the future enhance residual
CST transmission and promote paretic hand function in stroke survivors.

## Key facts

- **NIH application ID:** 10889693
- **Project number:** 1R21NS133605-01A1
- **Recipient organization:** UNIVERSITY OF TEXAS AT AUSTIN
- **Principal Investigator:** Sara J Hussain
- **Activity code:** R21 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $205,217
- **Award type:** 1
- **Project period:** 2024-04-01 → 2026-03-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10889693

## Citation

> US National Institutes of Health, RePORTER application 10889693, Developing real-time personalized TMS to target residual corticospinal connections after stroke (1R21NS133605-01A1). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10889693. Licensed CC0.

---

*[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*