Using brain activity patterns to improve hand recovery after stroke
Official title: Identifying Personalized Brain States Predicting Residual Corticospinal Tract Output After Stroke
Run by University of Texas at Austin · NCT06365099 · Data updated 2026-11-12
This study is testing if a special brain treatment can help stroke patients recover better use of their hands by focusing on their unique brain activity patterns.
Quick facts
| Phase | Phase 1 |
|---|---|
| Study type | Interventional |
| Ages | 18 Years and up |
| Sex | All |
| Enrollment | 20 (estimated) |
| Sponsor | University of Texas at Austin Academic / other |
| Locations | 1 site (Austin, Texas) |
| Status | Recruiting |
| Started | February 2, 2024 |
| Main results expected | March 31, 2026 |
| Study ends | March 31, 2026 |
| Registry record updated | February 10, 2025 |
| Trial ID | NCT06365099 on ClinicalTrials.gov |
Is this trial a fit for you?
✓Likely a good fit
Ideal candidates are individuals with residual upper extremity hemiparesis following a stroke that occurred at least six months prior.
✕Probably not a fit
Patients with a history of neurological diseases other than stroke or those with contraindications to TMS may not benefit from this study.
A plain-English reading of the study's rules. Only the study team can confirm whether you qualify.
What this trial studies
This study investigates the use of transcranial magnetic stimulation (TMS) to enhance recovery of hand motor function in stroke patients by targeting specific brain activity patterns. Researchers have developed a machine learning framework that identifies personalized brain states associated with strong corticospinal tract (CST) activation. Participants will undergo two days of testing to evaluate their brain activity and the integrity of their CST pathways. The goal is to optimize TMS delivery to maximize its effectiveness in promoting motor recovery.
Why it matters
Potential benefit: If successful, this approach could lead to more effective rehabilitation strategies for stroke patients, improving their hand motor recovery.
How similar studies have gone: While this approach is novel in applying machine learning to personalize TMS for stroke recovery, similar studies have shown promise in enhancing motor function through targeted interventions.
Who can join: the full rules
Show full inclusion / exclusion criteria
* Presence of residual upper extremity hemiparesis, defined as any of the following Fugl Meyer Upper Extremity Score \<66 Wolf Motor Function Test Score \<70 Affected hand performance on the 9-Hole Peg Test \>= 10% worse than unaffected hand Affected hand pinch, key, or power grip performance \>= 10% worse than unaffected hand * Occurrence of ischemic or hemorrhagic stroke \>= 6 months before participation * Willingness and ability to provide informed consent * No history of neurological disease and/or neurological injury other than stroke * No TMS contraindications, including but not limited to: Cardiac pacemaker Cochlear implant Cortical stimulator Deep brain stimulator Vagus nerve stimulator Cervical spine epidural stimulation Ventriculoperitoneal shunt Ferromagnetic metallic implants above the level of the seventh cervical vertebra Seizure in the last 12 months while taking anti-epilepsy medication History of adverse reactions to TMS or peripheral nerve stimulation Current, suspected, or planned pregnancy Any recent changes (within the last month) to medication use \- Presence of residual corticospinal connections innervating an affected upper extremity muscle following stimulation of the lesioned hemisphere (i.e., MEP+), evaluated at rest
Where it's running
-
University of Texas at Austin
Austin, Texas, United States Recruiting
Similar studies
Other studies for the same condition or treatment. Recruiting studies are listed first.
-
Deep phenotyping to explore recovery in young adults after stroke
This project will test whether a detailed health and sample-collection approach (deep phenotyping) is practical and acceptable for people aged 18–55 who have had a stroke.
-
Stem cell therapy for chronic stroke recovery
This study is testing whether using a patient's own stem cells can help people with long-term stroke recovery feel better and improve their daily functioning.
-
AI-based tools to help diagnose, treat and predict stroke outcomes across multiple hospitals
This study tests AI-powered tools on real-world stroke patients to see if they can improve pre-hospital triage, diagnosis, and outcome prediction while data stay securely within each hospital.
-
Web-based therapy using action observation and mirror therapy for arm weakness after stroke
This trial tests whether a mobile telerehabilitation app delivering action observation therapy (AOT) alone or combined AOT with mirror therapy (AOT-MT) can improve upper limb function in people with subacute stroke.
-
Remote Ischaemic Conditioning for Fatigue After Stroke 2
Aat least 12 weeks after stroke, adults with post-stroke fatigue will try a simple hand-arm cuff treatment at home to see if it helps reduce fatigue.
-
Dual-task balance training plus computer-based cognitive training after stroke
In adults who recently had a stroke, the study tests whether adding computerized cognitive exercises to balance training improves balance and other recovery outcomes.
How to take part
- Review the eligibility criteria above with your treating physician.
- Check the official listing on ClinicalTrials.gov for the latest contact details and recruitment status.
- Contact the study coordinator or principal investigator to ask for pre-screening. Pre-screening is free and never obligates you to enroll.