Using a computer model to guide treatment for acquired dyslexia
Official title: Evidence-based Modeling for Acquired Dyslexia Treatments
Run by Rutgers, The State University of New Jersey · NCT07209488 · Data updated 2026-11-12
This trial will test whether a computer (neural network) model can predict which of two 60-hour therapies will better help people with new reading problems after a left-hemisphere stroke.
Quick facts
| Study type | Interventional |
|---|---|
| Ages | 18 Years to 85 Years |
| Sex | All |
| Enrollment | 12 (estimated) |
| Sponsor | Rutgers, The State University of New Jersey Academic / other |
| Locations | 3 sites (Tallahassee, Florida and 2 other locations) |
| Status | Recruiting |
| Started | December 3, 2025 |
| Main results expected | September 2026 |
| Study ends | September 2026 |
| Registry record updated | May 6, 2026 |
| Trial ID | NCT07209488 on ClinicalTrials.gov |
Is this trial a fit for you?
✓Likely a good fit
Adults who were native English speakers before their left-hemisphere stroke, have normal or corrected vision and hearing, left-hemisphere ischemic or hemorrhagic stroke confirmed by brain scan, and measurable reading impairment on the Woodcock Reading Mastery Test - III Basic Skills cluster are ideal candidates.
✕Probably not a fit
People with pre-stroke developmental dyslexia, other pre-stroke neurological diseases, severe apraxia of speech, major current psychiatric disorders, or who cannot provide or undergo post-stroke brain imaging may not benefit or may be excluded.
A plain-English reading of the study's rules. Only the study team can confirm whether you qualify.
What this trial studies
This is a low-risk, early phase 1, multicenter trial that uses a computational neural-network model of reading to simulate acquired dyslexia and to predict which of two therapies should work best for each person. All participants receive two full rounds of treatment — each round is either phonomotor therapy (PMT) or semantic feature analysis (SFA), delivered as 60 hours of therapy spread across 2 hours a day, 5 days a week. The trial compares outcomes when participants receive the treatment the model predicts as advantageous versus the alternative treatment. Reading ability and related language measures will be used to determine whether model-guided treatment produces greater improvement.
Why it matters
Potential benefit: If successful, the approach could help personalize therapy to improve reading recovery after stroke by identifying which therapy is most likely to help each person.
How similar studies have gone: Behavioral therapies like phonomotor treatment and semantic feature analysis have shown benefit for post-stroke language and reading problems, but using a neural-network model to predict which therapy to give is a novel approach that has not been widely tested in clinical trials.
Who can join: the full rules
Show full inclusion / exclusion criteria
Inclusion Criteria: * Fluent in English as a first language pre-stroke by self report. * Normal or corrected-to-normal vision. * Normal or aided hearing. * Left hemisphere ischemic or hemorrhagic stroke (as verified by a structural brain scan). * Impaired reading as confirmed by significant impairment in the Basic Skills cluster of the Woodcock Reading Mastery Test - III. Exclusion Criteria: * Diagnosed pre-stroke neurological disease affecting the brain other than left hemisphere stroke. * Severe apraxia of speech (determined by consensus judgment among speech-language pathologists). * History of learning disabilities such as developmental dyslexia or current self-reported major psychiatric disorders. * Inability to undergo, or provide a copy of, a post-stroke brain imaging scan.
Where it's running
-
Florida State University
Tallahassee, Florida, United States Recruiting -
Johns Hopkins University School of Medicine
Baltimore, Maryland, United States Recruiting -
Kessler Foundation
West Orange, New Jersey, United States Recruiting
Study contacts
- Principal investigator: William W Graves, PhD — Rutgers, The State University of New Jersey
- Study coordinator: William W Graves, PhD
- Email: william.graves@rutgers.edu
- Phone: 973-353-3947
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.
-
Neurofeedback treatment for reading difficulties after stroke
This study is testing a new brain training method to help stroke survivors improve their reading skills by using real-time brain feedback.
-
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.
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.