Understanding different types of stuttering using brain imaging and machine learning
Official title: Predicting stuttering subtypes from functional connectivity using machine learning
Funded at Boston University (Charles River Campus) · NIH-11163836 · Data updated 2026-11-12
This study is looking at how the brains of people who stutter work differently to help us understand the different ways stuttering shows up, so we can find better ways to support and treat those who experience it.
Quick facts
| Grant type | Fellowship grant |
|---|---|
| Study type | NIH-funded research |
| Funding institution | Boston University (Charles River Campus) NIH-funded |
| Lab location | 1 site (Boston, United States) |
| Project start | January 1, 2025 |
| Project end | December 31, 2027 |
| Funding year | 2025 |
| Award (this year) | $42,880 |
| NIH project number | 1F31DC022184-01A1 |
| Project ID | NIH-11163836 on NIH RePORTER |
Who could benefit from this research
✓Could help
Ideal candidates for this research are adults over 21 years old who experience persistent developmental stuttering.
✕Less likely to help
Patients who do not stutter or those with stuttering that is not persistent may not benefit from this research.
What this research studies
This research investigates persistent developmental stuttering (PDS), a speech disorder affecting fluency, by examining the brain's neural networks through advanced imaging techniques. The study aims to identify distinct types of stuttering behaviors, such as repetitions and blocks, by analyzing resting-state functional magnetic resonance imaging (rs-fMRI) data. Using machine learning, the researchers will predict which type of disfluency a person may exhibit based on their brain connectivity patterns. This approach seeks to enhance our understanding of stuttering and improve treatment strategies for those affected.
Why it matters
Potential benefit: If successful, this research could lead to more personalized and effective treatments for individuals who stutter.
How similar studies have gone: Previous research has shown promise in using neuroimaging and machine learning to understand speech disorders, indicating potential for success in this novel approach.
Where this research is happening
-
Boston University (Charles River Campus)
Boston, United States Active
Researchers
- Principal investigator: Kim, Jackie Sihyun — Boston University (Charles River Campus)
- Study coordinator: Kim, Jackie Sihyun
Related research
Other NIH-funded projects on the same topics.
-
How Arousal Changes How We See the World
This grant studies how moments of alertness or mood changes alter how the brain processes what we see.
-
NGF Metabolism as a biomarker for cognitive decline in familial Alzheimer's disease
Researchers are studying whether patterns in nerve growth factor pathways in blood and CSF can signal early, preclinical stages of Alzheimer's in people with genetic risk.
-
NGF metabolism as an early biomarker for cognitive decline in familial Alzheimer's disease
This project tests whether NGF pathway proteins in blood and CSF can signal early, preclinical changes in people with familial Alzheimer’s disease.
-
Ultra-High Density wearable brain monitoring in real life
Aims to make advanced wearable brain sensors that work in daily life to track brain activity alongside movement, perception, and communication.
-
A new method to image brain activity noninvasively using speckle contrast spectroscopy
A new, noninvasive imaging technique called speckle contrast optical spectroscopy (SCOS) could help measure brain blood flow in people, including those with brain injuries or neurodegenerative conditions.
-
NGF Metabolic Dysfunction as a Marker of Cognitive Decline in Autosomal Dominant Alzheimer's Disease
This project looks at nerve growth factor pathways in people with a genetic form of Alzheimer's to see if blood and spinal fluid markers can identify early brain changes before symptoms appear.
About this research
- This is an active NIH-funded research project — typically early-stage science, not a clinical trial accepting patient enrollment.
- Some NIH-funded labs run parallel clinical studies or seek volunteers for related work. To check, contact the principal investigator or institution listed above.
- For full project details, budget, and progress reports, visit the official NIH RePORTER page.