Predicting outcomes of epilepsy surgery using brain imaging
Official title: Predicting epilepsy surgery outcomes from individualized resting state functional anomalies
Funded at University of California, San Diego · NIH-10985416 · Data updated 2026-11-12
This study is looking at how special brain scans can help doctors better predict how well epilepsy surgery will work for both kids and adults, so they can make smarter choices about treatment.
Quick facts
| Grant type | NIH-funded research |
|---|---|
| Study type | NIH-funded research |
| Funding institution | University of California, San Diego NIH-funded |
| Lab location | 1 site (La Jolla, United States) |
| Project start | September 1, 2024 |
| Project end | August 31, 2029 |
| Funding year | 2024 |
| Award (this year) | $234,276 |
| NIH project number | 1K23NS135108-01A1 |
| Project ID | NIH-10985416 on NIH RePORTER |
Who could benefit from this research
✓Could help
Ideal candidates for this research include both adult and pediatric patients with focal epilepsy who are being considered for surgical treatment.
✕Less likely to help
Patients with generalized epilepsy or those who are not candidates for surgery may not benefit from this research.
What this research studies
This research investigates how individualized resting state functional MRI (fMRI) can be used to predict the outcomes of epilepsy surgery. By analyzing brain networks through advanced statistical and machine learning techniques, the study aims to improve the accuracy of surgical planning for patients with epilepsy. The research will involve both retrospective analysis of existing data and prospective collection of new data from patients, focusing on both adults and children. The goal is to identify key predictors that can guide clinical decisions regarding surgical interventions.
Why it matters
Potential benefit: If successful, this research could lead to more accurate predictions of surgical outcomes for epilepsy patients, potentially improving their quality of life.
How similar studies have gone: Previous research has shown promise in using fMRI and machine learning techniques to predict surgical outcomes in epilepsy, indicating that this approach is both relevant and potentially impactful.
Where this research is happening
-
University of California, San Diego
La Jolla, United States Active
Researchers
- Principal investigator: Gholipour, Taha — University of California, San Diego
- Study coordinator: Gholipour, Taha
Related research
Other NIH-funded projects on the same topics.
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Scaling Up Task-Shifted Epilepsy Care for Children in Africa
A study to help children with epilepsy in Africa get diagnosed and receive seizure care through trained community health workers.
-
Closed-loop control of interneuron timing to influence seizures and memory in epilepsy
A study tests a brain-quieting system that uses light to precisely time certain brain cells in epilepsy-prone mice to see if this can lessen seizures and improve memory.
-
Epilepsy Data Ecosystem: A Big-Data Sharing Platform for Better Care
A national effort to collect and share large, multi‑modal brain data from people with medication‑resistant epilepsy to speed discoveries and improve treatment options.
-
Exploring epilepsy genes in zebrafish to understand human brain seizures
A study in zebrafish will model human epilepsy genes to help explain how certain brain mutations contribute to seizures.
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New MRI tools to better plan pediatric epilepsy surgery
A new MRI-based approach helps doctors decide the best time to surgically remove brain tissue in children with difficult-to-control seizures.
-
Sahel Epilepsy Study: finding patterns in epilepsy in rural Africa and its links to genes
A plan to study epilepsy in rural Nigeria to discover how common it is, what factors affect risk, and how genetics may influence it, with results that could improve care for people with epilepsy.
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.