# Predicting epilepsy surgery outcomes from individualized resting state functional anomalies

> **NIH NIH K23** · UNIVERSITY OF CALIFORNIA, SAN DIEGO · 2024 · $234,276

## Abstract

Project Summary/Abstract:
This proposal details a 5-year plan to provide the candidate, Dr. Taha Gholipour, with the knowledge
and expertise to become an independent investigator. He is a board-certified neurologist and
epileptologist with research training in neuroimaging. The candidate's training will be guided by
established mentors with expertise in the field of epilepsy research, functional imaging, advanced
statistics and machine learning, and an advisory committee of scientists with collective expertise in
clinical neuroscience and image analysis across prominent institutions. Uncontrolled seizures from
epilepsy are associated with high morbidity, mortality, and cost. Current clinical and imaging predictors
of response to surgery are inadequate, and surgical treatment outcomes are mixed. Predicting
treatment outcome is critical for clinical decision making. Functional MRI (fMRI) offers noninvasive and
accessible means for assessment of brain networks and may complement current methods of surgical
planning to guide treatment. Statistical constraints from abundance of variables and data heterogeneity
in fMRI analysis can be addressed by application of novel statistical and machine learning methods.
The candidate will conduct a study with retrospective analysis of large multicenter datasets of resting
state fMRI studies from adult and pediatric focal epilepsy patients, and a prospective arm to identify
preliminary predictors of treatment response to guide future multi-site studies. The candidate will use
functional anomaly mapping method to identify associations of this method with commonly used
functional connectivity analysis and treatment outcomes 12 months after surgery. Post-surgical
resection masks, clinical outcomes of seizure control and cognitive decline from surgery are collected in
prospective arm. The goal is to identify common features in patients who become seizure-free following
surgery. This study will use innovative methods to improve non-invasive evaluation of patients with
refractory epilepsy, which can expand surgical candidacy for patients with or without apparent lesions
on MRI. This project aims to help overcome current barriers to personalized care for people with
epilepsy. The innovative use of advances statistics for solving clinical challenges in epilepsy imaging
will have a fundamental impact on designing future investigations focused on developing biomarkers,
predicting response to treatment, and understanding the disease mechanisms in epilepsy, as
advocated by the 2021 AES/NINDS Epilepsy Research Benchmarks.

## Key facts

- **NIH application ID:** 10985416
- **Project number:** 1K23NS135108-01A1
- **Recipient organization:** UNIVERSITY OF CALIFORNIA, SAN DIEGO
- **Principal Investigator:** Taha Gholipour
- **Activity code:** K23 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $234,276
- **Award type:** 1
- **Project period:** 2024-09-01 → 2029-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10985416

## Citation

> US National Institutes of Health, RePORTER application 10985416, Predicting epilepsy surgery outcomes from individualized resting state functional anomalies (1K23NS135108-01A1). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10985416. Licensed CC0.

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