# Directed connectivity analysis of resting-state SEEG and DWI to improve lateralization and localization in focal epilepsy

> **NIH NIH F31** · VANDERBILT UNIVERSITY · 2021 · $31,270

## Abstract

PROJECT SUMMARY/ABSTRACT
Focal epilepsy is the most common form of epilepsy, a debilitating disorder that affects 50 million people
worldwide. Approximately 30-40% of patients with focal epilepsy continue to have debilitating seizures despite
maximal medical therapy. Epilepsy surgery can eliminate or reduce seizures using resection, ablation, or
neurostimulation of regions that generate seizures (“Epileptogenic Zones”, EZs). However, 33-50% of patients
that undergo surgery continue to have seizures post-operatively. An important determinate of post-operative
outcome is accurate pre-surgical lateralization and localization of EZs. In 50% of patients, lateralization and
localization requires invasive intracranial monitoring with stereo-electroencephalography (SEEG) in the hospital
for days to weeks to record multiple seizures. This invasive diagnostic process causes significant morbidity to
the patient, and interpretation of ictal (seizure) activity from SEEG may sometimes be challenging, inaccurate,
and incapable of capturing all the patient’s seizure types. Resting-state (between seizures) SEEG analysis may
supplement clinical interpretation by identifying EZs without requiring ictal recordings. Beyond SEEG, diffusion
MRI (DWI) and neurostimulation have also been used to attempt EZ lateralization and localization. These studies
rely on generating connectivity networks of brain regions and extracting features that predict EZ locations, but
EZ lateralization and localization accuracy with these data has been suboptimal. However, few studies have
evaluated the directionality of connectivity patterns involving EZs. Therefore, building from previous
neurophysiological work that shows tonic inhibition of EZs in focal epilepsy, we hypothesize that
electrophysiological resting-state inhibitory inward directed connectivity of EZs will be markedly increased vs.
that of Non-EZs, and thus key to predicting epileptogenicity of brain regions. Further, integrating previous work
done across the fields of neuroscience and neuropsychology, we also hypothesize specific DWI-derived
structural network alterations that are important to lateralize EZs and predict surgical outcome. Our first goal is
to develop directed connectivity measures to reliably identify EZs using brief resting-state SEEG recordings and
neurostimulation sessions (Aim 1). We then seek to identify noninvasive structural connectivity measures to
lateralize EZs and predict surgical outcome using DWI to ultimately reduce the need for invasive intracranial
monitoring. We will do this through advanced network analysis of DWI-generated structural connectivity maps
(Aim 2). This proposed fellowship will provide research training in a collaborative research atmosphere with
expert mentors in translational neuroscience and engineering research. Research training will be conducted in
an environment that combines an academic medical center with a level 4 epilepsy center, world class imaging
institute, and engi...

## Key facts

- **NIH application ID:** 10311253
- **Project number:** 1F31NS120401-01A1
- **Recipient organization:** VANDERBILT UNIVERSITY
- **Principal Investigator:** Graham Walter Johnson
- **Activity code:** F31 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $31,270
- **Award type:** 1
- **Project period:** 2021-08-01 → 2024-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10311253

## Citation

> US National Institutes of Health, RePORTER application 10311253, Directed connectivity analysis of resting-state SEEG and DWI to improve lateralization and localization in focal epilepsy (1F31NS120401-01A1). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10311253. Licensed CC0.

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