# Dynamics of long range network interactions  in focal epilepsy

> **NIH NIH R01** · COLUMBIA UNIVERSITY HEALTH SCIENCES · 2022 · $609,033

## Abstract

PROJECT SUMMARY
Epilepsy is the world’s most prominent serious brain disorder, affecting nearly 50 million people worldwide. For
about 30% of these patients, seizures remain poorly controlled despite optimal medical management, with
attendant effects on health and quality of life. In order to enable advances in the therapeutic management of
epilepsy, a thorough understanding of how cellular processes that drive seizures are linked to large-scale
network effects is needed. While seizures impact large brain areas and often multiple lobes, the driving
processes span regions on the scale of millimeters. These have been well characterized in animal models,
but the relevance to human seizures, i.e. how seizures are driven by brain signals from small-scale processes
remains unclear. Instead, the view that naturally-occurring seizures may be attributable instead to large-scale
neural mass effects (i.e., the epileptic network) is a subject of ongoing debate. Previously, we defined a key
role for surround inhibition in shaping EEG recordings of seizures at the onset site and on small spatial scales.
We now propose that surround inhibition has a dual role. On a millimeter scale, its abrupt failure permits the
advance of a seizure. At long distances from the seizure focus, strong local inhibition serves to mask the
excitatory effects of seizures and may help to hasten seizure termination, while weakened inhibition may
permit emergence of ictal activity at a distant, noncontiguous seizure site. Multiple seizure foci may go
unrecognized with standard EEG interpretation methods, and are likely a critical factor in epilepsy surgery
failures. We hypothesize that once established, multiple ictal generators behave as delay-coupled oscillators,
demonstrating activity that is synchronized or even temporally reversed. This results in complex and at times
counterintuitive network behavior that can be challenging to reverse engineer from EEG recordings. Typically,
however, even intracranial EEG recordings provide only a limited view of neural activity. In this project, an
interdisciplinary research group with combined expertise in epilepsy, clinical neurophysiology, computational
modeling, and mathematics will conduct a comprehensive study of the neuronal contributors to epileptic
networks utilizing a unique combined dataset of simultaneous microelectrode and macroelectrode recordings
of human seizures. Using a machine learning approach, we will apply this information to develop a
multivariate EEG biomarker based on the inferred source of EEG discharges, high frequency oscillations, and
very low frequency (DC) shifts and assess its predictive value for post-resection surgical outcome. We
anticipate that the project will lead to a theoretical framework for rational development of innovative strategies
for developing interventions to control seizures.

## Key facts

- **NIH application ID:** 10456050
- **Project number:** 5R01NS084142-10
- **Recipient organization:** COLUMBIA UNIVERSITY HEALTH SCIENCES
- **Principal Investigator:** Catherine A Schevon
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $609,033
- **Award type:** 5
- **Project period:** 2013-09-01 → 2024-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10456050

## Citation

> US National Institutes of Health, RePORTER application 10456050, Dynamics of long range network interactions  in focal epilepsy (5R01NS084142-10). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10456050. Licensed CC0.

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