# Understanding the fast and slow spatiotemporal dynamics of human seizures

> **NIH NIH R01** · BOSTON UNIVERSITY (CHARLES RIVER CAMPUS) · 2022 · $456,892

## Abstract

PROJECT SUMMARY
Epilepsy is the world’s most prominent serious brain disorder, affecting nearly 50 million people worldwide. For
an estimated 30% of these patients, seizures remain poorly controlled despite maximal medical management,
with significant financial costs and effects on health and quality of life. To advance the therapeutic
management of epilepsy requires a more detailed understanding of the spatiotemporal dynamics that drive
seizures. Characterizing these dynamics is especially difficult because, like many brain functions, the
processes span spatial and temporal scales, from the fast activity of small neural populations to the slow
evolution from seizure onset to termination of large brain regions. How brain signals at one scale relate to
those at other scales is a significant and poorly understood issue. While animal models of epilepsy provide
powerful techniques to investigate detailed neural activity within and between spatial scales, the relationship of
these models to human epilepsy is unclear. An alternative to animal models of epilepsy is to study
spontaneously occurring seizures in vivo from a population of human patients. However, typical in vivo clinical
recordings provide only a limited view of a seizure’s multiscale dynamics. In this project, an interdisciplinary
research group consisting of epileptologists and clinical neurophysiologists, a statistician, and a
mathematician will study the spatiotemporal dynamics of human seizures. To do so, the team will analyze
simultaneous microelectrode and macroelectrode recordings from human patients during seizures, with a
particular focus on the organized spatiotemporal patterns and high frequency oscillations common in epilepsy.
To make sense of these data, the team will develop and apply new methods to characterize these patterns,
and link these activities to candidate mechanisms in computational models. Completion of the proposed
research will represent significant progress towards a deeper understanding of human seizures, new methods
to analyze and model the spatiotemporal dynamics of seizures observed in complex multiscale data, new
methods to estimate model parameters and variables from brain voltage recordings, and new candidate
targets for surgical treatment of epilepsy.

## Key facts

- **NIH application ID:** 10361503
- **Project number:** 5R01NS110669-04
- **Recipient organization:** BOSTON UNIVERSITY (CHARLES RIVER CAMPUS)
- **Principal Investigator:** SYDNEY S CASH
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $456,892
- **Award type:** 5
- **Project period:** 2019-07-01 → 2024-03-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10361503

## Citation

> US National Institutes of Health, RePORTER application 10361503, Understanding the fast and slow spatiotemporal dynamics of human seizures (5R01NS110669-04). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10361503. Licensed CC0.

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