# The Epilepsy Bioinformatics Study for Antiepileptogenic Therapy (EpiBioS4Rx) Informatics and Analytics Core

> **NIH NIH U54** · UNIVERSITY OF SOUTHERN CALIFORNIA · 2020 · $903,807

## Abstract

ABSTRACT – INFORMATICS AND ANALYTICS CORE
The Informatics and Analytics Core (IAC) will centralize an enduring data archive and analytic tools that will
allow the broader epilepsy research community to identify and validate biomarkers of epileptogenesis in
images, electrophysiology, and molecular/serological/tissue studies. Beyond creating a centralized data
repository, the IAC will pioneer innovative standardization/co-registration methods, fully supported by novel
image and electrophysiology processing methods to extract candidate biomarkers from the diverse data. Not
only will a well-curated and standardized multi-modal data set facilitate the development of models of
epileptogenesis, it will also ensure that such models are statistically significant and can be validated. Based on
our previous experience with similar multicenter projects, we are confident that our infrastructure will lead to
success in this project.
The amount of data to be collected in these studies is unprecedented: video-EEG from animals after TBI
recorded continuously for 6 months, in addition to prolonged continuous ICU EEG recordings from humans and
intermittent sampling of brain images, blood, and tissue data. To analyze these data properly, it requires a
diverse, accomplished group of investigators spanning neurology, neuroscience, imaging, mathematics,
engineering, and computer science, as well as collecting comprehensive data in parallel from humans and
animal models after TBI. The IAC will be seamlessly integrated with Projects 1-3, assisting in collecting data
and providing analytic tools that will lead to biomarkers of epileptogenesis. By combining new data capabilities
and our powerful, best-in-class, interdisciplinary team, quantitative models of epileptogenesis may be possible.
These types of models will enrich preclinical trial populations, expedite interventions to prevent epilepsy after
brain insults, and document epilepsy before late seizures occur. Based on previous studies, it is likely that
there are reproducible changes in biomarkers, which identify the presence of epilepsy before its overt clinical
expression61,71,72.
The IAC will bring big data techniques and rigorous analysis to longitudinal data collected from humans and
animal models of TBI, epilepsy, and their interaction. It will develop and implement new approaches, including
novel graphical methods to visualize multivariable interactions, to quantify phenotype and molecular profiles in
these disorders. A first-rate bioinformatics platform, LONI, will focus on TBI and epileptogenesis research. The
tools, pipelines, and protocols developed for this proposal will be made available to the epilepsy research
community, with the potential to change, long-term, the way that images, video, electrophysiology, proteomics,
and metadata are analyzed in these fields. Quantitative and data mining methods will enable investigators to
record and analyze gold-standard data and create a shared bioinformatics reso...

## Key facts

- **NIH application ID:** 9849339
- **Project number:** 5U54NS100064-04
- **Recipient organization:** UNIVERSITY OF SOUTHERN CALIFORNIA
- **Principal Investigator:** ARTHUR W TOGA
- **Activity code:** U54 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $903,807
- **Award type:** 5
- **Project period:** — → —

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9849339

## Citation

> US National Institutes of Health, RePORTER application 9849339, The Epilepsy Bioinformatics Study for Antiepileptogenic Therapy (EpiBioS4Rx) Informatics and Analytics Core (5U54NS100064-04). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/9849339. Licensed CC0.

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