# SCH: INT: Collaborative Research: Multimodal Signal Analysis and Data Fusion for Post-traumatic Epilepsy

> **NIH NIH R01** · UNIVERSITY OF SOUTHERN CALIFORNIA · 2021 · $243,545

## Abstract

The research objective of this proposal, Multimodal Signal Analysis and Data Fusion for Post-traumatic
Epilepsy Prediction, with Pl Dominique Duncan from the University of Southern California, is to predict the
onset of epileptic seizures following traumatic brain injury (TBI), using innovative analytic tools from machine
learning and applied mathematics to identify features of epileptiform activity, from a multimodal dataset
collected from both an animal model and human patients. The proposed research will accelerate the
discovery of salient and robust features of epileptogenesis following TBI from a rich dataset, collected from
the Epilepsy Bioinformatics Study for Antiepileptogenic Therapy (EpiBioS4Rx), as it is being acquired by
investigating state-of-the-art models, methods, and algorithms from contemporary machine learning theory.
This secondary use of data to support automated discovery of reliable knowledge from aggregated records
of animal model and human patient data will lead to innovative models to predict post-traumatic epilepsy
(PTE). This machine learning based investigation of a rich dataset complements ongoing data acquisition
and classical biostatistics-based analyses ongoing in the study and can lead to rigorous outcomes for the
development of antiepileptogenic therapies, which can prevent this disease. Identifying salient features in
time series and images to help design a predictor of PTE using data from two species and multiple individuals
with heterogeneous TBI conditions presents significant theoretical challenges that need to be tackled. In this
project, it is proposed to adopt transfer learning and domain adaptation perspectives to accomplish these
goals in multimodal biomedical datasets across two populations. Specifically, techniques emerging from d,eep
learning literature will be exploited to augment data, share parameters across model components to reduce
the number of parameters that need to be optimized, and use state-of-the-art architectures to develop models
for feature extraction. These will be compared against established pipelines of hand-crafted feature extraction
in rigorous cross-validation analyses. Developed techniques for transfer learning will be able to extract
features that generalize across animal and human data. Moreover, these theoretical techniques with
associated models and optimization methods will be applicable to other multi-species transfer learning
challenges that may arise in the context of health and medicine. Multimodal feature extraction and
discriminative model learning for disease onset prediction using novel classifiers also offer insights into
biomarker discovery using advanced machine learning techniques through joint multimodal data analysis.

## Key facts

- **NIH application ID:** 10093160
- **Project number:** 5R01NS111744-03
- **Recipient organization:** UNIVERSITY OF SOUTHERN CALIFORNIA
- **Principal Investigator:** Dominique Duncan
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $243,545
- **Award type:** 5
- **Project period:** 2019-05-01 → 2023-02-28

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10093160

## Citation

> US National Institutes of Health, RePORTER application 10093160, SCH: INT: Collaborative Research: Multimodal Signal Analysis and Data Fusion for Post-traumatic Epilepsy (5R01NS111744-03). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10093160. Licensed CC0.

---

*[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*