# Network dysfunction and impaired consciousness in epilepsy

> **NIH NIH K99** · YALE UNIVERSITY · 2024 · $127,467

## Abstract

PROJECT SUMMARY/ABSTRACT
Epilepsy is a devastating neurological disorder affecting 65 million people worldwide and more than 3 million
people in the United States. Seizures impairing consciousness severely affect quality of life of people with
epilepsy. However, the dysfunction in brain networks associated with these seizures is not fully understood.
Previous studies explored limited set of features that can partially explain the underlying network dysfunction.
Additionally, none of these studies investigated the association between ictal and interictal dysfunctional
network connectivity patterns. Recently, we studied EEG characteristics of seizures impairing consciousness
and developed a promising machine learning approach to predict impaired consciousness in absence epilepsy.
The current proposal extends more broadly to other seizure disorders and interictal cognitive deficits. My
central hypothesis is that seizures impairing consciousness are associated with both transient and chronic
dysfunction in the same networks and that the characteristics of the transient dysfunction can be leveraged to
develop a clinical tool to predict impaired consciousness during seizures based on scalp EEG without the need
for behavioral testing. To address this hypothesis, a large EEG dataset of seizures impairing and sparing
consciousness (impaired and spared seizures), as well as interictal recordings will be created. The
spatiotemporal and spectral characteristics of behaviorally impaired and spared seizures will be investigated
(Aim 1). A clinical tool based on conventional machine learning and deep learning methods will be developed
to predict impaired consciousness based on pre-ictal, ictal, and post-ictal EEG and transfer learning will be
used to enable model generalization across hospital settings (Aim 2). To characterize the relation between ictal
and interictal connectivity dysfunction, we will investigate functional and effective connectivity patterns in
relation to impairment during and between seizures (Aim 3). It is anticipated that spatiotemporal and spectral
analyses will reveal statistically significant differences between impaired and spared seizures during pre-ictal,
ictal, and post-ictal periods (Aim 1). We expect the developed clinical tool will be capable of predicting
impairment in consciousness regardless of seizure type and origin and will perform efficiently across different
hospital settings (Aim 2). Furthermore, the predictive models are expected to identify innovative information
about evolution and stability of neural representations underlying impaired and spared seizures (Aim 2).
Finally, we hypothesize that recurrent transient dysfunction in connectivity due to impaired seizures will be
associated with chronic connectivity dysfunction in the same networks (Aim 3). A detailed understanding of
transient and chronic network dysfunction associated with seizures may lead to development of novel
biomarkers and targets for therapeutic neu...

## Key facts

- **NIH application ID:** 10885569
- **Project number:** 1K99NS133494-01A1
- **Recipient organization:** YALE UNIVERSITY
- **Principal Investigator:** Aya Khalaf
- **Activity code:** K99 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $127,467
- **Award type:** 1
- **Project period:** 2024-04-01 → 2026-03-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10885569

## Citation

> US National Institutes of Health, RePORTER application 10885569, Network dysfunction and impaired consciousness in epilepsy (1K99NS133494-01A1). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10885569. Licensed CC0.

---

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