# Explaining cognitive heterogeneity in temporal lobe epilepsy (TLE): Identifying unique imaging features, health-related risk factors and protective factors in TLE subtypes

> **NIH NIH F31** · UNIVERSITY OF CALIFORNIA, SAN DIEGO · 2020 · $38,362

## Abstract

Project Summary/Abstract
Temporal lobe epilepsy (TLE) is the most common form of focal epilepsy, and it is often characterized by
debilitating and progressive cognitive impairment. As a result, patients with TLE often report poor quality of life
and impaired daily functioning. However, there is significant variability in the nature and severity of cognitive
impairments observed across patients with TLE, with some patients demonstrating generalized impairment and
others demonstrating relatively normal cognitive profiles. Despite the well-known variability in cognitive
impairment that is observed across patients, few studies have focused on identifying distinct cognitive
phenotypes within the syndrome of TLE. In addition, very little is known about other health-related risk or
individual factors that may contribute to this variability. Health-related risk factors such as vascular factors and
metabolic biomarkers, and modifiable risk factors such as hypertension, obesity, physical inactivity, and smoking
have been linked to accelerated cognitive aging and even dementia. In contrast, individual factors such as high
pre-morbid IQ, more years of education, higher occupational attainment, and bilingualism have been shown to
offer a protective factor against the effects of neuropathology on cognition. Given that TLE is now understood to
represent a spectrum of disorders, identifying groups of patients with similar cognitive profiles may provide
unique insight into the underlying neuropathology that exists in individual patients with TLE, which could have
important prognostic value. The proposed study will be the first to integrate advanced neuroimaging data,
cognitive data, and health-related risk factors and protective factors in an effort to unravel the heterogeneity of
cognitive impairment in TLE. In this study, we will identify and define cognitive phenotypes in TLE based on
impairment across neuropsychological measures of language, memory, executive function, and motor speed.
We will then investigate brain network differences and clinical features associated with each phenotype.
Differences in brain networks will be evaluated using structural and diffusion data utilizing both a regional and a
novel connectome-based approach. In addition, we will identify important health-related risk factors (i.e., BMI,
pulse pressure proxy, fasting glucose, history of past or current vascular disease, history of smoking, diet, and
exercise) and protective factors (i.e., pre-morbid IQ, education, occupation attainment, bilingualism) that
moderate the relationship between brain network abnormalities and cognitive dysfunction. The ultimate goal of
this proposal is to address the National Institutes of Neurological Diseases and Strokes (NINDS) major
benchmark focused on the prevention and reversal of comorbidities in epilepsy. Given that cognitive impairment
is the most common and problematic comorbidity in TLE and epilepsy in general, the results from this proposal
ma...

## Key facts

- **NIH application ID:** 9966711
- **Project number:** 5F31NS111883-02
- **Recipient organization:** UNIVERSITY OF CALIFORNIA, SAN DIEGO
- **Principal Investigator:** Anny Reyes
- **Activity code:** F31 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $38,362
- **Award type:** 5
- **Project period:** 2019-07-01 → 2022-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9966711

## Citation

> US National Institutes of Health, RePORTER application 9966711, Explaining cognitive heterogeneity in temporal lobe epilepsy (TLE): Identifying unique imaging features, health-related risk factors and protective factors in TLE subtypes (5F31NS111883-02). Retrieved via AI Analytics 2026-05-27 from https://api.ai-analytics.org/grant/nih/9966711. Licensed CC0.

---

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