# Multimodal imaging of memory reorganization in epilepsy from whole brain networks to local neuronal responses:  Implications for surgical-decision-making

> **NIH NIH R56** · UNIVERSITY OF CALIFORNIA, SAN DIEGO · 2020 · $467,231

## Abstract

Anterior temporal lobectomy (ATL) is a highly sucessful treatment for eliminating seizures in patients with
temporal lobe epilepsy (TLE). However, ATL-induced memory decline is frequent and often severe, having a
deleterious impact on quality of life and functional outcomes. Stereotactic laser amygdalohippocampectomy
(SLAH) has been introduced as a minimally-invasive alternative that could minimize risk of memory
decline. However, it is unclear which patients would benefit the most from SLAH and whether SLAH
decreases risk for targeted aspects of episodic memory decline compared to ATL. During the previous
grant funding period, we demonstrated the clinical value of combining information from structural (sMRI),
diffusion (DTI), and functional (fMRI) imaging to better characterize the neural networks that underlie
preoperative language and memory impairment and (re)organization and in TLE. We propose that the same
multimodal imaging (MMI) approach can be used to quantify risk for postoperative memory decline. In this
competing renewal, we extend our MMI approach, combining sMRI/DTI/fMRI with intracranial recordings (iEEG),
enabling us to delve deep into the spatial and temporal dynamics of episodic memory networks in TLE. We
employ multimodal associative learning tasks with real-world implications (i.e., pairing a face with a name) that
have not before been studied in the surgical context. In addition, we draw from preclinical and computational
models of hippocampal functioning that may inform why many patients struggle to make fine-grain distinctions
in memory (i.e., impaired pattern separation), even when simple item memory appears intact. We propose that
our MMI approach will yield a more complete characterization of episodic memory networks in TLE, reveal
patterns of structural and functional reorganization in individual patients, and enable a personalized approach to
risk assessment when considering surgical options. Finally, we will track cognitive and imaging changes post-
ATL and SLAH and identify patient-specific factors that promote reorganization and improved cognitive
outcomes. The goals of this renewal are perfectly aligned with the 2014 NINDS Benchmarks for Epilepsy
Research (Part IV, Limit or prevent adverse consequences of seizures and their treatment across the
lifespan), which encourage mitigating the effects of surgical interventions on cognitive co-morbidities
in epilepsy. Our renewal directly addresses this request, striving to improve surgical decision-making, which will
have an immediate and sustained impact on patient care. Epilepsy is a common neurological disease that costs
the healthcare system approximately $15.5 billion annually and can negatively impact quality of life, employment,
and health status. The current project has strong implications for public health because it strives to improve
health outcomes in patients with epilepsy by using advanced, noninvasive technology to identify individual
predictors of memory de...

## Key facts

- **NIH application ID:** 10118551
- **Project number:** 2R56NS065838-10
- **Recipient organization:** UNIVERSITY OF CALIFORNIA, SAN DIEGO
- **Principal Investigator:** CARRIE R MCDONALD
- **Activity code:** R56 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $467,231
- **Award type:** 2
- **Project period:** 2010-07-01 → 2021-11-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10118551

## Citation

> US National Institutes of Health, RePORTER application 10118551, Multimodal imaging of memory reorganization in epilepsy from whole brain networks to local neuronal responses:  Implications for surgical-decision-making (2R56NS065838-10). Retrieved via AI Analytics 2026-06-08 from https://api.ai-analytics.org/grant/nih/10118551. Licensed CC0.

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