# Optimized Intracranial EEG Targeting in Focal Epilepsy based upon Neuroimaging Connectomics

> **NIH NIH R01** · UNIVERSITY OF PENNSYLVANIA · 2021 · $660,904

## Abstract

Despite recent advances in neuroimaging, approximately 2/3 of intractable epilepsy patients that undergo
surgical evaluation continue to require intracranial EEG (IEEG), arguably the most invasive diagnostic test in
medicine. We currently lack methods to quantitatively map noninvasive imaging measures of structure and
function to IEEG. Specifically, there is a critical need to validate whole-brain noninvasive neuroimaging network-
based biomarkers to guide precise placement of electrodes and translate noninvasive network neuroimaging to
change the paradigms of clinical care. The long-term goal of this proposal is to predict IEEG functional dynamics
and surgical outcomes using noninvasive MRI-based measures of structure and function. Our overall objective,
which is the next step toward attaining our long-term goal, is to develop open-source noninvasive imaging tools
that map epileptic networks by integrating MRI and IEEG data. Our central hypothesis is that noninvasive
measures of structure and function relate to and can predict the intricate functional dynamics captured on IEEG.
The central hypothesis will be tested in patients undergoing IEEG targeting the temporal lobe network by
pursuing three specific aims: 1) To map the patient specific structural connectome to IEEG seizure onset and
propagation, 2) To correlate seizure onset and propagation on IEEG with network measures derived from resting
state functional MRI (rsfMRI), and 3) To integrate the structural (Aim 1) and functional (Aim 2) connectome with
standard qualitative clinical data to predict IEEG network dynamics and surgical outcomes. Under the first aim
patients will undergo diffusion tensor imaging (DTI) prior to stereotactic IEEG, an IEEG method that inherently
samples long range networks. The functional IEEG network will be mapped to DTI thus defining how seizures
are constrained by the underlying structural connectome as they propagate. Under the second aim patients with
temporal lobe epilepsy will undergo rsfMRI on 7T MRI prior to stereotactic IEEG. Functional network measures
from rsfMRI and IEEG will be coregistered and rsfMRI will be used to predict functional EEG ictal and interictal
networks. In the third aim two models predicting IEEG network dynamics and epilepsy surgical outcomes will be
created building off of methods developed in Aims 1 and 2. The proposed research is innovative because it
represents a substantive departure from the status quo by directly connecting noninvasive multimodal imaging
with measures of functional network dynamics in IEEG. The proposed research is significant because it is
expected that successful completion of these aims will yield personalized strategies for IEEG targeting based on
noninvasive neuroimaging.

## Key facts

- **NIH application ID:** 10122346
- **Project number:** 1R01NS116504-01A1
- **Recipient organization:** UNIVERSITY OF PENNSYLVANIA
- **Principal Investigator:** Kathryn Adamiak Davis
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $660,904
- **Award type:** 1
- **Project period:** 2021-03-01 → 2026-02-28

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10122346

## Citation

> US National Institutes of Health, RePORTER application 10122346, Optimized Intracranial EEG Targeting in Focal Epilepsy based upon Neuroimaging Connectomics (1R01NS116504-01A1). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10122346. Licensed CC0.

---

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