Localizing Epileptic Networks Using MRI and iEEG

Epilepsy Clinical Trial

Official title: Optimized Intracranial EEG Targeting in Focal Epilepsy Based Upon Neuroimaging Connectomics

Status Recruiting

Clinical Trial Summary

Upon successful completion of this study, the investigators expect the study's contribution to be the development of noninvasive imaging biomarkers to predict IEEG functional dynamics and epilepsy surgical outcomes. Findings from the present study may inform current and new therapies to map and alter seizure spread, and pave the way for less invasive, better- targeted, patient-specific interventions with improved surgical outcomes. This research is relevant to public health because over 20 million people worldwide suffer from focal drug-resistant epilepsy and are potential candidates for cure with epilepsy surgical interventions.

Clinical Trial Description

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. Clinicians 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 study is to predict IEEG functional dynamics and surgical outcomes using noninvasive MRI-based measures of structure and function. The investigators' overall objective, which is the next step toward attaining the study's long-term goal, is to develop open-source noninvasive imaging tools that map epileptic networks by integrating MRI and IEEG data. The 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. This 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. This research is also significant because it is expected that successful completion of these aims will yield personalized strategies for IEEG targeting based on noninvasive neuroimaging. ;

Related Conditions & MeSH terms

• Drug Resistant Epilepsy
• Epilepsy
• Epilepsy Intractable
• Epilepsy, Temporal Lobe

• NCT number: NCT04649008
• Study type: Interventional
• Source: University of Pennsylvania
• Contact: Kathryn A Davis, MD, MSTR
• Phone: 215-349-5166
• Email: katedavis@pennmedicine.upenn.edu
• Status: Recruiting
• Phase: Early Phase 1
• Start date: March 1, 2021
• Completion date: February 28, 2026