Localizing Epileptic Networks Using MRI and iEEG

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.

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.

Patients recruited for the study with intractable epilepsy who are anticipated to undergo epilepsy surgery

Epilepsy patients may undergo implantation of intracranial electroencephalography (iEEG) electrodes for localization of epileptogenic foci, which also provide a means to record localized brain activity during memory or other tasks for research purposes.

This study aims to analyze MRI and iEEG to quantify structure-function coupling (SC-FC), specifically between white matter connections and IEEG, across preictal and ictal periods in consecutive patients undergoing IEEG monitoring with SEEG targeting the temporal lobe networks at the University of Pennsylvania and Medical University of South Carolina.

Measure will be assessed upon collection of patient pre-implant MRI study and iEEG recordings, and control MRI

This study aims to correlate seizure onset and propagation on IEEG with network measures derived from resting-state functional 7T MRI (rsfMRI) in MRI-negative TLE.

Measure will be assessed upon collection of patient pre-implant 7T rsfMRI and iEEG recordings, and control 7T MRI

This study aims to correlate non-invasive structural (Outcome 1) and functional (Outcome 2) connectomes and standard clinical data with IEEG network dynamics and surgical outcomes. This represents the first quantitative multi-modal imaging study linking noninvasive imaging to IEEG functional dynamics and epilepsy surgical outcomes.

Measure will be assessed upon collection of patient pre-implant 3T and 7T MRI studies and iEEG recordings, and control MRI

Inclusion Criteria: Patients with medication-refractory epilepsy Planned intracranial EEG (IEEG) placement Hypothesized to have temporal lobe epilepsy Exclusion Criteria: Contraindication to 3T MRI (e.g. metal implants or claustrophobia), clinical features that typically preclude the use of IEEG (e.g. pregnancy), prior intracranial surgery or device, and IEEG findings that are non-diagnostic (e.g. seizure onset zone(s) not identified)

Upon completion of this study and dissemination of primary study results, the analysis data files will be made available to the public, along with the final version of the study protocol, the data dictionary, and brief instructions ("Readme" file). Public use data files and the accompanying documents will be made available through the National Technical Information Service (NTIS). Each data file (including MRI images) will be stripped of any and all personal identifiers and will undergo de-identification. The datasets, analytical results, and analysis software used in the investigators' applications will be hosted on the NIH-funded IEEG.org portal that has been co-developed over the past almost 10 years. IEEG.org already hosts over 150 IEEG datasets and has over 3900 users, and links to software and algorithms on GitHub.

Data will be uploaded to IEEG.org as it is collected in this study will be hosted there while funding remains available for the portal.

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