High-frequency Oscillations Captured on Micro-electrocorticographic Arrays During Intraoperative Recordings
Abstract number :
Submission category :
2. Translational Research / 2B. Devices, Technologies, Stem Cells
Submission ID :
Presentation date :
12/5/2022 12:00:00 PM
Published date :
Nov 22, 2022, 05:27 AM
Katrina Barth, BS,MS – Duke University; Chia-Han Chiang, PhD – Biomedical Engineering – Duke University; Shervin Rahimpour, MD – Neurosurgery, Biomedical Engineering – University of Utah; Charles Wang, MS – Biomedical Engineering – Duke University; Michael Trumpis, PhD – Biomedical Engineering – Duke University; James Sun, BS – Center for Neural Science – New York University; Suseendrakumar Duraivel, MS – Biomedical Engineering – Duke University; Agrita Dubey, PhD – Center for Neural Science – New York University; Katie Wingel, BS – Center for Neural Science – New York University; Shaoyu Qiao, PhD – Center for Neural Science – New York University; Alex Voinas, BS – Center for Neural Science – New York University; Breonna Ferrentino, BS – Center for Neural Science – New York University; Werner Doyle, MD – Neurosurgery – New York University Langone Medical Center; Derek Southwell, MD,PhD – Neurosurgery, Neurobiology – Duke University; Michael Haglund, MD,PhD – Neurosurgery – Duke University; Allan Friedman, MD – Neurosurgery – Duke University; Shivanand Lad, MD,PhD – Neurosurgery – Duke University; Florian Solzbacher, PhD – Biomedical Engineering, Electrical and Computer Engineering, Materials Science and Engineering – University of Utah; Sasha Devore, PhD – Neurology – New York University Grossman School of Medicine; Orrin Devinsky, MD – Neurosurgery, Neurology, Comprehensive Epilepsy Center – New York University Langone Health; New York University Grossman School of Medicine; Saurabh Sinha, MD,PhD – Neurology, Comprehensive Epilepsy Center – Duke University; Daniel Friedman, MD – Neurology – New York University Grossman School of Medicine; Bijan Pesaran, PhD – Center for Neural Science, Neurology – New York University; New York University Grossman School of Medicine; Gregory Cogan, PhD – Neurosurgery, Neurology, Comprehensive Epilepsy Center, Psychology and Neuroscience, Center for Cognitive Neuroscience – Duke University; Justin Blanco, PhD – Electrical and Computer Engineering – United States Naval Academy; Jonathan Viventi, PhD – Biomedical Engineering, Neurobiology, Neurosurgery, Comprehensive Epilepsy Center – Duke University
Rationale: Identification of the seizure onset zone (SOZ) is essential to the success of surgical resection or implantation of a responsive neurostimulator to treat drug resistant epilepsy. However, the ability to accurately localize the SOZ may be limited by a mismatch between the fine micro-scale electrophysiology of epileptic signals and the coarse spatial scale of clinical recording arrays. Previous research using penetrating micro-electrode arrays (MEAs) has shown that interictal events related to the SOZ, such as high-frequency oscillations (HFOs), are initiated in < 1mm2 of brain (Schevon, et al. Journal of Clinical Neurophysiology. 2008). However, MEAs are limited in their spatial coverage (4x4mm) and therefore unable to capture wide propagation of signals or delineate between regions of SOZ and non-SOZ at a surgically-usable scale.
Methods: Here we report the use of novel micro-electrocorticographic (µECoG) arrays to record HFOs intraoperatively from 10 human subjects with epilepsy undergoing resective surgery. Our liquid crystal polymer thin-film µECoG arrays offer between 128-512 gold micro-contacts (200 µm diameter) at high resolution (0.76-1.72 mm spacing) while maintaining spatial coverage (148 - 1560 mm2) comparable to standard clinical electrodes (Chiang, et al. Journal of Neural Engineering, 2021). In each intraoperative recording, the array was placed on the cortical surface as near the clinically determined SOZ as allowed by the craniotomy. We utilized a standard automated detection algorithm (Staba, et al. Journal of Neurophysiology. 2002) along with automated and visual artifact rejection methods to identify HFOs in our intraoperative µECoG recordings. The spatiotemporal dynamics of HFOs were evaluated through 2D visualizations of HFO activity across the array, exponential fits of the signal covariance between HFOs and surrounding activity, and detection of HFOs on spatially averaged data to simulate recordings on larger contacts.
Results: We found that specific regions of elevated HFO activity were delineable within our µECoG arrays. From our covariance analysis, we determined that ~45% of HFOs occurred on single channels and that multichannel HFOs primarily occurred within ~1mm radius of a central HFO. We also show that recordings from larger contacts simulated through spatial averaging would fail to capture most HFO events seen in our µECoG recordings.
Conclusions: These results demonstrate that HFOs occur at the micro-scale and that capturing these events using arrays with both high resolution and large coverage may offer a more accurate tool for SOZ localization to improve epilepsy treatment.
Funding: This work is supported by an NSF GRFP (DGE-1644868), CTSA grant (UL1TR002553), NIH 1UG3NS120172, and Finding a Cure for Epilepsy and Seizures (FACES). Parts of the technology described here are patent pending under “Electroencephalography (EEG) Electrode Arrays and Related Methods of Use” U.S. Patent Application # PCT/US2020/051400. Florian Solzbacher has financial interest in Blackrock Microsystems. Conflict of interest is managed through University of Utah Conflict of Interest Management.