A novel method for dynamically altering the surface area of intracranial EEG electrodes.

Abstract
Intracranial EEG (iEEG) plays a critical role in the treatment of neurological diseases, such as epilepsy and Parkinson's disease, as well as the development of neural prostheses and brain computer interfaces. While electrode geometries vary widely across these applications, the impact of electrode size on iEEG features and morphology is not well understood. Some insight has been gained from computer simulations, as well as experiments in which signals are recorded using electrodes of different sizes concurrently in different brain regions. Here, we introduce a novel method to record from electrodes of different sizes in the exact same location by changing the size of iEEG electrodes after implantation in the brain. We first present a theoretical model and an in vitro validation of the method. We then report the results of an in vivo implementation in three human subjects with refractory epilepsy. We recorded iEEG data from three different electrode sizes and compared the amplitudes, power spectra, inter-channel correlations, and signal-to-noise ratio (SNR) of interictal epileptiform discharges, i.e., epileptic spikes. We found that iEEG amplitude and power decreased as electrode size increased, while inter-channel correlation did not change significantly with electrode size. The SNR of epileptic spikes was generally highest in the smallest electrodes, but 39% of spikes had maximal SNR in larger electrodes. This likely depends on the precise location and spatial spread of each spike. Overall, this new method enables multi-scale measurements of electrical activity in the human brain that can facilitate our understanding of neurophysiology, treatment of neurological disease, and development of novel technologies.

Citation
Remakanthakurup Sindhu, K., Ngo, D., Ombao, H., Olaya, J. E., Shrey, D. W., & Lopour, B. A. (2023). A novel method for dynamically altering the surface area of intracranial EEG electrodes. Journal of Neural Engineering. https://doi.org/10.1088/1741-2552/acb79f

Acknowledgements
Research reported in this publication was supported by the National Institute of Neurological Disorders and Stroke of the National Institutes of Health under award number R01NS116273 and a CHOC PSF Tithe grant. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. We would also like to acknowledge Zoran Nenadic and Jeffrey Lim for their support and helpful feedback, particularly in the early stages of this work.

Publisher
IOP Publishing

Journal
Journal of neural engineering

DOI
10.1088/1741-2552/acb79f

PubMed ID
36720162

Additional Links
https://iopscience.iop.org/article/10.1088/1741-2552/acb79f

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