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Abstracts

Modeling Cortico-Cortical Evoked Potentials with Gamma Functions in Patients with Drug-Resistant Epilepsy

Abstract number : 2.087
Submission category : 3. Neurophysiology / 3G. Computational Analysis & Modeling of EEG
Year : 2023
Submission ID : 363
Source : www.aesnet.org
Presentation date : 12/3/2023 12:00:00 AM
Published date :

Authors :
Presenting Author: Doris Xu, Undergraduate Student – University of Pennsylvania

Erin Conrad, MD – University of Pennsylvania; Brian Litt, MD – University of Pennsylvania; Carlyn Patterson Gentile, MD, PhD – University of Pennsylvania; Joshua LaRocque, MD, PhD – University of Pennsylvania

Rationale:
Developing accurate methods of localizing the seizure onset zone (SOZ) is critical to improve surgical planning for drug-resistant epilepsy. Cortico-cortical evoked potentials (CCEPs), measured during low-frequency direct cortical stimulation, can actively probe cortical networks and may localize the SOZ. However, current approaches to interpreting CCEPs are limited to detecting just one or two features of the waveform. We propose a full-waveform modeling technique using gamma functions, an approach previously used for modeling other neurophysiologic data. We hypothesize that this method can better characterize CCEPs, which may allow more accurate SOZ localization.

Methods:
We performed low frequency cortical stimulation (3 mA current, 300-500 µs pulse width, 1 Hz frequency, 30 trials per stimulation site) in six patients (five female, one male, aged 29-44 years) with drug-resistant epilepsy using stereoelectroencephalography (SEEG) depth electrodes targeting the mesial temporal regions bilaterally. We averaged across 30 trials, generating 450 total waveforms, one for each stimulation-response channel pair. Using a minimization algorithm and adapting a gamma fitting method previously used for bold fMRI data,1 the 0-200 ms portion of each waveform was fitted with the sum of three gamma functions and the linear correlation coefficient (R2) was calculated. We compared gamma fitting results to a previously-validated method of detecting the first negative (N1) peak,1 by measuring the correlation between these N1 peaks and the amplitude of the gamma model at the same time point.

Results:
The majority (386 of 450) of waveforms were fitted with converging gamma functions, while N1 peaks were only detected in 109 of 450 waveforms. The mean R2 value of fit was 0.85 ± 0.19 (0.92 ± 0.14 for waveforms in which an N1 was detected, 0.82 ± 0.20 for waveforms in which no N1 was detected). In waveforms with an N1, the mean R2 of the correlation between the N1 magnitude and the gamma model at the same timepoint was 0.87 ± 0.12. 

Conclusions:
These results suggest that gamma modeling is an accurate, physiologically valid, more complete approach to characterizing CCEPs. Next steps include exploring the correlation between gamma model parameters and the epileptic state of the tissue in which a CCEP is recorded.

1 Glover G. H. (1999). Deconvolution of impulse response in event-related BOLD fMRI. NeuroImage, 9(4), 416–429. https://doi.org/10.1006/nimg.1998.0419.

2 Cornblath, E. J., Lucas, A., Armstrong, C., Greenblatt, A. S., Stein, J. M., Hadar, P. N., Raghupathi, R., Marsh, E., Litt, B., Davis, K. A., & Conrad, E. C. (2023). Quantifying trial-by-trial variability during cortico-cortical evoked potential mapping of epileptogenic tissue. Epilepsia, 64(4), 1021–1034. https://doi.org/10.1111/epi.17528.

Funding:
NINDS 5T32NS091006-08 (Joshua J. LaRocque)

NINDS 1K23NS121401-01, the Burroughs Wellcome Career Award (Erin Conrad)

Mirowski Fund (Brian Litt)
Neurophysiology