The Impact of the Choice of the Time Segment on the Distribution of Spikes in Intracranial EEG
Abstract number :
3.128
Submission category :
2. Translational Research / 2C. Biomarkers
Year :
2022
Submission ID :
2204991
Source :
www.aesnet.org
Presentation date :
12/5/2022 12:00:00 PM
Published date :
Nov 22, 2022, 05:27 AM
Authors :
Vojtech Travnicek, MSc – International Clinical Research Center, St. Anne’s University Hospital, Brno, Czech Republic; Jan Cimbalnik, PhD – International Clinical Research Center, St. Anne’s University Hospital, Brno, Czech Republic; Petr Klimes, PhD – Institute of Scientific Instruments, The Czech Academy of Sciences, Brno, Czech Republic; Chifaou Abdallah, MD – Analytical Neurophysiology Lab, Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada; François Dubeau, MD – Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada; Birgit Frauscher, prof. – Analytical Neurophysiology Lab, Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada
Rationale: Epileptic spikes are the traditional interictal biomarker of epilepsy. When a patient is admitted for intracranial EEG (iEEG) recording, he is typically implanted for 1 to 3 weeks, while his antiseizure medication (ASM) dosage is reduced to facilitate seizures. Studies on the influence of ASM dosage, seizures, and time on spike rate and distribution remain ambiguous. This study describes the spike rate and distribution across two 24 hr recording segments separated by a minimum of 7 days.
Methods: We analyzed 11 consecutive focal epileptic patients from the Montreal Neurological Institute iEEG database which underwent subsequent epilepsy surgery. For each patient we chose two 24-h segments. The first segment started at least 72 h after electrode implantation and the second section started at least 7 days after segment 1. Both segments contained at least 20 10-min segments of non-artificial data. Every 10-min segment was pulled from a one-hour segment. All data were sampled at 2 kHz and contained no seizures. Sleep scoring was done automatically.1 The medication level for every day was evaluated using a published equation.2 Spikes were detected by the Janca detector.3 We classified the spikes according to their anatomical locations, and evaluated Wake, Non-REM, and REM sleep stages separately. For each patient and sleep stage, we compared the absolute spike number between the segments using a Wilcoxon rank-sum test with Bonferroni correction. To compare the spike distribution between the first and the second segment we performed a Spearman correlation analysis. To evaluate the stability of the most spiking regions, we computed the overlap percentage between the three most spiking regions between the segments.
Results: The medication load between the two segments differed by 48±21%, while 100% is the maximum daily dosage during implantation. The seizure rate between the segments differed by 1.1±1.4 seizures/day. The median Spearman correlation was 0.9 [interquartile range (IQ), 0.77-0.96]. In 25 of 30 cases, there was either no change (n=14) or only a change in one of the three most spiking regions (n=11). The absolute spike number changed in 2 out of 30 cases (only in 1 of the 3 evaluated vigilance states per patient).
Conclusions: Our study shows that spike is a robust interictal biomarker of epilepsy. Neither ASM reduction, seizures, nor prolonged implantation time have a significant effect on the spike rate and its distribution.
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References:_x000D_
1. von Ellenrieder, et al. B. SleepSEEG: automatic sleep scoring using intracranial EEG recordings only. J Neural Eng 2022;19_x000D_
2. Paulo DL, et al. SEEG functional connectivity measures to identify epileptogenic zones: stability, medication influence, and recording condition. Neurology. 2022;98:e2060-e2072._x000D_
3. Janca R, et al. Detection of interictal epileptiform discharges using signal envelope distribution modelling: application to epileptic and non-epileptic intracranial recordings. Brain Topogr. 2015;28:172-183.
Funding: Inter-Excellence LTAUSA18, FEKT-K-22-7649, CIHR PJT-175056
Translational Research