Poster No:
1645
Submission Type:
Abstract Submission
Authors:
Isa Dallmer-Zerbe1,2, Jakub Kopal3,1, Anna Pidnebesna1,4, Jonathan Curot5,6, Marie Denuelle5, Jean-Christophe Sol5, Luc Valton5, Emmanuel Barbeau6, Jaroslav Hlinka1,4
Institutions:
1Institute of Computer Science of the Czech Academy of Sciences, Prague, Czech Republic, 2Second Faculty of Medicine, Charles University, Prague, Czech Republic, 3McGill University, Montreal, Quebec, 4National Institute of Mental Health, Klecany, Czech Republic, 5Brain electrophysiology, Epilepsy and Sleep Unit, Neurology Department, University Hospital, Toulouse, France, 6Centre de Recherche Cerveau et Cognition (CerCo), Toulouse, France
First Author:
Isa Dallmer-Zerbe
Institute of Computer Science of the Czech Academy of Sciences|Second Faculty of Medicine, Charles University
Prague, Czech Republic|Prague, Czech Republic
Co-Author(s):
Jakub Kopal
McGill University|Institute of Computer Science of the Czech Academy of Sciences
Montreal, Quebec|Prague, Czech Republic
Anna Pidnebesna
Institute of Computer Science of the Czech Academy of Sciences|National Institute of Mental Health
Prague, Czech Republic|Klecany, Czech Republic
Jonathan Curot
Brain electrophysiology, Epilepsy and Sleep Unit, Neurology Department, University Hospital|Centre de Recherche Cerveau et Cognition (CerCo)
Toulouse, France|Toulouse, France
Marie Denuelle
Brain electrophysiology, Epilepsy and Sleep Unit, Neurology Department, University Hospital
Toulouse, France
Jean-Christophe Sol
Brain electrophysiology, Epilepsy and Sleep Unit, Neurology Department, University Hospital
Toulouse, France
Luc Valton
Brain electrophysiology, Epilepsy and Sleep Unit, Neurology Department, University Hospital
Toulouse, France
Emmanuel Barbeau
Centre de Recherche Cerveau et Cognition (CerCo)
Toulouse, France
Jaroslav Hlinka
Institute of Computer Science of the Czech Academy of Sciences|National Institute of Mental Health
Prague, Czech Republic|Klecany, Czech Republic
Introduction:
The clinical workup during the pre-surgical evaluation for epilepsy relies on the electrophysiological recording of seizures. The interval until first seizure occurrence is characterized by an increase in seizure likelihood caused by progressive drug dose decreases, during which the epileptic brain transitions from a state of low to a state of high seizure likelihood, so-called pro-ictal state (Baud et al., 2020). This study aimed to identify and characterize the dynamic brain changes characteristic of this transition.
Methods:
We analyzed 386 ten-minute segments of intracranial EEG recordings of 29 patients with drug-refractory temporal lobe epilepsy, irregularly sampled between electrode implantation and first seizure. As measures of brain dynamics we studied the mean phase coherence (gMPC; Mormann et al., 2000) and the relative power (gPR; Panagiotopoulou et al., 2022) in gamma frequency band, as well as the autocorrelation function width (ACFW; Maturana et al., 2020). We further investigate the interaction of those brain dynamics with various susceptibility factors, such as the rate of interictal spikes and high frequency oscillations (Roehri et al., 2018), circadian and multi-day cycles (Schroeder et al., 2020), and clinical outcomes.
Results:
We observe a significant increase in relative gamma power in the epileptogenic zone between the beginning and the end of the measured interval (Z = 2.998, pFDR = .008, Cohen's d = .678), and an increase in critical slowing in both the epileptogenic zone (Z = 1.992, pFDR = .046, d = -.545) as well as the healthy cortex (Z = 2.757, pFDR = .009, d = -.680). These brain dynamic changes were linked with increases in spike and high frequency oscillations rate. While the brain dynamic changes occurred on the slow time scale - from the beginning to the end of the multi-day interval - they did not change in the short-term during the pre-ictal interval.
Conclusions:
We highlight gamma power and critical slowing as markers of pro-ictal (as opposed to pre-ictal) brain states, as well as their potential to track the seizure-related brain mechanisms during the presurgical evaluation of epilepsy patients.
Disorders of the Nervous System:
Neurodegenerative/ Late Life (eg. Parkinson’s, Alzheimer’s) 2
Neurodevelopmental/ Early Life (eg. ADHD, autism)
Modeling and Analysis Methods:
EEG/MEG Modeling and Analysis 1
Keywords:
Data analysis
Electroencephaolography (EEG)
Epilepsy
Neurological
1|2Indicates the priority used for review
Provide references using author date format
Baud, M. O., Proix, T., Rao, V. R., & Schindler, K. (2020). ‘Chance and risk in epilepsy.’ Current Opinion in Neurology, vol. 33, no. 2, pp. 163–172.
Maturana, M. I., Meisel, C., Dell, K., Karoly, P. J., D’Souza, W., Grayden, D. B., Burkitt, A. N., Jiruska, P., Kudlacek, J., Hlinka, J., Cook, M. J., Kuhlmann, L., & Freestone, D. R. (2020). ‘Critical slowing down as a biomarker for seizure susceptibility.’ Nature Communications, vol. 11, no. 1, p. 2172.
Mormann, F., Lehnertz, K., David, P., & E. Elger, C. (2000). ‘Mean phase coherence as a measure for phase synchronization and its application to the EEG of epilepsy patients.’ Physica D: Nonlinear Phenomena, vol. 144, no. 3–4, pp. 358–369.
Panagiotopoulou, M., Papasavvas, C. A., Schroeder, G. M., Thomas, R. H., Taylor, P. N., & Wang, Y. (2022). ‘Fluctuations in EEG band power at subject‐specific timescales over minutes to days explain changes in seizure evolutions.’ Human Brain Mapping, vol. 43, no. 8, pp. 2460–2477.
Schroeder, G. M., Diehl, B., Chowdhury, F. A., Duncan, J. S., de Tisi, J., Trevelyan, A. J., Forsyth, R., Jackson, A., Taylor, P. N., & Wang, Y. (2020). ‘Seizure pathways change on circadian and slower timescales in individual patients with focal epilepsy.’ Proceedings of the National Academy of Sciences, vol. 117, no. 20, pp. 11048-11058.