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Volume 13
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Article

The Pre-Interictal Network State in Idiopathic Generalized Epilepsies

by
Dimitrios Pitetzis
1,2,*,
Christos Frantzidis
2,3,
Elizabeth Psoma
4,
Smaranda Nafsika Ketseridou
2,
Georgia Deretzi
1,
Anna Kalogera-Fountzila
4,
Panagiotis D. Bamidis
2 and
Martha Spilioti
5
1
Department of Neurology, Papageorgiou General Hospital, 56403 Thessaloniki, Greece
2
Lab of Medical Physics and Digital Innovation, School of Medicine, Faculty of Health Sciences, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece
3
School of Computer Science, University of Lincoln, Lincoln LN6 7TS, UK
4
Department of Radiology, AHEPA General Hospital, School of Medicine, Faculty of Health Sciences, Aristotle University of Thessaloniki, 54636 Thessaloniki, Greece
5
1st Department of Neurology, AHEPA General Hospital, School of Medicine, Faculty of Health Sciences, Aristotle University of Thessaloniki, 54636 Thessaloniki, Greece
*
Author to whom correspondence should be addressed.
Brain Sci. 2023, 13(12), 1671; https://doi.org/10.3390/brainsci13121671
Submission received: 29 October 2023 / Revised: 24 November 2023 / Accepted: 30 November 2023 / Published: 2 December 2023
(This article belongs to the Section Computational Neuroscience and Neuroinformatics)
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Abstract

Generalized spike wave discharges (GSWDs) are the typical electroencephalographic findings of Idiopathic Generalized Epilepsies (IGEs). These discharges are either interictal or ictal and recent evidence suggests differences in their pathogenesis. The aim of this study is to investigate, through functional connectivity analysis, the pre-interictal network state in IGEs, which precedes the formation of the interictal GSWDs. A high-density electroencephalogram (HD-EEG) was recorded in twenty-one patients with IGEs, and cortical connectivity was analyzed based on lagged coherence and individual anatomy. Graph theory analysis was used to estimate network features, assessed using the characteristic path length and clustering coefficient. The functional connectivity analysis identified two distinct networks during the pre-interictal state. These networks exhibited reversed connectivity attributes, reflecting synchronized activity at 3–4 Hz (delta2), and desynchronized activity at 8–10.5 Hz (alpha1). The delta2 network exhibited a statistically significant (p < 0.001) decrease in characteristic path length and an increase in the mean clustering coefficient. In contrast, the alpha1 network showed opposite trends in these features. The nodes influencing this state were primarily localized in the default mode network (DMN), dorsal attention network (DAN), visual network (VIS), and thalami. In conclusion, the coupling of two networks defined the pre-interictal state in IGEs. This state might be considered as a favorable condition for the generation of interictal GSWDs.
Keywords: EEG; cortical connectivity; graph theory analysis EEG; cortical connectivity; graph theory analysis

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MDPI and ACS Style

Pitetzis, D.; Frantzidis, C.; Psoma, E.; Ketseridou, S.N.; Deretzi, G.; Kalogera-Fountzila, A.; Bamidis, P.D.; Spilioti, M. The Pre-Interictal Network State in Idiopathic Generalized Epilepsies. Brain Sci. 2023, 13, 1671. https://doi.org/10.3390/brainsci13121671

AMA Style

Pitetzis D, Frantzidis C, Psoma E, Ketseridou SN, Deretzi G, Kalogera-Fountzila A, Bamidis PD, Spilioti M. The Pre-Interictal Network State in Idiopathic Generalized Epilepsies. Brain Sciences. 2023; 13(12):1671. https://doi.org/10.3390/brainsci13121671

Chicago/Turabian Style

Pitetzis, Dimitrios, Christos Frantzidis, Elizabeth Psoma, Smaranda Nafsika Ketseridou, Georgia Deretzi, Anna Kalogera-Fountzila, Panagiotis D. Bamidis, and Martha Spilioti. 2023. "The Pre-Interictal Network State in Idiopathic Generalized Epilepsies" Brain Sciences 13, no. 12: 1671. https://doi.org/10.3390/brainsci13121671

APA Style

Pitetzis, D., Frantzidis, C., Psoma, E., Ketseridou, S. N., Deretzi, G., Kalogera-Fountzila, A., Bamidis, P. D., & Spilioti, M. (2023). The Pre-Interictal Network State in Idiopathic Generalized Epilepsies. Brain Sciences, 13(12), 1671. https://doi.org/10.3390/brainsci13121671

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