Temporal and spatial dynamic propagation of electroencephalogram by combining power spectral and synchronization in childhood absence epilepsy

Zhong, Lisha; Wan, Jiangzhong; Wu, Jia; He, Suling; Zhong, Xuefei; Huang, Zhiwei; Li, Zhangyong

doi:10.3389/fninf.2022.962466

Cited by 9 publications

(8 citation statements)

References 60 publications

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“…4c). This suggests their role in initiating and propagating the hyper-synchronized state, consistent with extensive observations and predictions in literature 31–36 . Additionally, we find certain cortical networks (orbital, sensorimotor) to be more critical for synchronization propagation than others (visual) 23 .…”

Section: Discussionsupporting

confidence: 90%

Interplay between resource dynamics, network structure and spatial propagation of transient explosive synchronization in an adaptively coupled mouse brain network model

Ranjan,

Gandhi

2023

Preprint

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Generalized epileptic attacks, which exhibit widespread disruption of brain activity, are characterized by recurrent, spontaneous and synchronized bursts of neural activity that self-initiate and self-terminate through critical transitions. Here we utilize the general framework of explosive synchronization (ES) from complex systems science to study the role of network structure and resource dynamics in the generation and propagation of seizures. We show that a combination of resource constraint and adaptive coupling in a Kuramoto network oscillator model can reliably generate seizure-like synchronization activity across different network topologies, including a biologically derived mesoscale mouse brain network. The model, coupled with a novel algorithm for tracking seizure propagation, provides mechanistic insight into the dynamics of transition to the synchronized state and its dependence on resources; and identifies key brain areas that may be involved in the initiation and spatial propagation of the seizure. The model, though minimal, efficiently recapitulates several experimental and theoretical predictions from more complex models, and makes novel experimentally testable predictions.Significance statement / Author SummaryUnderstanding seizure dynamics at the whole-brain level is crucial for controlling abnormal hypersynchronous activity. Currently, complete brain coverage recordings are lacking in both patients and animal models. We employ network science tools to investigate epileptic seizure-like synchronization in a mouse whole brain network, leveraging network structure and supported dynamics as the basis for seizure evolution. Our results align with experimental findings, suggesting that seizure activity initiates in the cortico-thalamic circuit. Importantly, our novel analysis identifies key nodes, primarily in the cortex, driving this hypersynchronous activity. Our findings highlight network structure’s role in shaping seizure dynamics and the techniques developed here could enhance our control of generalized seizures when combined with patient-specific data.

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Section: Discussionsupporting

confidence: 90%

Interplay between resource dynamics, network structure and spatial propagation of transient explosive synchronization in an adaptively coupled mouse brain network model

Ranjan,

Gandhi

2023

Preprint

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“…Previous studies have suggested that epileptiform discharges may occur before seizures ( 55 ). In EEG-fMRI studies, patients with JAE have partially activated brain areas in the pre-ictal state ( 56 , 57 ).…”

Section: Discussionmentioning

confidence: 99%

Exploring brain network oscillations during seizures in drug-naïve patients with juvenile absence epilepsy

Tan,

Tang,

Luo

et al. 2024

Front. Neurol.

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ObjectiveWe aimed to investigate the brain network activity during seizures in patients with untreated juvenile absence epilepsy.MethodsThirty-six juvenile absence epilepsy (JAE) patients with a current high frequency of seizures (more than five seizures during a 2 h EEG examination) were included. Each participant underwent a 2 h video EEG examination. Five 10 s EEG epochs for inter-ictal, pre-ictal, and post-ictal, and five 5 s EEG epochs for ictal states were extracted. Five 10 s resting-state EEG epochs for each participant from a sex- and age-matched healthy control (HC) were enrolled. The topological parameters of the brain networks were calculated using a graph theory analysis.ResultsCompared with the resting state of the HC group, the global efficiency, local efficiency, and clustering coefficients of the JAE group decreased in the inter-ictal state. In addition, the ictal state showed significantly increased global and local efficiency and clustering coefficients (p < 0.05) and a decreased small-world index and the shortest path length (p < 0.05) in the theta and alpha bands, compared to the remaining states within the JAE group. Moreover, subgroup analysis revealed that those JAE patients with typical 3 Hz discharges had upgraded global efficiency, local efficiency, and clustering coefficients in both delta and beta1 bands, compared to those JAE patients with non-3 Hz discharges during seizures.ConclusionThe present study supported the idea that the changes in the EEG brain networks in JAE patients are characterized by decreased global and local efficiency and clustering coefficient in the alpha band. Moreover, the onset of seizures is accompanied by excessively enhanced network efficiency. JAE patients with different ictal discharge patterns may have different functional network oscillations.

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Section: Discussionmentioning

confidence: 99%

“…PSD analysis of EEG is a widely used method for quantifying power in a specific frequency band reflecting the process of regional brain activities 27 , 28 , which has been used for evaluating dynamic changes in neural activity during the initiation and propagation of seizures 29 and for exploring markers to predict the risk of seizures 30 . According to previous literature 29 , 30 , increased PSD can represent increases in neural activities, which may be a predisposition to seizure. Therefore, our findings of increased PSD in the entire areas of the delta, theta, and beta bands and in the frontal areas of the alpha band in the AIS group, compared with the seizure-free group, suggest that the AIS group may have inherent seizure susceptibility, even in the resting-state.…”

Section: Discussionmentioning

confidence: 99%

Inherent seizure susceptibility in patients with antihistamine-induced acute symptomatic seizure: a resting-state EEG analysis

Kim

Wang

Park

et al. 2023

Sci Rep

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We compared neural activities and network properties between the antihistamine-induced seizures (AIS) and seizure-free groups, with the hypothesis that patients with AIS might have inherently increased neural activities and network properties that are easily synchronized. Resting-state electroencephalography (EEG) data were collected from 27 AIS patients and 30 healthy adults who had never had a seizure. Power spectral density analysis was used to compare neural activities in each localized region. Functional connectivity (FC) was measured using coherence, and graph theoretical analyses were performed to compare network properties between the groups. Machine learning algorithms were applied using measurements found to be different between the groups in the EEG analyses as input features. Compared with the seizure-free group, the AIS group showed a higher spectral power in the entire regions of the delta, theta, and beta bands, as well as in the frontal areas of the alpha band. The AIS group had a higher overall FC strength, as well as a shorter characteristic path length in the theta band and higher global efficiency, local efficiency, and clustering coefficient in the beta band than the seizure-free group. The Support Vector Machine, k-Nearest Neighbor, and Random Forest models distinguished the AIS group from the seizure-free group with a high accuracy of more than 99%. The AIS group had seizure susceptibility considering both regional neural activities and functional network properties. Our findings provide insights into the underlying pathophysiological mechanisms of AIS and may be useful for the differential diagnosis of new-onset seizures in the clinical setting.

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Temporal and spatial dynamic propagation of electroencephalogram by combining power spectral and synchronization in childhood absence epilepsy

Cited by 9 publications

References 60 publications

Interplay between resource dynamics, network structure and spatial propagation of transient explosive synchronization in an adaptively coupled mouse brain network model

Interplay between resource dynamics, network structure and spatial propagation of transient explosive synchronization in an adaptively coupled mouse brain network model

Exploring brain network oscillations during seizures in drug-naïve patients with juvenile absence epilepsy

Inherent seizure susceptibility in patients with antihistamine-induced acute symptomatic seizure: a resting-state EEG analysis

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