Frequency-Dependent Dynamics of Functional Connectivity Networks During Seizure Termination in Childhood Absence Epilepsy: A Magnetoencephalography Study

Sun, Jiao; Li, Yihan; Zhang, Ke; Sun, Yulei; Wang, Yingfan; Miao, Ailiang; Xiang, Jing; Wang, Xiaoshan

doi:10.3389/fneur.2021.744749

Cited by 7 publications

(7 citation statements)

References 67 publications

(103 reference statements)

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“…Comparing (D,E) of DTGCN in Figure 6 , it can be found that the difference between the adjacency matrix of generalized seizure and no seizure is greater than that of focal seizure and no seizure, which indicates that generalized seizure can disrupt normal brain connections to a greater extent than focal epilepsy. This is consistent with the previous description in the literature: brain functional connectivity is significantly altered in global seizures [ 31 ]. Comparing (I,J) of DCRNN in Figure 6 , we find that there is no obvious difference in their graph structures.…”

Section: Results Analysissupporting

confidence: 93%

Time-Series Anomaly Detection Based on Dynamic Temporal Graph Convolutional Network for Epilepsy Diagnosis

Wu,

Yu,

Zhou

et al. 2024

Bioengineering

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Electroencephalography (EEG) is typical time-series data. Designing an automatic detection model for EEG is of great significance for disease diagnosis. For example, EEG stands as one of the most potent diagnostic tools for epilepsy detection. A myriad of studies have employed EEG to detect and classify epilepsy, yet these investigations harbor certain limitations. Firstly, most existing research concentrates on the labels of sliced EEG signals, neglecting epilepsy labels associated with each time step in the original EEG signal—what we term fine-grained labels. Secondly, a majority of these studies utilize static graphs to depict EEG’s spatial characteristics, thereby disregarding the dynamic interplay among EEG channels. Consequently, the efficient nature of EEG structures may not be captured. In response to these challenges, we propose a novel seizure detection and classification framework—the dynamic temporal graph convolutional network (DTGCN). This method is specifically designed to model the interdependencies in temporal and spatial dimensions within EEG signals. The proposed DTGCN model includes a unique seizure attention layer conceived to capture the distribution and diffusion patterns of epilepsy. Additionally, the model incorporates a graph structure learning layer to represent the dynamically evolving graph structure inherent in the data. We rigorously evaluated the proposed DTGCN model using a substantial publicly available dataset, TUSZ, consisting of 5499 EEGs. The subsequent experimental results convincingly demonstrated that the DTGCN model outperformed the existing state-of-the-art methods in terms of efficiency and accuracy for both seizure detection and classification tasks.

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Section: Results Analysissupporting

confidence: 93%

Time-Series Anomaly Detection Based on Dynamic Temporal Graph Convolutional Network for Epilepsy Diagnosis

Wu,

Yu,

Zhou

et al. 2024

Bioengineering

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“…An iEEG study on patients with different types of epilepsies has shown that the initial part of the rapid discharge at the beginning of a seizure corresponds to a synchrony decrease across brain areas; later parts of the seizure, as well as pre-ictal states, correspond to increased synchrony 10 . Since FC quanti es the synchronicity among regions 45 , we expect the FC between the different iEEG contacts to be lower before the seizure onset and increased later during the seizure course. Our ndings support this notion since FC was higher in ictal compared to interictal and pre-ictal states while the most prominent changes were observed in higher frequency bands (i.e., beta, low-and high-gamma).…”

Section: Discussionmentioning

confidence: 99%

Functional connectivity discriminates epileptogenic states and predicts surgical outcome in children with drug resistant epilepsy

Rijal

Corona

Perry

et al. 2022

Preprint

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Normal brain functioning emerges from a complex interplay among regions forming networks. In epilepsy, these networks are disrupted causing seizures. Nodes of these networks are the target of epilepsy surgery. Here, we assess whether functional connectivity (FC) using intracranial electroencephalography (iEEG) can quantify epileptogenicity and predict surgical outcome in children with drug-resistant epilepsy (DRE). We computed Amplitude Envelope Correlation (AEC) and Phase Locking Value (PLV) for different states (i.e., interictal with no spikes, interictal with spikes, pre-ictal, ictal, and post-ictal) and for different frequency bands. We then computed each node’s strength (i.e., AEC or PLV at iEEG electrodes). We observed differences in nodal strength among the different states following a hierarchical epileptogenic organization: lower FC in interictal and pre-ictal states followed by higher FC values in ictal and post-ictal states (p < 0.05). We also observed higher nodal strength within resection for patients with good outcome (n = 22, Engel I), but not for poor outcome (n = 9, Engel II-IV), for all states (except ictal) and all bands (p < 0.05). Resection of hubs with high nodal strength was predictive of outcome (75–92% positive and 47–63% negative predictive values). Our findings suggest that FC can discriminate epileptogenic states and predict outcome in children with DRE.

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“…MEG studies of patients with childhood absence epilepsy have shown early ictal activity to be predominantly localized to the frontal and parietal cortices. 23,25,26,29 Some groups have also demonstrated altered activity of the precuneus and other regions of the DMN during absence seizures, although the exact nature of these changes was inconsistent between studies. 26,31 Similar to fMRI and EEG, MEG has been used to contrast ictal and subclinical interictal generalized spike-wave discharges in an attempt to uncover the mechanism behind altered consciousness in absence seizures.…”

Section: Evidence From Megmentioning

confidence: 99%

Understanding of Consciousness in Absence Seizures: A Literature Review

Groulx-Boivin,

Bouchet,

Myers

2024

NDT

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Frequency-Dependent Dynamics of Functional Connectivity Networks During Seizure Termination in Childhood Absence Epilepsy: A Magnetoencephalography Study

Cited by 7 publications

References 67 publications

Time-Series Anomaly Detection Based on Dynamic Temporal Graph Convolutional Network for Epilepsy Diagnosis

Time-Series Anomaly Detection Based on Dynamic Temporal Graph Convolutional Network for Epilepsy Diagnosis

Functional connectivity discriminates epileptogenic states and predicts surgical outcome in children with drug resistant epilepsy

Understanding of Consciousness in Absence Seizures: A Literature Review

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