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The association of magnetoencephalography high-frequency oscillations with epilepsy types and a ripple-based method with source-level connectivity for mapping epilepsy sources

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机构: [1]School of Biological Science and Medical Engineering, Beihang University, Beijing, China. [2]Beijing Advanced Innovation Centre for Big Data-Based Precision Medicine, Beihang University, Beijing, China. [3]Beijing Advanced Innovation Centre for Biomedical Engineering, Beihang University, Beijing, China. [4]Department of Neurology, Xuanwu Hospital, Capital Medical University, Beijing, China. [5]Brain Functional Disease and Neuromodulation of Beijing Key Laboratory, Beijing, China. [6]Hefei Innovation Research Institute, Beihang University, Hefei, Anhui, China.
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关键词: functional connectivity MEG ripple source location spatial complexity tucker decomposition

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To explore the association between high-frequency oscillations (HFOs) and epilepsy types and to improve the accuracy of source localization.Magnetoencephalography (MEG) ripples of 63 drug-resistant epilepsy patients were detected. Ripple rates, distribution, spatial complexity, and the clustering coefficient of ripple channels were used for the preliminary classification of lateral temporal lobe epilepsy (LTLE), mesial temporal lobe epilepsy (MTLE), and nontemporal lobe epilepsy (NTLE), mainly frontal lobe epilepsy (FLE). Furthermore, the seizure site identification was improved using the Tucker LCMV method and source-level betweenness centrality.Ripple rates were significantly higher in MTLE than in LTLE and NTLE (p < 0.05). The LTLE and MTLE were mainly distributed in the temporal lobe, followed by the parietal lobe, occipital lobe, and frontal lobe, whereas MTLE ripples were mainly distributed in the frontal lobe, then parietal lobe and occipital lobe. Nevertheless, the NTLE ripples were primarily in the frontal lobe and partially in the occipital lobe (p < 0.05). Meanwhile, the spatial complexity of NTLE was significantly higher than that of LTLE and MTLE and was lowest in MTLE (p < 0.01). However, an opposite trend was observed for the standardized clustering coefficient compared with spatial complexity (p < 0.01). Finally, the tucker algorithm showed a higher percentage of ripples at the surgical site when the betweenness centrality was added (p < 0.01).This study demonstrated that HFO rates, distribution, spatial complexity, and clustering coefficient of ripple channels varied considerably among the three epilepsy types. Additionally, tucker MEG estimation combined with ripple rates based on the source-level functional connectivity is a promising approach for presurgical epilepsy evaluation.© 2023 The Authors. CNS Neuroscience & Therapeutics published by John Wiley & Sons Ltd.

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出版当年[2022]版:
大类 | 1 区 医学
小类 | 1 区 药学 1 区 神经科学
最新[2023]版:
大类 | 1 区 医学
小类 | 2 区 神经科学 2 区 药学
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出版当年[2021]版:
Q1 NEUROSCIENCES Q1 PHARMACOLOGY & PHARMACY
最新[2023]版:
Q1 PHARMACOLOGY & PHARMACY Q1 NEUROSCIENCES

影响因子: 最新[2023版] 最新五年平均 出版当年[2021版] 出版当年五年平均 出版前一年[2020版] 出版后一年[2022版]

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第一作者机构: [1]School of Biological Science and Medical Engineering, Beihang University, Beijing, China. [2]Beijing Advanced Innovation Centre for Big Data-Based Precision Medicine, Beihang University, Beijing, China. [3]Beijing Advanced Innovation Centre for Biomedical Engineering, Beihang University, Beijing, China.
通讯作者:
通讯机构: [1]School of Biological Science and Medical Engineering, Beihang University, Beijing, China. [2]Beijing Advanced Innovation Centre for Big Data-Based Precision Medicine, Beihang University, Beijing, China. [3]Beijing Advanced Innovation Centre for Biomedical Engineering, Beihang University, Beijing, China. [4]Department of Neurology, Xuanwu Hospital, Capital Medical University, Beijing, China. [5]Brain Functional Disease and Neuromodulation of Beijing Key Laboratory, Beijing, China. [6]Hefei Innovation Research Institute, Beihang University, Hefei, Anhui, China. [*1]School of Biological Science and Medical Engineering, Beihang University, Beijing 100191, China. [*2]Department of Neurology, Xuanwu Hospital, Capital Medical University, Beijing 100053, China. [*3]Hefei Innovation Research Institute, Beihang University, Hefei, Anhui, China.
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