• 文献检索
  • 文档翻译
  • 深度研究
  • 学术资讯
  • Suppr Zotero 插件Zotero 插件
  • 邀请有礼
  • 套餐&价格
  • 历史记录
应用&插件
Suppr Zotero 插件Zotero 插件浏览器插件Mac 客户端Windows 客户端微信小程序
定价
高级版会员购买积分包购买API积分包
服务
文献检索文档翻译深度研究API 文档MCP 服务
关于我们
关于 Suppr公司介绍联系我们用户协议隐私条款
关注我们

Suppr 超能文献

核心技术专利：CN118964589B侵权必究
粤ICP备2023148730 号-1Suppr @ 2026

文献检索

告别复杂PubMed语法，用中文像聊天一样搜索，搜遍4000万医学文献。AI智能推荐，让科研检索更轻松。

立即免费搜索

文件翻译

保留排版，准确专业，支持PDF/Word/PPT等文件格式，支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述，25分钟生成高质量综述，智能提取关键信息，辅助科研写作。

立即免费体验

基于噪声辅助的多变量经验模态分解的平均相位相干性分析用于评估癫痫患者的相位同步动力学。

Noise-Assisted Multivariate EMD-Based Mean-Phase Coherence Analysis to Evaluate Phase-Synchrony Dynamics in Epilepsy Patients.

出版信息

IEEE Trans Neural Syst Rehabil Eng. 2018 Dec;26(12):2270-2279. doi: 10.1109/TNSRE.2018.2881606. Epub 2018 Nov 15.

DOI:10.1109/TNSRE.2018.2881606
PMID:30452374
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC6326379/
Abstract

Spatiotemporal evolution of synchrony dynamics among neuronal populations plays an important role in decoding complicated brain function in normal cognitive processing as well as during pathological conditions such as epileptic seizures. In this paper, a non-linear analytical methodology is proposed to quantitatively evaluate the phase-synchrony dynamics in epilepsy patients. A set of finite neuronal oscillators was adaptively extracted from a multi-channel electrocorticographic (ECoG) dataset utilizing noise-assisted multivariate empirical mode de-composition (NA-MEMD). Next, the instantaneous phases of the oscillatory functions were extracted using the Hilbert transform in order to be utilized in the mean-phase coherence analysis. The phase-synchrony dynamics were then assessed using eigenvalue decomposition. The extracted neuronal oscillators were grouped with respect to their frequency range into wideband (1-600 Hz), ripple (80-250 Hz), and fast-ripple (250-600 Hz) bands in order to investigate the dynamics of ECoG activity in these frequency ranges as seizures evolve. Drug-refractory patients with frontal and temporal lobe epilepsy demonstrated a reduction in phase-synchrony around seizure onset. However, the network phase-synchrony started to increase toward seizure end and achieved its maximum level at seizure offset for both types of epilepsy. This result suggests that hyper-synchronization of the epileptic network may be an essential self-regulatory mechanism by which the brain terminates seizures.

摘要

神经元群体之间同步动力学的时空演化在正常认知处理以及癫痫发作等病理状态下解码复杂脑功能方面起着重要作用。在本文中，提出了一种非线性分析方法来定量评估癫痫患者的相位同步动力学。利用噪声辅助多变量经验模态分解（NA-MEMD），从多通道脑电描记图（ECoG）数据集中自适应地提取一组有限的神经元振荡器。然后，使用希尔伯特变换提取振荡函数的瞬时相位，以便用于平均相位相干分析。然后使用特征值分解评估相位同步动力学。提取的神经元振荡器根据其频率范围分组为宽带（1-600 Hz）、纹波（80-250 Hz）和快速纹波（250-600 Hz）带，以研究这些频率范围内 ECoG 活动的动力学随着癫痫发作的进展。具有额颞叶癫痫的耐药性患者在癫痫发作开始时表现出相位同步性降低。然而，对于两种类型的癫痫，网络相位同步性开始在癫痫发作结束时增加，并在癫痫发作结束时达到最大值。这一结果表明，癫痫网络的超同步可能是大脑终止癫痫发作的重要自调节机制。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b72a/6326379/74a69422bd5b/nihms-1516122-f0007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b72a/6326379/c7227fa39e20/nihms-1516122-f0001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b72a/6326379/3de1b564dee1/nihms-1516122-f0002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b72a/6326379/0fa9db63a029/nihms-1516122-f0003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b72a/6326379/8743d60c5a29/nihms-1516122-f0004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b72a/6326379/ec72008ed7ef/nihms-1516122-f0005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b72a/6326379/d527dbb82b6a/nihms-1516122-f0006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b72a/6326379/74a69422bd5b/nihms-1516122-f0007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b72a/6326379/c7227fa39e20/nihms-1516122-f0001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b72a/6326379/3de1b564dee1/nihms-1516122-f0002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b72a/6326379/0fa9db63a029/nihms-1516122-f0003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b72a/6326379/8743d60c5a29/nihms-1516122-f0004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b72a/6326379/ec72008ed7ef/nihms-1516122-f0005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b72a/6326379/d527dbb82b6a/nihms-1516122-f0006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b72a/6326379/74a69422bd5b/nihms-1516122-f0007.jpg

相似文献

1
Noise-Assisted Multivariate EMD-Based Mean-Phase Coherence Analysis to Evaluate Phase-Synchrony Dynamics in Epilepsy Patients.基于噪声辅助的多变量经验模态分解的平均相位相干性分析用于评估癫痫患者的相位同步动力学。
IEEE Trans Neural Syst Rehabil Eng. 2018 Dec;26(12):2270-2279. doi: 10.1109/TNSRE.2018.2881606. Epub 2018 Nov 15.
2
EMD-Based, Mean-Phase Coherence Analysis to Assess Instantaneous Phase-Synchrony Dynamics in Epilepsy Patients.基于经验模态分解的平均相位相干分析用于评估癫痫患者的瞬时相位同步动力学
Annu Int Conf IEEE Eng Med Biol Soc. 2018 Jul;2018:2406-2409. doi: 10.1109/EMBC.2018.8512794.
3
Synchrony dynamics across brain structures in limbic epilepsy vary between initiation and termination phases of seizures.边缘性癫痫发作起始和终止阶段各脑区间的同步动力学变化。
IEEE Trans Biomed Eng. 2013 Mar;60(3):821-9. doi: 10.1109/TBME.2012.2189113. Epub 2012 Feb 27.
4
Transient seizure onset network for localization of epileptogenic zone: effective connectivity and graph theory-based analyses of ECoG data in temporal lobe epilepsy.短暂性发作起始网络用于致痫灶定位：基于 ECoG 数据的有效连通性和图论分析在颞叶癫痫中的应用。
J Neurol. 2019 Apr;266(4):844-859. doi: 10.1007/s00415-019-09204-4. Epub 2019 Jan 25.
5
Assessing instantaneous synchrony of nonlinear nonstationary oscillators in the brain.评估大脑中非线性非平稳振荡器的瞬时同步。
J Neurosci Methods. 2010 Jan 30;186(1):42-51. doi: 10.1016/j.jneumeth.2009.10.023. Epub 2009 Nov 10.
6
Temporal-spatial characteristics of phase-amplitude coupling in electrocorticogram for human temporal lobe epilepsy.人类颞叶癫痫患者皮质脑电图中相位-振幅耦合的时空特征
Clin Neurophysiol. 2017 Sep;128(9):1707-1718. doi: 10.1016/j.clinph.2017.05.020. Epub 2017 Jun 28.
7
Spatio-temporal dynamics prior to neocortical seizures: amplitude versus phase couplings.新皮层癫痫发作前的时空动力学：幅度与相位耦合
IEEE Trans Biomed Eng. 2003 May;50(5):571-83. doi: 10.1109/TBME.2003.810696.
8
Frequency flows and the time-frequency dynamics of multivariate phase synchronization in brain signals.大脑信号中多变量相位同步的频率流及时频动态
Neuroimage. 2006 May 15;31(1):209-27. doi: 10.1016/j.neuroimage.2005.11.021. Epub 2006 Jan 18.
9
Modulation of instantaneous synchrony during seizures by deep brain stimulation.通过深部脑刺激对癫痫发作期间瞬时同步性的调节。
Annu Int Conf IEEE Eng Med Biol Soc. 2009;2009:3310-3. doi: 10.1109/IEMBS.2009.5333714.
10
Network dynamics of the brain and influence of the epileptic seizure onset zone.大脑的网络动力学与癫痫发作起始区的影响。
Proc Natl Acad Sci U S A. 2014 Dec 9;111(49):E5321-30. doi: 10.1073/pnas.1401752111. Epub 2014 Nov 17.

引用本文的文献

1
Bridging functional and anatomical neural connectivity through cluster synchronization.通过簇同步实现功能和解剖神经连接的桥接。
Sci Rep. 2023 Dec 17;13(1):22430. doi: 10.1038/s41598-023-49746-2.
2
Connectivity Analysis for Multivariate Time Series: Correlation vs. Causality.多元时间序列的连通性分析：相关性与因果关系
Entropy (Basel). 2021 Nov 25;23(12):1570. doi: 10.3390/e23121570.
3
Within-cycle instantaneous frequency profiles report oscillatory waveform dynamics.周期内瞬时频率谱报告振荡波形动力学。

本文引用的文献

1
Resection of high frequency oscillations predicts seizure outcome in the individual patient.切除高频振荡可预测个体患者的癫痫发作结果。
Sci Rep. 2017 Oct 23;7(1):13836. doi: 10.1038/s41598-017-13064-1.
2
Update on the mechanisms and roles of high-frequency oscillations in seizures and epileptic disorders.癫痫发作及癫痫性疾病中高频振荡的机制与作用的最新进展
Epilepsia. 2017 Aug;58(8):1330-1339. doi: 10.1111/epi.13830. Epub 2017 Jul 6.
3
Detecting and characterizing high-frequency oscillations in epilepsy: a case study of big data analysis.
J Neurophysiol. 2021 Oct 1;126(4):1190-1208. doi: 10.1152/jn.00201.2021. Epub 2021 Aug 18.
4
Stimulating Solutions for Intractable Epilepsy.难治性癫痫的刺激解决方案
Epilepsy Curr. 2021 Apr 30;21(5):15357597211012466. doi: 10.1177/15357597211012466.
5
Phase Synchrony Analysis of Rolling Bearing Vibrations and Its Application to Failure Identification.滚动轴承振动的相位同步分析及其在故障识别中的应用。
Sensors (Basel). 2020 May 23;20(10):2964. doi: 10.3390/s20102964.
癫痫中高频振荡的检测与特征分析：大数据分析的案例研究
R Soc Open Sci. 2017 Jan 18;4(1):160741. doi: 10.1098/rsos.160741. eCollection 2017 Jan.
4
Automatic Detection and Classification of High-Frequency Oscillations in Depth-EEG Signals.深度脑电图信号中高频振荡的自动检测与分类
IEEE Trans Biomed Eng. 2017 Sep;64(9):2230-2240. doi: 10.1109/TBME.2016.2633391. Epub 2016 Nov 29.
5
Assessing Granger Causality in Electrophysiological Data: Removing the Adverse Effects of Common Signals via Bipolar Derivations.评估电生理数据中的格兰杰因果关系：通过双极导联消除共同信号的不利影响。
Front Syst Neurosci. 2016 Jan 20;9:189. doi: 10.3389/fnsys.2015.00189. eCollection 2015.
6
Stimulus-induced visual cortical networks are recapitulated by spontaneous local and interareal synchronization.刺激诱发的视觉皮层网络可通过自发的局部和区域间同步再现。
Proc Natl Acad Sci U S A. 2016 Feb 2;113(5):E606-15. doi: 10.1073/pnas.1513773113. Epub 2016 Jan 19.
7
Quantifying Neural Oscillatory Synchronization: A Comparison between Spectral Coherence and Phase-Locking Value Approaches.量化神经振荡同步：谱相干与锁相值方法的比较
PLoS One. 2016 Jan 8;11(1):e0146443. doi: 10.1371/journal.pone.0146443. eCollection 2016.
8
Network Mechanisms Generating Abnormal and Normal Hippocampal High-Frequency Oscillations: A Computational Analysis.产生异常和正常海马高频振荡的网络机制：一项计算分析。
eNeuro. 2015 May-Jun;2(3). doi: 10.1523/ENEURO.0024-15.2015.
9
Collaborating and sharing data in epilepsy research.癫痫研究中的数据协作与共享。
J Clin Neurophysiol. 2015 Jun;32(3):235-9. doi: 10.1097/WNP.0000000000000159.
10
Neuronal ensemble synchrony during human focal seizures.人类局灶性癫痫发作期间的神经元集合同步。
J Neurosci. 2014 Jul 23;34(30):9927-44. doi: 10.1523/JNEUROSCI.4567-13.2014.