• 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

正常清醒脑电图理论:从单个神经元到α、β和γ频率范围内的波形

Theory of the normal waking EEG: from single neurones to waveforms in the alpha, beta and gamma frequency ranges.

作者信息

Miller Robert

机构信息

Otago Centre for Theoretical Studies in Psychiatry and Neuroscience, Department of Anatomy and Structural Biology, School of Medical Science, University of Otago, P.O. Box 913, Dunedin, New Zealand.

出版信息

Int J Psychophysiol. 2007 Apr;64(1):18-23. doi: 10.1016/j.ijpsycho.2006.07.009. Epub 2006 Sep 25.

DOI:10.1016/j.ijpsycho.2006.07.009
PMID:16997407
Abstract

The classic alpha rhythm, recorded intracortically, consists of alternating surface-negative troughs and briefer surface-positive peaks. The troughs are associated with neuronal hyperpolarization, the peaks with brief depolarization and burst firing. Each hyperpolarization is mainly a potassium potential, lasting approximately 100 ms. Depolarization and burst firing arise when this inactivates. In the desynchronized state, membrane potential is poised just below threshold. Firing in vivo is somewhat irregular and non-bursting. It is suggested that EEG bistability (classic alpha vs desynchronization) corresponds to bistability of single pyramidal cells. In vitro, paired pulses lead to depression of synaptic transmission in synapses linking two pyramidal cells, but to facilitation in synapses linking pyramidal cells to inhibitory neurones. These effects should be recruited by burst firing in vivo. Thus, enhancement of inhibitory and excitatory transmission occur respectively during the classic alpha rhythm, and the desynchronized state. As a result both states tend to be self-sustaining. In the desynchronized state high frequency (gamma or beta) activity predominates. In simulations, gamma activity has been modeled as the behaviour of cortical networks where populations of excitatory and inhibitory neurones interact. These simulations assume conduction times between neurones to be negligible. However, this is not true for long-distance interactions. Introduction into the models of plausible conduction delays should slow the oscillation frequency. The activated cortex can then produce not only gamma activity but also beta, and sometimes alpha activity. Thus, alpha frequencies can arise both in the "idling" cortex (classic alpha), and in the activated cortex, although the respective mechanisms are quite different.

摘要

皮层内记录到的经典α节律由交替出现的表面负向波谷和更短暂的表面正向波峰组成。波谷与神经元超极化相关,波峰与短暂去极化和爆发性放电相关。每次超极化主要是一个钾离子电位,持续约100毫秒。当这种情况失活时会出现去极化和爆发性放电。在去同步化状态下,膜电位刚好处于阈值以下。体内放电有点不规则且非爆发性。有人提出脑电图双稳态(经典α波与去同步化)对应于单个锥体细胞的双稳态。在体外,成对脉冲会导致连接两个锥体细胞的突触中突触传递抑制,但在连接锥体细胞与抑制性神经元的突触中会导致促进。这些效应在体内应通过爆发性放电来募集。因此,在经典α节律和去同步化状态下分别会出现抑制性和兴奋性传递的增强。结果,这两种状态往往都是自我维持的。在去同步化状态下,高频(γ或β)活动占主导。在模拟中,γ活动被建模为兴奋性和抑制性神经元群体相互作用的皮层网络行为。这些模拟假设神经元之间的传导时间可以忽略不计。然而,对于长距离相互作用并非如此。在模型中引入合理的传导延迟应该会减慢振荡频率。然后,激活的皮层不仅可以产生γ活动,还可以产生β活动,有时还可以产生α活动。因此,α频率既可以在“闲置”皮层(经典α波)中出现,也可以在激活的皮层中出现,尽管各自的机制有很大不同。

相似文献

1
Theory of the normal waking EEG: from single neurones to waveforms in the alpha, beta and gamma frequency ranges.正常清醒脑电图理论:从单个神经元到α、β和γ频率范围内的波形
Int J Psychophysiol. 2007 Apr;64(1):18-23. doi: 10.1016/j.ijpsycho.2006.07.009. Epub 2006 Sep 25.
2
Generation and control of cortical gamma: findings from simulation at two scales.皮层γ波的产生与控制:两个尺度模拟的研究结果
Neural Netw. 2009 May;22(4):373-84. doi: 10.1016/j.neunet.2008.11.001. Epub 2008 Nov 17.
3
A model of event-related EEG synchronization changes in beta and gamma frequency bands.一个与事件相关的脑电图在β和γ频段同步变化的模型。
J Theor Biol. 2006 Feb 21;238(4):901-13. doi: 10.1016/j.jtbi.2005.07.001. Epub 2005 Aug 15.
4
Neuronal activity of orexin and non-orexin waking-active neurons during wake-sleep states in the mouse.小鼠清醒-睡眠状态下食欲素及非食欲素觉醒激活神经元的神经活动
Neuroscience. 2008 May 15;153(3):860-70. doi: 10.1016/j.neuroscience.2008.02.058. Epub 2008 Mar 6.
5
Two differential frequency-dependent mechanisms regulating tonic firing of thalamic reticular neurons.两种调节丘脑网状核神经元紧张性放电的频率依赖性差异机制。
Eur J Neurosci. 2008 May;27(10):2643-56. doi: 10.1111/j.1460-9568.2008.06246.x.
6
Cortical network modeling: analytical methods for firing rates and some properties of networks of LIF neurons.皮层网络建模:LIF神经元网络放电率的分析方法及网络的一些特性
J Physiol Paris. 2006 Jul-Sep;100(1-3):88-99. doi: 10.1016/j.jphysparis.2006.09.001. Epub 2006 Oct 24.
7
Thalamic mechanisms of EEG alpha rhythms and their pathological implications.脑电图α节律的丘脑机制及其病理意义。
Neuroscientist. 2005 Aug;11(4):357-72. doi: 10.1177/1073858405277450.
8
Comparison of spontaneous activity of mesencephalic reticular neurones in the waking state and during pentobarbital anaesthesia.中脑网状神经元在清醒状态和戊巴比妥麻醉期间的自发活动比较。
Physiol Bohemoslov. 1977;26(1):21-30.
9
Callosal responses of fast-rhythmic-bursting neurons during slow oscillation in cats.猫慢振荡期间快速节律性爆发神经元的胼胝体反应
Neuroscience. 2007 Jun 29;147(2):272-6. doi: 10.1016/j.neuroscience.2007.04.025. Epub 2007 May 23.
10
[Changes in EEG-complexity after subcortical ischemic brain damage].[皮层下缺血性脑损伤后脑电图复杂性的变化]
Ideggyogy Sz. 2006 May 20;59(5-6):185-92.

引用本文的文献

1
Objective Detection of Newborn Infant Acute Procedural Pain Using EEG and Machine Learning Algorithms.使用脑电图(EEG)和机器学习算法检测新生儿急性程序性疼痛
Paediatr Neonatal Pain. 2025 Mar 10;7(1):e70001. doi: 10.1002/pne2.70001. eCollection 2025 Mar.
2
What can neurofeedback and transcranial alternating current stimulation reveal about cross-frequency coupling?神经反馈和经颅交流电刺激能揭示关于交叉频率耦合的哪些信息?
Front Neurosci. 2025 Feb 12;19:1465773. doi: 10.3389/fnins.2025.1465773. eCollection 2025.
3
Brain Punch: K-1 Fights Affect Brain Wave Activity in Professional Kickboxers.
脑冲击:K-1 格斗影响职业踢拳手的脑电波活动。
Sports Med. 2024 Dec;54(12):3169-3179. doi: 10.1007/s40279-024-02082-5. Epub 2024 Aug 7.
4
A systematic review of EEG based automated schizophrenia classification through machine learning and deep learning.通过机器学习和深度学习对基于脑电图的精神分裂症自动分类进行的系统综述。
Front Hum Neurosci. 2024 Feb 14;18:1347082. doi: 10.3389/fnhum.2024.1347082. eCollection 2024.
5
Fatigue factors and fatigue indices in SSVEP-based brain-computer interfaces: a systematic review and meta-analysis.基于稳态视觉诱发电位的脑机接口中的疲劳因素和疲劳指标:系统评价与荟萃分析。
Front Hum Neurosci. 2023 Nov 16;17:1248474. doi: 10.3389/fnhum.2023.1248474. eCollection 2023.
6
Risk of data leakage in estimating the diagnostic performance of a deep-learning-based computer-aided system for psychiatric disorders.在估计基于深度学习的计算机辅助精神障碍诊断性能的系统中,存在数据泄露的风险。
Sci Rep. 2023 Oct 3;13(1):16633. doi: 10.1038/s41598-023-43542-8.
7
The neural correlates of psychosocial stress: A systematic review and meta-analysis of spectral analysis EEG studies.心理社会应激的神经关联:脑电图频谱分析研究的系统评价与荟萃分析
Neurobiol Stress. 2022 Apr 26;18:100452. doi: 10.1016/j.ynstr.2022.100452. eCollection 2022 May.
8
Nanopower Integrated Gaussian Mixture Model Classifier for Epileptic Seizure Prediction.用于癫痫发作预测的纳米功率集成高斯混合模型分类器
Bioengineering (Basel). 2022 Apr 5;9(4):160. doi: 10.3390/bioengineering9040160.
9
Epileptic seizure detection using EEG signals and extreme gradient boosting.基于脑电图信号和极端梯度提升的癫痫发作检测
J Biomed Res. 2019 Aug 30;34(3):228-239. doi: 10.7555/JBR.33.20190016.
10
Trial-by-trial source-resolved EEG responses to gait task challenges predict subsequent step adaptation.针对步态任务挑战的逐试源分辨 EEG 反应可预测后续的步伐适应。
Neuroimage. 2019 Oct 1;199:691-703. doi: 10.1016/j.neuroimage.2019.06.018. Epub 2019 Jun 7.