Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关概念视频

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

Learning shapes neural geometry in the primate prefrontal cortex.

Nature neuroscience·2026
Same author

Modelling discrete states and long-term dynamics in functional brain networks.

Imaging neuroscience (Cambridge, Mass.)·2026
Same author

Varying patterns of association between cortical large-scale networks and subthalamic nucleus activity in Parkinson's disease.

NPJ Parkinson's disease·2026
Same author

Modelling variability in functional brain networks using embeddings.

Imaging neuroscience (Cambridge, Mass.)·2026
Same author

Canonical Hidden Markov Model Networks for studying M/EEG.

Imaging neuroscience (Cambridge, Mass.)·2026
Same author

Effects of Age on Resting-State Cortical Networks.

Human brain mapping·2026
Same journal

Individualized mapping of functional brain networks in older adulthood.

Imaging neuroscience (Cambridge, Mass.)·2026
Same journal

Is the whole more than the sum of its parts? Considering global and local features of the connectome improves prediction of individuals and phenotypes.

Imaging neuroscience (Cambridge, Mass.)·2026
Same journal

The language network responds robustly to sentences across tasks.

Imaging neuroscience (Cambridge, Mass.)·2026
Same journal

Neighborhood disadvantage and brain myelination: Insights from infancy to childhood.

Imaging neuroscience (Cambridge, Mass.)·2026
Same journal

Meditation and neurofeedback: A systematic scoping review, synthesis, and future directions.

Imaging neuroscience (Cambridge, Mass.)·2026
Same journal

Interactive shape and color representation in visual working memory for colored objects in the human occipitotemporal cortex.

Imaging neuroscience (Cambridge, Mass.)·2026
查看所有相关文章

相关实验视频

Updated: Sep 11, 2025

Network Analysis of Foramen Ovale Electrode Recordings in Drug-resistant Temporal Lobe Epilepsy Patients
09:32

Network Analysis of Foramen Ovale Electrode Recordings in Drug-resistant Temporal Lobe Epilepsy Patients

Published on: December 18, 2016

12.5K

电生理学任务数据的动态网络分析.

Chetan Gohil1, Oliver Kohl1, Rukuang Huang1

  • 1Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, United Kingdom.

Imaging neuroscience (Cambridge, Mass.)
|August 13, 2025
PubMed
概括
此摘要是机器生成的。

新的方法,动态网络模式 (DyNeMo) 和隐藏的马尔科夫模型 (HMM),分析任务期间的大脑网络振荡. DyNeMo识别了传统方法错过的动态大脑网络活动.

关键词:
动力学 动力学 动力学电力生理学 电力生理学网络 网络 网络 网络 网络 网络振荡的振荡是如何发生的任务数据 任务数据

更多相关视频

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms
08:51

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms

Published on: November 1, 2019

5.7K
Author Spotlight: Advancing Large-Scale Neural Dynamics Through HD-MEA Technology
09:44

Author Spotlight: Advancing Large-Scale Neural Dynamics Through HD-MEA Technology

Published on: March 8, 2024

5.1K

相关实验视频

Last Updated: Sep 11, 2025

Network Analysis of Foramen Ovale Electrode Recordings in Drug-resistant Temporal Lobe Epilepsy Patients
09:32

Network Analysis of Foramen Ovale Electrode Recordings in Drug-resistant Temporal Lobe Epilepsy Patients

Published on: December 18, 2016

12.5K
Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms
08:51

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms

Published on: November 1, 2019

5.7K
Author Spotlight: Advancing Large-Scale Neural Dynamics Through HD-MEA Technology
09:44

Author Spotlight: Advancing Large-Scale Neural Dynamics Through HD-MEA Technology

Published on: March 8, 2024

5.1K

科学领域:

  • 神经科学是一个神经科学.
  • 认知科学 认知科学
  • 计算神经科学是一种神经科学.

背景情况:

  • 功能神经成像与任务相结合,对于研究人类大脑至关重要.
  • 传统的电子生理学数据的时间频率分析单独检查大脑区域,导致解释挑战和多重比较问题.
  • 大脑的任务反应涉及神经网络之间的协调活动,需要全脑网络分析技术.

研究的目的:

  • 从网络角度引入和评估用于分析振荡任务响应的新方法.
  • 展示隐藏马尔科夫模型 (HMM) 和动态网络模式 (DyNeMo) 如何在网络层面更节地代表大脑活动.
  • 为了比较DyNeMo,HMM和传统时间频率分析在检测与任务相关的大脑网络动态方面的有效性.

主要方法:

  • 应用两种最先进的方法:隐藏马尔科夫模型 (HMM) 和动态网络模式 (DyNeMo).
  • 在网络层面以毫秒分辨率表示振荡任务响应.
  • 对DyneMo,HMM和传统时间频率分析进行比较分析.

主要成果:

  • 无论是HMM还是DyNeMo,都显示了振荡活动的频率分辨率网络.
  • 与HMM和传统方法相比,DyNeMo在识别与任务相关的激活和关闭方面表现出卓越的能力.
  • 该研究强调了网络层面分析的潜力,以了解任务期间的大脑功能.

结论:

  • DyNeMo提供了一种强大的新方法,通过动态大脑网络的透视来分析基于任务的电生理学数据.
  • 网络层面的分析比特定区域的分析更全面地了解大脑对任务的反应.
  • 这些发现表明,神经成像研究中转向以网络为中心的方法.