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

Enhanced source localization accuracy through bidirectional deep brain stimulation (DBS) electrodes: A comparative study with non-invasive EEG methods.

Journal of neural engineering·2026
Same author

Corrigendum to "Biomimetic bone calcium phosphate-based scaffolds fabricated via ceramic vat photopolymerization: Effect of porosity, sintering temperature, mineralogical phases and trace elements on the osteogenic potential" [Mater. Today Bio 38 (2026) 103074].

Materials today. Bio·2026
Same author

Corrigendum to "Biomimetic bone calcium phosphate-based scaffolds fabricated via ceramic vat photopolymerization: Effect of porosity, sintering temperature, mineralogical phases and trace elements on the osteogenic potential" [Mater. Today Bio 38 (2026) 103074].

Materials today. Bio·2026
Same author

Three-Dimensional Micro-Computed Tomographic Imaging Reveals Early Mucosal Remodeling in Individuals With Potential Celiac Disease.

Gastroenterology·2026
Same author

Biomimetic bone calcium phosphate-based scaffolds fabricated via ceramic vat photopolymerization: Effect of porosity, sintering temperature, mineralogical phases and trace elements on the osteogenic potential.

Materials today. Bio·2026
Same author

Oxygen concentration measurement in 3D cell culture using multifocal optical projection microscopy.

Analytical methods : advancing methods and applications·2026

相关实验视频

Updated: Jun 7, 2025

Author Spotlight: Advancing Pediatric Epilepsy Surgery in Children Through Novel Biomarkers and Enhanced Localization
09:57

Author Spotlight: Advancing Pediatric Epilepsy Surgery in Children Through Novel Biomarkers and Enhanced Localization

Published on: September 20, 2024

2.5K

对神经质量模型的灵敏度分析引导的贝叶斯参数估计:在中应用.

Narayan Puthanmadam Subramaniyam1, Jari Hyttinen1

  • 1Faculty of Medicine and Health Technology, <a href="https://ror.org/033003e23">Tampere University</a>, 33520 Tampere, Finland.

Physical review. E
|November 20, 2024
PubMed
概括
此摘要是机器生成的。

这项研究确定了神经质量模型中的关键参数,用于精确的脑电图 (EEG) 分析. 这些发现使得这些参数的可靠估计成为可能,这对于理解大脑动态和制定控制策略至关重要.

更多相关视频

Analyzing Neural Activity and Connectivity Using Intracranial EEG Data with SPM Software
06:50

Analyzing Neural Activity and Connectivity Using Intracranial EEG Data with SPM Software

Published on: October 30, 2018

9.4K
Using a Bipolar Electrode to Create a Temporal Lobe Epilepsy Mouse Model by Electrical Kindling of the Amygdala
09:49

Using a Bipolar Electrode to Create a Temporal Lobe Epilepsy Mouse Model by Electrical Kindling of the Amygdala

Published on: June 29, 2022

2.4K

相关实验视频

Last Updated: Jun 7, 2025

Author Spotlight: Advancing Pediatric Epilepsy Surgery in Children Through Novel Biomarkers and Enhanced Localization
09:57

Author Spotlight: Advancing Pediatric Epilepsy Surgery in Children Through Novel Biomarkers and Enhanced Localization

Published on: September 20, 2024

2.5K
Analyzing Neural Activity and Connectivity Using Intracranial EEG Data with SPM Software
06:50

Analyzing Neural Activity and Connectivity Using Intracranial EEG Data with SPM Software

Published on: October 30, 2018

9.4K
Using a Bipolar Electrode to Create a Temporal Lobe Epilepsy Mouse Model by Electrical Kindling of the Amygdala
09:49

Using a Bipolar Electrode to Create a Temporal Lobe Epilepsy Mouse Model by Electrical Kindling of the Amygdala

Published on: June 29, 2022

2.4K

科学领域:

  • 计算神经科学是一种神经科学.
  • 生物物理学的生物物理.
  • 信号处理 信号处理

背景情况:

  • 神经质量模型 (NMMs) 模拟大脑活动,就像脑电图 (EEG).
  • 在EEG分析中高维NMM中估计参数具有挑战性.
  • 识别敏感的NMM参数对于可靠的大脑动态洞察至关重要.

研究的目的:

  • 为了确定詹森和里特NMM (JR-NMM) 的最敏感的参数,以可靠地估计EEG数据参数.
  • 开发和验证一个贝叶斯的方法来估计JR-NMM状态和参数.
  • 为了研究发作期间这些参数的变化.

主要方法:

  • 在JR-NMM上使用莫里斯和索博尔方法进行参数灵敏度分析.
  • 一个贝叶斯估计框架,将预期最大化 (EM) 与无气味的卡尔曼光滑器 (UKS-EM) 结合起来.
  • 用模拟的EEG数据进行验证,并应用于患者的内EEG.

主要成果:

  • 平均抑制性突触增益 (B) 和相互时间常数 (b) 被确定为最敏感的JR-NMM参数.
  • 在不同的噪音水平下,UKS-EM方法准确地估计了B和b.
  • 在患者中,在前,前和后期间观察到B和b的显著变化.

结论:

  • 灵敏度分析有效地减少NMM参数空间,以获得可靠的估计.
  • 该UKS-EM算法提供了一个强大的工具,用于估计从EEG敏感的NMM参数.
  • 在B和b的参数变化提供了对动态和实时跟踪潜力的洞察.