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

Neural Circuits01:25

Neural Circuits

1.1K
Neural circuits and neuronal pools are two of the main structures found in the nervous system. Neural circuits are networks of neurons that work together to carry out a specific task or process. They consist of interconnected neurons and glial cells, which provide structural and metabolic support.
Neuronal pools are collections of nerve cells with similar functions and interact through chemical and electrical signals. These pools include both interneurons (the central neural circuit nodes that...
1.1K

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

IL7R depletion mitigates neuroinflammation and ischemic stroke via AKT dephosphorylation dependent LCP1 suppression in mice.

Neurotherapeutics : the journal of the American Society for Experimental NeuroTherapeutics·2026
Same author

m6A modification of LINC00458 enhances HMOX1 stability via ELAVL1 recruitment to promote ferroptosis and aggravate asthma.

Molecular immunology·2026
Same author

Application of delta radiomics based on cone-beam computed tomography in predicting radiotherapy efficacy for nasopharyngeal carcinoma.

Strahlentherapie und Onkologie : Organ der Deutschen Rontgengesellschaft ... [et al]·2026
Same author

Adipocytes and macrophages after stroke: Interactions between metabolism and immunity.

Neural regeneration research·2026
Same author

UPLC-MS/MS-Based Cellular Pharmacokinetics of Four Active Components of Total Glucosides of Picrorhizae Rhizome and Target Validation for ACLY.

Biomedical chromatography : BMC·2026
Same author

BrainPrompt+: Multi-Level Brain Prompt Learning for Knowledge-Guided Neurological Disorder Identification.

IEEE transactions on medical imaging·2026

相关实验视频

Updated: Jun 13, 2025

Modeling the Functional Network for Spatial Navigation in the Human Brain
05:55

Modeling the Functional Network for Spatial Navigation in the Human Brain

Published on: October 13, 2023

1.0K

对比图汇集用于大脑网络的可解释分类.

Jiaxing Xu1, Qingtian Bian1, Xinhang Li2

  • 1School of Computer Science and Engineering, Nanyang Technological University, Singapore, 639798 Singapore.

ArXiv
|September 16, 2024
PubMed
概括

本研究介绍了 ContrastPool,这是一种用于分析功能磁共振成像 (fMRI) 数据的新型图形神经网络方法. ContrastPool有效提取大脑网络特征,有助于理解神经退行性疾病.

关键词:
大脑网络 大脑网络深度学习用于神经成像.图形分类的图形分类图形神经网络的神经网络fMRI生物标志物 fMRI生物标志物

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

5.6K
Dynamic Inter-subject Functional Connectivity Reveals Moment-to-Moment Brain Network Configurations Driven by Continuous or Communication Paradigms
08:36

Dynamic Inter-subject Functional Connectivity Reveals Moment-to-Moment Brain Network Configurations Driven by Continuous or Communication Paradigms

Published on: March 21, 2019

7.2K

相关实验视频

Last Updated: Jun 13, 2025

Modeling the Functional Network for Spatial Navigation in the Human Brain
05:55

Modeling the Functional Network for Spatial Navigation in the Human Brain

Published on: October 13, 2023

1.0K
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

5.6K
Dynamic Inter-subject Functional Connectivity Reveals Moment-to-Moment Brain Network Configurations Driven by Continuous or Communication Paradigms
08:36

Dynamic Inter-subject Functional Connectivity Reveals Moment-to-Moment Brain Network Configurations Driven by Continuous or Communication Paradigms

Published on: March 21, 2019

7.2K

科学领域:

  • 神经科学是一个神经科学.
  • 机器学习 机器学习
  • 医疗成像医学成像

背景情况:

  • 功能磁共振成像 (fMRI) 对于测量神经激活和识别神经退行性疾病,如帕金森病,阿尔茨海默病和自闭症至关重要.
  • 使用图形神经网络 (GNN) 分析fMRI数据需要专门的设计,因为fMRI数据的独特特性.
  • 开发能为大脑网络产生有效和域内可解释的特征的GNN仍然是一个重大挑战.

研究的目的:

  • 为了提出一种新的GNN方法,ContrastPool,适用于fMRI数据分析.
  • 通过解决fMRI特定要求,增强GNN对大脑网络的利用.
  • 改善从fMRI数据中提取有效和可解释的特征,用于神经退行性疾病研究.

主要方法:

  • 为GNN引入了对比的双重注意力区块.
  • 开发了一种名为ContrastPool的可微分图集结方法.
  • 将拟议的方法应用于3种疾病的5个静止状态fMRI大脑网络数据集.

主要成果:

  • 证明了ContrastPool对最先进的基线方法的优越性.
  • 与现有的神经科学领域知识对提取的模式进行了验证.
  • 确认ContrastPool为神经退行性疾病提供了直接和有洞察力的研究结果.

结论:

  • ContrastPool提供了一种强大的工具,可以促进对大脑网络的理解.
  • 该方法在改善神经退行性疾病研究中神经成像数据的分析方面具有显著的潜力.
  • 拟议的方法促进了对大脑功能和功能障碍的新见解的发现.