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

The individuality of single-frame functional brain connectivity.

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

Myrtle Syrup Improves Proteinuria in Type 2 Diabetic Patients: A Randomized Double-blinded Placebo-controlled Clinical Trial : Myrtle Syrup Improves Proteinuria in Type 2 Diabetes.

Galen medical journal·2026
Same author

The Effects of Mindfulness on Brain Network Dynamics Following an Acute Stressor in a Population of Drinking Adults.

Brain sciences·2026
Same author

Introducing Structural Reliance: A New Method to Assess Structure-Function Coupling in the Brain.

Human brain mapping·2026
Same author

Dynamic Resting-State Network Markers of Disruptive Behavior Problems in Youth.

Biological psychiatry global open science·2026
Same author

Derivation of machine learning brain aging biomarkers for a set of forty thousand functional connectomes.

Brain research bulletin·2026
Same journal

Integrative Analysis of Multimodal Omics Data.

Annual review of statistics and its application·2026
Same journal

Statistical Methods in Aging Research: Improving Current Practices and Embracing Emerging Approaches.

Annual review of statistics and its application·2026
Same journal

Excess Mortality Estimation.

Annual review of statistics and its application·2026
Same journal

Causal Mediation Analysis for Integrating Exposure, Genomic, and Phenotype Data.

Annual review of statistics and its application·2025
Same journal

High-Dimensional Gene-Environment Interaction Analysis.

Annual review of statistics and its application·2025
Same journal

Identification and Inference with Invalid Instruments.

Annual review of statistics and its application·2025
查看所有相关文章

相关实验视频

Updated: Jun 15, 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

统计大脑网络分析

Sean L Simpson1,2, Heather M Shappell1,2, Mohsen Bahrami2,3

  • 1Department of Biostatistics and Data Science, Wake Forest University School of Medicine, Winston-Salem, North Carolina, USA.

Annual review of statistics and its application
|August 26, 2024
PubMed
概括
此摘要是机器生成的。

新的统计框架通过整合网络科学和神经科学来增强大脑网络分析. 这些方法有助于了解大脑功能和临床结果.

关键词:
连接性的连接性.连接经济学是连接经济学.图表,图表,图表,图表,图表,图表,图表,图表,图表,图表,图表网络神经科学 网络神经科学神经成像是一种神经成像.在统计模型中使用统计模型.

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
Network Analysis of the Default Mode Network Using Functional Connectivity MRI in Temporal Lobe Epilepsy
12:09

Network Analysis of the Default Mode Network Using Functional Connectivity MRI in Temporal Lobe Epilepsy

Published on: August 5, 2014

18.0K

相关实验视频

Last Updated: Jun 15, 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
Network Analysis of the Default Mode Network Using Functional Connectivity MRI in Temporal Lobe Epilepsy
12:09

Network Analysis of the Default Mode Network Using Functional Connectivity MRI in Temporal Lobe Epilepsy

Published on: August 5, 2014

18.0K

科学领域:

  • 神经科学是一个神经科学.
  • 网络科学 网络科学
  • 计算生物学 计算生物学

背景情况:

  • 网络科学和神经科学的整合已经建立了大脑网络分析.
  • 大脑网络分析提供了对正常和异常大脑功能和临床结果的见解.
  • 对群体和个体大脑网络分析的统计方法是不发达的.

研究的目的:

  • 解决大脑网络分析中先进统计方法的需求.
  • 引入三个新的统计框架来分析全脑网络数据.
  • 促进该领域的进一步研究和方法开发.

主要方法:

  • 开发一个混合建模框架.
  • 开发一个距离回归框架.
  • 开发一个隐藏的半马尔科夫模型框架.

主要成果:

  • 拟议的框架提供了统计方法与网络科学的协同融合.
  • 这些工具为全脑网络数据提供了必要的分析基础.
  • 该研究概述了这些方法,并调查了相关工具.

结论:

  • 开发的统计框架推动了对大脑网络的分析.
  • 这些方法有可能加深我们对大脑功能和临床应用的理解.
  • 鼓励在脑网络分析方面进行进一步的统计和方法创新.