Jove
Visualize
联系我们

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

Repetitive Transcranial Magnetic Stimulation as a Cognitive Rehabilitation Approach for Veterans With Parkinson Disease and Mild Cognitive Impairment: Protocol for a Randomized Sham-Controlled Trial.

JMIR research protocols·2026
Same author

Bridging technology and education: The use of ChatGPT in grading pharmacy student exams.

Currents in pharmacy teaching & learning·2026
Same author

Gut-derived metabolic reprogramming drives immune aging and tissue degeneration.

bioRxiv : the preprint server for biology·2026
Same author

A novel strategy for detecting multiple mediators in high-dimensional mediation models.

Frontiers in psychiatry·2025
Same author

Research Letter: Safety, Feasibility, Acceptability and Preliminary Findings From Veterans' Intervention Blending NeuRomodulation and YogA for Chronic PaiN Treatment: VIBRANT-MTBI and Chronic Pain Pilot.

The Journal of head trauma rehabilitation·2025
Same author

A Customized Neural Transcranial Magnetic Stimulation Target for Functional Disability Among Veterans With Co-Occurring Alcohol Use Disorder and Mild Traumatic Brain Injury: Protocol for a Pilot Randomized Controlled Trial.

JMIR research protocols·2025
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

相关实验视频

Updated: Jul 17, 2025

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

7.3K

检测稀疏差异依赖的功能性大脑连接的检测.

Nairita Ghosal1, Sanjb Basu2, Dulal Bhaumik2,3

  • 1Biostatistics and Research Decision Sciences, Merck & Co., Inc., Rahway, New Jersey, USA.

Statistics in medicine
|August 30, 2023
PubMed
概括

这项研究引入了一种新的贝叶斯模型,用于发现自闭症研究中大脑连接的差异. 该模型有助于识别休息状态fMRI数据中的特定大脑区域连接.

关键词:
在ABIDE数据库中,您可以使用ABIDE数据库.贝叶斯模型是贝叶斯模型.迪里克莱特过程是指迪里克莱特过程.有关fMRI数据的数据.空间自回归的空间自回归

更多相关视频

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
14:27

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

15.7K
Cerebral Blood Flow-Based Resting State Functional Connectivity of the Human Brain using Optical Diffuse Correlation Spectroscopy
07:13

Cerebral Blood Flow-Based Resting State Functional Connectivity of the Human Brain using Optical Diffuse Correlation Spectroscopy

Published on: May 27, 2020

6.7K

相关实验视频

Last Updated: Jul 17, 2025

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

7.3K
Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
14:27

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

15.7K
Cerebral Blood Flow-Based Resting State Functional Connectivity of the Human Brain using Optical Diffuse Correlation Spectroscopy
07:13

Cerebral Blood Flow-Based Resting State Functional Connectivity of the Human Brain using Optical Diffuse Correlation Spectroscopy

Published on: May 27, 2020

6.7K

科学领域:

  • 神经科学是一个神经科学.
  • 精神病学是一个精神病学.
  • 自闭症研究 自闭症研究

背景情况:

  • 功能性大脑连接分析在神经科学和精神病学中至关重要.
  • 休息状态功能磁共振成像 (rs-fMRI) 测量大脑区域的协同激活.
  • 识别差异性功能连接是理解神经系统疾病 (如自闭症谱系障碍) 的关键.

研究的目的:

  • 提出一种新的贝叶斯模型,用于检测兴趣区域 (ROI) 对之间的交叉相关功能连接中的差异连接.
  • 引入一个稀疏的集群邻近模型,使用非参数的贝叶斯方法来识别稀疏的差异连接的ROI对.
  • 通过结构化的依赖模型,模拟ROI对之间的潜在依赖.

主要方法:

  • 开发了一种新的贝叶斯稀疏集群邻里模型.
  • 采用非参数贝叶斯方法来处理稀疏性和聚类.
  • 纳入了ROI对关系的结构依赖模型.
  • 通过模拟研究证明了贝叶斯推理和模型性能.
  • 将拟议的模型与标准模型进行比较.

主要成果:

  • 提出的贝叶斯模型有效地检测差异功能连接.
  • 该模型在模拟研究中表现出强的性能.
  • 稀疏集群邻里模型成功地识别了稀疏的差异连接的ROI对.
  • 结构依赖模型捕捉了ROI对之间的潜在相互依赖.

结论:

  • 新的贝叶斯模型为分析rs-fMRI数据中的差异功能连接提供了一个强大的方法.
  • 该模型有效地识别了与自闭症谱系障碍相关的大脑连接差异.
  • 这种方法增强了我们对神经发育条件中的大脑网络改变的理解.