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

Mapping the genetic architecture of human cortical expansion and its links to neuropsychiatric disorders.

bioRxiv : the preprint server for biology·2026
Same author

Brain-gut axis imaging, motion correction with [ <sup>11</sup> C]-carfentanil total-body PET.

medRxiv : the preprint server for health sciences·2026
Same author

Racialized Heteroscedasticity in Neuroimaging Features, Behavior Measures, and Neuroimaging-Based Predictive Models.

Research square·2026
Same author

The Clinical and Surgical Landscape of Gender Affirming Vocal Care: A Scoping Review.

Laryngoscope investigative otolaryngology·2026
Same author

Evaluating Components of Vocal Effort in Transgender Women.

Laryngoscope investigative otolaryngology·2026
Same author

Using connectome-based predictive models to reveal the systems standardized tests and clinical symptoms are reflecting.

Nature communications·2026
Same journal

Measurement prediction and power analysis for fNIRS and DOT.

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

相关实验视频

Updated: Sep 11, 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

1.2K

在基于connectome的预测建模中挽救丢失的数据.

Qinghao Liang1, Rongtao Jiang2, Brendan D Adkinson3

  • 1Department of Biomedical Engineering, Yale University, New Haven, CT, United States.

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

大型样本大小用于大脑表型预测增加了缺失的数据. 推算方法有效地挽救缺失的连接组和表型数据,显著提高预测性能和样本大小.

关键词:
功能连接性的功能连接性.功能磁力共振成像 (fMRI) 是一种归算是指指责一个人.机器学习是机器学习.缺失的数据 缺失的数据

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
Using Informational Connectivity to Measure the Synchronous Emergence of fMRI Multi-voxel Information Across Time
07:12

Using Informational Connectivity to Measure the Synchronous Emergence of fMRI Multi-voxel Information Across Time

Published on: July 1, 2014

12.4K

相关实验视频

Last Updated: Sep 11, 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

1.2K
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
Using Informational Connectivity to Measure the Synchronous Emergence of fMRI Multi-voxel Information Across Time
07:12

Using Informational Connectivity to Measure the Synchronous Emergence of fMRI Multi-voxel Information Across Time

Published on: July 1, 2014

12.4K

科学领域:

  • 神经成像是一种神经成像.
  • 计算神经科学是一种神经科学.
  • 数据科学数据科学数据科学

背景情况:

  • 大脑现象型预测模型通常需要大样本大小.
  • 样本规模的增加加剧了缺失数据的问题.
  • 像完整案例分析这样的传统方法减少了有效的样本大小,丢弃了有价值的信息.

研究的目的:

  • 在基于Connectome的预测建模 (CPM) 框架中整合和评估归算方法.
  • 为了将归算的性能与缺失的连接组和表型数据的完整案例分析进行比较.
  • 评估归算精度作为方法选择指南的实用性.

主要方法:

  • 在CPM框架中集成了各种归算技术.
  • 使用四个大型开源数据集 (HCP,PNC,CNP,HBN) 验证了该方法.
  • 将归算策略与缺失连接体和表型措施的完整案例分析进行比较.

主要成果:

  • 假定缺失的连接体显著改善了预测性能,而不是完整案例分析.
  • 推算准确度是选择缺少表型数据的方法的可靠指标,但不是连接组.
  • 在现实世界的认知预测任务中,归算使样本大小增加了一倍,解释变量增加了45%.

结论:

  • 推算是一种强大的策略,用于解决连接组和表型数据集中缺失的数据,用于预测建模.
  • 有效的归算方法通过保留参与者来提高预测性能,与完整案例分析不同.
  • 这项研究为神经成像预测建模中的归算技术提供了基准.