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

One-Way ANOVA: Unequal Sample Sizes01:15

One-Way ANOVA: Unequal Sample Sizes

5.7K
One-way ANOVA can be performed on three or more samples of unequal sizes. However, calculations get complicated when sample sizes are not always the same. So, while performing ANOVA with unequal samples size, the following equation is used:
5.7K
One-Way ANOVA: Equal Sample Sizes01:15

One-Way ANOVA: Equal Sample Sizes

3.2K
One-Way ANOVA can be performed on three or more samples with equal or unequal sample sizes. When one-way ANOVA is performed on two datasets with samples of equal sizes, it can be easily observed that the computed F statistic is highly sensitive to the sample mean.
Different sample means can result in different values for the variance estimate: variance between samples. This is because the variance between samples is calculated as the product of the sample size and the variance between the...
3.2K
Variability: Analysis01:11

Variability: Analysis

132
Measures of variability are statistical metrics that reveal the dispersion pattern within a dataset. They are pivotal in biostatistics, providing insights into the heterogeneity within health and biological data. Variability signifies the degree to which data points diverge from one another, helping researchers understand the potential range of values and associated uncertainty within the data.
The range is a simple measure of variability, indicating the difference between the highest and...
132
Stratified Sampling Method01:16

Stratified Sampling Method

11.8K
Sampling is a technique to select a portion (or subset) of the larger population and study that portion (the sample) to gain information about the population. The sampling method ensures that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
To choose a stratified sample, divide the population into groups called strata and then take a...
11.8K
Friedman Two-way Analysis of Variance by Ranks01:21

Friedman Two-way Analysis of Variance by Ranks

158
Friedman's Two-Way Analysis of Variance by Ranks is a nonparametric test designed to identify differences across multiple test attempts when traditional assumptions of normality and equal variances do not apply. Unlike conventional ANOVA, which requires normally distributed data with equal variances, Friedman's test is ideal for ordinal or non-normally distributed data, making it particularly useful for analyzing dependent samples, such as matched subjects over time or repeated measures...
158
Comparing the Survival Analysis of Two or More Groups01:20

Comparing the Survival Analysis of Two or More Groups

156
Survival analysis is a cornerstone of medical research, used to evaluate the time until an event of interest occurs, such as death, disease recurrence, or recovery. Unlike standard statistical methods, survival analysis is particularly adept at handling censored data—instances where the event has not occurred for some participants by the end of the study or remains unobserved. To address these unique challenges, specialized techniques like the Kaplan-Meier estimator, log-rank test, and...
156

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

Excessive Censoring Degrades Individual-Specific Cortical Parcellations and Personalized TMS Targets.

bioRxiv : the preprint server for biology·2026
Same author

Atrophy in preclinical Alzheimer's disease maps to a network that predicts longitudinal decline.

Molecular psychiatry·2026
Same author

Individual-specific resting-state networks predict language dominance in drug-resistant epilepsy.

Epilepsia·2026
Same author

Widespread use of invalid statistical tests in biomedical machine learning.

bioRxiv : the preprint server for biology·2026
Same author

Developing a multi-modal neuroimaging-based BrainAge model across childhood.

bioRxiv : the preprint server for biology·2026
Same author

Convergent and divergent brain-cognition development in early adolescence.

Nature communications·2026

相关实验视频

Updated: Jun 11, 2025

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

15.6K

深度ResBat:深度剩余批量协调,考虑共变量分布差异.

Lijun An1, Chen Zhang1, Naren Wulan1

  • 1Centre for Sleep and Cognition & Centre for Translational MR Research, Yong Loo Lin School of Medicine, National University of Singapore, Singapore; Department of Electrical and Computer Engineering, National University of Singapore, Singapore; Department of Medicine, Healthy Longevity Translational Research Programme, Human Potential Translational Research Programme & Institute for Digital Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore; N.1 Institute for Health, National University of Singapore, Singapore.

Medical image analysis
|October 5, 2024
PubMed
概括

在不同地点协调MRI数据至关重要. 新的深度学习方法DeepResBat和coVAE通过计算共变量来提高协调性,DeepResBat表现出卓越的性能并避免与coVAE不同的是错误的阳性.

关键词:
共同变量 共同变量 共同变量深度学习是一种深度学习.这是错误的阳性.核磁共振成像协调协调工作

更多相关视频

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
04:35

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

3.3K
Basics of Multivariate Analysis in Neuroimaging Data
06:35

Basics of Multivariate Analysis in Neuroimaging Data

Published on: July 24, 2010

16.8K

相关实验视频

Last Updated: Jun 11, 2025

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

15.6K
Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
04:35

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

3.3K
Basics of Multivariate Analysis in Neuroimaging Data
06:35

Basics of Multivariate Analysis in Neuroimaging Data

Published on: July 24, 2010

16.8K

科学领域:

  • 神经成像分析分析神经成像分析
  • 医疗数据协调与协调
  • 机器学习在神经科学中的应用

背景情况:

  • 从多个站点汇集MRI数据需要协调,以减少站点间的变化.
  • 像ComBat这样的传统方法使用混合效应模型,而像cVAE这样的深度学习方法正在出现.
  • 当前的深度学习方法往往忽略了共变量分布差异,可能导致低于最佳的协调.

研究的目的:

  • 开发和评估基于深度学习的新型MRI协调方法,明确考虑共变量.
  • 将共变量意识深度学习方法的性能与现有方法进行比较.
  • 解决当前深度学习协调技术在共变量处理方面的局限性.

主要方法:

  • 提出了两个共变量意识深度学习协调方法:共变量 VAE (coVAE) 和DeepRes.Bat.
  • coVAE通过将共变量纳入潜伏表示来扩展cVAE.
  • DeepResBat使用剩余框架,首先删除共变量效应,然后网站效应,最后重新引入共变量效应.

主要成果:

  • 在减少数据集差异和保持生物效应方面,DeepResBat和coVAE的表现优于ComBat,CovBat和cVAE.
  • coVAE 显示了一种倾向,在MRI 数据和共变量之间幻觉虚假的关联.
  • DeepResBat被证明是一个有效的深度学习替代ComBat对MRI协调.

结论:

  • 共变量意识的深度学习方法可以显著改善MRI数据的协调.
  • DeepResBat是一种有前途的深度学习方法,用于协调多站点MRI数据,为ComBat.Bat提供一种有效的替代方案.
  • 研究人员应该小心一些深度学习协调方法 (如coVAE) 的潜在假阳性结果.