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

5.8K
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:
5.8K
One-Way ANOVA: Equal Sample Sizes01:15

One-Way ANOVA: Equal Sample Sizes

3.3K
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...
3.3K
Variability: Analysis01:11

Variability: Analysis

143
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...
143
Stratified Sampling Method01:16

Stratified Sampling Method

12.0K
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...
12.0K
Friedman Two-way Analysis of Variance by Ranks01:21

Friedman Two-way Analysis of Variance by Ranks

197
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...
197
Comparing the Survival Analysis of Two or More Groups01:20

Comparing the Survival Analysis of Two or More Groups

188
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...
188

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

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
Same author

Network-based near-scalp personalized brain stimulation targets.

Imaging neuroscience (Cambridge, Mass.)·2026

相关实验视频

Updated: Jul 4, 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

15.7K

深度ResBat:深度剩余批量协调,考虑共变量分布差异.

Lijun An1,2,3,4, Chen Zhang1,2,3,4, Naren Wulan1,2,3,4

  • 1Centre for Sleep and Cognition & Centre for Translational MR Research, Yong Loo Lin School of Medicine, National University of Singapore, Singapore.

bioRxiv : the preprint server for biology
|January 31, 2024
PubMed
概括

在不同地点协调MRI数据至关重要. 像DeepResBat这样的深度学习方法有效地减少了站点变异性,同时保留了生物信号,优于现有的方法.

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

3.4K
Basics of Multivariate Analysis in Neuroimaging Data
06:35

Basics of Multivariate Analysis in Neuroimaging Data

Published on: July 24, 2010

16.9K

相关实验视频

Last Updated: Jul 4, 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

15.7K
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

3.4K
Basics of Multivariate Analysis in Neuroimaging Data
06:35

Basics of Multivariate Analysis in Neuroimaging Data

Published on: July 24, 2010

16.9K

科学领域:

  • 神经成像是一种神经成像.
  • 医疗数据分析 医学数据分析
  • 机器学习 机器学习

背景情况:

  • 从多个来源汇集MRI数据需要协调,以最大限度地减少特定地点的变化.
  • 像ComBat这样的现有方法使用混合效果模型,而像条件变化自编码器 (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的差异,这可能会影响协调质量.

研究的目的:

  • 评估共变量分布差异对MRI协调的影响.
  • 提出基于深度神经网络 (DNN) 的新协调方法,明确考虑共变量.
  • 将新方法的性能与现有技术进行比较.

主要方法:

  • 开发了两个共变量意识的DNN协调方法:共变量VAE (coVAE) 和DeepResBat.
  • coVAE通过将共变量纳入潜伏表示来扩展cVAE.
  • DeepResBat使用剩余框架,去除协变效应,与cVAE协调站点差异,并重新引入协变效应.

主要成果:

  • 与ComBat,CovBat和cVAE相比,DeepResBat和coVAE在减少数据集差异和增强生物效应方面表现出卓越的表现.
  • 这项研究使用了三大数据集,共有2787名参与者和10085个T1扫描.
  • coVAE表现出产生MRI数据和共变量之间的虚假阳性关联的趋势.

结论:

  • 考虑共变量的基于深度学习的协调方法可以超过传统和非共变量意识的DNN方法.
  • DeepResBat被介绍为一个强大的深度学习替代ComBat用于MRI数据协调.
  • 研究人员应该小心某些DNN协调方法 (如coVAE) 的潜在假阳性结果.