Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Introduction to Test of Independence01:21

Introduction to Test of Independence

In statistics, the term independence means that one can directly obtain the probability of any event involving both variables by multiplying their individual probabilities. Tests of independence are chi-square tests involving the use of a contingency table of observed (data) values.
The test statistic for a test of independence is similar to that of a goodness-of-fit test:
Hypothesis Test for Test of Independence01:16

Hypothesis Test for Test of Independence

The test of independence is a chi-square-based test used to determine whether two variables or factors are independent or dependent. This hypothesis test is used to examine the independence of the variables. One can construct two qualitative survey questions or experiments based on the variables in a contingency table. The goal is to see if the two variables are unrelated (independent) or related (dependent). The null and alternative hypotheses for this test are:
H0: The two variables (factors)...
Independent and Dependent Sources01:18

Independent and Dependent Sources

In electrical circuits, sources play a crucial role in providing power for the operation of the circuit. These sources can be broadly categorized into two types: independent and dependent.
Independent voltage or current sources supply a fixed amount of voltage or current, respectively, which is unaffected by other elements within the circuit. These are represented using specific symbols. Independent voltage sources are symbolized with polarities (+ and -), indicating the direction of the...
Variability: Analysis01:11

Variability: Analysis

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...
The R Chart01:02

The R Chart

In statistical process control, control charts, particularly R charts, are instrumental in monitoring process variations and identifying non-random patterns that run charts might miss. R charts track the variability within process subgroups, which is crucial when standard deviation use is impractical or unknown process variations exist.
R charts are pivotal for pinpointing shifts in process variability. Stability is indicated when all data points remain within the defined upper and lower...
Friedman Two-way Analysis of Variance by Ranks01:21

Friedman Two-way Analysis of Variance by Ranks

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 from...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Functional brain organization is stable within individuals across years.

bioRxiv : the preprint server for biology·2026
Same author

Efficient Deep Learning Models for Predicting Individualized Task Activation From Resting-State Functional Connectivity.

Human brain mapping·2026
Same author

Refining RDoC Using Individual-Level Task fMRI Factor Models Reveals Reproducible and Clinically Relevant Brain-Wide Motifs.

bioRxiv : the preprint server for biology·2026
Same author

Building an open ecosystem for molecular neuroimaging: standards and tools from the OpenNeuroPET initiative.

bioRxiv : the preprint server for biology·2026
Same author

Quality assessment and control of unprocessed anatomical, functional and diffusion MRI of the human brain using MRIQC.

Nature protocols·2026
Same author

Investigating the analytical robustness of the social and behavioural sciences.

Nature·2026

Related Experiment Video

Updated: Jun 22, 2026

Gaze in Action: Head-mounted Eye Tracking of Children's Dynamic Visual Attention During Naturalistic Behavior
07:09

Gaze in Action: Head-mounted Eye Tracking of Children's Dynamic Visual Attention During Naturalistic Behavior

Published on: November 14, 2018

Independence in ROI analysis: where is the voodoo?

Russell A Poldrack1, Jeanette A Mumford

  • 1Department of Psychology, University of California, Los Angeles, CA 90095-1563, USA. poldrack@ucla.edu

Social Cognitive and Affective Neuroscience
|May 28, 2009
PubMed
Summary

Non-independence in functional magnetic resonance imaging (fMRI) region of interest (ROI) analyses can yield high correlations, even with independent data. This study suggests previous claims of implausibility are incorrect, offering recommendations to mitigate issues.

More Related Videos

Kinetic Analysis of Vasculogenesis Quantifies Dynamics of Vasculogenesis and Angiogenesis In Vitro
11:03

Kinetic Analysis of Vasculogenesis Quantifies Dynamics of Vasculogenesis and Angiogenesis In Vitro

Published on: January 31, 2018

In Vivo Vascular Injury Readouts in Mouse Retina to Promote Reproducibility
07:35

In Vivo Vascular Injury Readouts in Mouse Retina to Promote Reproducibility

Published on: April 21, 2022

Related Experiment Videos

Last Updated: Jun 22, 2026

Gaze in Action: Head-mounted Eye Tracking of Children's Dynamic Visual Attention During Naturalistic Behavior
07:09

Gaze in Action: Head-mounted Eye Tracking of Children's Dynamic Visual Attention During Naturalistic Behavior

Published on: November 14, 2018

Kinetic Analysis of Vasculogenesis Quantifies Dynamics of Vasculogenesis and Angiogenesis In Vitro
11:03

Kinetic Analysis of Vasculogenesis Quantifies Dynamics of Vasculogenesis and Angiogenesis In Vitro

Published on: January 31, 2018

In Vivo Vascular Injury Readouts in Mouse Retina to Promote Reproducibility
07:35

In Vivo Vascular Injury Readouts in Mouse Retina to Promote Reproducibility

Published on: April 21, 2022

Area of Science:

  • Neuroimaging
  • Cognitive Neuroscience
  • Statistical Analysis in Neuroscience

Background:

  • Region of Interest (ROI) analysis is a common technique in functional magnetic resonance imaging (fMRI).
  • Concerns regarding non-independence in fMRI ROI analyses have been raised, questioning the validity of high correlation findings.
  • The potential for inflated correlations due to non-independence requires careful examination.

Purpose of the Study:

  • To investigate the impact of non-independence on fMRI ROI analyses.
  • To re-evaluate the claims made by Vul et al. regarding the implausibility of high correlations in fMRI.
  • To provide recommendations for addressing non-independence issues in fMRI research.

Main Methods:

  • Utilized a previously published functional magnetic resonance imaging (fMRI) dataset.
  • Examined the effects of non-independence on region of interest (ROI) analyses.
  • Performed statistical analyses to assess correlation strengths under conditions of non-independence.

Main Results:

  • Demonstrated that very strong correlations (exceeding 0.8) can arise in fMRI ROI analyses.
  • Showed these high correlations can occur even when the ROI is statistically independent from the analyzed data.
  • Contradicted the assertion by Vul et al. that such high correlations are inherently implausible.

Conclusions:

  • The findings challenge the conclusions of Vul et al. regarding the interpretation of high correlations in fMRI ROI analyses.
  • Non-independence does not automatically render high correlations invalid, but warrants careful consideration.
  • Recommendations are provided to help researchers identify and mitigate potential problems arising from non-independence in fMRI studies.