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

Theory of Attribution II: Kelley's Covariation Theory01:29

Theory of Attribution II: Kelley's Covariation Theory

Attribution theory plays a crucial role in social psychology, helping to explain how individuals interpret the causes of behavior. One prominent model within this field is Harold Kelley's covariation theory, which provides a systematic approach to determining whether internal traits or external circumstances drive a person's actions. The model posits that individuals rely on three key types of information—consensus, consistency, and distinctiveness—to make these judgments.Consensus: Comparing...
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...
Multicompartment Models: Overview01:14

Multicompartment Models: Overview

Multicompartment models are mathematical constructs that depict how drugs are distributed and eliminated within the body. They segment the body into several compartments, symbolizing various physiological or anatomical areas connected through drug transfer processes such as absorption, metabolism, distribution, and elimination.
These models offer a more comprehensive representation of drug behavior in the body than one-compartment models. They accommodate the complexity of drug distribution,...
Strategies for Assessing and Addressing Confounding01:25

Strategies for Assessing and Addressing Confounding

Confounding is a critical issue in epidemiological studies, often leading to misleading conclusions about associations between exposures and outcomes. It occurs when the relationship between the exposure and the outcome is mixed with the effects of other factors that influence the outcome. Given that, addressing confounding is of high importance for drawing accurate inferences in research.
Confounding can be addressed at both the design phase of a study and through analytical methods after data...
Law of Independent Assortment02:03

Law of Independent Assortment

While Mendel’s Law of Segregation states that the two alleles for one gene are separated into different gametes, a different question of how different genes are inherited remains. For example, is the gene for tall plants inherited with the gene for green peas? Mendel asked this question by experimenting with a dihybrid cross; a cross in which both parents are homozygous for two distinct traits resulting in an F1 generation that are heterozygous for both traits.
Law of Independent Assortment02:03

Law of Independent Assortment

While Mendel’s Law of Segregation states that the two alleles for one gene are separated into different gametes, a different question of how different genes are inherited remains. For example, is the gene for tall plants inherited with the gene for green peas? Mendel asked this question by experimenting with a dihybrid cross; a cross in which both parents are homozygous for two distinct traits resulting in an F1 generation that are heterozygous for both traits.

You might also read

Related Articles

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

Sort by
Same author

Author Correction: Cerebellar aging is spatially heterogeneous and supports cognitive resilience in later life.

Nature neuroscience·2026
Same author

Cerebellar aging is spatially heterogeneous and supports cognitive resilience in later life.

Nature neuroscience·2026
Same author

A Multiple-Well Framework for Human Perceptual Decision-Making.

Entropy (Basel, Switzerland)·2026
Same author

AI-Enhanced Semantic Feature Norms for 786 Concepts.

Topics in cognitive science·2025
Same author

Learning expectations shape cognitive control allocation.

Proceedings of the National Academy of Sciences of the United States of America·2025
Same author

Test-retest reliability of the human connectome: An OPM-MEG study.

Imaging neuroscience (Cambridge, Mass.)·2025
Same journal

Investigating the Neural Origins of Ear-EEG: A Correlation Study Using Scalp EEG Source Reconstruction.

NeuroImage·2026
Same journal

Hysteresis effects in visual and auditory perception and the comparison of underlying neural mechanisms - an EEG study.

NeuroImage·2026
Same journal

Short-term audio-tactile training affects cortical auditory speech-envelope tracking for incongruent but not congruent stimuli.

NeuroImage·2026
Same journal

Dissociable Neurocognitive Mechanisms of State and Trait Anxiety in Working Memory: Threat-Induced Alterations in Decision Dynamics and Attenuation of Large-Scale Network Reconfiguration.

NeuroImage·2026
Same journal

Neuro-Ocular Amyloid Characterization in Alzheimer's Disease via Cross-Site PET-MRI and Hierarchical Cross-Attention Driven Multimodal Representation Learning.

NeuroImage·2026
Same journal

Whole-brain network dynamics underlying intolerance of uncertainty.

NeuroImage·2026
See all related articles

Related Experiment Video

Updated: May 12, 2026

Cross-Modal Multivariate Pattern Analysis
13:51

Cross-Modal Multivariate Pattern Analysis

Published on: November 9, 2011

Confounds in multivariate pattern analysis: Theory and rule representation case study.

Michael T Todd1, Leigh E Nystrom, Jonathan D Cohen

  • 1Department of Psychology and Princeton Neuroscience Institute, Princeton University, Princeton, NJ 08540, USA. mttodd@berkeley.edu

Neuroimage
|April 6, 2013
PubMed
Summary
This summary is machine-generated.

Multivariate pattern analysis (MVPA) in functional magnetic resonance imaging (fMRI) may yield false positives due to confounds. Standard MVPA practices discard effect direction, unlike general linear model analysis (GLMA), potentially misinterpreting confounds as representations.

More Related Videos

Basics of Multivariate Analysis in Neuroimaging Data
06:35

Basics of Multivariate Analysis in Neuroimaging Data

Published on: July 24, 2010

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

Related Experiment Videos

Last Updated: May 12, 2026

Cross-Modal Multivariate Pattern Analysis
13:51

Cross-Modal Multivariate Pattern Analysis

Published on: November 9, 2011

Basics of Multivariate Analysis in Neuroimaging Data
06:35

Basics of Multivariate Analysis in Neuroimaging Data

Published on: July 24, 2010

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

Area of Science:

  • Neuroimaging techniques
  • Cognitive neuroscience
  • Data analysis in fMRI

Background:

  • Multivariate pattern analysis (MVPA) is a popular fMRI analysis method.
  • MVPA is often preferred over general linear model analysis (GLMA) for detecting fine-grained distributed patterns.
  • Concerns exist regarding potential confounds in standard MVPA approaches.

Purpose of the Study:

  • To investigate whether common MVPA practices can introduce confounds.
  • To compare MVPA with GLMA in controlling for experimental confounds.
  • To re-evaluate findings of rule representations in prefrontal cortex using MVPA.

Main Methods:

  • Analysis of functional magnetic resonance imaging (fMRI) data using both MVPA and GLMA.
  • Examination of group testing procedures in MVPA versus GLMA.
  • Application of linear regression to control for confounds in MVPA.
  • Case study using fMRI data on rule representations.

Main Results:

  • Standard MVPA approaches can systematically admit confounds that GLMA eliminates.
  • Discarding the sign of effects in group-level MVPA undermines experimental control.
  • Controlling for reaction time, a confound at the individual-subject level, eliminated apparent rule representations in MVPA.
  • GLMA produced null results where standard MVPA showed significant effects.

Conclusions:

  • Widely used MVPA methods may produce false positive results due to confounds.
  • GLMA offers better control for confounds compared to standard MVPA group analyses.
  • Recent findings of rule representations in prefrontal cortex may be attributable to confounds like reaction time or task difficulty rather than true representations.