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

Multiple Regression01:25

Multiple Regression

Multiple regression assesses a linear relationship between one response or dependent variable and two or more independent variables. It has many practical applications.
Farmers can use multiple regression to determine the crop yield based on more than one factor, such as water availability, fertilizer, soil properties, etc. Here, the crop yield is the response or dependent variable as it depends on the other independent variables. The analysis requires the construction of a scatter plot...
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,...

You might also read

Related Articles

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

Sort by
Same author

Ultra-high-field fMRI study of insular discrimination of taste quality and perceived taste valence.

Chemical senses·2026
Same author

Aligning statistical models with inference goals in the neuroscience of language: A dual-dependency taxonomy.

Imaging neuroscience (Cambridge, Mass.)·2026
Same author

Deep Sound Synthesis Matched to Brain Activity Recapitulates Preferential Responses to Speech and Music.

The Journal of neuroscience : the official journal of the Society for Neuroscience·2026
Same author

Unraveling the anti-inflammatory effects of Mediterranean diet in patients with cancer remission.

Frontiers in immunology·2025
Same author

Two Seconds to Speak: Increasing Communication Speed for fMRI-Based Brain-Computer Interfaces.

Brain connectivity·2025
Same author

Mapping curvature domains in human V4 using CBV-sensitive layer-fMRI at 3T.

Frontiers in neuroscience·2025

Related Experiment Video

Updated: May 9, 2026

Basics of Multivariate Analysis in Neuroimaging Data
06:35

Basics of Multivariate Analysis in Neuroimaging Data

Published on: July 24, 2010

Multivariate linear regression of high-dimensional fMRI data with multiple target variables.

Giancarlo Valente1, Agustin Lage Castellanos, Gianluca Vanacore

  • 1Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, The Netherlands; Maastricht Brain Imaging Center, M-Bic, Faculty of Psychology and Neuroscience, Maastricht, The Netherlands.

Human Brain Mapping
|July 25, 2013
PubMed
Summary

Multivariate linear regression (MLR) in fMRI can misinterpret brain activity. A new model improves accuracy by considering all experimental targets simultaneously, enhancing neuroimaging analysis.

Keywords:
fMRIkernel methodsmultivariate regression

More Related Videos

Simultaneous Data Collection of fMRI and fNIRS Measurements Using a Whole-Head Optode Array and Short-Distance Channels
08:19

Simultaneous Data Collection of fMRI and fNIRS Measurements Using a Whole-Head Optode Array and Short-Distance Channels

Published on: October 20, 2023

Cross-Modal Multivariate Pattern Analysis
13:51

Cross-Modal Multivariate Pattern Analysis

Published on: November 9, 2011

Related Experiment Videos

Last Updated: May 9, 2026

Basics of Multivariate Analysis in Neuroimaging Data
06:35

Basics of Multivariate Analysis in Neuroimaging Data

Published on: July 24, 2010

Simultaneous Data Collection of fMRI and fNIRS Measurements Using a Whole-Head Optode Array and Short-Distance Channels
08:19

Simultaneous Data Collection of fMRI and fNIRS Measurements Using a Whole-Head Optode Array and Short-Distance Channels

Published on: October 20, 2023

Cross-Modal Multivariate Pattern Analysis
13:51

Cross-Modal Multivariate Pattern Analysis

Published on: November 9, 2011

Area of Science:

  • Neuroimaging
  • Cognitive Neuroscience
  • Machine Learning in Neuroscience

Background:

  • Multivariate linear regression (MLR) is a common tool for analyzing fMRI data, linking brain activity to stimuli or behavior.
  • Current MLR methods may produce inaccurate interpretations by analyzing targets separately, especially when neural activity overlaps.
  • This limitation hinders the precise identification of brain regions associated with specific cognitive processes.

Purpose of the Study:

  • To address the limitations of standard MLR in fMRI analysis.
  • To develop a novel model for accurately identifying spatial patterns of brain activation related to specific experimental targets.
  • To improve the generalization and interpretability of fMRI-MLR models.

Main Methods:

  • Proposed a new multivariate linear regression formulation that trains on an augmented dataset including all experimental targets.
  • Incorporated interaction coefficients to disentangle specific neural effects from overlapping predictive maps.
  • Validated the model using simulated fMRI data and a publicly available dataset.

Main Results:

  • The proposed method accurately identifies spatial patterns associated with specific targets.
  • Demonstrated high spatial sensitivity and improved generalization compared to standard MLR.
  • Successfully disentangled specific neural effects from interactions with other predictive maps.

Conclusions:

  • The novel MLR approach offers a more reliable method for analyzing fMRI data.
  • This advancement aids in correctly interpreting the link between brain activity and cognitive/behavioral variables.
  • The formulation enhances the precision of neuroimaging studies by accounting for target interactions.