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

3.7K
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...
3.7K
Aggregates Classification01:29

Aggregates Classification

774
Aggregate classification is generally based on its size, petrographic characteristics, weight, and source. Size classification ranges from coarse to fine aggregates, defined by the size of the particles. Coarse aggregates are particles that do not pass through ASTM sieve No. 4, and aggregates that pass through the sieve are fine aggregates.
Petrographic classification groups aggregates based on common mineralogical characteristics. Some of the common mineral groups found in aggregates are...
774
Classification of Signals01:30

Classification of Signals

1.2K
In signal processing, signals are classified based on various characteristics: continuous-time versus discrete-time, periodic versus aperiodic, analog versus digital, and causal versus noncausal. Each category highlights distinct properties crucial for understanding and manipulating signals.
A continuous-time signal holds a value at every instant in time, representing information seamlessly. In contrast, a discrete-time signal holds values only at specific moments, often denoted as x(n), where...
1.2K
Classification of Systems-II01:31

Classification of Systems-II

422
Continuous-time systems have continuous input and output signals, with time measured continuously. These systems are generally defined by differential or algebraic equations. For instance, in an RC circuit, the relationship between input and output voltage is expressed through a differential equation derived from Ohm's law and the capacitor relation,
422
Classification of Systems-I01:26

Classification of Systems-I

490
Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
Homogeneity dictates that if an input x(t) is multiplied by a constant c, the output y(t) is multiplied by the same constant. Mathematically, this is expressed as:
490
Multicompartment Models: Overview01:14

Multicompartment Models: Overview

430
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,...
430

You might also read

Related Articles

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

Sort by
Same author

Targeted memory reactivation elicits temporally compressed reactivation linked to spindles.

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

Introduction to Large Language Models (LLMs) for dementia care and research.

Frontiers in dementia·2024
Same author

Targeted memory reactivation in human REM sleep elicits detectable reactivation.

eLife·2023
Same author

A Skewed Loss Function for Correcting Predictive Bias in Brain Age Prediction.

IEEE transactions on medical imaging·2023
Same author

Targeting targeted memory reactivation: Characteristics of cued reactivation in sleep.

NeuroImage·2022
Same author

Towards an objective evaluation of EEG/MEG source estimation methods - The linear approach.

NeuroImage·2022
Same journal

Evaluation of an open-face 8-channel transmit 64-channel receive 7T head coil for neuroimaging.

Frontiers in neuroscience·2026
Same journal

Acoustic stimulation in pain management: neurobiological mechanisms and clinical applications-a narrative review.

Frontiers in neuroscience·2026
Same journal

Local brain connectome parameters across the spectrum of clinical cognitive decline.

Frontiers in neuroscience·2026
Same journal

Body mass index affects EEG microstate dynamics through blood viscosity in high-altitude environments.

Frontiers in neuroscience·2026
Same journal

Disrupted glymphatic function and its relationship with sleep and cognitive impairment in ME/CFS assessed via DTI-ALPS.

Frontiers in neuroscience·2026
Same journal

Neuromorphic-inspired multi-view global-local fusion for IR-UWB radar dynamic gesture recognition.

Frontiers in neuroscience·2026
See all related articles

Related Experiment Video

Updated: Dec 17, 2025

Cross-Modal Multivariate Pattern Analysis
13:51

Cross-Modal Multivariate Pattern Analysis

Published on: November 9, 2011

20.3K

MVPA-Light: A Classification and Regression Toolbox for Multi-Dimensional Data.

Matthias S Treder1

  • 1School of Computer Science & Informatics, Cardiff University, Cardiff, United Kingdom.

Frontiers in Neuroscience
|June 26, 2020
PubMed
Summary
This summary is machine-generated.

MVPA-Light is a MATLAB toolbox for multivariate pattern analysis (MVPA). It offers advanced tools for analyzing complex neuroimaging data, including MEG and fMRI.

Keywords:
MVPAclassificationcross-validationdecodingmachine learningregressionregularizationtoolbox

More Related Videos

Using Informational Connectivity to Measure the Synchronous Emergence of fMRI Multi-voxel Information Across Time
07:12

Using Informational Connectivity to Measure the Synchronous Emergence of fMRI Multi-voxel Information Across Time

Published on: July 1, 2014

12.6K
Author Spotlight: Generating Neuronal Phenotypic Profiles - A Protocol to Culture and Image Human Midbrain Dopaminergic Neurons
09:21

Author Spotlight: Generating Neuronal Phenotypic Profiles - A Protocol to Culture and Image Human Midbrain Dopaminergic Neurons

Published on: July 7, 2023

1.9K

Related Experiment Videos

Last Updated: Dec 17, 2025

Cross-Modal Multivariate Pattern Analysis
13:51

Cross-Modal Multivariate Pattern Analysis

Published on: November 9, 2011

20.3K
Using Informational Connectivity to Measure the Synchronous Emergence of fMRI Multi-voxel Information Across Time
07:12

Using Informational Connectivity to Measure the Synchronous Emergence of fMRI Multi-voxel Information Across Time

Published on: July 1, 2014

12.6K
Author Spotlight: Generating Neuronal Phenotypic Profiles - A Protocol to Culture and Image Human Midbrain Dopaminergic Neurons
09:21

Author Spotlight: Generating Neuronal Phenotypic Profiles - A Protocol to Culture and Image Human Midbrain Dopaminergic Neurons

Published on: July 7, 2023

1.9K

Area of Science:

  • Neuroimaging analysis
  • Machine learning in neuroscience
  • Computational neuroscience

Background:

  • Multivariate pattern analysis (MVPA) is crucial for decoding brain activity from neuroimaging data.
  • Existing toolboxes may lack comprehensive features or modern algorithmic implementations.

Purpose of the Study:

  • Introduce MVPA-Light, a novel MATLAB toolbox for advanced MVPA.
  • Provide researchers with a flexible and efficient platform for neuroimaging data analysis.

Main Methods:

  • Developed native implementations of classifiers and regression models with modern optimization.
  • Integrated high-level functions for generalization and searchlight analyses.
  • Included cross-validation, hyperparameter tuning, and nested preprocessing.

Main Results:

  • MVPA-Light offers modularity and extensibility for diverse analytical needs.
  • Demonstrated example analyses on MEG and fMRI datasets.
  • Benchmarking confirmed the performance of implemented classifiers and regression models.

Conclusions:

  • MVPA-Light provides a robust and versatile solution for MVPA in neuroimaging.
  • Facilitates sophisticated multivariate analyses and statistical significance testing.
  • Enhances the capabilities of researchers using MATLAB for brain data analysis.