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

Magnetic Resonance Imaging01:24

Magnetic Resonance Imaging

Magnetic resonance imaging (MRI) is a noninvasive medical imaging technique based on a phenomenon of nuclear physics discovered in the 1930s, in which matter exposed to magnetic fields and radio waves was found to emit radio signals. In 1970, a physician and researcher named Raymond Damadian noticed that malignant (cancerous) tissue gave off different signals than normal body tissue. He applied for a patent for the first MRI scanning device in clinical use by the early 1980s. The early MRI...
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,...
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least squares (OLS)...
Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
In the absence of...

You might also read

Related Articles

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

Sort by
Same author

The dynamic functional connectivity peak index: Detection of interictal epileptic activity with fMRI.

Epilepsia·2026
Same author

Unique Digital Images as Incentives in Clinical Trials: A Digital Shift Toward Meaningful Participation.

Journal of medical Internet research·2026
Same author

Network-based near-scalp personalized brain stimulation targets.

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

Disrupted Coupling of Heart Rate-Dependent Brain Network Switching and Attentional Task Performance in Schizophrenia Spectrum Disorders.

medRxiv : the preprint server for health sciences·2026
Same author

A Novel Therapeutic Mechanism for Nicotine Craving in Schizophrenia.

medRxiv : the preprint server for health sciences·2026
Same author

Parietal Default Mode Network Connectivity is Associated with Tobacco Use in Psychosis.

medRxiv : the preprint server for health sciences·2026
Same journal

Foot dynamic contrast-enhanced MRI for assessing microcirculatory changes after endovascular therapy in peripheral artery disease: A prospective pilot study.

Magnetic resonance imaging·2026
Same journal

Reconstruction of MRI from undersampled k-spaces of double-contrast volume acquisitions using deep neural networks.

Magnetic resonance imaging·2026
Same journal

Radiofrequency-induced heating safety of brain MRI scans at 7 T in the presence of a shoulder implant.

Magnetic resonance imaging·2026
Same journal

Incremental diagnostic value of microstructural time-dependent diffusion MRI in differentiating PCNSL from glioblastoma over conventional MRI.

Magnetic resonance imaging·2026
Same journal

Enhanced motion compensation for free-breathing dynamic contrast-enhanced MRI with GROG-facilitated bunch phase encoding and Golden angle radial sampling.

Magnetic resonance imaging·2026
Same journal

The allegory of the cave: 10 years of AI shadows in radiology.

Magnetic resonance imaging·2026
See all related articles

Related Experiment Video

Updated: Jun 13, 2026

Basics of Multivariate Analysis in Neuroimaging Data
06:35

Basics of Multivariate Analysis in Neuroimaging Data

Published on: July 24, 2010

Functional MRI and multivariate autoregressive models.

Baxter P Rogers1, Santosh B Katwal, Victoria L Morgan

  • 1Institute of Imaging Science, Vanderbilt University, Nashville, TN 37232-2310, USA. baxter.rogers@vanderbilt.edu

Magnetic Resonance Imaging
|May 7, 2010
PubMed
Summary
This summary is machine-generated.

Functional magnetic resonance imaging (fMRI) can measure brain signal timing differences using multivariate autoregressive models. Careful experimental design can enhance the utility of these models for understanding brain connectivity.

More Related Videos

Cross-Modal Multivariate Pattern Analysis
13:51

Cross-Modal Multivariate Pattern Analysis

Published on: November 9, 2011

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

Related Experiment Videos

Last Updated: Jun 13, 2026

Basics of Multivariate Analysis in Neuroimaging Data
06:35

Basics of Multivariate Analysis in Neuroimaging Data

Published on: July 24, 2010

Cross-Modal Multivariate Pattern Analysis
13:51

Cross-Modal Multivariate Pattern Analysis

Published on: November 9, 2011

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

Area of Science:

  • Neuroscience
  • Brain Imaging
  • Systems Neuroscience

Background:

  • Brain connectivity describes relationships between brain regions.
  • Functional magnetic resonance imaging (fMRI) quantifies relationships between hemodynamic signals.
  • Timing differences in fMRI signals can reveal functional organization and circuit response dynamics.

Purpose of the Study:

  • To review the application of multivariate autoregressive (MAR) models for fMRI.
  • To discuss challenges and extensions of MAR models for hemodynamic time series.
  • To explore the utility of MAR-based connectivity measures for fMRI data analysis.

Main Methods:

  • Application of multivariate autoregressive time series models to fMRI data.
  • Analysis of hemodynamic signal timing differences (delays) within brain regions.
  • Consideration of Granger causality and its relationship to neuronal connectivity.

Main Results:

  • MAR models are suitable for measuring small timing differences (≤100 ms) in fMRI signals.
  • MAR-based connectivity measures, like Granger causality, may not always reflect true neuronal connectivity.
  • Potential for MAR models to extend information obtainable from fMRI analyses.

Conclusions:

  • Multivariate autoregressive models offer a valuable approach for analyzing fMRI-derived brain connectivity.
  • Addressing methodological issues is crucial for accurate interpretation of MAR-based findings.
  • Strategic experimental design can optimize the use of MAR models to enhance fMRI insights into brain function.