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

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

Computational representation of white matter fiber orientations.

International journal of biomedical imaging·2013
Same author

Bayesian mixture models of variable dimension for image segmentation.

Computer methods and programs in biomedicine·2008
Same author

A Dirichlet process mixture model for brain MRI tissue classification.

Medical image analysis·2007
Same journal

MMFVS-Net: A triple-symmetric cross-attention network for multimodal optical image fusion and high-accuracy virtual staining of breast cancer tissues.

Computer methods and programs in biomedicine·2026
Same journal

A novel Milstein-stochastic epidemiologically-informed neural network for approaching epidemic dynamics: Application to Mpox disease.

Computer methods and programs in biomedicine·2026
Same journal

Accounting for approximation errors using surrogate-based parameter estimation of cardiac mechanics digital twins.

Computer methods and programs in biomedicine·2026
Same journal

Facial iPPG heatmap patterns based on period-aware autoencoder show association with carotid atherosclerosis towards non-contact hemodynamic assessment.

Computer methods and programs in biomedicine·2026
Same journal

Explainable machine learning models predict liver fibrosis risk and outcome in the general population: Development and multi-cohort external validation.

Computer methods and programs in biomedicine·2026
Same journal

Evaluation of surrogate endpoints for survival outcomes using the surrogate package in R.

Computer methods and programs in biomedicine·2026
See all related articles

Related Experiment Video

Updated: Jun 11, 2026

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

A Bayesian multilevel model for fMRI data analysis.

Adelino R Ferreira da Silva1

  • 1Dep. de Eng. Electrotécnica, Faculdade de Ciências e Tecnologia, FCT, Universidade Nova de Lisboa, Caparica, Portugal. afs@fct.unl.pt

Computer Methods and Programs in Biomedicine
|June 29, 2010
PubMed
Summary
This summary is machine-generated.

This study introduces a Bayesian multilevel model to analyze functional magnetic resonance imaging (fMRI) data, addressing limitations of traditional methods. The model effectively handles spatial variations and noise in fMRI activation patterns.

More Related Videos

Basics of Multivariate Analysis in Neuroimaging Data
06:35

Basics of Multivariate Analysis in Neuroimaging Data

Published on: July 24, 2010

Network Analysis of the Default Mode Network Using Functional Connectivity MRI in Temporal Lobe Epilepsy
12:09

Network Analysis of the Default Mode Network Using Functional Connectivity MRI in Temporal Lobe Epilepsy

Published on: August 5, 2014

Related Experiment Videos

Last Updated: Jun 11, 2026

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

Basics of Multivariate Analysis in Neuroimaging Data
06:35

Basics of Multivariate Analysis in Neuroimaging Data

Published on: July 24, 2010

Network Analysis of the Default Mode Network Using Functional Connectivity MRI in Temporal Lobe Epilepsy
12:09

Network Analysis of the Default Mode Network Using Functional Connectivity MRI in Temporal Lobe Epilepsy

Published on: August 5, 2014

Area of Science:

  • Neuroimaging
  • Statistical Modeling
  • Brain Imaging Analysis

Background:

  • Functional magnetic resonance imaging (fMRI) analysis faces limitations with current statistical parametric mapping methods.
  • Widespread adoption of Bayesian approaches in neuroimaging is hindered by challenges in subjective prior elicitation.
  • Existing Bayesian methods often impose specific spatial or temporal priors, limiting flexibility.

Purpose of the Study:

  • To present a novel Bayesian multilevel model for analyzing brain fMRI data.
  • To overcome the limitations of statistical parametric mapping and subjective prior elicitation in Bayesian methods.
  • To offer a flexible approach for estimating fMRI activation patterns without rigid spatial or temporal constraints.

Main Methods:

  • A Bayesian multilevel model is proposed, treating group effects (fMRI activation patterns) as exchangeable.
  • Voxel time series are considered manifestations of common underlying phenomena.
  • A two-stage empirical Bayes prior approach relates voxel regression equations via correlations, avoiding specific spatial/temporal priors.

Main Results:

  • The Bayesian multilevel model leverages adaptive shrinkage properties to manage spatial variations and noise outliers.
  • The model's performance and characteristics were evaluated using two real fMRI datasets.
  • The approach demonstrated effectiveness in handling complex spatial variations and noise in fMRI data.

Conclusions:

  • The proposed Bayesian multilevel model offers a robust and flexible alternative for fMRI data analysis.
  • This method effectively addresses inherent noise and spatial variations in neuroimaging data.
  • The empirical Bayes approach facilitates the adoption of Bayesian methodologies in the broader neuroimaging community.