Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

You might also read

Related Articles

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

Sort by

Same author

Discordance between fractional flow reserve and nonhyperemic index with a fiber-optic pressure wire. READI EPIC-14.

REC, interventional cardiology·2025

Same author

Use of a multistate model in survival predictions in cardiology studies.

REC, interventional cardiology·2025

Same author

Bayesian Modeling for Nonstationary Spatial Point Process via Spatial Deformations.

Entropy (Basel, Switzerland)·2024

Same author

More than 10,000 pre-Columbian earthworks are still hidden throughout Amazonia.

Science (New York, N.Y.)·2023

Same author

Letter: Composite endpoints in clinical trials - simplicity or perfection?

EuroIntervention : journal of EuroPCR in collaboration with the Working Group on Interventional Cardiology of the European Society of Cardiology·2022

Same author

Design and interim results of a registry of left atrial appendage occlusion with the Watchman device in patients on hemodialysis: EPIC06-WATCH-HD.

Revista espanola de cardiologia (English ed.)·2021

Same journal

Segmentation of the parasagittal dura mater on multi-center 3D-FLAIR MRI.

NeuroImage·2026

Same journal

Spatial frequency channels implement a mental ruler in spatial vision.

NeuroImage·2026

Same journal

Exploring the Link Between Intravoxel Incoherent Motion Measured Brain Diffusivity During Wakefulness and Sleep Macrostructure in the Elderly.

NeuroImage·2026

Same journal

Closed-loop adaptation of transcranial magnetic stimulation intensity with electroencephalography feedback.

NeuroImage·2026

Same journal

Volumetric postmortem MRI of the medial temporal lobe in Alzheimer's disease and related disorders: methodological advances and implications for in vivo biomarker development.

NeuroImage·2026

Same journal

Neural responses to equity and inequity when receiving vicarious rewards for self and charity during adolescence.

NeuroImage·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Jun 21, 2026

Concurrent EEG and Functional MRI Recording and Integration Analysis for Dynamic Cortical Activity Imaging

Concurrent EEG and Functional MRI Recording and Integration Analysis for Dynamic Cortical Activity Imaging

Published on: June 30, 2018

Bayesian spatiotemporal model of fMRI data.

Alicia Quirós¹, Raquel Montes Diez, Dani Gamerman

¹Departamento de Estadística e Investigación Operativa, Universidad Rey Juan Carlos, C/Tulipan s/n, Mostoles, Madrid, Spain. alicia.quiros@urjc.es

|August 4, 2009

Summary

This summary is machine-generated.

This study introduces a novel Bayesian spatiotemporal model for analyzing functional magnetic resonance imaging (fMRI) data. The model enhances the analysis of brain activity by integrating temporal and spatial information for more accurate results.

More Related Videos

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

Analyzing Neural Activity and Connectivity Using Intracranial EEG Data with SPM Software

Analyzing Neural Activity and Connectivity Using Intracranial EEG Data with SPM Software

Published on: October 30, 2018

Related Experiment Videos

Last Updated: Jun 21, 2026

Concurrent EEG and Functional MRI Recording and Integration Analysis for Dynamic Cortical Activity Imaging

Concurrent EEG and Functional MRI Recording and Integration Analysis for Dynamic Cortical Activity Imaging

Published on: June 30, 2018

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

Analyzing Neural Activity and Connectivity Using Intracranial EEG Data with SPM Software

Analyzing Neural Activity and Connectivity Using Intracranial EEG Data with SPM Software

Published on: October 30, 2018

Area of Science:

Neuroimaging
Statistical Modeling
Biophysics

Background:

Functional magnetic resonance imaging (fMRI) is crucial for understanding brain function.
Analyzing block-design fMRI data requires robust statistical models to capture spatiotemporal dynamics.
Existing models may not fully capture the complex interplay between spatial contiguity and temporal signal characteristics.

Purpose of the Study:

To develop a novel Bayesian spatiotemporal model for block-design BOLD fMRI studies.
To parameterize the hemodynamic response function (HRF) shape and spatial activation characteristics.
To create an interpretable model with a reduced number of parameters.

Main Methods:

A Bayesian spatiotemporal model was developed.
The temporal dimension utilized a parameterized hemodynamic response function (HRF) with signal increase and exponential decay.
The spatial dimension employed Gaussian Markov random fields (GMRF) priors for activation parameters, enforcing spatial contiguity and homogeneity.

Main Results:

The model demonstrated effective parameter estimation and sampling scheme performance in simulations.
Model sensitivity to signal-to-noise ratio was assessed.
The model was validated on both synthetic and real block-design fMRI data.

Conclusions:

The proposed Bayesian spatiotemporal model offers a powerful and interpretable approach for analyzing fMRI data.
The integration of GMRF priors effectively incorporates prior knowledge about spatial activation patterns.
The model shows promise for advancing the analysis of brain activity in neuroimaging research.