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 Experiment Videos

Variational Bayesian inference for fMRI time series.

Will Penny1, Stefan Kiebel, Karl Friston

  • 1Wellcome Department of Imaging Neuroscience, University College, London WC1N 3BG, UK. wpenny,karl@fil.ion.ucl.ac.uk

Neuroimage
|July 26, 2003
PubMed
Summary
This summary is machine-generated.

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

Scale-invariant evolution: Comment on "homo informatio" by Michael Walker.

Physics of life reviews·2026
Same author

The body does not keep the score: trauma, predictive coding, and the restoration of metastability.

Frontiers in systems neuroscience·2026
Same author

The dysconnection hypothesis of schizophrenia: a 30-year update.

World psychiatry : official journal of the World Psychiatric Association (WPA)·2026
Same author

The methodological foundations of lesion network mapping remain sound.

bioRxiv : the preprint server for biology·2026
Same author

Insula Structure Is Linked to Autonomic Cardiac Dysregulation in Depression.

Biological psychiatry·2026
Same author

Correction: Dynamic causal models in infectious disease epidemiology-an assessment of their predictive validity based on the COVID-19 epidemic in the UK 2020 to 2024.

Frontiers in public health·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
Same journal

Cognitive Strategy-based neuromodulation optimizes neural communication to improve working memory.

NeuroImage·2026
See all related articles

This study introduces a Bayesian method for analyzing fMRI time series using General Linear Models with Autoregressive (AR) errors. The Variational Bayesian (VB) framework offers efficient estimation and automatic AR order selection for fMRI data.

Area of Science:

  • Neuroimaging
  • Statistical modeling
  • Computational neuroscience

Background:

  • Functional magnetic resonance imaging (fMRI) generates complex time series data.
  • Accurate statistical modeling is crucial for interpreting fMRI results.
  • Traditional methods may not fully capture the temporal dependencies in fMRI signals.

Purpose of the Study:

  • To present a novel Bayesian estimation and inference procedure for fMRI time series.
  • To utilize the Variational Bayesian (VB) framework for improved analysis.
  • To enable automatic selection of the Autoregressive (AR) error process order.

Main Methods:

  • Bayesian estimation and inference for fMRI time series.
  • General Linear Models (GLM) incorporating Autoregressive (AR) error processes.

Related Experiment Videos

  • Variational Bayesian (VB) approximation of posterior densities, validated with Gibbs sampling.

    • Main Results:

    • The VB approach effectively approximates true posterior densities.
    • VB accounts for hyperparameter variability with minimal computational overhead.
    • Automatic selection of the AR process order was achieved.
    • The method demonstrated successful application on simulated and real fMRI data.

    Conclusions:

    • The proposed VB framework provides an advanced Bayesian approach for fMRI time series analysis.
    • This method offers advantages over Empirical Bayes, including computational efficiency and automatic model order selection.
    • The technique is robust and applicable to event-related fMRI experiments.