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

Classical and Bayesian inference in neuroimaging: applications.

K J Friston1, D E Glaser, R N A Henson

  • 1The Wellcome Department of Cognitive Neurology and The Institute of Cognitive Neuroscience, University College London, Queen Square, London, WC1N 3BG, United Kingdom.

Neuroimage
|May 29, 2002
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

DNA tests for ancestry and phenotype inference applied to the UK Metropolitan Police Operation Minstead: The investigation of serial sex offender Delroy Grant.

Forensic science international. Genetics·2026
Same author

Internal validation of the fully continuous model in EuroForMix for its implementation in routine forensic DNA profiling.

Forensic science international. Genetics·2026
Same author

A biofidelic mock residual limb for prosthetic socket testing.

Canadian prosthetics & orthotics journal·2025
Same author

Prosthetic treatment of women with lower limb absence during pregnancy & the postpartum period: A chart review.

Canadian prosthetics & orthotics journal·2025
Same author

Development of a social vulnerability index: Enhancing approaches to support climate justice.

MethodsX·2025
Same author

Evaluating the effect of marker panel sizes on estimation of bio-geographical co-ancestry proportions.

Forensic science international. Genetics·2025
Same journal

Investigating the Neural Origins of Ear-EEG: A Correlation Study Using Scalp EEG Source Reconstruction.

NeuroImage·2026
Same journal

Hysteresis effects in visual and auditory perception and the comparison of underlying neural mechanisms - an EEG study.

NeuroImage·2026
Same journal

Short-term audio-tactile training affects cortical auditory speech-envelope tracking for incongruent but not congruent stimuli.

NeuroImage·2026
Same journal

Dissociable Neurocognitive Mechanisms of State and Trait Anxiety in Working Memory: Threat-Induced Alterations in Decision Dynamics and Attenuation of Large-Scale Network Reconfiguration.

NeuroImage·2026
Same journal

Neuro-Ocular Amyloid Characterization in Alzheimer's Disease via Cross-Site PET-MRI and Hierarchical Cross-Attention Driven Multimodal Representation Learning.

NeuroImage·2026
Same journal

Whole-brain network dynamics underlying intolerance of uncertainty.

NeuroImage·2026
See all related articles

Empirical Bayes offers a powerful framework for hierarchical models in neuroimaging. This approach enhances statistical inference in fMRI and PET studies, improving precision and addressing limitations of classical methods.

Area of Science:

  • Neuroimaging and statistical modeling
  • Brain imaging analysis techniques

Background:

  • Introduced empirical Bayes for hierarchical models in Friston et al. (2002).
  • Classical and empirical Bayesian approaches can be framed as covariance component estimation.
  • Hierarchical models are crucial for multisubject neuroimaging studies.

Purpose of the Study:

  • To present diverse models within the empirical Bayesian framework for hierarchical data.
  • To illustrate the Expectation-Maximization (EM) algorithm for covariance component estimation in fMRI.
  • To explore parametric empirical Bayesian (PEB) estimators versus classical maximum likelihood (ML) estimates.

Main Methods:

  • Application of the Expectation-Maximization (EM) algorithm for covariance component estimation.
  • Development of parametric empirical Bayesian (PEB) estimators.

Related Experiment Videos

  • Bayesian inference at the within-voxel level using PET data to derive priors from between-voxel activation distributions.

    • Main Results:

    • PEB estimators provide distinct advantages over ML estimates, particularly in estimating intersubject variability and understanding fixed- vs. random-effect analyses.
    • Bayesian inference demonstrates improved specificity and sensitivity.
    • Posterior probability maps (PPMs) derived from voxel-level Bayesian inference show enhanced anatomical precision and validity, overcoming multiple comparison issues.

    Conclusions:

    • Empirical Bayesian approaches offer a robust and flexible framework for neuroimaging data analysis.
    • PEB estimators and voxel-level Bayesian inference provide significant improvements over classical methods.
    • Bayesian methods enhance the precision and interpretability of neuroimaging results.