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

Assessing brain activity through spatial Bayesian variable selection.

Michael Smith1, Benno Pütz, Dorothee Auer

  • 1University of Sydney, Sydney, Australia. mikes@econ.usyd.edu.au

Neuroimage
|October 22, 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

Randomized, double-blind, sham-controlled pilot trial of theta-band transcranial alternating current stimulation during cognitive training in mild Alzheimer's disease.

Translational psychiatry·2026
Same author

Suicide attempt history, childhood trauma, and functional brain network alterations in major depressive disorder: a resting-state functional connectivity-based multivariate pattern analysis.

Neuropsychopharmacology : official publication of the American College of Neuropsychopharmacology·2026
Same author

Algebraic methods and computational strategies for pseudoinverse-based MR image reconstruction (Pinv-Recon).

Scientific reports·2025
Same author

NSAID ingestion augments training-induced muscle hypertrophy and differentially affects muscle mRNA expression, but not strength gains, in trained men.

The Journal of physiology·2025
Same author

Childhood trauma and frontal-limbic network abnormalities in major depressive disorder: Resting-state functional connectivity and brain network analysis.

Journal of affective disorders·2025
Same author

Predictive survival modelings for HIV-related cryptococcosis: comparing machine learning approaches.

Frontiers in cellular and infection microbiology·2025
Same journal

Lifespan Trajectories of the Brain's Functional Complexity Characterized by Multiscale Sample Entropy.

NeuroImage·2026
Same journal

Pleasant fragrance modulates dyadic social sharing of positive emotion: Sharer-centered socioemotional enhancement effect and its neural couplings.

NeuroImage·2026
Same journal

Altered Functional Hierarchical and Sequential Organization in Individuals with Schizophrenia during Auditory Processing.

NeuroImage·2026
Same journal

Mechanical Deformation Explains Distinct Neuroimaging Patterns and Etiologies in Brain Trauma.

NeuroImage·2026
Same journal

Ventral striatum temporal interference brain stimulation enhances the reward-positivity event-related potential and reduces anxiety.

NeuroImage·2026
Same journal

NeuroHarm‑Kit: An Open‑Source Toolbox for Benchmarking Deep‑Learning Harmonization of Multi‑Site T1‑Weighted MRI.

NeuroImage·2026
See all related articles

This study introduces a novel Bayesian approach for analyzing functional magnetic resonance imaging (fMRI) data, improving the detection of brain activity and preserving structural details. The new method enhances sensitivity and spatial accuracy in brain imaging analysis.

Area of Science:

  • Neuroimaging
  • Statistical modeling
  • Computational neuroscience

Background:

  • Statistical Parametric Mapping (SPM) is a standard frequentist method for fMRI analysis.
  • Friston et al. highlighted limitations of frequentist approaches and suggested Bayesian methods for brain activity assessment.
  • Bayesian formulations offer explicit modeling and estimation of brain activation probabilities.

Purpose of the Study:

  • To develop a novel regression-based approach using spatial Bayesian variable selection for fMRI data analysis.
  • To address limitations in current brain activity assessment methods.
  • To improve the modeling of activation probabilities and amplitudes in fMRI.

Main Methods:

  • A new regression-based approach employing spatial Bayesian variable selection was developed.

Related Experiment Videos

  • Spatial correlation is directly modeled for activation probabilities and indirectly for amplitudes.
  • Anatomical prior information, including grey matter distribution and expert knowledge, is incorporated into the model.

    • Main Results:

    • The developed method demonstrates superior edge-preservation properties and computational speed.
    • Improved sensitivity in detecting activated cortical areas was observed.
    • Enhanced preservation of details within activated brain structures was achieved.

    Conclusions:

    • The novel spatial Bayesian variable selection method offers significant advantages over traditional frequentist approaches for fMRI analysis.
    • This approach allows for direct modeling of spatial correlation and integration of anatomical priors.
    • The method provides more sensitive and detailed insights into human brain activity patterns.