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

Multigrid priors for a Bayesian approach to fMRI.

Selene da Rocha Amaral1, Said R Rabbani, Nestor Caticha

  • 1Instituto de Física, Universidade de São Paulo, São Paulo, SP, Brazil.

Neuroimage
|October 19, 2004
PubMed
Summary

This study introduces a Bayesian-inspired method using multigrid priors to assess brain activity in functional magnetic resonance imaging (fMRI). The novel approach improves the robustness and accuracy of fMRI analysis, even with reduced data quality.

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

Legislative rebellions and impeachments in a neural network society.

Physical review. E·2024
Same author

Entropic Dynamics in Neural Networks, the Renormalization Group and the Hamilton-Jacobi-Bellman Equation.

Entropy (Basel, Switzerland)·2020
Same author

A Data-Driven Multi-scale Technique for fMRI Mapping of the Human Somatosensory Cortex.

Brain topography·2019
Same author

Normal aging in human lumbar discs: An ultrastructural comparison.

PloS one·2019
Same author

Exploration and recency as the main proximate causes of probability matching: a reinforcement learning analysis.

Scientific reports·2017
Same author

Backward renormalization-group inference of cortical dipole sources and neural connectivity efficacy.

Physical review. E·2017

Area of Science:

  • Neuroimaging
  • Computational Neuroscience
  • Statistical Modeling

Background:

  • Functional magnetic resonance imaging (fMRI) is crucial for understanding brain activity.
  • Accurate assessment of brain activity in fMRI data can be challenging due to noise and data limitations.
  • Existing methods may lack robustness under conditions of information loss.

Purpose of the Study:

  • To develop a novel Bayesian-inspired method for assessing brain activity in fMRI data.
  • To introduce multigrid priors to enhance the analysis of fMRI signals.
  • To evaluate the robustness of the proposed method against information loss and varying signal-to-noise ratios.

Main Methods:

  • Construction of a sequence of different scale grids over the fMRI image.

Related Experiment Videos

  • Sequential definition of coarse-grained data variables and posterior probabilities across scales.
  • Bayesian inference where posterior probabilities from coarser scales inform priors for finer scales.
  • Utilizing a linear model with a hemodynamic response function for likelihood construction.
  • Application to both simulated and real fMRI data from a boxcar experiment.

    • Main Results:

    • The method demonstrates robust performance in assessing brain activity.
    • Receiver operating characteristic (ROC) curves were used to analyze performance based on thresholds and hyperparameters.
    • The study quantified the method's performance under simulated information loss, including reduced image counts and signal-to-noise ratios.
    • Comparison of robustness against other existing methods was performed.

    Conclusions:

    • The proposed multigrid prior method offers a robust approach for brain activity assessment in fMRI.
    • The Bayesian-inspired framework effectively handles varying data quality and information loss.
    • This method has potential for improving the reliability of neuroimaging analyses in challenging conditions.