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

Estimation of general linear model coefficients for real-time application.

E Bagarinao1, K Matsuo, T Nakai

  • 1Life Electronics Research Laboratory, National Institute of Advanced Industrial Science and Technology, Osaka, Japan. baggy@ni.aist.go.jp

Neuroimage
|June 20, 2003
PubMed
Summary

This study introduces an efficient algorithm for analyzing functional magnetic resonance imaging (fMRI) data using general linear models (GLM). The novel method enhances real-time estimation and reduces memory usage in fMRI analysis.

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

Involvement of the Thalamus, Hippocampus, and Brainstem in Hypsarrhythmia of West Syndrome: Simultaneous Recordings of Electroencephalography and fMRI Study.

AJNR. American journal of neuroradiology·2022
Same author

Enabling on-demand real-time functional MRI analysis using grid technology.

Methods of information in medicine·2006
Same author

Reconstructing bifurcation diagrams from noisy time series using nonlinear autoregressive models.

Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics·2002
Same author

Variability of the electric organ discharge interval duration in resting Gymnotus carapo.

Biological cybernetics·2001
Same author

Reconstructing bifurcation diagrams of dynamical systems using measured time series.

Methods of information in medicine·2000
Same author

Brainstem topology of the vagal motoneurons projecting to the esophagus and stomach in the house musk shrew, Suncus murinus.

Journal of the autonomic nervous system·1998

Area of Science:

  • Neuroimaging
  • Computational Neuroscience
  • Statistical Modeling

Background:

  • Functional magnetic resonance imaging (fMRI) relies on general linear models (GLM) for data analysis.
  • Estimating GLM coefficients in fMRI can be computationally intensive, limiting real-time applications.
  • Existing methods may require significant memory resources for storing image data.

Purpose of the Study:

  • To develop an efficient algorithm for estimating GLM coefficients in fMRI.
  • To enable real-time analysis of fMRI data.
  • To reduce memory requirements during fMRI data processing.

Main Methods:

  • Utilized the Gram-Schmidt orthogonalization procedure to convert GLM basis functions into orthogonal functions.
  • Estimated auxiliary coefficients using the orthogonality condition.

Related Experiment Videos

  • Employed Cholesky decomposition for computational efficiency, achieving a twofold speed increase over standard recursive least-squares methods.

    • Main Results:

    • The algorithm successfully estimates GLM coefficients for fMRI data.
    • The orthogonalization approach allows for real-time updates as new image data become available.
    • Memory requirements are minimized as data does not need to be stored during estimation.

    Conclusions:

    • The described algorithm offers a computationally efficient and memory-saving approach for fMRI data analysis.
    • Its real-time estimation capabilities make it suitable for dynamic neuroimaging studies.
    • This method has the potential to advance the field of real-time fMRI analysis.