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

Cluster analysis of muscle functional MRI data.

Bruce M Damon1, Danielle M Wigmore, Zhaohua Ding

  • 1Department of Radiology and Radiological Sciences, Vanderbilt University, Nashville, TN 37232-2675, USA. bruce.damon@vanderbilt.edu

Journal of Applied Physiology (Bethesda, Md. : 1985)
|May 27, 2003
PubMed
Summary

This study introduces a novel cluster analysis method for muscle functional MRI (mfMRI) data. This technique reveals functional muscle compartments more effectively than traditional analysis methods.

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

Non-invasive detection of local microstructural damage in tendon using Diffusion Tensor MRI.

Acta biomaterialia·2026
Same author

The effect of microstructural variations in tendon and ligament on diffusion tensor MRI.

bioRxiv : the preprint server for biology·2026
Same author

Correction: Ultrashort-T2* mapping at 7 tesla using an optimized pointwise encoding time reduction with radial acquisition (PETRA) sequence at standard and extended echo times.

PloS one·2026
Same author

Study protocol for the Champaign-Urbana population study.

Frontiers in neuroimaging·2026
Same author

MRI-based 3D estimation of skeletal muscle architecture and strain during contraction.

Journal of biomechanics·2025
Same author

A Comparison of Skeletal Muscle Diffusion Tensor Imaging Tractography Seeding Methods.

NMR in biomedicine·2025

Area of Science:

  • Physiology
  • Biomedical Engineering
  • Imaging Science

Background:

  • Muscle functional magnetic resonance imaging (mfMRI) is crucial for understanding muscle activation during exercise.
  • Heterogeneity in signal intensity (SI) within synergistic muscles complicates mfMRI data interpretation.
  • Existing methods struggle to resolve fine-scale functional differences within muscle groups.

Purpose of the Study:

  • To develop and validate a novel cluster analysis algorithm for organizing heterogeneous mfMRI data.
  • To apply this algorithm to identify functional compartmentalization in the anterior leg muscles.
  • To compare the efficacy of cluster analysis with traditional mfMRI analysis techniques.

Main Methods:

  • A new algorithm was developed to group voxels with similar SI time courses.

Related Experiment Videos

  • The algorithm was tested using simulated datasets with known SI variations.
  • The method was applied to mfMRI data from six male subjects performing isometric dorsiflexion contractions.

    • Main Results:

    • Cluster analysis successfully identified functional distinctions not apparent with traditional methods.
    • Clusters generally corresponded to regions with a contrast-to-noise ratio (CNR) exceeding 5.
    • The novel method demonstrated greater sensitivity in detecting muscle functional compartmentalization.

    Conclusions:

    • Cluster analysis, utilizing the full SI time course, offers enhanced sensitivity for muscle functional compartmentalization.
    • This approach improves the understanding of spatial patterns of muscle involvement during exercise.
    • The developed algorithm provides a more refined tool for mfMRI data analysis in human physiology studies.