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Physiological basis of muscle functional MRI: predictions using a computer model.

Bruce M Damon1, John C Gore

  • 1Dept. of Radiology and Radiological Sciences, Vanderbilt University, 1161 21st Ave S., CCC-1121, Nashville, TN 37232-2675, USA. bruce.damon@vanderbilt.edu

Journal of Applied Physiology (Bethesda, Md. : 1985)
|August 31, 2004
PubMed
Summary

This study developed a computer model to understand muscle functional MRI (mfMRI) signals during exercise. The model predicts that blood oxygenation and creatine kinase reactions dominate early signals, while glycolysis influences later responses.

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Area of Science:

  • Physiology
  • Biophysics
  • Medical Imaging

Background:

  • Muscle functional MRI (mfMRI) offers noninvasive measurement of muscle metabolic and hemodynamic responses.
  • The theoretical underpinnings and contributing factors to mfMRI signal changes during muscle activation are not fully understood.
  • Isolating individual physiological variables influencing mfMRI signal magnitude and temporal patterns presents a significant challenge.

Purpose of the Study:

  • To develop a computational model simulating the effects of physiological changes during exercise on the mfMRI signal intensity time course.
  • To predict the individual contributions of various physiological factors to the mfMRI response.
  • To validate the model against existing experimental mfMRI data for human anterior tibialis muscle isometric contractions.

Main Methods:

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  • A detailed computational model of a muscle (39,204 fibers) was created, reflecting human anterior tibialis muscle properties.
  • Simulations included isometric contractions at 25% and 40% maximum voluntary contraction, incorporating vascular and metabolic responses.
  • The model estimated effects on MRI signal transverse relaxation and measured the mfMRI signal intensity time course from simulated images.

Main Results:

  • The model's simulated mfMRI data demonstrated good qualitative agreement with published experimental data.
  • Quantitative agreement between the model and experimental data was achieved, particularly for longer exercise durations.
  • The model predicted that early mfMRI signals (up to ~45s) are dominated by NMR relaxation effects from blood volume/oxygenation changes and the creatine kinase reaction.

Conclusions:

  • The developed computational model provides a valuable tool for understanding the physiological basis of mfMRI signals.
  • The model predicts distinct contributions of metabolic and hemodynamic factors to the mfMRI signal over time.
  • Glycolysis emerges as a primary contributor to the mfMRI signal at later exercise durations, following initial responses driven by oxygenation and creatine kinase activity.