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Interactions between adjacent fibers in a cardiac muscle bundle
S Wang1, L J Leon, F A Roberge
1Institute of Biomedical Engineering, Ecole Polytechnique, Montréal, Québec, Canada.
Annals of Biomedical Engineering
|November 1, 1996
Summary
Cardiac muscle fiber arrangement significantly impacts electrical signal propagation. Close fiber proximity in uncoupled bundles slows signals, while coupling synchronizes electrical activity, influencing cardiac electrophysiology models.
Area of Science:
- Computational electrophysiology
- Biophysics of cardiac tissue
Background:
- Cardiac muscle electrical activity is complex, influenced by fiber arrangement and coupling.
- Understanding interstitial microstructural organization is key to modeling cardiac electrophysiology.
Purpose of the Study:
- To model cardiac muscle as a bundle of fibers to investigate the impact of interstitial microstructure on electrical propagation.
- To compare the behavior of coupled and uncoupled fiber bundles under varying fiber spacing.
Main Methods:
- A computational model of a cardiac muscle strand as a bundle of concentric cylindrical fibers.
- Simulation of electrical propagation in the presence and absence of transverse resistive coupling.
- Analysis of interstitial potential and action potential propagation velocity as a function of fiber spacing (d).
Main Results:
- In uncoupled bundles, reduced fiber spacing (d < 0.01 micron) significantly decreases central fiber velocity and increases interstitial potential.
- Surface fibers in uncoupled bundles are less sensitive to microstructural changes due to external volume conductor influence.
- Coupled bundles show less pronounced changes in velocity and interstitial potential with reduced spacing due to strong resistive coupling.
Conclusions:
- Fiber spacing and coupling significantly alter cardiac electrical propagation, particularly in uncoupled bundles.
- The model's behavior converges towards bidomain formulation as fiber spacing increases (d > 0.01 micron).
- These findings are crucial for developing accurate computational models of cardiac electrophysiology.