Plausibility Tracking: a method to evaluate anatomical connectivity and microstructural properties along fiber...

Area of Science:

• Neuroimaging
• Diffusion MRI
• White Matter Tractography

Background:

• Diffusion MRI provides non-invasive insights into brain white matter structure, axonal properties, and connection pathways.
• Existing tractography methods face challenges with local noise and error propagation, limiting detailed analysis.

Purpose of the Study:

• To introduce Plausibility Tracking, a novel global tractography method for generating plausible white matter pathways.
• To develop a framework for assessing diffusion-derived tissue properties using advanced, bundle-specific indices.

Main Methods:

• Plausibility Tracking models brain pathways as smooth spline curves, optimizing globally using fiber orientation density functions.
• The method combines probabilistic tractography's connectivity patterns with globally optimized smooth tracks.
• A framework for assessing diffusion MRI properties includes atlas-guided parameterization and bundle-specific indices (fiber density, spread, complexity).

Main Results:

• Plausibility Tracking demonstrated robustness against local noise and error propagation on phantom and biological data.
• The method provides reliable local directions along fiber pathways, suitable for direction-dependent diffusion MRI indices.
• Analysis of real data revealed more specific interpretations of white matter microstructure compared to traditional diffusion tensor indices.

Conclusions:

• Plausibility Tracking offers a robust and accurate global tractography approach for diffusion MRI.
• The developed framework enables detailed, bundle-specific assessment of white matter microstructure.
• This method enhances the interpretation of diffusion MRI data for understanding brain connectivity and tissue properties.