Streamline-Based Analysis: A novel framework for tractogram-driven streamline-wise statistical inference

Area of Science:

• Neuroimaging
• Diffusion MRI
• White Matter Tractography

Background:

• Current diffusion MRI statistical methods struggle with high-resolution, whole-brain analysis of white matter tracts.
• Existing frameworks lack simultaneous pathway-level localization, broad coverage, and robust statistical power with error control.

Purpose of the Study:

• Introduce Streamline-Based Analysis (SBA), a novel framework for statistical inference directly on individual tractography streamlines.
• Address limitations in current diffusion MRI statistical analysis for white matter studies.

Main Methods:

• SBA performs statistical inference directly on individual tractography streamlines.
• Utilizes streamline similarity for smoothing and statistical enhancement.
• Employs a non-parametric permutation testing framework with family-wise error control.

Main Results:

• SBA successfully recapitulated known patterns in healthy aging compared to Fixel-Based Analysis.
• Identified novel, previously unreported effects in white matter.
• Produced more spatially coherent, pathway-level effects with enhanced anatomical interpretability.

Conclusions:

• SBA provides pathway-level specificity, whole-brain coverage, and family-wise error control.
• Fills a critical analytical gap between voxel-, fixel-, and tract-level methods.
• Offers a flexible framework for detecting white matter changes in diverse diffusion MRI studies.