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arXiv · 2609.31058

Automatic sub-bundle tractometry reveals localized microstructural variations in complex white matter topographies

Nathan Decaux·Jean-Charles Roy·Thomas Desmidt·Marie-Laure Paillère Martinot·Gabriel Robert·Julie Coloigner
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Abstract

Conventional tractometry averages white matter microstructure into a single profile, masking intra-bundle spatial heterogeneity and diluting localized alterations. We propose an automatic sub-bundle tractometry framework using Fr{é}chet-based hierarchical clustering to decompose bundles into geometrically coherent sub-bundles, resolving complex fanning configurations without empirical thresholds. Multi-compartment metrics (fractional anisotropy, FA; isotropic free-water fraction, IFW) are projected onto these clusters. We evaluated this framework in 140 participants across early-life (ELD) and late-life (LLD) depression cohorts, alongside age-matched healthy controls. We assessed performance based on microstructural homogeneity, sensitivity to age, and clinical group differences. Proposed sub-bundle metrics were significantly more homogeneous than classical full bundle profiles (p < 0.001). Our approach showed enhanced sensitivity, detecting more age-correlated bundles in elderly controls (11 vs. 5) and more clinical group differences in LLD (FA: 6 vs. 4; IFW: 23 vs. 17) with larger effect sizes. In ELD, it improved the detection of subtle FA differences between remitted and resistant individuals (2.9% vs. 1.8%). Gains were particularly pronounced in LLD, reflecting the framework's ability to resolve fanning tracts susceptible to brain aging. By preserving spatial heterogeneity, this automatic decomposition provides a robust foundation for large-scale clinical tractometry.

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Nathan Decaux, Jean-Charles Roy, Thomas Desmidt, Marie-Laure Paillère Martinot, Gabriel Robert, Julie Coloigner. 2026-09-25. Automatic sub-bundle tractometry reveals localized microstructural variations in complex white matter topographies. https://arxiv.org/abs/2609.31058

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