Graph-Based White Matter Tractometry: Methods, Applications, and the Path to Validation

Neuroinformatics. 2026 Jul 24;24(3):49. doi: 10.1007/s12021-026-09804-2.

ABSTRACT

Graph-based white matter tractometry represents bundles as networks, preserving spatial topology that traditional along-tract profiles collapse. This enables the detection of distributed pathology patterns organized across connected regions. The field is at a critical juncture: methods are proliferating rapidly, but validation infrastructure lags. We systematically assess maturity across the analytical pipeline, from diffusion measurements through graph construction, detection models, clinical applications, and validation resources. Diffusion metrics measure water behavior rather than directly measuring tissue microstructure. Their biological interpretation relies on model assumptions that have been validated only in restricted contexts. Tractography provides spatial scaffolding with known limitations, and graph-construction choices encode implicit hypotheses about the organization of pathology but are often underspecified. Clinical studies demonstrate consistent group differences across disorders. Where controlled comparisons exist, graph methods show modest improvements over traditional approaches. However, comprehensive benchmarking against TBSS and AFQ is absent. Dedicated validation platforms for tractometry detection models do not yet exist. We document concrete barriers to systematic validation and assess emerging infrastructure addressing these gaps. Graph-based tractometry shows promise as a research tool. Realizing its clinical potential requires validation matching the maturity of traditional approaches.

PMID:42496798 | DOI:10.1007/s12021-026-09804-2