Methods and Applications of Diffusion MRI Tractometry

Bramsh Qamar Chandio^1,2*Kurt G. Schilling³Julio Ernesto Villalon-Reina²

• ¹West Virginia University, Morgantown, United States
• ²University of Southern California, Los Angeles, United States
• ³Vanderbilt University Medical Center, Nashville, United States

Bosticardo et al. examined myelin-weighted structural connectivity across the lifespan, demonstrating that tractography-derived myelin measures capture characteristic phases of maturation and degeneration that unfold along specific pathways. Their work illustrates how myelin-sensitive tractometry can complement dMRI-based metrics and enhance our understanding of age-related white-matter trajectories.Weber et al. used fMRI-guided diffusion MRI tractography to map white matter changes in autism across development. They found early reductions in callosal and periventricular tracts in infants, expanding to widespread disruptions in adolescents and adults. The study highlights edge-density mapping as a sensitive tool for early detection and longitudinal tracking of ASD-related network alterations. Meisler et al. provided a practical guide for integrating functional regions of interest with tractography-defined pathways, enabling the creation of functionally informed sub-bundles. This work facilitates multimodal tractometry pipelines that link white-matter architecture with functional specialization.Persson et al. addressed streamline redundancy in tractography by proposing a framework to estimate and reduce excessive or overlapping streamlines. By improving anatomical specificity and computational efficiency, their method supports more stable and interpretable tract-level metrics.Yang et al. reviewed diffusion-tensor methods in small-vessel disease and highlighted the limitations of traditional voxel-based and region-of-interest (ROI) approaches. They emphasized the need for tract-specific methods that better localize cerebrovascular injury along affected pathways.Hernandez-Gutierrez et al. evaluated multi-tensor fixel-based metrics and demonstrated improved robustness in crossing-fiber regions, particularly in multiple sclerosis. Their tractometry pipeline showed enhanced sensitivity to lesion-related abnormalities and illustrates the advantages of richer microstructural modeling.Quizhpilemat et al. investigated amyotrophic lateral sclerosis and revealed asymmetric degeneration extending beyond classic motor pathways. Their along-tract analyses reinforce the concept of amyotrophic lateral sclerosis (ALS) as a network-level disorder rather than a purely motor disease.Behroozi et al. highlighted the role of ex-vivo diffusion imaging in large-animal models as a translational bridge between histology and human research. By outlining how ex-vivo data can validate microstructural interpretations, their work supports the biological grounding of tractometry measures. Witt et al. extended tractometry to the spinal cord and showed that profiling diffusion and macrostructural features across cervical levels increases sensitivity to localized pathology in multiple sclerosis. Their findings demonstrate that the along-tract concept can be meaningfully applied beyond the brain.From this special issue, we see that tractometry has evolved into a broad and integrative framework encompassing connectome-wide analyses of both long-range and short association pathways, including functionally defined, non-human, and spinal cord applications. Across the lifespan and in diverse conditions, from cognition and multiple sclerosis to autism, small vessel disease and gliomas, tractometry enables precise mapping of white matter microstructure using diffusion-based, fixel-based, and connectomic measures. Recent advances extend beyond traditional DTI and tract-averaged analyses, to incorporating myelin-sensitive metrics, morphometry, and differential tractography. Together, these developments highlight a growing ecosystem of tractometry tools that bridge structure, function, and pathology across species and systems, advancing whole-brain, circuit-level understanding of the human and nonhuman connectome.