AGFS-Tractometry: A Novel Atlas-Guided Fine-Scale Tractometry Approach for Enhanced Along-Tract Group Statistical Comparison Using Diffusion MRI Tractography

2025-07-12

AI Summary

To address insufficient sensitivity and specificity in detecting local differences during tract-wise statistical comparisons of white matter fiber bundles, this paper introduces AGFS-Tractometry: an atlas-guided, fine-scale tractometry framework. It constructs a reproducible, high-resolution along-tract parcellation template by integrating atlas-guided registration, sub-bundle-level segmentation, nonparametric statistical inference via permutation testing, and rigorous multiple-comparison correction. Compared to AFQ and BUAN, AGFS-Tractometry significantly improves detection power on both synthetic and real diffusion MRI data. It identifies a greater number of anatomically plausible, focal microstructural differences—particularly those reflecting subtle white matter alterations—enabling precise localization and quantification of such changes.

Technology Category

• Humans and AI: Brain-Sensing and Analysis
• Computer Vision: Motion & Tracking
• Multiagent Systems: Teamwork

Application Category

• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs
• Web Mining and Content Analysis: Content-based information diffusion
• Semantics and Knowledge: Scalable techniques for the creation, curation, publication, maintenance, and consumption of large, Web-based, structured, reusable, knowledge graphs and ontologies

Abstract

Diffusion MRI (dMRI) tractography is currently the only method for in vivo mapping of the brain's white matter (WM) connections. Tractometry is an advanced tractography analysis technique for along-tract profiling to investigate the morphology and microstructural properties along the fiber tracts. Tractometry has become an essential tool for studying local along-tract differences between different populations (e.g., health vs disease). In this study, we propose a novel atlas-guided fine-scale tractometry method, namely AGFS-Tractometry, that leverages tract spatial information and permutation testing to enhance the along-tract statistical analysis between populations. There are two major contributions in AGFS-Tractometry. First, we create a novel atlas-guided tract profiling template that enables consistent, fine-scale, along-tract parcellation of subject-specific fiber tracts. Second, we propose a novel nonparametric permutation testing group comparison method to enable simultaneous analysis across all along-tract parcels while correcting for multiple comparisons. We perform experimental evaluations on synthetic datasets with known group differences and in vivo real data. We compare AGFS-Tractometry with two state-of-the-art tractometry methods, including Automated Fiber-tract Quantification (AFQ) and BUndle ANalytics (BUAN). Our results show that the proposed AGFS-Tractometry obtains enhanced sensitivity and specificity in detecting local WM differences. In the real data analysis experiments, AGFS-Tractometry can identify more regions with significant differences, which are anatomically consistent with the existing literature. Overall, these demonstrate the ability of AGFS-Tractometry to detect subtle or spatially localized WM group-level differences. The created tract profiling template and related code are available at: https://github.com/ZhengRuixi/AGFS-Tractometry.git.

Problem

• Enhances along-tract statistical analysis of white matter using dMRI.
• Improves detection of local white matter differences between populations.
• Provides consistent fine-scale parcellation of subject-specific fiber tracts.

Innovation

• Atlas-guided fine-scale tract profiling template
• Nonparametric permutation testing group comparison
• Enhanced sensitivity in detecting WM differences