arXiv subjects

Ruixi Zheng

Publications and source records attributed to Ruixi Zheng.

2 recordsLinked to original sources

arXiv2025

Cross-Population White Matter Atlas Creation for Concurrent Mapping of Brain Connections in Neonates and Adults with Diffusion MRI Tractography

Comparing white matter (WM) connections between adults and neonates using diffusion MRI (dMRI) can advance our understanding of typical brain development and potential biomarkers for neurological disorders. However, existing WM atlases are population-specific (adult or neonatal) and reside in separate spaces, preventing direct cross-population comparisons. A unified WM atlas spanning both neonates and adults is still lacking. In this study, we propose a neonatal/adult brain atlas (NABA), a WM tractography atlas built from dMRI data of both neonates and adults. NABA is constructed using a robust, data-driven fiber clustering pipeline, enabling group-wise WM atlasing across populations despite substantial anatomical variability. The atlas provides a standardized template for WM parcellation, allowing direct comparison of WM tracts between neonates and adults. Using NABA, we conduct four analyses: (1) evaluating the feasibility of joint WM mapping across populations, (2) characterizing WM development across neonatal ages relative to adults, (3) assessing sex-related differences in neonatal WM development, and (4) examining the effects of preterm birth. Our results show that NABA robustly identifies WM tracts in both populations. We observe rapid fractional anisotropy (FA) development in long-range association tracts, including the arcuate fasciculus and superior longitudinal fasciculus II, whereas intra-cerebellar tracts develop more slowly. Neonatal females exhibit faster overall FA development than males. Although preterm neonates show lower overall FA development rates, they demonstrate relatively higher FA growth in specific tracts, including the corticospinal tract, corona radiata-pontine pathway, and intracerebellar tracts. These findings demonstrate that NABA is a useful tool for investigating WM development across neonates and adults.

cs.NE↗

arXiv2025

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

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.

q-bio.QM↗