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

TL;DR

The study develops NABA, a cross-population white matter atlas derived from neonatal and adult diffusion MRI datasets to enable concurrent tract mapping and direct neonate–adult comparisons. Using a data-driven fiber clustering pipeline, groupwise registration, and ORG-based labeling, NABA defines 78 major WM tracts applicable across populations and allows extraction of FA and NoS metrics. The analyses reveal that neonates exhibit rapid FA development in long-range association tracts (e.g., arcuate and SLF II) with sex effects favoring faster maturation in females, while preterm birth generally slows overall FA development but accelerates growth in select motor pathways, demonstrating NABA’s utility for tract-wise developmental investigations. The atlas thus provides a standardized framework for cross-population WM studies and has potential to inform typical development and biomarkers in neurological disorders, with future work extending to additional age ranges and diffusion metrics.

Abstract

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.

Figures (12)

• Figure 1: Method Overview. (a) First, the dMRI data of 490 dHCP and 1065 HCP-YA subjects are preprocessed, and tractography is performed. Next, the anatomical atlas is constructed. (b) A total of 100 subjects with balanced age and sex are selected from each dataset. Their tractography is downsampled to the same level. (c) Their tractography data is combined and processed using fiber clustering. Anatomical labels are then assigned to each fiber cluster to define white matter tracts. Different clusters are color-coded for visualization, and those having same anatomical label are grouped together to anatomical tracts. (d) Then, subject-specific tract identification is performed for all individuals using the constructed atlas. Finally, statistical analysis of white matter tracts is conducted across different subject groups.
• Figure 2: IR comparison between NABA atlas and ORG atlas on dHCP and HCP-YA.
• Figure 3: Visualization of the tract parcellation results with the NABA atlas and ORG atlas. Six example tracts are visualized. For each tract, the parcellation results in three dHCP subjects (left panel) and three HCP-YA subjects (right panel) are visualized.
• Figure 4: Rate of change with age (beta coefficient) of FA in each of the dHCP and HCP-YA populations. The two groups are distinguished using different marker styles for the bars. Tracts of different categories are color-coded. The upper right corner displays the rate of change at the category level for both populations.
• Figure 5: Changes in FA with increasing age in the AF, SLF II, and Intra-CBLM tracts.