Abstract / Summary
Abstract The thalamus is critical relay hub of brain connectivity, yet its topographical organization during the early human brain development remains incompletely understood. Our study characterized the critical perinatal developmental period by optimizing and applying a clustering framework to parcellate the neonatal thalamus and delineate its developmental trajectory. Thalamus topography was studied using Gaussian Mixture Model (GMM) based clustering applied to voxel-level fiber orientation distribution functions, obtained from diffusion MRI data. Three perinatal datasets were used: term-born infants from developing human connectome project (dHCP, n = 68, age at MRI: 37.4–44.7 weeks), dHCP preterm infant data ( n = 48, 29.3–37.4 weeks) and a neonatal control dataset from Zürich ( n = 42, 39.0-48.7 weeks). Clustering was optimized using an adult test-retest diffusion MRI dataset ( n = 30). We subdivided the thalamus into seven clusters, where GMM showed moderate reproducibility (test-retest overlap ranged from 0.65 to 0.73), and comparable anatomical alignment with digital Morel atlas. Robust regression analysis revealed significant age-related volume increases in absolute volumes across thalamic clusters (FDR-corrected p < 0.001). Topography by means of relative volumes remained stable, suggesting preservation of thalamic topography during late gestation. Two clusters, closely corresponding to the ventral-anterior nucleus and pulvinar group, showed moderate age-related topographical changes (FDR-corrected p < 0.05, adjusted R²=0.039 and 0.101). Group-wise analysis of harmonized volumes revealed no significant group effects between preterm and term born neonates. No sex differences were observed. Our findings demonstrate that thalamic topography is largely established across the perinatal period; however, the lack of systematic term-equivalent scans in preterm infants limits our ability to draw conclusions about the effects of prematurity. The interpretation might be limited by the indirect nature of diffusion MRI features and the moderate reproducibility of the clustering techniques.