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Mapping corpus callosum architecture: developmental, genetic, and cognitive correlates in youth

Siffredi, V.; Schmidt, L.; Hofmeister, R.; Patino Lopez, J.; Kutalik, Z.; Richiardi, J.

2026-04-28 neuroscience
10.64898/2026.04.25.720714 bioRxiv
Show abstract

The corpus callosum (CC), the largest interhemispheric white-matter tract, plays a central role in higher-order cognitive functions and is frequently altered in neurodevelopmental and psychiatric conditions. However, most diffusion MRI studies rely on tract-averaged measures, which obscure spatially specific microstructural variation along the tract that may hold biologically and functionally meaningful information. Leveraging multimodal data from the Philadelphia Neurodevelopmental Cohort (n=1342) and a functional data analysis approach, we characterised fine-grained spatial variation in callosal microstructure and its developmental, genetic, and behavioural correlates. Our findings revealed distinct midline-to-cortical variations of age-related diffusion metrics change across callosal subdivisions. Frontal and parietal heteromodal callosal pathways showed pronounced distal-segment maturation (F = 13 - 23, p [≤] 2.8x10e-16), whereas posterior sensorimotor and occipital callosal tracts exhibited more stable age associations along their lengths (F = 4.2 - 4.3, p = 10e-3). Genetic variations in callosal axon-guidance genes (ROBO1, IQCJ-SCHIP1, NRP1 and DCC) were associated with spatial variation in callosal diffusion metrics, particularly in anterior (rostrum) and posterior callosal subdivisions (isthmus and splenium) (F = 4.29 - 18.61, p-values = 2.2e-16 - 1.4e-04). These regions correspond to early-forming callosal compartments, suggesting that prenatal axon-guidance mechanisms leave enduring spatial patterns on callosal organisation. Finally, spatial variation in callosal microstructure was significantly associated with behavioural performance (F = 2.9 - 21.3, p-values = 2.8x10e-16 - 0.04), with the strongest and most spatially heterogeneous effects observed for complex cognition and executive functioning. Across all analyses, functional data models generally outperformed tract-averaged linear models, supporting the value of explicitly preserving spatial variation along callosal tracts.

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