Back

Diffusion Neuroimaging of Speech Acquisition in Infants

Liu, F.; Liu, J.; Gu, J.; Wang, Y.; Cai, X.; Wang, Z.; Wu, H.; Feng, J.; Zhang, H.; Shen, D.

2025-05-25 neuroscience
10.1101/2025.05.23.655876 bioRxiv
Show abstract

How white matter maturation supports speech -- an emergent property of integrated human brain networks -- remains unclear. Leveraging the Baby Connectome Project, the largest longitudinal infant neuroimaging and behavioral dataset available, we characterized white matter maturation across five major pathways (motor, auditory, visual, ventral language, and dorsal language) from birth to 24 months and correlated these trajectories with emerging motor, perceptual, and cognitive skills. We found that dorsal language tracts, initially immature at birth, rapidly matured in parallel with motor tracts, reaching maturity levels comparable to primary sensory tracts by approximately 24 months. Our data suggest two potential mechanisms: (i) Widespread brain-behavior interdependencies, wherein both ventral and dorsal tracts correlated significantly not only with speech but also broadly with other behavioral domains, and advanced speech skills were associated across multiple tracts; and (ii) a nonlinear developmental cascade, in which early gross motor skills promoted subsequent social interactions thus influencing later speech development, meanwhile, early social interactions fostered subsequent fine motor refinement indirectly influencing later speech development. Our data thus portray early brain networks as a malleable, integrative scaffold, coordinating information across motor, auditory, and visual systems into a coherent whole immediately after birth. Collectively, these findings reveal speech acquisition as an emergent property reflecting a fundamental integrative principle of early brain development.

Published in Communications Biology (predicted rank #8) · training set

Matching journals

The top 2 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.