Development of visual cortex in human neonates are selectively modified by postnatal experience

Abstract

Experience-dependent cortical plasticity is a pivotal process of human brain development and essential for the formation of most cognitive functions. Although studies found that early visual experience could influence the endogenous development of visual cortex in animals, little is known about such impact on human infants. Using the multi-modal MRI data from developing human connectome project, we revealed the early structural and functional maps in the ventral visual cortex and their development across the first month of age. Particularly, we found the postnatal experience could modulate both the cortical morphology in ventral visual cortex and the functional circuit between bilateral primary visual cortices. But the cortical myelination and overall functional circuits of ventral cortex, particularly that of the high-order visual cortex, developed without significant influence of postnatal experience in such early period. These experience-dependent cortical properties were further validated in the preterm-born infants who have longer postnatal time but immature cortical development at birth. The results confirmed that the development of cortical thickness was dominated by the postnatal experience but the functional circuit might be determined by the overall maturity of visual cortex. These data suggest in human newborns that early postnatal experience shapes the structural and functional development of the visual cortex in selective and organized pattern.

Multi-modal multi-resolution atlas of the human neonatal cerebral cortex based on microstructural similarity

“…We collected the minimally preprocessed image data from dHCP database and the detailed preprocessing as described in the previous paper (Li et al, 2022) Briefly, for the anatomical data, the dHCP pipeline included super-resolution reconstruction (Kuklisova-Murgasova et al, 2012), registration (from T1w to T2w), bias correction, brain extraction, segmentation (Makropoulos et al, 2014), surface extraction (Schuh et al, 2017), and surface registration (Robinson et al, 2018). We used the cortical metrics obtained from dHCP, including the cortical thickness (CT) and myelination (CM) in individual brains and the corresponding transformation files from individual to the dHCP 40-weeks surface template (Bozek et al, 2018).…”

“…The functional data from dHCP included the r-fMRI data in individual spaces and the motion parameters. We further processed these data by following steps using custom codes and the DPABI toolbox (Yan et al, 2016) in MATLAB (v2018a) as described in our previous study (Li et al, 2022). 1) To decrease the influence of head motion on the data, we selected a continuous subset (1600 volumes, around 70%) of the total volume with the lowest head motion for each neonate.…”