bioRxivNeurosciencePreprint
Division-Specific Organization of a Shared Functional Scaffold in the Early-Life Human Brain
Dan HuiD, Jiale ChengiD, Kangfu HaniD, Zhengwang WuiD, Weiyan YiniD, Yue SuniD, Jiameng Liu, Sheng‐Che HungiD, Li WangiD, Jessica Rachel CoheniD, Weili LiniD, Gang Li
Abstract
Early cognition and behavior emerge from coordinated maturation of functional systems spanning the brain, making cross-division integration essential for defining whole-brain architecture and understanding its role in neurodevelopment. Yet early human functional development is still largely investigated through cortical networks or through isolated, coarsely resolved noncortical structures, leaving unclear how this integrated architecture is organized across the cerebral cortex, subcortex and cerebellum. Moving beyond structure-isolated and adult-derived maps, we create a reproducible, fine-grained Early-Life Whole-Brain Functional Parcellation (UNC-ELF) spanning birth to six years. Using this framework, we identify a shared functional scaffold expressed through distinct division-specific modes: differentiated and dual-polarity cortical organization, spatially graded cerebellar organization with blended functional transition zones, and nucleus-constrained subcortical mosaics of multi-system affiliation. Across early childhood, the scaffold is selectively refined through heterogeneous, nonlinear connectivity trajectories, with major inflections concentrated in infancy. Age-, sex- and prospective cognition-related variation is differentially encoded across distinct connectomic components. Together, these findings reframe early functional brain development as coordinated refinement of a shared but nonuniform whole-brain scaffold and establish UNC-ELF as a unified reference for resolving typical and atypical functional organization across the developing brain.
- organism: human
- assay: spatial transcriptomics
- research focus: clustering
- research focus: dimensionality reduction
- research focus: benchmarking
- research focus: database / resource
- approach: statistical model
- Citations
- 0
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