Cerebral CortexNeurosciencePeer-reviewed journal article
The Generalizability of Cortical Area Parcellations Across Early Childhood
Jiaxin Cindy TuiD, Michael J. MyersiD, Wei Li, J. Jenny LiiD, Xintian Wang, Donna L. DierkeriD, Trevor K. M. DayiD, Abraham Z. SnyderiD, Aidan R. LathamiD, Jeanette K. KenleyiD, Chloe M. Sobolewski, Yu Wang, Alyssa K. LabonteiD, Eric FeczkoiD, Omid KardaniD, Lucille A. MooreiD, Chad M. SylvesteriD, Damien A. FairiD, Jed T. ElisoniD, Barbara B. WarneriD, Deanna M. BarchiD, Cynthia E. RogersiD, Joan L. LubyiD, Christopher D. SmyseriD, Evan M. GordoniD, , Muriah D. WheelockiD
Abstract
The cerebral cortex consists of distinct areas that develop through intrinsic embryonic patterning and postnatal experiences. Accurate parcellation of these areas in neuroimaging studies improves statistical power and cross-study comparability. Given significant brain changes in volume, microstructure, and connectivity during early life, we hypothesized that cortical areas in 1- to 3-year-olds would differ markedly from neonates and increasingly resemble adult patterns as development progresses. Here, we parcellated the cerebral cortex into putative areas using local functional connectivity gradients in 92 toddlers at 2 years old. We demonstrate high reproducibility of these cortical regions across 1- to 3-year-olds in two independent datasets. The area boundaries in 1- to 3-year-olds were more similar to those in adults than those in neonates. While the age-specific group area parcellation better fit the underlying functional connectivity in individuals during the first 3 years, adult area parcellations might still have some utility in developmental studies, especially in children older than 6 years. Additionally, we provide connectivity-based community assignments of the parcels, showing fragmented anterior and posterior components based on the strongest connectivity, yet alignment with adult systems when weaker connectivity was included.
- organism: human
- assay: imaging
- research focus: clustering
- research focus: cell type annotation
- approach: statistical model
- Citations
- 11
- FWCI
- 6.27
- Field percentile
- top 3%
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