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Growth charts of brain morphometry for preschool children.

Author information

Affiliations

  • 1. Department of Radiology, Children's Hospital, Zhejiang University School of Medicine, Hangzhou, China.
      Authors
    • Zhang H 1
    • Su X 1
    • Liu T 1
    (3 authors)
  • 2. Laboratory of Psychological Health and Imaging, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China; Faculty of Psychology and Educational Sciences, KU Leuven, Leuven, Belgium.
      Authors
    • Li J 2
    (1 author)
  • 3. Laboratory of Psychological Health and Imaging, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China; Shanghai Key laboratory of Psychotic disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
      Authors
    • Hu Y 3
    (1 author)
  • 4. Clinical Trial Institute, Children's Hospital, Zhejiang University School of Medicine, Hangzhou, China.
      Authors
    • Ni S 4
    (1 author)
  • 5. Department of Rehabilitation, Children's Hospital, Zhejiang University School of Medicine, Hangzhou, China.
      Authors
    • Li H 5
    (1 author)
    • Show all (9)

ORCIDs linked to this article

Neuroimage, 14 Apr 2022, 255:119178
https://doi.org/10.1016/j.neuroimage.2022.119178 PMID: 35430358 

This article is based on a previously available preprint.

Abstract 

Brain development from 1 to 6 years of age anchors a wide range of functional capabilities and carries early signs of neurodevelopmental disorders. However, quantitative models for depicting brain morphology changes and making individualized inferences are lacking, preventing the identification of early brain atypicality during this period. With a sample size of 285, we characterized the age dependence of the cortical thickness and subcortical volume in neurologically normal children and constructed quantitative growth charts of all brain regions for preschool children. While the cortical thickness of most brain regions decreased with age, the entorhinal and parahippocampal regions displayed an inverted-U shape of age dependence. Compared to the cortical thickness, the normalized volume of subcortical regions exhibited more divergent trends, with some regions increasing, some decreasing, and some displaying inverted-U-shaped trends. The growth curve models for all brain regions demonstrated utilities in identifying brain atypicality. The percentile measures derived from the growth curves facilitate the identification of children with developmental speech and language disorders with an accuracy of 0.875 (area under the receiver operating characteristic curve: 0.943). Our results fill the knowledge gap in brain morphometrics in a critical development period and provide an avenue for individualized brain developmental status evaluation with demonstrated sensitivity. The brain growth charts are shared with the public (http://phi-group.top/resources.html).

Full text links 

Read article at publisher's site: https://doi.org/10.1016/j.neuroimage.2022.119178

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