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Volumetric Analysis of Amygdala and Hippocampal Subfields for Infants with Autism

  • Guannan Li
  • , Meng Hsiang Chen
  • , Gang Li
  • , Di Wu
  • , Chunfeng Lian
  • , Quansen Sun
  • , R. Jarrett Rushmore
  • , Li Wang
  • Nanjing University of Science and Technology
  • University of North Carolina at Chapel Hill
  • Chang Gung University
  • Boston University
  • Brigham and Women’s Hospital
  • Massachusetts General Hospital

科研成果: 期刊稿件文章同行评审

20 引用 (Scopus)

摘要

Previous studies have demonstrated abnormal brain overgrowth in children with autism spectrum disorder (ASD), but the development of specific brain regions, such as the amygdala and hippocampal subfields in infants, is incompletely documented. To address this issue, we performed the first MRI study of amygdala and hippocampal subfields in infants from 6 to 24 months of age using a longitudinal dataset. A novel deep learning approach, Dilated-Dense U-Net, was proposed to address the challenge of low tissue contrast and small structural size of these subfields. We performed a volume-based analysis on the segmentation results. Our results show that infants who were later diagnosed with ASD had larger left and right volumes of amygdala and hippocampal subfields than typically developing controls.

源语言英语
页(从-至)2475-2489
页数15
期刊Journal of Autism and Developmental Disorders
53
6
DOI
出版状态已出版 - 6月 2023
已对外发布

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