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*
  • *Corresponding author for this work

Research output: Contribution to journalJournal Article peer-review

16 Scopus citations

Abstract

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.

Original languageEnglish
Pages (from-to)2475-2489
Number of pages15
JournalJournal of Autism and Developmental Disorders
Volume53
Issue number6
DOIs
StatePublished - 06 2023

Bibliographical note

Publisher Copyright:
© 2022, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.

Keywords

  • Amygdala
  • Autism spectrum disorder (ASD)
  • Deep learning
  • Hippocampus subfields
  • Infant structural MRI

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