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A Longitudinal MRI Study of Amygdala and Hippocampal Subfields for Infants with Risk of Autism

  • Guannan Li
  • Meng-Hsiang Chen
  • Gang Li
  • Di Wu
  • Chunfeng Lian
  • Quansen Sun
  • Dinggang ShenEmail author
  • Li WangEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11849)

Abstract

Currently, there are still no early biomarkers to detect infants with risk of autism spectrum disorder (ASD), which is mainly diagnosed based on behavioral observations at three or four years of age. Since intervention efforts may miss a critical developmental window after 2 years old, it is clinically significant to identify imaging-based biomarkers at an early stage for better intervention, before behavioral diagnostic signs of ASD typically arising. Previous studies on older children and young adults with ASD demonstrate altered developmental trajectories of the amygdala and hippocampus. However, our knowledge on their developmental trajectories in early postnatal stages remains very limited. In this paper, for the first time, we propose a volume-based analysis of the amygdala and hippocampal subfields of the infant subjects with risk of ASD at 6, 12, and 24 months of age. To address the challenge of low tissue contrast and small structural size of infant amygdala and hippocampal subfields, we propose a novel deep-learning approach, dilated-dense U-Net, to digitally segment the amygdala and hippocampal subfields in a longitudinal dataset, the National Database for Autism Research (NDAR). A volume-based analysis is then performed based on the segmentation results. Our study shows that the overgrowth of amygdala and cornu ammonis sectors (CA) 1–3 May start from 6 months of age, which may be related to the emergence of autistic spectrum disorder.

Keywords

Autism Convolutional neural network Trajectory Amygdala Hippocampus 

Notes

Acknowledgements

Part of this work was done when Guannan Li was in UNC (supported in part by NIH grants MH109773). Gang Li and Dinggang Shen were supported in part by NIH grants (MH117943). Li Wang was supported by NIH grants MH109773 and MH117943.

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Guannan Li
    • 1
    • 2
  • Meng-Hsiang Chen
    • 3
  • Gang Li
    • 2
  • Di Wu
    • 4
  • Chunfeng Lian
    • 2
  • Quansen Sun
    • 1
  • Dinggang Shen
    • 2
    Email author
  • Li Wang
    • 2
    Email author
  1. 1.School of Computer Science and EngineeringNanjing University of Science and TechnologyNanjingChina
  2. 2.Department of Radiology and Biomedical Research Imaging CenterUniversity of North Carolina at Chapel HillChapel HillUSA
  3. 3.Department of Diagnostic Radiology, Kaohsiung Chang Gung Memorial HospitalChang Gung University College of MedicineKaohsiungTaiwan
  4. 4.Department of BiostatisticsUniversity of North Carolina at Chapel HillChapel HillUSA

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