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 Shen; Li Wang

doi:10.1007/978-3-030-35817-4_20

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 Shen, Li Wang

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

19 Citations (Scopus)

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.

Original language	English
Title of host publication	Graph Learning in Medical Imaging - 1st International Workshop, GLMI 2019, held in Conjunction with MICCAI 2019, Proceedings
Editors	Daoqiang Zhang, Luping Zhou, Biao Jie, Mingxia Liu
Publisher	Springer
Pages	164-171
Number of pages	8
ISBN (Print)	9783030358167
DOIs	https://doi.org/10.1007/978-3-030-35817-4_20
Publication status	Published - 2019
Event	1st International Workshop on Graph Learning in Medical Imaging, GLMI 2019 held in conjunction with the 22nd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2019 - Shenzhen, China Duration: 2019 Oct 17 → 2019 Oct 17

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	11849 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	1st International Workshop on Graph Learning in Medical Imaging, GLMI 2019 held in conjunction with the 22nd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2019
Country/Territory	China
City	Shenzhen
Period	19/10/17 → 19/10/17

Bibliographical note

Publisher Copyright:
© 2019, Springer Nature Switzerland AG.

Keywords

Amygdala
Autism
Convolutional neural network
Hippocampus
Trajectory

ASJC Scopus subject areas

Theoretical Computer Science
General Computer Science

Access to Document

10.1007/978-3-030-35817-4_20

Cite this

Li, G., Chen, M. H., Li, G., Wu, D., Lian, C., Sun, Q., Shen, D., & Wang, L. (2019). A Longitudinal MRI Study of Amygdala and Hippocampal Subfields for Infants with Risk of Autism. In D. Zhang, L. Zhou, B. Jie, & M. Liu (Eds.), Graph Learning in Medical Imaging - 1st International Workshop, GLMI 2019, held in Conjunction with MICCAI 2019, Proceedings (pp. 164-171). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11849 LNCS). Springer. https://doi.org/10.1007/978-3-030-35817-4_20

A Longitudinal MRI Study of Amygdala and Hippocampal Subfields for Infants with Risk of Autism. / Li, Guannan; Chen, Meng Hsiang; Li, Gang et al.
Graph Learning in Medical Imaging - 1st International Workshop, GLMI 2019, held in Conjunction with MICCAI 2019, Proceedings. ed. / Daoqiang Zhang; Luping Zhou; Biao Jie; Mingxia Liu. Springer, 2019. p. 164-171 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11849 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Li, G, Chen, MH, Li, G, Wu, D, Lian, C, Sun, Q, Shen, D & Wang, L 2019, A Longitudinal MRI Study of Amygdala and Hippocampal Subfields for Infants with Risk of Autism. in D Zhang, L Zhou, B Jie & M Liu (eds), Graph Learning in Medical Imaging - 1st International Workshop, GLMI 2019, held in Conjunction with MICCAI 2019, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 11849 LNCS, Springer, pp. 164-171, 1st International Workshop on Graph Learning in Medical Imaging, GLMI 2019 held in conjunction with the 22nd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2019, Shenzhen, China, 19/10/17. https://doi.org/10.1007/978-3-030-35817-4_20

Li G, Chen MH, Li G, Wu D, Lian C, Sun Q et al. A Longitudinal MRI Study of Amygdala and Hippocampal Subfields for Infants with Risk of Autism. In Zhang D, Zhou L, Jie B, Liu M, editors, Graph Learning in Medical Imaging - 1st International Workshop, GLMI 2019, held in Conjunction with MICCAI 2019, Proceedings. Springer. 2019. p. 164-171. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-030-35817-4_20

Li, Guannan ; Chen, Meng Hsiang ; Li, Gang et al. / A Longitudinal MRI Study of Amygdala and Hippocampal Subfields for Infants with Risk of Autism. Graph Learning in Medical Imaging - 1st International Workshop, GLMI 2019, held in Conjunction with MICCAI 2019, Proceedings. editor / Daoqiang Zhang ; Luping Zhou ; Biao Jie ; Mingxia Liu. Springer, 2019. pp. 164-171 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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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.",

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

Abstract

Publication series

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Bibliographical note

Keywords

ASJC Scopus subject areas

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