TY - GEN
T1 - A Longitudinal MRI Study of Amygdala and Hippocampal Subfields for Infants with Risk of Autism
AU - Li, Guannan
AU - Chen, Meng Hsiang
AU - Li, Gang
AU - Wu, Di
AU - Lian, Chunfeng
AU - Sun, Quansen
AU - Shen, Dinggang
AU - Wang, Li
N1 - Funding Information:
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.
Publisher Copyright:
© 2019, Springer Nature Switzerland AG.
PY - 2019
Y1 - 2019
N2 - 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.
AB - 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.
KW - Amygdala
KW - Autism
KW - Convolutional neural network
KW - Hippocampus
KW - Trajectory
UR - http://www.scopus.com/inward/record.url?scp=85076295962&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-35817-4_20
DO - 10.1007/978-3-030-35817-4_20
M3 - Conference contribution
AN - SCOPUS:85076295962
SN - 9783030358167
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 164
EP - 171
BT - Graph Learning in Medical Imaging - 1st International Workshop, GLMI 2019, held in Conjunction with MICCAI 2019, Proceedings
A2 - Zhang, Daoqiang
A2 - Zhou, Luping
A2 - Jie, Biao
A2 - Liu, Mingxia
PB - Springer
T2 - 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
Y2 - 17 October 2019 through 17 October 2019
ER -