Li Guannan, Chen Meng-Hsiang, Li Gang, Wu Di, Lian Chunfeng, Sun Quansen, Shen Dinggang, Wang Li
School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing 210094, China.
Department of Radiology and Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA.
Graph Learn Med Imaging (2019). 2019 Oct;11849:164-171. doi: 10.1007/978-3-030-35817-4_20. Epub 2019 Nov 14.
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.
目前，仍没有早期生物标志物可用于检测患有自闭症谱系障碍（ASD）风险的婴儿，ASD主要是在三四岁时根据行为观察来诊断。由于干预措施可能会错过2岁后的关键发育窗口期，因此在ASD典型行为诊断体征出现之前，早期识别基于影像学的生物标志物以进行更好的干预具有临床意义。先前针对患有ASD的大龄儿童和青年的研究表明，杏仁核和海马体的发育轨迹发生了改变。然而，我们对它们在出生后早期阶段的发育轨迹的了解仍然非常有限。在本文中，我们首次对6、12和24个月大的有ASD风险的婴儿受试者的杏仁核和海马亚区进行基于体积的分析。为应对婴儿杏仁核和海马亚区组织对比度低和结构尺寸小的挑战，我们提出了一种新颖的深度学习方法——扩张密集U-Net，用于在纵向数据集“国家自闭症研究数据库”（NDAR）中对杏仁核和海马亚区进行数字分割。然后根据分割结果进行基于体积的分析。我们的研究表明，杏仁核和海马1-3角（CA）的过度生长可能从6个月大时就开始了，这可能与自闭症谱系障碍的出现有关。
