| Literature DB >> 35389185 |
Guannan Li1,2, Meng-Hsiang Chen3, Gang Li2, Di Wu4, Chunfeng Lian2, Quansen Sun1, R Jarrett Rushmore5,6,7, Li Wang8.
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.Entities:
Keywords: Amygdala; Autism spectrum disorder (ASD); Deep learning; Hippocampus subfields; Infant structural MRI
Year: 2022 PMID: 35389185 PMCID: PMC9537344 DOI: 10.1007/s10803-022-05535-w
Source DB: PubMed Journal: J Autism Dev Disord ISSN: 0162-3257