Volumetric Analysis of Amygdala and Hippocampal Subfields for Infants with Autism.
Guannan LiMeng-Hsiang ChenGang LiDi WuChunfeng LianQuansen SunR Jarrett RushmoreLi WangPublished in: Journal of autism and developmental disorders (2022)
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.
Keyphrases
- temporal lobe epilepsy
- resting state
- functional connectivity
- cerebral ischemia
- autism spectrum disorder
- deep learning
- prefrontal cortex
- attention deficit hyperactivity disorder
- convolutional neural network
- magnetic resonance imaging
- white matter
- subarachnoid hemorrhage
- magnetic resonance
- contrast enhanced
- brain injury
- stress induced
- multiple sclerosis
- blood brain barrier