[article] 
inJournal of Autism and Developmental Disorders > 53-6 (June 2023) . - p.2475-2489

Titre :	Volumetric Analysis of Amygdala and Hippocampal Subfields for Infants with Autism : Journal of Autism and Developmental Disorders
Type de document :	Texte imprimé et/ou numérique
Auteurs :	Guannan Li, Auteur ; Meng-Hsiang Chen, Auteur ; Gang LI, Auteur ; Di Wu, Auteur ; Chunfeng Lian, Auteur ; Quansen Sun, Auteur ; R. Jarrett Rushmore, Auteur ; Li WANG, Auteur
Article en page(s) :	p.2475-2489
Langues :	Anglais (eng)
Index. décimale :	PER Périodiques
Résumé :	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.
En ligne :	https://doi.org/10.1007/s10803-022-05535-w
Permalink :	https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=506

[article] Volumetric Analysis of Amygdala and Hippocampal Subfields for Infants with Autism : Journal of Autism and Developmental Disorders [Texte imprimé et/ou numérique] / Guannan Li, Auteur ; Meng-Hsiang Chen, Auteur ; Gang LI, Auteur ; Di Wu, Auteur ; Chunfeng Lian, Auteur ; Quansen Sun, Auteur ; R. Jarrett Rushmore, Auteur ; Li WANG, Auteur . - p.2475-2489.
Langues : Anglais (eng)
in Journal of Autism and Developmental Disorders > 53-6 (June 2023) . - p.2475-2489

Index. décimale :	PER Périodiques
Résumé :	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.
En ligne :	https://doi.org/10.1007/s10803-022-05535-w
Permalink :	https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=506