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Auteur Haijing NIU |
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Atypical brain network development of infants at elevated likelihood for autism spectrum disorder during the first year of life / Fen ZHANG in Autism Research, 15-12 (December 2022)
[article]
Titre : Atypical brain network development of infants at elevated likelihood for autism spectrum disorder during the first year of life Type de document : Texte imprimé et/ou numérique Auteurs : Fen ZHANG, Auteur ; Floor MOERMAN, Auteur ; Haijing NIU, Auteur ; Petra WARREYN, Auteur ; Herbert ROEYERS, Auteur Article en page(s) : p.2223-2237 Langues : Anglais (eng) Mots-clés : Infant Humans Autism Spectrum Disorder Neurodevelopmental Disorders Brain/diagnostic imaging Phenotype autism spectrum disorder brain network functional connectivity functional near-infrared spectroscopy neurodevelopmental disorder Index. décimale : PER Périodiques Résumé : Autism spectrum disorder (ASD) is a neurodevelopmental disorder characterized by behavioral features that appear early in life. Although studies have shown that atypical brain functional and structural connectivity are associated with these behavioral traits, the occurrence and initial alterations of brain networks have not been fully investigated. The current study aimed to map early brain network efficiency and information transferring in infants at elevated likelihood (EL) compared to infants at typical likelihood (TL) for ASD in the first year of life. This study used a resting-state functional near-infrared spectroscopy (fNIRS) approach to obtain the length and strength of functional connections in the frontal and temporal areas in 45 5-month-old and 38 10-month-old infants. Modular organization and small-world properties were detected in both EL and TL infants at 5 and 10Â months. In 5-month-old EL infants, local and nodal efficiency were significantly greater than age-matched TL infants, indicating overgrown local connections. Furthermore, we used a support vector machine (SVM) model to classify infants with or without EL based on the obtained global properties of the network, achieving an accuracy of 77.6%. These results suggest that infants with EL for ASD exhibit inefficiencies in the organization of brain networks during the first year of life. En ligne : http://dx.doi.org/10.1002/aur.2827 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=488
in Autism Research > 15-12 (December 2022) . - p.2223-2237[article] Atypical brain network development of infants at elevated likelihood for autism spectrum disorder during the first year of life [Texte imprimé et/ou numérique] / Fen ZHANG, Auteur ; Floor MOERMAN, Auteur ; Haijing NIU, Auteur ; Petra WARREYN, Auteur ; Herbert ROEYERS, Auteur . - p.2223-2237.
Langues : Anglais (eng)
in Autism Research > 15-12 (December 2022) . - p.2223-2237
Mots-clés : Infant Humans Autism Spectrum Disorder Neurodevelopmental Disorders Brain/diagnostic imaging Phenotype autism spectrum disorder brain network functional connectivity functional near-infrared spectroscopy neurodevelopmental disorder Index. décimale : PER Périodiques Résumé : Autism spectrum disorder (ASD) is a neurodevelopmental disorder characterized by behavioral features that appear early in life. Although studies have shown that atypical brain functional and structural connectivity are associated with these behavioral traits, the occurrence and initial alterations of brain networks have not been fully investigated. The current study aimed to map early brain network efficiency and information transferring in infants at elevated likelihood (EL) compared to infants at typical likelihood (TL) for ASD in the first year of life. This study used a resting-state functional near-infrared spectroscopy (fNIRS) approach to obtain the length and strength of functional connections in the frontal and temporal areas in 45 5-month-old and 38 10-month-old infants. Modular organization and small-world properties were detected in both EL and TL infants at 5 and 10Â months. In 5-month-old EL infants, local and nodal efficiency were significantly greater than age-matched TL infants, indicating overgrown local connections. Furthermore, we used a support vector machine (SVM) model to classify infants with or without EL based on the obtained global properties of the network, achieving an accuracy of 77.6%. These results suggest that infants with EL for ASD exhibit inefficiencies in the organization of brain networks during the first year of life. En ligne : http://dx.doi.org/10.1002/aur.2827 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=488