Centre d'Information et de documentation du CRA Rhône-Alpes
CRA
Informations pratiques
-
Adresse
Centre d'information et de documentation
du CRA Rhône-Alpes
Centre Hospitalier le Vinatier
bât 211
95, Bd Pinel
69678 Bron CedexHoraires
Lundi au Vendredi
9h00-12h00 13h30-16h00Contact
Tél: +33(0)4 37 91 54 65
Mail
Fax: +33(0)4 37 91 54 37
-
Détail de l'auteur
Auteur Yunhong LIU |
Documents disponibles écrits par cet auteur (1)
Faire une suggestion Affiner la recherche
Identification and analysis of autism spectrum disorder via large-scale dynamic functional network connectivity / Wenwen ZHUANG in Autism Research, 16-8 (August 2023)
[article]
Titre : Identification and analysis of autism spectrum disorder via large-scale dynamic functional network connectivity Type de document : Texte imprimé et/ou numérique Auteurs : Wenwen ZHUANG, Auteur ; Hai JIA, Auteur ; Yunhong LIU, Auteur ; Jing CONG, Auteur ; Kai CHEN, Auteur ; Dezhong YAO, Auteur ; Xiaodong KANG, Auteur ; Peng XU, Auteur ; Tao ZHANG, Auteur Article en page(s) : p.1512-1526 Langues : Anglais (eng) Index. décimale : PER Périodiques Résumé : Abstract Autism spectrum disorder (ASD) is a prevalent neurodevelopmental disorder with severe cognitive impairment. Several studies have reported that brain functional network connectivity (FNC) has great potential for identifying ASD from healthy control (HC) and revealing the relationships between the brain and behaviors of ASD. However, few studies have explored dynamic large-scale FNC as a feature to identify individuals with ASD. This study used a time-sliding window method to study the dynamic FNC (dFNC) on the resting-state fMRI. To avoid arbitrarily determining the window length, we set a window length range of 10-75 TRs (TR=2?s). We constructed linear support vector machine classifiers for all window length conditions. Using a nested 10-fold cross-validation framework, we obtained a grand average accuracy of 94.88% across window length conditions, which is higher than those reported in previous studies. In addition, we determined the optimal window length using the highest classification accuracy of 97.77%. Based on the optimal window length, we found that the dFNCs were located mainly in dorsal and ventral attention networks (DAN and VAN) and exhibited the highest weight in classification. Specifically, we found that the dFNC between DAN and temporal orbitofrontal network (TOFN) was significantly negatively correlated with social scores of ASD. Finally, using the dFNCs with high classification weights as features, we construct a model to predict the clinical score of ASD. Overall, our findings demonstrated that the dFNC could be a potential biomarker to identify ASD and provide new perspectives to detect cognitive changes in ASD. En ligne : https://doi.org/10.1002/aur.2974 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=510
in Autism Research > 16-8 (August 2023) . - p.1512-1526[article] Identification and analysis of autism spectrum disorder via large-scale dynamic functional network connectivity [Texte imprimé et/ou numérique] / Wenwen ZHUANG, Auteur ; Hai JIA, Auteur ; Yunhong LIU, Auteur ; Jing CONG, Auteur ; Kai CHEN, Auteur ; Dezhong YAO, Auteur ; Xiaodong KANG, Auteur ; Peng XU, Auteur ; Tao ZHANG, Auteur . - p.1512-1526.
Langues : Anglais (eng)
in Autism Research > 16-8 (August 2023) . - p.1512-1526
Index. décimale : PER Périodiques Résumé : Abstract Autism spectrum disorder (ASD) is a prevalent neurodevelopmental disorder with severe cognitive impairment. Several studies have reported that brain functional network connectivity (FNC) has great potential for identifying ASD from healthy control (HC) and revealing the relationships between the brain and behaviors of ASD. However, few studies have explored dynamic large-scale FNC as a feature to identify individuals with ASD. This study used a time-sliding window method to study the dynamic FNC (dFNC) on the resting-state fMRI. To avoid arbitrarily determining the window length, we set a window length range of 10-75 TRs (TR=2?s). We constructed linear support vector machine classifiers for all window length conditions. Using a nested 10-fold cross-validation framework, we obtained a grand average accuracy of 94.88% across window length conditions, which is higher than those reported in previous studies. In addition, we determined the optimal window length using the highest classification accuracy of 97.77%. Based on the optimal window length, we found that the dFNCs were located mainly in dorsal and ventral attention networks (DAN and VAN) and exhibited the highest weight in classification. Specifically, we found that the dFNC between DAN and temporal orbitofrontal network (TOFN) was significantly negatively correlated with social scores of ASD. Finally, using the dFNCs with high classification weights as features, we construct a model to predict the clinical score of ASD. Overall, our findings demonstrated that the dFNC could be a potential biomarker to identify ASD and provide new perspectives to detect cognitive changes in ASD. En ligne : https://doi.org/10.1002/aur.2974 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=510