Heterogeneity of dynamic synergetic configurations of salience network in children with autism spectrum disorder / Guangjin ZHAI ; Junfeng LIU ; Xia ZHANG ; Tao ZHANG ; Dong CUI ; Rongjuan ZHOU ; Le GAO in Autism Research, 16-12 (December 2023)  

Public ISBD [article]  inAutism Research > 16-12 (December 2023) . - p.2275-2290 Titre : Heterogeneity of dynamic synergetic configurations of salience network in children with autism spectrum disorder Type de document : Texte imprimé et/ou numérique Auteurs : Guangjin ZHAI, Auteur ; Junfeng LIU, Auteur ; Xia ZHANG, Auteur ; Tao ZHANG, Auteur ; Dong CUI, Auteur ; Rongjuan ZHOU, Auteur ; Le GAO, Auteur Article en page(s) : p.2275-2290 Index. décimale : PER Périodiques Résumé : Abstract Atypical functional connectivity (FC) patterns have been identified in autism spectrum disorders (ASD), especially within salience network (SN) and between SN and default mode network (DMN) and central executive network (CEN). But whether the dynamic configuration of intra-SN and inter-SN (SN with DMN and CEN) FC in ASD is also heterogeneous remains unknown. Based on the resting-state functional magnetic resonance imaging data from 105 ASD and 102 typically-developing controls (TC), we calculated the time-varying FC of intra-SN and inter-SN (SN with DMN and CEN). Then, the joint recurrence features for the time-varying FC were calculated to assess how the SN dynamically recruits different configurations of network segregation and integration in ASD, that is, synergies, from the dynamical systems perspective. We analyzed the differences in synergetic patterns between ASD subtypes obtained by k-means clustering algorithm based on the synergy of SN and TC, and investigated the relationships between synergy of SN and severity of clinical symptoms of ASD for ASD subtypes. Two ASD subtypes were revealed, where the synergy of SN in ASD subtype 1 has lower stability and periodicity compared to the TC, and ASD subtype 2 exhibits the opposite alteration. Synergy of SN for ASD subtype 1 and 2 was found to predict the severity of communication impairments and restricted and repetitive behaviors in ASD, respectively. These results suggest the existence of subtypes with distinct patterns of the synergy of SN in ASD, and provide insight into the complex pathophysiological mechanism of clinical manifestations of ASD. En ligne : https://doi.org/10.1002/aur.3037 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=518 [article] Heterogeneity of dynamic synergetic configurations of salience network in children with autism spectrum disorder [Texte imprimé et/ou numérique] / Guangjin ZHAI, Auteur ; Junfeng LIU, Auteur ; Xia ZHANG, Auteur ; Tao ZHANG, Auteur ; Dong CUI, Auteur ; Rongjuan ZHOU, Auteur ; Le GAO, Auteur . - p.2275-2290. in Autism Research > 16-12 (December 2023) . - p.2275-2290 Index. décimale : PER Périodiques Résumé : Abstract Atypical functional connectivity (FC) patterns have been identified in autism spectrum disorders (ASD), especially within salience network (SN) and between SN and default mode network (DMN) and central executive network (CEN). But whether the dynamic configuration of intra-SN and inter-SN (SN with DMN and CEN) FC in ASD is also heterogeneous remains unknown. Based on the resting-state functional magnetic resonance imaging data from 105 ASD and 102 typically-developing controls (TC), we calculated the time-varying FC of intra-SN and inter-SN (SN with DMN and CEN). Then, the joint recurrence features for the time-varying FC were calculated to assess how the SN dynamically recruits different configurations of network segregation and integration in ASD, that is, synergies, from the dynamical systems perspective. We analyzed the differences in synergetic patterns between ASD subtypes obtained by k-means clustering algorithm based on the synergy of SN and TC, and investigated the relationships between synergy of SN and severity of clinical symptoms of ASD for ASD subtypes. Two ASD subtypes were revealed, where the synergy of SN in ASD subtype 1 has lower stability and periodicity compared to the TC, and ASD subtype 2 exhibits the opposite alteration. Synergy of SN for ASD subtype 1 and 2 was found to predict the severity of communication impairments and restricted and repetitive behaviors in ASD, respectively. These results suggest the existence of subtypes with distinct patterns of the synergy of SN in ASD, and provide insight into the complex pathophysiological mechanism of clinical manifestations of ASD. En ligne : https://doi.org/10.1002/aur.3037 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=518

Identification and analysis of autism spectrum disorder via large-scale dynamic functional network connectivity / Wenwen ZHUANG in Autism Research, 16-8 (August 2023)  

Public ISBD [article]  inAutism Research > 16-8 (August 2023) . - p.1512-1526 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 [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

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