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Auteur G. BUSSU |
Documents disponibles écrits par cet auteur (3)



Latent trajectories of adaptive behaviour in infants at high and low familial risk for autism spectrum disorder / G. BUSSU in Molecular Autism, 10 (2019)
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[article]
Titre : Latent trajectories of adaptive behaviour in infants at high and low familial risk for autism spectrum disorder Type de document : Texte imprimé et/ou numérique Auteurs : G. BUSSU, Auteur ; E. J. H. JONES, Auteur ; Tony CHARMAN, Auteur ; M. H. JOHNSON, Auteur ; Jan K. BUITELAAR, Auteur Article en page(s) : 13 p. Langues : Anglais (eng) Mots-clés : Adaptive behaviour Autism Infant siblings Subgroups Trajectories Central NREC (approval codes 06/MRE02/73, 08/H0718/76), and one or both parents gave informed consent to participate in the study.Not applicable.JKB has been a consultant to/member of, an advisory board of, and/or a speaker for Janssen Cilag BV, Eli Lilly, Lundbeck, Shire, Roche, Novartis, Medice, and Servier. He is neither an employee nor a stock shareholder of any of these companies. TC has received research grant support from the Medical Research Council (UK), the National Institute of Health Research, Horizon 2020 and the Innovative Medicines Initiative (both European Commission), MQ, Autistica, FP7 (European Commission), the Charles Hawkins Fund, and the Waterloo Foundation. He has served as a consultant to F. Hoffmann-La Roche, Ltd. He has received royalties from Sage Publications and Guilford Publications. The present work is unrelated to these relationships. The other authors declare that they have no competing interests.Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Index. décimale : PER Périodiques Résumé : Background: Autism spectrum disorder (ASD) is characterised by persisting difficulties in everyday functioning. Adaptive behaviour is heterogeneous across individuals with ASD, and it is not clear to what extent early development of adaptive behaviour relates to ASD outcome in toddlerhood. This study aims to identify subgroups of infants based on early development of adaptive skills and investigate their association with later ASD outcome. Methods: Adaptive behaviour was assessed on infants at high (n = 166) and low (n = 74) familial risk for ASD between 8 and 36 months using the Vineland Adaptive Behavior Scales (VABS-II). The four domains of VABS-II were modelled in parallel using growth mixture modelling to identify distinct classes of infants based on adaptive behaviour. Then, we associated class membership with clinical outcome and ASD symptoms at 36 months and longitudinal measures of cognitive development. Results: We observed three classes characterised by decreasing trajectories below age-appropriate norms (8.3%), stable trajectories around age-appropriate norms (73.8%), and increasing trajectories reaching average scores by age 2 (17.9%). Infants with declining adaptive behaviour had a higher risk (odds ratio (OR) = 4.40; confidence interval (CI) 1.90; 12.98) for ASD and higher parent-reported symptoms in the social, communication, and repetitive behaviour domains at 36 months. Furthermore, there was a discrepancy between adaptive and cognitive functioning as the class with improving adaptive skills showed stable cognitive development around average scores. Conclusions: Findings confirm the heterogeneity of trajectories of adaptive functioning in infancy, with a higher risk for ASD in toddlerhood linked to a plateau in the development of adaptive functioning after the first year of life. En ligne : https://dx.doi.org/10.1186/s13229-019-0264-6 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=389
in Molecular Autism > 10 (2019) . - 13 p.[article] Latent trajectories of adaptive behaviour in infants at high and low familial risk for autism spectrum disorder [Texte imprimé et/ou numérique] / G. BUSSU, Auteur ; E. J. H. JONES, Auteur ; Tony CHARMAN, Auteur ; M. H. JOHNSON, Auteur ; Jan K. BUITELAAR, Auteur . - 13 p.
Langues : Anglais (eng)
in Molecular Autism > 10 (2019) . - 13 p.
Mots-clés : Adaptive behaviour Autism Infant siblings Subgroups Trajectories Central NREC (approval codes 06/MRE02/73, 08/H0718/76), and one or both parents gave informed consent to participate in the study.Not applicable.JKB has been a consultant to/member of, an advisory board of, and/or a speaker for Janssen Cilag BV, Eli Lilly, Lundbeck, Shire, Roche, Novartis, Medice, and Servier. He is neither an employee nor a stock shareholder of any of these companies. TC has received research grant support from the Medical Research Council (UK), the National Institute of Health Research, Horizon 2020 and the Innovative Medicines Initiative (both European Commission), MQ, Autistica, FP7 (European Commission), the Charles Hawkins Fund, and the Waterloo Foundation. He has served as a consultant to F. Hoffmann-La Roche, Ltd. He has received royalties from Sage Publications and Guilford Publications. The present work is unrelated to these relationships. The other authors declare that they have no competing interests.Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Index. décimale : PER Périodiques Résumé : Background: Autism spectrum disorder (ASD) is characterised by persisting difficulties in everyday functioning. Adaptive behaviour is heterogeneous across individuals with ASD, and it is not clear to what extent early development of adaptive behaviour relates to ASD outcome in toddlerhood. This study aims to identify subgroups of infants based on early development of adaptive skills and investigate their association with later ASD outcome. Methods: Adaptive behaviour was assessed on infants at high (n = 166) and low (n = 74) familial risk for ASD between 8 and 36 months using the Vineland Adaptive Behavior Scales (VABS-II). The four domains of VABS-II were modelled in parallel using growth mixture modelling to identify distinct classes of infants based on adaptive behaviour. Then, we associated class membership with clinical outcome and ASD symptoms at 36 months and longitudinal measures of cognitive development. Results: We observed three classes characterised by decreasing trajectories below age-appropriate norms (8.3%), stable trajectories around age-appropriate norms (73.8%), and increasing trajectories reaching average scores by age 2 (17.9%). Infants with declining adaptive behaviour had a higher risk (odds ratio (OR) = 4.40; confidence interval (CI) 1.90; 12.98) for ASD and higher parent-reported symptoms in the social, communication, and repetitive behaviour domains at 36 months. Furthermore, there was a discrepancy between adaptive and cognitive functioning as the class with improving adaptive skills showed stable cognitive development around average scores. Conclusions: Findings confirm the heterogeneity of trajectories of adaptive functioning in infancy, with a higher risk for ASD in toddlerhood linked to a plateau in the development of adaptive functioning after the first year of life. En ligne : https://dx.doi.org/10.1186/s13229-019-0264-6 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=389 Prediction of Autism at 3 Years from Behavioural and Developmental Measures in High-Risk Infants: A Longitudinal Cross-Domain Classifier Analysis / G. BUSSU in Journal of Autism and Developmental Disorders, 48-7 (July 2018)
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Titre : Prediction of Autism at 3 Years from Behavioural and Developmental Measures in High-Risk Infants: A Longitudinal Cross-Domain Classifier Analysis Type de document : Texte imprimé et/ou numérique Auteurs : G. BUSSU, Auteur ; E. J. H. JONES, Auteur ; Tony CHARMAN, Auteur ; M. H. JOHNSON, Auteur ; Jan K. BUITELAAR, Auteur Article en page(s) : p.2418-2433 Langues : Anglais (eng) Mots-clés : Autism Data integration Early prediction High-risk Individual prediction Longitudinal study Machine learning Index. décimale : PER Périodiques Résumé : We integrated multiple behavioural and developmental measures from multiple time-points using machine learning to improve early prediction of individual Autism Spectrum Disorder (ASD) outcome. We examined Mullen Scales of Early Learning, Vineland Adaptive Behavior Scales, and early ASD symptoms between 8 and 36 months in high-risk siblings (HR; n = 161) and low-risk controls (LR; n = 71). Longitudinally, LR and HR-Typical showed higher developmental level and functioning, and fewer ASD symptoms than HR-Atypical and HR-ASD. At 8 months, machine learning classified HR-ASD at chance level, and broader atypical development with 69.2% Area Under the Curve (AUC). At 14 months, ASD and broader atypical development were classified with approximately 71% AUC. Thus, prediction of ASD was only possible with moderate accuracy at 14 months. En ligne : http://dx.doi.org/10.1007/s10803-018-3509-x Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=367
in Journal of Autism and Developmental Disorders > 48-7 (July 2018) . - p.2418-2433[article] Prediction of Autism at 3 Years from Behavioural and Developmental Measures in High-Risk Infants: A Longitudinal Cross-Domain Classifier Analysis [Texte imprimé et/ou numérique] / G. BUSSU, Auteur ; E. J. H. JONES, Auteur ; Tony CHARMAN, Auteur ; M. H. JOHNSON, Auteur ; Jan K. BUITELAAR, Auteur . - p.2418-2433.
Langues : Anglais (eng)
in Journal of Autism and Developmental Disorders > 48-7 (July 2018) . - p.2418-2433
Mots-clés : Autism Data integration Early prediction High-risk Individual prediction Longitudinal study Machine learning Index. décimale : PER Périodiques Résumé : We integrated multiple behavioural and developmental measures from multiple time-points using machine learning to improve early prediction of individual Autism Spectrum Disorder (ASD) outcome. We examined Mullen Scales of Early Learning, Vineland Adaptive Behavior Scales, and early ASD symptoms between 8 and 36 months in high-risk siblings (HR; n = 161) and low-risk controls (LR; n = 71). Longitudinally, LR and HR-Typical showed higher developmental level and functioning, and fewer ASD symptoms than HR-Atypical and HR-ASD. At 8 months, machine learning classified HR-ASD at chance level, and broader atypical development with 69.2% Area Under the Curve (AUC). At 14 months, ASD and broader atypical development were classified with approximately 71% AUC. Thus, prediction of ASD was only possible with moderate accuracy at 14 months. En ligne : http://dx.doi.org/10.1007/s10803-018-3509-x Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=367 Temperament as an Early Risk Marker for Autism Spectrum Disorders? A Longitudinal Study of High-Risk and Low-Risk Infants / M. K. J. PIJL in Journal of Autism and Developmental Disorders, 49-5 (May 2019)
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Titre : Temperament as an Early Risk Marker for Autism Spectrum Disorders? A Longitudinal Study of High-Risk and Low-Risk Infants Type de document : Texte imprimé et/ou numérique Auteurs : M. K. J. PIJL, Auteur ; G. BUSSU, Auteur ; Tony CHARMAN, Auteur ; M. H. JOHNSON, Auteur ; E. J. H. JONES, Auteur ; G. PASCO, Auteur ; I. J. OOSTERLING, Auteur ; Nanda N. ROMMELSE, Auteur ; Jan K. BUITELAAR, Auteur Article en page(s) : p.1825-1836 Langues : Anglais (eng) Mots-clés : Autism spectrum disorder High-risk Longitudinal Machine learning Temperament Index. décimale : PER Périodiques Résumé : To investigate temperament as an early risk marker for autism spectrum disorder (ASD), we examined parent-reported temperament for high-risk (HR, n = 170) and low-risk (LR, n = 77) siblings at 8, 14, and 24 months. Diagnostic assessment was performed at 36 months. Group-based analyses showed linear risk gradients, with more atypical temperament for HR-ASD, followed by HR-Atypical, HR-Typical, and LR siblings. Temperament differed significantly between outcome groups (0.03 = etap(2) = 0.34). Machine learning analyses showed that, at an individual level, HR-ASD siblings could not be identified accurately, whereas HR infants without ASD could. Our results emphasize the discrepancy between group-based and individual-based predictions and suggest that while temperament does not facilitate early identification of ASD individually, it may help identify HR infants who do not develop ASD. En ligne : http://dx.doi.org/10.1007/s10803-018-3855-8 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=393
in Journal of Autism and Developmental Disorders > 49-5 (May 2019) . - p.1825-1836[article] Temperament as an Early Risk Marker for Autism Spectrum Disorders? A Longitudinal Study of High-Risk and Low-Risk Infants [Texte imprimé et/ou numérique] / M. K. J. PIJL, Auteur ; G. BUSSU, Auteur ; Tony CHARMAN, Auteur ; M. H. JOHNSON, Auteur ; E. J. H. JONES, Auteur ; G. PASCO, Auteur ; I. J. OOSTERLING, Auteur ; Nanda N. ROMMELSE, Auteur ; Jan K. BUITELAAR, Auteur . - p.1825-1836.
Langues : Anglais (eng)
in Journal of Autism and Developmental Disorders > 49-5 (May 2019) . - p.1825-1836
Mots-clés : Autism spectrum disorder High-risk Longitudinal Machine learning Temperament Index. décimale : PER Périodiques Résumé : To investigate temperament as an early risk marker for autism spectrum disorder (ASD), we examined parent-reported temperament for high-risk (HR, n = 170) and low-risk (LR, n = 77) siblings at 8, 14, and 24 months. Diagnostic assessment was performed at 36 months. Group-based analyses showed linear risk gradients, with more atypical temperament for HR-ASD, followed by HR-Atypical, HR-Typical, and LR siblings. Temperament differed significantly between outcome groups (0.03 = etap(2) = 0.34). Machine learning analyses showed that, at an individual level, HR-ASD siblings could not be identified accurately, whereas HR infants without ASD could. Our results emphasize the discrepancy between group-based and individual-based predictions and suggest that while temperament does not facilitate early identification of ASD individually, it may help identify HR infants who do not develop ASD. En ligne : http://dx.doi.org/10.1007/s10803-018-3855-8 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=393