Profiles of autism characteristics in thirteen genetic syndromes: a machine learning approach / Alice WELHAM ; Dawn ADAMS ; Stacey BISSELL ; Hilgo BRUINING ; Hayley CRAWFORD ; Kate EDEN ; Lisa NELSON ; Christopher OLIVER ; Laurie POWIS ; Caroline RICHARDS ; Jane WAITE ; Peter WATSON ; Hefin RHYS ; Lucy WILDE ; Kate WOODCOCK ; Joanna MOSS in Molecular Autism, 14 (2023)  

Public ISBD [article]  inMolecular Autism > 14 (2023) . - 3 p. Titre : Profiles of autism characteristics in thirteen genetic syndromes: a machine learning approach Type de document : texte imprimé Auteurs : Alice WELHAM, Auteur ; Dawn ADAMS, Auteur ; Stacey BISSELL, Auteur ; Hilgo BRUINING, Auteur ; Hayley CRAWFORD, Auteur ; Kate EDEN, Auteur ; Lisa NELSON, Auteur ; Christopher OLIVER, Auteur ; Laurie POWIS, Auteur ; Caroline RICHARDS, Auteur ; Jane WAITE, Auteur ; Peter WATSON, Auteur ; Hefin RHYS, Auteur ; Lucy WILDE, Auteur ; Kate WOODCOCK, Auteur ; Joanna MOSS, Auteur Article en page(s) : 3 p. Langues : Anglais (eng) Index. décimale : PER Périodiques Résumé : BACKGROUND: Phenotypic studies have identified distinct patterns of autistic characteristics in genetic syndromes associated with intellectual disability (ID), leading to diagnostic uncertainty and compromised access to autism-related support. Previous research has tended to include small samples and diverse measures, which limits the generalisability of findings. In this study, we generated detailed profiles of autistic characteristics in a large sample of>1500 individuals with rare genetic syndromes. METHODS: Profiles of autistic characteristics based on the Social Communication Questionnaire (SCQ) scores were generated for thirteen genetic syndrome groups (Angelman n=154, Cri du Chat n=75, Cornelia de Lange n=199, fragile X n=297, Prader-Willi n=278, Lowe n=89, Smith-Magenis n=54, Down n=135, Sotos n=40, Rubinstein-Taybi n=102, 1p36 deletion n=41, tuberous sclerosis complex n=83 and Phelan-McDermid n=35 syndromes). It was hypothesised that each syndrome group would evidence a degree of specificity in autistic characteristics. To test this hypothesis, a classification algorithm via support vector machine (SVM) learning was applied to scores from over 1500 individuals diagnosed with one of the thirteen genetic syndromes and autistic individuals who did not have a known genetic syndrome (ASD; n=254). Self-help skills were included as an additional predictor. RESULTS: Genetic syndromes were associated with different but overlapping autism-related profiles, indicated by the substantial accuracy of the entire, multiclass SVM model (55% correctly classified individuals). Syndrome groups such as Angelman, fragile X, Prader-Willi, Rubinstein-Taybi and Cornelia de Lange showed greater phenotypic specificity than groups such as Cri du Chat, Lowe, Smith-Magenis, tuberous sclerosis complex, Sotos and Phelan-McDermid. The inclusion of the ASD reference group and self-help skills did not change the model accuracy. LIMITATIONS: The key limitations of our study include a cross-sectional design, reliance on a screening tool which focuses primarily on social communication skills and imbalanced sample size across syndrome groups. CONCLUSIONS: These findings replicate and extend previous work, demonstrating syndrome-specific profiles of autistic characteristics in people with genetic syndromes compared to autistic individuals without a genetic syndrome. This work calls for greater precision of assessment of autistic characteristics in individuals with genetic syndromes associated with ID. En ligne : http://dx.doi.org/10.1186/s13229-022-00530-5 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=513 [article] Profiles of autism characteristics in thirteen genetic syndromes: a machine learning approach [texte imprimé] / Alice WELHAM, Auteur ; Dawn ADAMS, Auteur ; Stacey BISSELL, Auteur ; Hilgo BRUINING, Auteur ; Hayley CRAWFORD, Auteur ; Kate EDEN, Auteur ; Lisa NELSON, Auteur ; Christopher OLIVER, Auteur ; Laurie POWIS, Auteur ; Caroline RICHARDS, Auteur ; Jane WAITE, Auteur ; Peter WATSON, Auteur ; Hefin RHYS, Auteur ; Lucy WILDE, Auteur ; Kate WOODCOCK, Auteur ; Joanna MOSS, Auteur . - 3 p. Langues : Anglais (eng) in Molecular Autism > 14 (2023) . - 3 p. Index. décimale : PER Périodiques Résumé : BACKGROUND: Phenotypic studies have identified distinct patterns of autistic characteristics in genetic syndromes associated with intellectual disability (ID), leading to diagnostic uncertainty and compromised access to autism-related support. Previous research has tended to include small samples and diverse measures, which limits the generalisability of findings. In this study, we generated detailed profiles of autistic characteristics in a large sample of>1500 individuals with rare genetic syndromes. METHODS: Profiles of autistic characteristics based on the Social Communication Questionnaire (SCQ) scores were generated for thirteen genetic syndrome groups (Angelman n=154, Cri du Chat n=75, Cornelia de Lange n=199, fragile X n=297, Prader-Willi n=278, Lowe n=89, Smith-Magenis n=54, Down n=135, Sotos n=40, Rubinstein-Taybi n=102, 1p36 deletion n=41, tuberous sclerosis complex n=83 and Phelan-McDermid n=35 syndromes). It was hypothesised that each syndrome group would evidence a degree of specificity in autistic characteristics. To test this hypothesis, a classification algorithm via support vector machine (SVM) learning was applied to scores from over 1500 individuals diagnosed with one of the thirteen genetic syndromes and autistic individuals who did not have a known genetic syndrome (ASD; n=254). Self-help skills were included as an additional predictor. RESULTS: Genetic syndromes were associated with different but overlapping autism-related profiles, indicated by the substantial accuracy of the entire, multiclass SVM model (55% correctly classified individuals). Syndrome groups such as Angelman, fragile X, Prader-Willi, Rubinstein-Taybi and Cornelia de Lange showed greater phenotypic specificity than groups such as Cri du Chat, Lowe, Smith-Magenis, tuberous sclerosis complex, Sotos and Phelan-McDermid. The inclusion of the ASD reference group and self-help skills did not change the model accuracy. LIMITATIONS: The key limitations of our study include a cross-sectional design, reliance on a screening tool which focuses primarily on social communication skills and imbalanced sample size across syndrome groups. CONCLUSIONS: These findings replicate and extend previous work, demonstrating syndrome-specific profiles of autistic characteristics in people with genetic syndromes compared to autistic individuals without a genetic syndrome. This work calls for greater precision of assessment of autistic characteristics in individuals with genetic syndromes associated with ID. En ligne : http://dx.doi.org/10.1186/s13229-022-00530-5 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=513

Self-injury and aggression in tuberous sclerosis complex: cross syndrome comparison and associated risk markers / Kate E. EDEN in Journal of Neurodevelopmental Disorders, 6-1 (December 2014)  

Public ISBD [article]  inJournal of Neurodevelopmental Disorders > 6-1 (December 2014) . - p.10 Titre : Self-injury and aggression in tuberous sclerosis complex: cross syndrome comparison and associated risk markers Type de document : texte imprimé Auteurs : Kate E. EDEN, Auteur ; Petrus J. DE VRIES, Auteur ; Jo MOSS, Auteur ; Caroline RICHARDS, Auteur ; Chris OLIVER, Auteur Article en page(s) : p.10 Langues : Anglais (eng) Mots-clés : Asd  Aggression  Impulsivity  Pain  Repetitive/stereotyped behaviour  Self-injury  Tuberous sclerosis complex Index. décimale : PER Périodiques Résumé : BACKGROUND: Research reporting prevalence rates of self-injurious and aggressive behaviour in people with tuberous sclerosis complex (TSC) is limited. No studies have compared rates of these behaviours in TSC with those in other syndrome groups matched for degree of disability or investigated risk markers for these behaviours in TSC. METHODS: Data from the Challenging Behaviour Questionnaire were collected for 37 children, aged 4 to 15 years, with TSC. Odds ratios were used to compare rates of self-injury and aggression in children with TSC with children with idiopathic autism spectrum disorder (ASD), fragile X, Cornelia de Lange and Down syndromes. Characteristics were measured using the Mood Interest and Pleasure Questionnaire, the Activity Questionnaire, the Social Communication Questionnaire, the Repetitive Behaviour Questionnaire, the Wessex Behaviour Schedule and the revised Non-communicating Children Pain Checklist. Mann-Whitney U analyses were used to compare characteristics between individuals with self-injury and aggression and those not showing these behaviours. RESULTS: Rates of self-injury and aggression in TSC were 27% and 50%, respectively. These are high but not significantly different from rates in children with Down syndrome or other syndrome groups. Both self-injury and aggression were associated with stereotyped and pain-related behaviours, low mood, hyperactivity, impulsivity and repetitive use of language. Children who engaged in self-injury also had lower levels of interest and pleasure and showed a greater degree of 'insistence on sameness' than children who did not self-injure. Aggression was associated with repetitive behaviour. The majority of these associations remained significant when the association with level of adaptive functioning was controlled for. CONCLUSIONS: Behavioural profiles can be used to identify those most at risk of developing self-injury and aggression. Further research is warranted to understand the influence of such internal factors as mood, ASD symptomatology and pain on challenging behaviour in people with intellectual disability. En ligne : http://dx.doi.org/10.1186/1866-1955-6-10 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=346 [article] Self-injury and aggression in tuberous sclerosis complex: cross syndrome comparison and associated risk markers [texte imprimé] / Kate E. EDEN, Auteur ; Petrus J. DE VRIES, Auteur ; Jo MOSS, Auteur ; Caroline RICHARDS, Auteur ; Chris OLIVER, Auteur . - p.10. Langues : Anglais (eng) in Journal of Neurodevelopmental Disorders > 6-1 (December 2014) . - p.10 Mots-clés : Asd  Aggression  Impulsivity  Pain  Repetitive/stereotyped behaviour  Self-injury  Tuberous sclerosis complex Index. décimale : PER Périodiques Résumé : BACKGROUND: Research reporting prevalence rates of self-injurious and aggressive behaviour in people with tuberous sclerosis complex (TSC) is limited. No studies have compared rates of these behaviours in TSC with those in other syndrome groups matched for degree of disability or investigated risk markers for these behaviours in TSC. METHODS: Data from the Challenging Behaviour Questionnaire were collected for 37 children, aged 4 to 15 years, with TSC. Odds ratios were used to compare rates of self-injury and aggression in children with TSC with children with idiopathic autism spectrum disorder (ASD), fragile X, Cornelia de Lange and Down syndromes. Characteristics were measured using the Mood Interest and Pleasure Questionnaire, the Activity Questionnaire, the Social Communication Questionnaire, the Repetitive Behaviour Questionnaire, the Wessex Behaviour Schedule and the revised Non-communicating Children Pain Checklist. Mann-Whitney U analyses were used to compare characteristics between individuals with self-injury and aggression and those not showing these behaviours. RESULTS: Rates of self-injury and aggression in TSC were 27% and 50%, respectively. These are high but not significantly different from rates in children with Down syndrome or other syndrome groups. Both self-injury and aggression were associated with stereotyped and pain-related behaviours, low mood, hyperactivity, impulsivity and repetitive use of language. Children who engaged in self-injury also had lower levels of interest and pleasure and showed a greater degree of 'insistence on sameness' than children who did not self-injure. Aggression was associated with repetitive behaviour. The majority of these associations remained significant when the association with level of adaptive functioning was controlled for. CONCLUSIONS: Behavioural profiles can be used to identify those most at risk of developing self-injury and aggression. Further research is warranted to understand the influence of such internal factors as mood, ASD symptomatology and pain on challenging behaviour in people with intellectual disability. En ligne : http://dx.doi.org/10.1186/1866-1955-6-10 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=346

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