Identifying the neurodevelopmental and psychiatric signatures of genomic disorders associated with intellectual disability: a machine learning approach / Adam CUNNINGHAM ; Sergio Marco SALAS ; Matthew BRACHER-SMITH ; Samuel CHAWNER ; Jan STOCHL ; Tamsin FORD ; F. Lucy RAYMOND ; Valentina ESCOTT PRICE ; Marianne B.M. VAN DEN BREE in Molecular Autism, 14 (2023)  

Public ISBD [article]  inMolecular Autism > 14 (2023) . - 19 p. Titre : Identifying the neurodevelopmental and psychiatric signatures of genomic disorders associated with intellectual disability: a machine learning approach Type de document : texte imprimé Auteurs : Adam CUNNINGHAM, Auteur ; Sergio Marco SALAS, Auteur ; Matthew BRACHER-SMITH, Auteur ; Samuel CHAWNER, Auteur ; Jan STOCHL, Auteur ; Tamsin FORD, Auteur ; F. Lucy RAYMOND, Auteur ; Valentina ESCOTT PRICE, Auteur ; Marianne B.M. VAN DEN BREE, Auteur Article en page(s) : 19 p. Langues : Anglais (eng) Index. décimale : PER Périodiques Résumé : BACKGROUND: Genomic conditions can be associated with developmental delay, intellectual disability, autism spectrum disorder, and physical and mental health symptoms. They are individually rare and highly variable in presentation, which limits the use of standard clinical guidelines for diagnosis and treatment. A simple screening tool to identify young people with genomic conditions associated with neurodevelopmental disorders (ND-GCs) who could benefit from further support would be of considerable value. We used machine learning approaches to address this question. METHOD: A total of 493 individuals were included: 389 with a ND-GC, mean age=9.01, 66% male) and 104 siblings without known genomic conditions (controls, mean age=10.23, 53% male). Primary carers completed assessments of behavioural, neurodevelopmental and psychiatric symptoms and physical health and development. Machine learning techniques (penalised logistic regression, random forests, support vector machines and artificial neural networks) were used to develop classifiers of ND-GC status and identified limited sets of variables that gave the best classification performance. Exploratory graph analysis was used to understand associations within the final variable set. RESULTS: All machine learning methods identified variable sets giving high classification accuracy (AUROC between 0.883 and 0.915). We identified a subset of 30 variables best discriminating between individuals with ND-GCs and controls which formed 5 dimensions: conduct, separation anxiety, situational anxiety, communication and motor development. LIMITATIONS: This study used cross-sectional data from a cohort study which was imbalanced with respect to ND-GC status. Our model requires validation in independent datasets and with longitudinal follow-up data for validation before clinical application. CONCLUSIONS: In this study, we developed models that identified a compact set of psychiatric and physical health measures that differentiate individuals with a ND-GC from controls and highlight higher-order structure within these measures. This work is a step towards developing a screening instrument to identify young people with ND-GCs who might benefit from further specialist assessment. En ligne : http://dx.doi.org/10.1186/s13229-023-00549-2 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=513 [article] Identifying the neurodevelopmental and psychiatric signatures of genomic disorders associated with intellectual disability: a machine learning approach [texte imprimé] / Adam CUNNINGHAM, Auteur ; Sergio Marco SALAS, Auteur ; Matthew BRACHER-SMITH, Auteur ; Samuel CHAWNER, Auteur ; Jan STOCHL, Auteur ; Tamsin FORD, Auteur ; F. Lucy RAYMOND, Auteur ; Valentina ESCOTT PRICE, Auteur ; Marianne B.M. VAN DEN BREE, Auteur . - 19 p. Langues : Anglais (eng) in Molecular Autism > 14 (2023) . - 19 p. Index. décimale : PER Périodiques Résumé : BACKGROUND: Genomic conditions can be associated with developmental delay, intellectual disability, autism spectrum disorder, and physical and mental health symptoms. They are individually rare and highly variable in presentation, which limits the use of standard clinical guidelines for diagnosis and treatment. A simple screening tool to identify young people with genomic conditions associated with neurodevelopmental disorders (ND-GCs) who could benefit from further support would be of considerable value. We used machine learning approaches to address this question. METHOD: A total of 493 individuals were included: 389 with a ND-GC, mean age=9.01, 66% male) and 104 siblings without known genomic conditions (controls, mean age=10.23, 53% male). Primary carers completed assessments of behavioural, neurodevelopmental and psychiatric symptoms and physical health and development. Machine learning techniques (penalised logistic regression, random forests, support vector machines and artificial neural networks) were used to develop classifiers of ND-GC status and identified limited sets of variables that gave the best classification performance. Exploratory graph analysis was used to understand associations within the final variable set. RESULTS: All machine learning methods identified variable sets giving high classification accuracy (AUROC between 0.883 and 0.915). We identified a subset of 30 variables best discriminating between individuals with ND-GCs and controls which formed 5 dimensions: conduct, separation anxiety, situational anxiety, communication and motor development. LIMITATIONS: This study used cross-sectional data from a cohort study which was imbalanced with respect to ND-GC status. Our model requires validation in independent datasets and with longitudinal follow-up data for validation before clinical application. CONCLUSIONS: In this study, we developed models that identified a compact set of psychiatric and physical health measures that differentiate individuals with a ND-GC from controls and highlight higher-order structure within these measures. This work is a step towards developing a screening instrument to identify young people with ND-GCs who might benefit from further specialist assessment. En ligne : http://dx.doi.org/10.1186/s13229-023-00549-2 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=513

Using kinematic analyses to explore sensorimotor control impairments in children with 22q11.2 deletion syndrome / Adam C. CUNNINGHAM in Journal of Neurodevelopmental Disorders, 11-1 (December 2019)  

Public ISBD [article]  inJournal of Neurodevelopmental Disorders > 11-1 (December 2019) . - 8 p. Titre : Using kinematic analyses to explore sensorimotor control impairments in children with 22q11.2 deletion syndrome Type de document : texte imprimé Auteurs : Adam C. CUNNINGHAM, Auteur ; Liam HILL, Auteur ; Mark MON-WILLIAMS, Auteur ; Kathryn J. PEALL, Auteur ; David E.J. LINDEN, Auteur ; Jeremy HALL, Auteur ; Michael J. OWEN, Auteur ; Marianne B.M. VAN DEN BREE, Auteur Article en page(s) : 8 p. Langues : Anglais (eng) Mots-clés : 22q11.2 deletion syndrome  Adhd  Asd  Anxiety  Coordination  Movement difficulties Index. décimale : PER Périodiques Résumé : BACKGROUND: The 22q11.2 deletion is associated with psychiatric and behavioural disorders, intellectual disability and multiple physical abnormalities. Recent research also indicates impaired coordination skills may be part of the clinical phenotype. This study aimed to characterise sensorimotor control abilities in children with 22q11.2 deletion syndrome (22q11.2DS) and investigate their relationships with co-occurring IQ impairments and psychopathology. METHODS: Fifty-four children with 22q11.2DS and 24 unaffected sibling controls, comparable in age and gender, underwent kinematic analysis of their hand movements, whilst performing a battery of three visuo-manual coordination tasks that measured their tracking, aiming and steering abilities. Additionally, standardised assessments of full-scale IQ (FSIQ), attention deficit hyperactivity disorder, indicative autism spectrum disorder (ASD) and anxiety disorder symptomatology were conducted. RESULTS: Children with 22q11.2DS showed deficits on seven of eight kinematic descriptors of movement quality across the three coordination tasks, compared to controls. Within 22q11.2DS cases, the extent of impairment on only three kinematic descriptors was significantly related to FSIQ after correction for multiple testing. Moreover, only error whilst visuo-manually tracking was nominally associated with ADHD symptom counts. CONCLUSIONS: Impairments in sensorimotor control are seen on a range of visuo-manual tasks in children with 22q11.2DS but the extent of these impairments are largely unrelated to the severity of other psychopathological and intellectual impairments commonly found in children with 22q11.2DS. En ligne : https://dx.doi.org/10.1186/s11689-019-9271-3 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=409 [article] Using kinematic analyses to explore sensorimotor control impairments in children with 22q11.2 deletion syndrome [texte imprimé] / Adam C. CUNNINGHAM, Auteur ; Liam HILL, Auteur ; Mark MON-WILLIAMS, Auteur ; Kathryn J. PEALL, Auteur ; David E.J. LINDEN, Auteur ; Jeremy HALL, Auteur ; Michael J. OWEN, Auteur ; Marianne B.M. VAN DEN BREE, Auteur . - 8 p. Langues : Anglais (eng) in Journal of Neurodevelopmental Disorders > 11-1 (December 2019) . - 8 p. Mots-clés : 22q11.2 deletion syndrome  Adhd  Asd  Anxiety  Coordination  Movement difficulties Index. décimale : PER Périodiques Résumé : BACKGROUND: The 22q11.2 deletion is associated with psychiatric and behavioural disorders, intellectual disability and multiple physical abnormalities. Recent research also indicates impaired coordination skills may be part of the clinical phenotype. This study aimed to characterise sensorimotor control abilities in children with 22q11.2 deletion syndrome (22q11.2DS) and investigate their relationships with co-occurring IQ impairments and psychopathology. METHODS: Fifty-four children with 22q11.2DS and 24 unaffected sibling controls, comparable in age and gender, underwent kinematic analysis of their hand movements, whilst performing a battery of three visuo-manual coordination tasks that measured their tracking, aiming and steering abilities. Additionally, standardised assessments of full-scale IQ (FSIQ), attention deficit hyperactivity disorder, indicative autism spectrum disorder (ASD) and anxiety disorder symptomatology were conducted. RESULTS: Children with 22q11.2DS showed deficits on seven of eight kinematic descriptors of movement quality across the three coordination tasks, compared to controls. Within 22q11.2DS cases, the extent of impairment on only three kinematic descriptors was significantly related to FSIQ after correction for multiple testing. Moreover, only error whilst visuo-manually tracking was nominally associated with ADHD symptom counts. CONCLUSIONS: Impairments in sensorimotor control are seen on a range of visuo-manual tasks in children with 22q11.2DS but the extent of these impairments are largely unrelated to the severity of other psychopathological and intellectual impairments commonly found in children with 22q11.2DS. En ligne : https://dx.doi.org/10.1186/s11689-019-9271-3 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=409

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