Phenotypic characterization of individuals with SYNGAP1 pathogenic variants reveals a potential correlation between posterior dominant rhythm and developmental progression / A. JIMENEZ-GOMEZ in Journal of Neurodevelopmental Disorders, 11-1 (December 2019)  

Public ISBD [article]  inJournal of Neurodevelopmental Disorders > 11-1 (December 2019) . - 18 p. Titre : Phenotypic characterization of individuals with SYNGAP1 pathogenic variants reveals a potential correlation between posterior dominant rhythm and developmental progression Type de document : Texte imprimé et/ou numérique Auteurs : A. JIMENEZ-GOMEZ, Auteur ; S. NIU, Auteur ; F. ANDUJAR-PEREZ, Auteur ; E. A. MCQUADE, Auteur ; A. BALASA, Auteur ; D. HUSS, Auteur ; R. COORG, Auteur ; M. QUACH, Auteur ; S. VINSON, Auteur ; S. RISEN, Auteur ; J. L. HOLDER, Auteur Article en page(s) : 18 p. Langues : Anglais (eng) Mots-clés : Autism  Electroencephalogram  Neurodevelopment  Posterior dominant rhythm  Syngap1 Index. décimale : PER Périodiques Résumé : BACKGROUND: The SYNGAP1 gene encodes for a small GTPase-regulating protein critical to dendritic spine maturation and synaptic plasticity. Mutations have recently been identified to cause a breadth of neurodevelopmental disorders including autism, intellectual disability, and epilepsy. The purpose of this work is to define the phenotypic spectrum of SYNGAP1 gene mutations and identify potential biomarkers of clinical severity and developmental progression. METHODS: A retrospective clinical data analysis of individuals with SYNGAP1 mutations was conducted. Data included genetic diagnosis, clinical history and examinations, neurophysiologic data, neuroimaging, and serial neurodevelopmental/behavioral assessments. All patients were seen longitudinally within a 6-year period; data analysis was completed on June 30, 2018. Records for all individuals diagnosed with deleterious SYNGAP1 variants (by clinical sequencing or exome sequencing panels) were reviewed. RESULTS: Fifteen individuals (53% male) with seventeen unique SYNGAP1 mutations are reported. Mean age at genetic diagnosis was 65.9 months (28-174 months). All individuals had epilepsy, with atypical absence seizures being the most common semiology (60%). EEG abnormalities included intermittent rhythmic delta activity (60%), slow or absent posterior dominant rhythm (87%), and epileptiform activity (93%), with generalized discharges being more common than focal. Neuroimaging revealed nonspecific abnormalities (53%). Neurodevelopmental evaluation revealed impairment in all individuals, with gross motor function being the least affected. Autism spectrum disorder was diagnosed in 73% and aggression in 60% of cases. Analysis of biomarkers revealed a trend toward a moderate positive correlation between visual-perceptual/fine motor/adaptive skills and language development, with posterior dominant rhythm on electroencephalogram (EEG), independent of age. No other neurophysiology-development associations or correlations were identified. CONCLUSIONS: A broad spectrum of neurologic and neurodevelopmental features are found with pathogenic variants of SYNGAP1. An abnormal posterior dominant rhythm on EEG correlated with abnormal developmental progression, providing a possible prognostic biomarker. En ligne : https://dx.doi.org/10.1186/s11689-019-9276-y Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=409 [article] Phenotypic characterization of individuals with SYNGAP1 pathogenic variants reveals a potential correlation between posterior dominant rhythm and developmental progression [Texte imprimé et/ou numérique] / A. JIMENEZ-GOMEZ, Auteur ; S. NIU, Auteur ; F. ANDUJAR-PEREZ, Auteur ; E. A. MCQUADE, Auteur ; A. BALASA, Auteur ; D. HUSS, Auteur ; R. COORG, Auteur ; M. QUACH, Auteur ; S. VINSON, Auteur ; S. RISEN, Auteur ; J. L. HOLDER, Auteur . - 18 p. Langues : Anglais (eng) in Journal of Neurodevelopmental Disorders > 11-1 (December 2019) . - 18 p. Mots-clés : Autism  Electroencephalogram  Neurodevelopment  Posterior dominant rhythm  Syngap1 Index. décimale : PER Périodiques Résumé : BACKGROUND: The SYNGAP1 gene encodes for a small GTPase-regulating protein critical to dendritic spine maturation and synaptic plasticity. Mutations have recently been identified to cause a breadth of neurodevelopmental disorders including autism, intellectual disability, and epilepsy. The purpose of this work is to define the phenotypic spectrum of SYNGAP1 gene mutations and identify potential biomarkers of clinical severity and developmental progression. METHODS: A retrospective clinical data analysis of individuals with SYNGAP1 mutations was conducted. Data included genetic diagnosis, clinical history and examinations, neurophysiologic data, neuroimaging, and serial neurodevelopmental/behavioral assessments. All patients were seen longitudinally within a 6-year period; data analysis was completed on June 30, 2018. Records for all individuals diagnosed with deleterious SYNGAP1 variants (by clinical sequencing or exome sequencing panels) were reviewed. RESULTS: Fifteen individuals (53% male) with seventeen unique SYNGAP1 mutations are reported. Mean age at genetic diagnosis was 65.9 months (28-174 months). All individuals had epilepsy, with atypical absence seizures being the most common semiology (60%). EEG abnormalities included intermittent rhythmic delta activity (60%), slow or absent posterior dominant rhythm (87%), and epileptiform activity (93%), with generalized discharges being more common than focal. Neuroimaging revealed nonspecific abnormalities (53%). Neurodevelopmental evaluation revealed impairment in all individuals, with gross motor function being the least affected. Autism spectrum disorder was diagnosed in 73% and aggression in 60% of cases. Analysis of biomarkers revealed a trend toward a moderate positive correlation between visual-perceptual/fine motor/adaptive skills and language development, with posterior dominant rhythm on electroencephalogram (EEG), independent of age. No other neurophysiology-development associations or correlations were identified. CONCLUSIONS: A broad spectrum of neurologic and neurodevelopmental features are found with pathogenic variants of SYNGAP1. An abnormal posterior dominant rhythm on EEG correlated with abnormal developmental progression, providing a possible prognostic biomarker. En ligne : https://dx.doi.org/10.1186/s11689-019-9276-y Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=409

Unified framework for early stage status prediction of autism based on infant structural magnetic resonance imaging / K. GAO in Autism Research, 14-12 (December 2021)  

Public ISBD [article]  inAutism Research > 14-12 (December 2021) . - p.2512-2523 Titre : Unified framework for early stage status prediction of autism based on infant structural magnetic resonance imaging Type de document : Texte imprimé et/ou numérique Auteurs : K. GAO, Auteur ; Y. SUN, Auteur ; S. NIU, Auteur ; L. WANG, Auteur Article en page(s) : p.2512-2523 Langues : Anglais (eng) Mots-clés : Autism Spectrum Disorder/diagnostic imaging  Autistic Disorder  Brain/diagnostic imaging  Child, Preschool  Humans  Infant  Magnetic Resonance Imaging  Neuroimaging  autism Spectrum disorder (ASD)  deep learning algorithm  early-stage status prediction  infant structural MRI  subject-specific autism attention  interest. Index. décimale : PER Périodiques Résumé : Autism, or autism spectrum disorder (ASD), is a developmental disability that is diagnosed at about 2?years of age based on abnormal behaviors. Existing neuroimaging-based methods for the prediction of ASD typically focus on functional magnetic resonance imaging (fMRI); however, most of these fMRI-based studies include subjects older than 5?years of age. Due to challenges in the application of fMRI for infants, structural magnetic resonance imaging (sMRI) has increasingly received attention in the field for early status prediction of ASD. In this study, we propose an automated prediction framework based on infant sMRI at about 24?months of age. Specifically, by leveraging an infant-dedicated pipeline, iBEAT V2.0 Cloud, we derived segmentation and parcellation maps from infant sMRI. We employed a convolutional neural network to extract features from pairwise maps and a Siamese network to distinguish whether paired subjects were from the same or different classes. As compared to T1w imaging without segmentation and parcellation maps, our proposed approach with segmentation and parcellation maps yielded greater sensitivity, specificity, and accuracy of ASD prediction, which was validated using two datasets with different imaging protocols/scanners and was confirmed by receiver operating characteristic analysis. Furthermore, comparison with state-of-the-art methods demonstrated the superior effectiveness and robustness of the proposed method. Finally, attention maps were generated to identify subject-specific autism effects, supporting the reasonability of the predictive results. Collectively, these findings demonstrate the utility of our unified framework for the early-stage status prediction of ASD by sMRI. LAY SUMMARY: The status prediction of autism spectrum disorder (ASD) at an early age is highly desirable, as early intervention may significantly reduce autism symptoms. However, current methods for diagnosing young children are limited to behavioral assays. In this study, we propose an automated method for ASD status prediction at the age of 24?months that uses infant structural magnetic resonance imaging to identify neural features. En ligne : http://dx.doi.org/10.1002/aur.2626 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=450 [article] Unified framework for early stage status prediction of autism based on infant structural magnetic resonance imaging [Texte imprimé et/ou numérique] / K. GAO, Auteur ; Y. SUN, Auteur ; S. NIU, Auteur ; L. WANG, Auteur . - p.2512-2523. Langues : Anglais (eng) in Autism Research > 14-12 (December 2021) . - p.2512-2523 Mots-clés : Autism Spectrum Disorder/diagnostic imaging  Autistic Disorder  Brain/diagnostic imaging  Child, Preschool  Humans  Infant  Magnetic Resonance Imaging  Neuroimaging  autism Spectrum disorder (ASD)  deep learning algorithm  early-stage status prediction  infant structural MRI  subject-specific autism attention  interest. Index. décimale : PER Périodiques Résumé : Autism, or autism spectrum disorder (ASD), is a developmental disability that is diagnosed at about 2?years of age based on abnormal behaviors. Existing neuroimaging-based methods for the prediction of ASD typically focus on functional magnetic resonance imaging (fMRI); however, most of these fMRI-based studies include subjects older than 5?years of age. Due to challenges in the application of fMRI for infants, structural magnetic resonance imaging (sMRI) has increasingly received attention in the field for early status prediction of ASD. In this study, we propose an automated prediction framework based on infant sMRI at about 24?months of age. Specifically, by leveraging an infant-dedicated pipeline, iBEAT V2.0 Cloud, we derived segmentation and parcellation maps from infant sMRI. We employed a convolutional neural network to extract features from pairwise maps and a Siamese network to distinguish whether paired subjects were from the same or different classes. As compared to T1w imaging without segmentation and parcellation maps, our proposed approach with segmentation and parcellation maps yielded greater sensitivity, specificity, and accuracy of ASD prediction, which was validated using two datasets with different imaging protocols/scanners and was confirmed by receiver operating characteristic analysis. Furthermore, comparison with state-of-the-art methods demonstrated the superior effectiveness and robustness of the proposed method. Finally, attention maps were generated to identify subject-specific autism effects, supporting the reasonability of the predictive results. Collectively, these findings demonstrate the utility of our unified framework for the early-stage status prediction of ASD by sMRI. LAY SUMMARY: The status prediction of autism spectrum disorder (ASD) at an early age is highly desirable, as early intervention may significantly reduce autism symptoms. However, current methods for diagnosing young children are limited to behavioral assays. In this study, we propose an automated method for ASD status prediction at the age of 24?months that uses infant structural magnetic resonance imaging to identify neural features. En ligne : http://dx.doi.org/10.1002/aur.2626 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=450

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