5 recherche sur le mot-clé 'Clustering'

Verbal fluency in children with autism spectrum disorders: Clustering and switching strategies / Sander BEGEER in Autism, 18-8 (November 2014)  

Public ISBD [article]  inAutism > 18-8 (November 2014) . - p.1014-1018 Titre : Verbal fluency in children with autism spectrum disorders: Clustering and switching strategies Type de document : texte imprimé Auteurs : Sander BEGEER, Auteur ; Marlies WIERDA, Auteur ; Anke M. SCHEEREN, Auteur ; Jan-Pieter TEUNISSE, Auteur ; Hans M. KOOT, Auteur ; Hilde M. GEURTS, Auteur Article en page(s) : p.1014-1018 Langues : Anglais (eng) Mots-clés : autism spectrum disorders  clustering  cognitive flexibility  fluency  switching Index. décimale : PER Périodiques Résumé : This study highlights differences in cognitive strategies in children and adolescents with and without autism spectrum disorders (n = 52) on a verbal fluency task (naming as many words as possible (e.g. animals) within 60 s). The ability to form clusters of words (e.g. farm animals like “cow–horse–goat”) or to switch between unrelated words (e.g. “snake” and “cat”) was analyzed using a coding method that more stringently differentiates between these strategies. Results indicated that children and adolescents with autism spectrum disorders switched less frequently, but produced slightly larger clusters than the comparison group, resulting in equal numbers of total words produced. The currently used measures of cognitive flexibility suggest atypical, but possibly equally efficient, fluency styles used by individuals with autism spectrum disorders. En ligne : http://dx.doi.org/10.1177/1362361313500381 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=242 [article] Verbal fluency in children with autism spectrum disorders: Clustering and switching strategies [texte imprimé] / Sander BEGEER, Auteur ; Marlies WIERDA, Auteur ; Anke M. SCHEEREN, Auteur ; Jan-Pieter TEUNISSE, Auteur ; Hans M. KOOT, Auteur ; Hilde M. GEURTS, Auteur . - p.1014-1018. Langues : Anglais (eng) in Autism > 18-8 (November 2014) . - p.1014-1018 Mots-clés : autism spectrum disorders  clustering  cognitive flexibility  fluency  switching Index. décimale : PER Périodiques Résumé : This study highlights differences in cognitive strategies in children and adolescents with and without autism spectrum disorders (n = 52) on a verbal fluency task (naming as many words as possible (e.g. animals) within 60 s). The ability to form clusters of words (e.g. farm animals like “cow–horse–goat”) or to switch between unrelated words (e.g. “snake” and “cat”) was analyzed using a coding method that more stringently differentiates between these strategies. Results indicated that children and adolescents with autism spectrum disorders switched less frequently, but produced slightly larger clusters than the comparison group, resulting in equal numbers of total words produced. The currently used measures of cognitive flexibility suggest atypical, but possibly equally efficient, fluency styles used by individuals with autism spectrum disorders. En ligne : http://dx.doi.org/10.1177/1362361313500381 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=242

A 3D approach to understanding heterogeneity in early developing autisms / Veronica MANDELLI in Molecular Autism, 15 (2024)  

Public ISBD [article]  inMolecular Autism > 15 (2024) . - 41p. Titre : A 3D approach to understanding heterogeneity in early developing autisms Type de document : texte imprimé Auteurs : Veronica MANDELLI, Auteur ; Ines SEVERINO, Auteur ; Lisa T. EYLER, Auteur ; Karen PIERCE, Auteur ; Eric COURCHESNE, Auteur ; Michael V. LOMBARDO, Auteur Article en page(s) : 41p. Langues : Anglais (eng) Mots-clés : Humans  Child, Preschool  Autistic Disorder/diagnostic imaging/diagnosis  Female  Male  Child  Phenotype  Imaging, Three-Dimensional  Clustering  Gene expression  Stratification  Subtypes  fMRI  for the Collection in this journal entitled 'Neuroimaging in Autism Spectrum  Disorders'. All other authors have no competing interests to declare. Index. décimale : PER Périodiques Résumé : BACKGROUND: Phenotypic heterogeneity in early language, intellectual, motor, and adaptive functioning (LIMA) features are amongst the most striking features that distinguish different types of autistic individuals. Yet the current diagnostic criteria uses a single label of autism and implicitly emphasizes what individuals have in common as core social-communicative and restricted repetitive behavior difficulties. Subtype labels based on the non-core LIMA features may help to more meaningfully distinguish types of autisms with differing developmental paths and differential underlying biology. METHODS: Unsupervised data-driven subtypes were identified using stability-based relative clustering validation on publicly available Mullen Scales of Early Learning (MSEL) and Vineland Adaptive Behavior Scales (VABS) data (n = 615; age = 24-68 months) from the National Institute of Mental Health Data Archive (NDA). Differential developmental trajectories between subtypes were tested on longitudinal data from NDA and from an independent in-house dataset from UCSD. A subset of the UCSD dataset was also tested for subtype differences in functional and structural neuroimaging phenotypes and relationships with blood gene expression. The current subtyping model was also compared to early language outcome subtypes derived from past work. RESULTS: Two autism subtypes can be identified based on early phenotypic LIMA features. These data-driven subtypes are robust in the population and can be identified in independent data with 98% accuracy. The subtypes can be described as Type I versus Type II autisms differentiated by relatively high versus low scores on LIMA features. These two types of autisms are also distinguished by different developmental trajectories over the first decade of life. Finally, these two types of autisms reveal striking differences in functional and structural neuroimaging phenotypes and their relationships with gene expression and may highlight unique biological mechanisms. LIMITATIONS: Sample sizes for the neuroimaging and gene expression dataset are relatively small and require further independent replication. The current work is also limited to subtyping based on MSEL and VABS phenotypic measures. CONCLUSIONS: This work emphasizes the potential importance of stratifying autism by a Type I versus Type II distinction focused on LIMA features and which may be of high prognostic and biological significance. En ligne : https://dx.doi.org/10.1186/s13229-024-00613-5 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=538 [article] A 3D approach to understanding heterogeneity in early developing autisms [texte imprimé] / Veronica MANDELLI, Auteur ; Ines SEVERINO, Auteur ; Lisa T. EYLER, Auteur ; Karen PIERCE, Auteur ; Eric COURCHESNE, Auteur ; Michael V. LOMBARDO, Auteur . - 41p. Langues : Anglais (eng) in Molecular Autism > 15 (2024) . - 41p. Mots-clés : Humans  Child, Preschool  Autistic Disorder/diagnostic imaging/diagnosis  Female  Male  Child  Phenotype  Imaging, Three-Dimensional  Clustering  Gene expression  Stratification  Subtypes  fMRI  for the Collection in this journal entitled 'Neuroimaging in Autism Spectrum  Disorders'. All other authors have no competing interests to declare. Index. décimale : PER Périodiques Résumé : BACKGROUND: Phenotypic heterogeneity in early language, intellectual, motor, and adaptive functioning (LIMA) features are amongst the most striking features that distinguish different types of autistic individuals. Yet the current diagnostic criteria uses a single label of autism and implicitly emphasizes what individuals have in common as core social-communicative and restricted repetitive behavior difficulties. Subtype labels based on the non-core LIMA features may help to more meaningfully distinguish types of autisms with differing developmental paths and differential underlying biology. METHODS: Unsupervised data-driven subtypes were identified using stability-based relative clustering validation on publicly available Mullen Scales of Early Learning (MSEL) and Vineland Adaptive Behavior Scales (VABS) data (n = 615; age = 24-68 months) from the National Institute of Mental Health Data Archive (NDA). Differential developmental trajectories between subtypes were tested on longitudinal data from NDA and from an independent in-house dataset from UCSD. A subset of the UCSD dataset was also tested for subtype differences in functional and structural neuroimaging phenotypes and relationships with blood gene expression. The current subtyping model was also compared to early language outcome subtypes derived from past work. RESULTS: Two autism subtypes can be identified based on early phenotypic LIMA features. These data-driven subtypes are robust in the population and can be identified in independent data with 98% accuracy. The subtypes can be described as Type I versus Type II autisms differentiated by relatively high versus low scores on LIMA features. These two types of autisms are also distinguished by different developmental trajectories over the first decade of life. Finally, these two types of autisms reveal striking differences in functional and structural neuroimaging phenotypes and their relationships with gene expression and may highlight unique biological mechanisms. LIMITATIONS: Sample sizes for the neuroimaging and gene expression dataset are relatively small and require further independent replication. The current work is also limited to subtyping based on MSEL and VABS phenotypic measures. CONCLUSIONS: This work emphasizes the potential importance of stratifying autism by a Type I versus Type II distinction focused on LIMA features and which may be of high prognostic and biological significance. En ligne : https://dx.doi.org/10.1186/s13229-024-00613-5 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=538

Enhanced motor noise in an autism subtype with poor motor skills / Veronica MANDELLI in Molecular Autism, 15 (2024)  

Public ISBD [article]  inMolecular Autism > 15 (2024) . - 36p. Titre : Enhanced motor noise in an autism subtype with poor motor skills Type de document : texte imprimé Auteurs : Veronica MANDELLI, Auteur ; Isotta LANDI, Auteur ; Silvia Busti CECCARELLI, Auteur ; Massimo MOLTENI, Auteur ; Maria NOBILE, Auteur ; Alessandro D'AUSILIO, Auteur ; Luciano FADIGA, Auteur ; Alessandro CRIPPA, Auteur ; Michael V. LOMBARDO, Auteur Article en page(s) : 36p. Langues : Anglais (eng) Mots-clés : Humans  Child  Male  Female  Adolescent  Motor Skills  Autistic Disorder/physiopathology  Child, Preschool  Biomechanical Phenomena  Clustering  Kinematics  Motor  Stratification  Subtypes  competing interests to declare. Index. décimale : PER Périodiques Résumé : BACKGROUND: Motor difficulties are common in many, but not all, autistic individuals. These difficulties can co-occur with other problems, such as delays in language, intellectual, and adaptive functioning. Biological mechanisms underpinning such difficulties are less well understood. Poor motor skills tend to be more common in individuals carrying highly penetrant rare genetic mutations. Such mechanisms may have downstream consequences of altering neurophysiological excitation-inhibition balance and lead to enhanced behavioral motor noise. METHODS: This study combined publicly available and in-house datasets of autistic (n = 156), typically-developing (TD, n = 149), and developmental coordination disorder (DCD, n = 23) children (age 3-16 years). Autism motor subtypes were identified based on patterns of motor abilities measured from the Movement Assessment Battery for Children 2nd edition. Stability-based relative clustering validation was used to identify autism motor subtypes and evaluate generalization accuracy in held-out data. Autism motor subtypes were tested for differences in motor noise, operationalized as the degree of dissimilarity between repeated motor kinematic trajectories recorded during a simple reach-to-drop task. RESULTS: Relatively 'high' (n = 87) versus 'low' (n = 69) autism motor subtypes could be detected and which generalize with 89% accuracy in held-out data. The relatively 'low' subtype was lower in general intellectual ability and older at age of independent walking, but did not differ in age at first words or autistic traits or symptomatology. Motor noise was considerably higher in the 'low' subtype compared to 'high' (Cohen's d = 0.77) or TD children (Cohen's d = 0.85), but similar between autism 'high' and TD children (Cohen's d = 0.08). Enhanced motor noise in the 'low' subtype was also most pronounced during the feedforward phase of reaching actions. LIMITATIONS: The sample size of this work is limited. Future work in larger samples along with independent replication is important. Motor noise was measured only on one specific motor task. Thus, a more comprehensive assessment of motor noise on many other motor tasks is needed. CONCLUSIONS: Autism can be split into at least two discrete motor subtypes that are characterized by differing levels of motor noise. This suggests that autism motor subtypes may be underpinned by different biological mechanisms. En ligne : https://dx.doi.org/10.1186/s13229-024-00618-0 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=538 [article] Enhanced motor noise in an autism subtype with poor motor skills [texte imprimé] / Veronica MANDELLI, Auteur ; Isotta LANDI, Auteur ; Silvia Busti CECCARELLI, Auteur ; Massimo MOLTENI, Auteur ; Maria NOBILE, Auteur ; Alessandro D'AUSILIO, Auteur ; Luciano FADIGA, Auteur ; Alessandro CRIPPA, Auteur ; Michael V. LOMBARDO, Auteur . - 36p. Langues : Anglais (eng) in Molecular Autism > 15 (2024) . - 36p. Mots-clés : Humans  Child  Male  Female  Adolescent  Motor Skills  Autistic Disorder/physiopathology  Child, Preschool  Biomechanical Phenomena  Clustering  Kinematics  Motor  Stratification  Subtypes  competing interests to declare. Index. décimale : PER Périodiques Résumé : BACKGROUND: Motor difficulties are common in many, but not all, autistic individuals. These difficulties can co-occur with other problems, such as delays in language, intellectual, and adaptive functioning. Biological mechanisms underpinning such difficulties are less well understood. Poor motor skills tend to be more common in individuals carrying highly penetrant rare genetic mutations. Such mechanisms may have downstream consequences of altering neurophysiological excitation-inhibition balance and lead to enhanced behavioral motor noise. METHODS: This study combined publicly available and in-house datasets of autistic (n = 156), typically-developing (TD, n = 149), and developmental coordination disorder (DCD, n = 23) children (age 3-16 years). Autism motor subtypes were identified based on patterns of motor abilities measured from the Movement Assessment Battery for Children 2nd edition. Stability-based relative clustering validation was used to identify autism motor subtypes and evaluate generalization accuracy in held-out data. Autism motor subtypes were tested for differences in motor noise, operationalized as the degree of dissimilarity between repeated motor kinematic trajectories recorded during a simple reach-to-drop task. RESULTS: Relatively 'high' (n = 87) versus 'low' (n = 69) autism motor subtypes could be detected and which generalize with 89% accuracy in held-out data. The relatively 'low' subtype was lower in general intellectual ability and older at age of independent walking, but did not differ in age at first words or autistic traits or symptomatology. Motor noise was considerably higher in the 'low' subtype compared to 'high' (Cohen's d = 0.77) or TD children (Cohen's d = 0.85), but similar between autism 'high' and TD children (Cohen's d = 0.08). Enhanced motor noise in the 'low' subtype was also most pronounced during the feedforward phase of reaching actions. LIMITATIONS: The sample size of this work is limited. Future work in larger samples along with independent replication is important. Motor noise was measured only on one specific motor task. Thus, a more comprehensive assessment of motor noise on many other motor tasks is needed. CONCLUSIONS: Autism can be split into at least two discrete motor subtypes that are characterized by differing levels of motor noise. This suggests that autism motor subtypes may be underpinned by different biological mechanisms. En ligne : https://dx.doi.org/10.1186/s13229-024-00618-0 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=538

Deep phenotyping reveals movement phenotypes in mouse neurodevelopmental models / Ugne KLIBAITE in Molecular Autism, 13 (2022)  

Public ISBD [article]  inMolecular Autism > 13 (2022) . - 12 p. Titre : Deep phenotyping reveals movement phenotypes in mouse neurodevelopmental models Type de document : texte imprimé Auteurs : Ugne KLIBAITE, Auteur ; Mikhail KISLIN, Auteur ; Jessica L. VERPEUT, Auteur ; Silke BERGELER, Auteur ; Xiaoting SUN, Auteur ; Joshua W. SHAEVITZ, Auteur ; Samuel S.H. WANG, Auteur Article en page(s) : 12 p. Langues : Anglais (eng) Mots-clés : Animals  Autism Spectrum Disorder/genetics  Disease Models, Animal  Female  Male  Membrane Proteins/genetics  Mice  Mice, Inbred C57BL  Mice, Knockout  Nerve Tissue Proteins/genetics  Phenotype  Tuberous Sclerosis Complex 1 Protein/genetics  Autism  Behavior  Cerebellum  Clustering  Mouse  Pose estimation Index. décimale : PER Périodiques Résumé : BACKGROUND: Repetitive action, resistance to environmental change and fine motor disruptions are hallmarks of autism spectrum disorder (ASD) and other neurodevelopmental disorders, and vary considerably from individual to individual. In animal models, conventional behavioral phenotyping captures such fine-scale variations incompletely. Here we observed male and female C57BL/6J mice to methodically catalog adaptive movement over multiple days and examined two rodent models of developmental disorders against this dynamic baseline. We then investigated the behavioral consequences of a cerebellum-specific deletion in Tsc1 protein and a whole-brain knockout in Cntnap2 protein in mice. Both of these mutations are found in clinical conditions and have been associated with ASD. METHODS: We used advances in computer vision and deep learning, namely a generalized form of high-dimensional statistical analysis, to develop a framework for characterizing mouse movement on multiple timescales using a single popular behavioral assay, the open-field test. The pipeline takes virtual markers from pose estimation to find behavior clusters and generate wavelet signatures of behavior classes. We measured spatial and temporal habituation to a new environment across minutes and days, different types of self-grooming, locomotion and gait. RESULTS: Both Cntnap2 knockouts and L7-Tsc1 mutants showed forelimb lag during gait. L7-Tsc1 mutants and Cntnap2 knockouts showed complex defects in multi-day adaptation, lacking the tendency of wild-type mice to spend progressively more time in corners of the arena. In L7-Tsc1 mutant mice, failure to adapt took the form of maintained ambling, turning and locomotion, and an overall decrease in grooming. However, adaptation in these traits was similar between wild-type mice and Cntnap2 knockouts. L7-Tsc1 mutant and Cntnap2 knockout mouse models showed different patterns of behavioral state occupancy. LIMITATIONS: Genetic risk factors for autism are numerous, and we tested only two. Our pipeline was only done under conditions of free behavior. Testing under task or social conditions would reveal more information about behavioral dynamics and variability. CONCLUSIONS: Our automated pipeline for deep phenotyping successfully captures model-specific deviations in adaptation and movement as well as differences in the detailed structure of behavioral dynamics. The reported deficits indicate that deep phenotyping constitutes a robust set of ASD symptoms that may be considered for implementation in clinical settings as quantitative diagnosis criteria. En ligne : http://dx.doi.org/10.1186/s13229-022-00492-8 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=477 [article] Deep phenotyping reveals movement phenotypes in mouse neurodevelopmental models [texte imprimé] / Ugne KLIBAITE, Auteur ; Mikhail KISLIN, Auteur ; Jessica L. VERPEUT, Auteur ; Silke BERGELER, Auteur ; Xiaoting SUN, Auteur ; Joshua W. SHAEVITZ, Auteur ; Samuel S.H. WANG, Auteur . - 12 p. Langues : Anglais (eng) in Molecular Autism > 13 (2022) . - 12 p. Mots-clés : Animals  Autism Spectrum Disorder/genetics  Disease Models, Animal  Female  Male  Membrane Proteins/genetics  Mice  Mice, Inbred C57BL  Mice, Knockout  Nerve Tissue Proteins/genetics  Phenotype  Tuberous Sclerosis Complex 1 Protein/genetics  Autism  Behavior  Cerebellum  Clustering  Mouse  Pose estimation Index. décimale : PER Périodiques Résumé : BACKGROUND: Repetitive action, resistance to environmental change and fine motor disruptions are hallmarks of autism spectrum disorder (ASD) and other neurodevelopmental disorders, and vary considerably from individual to individual. In animal models, conventional behavioral phenotyping captures such fine-scale variations incompletely. Here we observed male and female C57BL/6J mice to methodically catalog adaptive movement over multiple days and examined two rodent models of developmental disorders against this dynamic baseline. We then investigated the behavioral consequences of a cerebellum-specific deletion in Tsc1 protein and a whole-brain knockout in Cntnap2 protein in mice. Both of these mutations are found in clinical conditions and have been associated with ASD. METHODS: We used advances in computer vision and deep learning, namely a generalized form of high-dimensional statistical analysis, to develop a framework for characterizing mouse movement on multiple timescales using a single popular behavioral assay, the open-field test. The pipeline takes virtual markers from pose estimation to find behavior clusters and generate wavelet signatures of behavior classes. We measured spatial and temporal habituation to a new environment across minutes and days, different types of self-grooming, locomotion and gait. RESULTS: Both Cntnap2 knockouts and L7-Tsc1 mutants showed forelimb lag during gait. L7-Tsc1 mutants and Cntnap2 knockouts showed complex defects in multi-day adaptation, lacking the tendency of wild-type mice to spend progressively more time in corners of the arena. In L7-Tsc1 mutant mice, failure to adapt took the form of maintained ambling, turning and locomotion, and an overall decrease in grooming. However, adaptation in these traits was similar between wild-type mice and Cntnap2 knockouts. L7-Tsc1 mutant and Cntnap2 knockout mouse models showed different patterns of behavioral state occupancy. LIMITATIONS: Genetic risk factors for autism are numerous, and we tested only two. Our pipeline was only done under conditions of free behavior. Testing under task or social conditions would reveal more information about behavioral dynamics and variability. CONCLUSIONS: Our automated pipeline for deep phenotyping successfully captures model-specific deviations in adaptation and movement as well as differences in the detailed structure of behavioral dynamics. The reported deficits indicate that deep phenotyping constitutes a robust set of ASD symptoms that may be considered for implementation in clinical settings as quantitative diagnosis criteria. En ligne : http://dx.doi.org/10.1186/s13229-022-00492-8 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=477

Finding Endophenotypes for Autism Spectrum Disorders (ASD): cDNA Microarrays and Brain Transcripts / Patrice BOURGEOIS

Public ISBD in Organism models of autism spectrum disorders / Pierre L. ROUBERTOUX Titre : Finding Endophenotypes for Autism Spectrum Disorders (ASD): cDNA Microarrays and Brain Transcripts Type de document : texte imprimé Auteurs : Patrice BOURGEOIS, Auteur ; Pierre L. ROUBERTOUX, Auteur Année de publication : 2015 Importance : p.217-238 Langues : Anglais (eng) Mots-clés : cDNA microarrays  Transcripts  Brain  Endophenotype  cDNA microarray technique  Oligonucleotide “probes”  RNA extraction  Labeling cDNA  Hybridizing  Reliability  Clustering  Gene expression Index. décimale : AUT-B AUT-B - L'Autisme - Ouvrages généraux et scientifiques Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=265 in Organism models of autism spectrum disorders / Pierre L. ROUBERTOUX Finding Endophenotypes for Autism Spectrum Disorders (ASD): cDNA Microarrays and Brain Transcripts [texte imprimé] / Patrice BOURGEOIS, Auteur ; Pierre L. ROUBERTOUX, Auteur . - 2015 . - p.217-238. Langues : Anglais (eng) Mots-clés : cDNA microarrays  Transcripts  Brain  Endophenotype  cDNA microarray technique  Oligonucleotide “probes”  RNA extraction  Labeling cDNA  Hybridizing  Reliability  Clustering  Gene expression Index. décimale : AUT-B AUT-B - L'Autisme - Ouvrages généraux et scientifiques Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=265

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