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 et/ou numérique Auteurs : Veronica MANDELLI, Auteur ; Ines SEVERINO, Auteur ; Lisa 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 et/ou numérique] / Veronica MANDELLI, Auteur ; Ines SEVERINO, Auteur ; Lisa 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 et/ou numérique 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 et/ou numérique] / 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

---

1 (1 - 2 / 2)