Motor Control Adherence to the Two-thirds Power Law Differs in Autistic Development / Emily FOURIE in Journal of Autism and Developmental Disorders, 55-3 (March 2025)  

Public ISBD [article]  inJournal of Autism and Developmental Disorders > 55-3 (March 2025) . - p.873-890 Titre : Motor Control Adherence to the Two-thirds Power Law Differs in Autistic Development Type de document : Texte imprimé et/ou numérique Auteurs : Emily FOURIE, Auteur ; Szu-Ching Lu, Auteur ; Jonathan Delafield-Butt, Auteur ; Susan M. RIVERA, Auteur Article en page(s) : p.873-890 Langues : Anglais (eng) Index. décimale : PER Périodiques Résumé : Autistic individuals often exhibit motor atypicalities, which may relate to difficulties in social communication. This study utilized a smart tablet activity to computationally characterize motor control by testing adherence to the two-thirds power law (2/3 PL), which captures a systematic covariation between velocity and curvature in motor execution and governs many forms of human movement. Children aged 4-8 years old participated in this study, including 24 autistic children and 33 typically developing children. Participants drew and traced ellipses on an iPad. We extracted data from finger movements on the screen, and computed adherence to the 2/3 PL and other kinematic metrics. Measures of cognitive and motor functioning were also collected. In comparison to the typically developing group, the autistic group demonstrated greater velocity modulation between curved and straight sections of movement, increased levels of acceleration and jerk, and greater intra- and inter-individual variability across several kinematic variables. Further, significant motor control development was observed in typically developing children, but not in those with autism. This study is the first to examine motor control adherence to the 2/3 PL in autistic children, revealing overall diminished motor control. Less smooth, more varied movement and an indication of developmental stasis in autistic children were observed. This study offers a novel tool for computational characterization of the autism motor signature in children?s development, demonstrating how smart tablet technology enables accessible assessment of children?s motor performance in an objective, quantifiable and scalable manner. En ligne : https://doi.org/10.1007/s10803-024-06240-6 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=548 [article] Motor Control Adherence to the Two-thirds Power Law Differs in Autistic Development [Texte imprimé et/ou numérique] / Emily FOURIE, Auteur ; Szu-Ching Lu, Auteur ; Jonathan Delafield-Butt, Auteur ; Susan M. RIVERA, Auteur . - p.873-890. Langues : Anglais (eng) in Journal of Autism and Developmental Disorders > 55-3 (March 2025) . - p.873-890 Index. décimale : PER Périodiques Résumé : Autistic individuals often exhibit motor atypicalities, which may relate to difficulties in social communication. This study utilized a smart tablet activity to computationally characterize motor control by testing adherence to the two-thirds power law (2/3 PL), which captures a systematic covariation between velocity and curvature in motor execution and governs many forms of human movement. Children aged 4-8 years old participated in this study, including 24 autistic children and 33 typically developing children. Participants drew and traced ellipses on an iPad. We extracted data from finger movements on the screen, and computed adherence to the 2/3 PL and other kinematic metrics. Measures of cognitive and motor functioning were also collected. In comparison to the typically developing group, the autistic group demonstrated greater velocity modulation between curved and straight sections of movement, increased levels of acceleration and jerk, and greater intra- and inter-individual variability across several kinematic variables. Further, significant motor control development was observed in typically developing children, but not in those with autism. This study is the first to examine motor control adherence to the 2/3 PL in autistic children, revealing overall diminished motor control. Less smooth, more varied movement and an indication of developmental stasis in autistic children were observed. This study offers a novel tool for computational characterization of the autism motor signature in children?s development, demonstrating how smart tablet technology enables accessible assessment of children?s motor performance in an objective, quantifiable and scalable manner. En ligne : https://doi.org/10.1007/s10803-024-06240-6 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=548

Motor Signature Differences Between Autism Spectrum Disorder and Developmental Coordination Disorder, and Their Neural Mechanisms / Christiana BUTERA ; Jonathan Delafield-Butt ; Szu-Ching Lu ; Krzysztof Sobota ; Timothy McGowan ; Laura HARRISON ; Emily KILROY ; Aditya JAYASHANKAR ; Lisa AZIZ-ZADEH in Journal of Autism and Developmental Disorders, 55-1 (January 2025)  

Public ISBD [article]  inJournal of Autism and Developmental Disorders > 55-1 (January 2025) . - p.353-368 Titre : Motor Signature Differences Between Autism Spectrum Disorder and Developmental Coordination Disorder, and Their Neural Mechanisms : Journal of Autism and Developmental Disorders Type de document : Texte imprimé et/ou numérique Auteurs : Christiana BUTERA, Auteur ; Jonathan Delafield-Butt, Auteur ; Szu-Ching Lu, Auteur ; Krzysztof Sobota, Auteur ; Timothy McGowan, Auteur ; Laura HARRISON, Auteur ; Emily KILROY, Auteur ; Aditya JAYASHANKAR, Auteur ; Lisa AZIZ-ZADEH, Auteur Article en page(s) : p.353-368 Langues : Anglais (eng) Index. décimale : PER Périodiques Résumé : Autism spectrum disorder (ASD) and Developmental Coordination Disorder (DCD) are distinct clinical groups with overlapping motor features. We attempted to (1) differentiate children with ASD from those with DCD, and from those typically developing (TD) (ages 8-17; 18 ASD, 16 DCD, 20 TD) using a 5-min coloring game on a smart tablet and (2) identify neural correlates of these differences. We utilized standardized behavioral motor assessments (e.g. fine motor, gross motor, and balance skills) and video recordings of a smart tablet task to capture any visible motor, behavioral, posture, or engagement differences. We employed machine learning analytics of motor kinematics during a 5-min coloring game on a smart tablet. Imaging data was captured using functional magnetic resonance imaging (fMRI) during action production tasks. While subject-rated motor assessments could not differentiate the two clinical groups, machine learning computational analysis provided good predictive discrimination: between TD and ASD (76% accuracy), TD and DCD (78% accuracy), and ASD and DCD (71% accuracy). Two kinematic markers which strongly drove categorization were significantly correlated with cerebellar activity. Findings demonstrate unique neuromotor patterns between ASD and DCD relate to cerebellar function and present a promising route for computational techniques in early identification. These are promising preliminary results that warrant replication with larger samples. En ligne : https://doi.org/10.1007/s10803-023-06171-8 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=547 [article] Motor Signature Differences Between Autism Spectrum Disorder and Developmental Coordination Disorder, and Their Neural Mechanisms : Journal of Autism and Developmental Disorders [Texte imprimé et/ou numérique] / Christiana BUTERA, Auteur ; Jonathan Delafield-Butt, Auteur ; Szu-Ching Lu, Auteur ; Krzysztof Sobota, Auteur ; Timothy McGowan, Auteur ; Laura HARRISON, Auteur ; Emily KILROY, Auteur ; Aditya JAYASHANKAR, Auteur ; Lisa AZIZ-ZADEH, Auteur . - p.353-368. Langues : Anglais (eng) in Journal of Autism and Developmental Disorders > 55-1 (January 2025) . - p.353-368 Index. décimale : PER Périodiques Résumé : Autism spectrum disorder (ASD) and Developmental Coordination Disorder (DCD) are distinct clinical groups with overlapping motor features. We attempted to (1) differentiate children with ASD from those with DCD, and from those typically developing (TD) (ages 8-17; 18 ASD, 16 DCD, 20 TD) using a 5-min coloring game on a smart tablet and (2) identify neural correlates of these differences. We utilized standardized behavioral motor assessments (e.g. fine motor, gross motor, and balance skills) and video recordings of a smart tablet task to capture any visible motor, behavioral, posture, or engagement differences. We employed machine learning analytics of motor kinematics during a 5-min coloring game on a smart tablet. Imaging data was captured using functional magnetic resonance imaging (fMRI) during action production tasks. While subject-rated motor assessments could not differentiate the two clinical groups, machine learning computational analysis provided good predictive discrimination: between TD and ASD (76% accuracy), TD and DCD (78% accuracy), and ASD and DCD (71% accuracy). Two kinematic markers which strongly drove categorization were significantly correlated with cerebellar activity. Findings demonstrate unique neuromotor patterns between ASD and DCD relate to cerebellar function and present a promising route for computational techniques in early identification. These are promising preliminary results that warrant replication with larger samples. En ligne : https://doi.org/10.1007/s10803-023-06171-8 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=547

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