Autism spectrum disorder (ASD) is a developmental disorder associated with a heterogeneous phenotype that includes social, linguistic, cognitive, and behavioral symptoms. Anecdotal reports and selected research results suggest that individuals with ASD exhibit difficulties in motor coordination, especially when interacting with dynamic objects, like catching a ball. Yet, these challenges are not included in the diagnostic criteria. Motivated by a theory recently developed by members of this collaborative team, the overarching hypothesis guiding this project is that individuals with ASD have impairment in prediction when interacting with moving objects and events. Using motor skills that involve interaction with a ball as a first test bed for analysis, three sets of experiments aim to quantify predictive impairments. In addition, fast actions such as ball interception require feedforward control, which relies on internal prediction of limb movements. To test predictive impairments three sets of experiments manipulate time for prediction and coordinative challenges.
Aim -1 examines naturalistic catching of a ball using 3D motion capture and electromyography to quantify predictive features of the complex task in ASD and neuro-typical children. Manual catching is compared to catching with a funnel that eliminates hand and finger coordination for the catch.
Aim -2 examines ball interaction in a virtual set-up that affords controlled manipulation of the time window for prediction, while simplifying the coordination challenges for the hand movement. Confining ball and hand movements to one dimension, tasks will comprise kinematic and dynamic interception of a ball, the latter requiring prediction of the ball trajectory before and after contact. Computationally advanced metrics of hand movements, postural adjustments and eye kinematics relative to the ball will rigorously test the hypothesis of predictive impairment in autism.
Aim -3 tests postural control and uni- and bimanual reaching to assess more elementary motor abilities, such as postural sway, reaction, movement time, and smoothness of hand movements. Potential impairments in these elementary movements will be entered as covariates in the statistical analyses. Both the theoretical framework and the rigorous experimental testing are innovative and promote a hypothesis-driven and unifying understanding of the heterogeneous profile of autism spectrum disorder. The investigator team combines Dr. Sternad's long- standing expertise in computational motor neuroscience at Northeastern University with Dr. Sinha's experience in computational neuroscience of visual cognition and autism, supported by Dr. Kjelgaard at Massachusetts General Hospital with long-standing expertise in autism. Given the safety risks to children with ASD acting in a world of dynamically evolving events, searching for the underpinnings of this pervasive impairment holds great significance. Better understanding of the disorder is relevant for making environments not only safer for autistic children and adults, but also for designing early biomarkers and interventions that address the underlying neuro-cognitive issue, i.e. prediction, and not merely the manifestations of the heterogeneous phenotype.
Autism spectrum disorder (ASD) is a common developmental disorder associated with a heterogeneous phenotype that includes social, linguistic, cognitive, and behavioral symptoms. A recently developed theory by members of this investigator team proposed that a common underlying mechanism is impairment in prediction, evidenced in difficulties in interacting with dynamic objects, such as catching a ball. This project aims to test this hypothesis in a range of motor tasks that involve catching and hitting a ball, using advanced quantitative measures of temporal prediction in hand, ball, and eye movements to test whether the difficulties described anecdotally result from an underlying impairment in temporal prediction.
|Sternad, Dagmar (2018) It's Not (Only) the Mean that Matters: Variability, Noise and Exploration in Skill Learning. Curr Opin Behav Sci 20:183-195|
|Zuzarte, Ian; Indic, Premananda; Sternad, Dagmar et al. (2018) Quantifying Movement in Preterm Infants Using Photoplethysmography. Ann Biomed Eng :|