Assessment of Speech and Fine Motor Coordination in Children With Autism Spectrum Disorder

Abstract

Autism Spectrum Disorder (ASD) is a developmental disorder characterized by difficulty in communication, which includes a high incidence of speech production errors. We hypothesize that these errors are partly due to underlying deficits in motor coordination and control, which are also manifested in degraded fine motor control of facial expressions and purposeful hand movements. In this pilot study, we computed correlations of acoustic, video, and handwriting time-series derived from five children with ASD and five children with neurotypical development during speech and handwriting tasks. These correlations and eigenvalues derived from the correlations act as a proxy for motor coordination across articulatory, laryngeal, and respiratory speech production systems and for fine motor skills. We utilized features derived from these correlations to discriminate between children with and without ASD. Eigenvalues derived from these correlations highlighted differences in complexity of coordination across speech subsystems and during handwriting, and helped discriminate between the two subject groups. These results suggest differences in coupling within speech production and fine motor skill systems in children with ASD. Our long-term goal is to create a platform assessing motor coordination in children with ASD in order to track progress from speech and motor interventions administered by clinicians.

Detecting Parkinson’s Disease from Wrist-Worn Accelerometry in the U.K. Biobank

“…A general approach was developed that quantifies movement dimensionality in this way, using correlation patterns among multichannel feature sets. This approach has been used in many studies to detect and estimate alterations in neuromotor coordination from speech [ 23 , 24 , 25 , 26 ], including motor and cognitive symptoms due to Parkinson’s disease [ 21 , 22 ]. The approach has also been used for detecting alterations in torso accelerations during gait due to load carriage [ 20 ] and mild traumatic brain injury [ 25 ], as well as for detecting alterations in hand movements during drawing due to autism [ 26 ].…”

“…Table 3 summarizes the GMM parameters, which have standard values that have been used in multiple previous studies [ 24 , 25 , 26 ]. The GMM output when presented with a feature vector from the test data is a likelihood score for each class model.…”

“…This is an important distinction from other analyses of accelerometry, which are designed to detect particular features such as tremor, bradykinesia, and gait freezing [ 18 ]. In a novel approach for PD detection, we investigate the use of correlation-structure analysis techniques that were originally developed for detecting changes in EEG functional connectivity to predict epileptic seizures [ 19 ] and that have since been shown to be advantageous for several biomedical applications [ 20 , 21 , 22 , 23 , 24 , 25 , 26 ].…”

Investigating the Interplay Between Affective, Phonatory and Motoric Subsystems in Autism Spectrum Disorder Using a Multimodal Dialogue Agent

“…Correlations between jaw movement and finger tapping suggest global altered temporal processing in ASD which may also explain poor cross-modal coordination during emotional speech production [20]. Indeed, prior work has suggested a coupling between speech motor coordination and fine motor skill systems in ASD [50].…”

Using Dynamics of Eye Movements, Speech Articulation and Brain Activity to Predict and Track mTBI Screening Outcomes

“…This approach has been used previously to differentiate mild traumatic brain injury (mTBI) subjects from control subjects based on gait movements ( 21 ), autistic subjects from control subjects based on hand drawing movements ( 22 ), and Parkinson's disease (PD) subjects from control subjects based on small-magnitude wrist movements ( 23 ). Whether neurological degradation results in greater or lower complexity depends on the specifics of the underlying behavior.…”