Motor Skills As Moderators of ASD Core Symptoms: Insights from the Artificial Networks Approach

Background:

In addition to the core symptoms, both fine and gross motor delays/disorders have been reported in children with Autism Spectrum Disorders (ASD). However, it is still unclear whether motor impairments are uniformly distributed across the entire ASD spectrum and whether they are related to DSM-5 specifiers (i.e. intelligence, language, comorbidity and associated conditions). In the light of the high heterogeneity in ASD it could be possible that “a single symptom approach analysis” do not provide comprehensive information. The strong inherent non-linearity of the relationships between clinical variables may account for the inability to grasp the core problem by the traditional analysis. Artificial neural networks (ANNs) are computational adaptive systems particularly adapting to solving non-linear problems. The goal of this data mining model is to discover hidden trends and associations among variables. Recently this approach has been successfully applied to the ASD field.

Objectives:

To investigate associations between motor skills and clinical/developmental features in preschoolers with ASD. We hypothesized that ANNs will be able to find hidden trends among the variables revealing the clinical profiles related to motor functioning in ASD.

Methods:

This study was carried out according to the standards for good ethical practice of the IRCCS Stella Maris Foundation and in accordance with the guidelines of the Declaration of Helsinki.

32 male with ASD (age range: 30-60 months; nonverbal IQ≥ 70) were recruited at the IRCCS Stella Maris Foundation, a tertiary care university hospital. Multidisciplinary comprehensive diagnostic evaluation was associated with a standardized assessment battery for motor skills, the Peabody Developmental Motor Scale- Second Edition (PDMS-2). The PDMS-2 consists of six motor subscales (Reflexes, Stationary, Locomotion, Object Manipulation, Grasping and Visual-Motor Integration) and three motor quotients (MQ) (Gross MQ, Fine MQ, Total MQ). According to PDMS-2, motor skills were classified into 7 categories: very superior, superior, above average, average, below average, poor and very poor.

Analyses were performed through the Auto Contractive Map system which is a fourth-generation unsupervised ANNs. Auto Contractive Map system ‘spatializes’ the correlation among variables (‘closeness’) providing a graph that identifies the relevant associations and organizes them into a coherent picture.

Results:

Preliminary linear correlation analysis revealed that motor impairment was associated with both cognitive skills and repetitive behaviors in children with ASD (Table 1). The ANNs analysis (Figure 1) showed that motor disorders were strongly related to low level of expressive language and high level of repetitive behaviors in preschoolers with ASD. In addition, the ANNs approach considered the entire spectrum of relationship among clinical variables, revealing hidden trends among motor, cognitive and social skills.

Conclusions:

The ANNs approach revealed motor skills as moderators of ASD core symptoms. This appears to be consistent with the growing literature suggesting that the systematic observation of motor development in ASD may improve the knowledge about clinical and neurobiological involvement as well as guide development of treatments.