Whole-Body Movement during Videogame Play Distinguishes Youth with Autism from Youth with Typical Development

Microstructural neural correlates of maximal grip strength in autistic children: the role of the cortico-cerebellar network and attention-deficit/hyperactivity disorder features

Introduction

Maximal grip strength, a measure of how much force a person's hand can generate when squeezing an object, may be an effective method for understanding potential neurobiological differences during motor tasks. Grip strength in autistic individuals may be of particular interest due to its unique developmental trajectory. While autism-specific differences in grip-brain relationships have been found in adult populations, it is possible that such differences in grip-brain relationships may be present at earlier ages when grip strength is behaviorally similar in autistic and non-autistic groups. Further, such neural differences may lead to the later emergence of diagnostic-group grip differences in adolescence. The present study sought to examine this possibility, while also examining if grip strength could elucidate the neuro-motor sources of phenotypic heterogeneity commonly observed within autism.

Methods

Using high resolution, multi-shell diffusion, and quantitative R1 relaxometry imaging, this study examined how variations in key sensorimotor-related white matter pathways of the proprioception input, lateral grasping, cortico-cerebellar, and corticospinal networks were associated with individual variations in grip strength in 68 autistic children and 70 non-autistic (neurotypical) children (6-11 years-old).

Results

In both groups, results indicated that stronger grip strength was associated with higher proprioceptive input, lateral grasping, and corticospinal (but not cortico-cerebellar modification) fractional anisotropy and R1, indirect measures concordant with stronger microstructural coherence and increased myelination. Diagnostic group differences in these grip-brain relationships were not observed, but the autistic group exhibited more variability particularly in the cortico-cerebellar modification indices. An examination into the variability within the autistic group revealed that attention-deficit/hyperactivity disorder (ADHD) features moderated the relationships between grip strength and both fractional anisotropy and R1 relaxometry in the premotor-primary motor tract of the lateral grasping network and the cortico-cerebellar network tracts. Specifically, in autistic children with elevated ADHD features (60% of the autistic group) stronger grip strength was related to higher fractional anisotropy and R1 of the cerebellar modification network (stronger microstructural coherence and more myelin), whereas the opposite relationship was observed in autistic children with reduced ADHD features.

Discussion

Together, this work suggests that while the foundational elements of grip strength are similar across school-aged autistic and non-autistic children, neural mechanisms of grip strength within autistic children may additionally depend on the presence of ADHD features. Specifically, stronger, more coherent connections of the cerebellar modification network, which is thought to play a role in refining and optimizing motor commands, may lead to stronger grip in children with more ADHD features, weaker grip in children with fewer ADHD features, and no difference in grip in non-autistic children. While future research is needed to understand if these findings extend to other motor tasks beyond grip strength, these results have implications for understanding the biological basis of neuromotor control in autistic children and emphasize the importance of assessing co-occurring conditions when evaluating brain-behavior relationships in autism.

Biosignal comparison for autism assessment using machine learning models and virtual reality

Clinical assessment procedures encounter challenges in terms of objectivity because they rely on subjective data. Computational psychiatry proposes overcoming this limitation by introducing biosignal-based assessments able to detect clinical biomarkers, while virtual reality (VR) can offer ecological settings for measurement. Autism spectrum disorder (ASD) is a neurodevelopmental disorder where many biosignals have been tested to improve assessment procedures. However, in ASD research there is a lack of studies systematically comparing biosignals for the automatic classification of ASD when recorded simultaneously in ecological settings, and comparisons among previous studies are challenging due to methodological inconsistencies. In this study, we examined a VR screening tool consisting of four virtual scenes, and we compared machine learning models based on implicit (motor skills and eye movements) and explicit (behavioral responses) biosignals. Machine learning models were developed for each biosignal within the virtual scenes and then combined into a final model per biosignal. A linear support vector classifier with recursive feature elimination was used and tested using nested cross-validation. The final model based on motor skills exhibited the highest robustness in identifying ASD, achieving an AUC of 0.89 (SD = 0.08). The best behavioral model showed an AUC of 0.80, while further research is needed for the eye-movement models due to limitations with the eye-tracking glasses. These findings highlight the potential of motor skills in enhancing objectivity and reliability in the early assessment of ASD compared to other biosignals.

The whole-body motor skills of children with autism spectrum disorder taking goal-directed actions in virtual reality

Many symptoms of the autism spectrum disorder (ASD) are evident in early infancy, but ASD is usually diagnosed much later by procedures lacking objective measurements. It is necessary to anticipate the identification of ASD by improving the objectivity of the procedure and the use of ecological settings. In this context, atypical motor skills are reaching consensus as a promising ASD biomarker, regardless of the level of symptom severity. This study aimed to assess differences in the whole-body motor skills between 20 children with ASD and 20 children with typical development during the execution of three tasks resembling regular activities presented in virtual reality. The virtual tasks asked to perform precise and goal-directed actions with different limbs vary in their degree of freedom of movement. Parametric and non-parametric statistical methods were applied to analyze differences in children's motor skills. The findings endorsed the hypothesis that when particular goal-directed movements are required, the type of action could modulate the presence of motor abnormalities in ASD. In particular, the ASD motor abnormalities emerged in the task requiring to take with the upper limbs goal-directed actions with low degree of freedom. The motor abnormalities covered (1) the body part mainly involved in the action, and (2) further body parts not directly involved in the movement. Findings were discussed against the background of atypical prospective control of movements and visuomotor discoordination in ASD. These findings contribute to advance the understanding of motor skills in ASD while deepening ecological and objective assessment procedures based on VR.

Dynamic balance improvement in children with Autism Spectrum Disorder after an extracurricular Service-Learning Physical Education program

This study aimed to examine the acute changes in dynamic balance Postural Control experienced by children with Autism Spectrum Disorder (ASD) who undertook a 6-month extracurricular Service-Learning Physical Education (PE) program. The study used a quasi-experimental design with 23 participants divided into an experimental group and a control group. Limits of Stability protocol was used to measure the children's postural control. The results showed that the experimental group achieved statistically significant improvements. To conclude, this study provides substantial input about how extracurricular PE activities aimed at developing the general motor proficiency of ASD children can improve their dynamic balance.

Prediction of autistic tendencies at 18 months of age via markerless video analysis of spontaneous body movements in 4-month-old infants

Early intervention is now considered the core treatment strategy for autism spectrum disorders (ASD). Thus, it is of significant clinical importance to establish a screening tool for the early detection of ASD in infants. To achieve this goal, in a longitudinal design, we analyzed spontaneous bodily movements of 4-month-old infants from general population and assessed their ASD-like behaviors at 18 months of age. A total of 26 movement features were calculated from video-recorded bodily movements of infants at 4 months of age. Their risk of ASD was assessed at 18 months of age with the Modified Checklist for Autism in Toddlerhood, a widely used screening questionnaire. Infants at high risk for ASD at 18 months of age exhibited less rhythmic and weaker bodily movement patterns at 4 months of age than low-risk infants. When the observed bodily movement patterns were submitted to a machine learning-based analysis, linear and non-linear classifiers successfully predicted ASD-like behavior at 18 months of age based on the bodily movement patterns at 4 months of age, at the level acceptable for practical use. This study analyzed the relationship between spontaneous bodily movements at 4 months of age and the ASD risk at 18 months of age. Experimental results suggested the utility of the proposed method for the early screening of infants at risk for ASD. We revealed that the signs of ASD risk could be detected as early as 4 months after birth, by focusing on the infant's spontaneous bodily movements.

Autistic Children Use Less Efficient Goal-Directed Whole Body Movements Compared to Neurotypical Development

Autistic children have differences in their movements which impact their functional performance. Virtual-reality enables researchers to study movement in safe, engaging environments. We used motion-capture to measure how 7-13-year-old autistic and neurotypical children make whole-body movements in a virtual-reality task. Although children in both groups were successful, we observed differences in their movements. Autistic children were less efficient moving to the target. Autistic children did not appear to use a movement strategy. While neurotypical children were more likely to overshoot near targets and undershoot far targets, autistic children did not modulate their strategy. Using kinematic data from tasks in virtual-reality, we can begin to understand the pattern of movement challenges experienced by autistic children.

Human activity recognition of children with wearable devices using LightGBM machine learning

Human activity recognition (HAR) using machine learning (ML) methods has been a continuously developed method for collecting and analyzing large amounts of human behavioral data using special wearable sensors in the past decade. Our main goal was to find a reliable method that could automatically detect various playful and daily routine activities in children. We defined 40 activities for ML recognition, and we collected activity motion data by means of wearable smartwatches with a special SensKid software. We analyzed the data of 34 children (19 girls, 15 boys; age range: 6.59–8.38; median age = 7.47). All children were typically developing first graders from three elementary schools. The activity recognition was a binary classification task which was evaluated with a Light Gradient Boosted Machine (LGBM) learning algorithm, a decision tree based method with a threefold cross validation. We used the sliding window technique during the signal processing, and we aimed at finding the best window size for the analysis of each behavior element to achieve the most effective settings. Seventeen activities out of 40 were successfully recognized with AUC values above 0.8. The window size had no significant effect. In summary, the LGBM is a very promising solution for HAR. In line with previous findings, our results provide a firm basis for a more precise and effective recognition system that can make human behavioral analysis faster and more objective.

Feasibility of a virtual reality-based exercise intervention and low-cost motion tracking method for estimation of motor proficiency in youth with autism spectrum disorder

Background

Motor impairment is widely acknowledged as a core feature in children with autism spectrum disorder (ASD), which can affect adaptive behavior and increase severity of symptoms. Low-cost motion capture and virtual reality (VR) game technologies hold a great deal of promise for providing personalized approaches to motor intervention in ASD. The present study explored the feasibility, acceptability and potential efficacy of a custom-designed VR game-based intervention (GaitWayXR™) for improving gross motor skills in youth with ASD.

Methods

Ten children and adolescents (10–17 years) completed six, 20-min VR-based motor training sessions over 2 weeks while whole-body movement was tracked with a low-cost motion capture system. We developed a methodology for using motion tracking data to quantify whole-body movement in terms of efficiency, synchrony and symmetry. We then studied the relationships of the above quantities with standardized measures of motor skill and cognitive flexibility.

Results

Our results supported our presumption that the VR intervention is safe, with no adverse events and very few minor to moderate side-effects, while a large proportion of parents said they would use the VR game at home, the most prohibitive reasons for adopting the system for home therapy were cost and space. Although there was little evidence of any benefits of the GaitWayXR™ intervention in improving gross motor skills, we showed several positive correlations between the standardized measures of gross motor skills in ASD and our measures of efficiency, symmetry and synchrony from low-cost motion capture.

Conclusions

These findings, though preliminary and limited by small sample size, suggest that low-cost motion capture of children with ASD is feasible with movement exercises in a VR-based game environment. Based on these preliminary findings, we recommend conducting larger-scale studies with methods for improving adherence to VR gaming interventions over longer periods.

Analysis of gait synchrony and balance in neurodevelopmental disorders using computer vision techniques

Gait tasks are commonly administered during motor assessments of children with neurodevelopmental disorders (NDDs). Gait analyses are often conducted in laboratory settings using costly and cumbersome experiments. In this paper, we propose a computational pipeline using computer vision techniques as an ecological and precise method to quantify gait in children with NDDs with challenging behaviors. We analyzed videos of 15 probands (PB) and 12 typically developing (TD) siblings, engaged in a preferred-pace walking task, using pose estimation software to track points of interest on their bodies over time. Analyzing the extracted information revealed that PB children had significantly less whole-body gait synchrony and poorer balance compared to their TD siblings. Our work offers a cost-effective method while preserving the validity of its results. This remote approach increases access to more diverse and distant cohorts and thus lowers barriers to research participation, further enriching our understanding of motor outcomes in NDDs.

Neurobiological and behavioural outcomes of biofeedback-based training in autism: a randomized controlled trial

The human brain has demonstrated the power to structurally change as a result of movement-based interventions. However, it is unclear whether these structural brain changes differ in autistic individuals compared to non-autistic individuals. The purpose of the present study was to pilot a randomized controlled trial to investigate brain, balance, autism symptom severity and daily living skill changes that result from a biofeedback-based balance intervention in autistic adolescents (13-17 years old). Thirty-four autistic participants and 28 age-matched non-autistic participants underwent diagnostic testing and pre-training assessment (neuroimaging, cognitive, autism symptom severity and motor assessments) and were then randomly assigned to 6 weeks of a balance-training intervention or a sedentary-control condition. After the 6 weeks, neuroimaging, symptom severity and motor assessments were repeated. Results found that both the autistic and non-autistic participants demonstrated similar and significant increases in balance times with training. Furthermore, individuals in the balance-training condition showed significantly greater improvements in postural sway and reductions in autism symptom severity compared to individuals in the control condition. Daily living scores did not change with training, nor did we observe hypothesized changes to the microstructural properties of the corticospinal tract. However, follow-up voxel-based analyses found a wide range of balance-related structures that showed changes across the brain. Many of these brain changes were specific to the autistic participants compared to the non-autistic participants, suggesting distinct structural neuroplasticity in response to balance training in autistic participants. Altogether, these findings suggest that biofeedback-based balance training may target postural stability challenges, reduce core autism symptoms and influence neurobiological change. Future research is encouraged to examine the superior cerebellar peduncle in response to balance training and symptom severity changes in autistic individuals, as the current study produced overlapping findings in this brain region.

Evolution of the Autism Literature and the Influence of Parents: A Scientific Mapping in Web of Science

Parents interventions are relevant to address autism spectrum disorder (ASD). The objective of this study is to analyze the importance and evolution of ASD and its relationship with the parents (ASD-PAR) in the publications indexed in Web of Science. For this, a bibliometric methodology has been used, based on a scientific mapping of the reported documents. We have worked with an analysis unit of 1381 documents. The results show that the beginnings of scientific production date back to 1971. There are two clearly differentiated moments in scientific production. A first moment (1971-2004), where the production volume is low. A second moment (2005-2019), where the volume of production increases considerably. Therefore, it can be said that the subject began to be relevant for the scientific community from 2005 to the present. The keyword match rate between set periods marks a high level of match between periods. It is concluded that the main focus of the research on ASD-PAR is on the stress that is generated in families with children with ASD, in addition to the family problems that the fact that these children also have behavior problems can cause.

Automatic Classification of Adult Males With and Without Autism Spectrum Disorder by Non-contact Measurement of Autonomic Nervous System Activation

People with autism spectrum disorder (ASD) exhibit atypicality in various domains of behavior. Previous psychophysiological studies have revealed an atypical pattern of autonomic nervous system (ANS) activation induced by psychosocial stimulation. Thus, it might be feasible to develop a novel assessment tool to evaluate the risk of ASD by measuring ANS activation in response to emotional stimulation. The present study investigated whether people with ASD could be automatically classified from neurotypical adults based solely on physiological data obtained by the recently introduced non-contact measurement of pulse wave. We video-recorded faces of adult males with and without ASD while watching emotion-inducing video clips. Features reflective of ANS activation were extracted from the temporal fluctuation of facial skin coloration and entered into a machine-learning algorithm. Though the performance was modest, the gradient boosting classifier succeeded in classifying people with and without ASD, which indicates that facial skin color fluctuation contains information useful for detecting people with ASD. Taking into consideration the fact that the current study recruited only high-functioning adults who have relatively mild symptoms and probably developed some compensatory strategies, ASD screening by non-contact measurement of pulse wave could be a promising assessment tool to evaluate ASD risk.

Trends in Autism Research in the Field of Education in Web of Science: A Bibliometric Study

Autism spectrum disorder (ASD) is conceived as a neurodevelopmental disorder. The scientific literature welcomes studies that reflect the possible singularities that people with ASD may present both in their daily lives and at an educational level. The main objective of this study is to analyze the scientific production on the term autism in Web of Science, focused on the educational field, in order to identify the research trends in this field of study. The intention is to offer researchers who study autism in the educational field some clear research directions. A bibliometric-type methodology was developed using the scientific mapping technique. For this purpose, a performance analysis and a co-word analysis were carried out. Work was conducted with an analysis unit of 5512 documents. The results show that the volume of production has been irregular from the beginning to the present. The collection of documents on the subject began to be relevant, in terms of the volume of production, from 2007, and this has persisted to the present. It is concluded that there are two lines of research. The first is the line focused on mothers of children with ASD and the second is the line of research focused on young people with ASD. In addition, since 2012, new lines of research have been generated, focused on the diagnosis and inclusion of these students in educational centers.

Documentary Analysis of the Scientific Literature on Autism and Technology in Web of Science

The objective of the study is to track the progression of the scientific literature on autism and the technology applied to this disorder. A bibliometric methodology has been used, based on a co-word analysis. The Web of Science database was chosen to perform the analysis of the literature. A unit of analysis of 1048 publications was configured. SciMAT software was used mainly for document analysis. The results indicate that the first studies appeared in 1992, but it was not until 2009 that the research volume increased considerably. The area of knowledge where these studies were compiled was rehabilitation, which marks the truly therapeutic nature of this type of study. One of the authors with the most studies, as well as the most relevant research, was Sarkar, N. Manuscripts were usually research articles written in English. It could be concluded that research in this field of study focused mainly on interventions carried out through the use of technological resources, with students or young people who present with ASD. This line of research, although not the only one, was the most relevant and the one that had aroused the most interest among the scientific community.