Intrinsic Visual-Motor Synchrony Correlates With Social Deficits in Autism

A handbook for Rhythmic Relating in autism: supporting social timing in play, learning and therapy

We present a handbook for Rhythmic Relating, an approach developed to support play, learning and therapy with young autistic children, unconventional communicators, and autistic people who have additional learning needs. Rhythmic Relating is based on the Movement Sensing perspective, a growing body of research that recognizes that autistic social difficulties stem from more basic sensory and motor differences. These sensorimotor differences directly affect embodied experience and social timing in communication. The Rhythmic Relating approach acknowledges that autistic/non-autistic interactive mismatch goes both ways and offers bidirectional support for social timing and expressive action in play. This handbook is presented in an accessible fashion, allowing the reader to develop at their own pace through three skill-levels and encouraging time out to practice. We begin with the basics of building rapport (seeing, copying, and celebrating interactional behaviors), introduce the basic foundations of sensory stability, and then move on to developing reciprocal play (using mirroring, matching, looping, and "Yes…and" techniques), and further to understanding sensory impetus (using sensory contours, accents and flows) and its potential in support of social timing. Rhythmic Relating is offered in support of each practitioner's creative practice and personal sense of fun and humor in play. The model is offered as a foundation for interaction and learning, as a base practice in schools, for Occupational Therapists, Speech Therapists and Physiotherapists, and can also provide a basis for tailoring creative arts therapies when working with autistic clients.

Disentangling the Individual-Shared and Individual-Specific Subspace of Altered Brain Functional Connectivity in Autism Spectrum Disorder

BACKGROUND

Despite considerable effort toward understanding the neural basis of autism spectrum disorder (ASD) using case-control analyses of resting-state functional magnetic resonance imaging data, findings are often not reproducible, largely due to biological and clinical heterogeneity among individuals with ASD. Thus, exploring the individual-shared and individual-specific altered functional connectivity (AFC) in ASD is important to understand this complex, heterogeneous disorder.

METHODS

We considered 254 individuals with ASD and 295 typically developing individuals from the Autism Brain Imaging Data Exchange to explore the individual-shared and individual-specific subspaces of AFC. First, we computed AFC matrices of individuals with ASD compared with typically developing individuals. Then, common orthogonal basis extraction was used to project AFC of ASD onto 2 subspaces: an individual-shared subspace, which represents altered connectivity patterns shared across ASD, and an individual-specific subspace, which represents the remaining individual characteristics after eliminating the individual-shared altered connectivity patterns.

RESULTS

Analysis yielded 3 common components spanning the individual-shared subspace. Common components were associated with differences of functional connectivity at the group level. AFC in the individual-specific subspace improved the prediction of clinical symptoms. The default mode network-related and cingulo-opercular network-related magnitudes of AFC in the individual-specific subspace were significantly correlated with symptom severity in social communication deficits and restricted, repetitive behaviors in ASD.

CONCLUSIONS

Our study decomposed AFC of ASD into individual-shared and individual-specific subspaces, highlighting the importance of capturing and capitalizing on individual-specific brain connectivity features for dissecting heterogeneity. Our analysis framework provides a blueprint for parsing heterogeneity in other prevalent neurodevelopmental conditions.

Diagnosis for autism spectrum disorder children using T1-based gray matter and arterial spin labeling-based cerebral blood flow network metrics

Introduction

Autism Spectrum Disorder (ASD) is a complex neurodevelopmental condition characterized by impairments in motor skills, communication, emotional expression, and social interaction. Accurate diagnosis of ASD remains challenging due to the reliance on subjective behavioral observations and assessment scales, lacking objective diagnostic indicators.

Methods

In this study, we introduced a novel approach for diagnosing ASD, leveraging T1-based gray matter and ASL-based cerebral blood flow network metrics. Thirty preschool-aged patients with ASD and twenty-two typically developing (TD) individuals were enrolled. Brain network features, including gray matter and cerebral blood flow metrics, were extracted from both T1-weighted magnetic resonance imaging (MRI) and ASL images. Feature selection was performed using statistical t-tests and Minimum Redundancy Maximum Relevance (mRMR). A machine learning model based on random vector functional link network was constructed for diagnosis.

Results

The proposed approach demonstrated a classification accuracy of 84.91% in distinguishing ASD from TD. Key discriminating network features were identified in the inferior frontal gyrus and superior occipital gyrus, regions critical for social and executive functions in ASD patients.

Discussion

Our study presents an objective and effective approach to the clinical diagnosis of ASD, overcoming the limitations of subjective behavioral observations. The identified brain network features provide insights into the neurobiological mechanisms underlying ASD, potentially leading to more targeted interventions.

Observational learning of atypical biological kinematics in autism

Observing and voluntarily imitating the biological kinematics displayed by a model underpins the acquisition of new motor skills via sensorimotor processes linking perception with action. Differences in voluntary imitation in autism could be related to sensorimotor processing activity during action-observation of biological motion, as well as how sensorimotor integration processing occurs across imitation attempts. Using an observational practice protocol, which minimized the active contribution of the peripheral sensorimotor system, we examined the contribution of sensorimotor processing during action-observation. The data showed that autistic participants imitated both the temporal duration and atypical kinematic profile of the observed movement with a similar level of accuracy as neurotypical participants. These findings suggest the lower-level perception-action processes responsible for encoding biological kinematics during the action-observation phase of imitation are operational in autism. As there was no task-specific engagement of the peripheral sensorimotor system during observational practice, imitation difficulties in autism are most likely underpinned by sensorimotor integration issues related to the processing of efferent and (re)afferent sensorimotor information during trial-to-trial motor execution.

Functional connectivity based brain signatures of behavioral regulation in children with ADHD, DCD, and ADHD-DCD

Behavioral regulation problems have been associated with daily-life and mental health challenges in children with neurodevelopmental conditions such as attention-deficit/hyperactivity disorder (ADHD) and developmental coordination disorder (DCD). Here, we investigated transdiagnostic brain signatures associated with behavioral regulation. Resting-state fMRI data were collected from 115 children (31 typically developing (TD), 35 ADHD, 21 DCD, 28 ADHD-DCD) aged 7-17 years. Behavioral regulation was measured using the Behavior Rating Inventory of Executive Function and was found to differ between children with ADHD (i.e., children with ADHD and ADHD-DCD) and without ADHD (i.e., TD children and children with DCD). Functional connectivity (FC) maps were computed for 10 regions of interest and FC maps were tested for correlations with behavioral regulation scores. Across the entire sample, greater behavioral regulation problems were associated with stronger negative FC within prefrontal pathways and visual reward pathways, as well as with weaker positive FC in frontostriatal reward pathways. These findings significantly increase our knowledge on FC in children with and without ADHD and highlight the potential of FC as brain-based signatures of behavioral regulation across children with differing neurodevelopmental conditions.

Alteration of resting-state network dynamics in autism spectrum disorder based on leading eigenvector dynamics analysis

Background

Neurobiological models to explain the vulnerability of autism spectrum disorders (ASDs) are scarce, and previous resting-state functional magnetic resonance imaging (rs-fMRI) studies mostly examined static functional connectivity (FC). Given that FC constantly evolves, it is critical to probe FC dynamic differences in ASD patients.

Methods

We characterized recurring phase-locking (PL) states during rest in 45 ASD patients and 47 age- and sex-matched healthy controls (HCs) using Leading Eigenvector Dynamics Analysis (LEiDA) and probed the organization of PL states across different fine grain sizes.

Results

Our results identified five different groups of discrete resting-state functional networks, which can be defined as recurrent PL state overtimes. Specifically, ASD patients showed an increased probability of three PL states, consisting of the visual network (VIS), frontoparietal control network (FPN), default mode network (DMN), and ventral attention network (VAN). Correspondingly, ASD patients also showed a decreased probability of two PL states, consisting of the subcortical network (SUB), somatomotor network (SMN), FPN, and VAN.

Conclusion

Our findings suggested that the temporal reorganization of brain discrete networks was closely linked to sensory to cognitive systems of the brain. Our study provides new insights into the dynamics of brain networks and contributes to a deeper understanding of the neurological mechanisms of ASD.

Multidimensional motor performance in children with autism mostly remains stable with age and predicts social communication delay, language delay, functional delay, and repetitive behavior severity after accounting for intellectual disability or cognitive delay: A SPARK dataset analysis

When motor difficulties continue into adolescence/adulthood, they could negatively impact an individual with autism spectrum disorder (ASD)'s daily living skills, physical fitness, as well as physical and mental health/well-being. Few studies have examined motor difficulties in children with ASD as a function of sex or age; however, greater cognitive challenges are associated with worse general motor performance. Based on the Developmental Coordination Disorder-Questionnaire (DCD-Q) data from the SPARK study sample, 87%-88% children with ASD were at-risk for a general motor impairment that persisted until 15 years and was related to their core and co-occurring difficulties. Bhat et al. confirmed motor difficulties in children with ASD on multiple motor dimensions that predicted core and co-occurring conditions after accounting for age and sex. However, presence of intellectual disability (ID) or cognitive delay was not controlled in the previous analysis. Additionally, the effects of age, sex, and cognitive ability on multidimensional motor difficulties of the SPARK sample have not been discussed before. Therefore, this analysis examines the effects of age, sex, and cognitive ability (presence of ID or level of cognitive delay) on the motor performance of children from the SPARK sample using the DCD-Q. Except fine motor skills, multiple motor domains did not change with age in children with ASD. Females without ID improved their fine motor scores with age, and performed better compared to males without ID. Children with ASD and ID had greater motor difficulties across multiple motor domains than those without ID. Even after controlling for age, sex, and presence of ID/cognitive delay; motor performance was predictive of social communication skills, repetitive behavior severity, as well as language and functional delays. Gross motor skills contributed more than fine motor and general motor competence skills in predicting social communication delay. However, fine motor and general motor competence skills contributed more than gross motor skills in predicting repetitive behavior severity and language delay. Both, fine and gross motor skills predicted functional delay. In light of consistent findings on motor difficulties in children with ASD, adding motor issues as a specifier within the ASD definition could provide a clear clinical route for movement clinicians to address motor difficulties of individuals with ASD.

A Prospective Evaluation of Infant Cerebellar-Cerebral Functional Connectivity in Relation to Behavioral Development in Autism Spectrum Disorder

Background

Autism spectrum disorder (ASD) is a neurodevelopmental disorder diagnosed based on social impairment, restricted interests, and repetitive behaviors. Contemporary theories posit that cerebellar pathology contributes causally to ASD by disrupting error-based learning (EBL) during infancy. The present study represents the first test of this theory in a prospective infant sample, with potential implications for ASD detection.

Methods

Data from the Infant Brain Imaging Study (n = 94, 68 male) were used to examine 6-month cerebellar functional connectivity magnetic resonance imaging in relation to later (12/24-month) ASD-associated behaviors and outcomes. Hypothesis-driven univariate analyses and machine learning-based predictive tests examined cerebellar-frontoparietal network (FPN; subserves error signaling in support of EBL) and cerebellar-default mode network (DMN; broadly implicated in ASD) connections. Cerebellar-FPN functional connectivity was used as a proxy for EBL, and cerebellar-DMN functional connectivity provided a comparative foil. Data-driven functional connectivity magnetic resonance imaging enrichment examined brain-wide behavioral associations, with post hoc tests of cerebellar connections.

Results

Cerebellar-FPN and cerebellar-DMN connections did not demonstrate associations with ASD. Functional connectivity magnetic resonance imaging enrichment identified 6-month correlates of later ASD-associated behaviors in networks of a priori interest (FPN, DMN), as well as in cingulo-opercular (also implicated in error signaling) and medial visual networks. Post hoc tests did not suggest a role for cerebellar connections.

Conclusions

We failed to identify cerebellar functional connectivity-based contributions to ASD. However, we observed prospective correlates of ASD-associated behaviors in networks that support EBL. Future studies may replicate and extend network-level positive results, and tests of the cerebellum may investigate brain-behavior associations at different developmental stages and/or using different neuroimaging modalities.

A Bibliometric and Visualization Analysis of Motor Learning in Preschoolers and Children over the Last 15 Years

Motor learning enables preschoolers and children to acquire fundamental skills that are critical to their development. The current study sought to conduct a bibliometric and visualization analysis to provide a comprehensive overview of motor-learning progress in preschoolers and children over the previous 15 years. The number of studies is constantly growing, with the United States and Australia, as well as other productive institutions and authors, at the leading edge. The dominant disciplines were Neurosciences and Neurology, Psychology, Rehabilitation, and Sport Sciences. The journals Developmental Medicine & Child Neurology, Human Movement Science, Physical Therapy, Neuropsychology, Journal of Motor Behavior, and Journal of Experimental Child Psychology have been the most productive and influential in this regard. The most common co-citations for clinical symptoms were for cerebral palsy, developmental coordination disorder, and autism. Research has focused on language impairment (speech disorders, explicit learning, and instructor-control feedback), as well as effective intervention strategies. Advances in brain mechanisms and diagnostic indicators, as well as new intervention and rehabilitation technologies (virtual reality, transcranial magnetic stimulation, and transcranial direct current stimulation), have shifted research frontiers and progress. The cognitive process is critical in intervention, rehabilitation, and new technology implementation and should not be overlooked. Overall, our broad overview identifies three major areas: brain mechanism research, clinical practice (intervention and rehabilitation), and new technology application.

Retained Primitive Reflexes and Potential for Intervention in Autistic Spectrum Disorders

We provide evidence to support the contention that many aspects of Autistic Spectrum Disorder (ASD) are related to interregional brain functional disconnectivity associated with maturational delays in the development of brain networks. We think a delay in brain maturation in some networks may result in an increase in cortical maturation and development in other networks, leading to a developmental asynchrony and an unevenness of functional skills and symptoms. The paper supports the close relationship between retained primitive reflexes and cognitive and motor function in general and in ASD in particular provided to indicate that the inhibition of RPRs can effect positive change in ASD.

Motor performance, praxis, and social skills in autism spectrum disorder and developmental coordination disorder

Previous research has shown that individuals with autism spectrum disorder (ASD) and developmental coordination disorder (DCD) may have overlapping social and motor skill impairments. This study compares ASD, DCD, and typically developing (TD) youth on a range of social, praxis and motor skills, and investigates the relationship between these skills in each group. Data were collected on participants aged 8–17 (n = 33 ASD, n = 28 DCD, n = 35 TD). Overall, the clinical groups showed some similar patterns of social and motor impairments but diverged in praxis impairments, cognitive empathy, and Theory of Mind ability. When controlling for both social and motor performance impairments, the ASD group showed significantly lower accuracy on imitation of meaningful gestures and gesture to command, indicating a prominent deficit in these praxis skills in ASD.

The effect of learning to drum on behavior and brain function in autistic adolescents

This current study aimed to investigate the impact of drum training on behavior and brain function in autistic adolescents with no prior drumming experience. Thirty-six autistic adolescents were recruited and randomly assigned to one of two groups. The drum group received individual drum tuition (two lessons per week over an 8-wk period), while the control group did not. All participants attended a testing session before and after the 8-wk period. Each session included a drumming assessment, an MRI scan, and a parent completing questionnaires relating to the participants’ behavioral difficulties. Results showed that improvements in drumming performance were associated with a significant reduction in hyperactivity and inattention difficulties in drummers compared to controls. The fMRI results demonstrated increased functional connectivity in brain areas responsible for inhibitory control, action outcomes monitoring, and self-regulation. In particular, seed-to-voxel analyses revealed an increased functional connectivity in the right inferior frontal gyrus and the right dorsolateral prefrontal cortex. A multivariate pattern analysis demonstrated significant changes in the medial frontal cortex, the left and right paracingulate cortex, the subcallosal cortex, the left frontal pole, the caudate, and the left nucleus accumbens. In conclusion, this study investigates the impact of a drum-based intervention on neural and behavioral outcomes in autistic adolescents. We hope that these findings will inform further research and trials into the potential use of drum-based interventions in benefitting clinical populations with inhibition-related disorders and emotional and behavioral difficulties.

Alterations in Connectome Dynamics in Autism Spectrum Disorder: A Harmonized Mega- and Meta-analysis Study Using the Autism Brain Imaging Data Exchange Dataset

BACKGROUND

Neuroimaging studies have reported functional connectome aberrancies in autism spectrum disorder (ASD). However, the time-varying patterns of connectome topology in individuals with ASD and the connection between these patterns and gene expression profiles remain unknown.

METHODS

To investigate case-control differences in dynamic connectome topology, we conducted mega- and meta-analyses of resting-state functional magnetic resonance imaging data of 939 participants (440 patients with ASD and 499 healthy control subjects, all males) from 18 independent sites, selected from the Autism Brain Imaging Data Exchange (ABIDE) dataset. Functional data were preprocessed and analyzed using harmonized protocols, and brain module dynamics was assessed using a multilayer network model. We further leveraged postmortem brain-wide gene expression data to identify transcriptomic signatures associated with ASD-related alterations in brain dynamics.

RESULTS

Compared with healthy control participants, individuals with ASD exhibited a higher global mean and lower standard deviation of whole-brain module dynamics, indicating an unstable and less regionally differentiated pattern. More specifically, individuals with ASD showed higher module switching, primarily in the medial prefrontal cortex, posterior cingulate gyrus, and angular gyrus, and lower switching in the visual regions. These alterations in brain dynamics were predictive of social impairments in individuals with ASD and were linked with expression profiles of genes primarily involved in the regulation of neurotransmitter transport and secretion as well as with previously identified autism-related genes.

CONCLUSIONS

This study is the first to identify consistent alterations in brain network dynamics in ASD and the transcriptomic signatures related to those alterations, furthering insights into the biological basis behind this disorder.

Collaborative learning of graph generation, clustering and classification for brain networks diagnosis

PURPOSE

Accurate diagnosis of autism spectrum disorder (ASD) plays a key role in improving the condition and quality of life for patients. In this study, we mainly focus on ASD diagnosis with functional brain networks (FBNs). The major challenge for brain networks modeling is the high dimensional connectivity in brain networks and limited number of subjects, which hinders the classification capability of graph convolutional networks (GCNs).

METHOD

To alleviate the influence of the limited data and high dimensional connectivity, we introduce a unified three-stage graph learning framework for brain network classification, involving multi-graph clustering, graph generation and graph classification. The framework combining Graph Generation, Clustering and Classification Networks (GraphCGC-Net) enhances the critical connections by multi-graph clustering (MGC) with a supervision scheme, and generates realistic brain networks by simultaneously preserving the global consistent distribution and local topology properties.

RESULTS

To demonstrate the effectiveness of our approach, we evaluate the performance of the proposed method on the Autism Brain Imaging Data Exchange (ABIDE) dataset and conduct extensive experiments on the ASD classification problem. Our proposed method achieves an average accuracy of 70.45% and an AUC of 72.76% on ABIDE. Compared with the traditional GCN model, the proposed GraphCGC-Net obtains 9.3%, and 10.64% improvement in terms of accuracy and AUC metrics, respectively.

CONCLUSION

The comprehensive experiments demonstrate that our GraphCGC-Net is effective for graph classification in brain disorders diagnosis. Moreover, we find that MGC can generate biologically meaningful subnetworks, which is highly consistent with the previous neuroimaging-derived biomarker evidence of ASD. More importantly, the promising results suggest that applying generative adversarial networks (GANs) in brain networks to improve the classification performance is worth further investigation.

Rhythmic Relating: Bidirectional Support for Social Timing in Autism Therapies

We propose Rhythmic Relating for autism: a system of supports for friends, therapists, parents, and educators; a system which aims to augment bidirectional communication and complement existing therapeutic approaches. We begin by summarizing the developmental significance of social timing and the social-motor-synchrony challenges observed in early autism. Meta-analyses conclude the early primacy of such challenges, yet cite the lack of focused therapies. We identify core relational parameters in support of social-motor-synchrony and systematize these using the communicative musicality constructs: pulse; quality; and narrative. Rhythmic Relating aims to augment the clarity, contiguity, and pulse-beat of spontaneous behavior by recruiting rhythmic supports (cues, accents, turbulence) and relatable vitality; facilitating the predictive flow and just-ahead-in-time planning needed for good-enough social timing. From here, we describe possibilities for playful therapeutic interaction, small-step co-regulation, and layered sensorimotor integration. Lastly, we include several clinical case examples demonstrating the use of Rhythmic Relating within four different therapeutic approaches (Dance Movement Therapy, Improvisational Music Therapy, Play Therapy, and Musical Interaction Therapy). These clinical case examples are introduced here and several more are included in the Supplementary Material (Examples of Rhythmic Relating in Practice). A suite of pilot intervention studies is proposed to assess the efficacy of combining Rhythmic Relating with different therapeutic approaches in playful work with individuals with autism. Further experimental hypotheses are outlined, designed to clarify the significance of certain key features of the Rhythmic Relating approach.

A further study of relations between motor impairment and social communication, cognitive, language, functional impairments, and repetitive behavior severity in children with ASD using the SPARK study dataset

Motor impairments are pervasive and persistent in children with autism spectrum disorder (ASD) throughout childhood and adolescence. Based on recent studies examining motor impairments in children with ASD between 5 and 15 years (i.e., SPARK study sample), 87-88% of this population is at-risk for a motor impairment, these problems persisted until 15 years, and related to their core (social communication skills and repetitive behaviors [RBs]) and comorbid (language, cognitive, and functional) impairments. Persistent motor impairments extending into adolescence/adulthood could negatively impact their independent daily living skills, physical fitness/activity levels, and physical/mental health. While multiple studies have examined relations between motor dimensions and core/comorbid impairments in young children with ASD, few studies have examined such relations in school-age children/adolescents with ASD. This paper conducts a further multidimensional study of which motor domains (i.e., gross-motor including visuo-motor or multilimb coordination/planning, fine motor [FM] or general coordination [GC] skills) best distinguish subgroups of school-age children/adolescents with ASD and help predict core and comorbid impairments after accounting for age and sex. Visuomotor, FM and certain GC skills were better at explaining variations in/predicting social communication impairments whereas FM skills were slightly better at explaining variations in/predicting RB severity. Multilimb coordination/planning and FM skills explained variations in/predicted cognitive delays whereas visuomotor and FM skills explained variations in and better predicted language delays. All three motor dimensions explained variations in/predicted functional delays. This study provides further evidence for inclusion of motor impairments within the ASD definition (criteria or specifiers). LAY SUMMARY: Gross-motor skills were related to social communication and functional delays of children with ASD (visuomotor skills related to language delays and multilimb coordination/planning skills related to cognitive delays). Fine-motor skills were related to repetitive behavior severity, language, cognitive, and functional delays in ASD. Diagnosticians should recommend systematic motor screening, further evaluations, and treatments for children at-risk for and diagnosed with ASD. Motor advocacy and enhanced public/clinical community awareness is needed to fulfill the unmet motor needs of children with ASD.

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.

Motor impairments in Chinese toddlers with autism spectrum disorder and its relationship with social communicative skills

OBJECTIVE

Motor impairments are prevalent in children with autism spectrum disorder (ASD) and persistent across age. Our current study was designed to investigate motor deficits in Chinese toddlers with ASD and to explore the relationships between motor deficits and social communication skills.

METHODS

For this cross-sectional study, we recruited a total of 210 Chinese toddlers with ASD aged between 18 and 36 months in the study during December 2017 to December 2020. Griffiths Developmental Scales-Chinese (GDS-C), Autism Diagnostic Observation Schedule-Second Edition (ADOS-2) and Communication and Symbolic Behavior Scales Developmental Profile-Infant-Toddler Checklist (CSBS-DP-ITC) were administered in these toddlers to evaluate their development, social communicative skills, and autism severity. We compared the developmental and social communicational profiles of ASD toddlers in different gross and fine motor subgroups, and explored potential associated factors. The univariate generalized linear model tested the relationship of fine and gross motor skills and social communicative skills.

RESULTS

The prevalence of gross and fine motor deficits were 59.5 and 82.5%, respectively, which are almost equivalent in boys and girls. The motor impairments tended to be more severe with age in toddlers. After adjusting for age, sex, non-verbal development quotient (DQ) and restricted, repetitive behaviors, severer gross motor impairments were significantly related to higher comparison score of ADOS-2 and higher social composite score of CSBS-DP-ITC, without interactions with other variables. Meanwhile, lower fine motor skills were associated with more deficits of social communication and higher severity of ASD, also depending on non-verbal DQ. In the lower non-verbal DQ subgroup, both fine motor deficits and restricted repetitive behaviors (RRBs) might have effects on autism symptomology.

CONCLUSION

Motor impairments are common in Chinese toddlers with ASD. Toddlers with weaker gross and fine motor skills have greater deficits in social communicative skills. Gross motor impairment might be an independent predictor of the severity of autism and social communication skills, while the effect of fine motor deficits might be affected by non-verbal DQ and RRBs of toddlers with ASD. We provide further justification for the inclusion of motor impairments in the early intervention for toddlers with ASD.

Connectivity-Based Brain Network Supports Restricted and Repetitive Behaviors in Autism Spectrum Disorder Across Development

INTRODUCTION

Autism spectrum disorder (ASD) is a lifelong condition. Autistic symptoms can persist into adulthood. Studies have reported that autistic symptoms generally improved in adulthood, especially restricted and repetitive behaviors and interests (RRBIs). We explored brain networks that are related to differences in RRBIs in individuals with ASDs among different ages.

METHODS

We enrolled 147 ASD patients from the Autism Brain Imaging Data Exchange II (ABIDEII) database. The participants were divided into four age groups: children (6-9 years old), younger adolescents (10-14 years old), older adolescents (15-19 years old), and adults (≥20 years old). RRBIs were evaluated using the Repetitive Behaviors Scale-Revised 6. We first explored differences in RRBIs between age groups using the Kruskal-Wallis test. Associations between improvements in RRBIs and age were analyzed using a general linear model. We then analyzed RRBIs associated functional connectivity (FC) links using the network-based statistic method by adjusting covariates. The association of the identified FC with age group, and mediation function of the FC on the association of age-group and RRBI were further analyzed.

RESULTS

Most subtypes of RRBIs improved with age, especially stereotyped behaviors, ritualistic behaviors, and restricted behaviors (p = 0.012, 0.014, and 0.012, respectively). Results showed that 12 FC links were closely related to overall RRBIs, 17 FC links were related to stereotyped behaviors. Among the identified 29 FC links, 15 were negatively related to age-groups. The mostly reported core brain regions included superior occipital gyrus, insula, rolandic operculum, angular, caudate, and cingulum. The decrease in FC between the left superior occipital lobe and right angular (effect = -0.125 and -0.693, respectively) and between the left insula and left caudate (effect = -0.116 and -0.664, respectively) might contribute to improvements in multiple RRBIs with age.

CONCLUSION

We identified improvements in RRBIs with age in ASD patients, especially stereotyped behaviors, ritualistic behaviors, and restricted behaviors. The decrease in FC between left superior occipital lobe and right angular and between left insula and left caudate might contribute to these improvements. Our findings improve our understanding of the pathogenesis of RRBIs and suggest potential intervention targets to improve prognosis in adulthood.

EYE-C: Eye-Contact Robust Detection and Analysis during Unconstrained Child-Therapist Interactions in the Clinical Setting of Autism Spectrum Disorders

The high level of heterogeneity in Autism Spectrum Disorder (ASD) and the lack of systematic measurements complicate predicting outcomes of early intervention and the identification of better-tailored treatment programs. Computational phenotyping may assist therapists in monitoring child behavior through quantitative measures and personalizing the intervention based on individual characteristics; still, real-world behavioral analysis is an ongoing challenge. For this purpose, we designed EYE-C, a system based on OpenPose and Gaze360 for fine-grained analysis of eye-contact episodes in unconstrained therapist-child interactions via a single video camera. The model was validated on video data varying in resolution and setting, achieving promising performance. We further tested EYE-C on a clinical sample of 62 preschoolers with ASD for spectrum stratification based on eye-contact features and age. By unsupervised clustering, three distinct sub-groups were identified, differentiated by eye-contact dynamics and a specific clinical phenotype. Overall, this study highlights the potential of Artificial Intelligence in categorizing atypical behavior and providing translational solutions that might assist clinical practice.

TE-HI-GCN: An Ensemble of Transfer Hierarchical Graph Convolutional Networks for Disorder Diagnosis

Accurate diagnosis of psychiatric disorders plays a critical role in improving the quality of life for patients and potentially supports the development of new treatments. Graph convolutional networks (GCNs) are shown to be successful in modeling applications with graph structures. However, training an accurate GCNs model for brain networks faces several challenges, including high dimensional and noisy correlation in the brain networks, limited labeled training data, and depth limitation of GCN learning. Generalization and interpretability are important in developing predictive models for clinical diagnosis. To address these challenges, we proposed an ensemble framework involving hierarchical GCN and transfer learning for sparse brain networks, which allows GCN to capture the intrinsic correlation among the subjects and domains, to improve the network embedding learning for disease diagnosis. Extensive experiments on two real medical clinical applications: diagnosis of Autism spectrum disorder (ASD) and diagnosis of Alzheimer's disease (AD) on both the ADNI and ABIDE databases, showing the effectiveness of the proposed framework. We achieved state-of-the-art accuracy and AUC for AD/MCI and ASD/NC (Normal control) classification in comparison with studies that used functional connectivity as features or GCN models. The proposed TE-HI-GCN model achieves the best classification performance, leading to about 27.93% (31.38%) improvement for ASD and 16.86% (44.50%) for AD in terms of accuracy and AUC compared with the traditional GCN model. Moreover, the obtained clustering results show high correspondence with the previous neuroimaging derived evidence of within and between-networks biomarkers for ASD. The discovered subnetworks are used as evidence for the proposed TE-HI-GCN model. Furthermore, this work is the first attempt of transfer learning on the two related disorder domains to uncover the correlation among the two diseases with a transfer learning scheme.

Deep sr-DDL: Deep structurally regularized dynamic dictionary learning to integrate multimodal and dynamic functional connectomics data for multidimensional clinical characterizations

We propose a novel integrated framework that jointly models complementary information from resting-state functional MRI (rs-fMRI) connectivity and diffusion tensor imaging (DTI) tractography to extract biomarkers of brain connectivity predictive of behavior. Our framework couples a generative model of the connectomics data with a deep network that predicts behavioral scores. The generative component is a structurally-regularized Dynamic Dictionary Learning (sr-DDL) model that decomposes the dynamic rs-fMRI correlation matrices into a collection of shared basis networks and time varying subject-specific loadings. We use the DTI tractography to regularize this matrix factorization and learn anatomically informed functional connectivity profiles. The deep component of our framework is an LSTM-ANN block, which uses the temporal evolution of the subject-specific sr-DDL loadings to predict multidimensional clinical characterizations. Our joint optimization strategy collectively estimates the basis networks, the subject-specific time-varying loadings, and the neural network weights. We validate our framework on a dataset of neurotypical individuals from the Human Connectome Project (HCP) database to map to cognition and on a separate multi-score prediction task on individuals diagnosed with Autism Spectrum Disorder (ASD) in a five-fold cross validation setting. Our hybrid model outperforms several state-of-the-art approaches at clinical outcome prediction and learns interpretable multimodal neural signatures of brain organization.

Visuomotor brain network activation and functional connectivity among individuals with autism spectrum disorder

Sensorimotor abnormalities are common in autism spectrum disorder (ASD) and predictive of functional outcomes, though their neural underpinnings remain poorly understood. Using functional magnetic resonance imaging, we examined both brain activation and functional connectivity during visuomotor behavior in 27 individuals with ASD and 30 typically developing (TD) controls (ages 9–35 years). Participants maintained a constant grip force while receiving visual feedback at three different visual gain levels. Relative to controls, ASD participants showed increased force variability, especially at high gain, and reduced entropy. Brain activation was greater in individuals with ASD than controls in supplementary motor area, bilateral superior parietal lobules, and contralateral middle frontal gyrus at high gain. During motor action, functional connectivity was reduced between parietal‐premotor and parietal‐putamen in individuals with ASD compared to controls. Individuals with ASD also showed greater age‐associated increases in functional connectivity between cerebellum and visual, motor, and prefrontal cortical areas relative to controls. These results indicate that visuomotor deficits in ASD are associated with atypical activation and functional connectivity of posterior parietal, premotor, and striatal circuits involved in translating sensory feedback information into precision motor behaviors, and that functional connectivity of cerebellar–cortical sensorimotor and nonsensorimotor networks show delayed maturation.

Socializing Sensorimotor Contingencies

The aim of this review is to highlight the idea of grounding social cognition in sensorimotor interactions shared across agents. We discuss an action-oriented account that emerges from a broader interpretation of the concept of sensorimotor contingencies. We suggest that dynamic informational and sensorimotor coupling across agents can mediate the deployment of action-effect contingencies in social contexts. We propose this concept of socializing sensorimotor contingencies (socSMCs) as a shared framework of analysis for processes within and across brains and bodies, and their physical and social environments. In doing so, we integrate insights from different fields, including neuroscience, psychology, and research on human-robot interaction. We review studies on dynamic embodied interaction and highlight empirical findings that suggest an important role of sensorimotor and informational entrainment in social contexts. Furthermore, we discuss links to closely related concepts, such as enactivism, models of coordination dynamics and others, and clarify differences to approaches that focus on mentalizing and high-level cognitive representations. Moreover, we consider conceptual implications of rethinking cognition as social sensorimotor coupling. The insight that social cognitive phenomena like joint attention, mutual trust or empathy rely heavily on the informational and sensorimotor coupling between agents may provide novel remedies for people with disturbed social cognition and for situations of disturbed social interaction. Furthermore, our proposal has potential applications in the field of human-robot interaction where socSMCs principles might lead to more natural and intuitive interfaces for human users.

Moving Toward Understanding Autism: Visual-Motor Integration, Imitation, and Social Skill Development

Autism spectrum disorder (ASD) is a neurodevelopmental disorder with a behavioral phenotype characterized by impaired development of social-communicative skills and excessive repetitive and stereotyped behaviors. Despite high phenotypic heterogeneity in ASD, a meaningful subpopulation of children with ASD (∼90%) show significant general motor impairment. More focused studies on the nature of motor impairment in ASD reveal that children with ASD are particularly impaired on tasks such as ball catching and motor imitation that require efficient visual-motor integration (VMI). Motor computational approaches also provide evidence for VMI impairment showing that children with ASD form internal sensorimotor representations that bias proprioceptive over visual feedback. Impaired integration of visual information to form internal representations of others' and the external world may explain observed impairments on VMI tasks and motor imitation of others. Motor imitation is crucial for acquiring both social and motor skills, and impaired imitation skill may contribute to the observed core behavioral phenotype of ASD. The current review examines evidence supporting VMI impairment as a core feature of ASD that may contribute to both impaired motor imitation and social-communicative skill development. We propose that understanding the neurobiological mechanisms underlying VMI impairment in ASD may be key to discovery of therapeutics to address disability in children and adults with ASD.

Movement kinematics and cortical activation in children with and without autism spectrum disorder during sway synchrony tasks: an fNIRS study

Children with Autism Spectrum Disorder (ASD) have difficulties with socially embedded movements such as imitation and interpersonal synchrony (IPS); however, related movement characteristics and underlying neural mechanisms are not well understood. This study compared the movement characteristics and cortical activation patterns of children with and without ASD during a whole-body, sway synchrony task when different levels of social information were provided. Thirty children with and without ASD (mean age: 12.6 years, SE: 0.6 years) participated. Movement kinematics and fNIRS-based cortical activation were recorded when the child observed an adult tester sway side to side, when they swayed solo, or when they swayed face to face with the tester with or without fingertips touching (i.e., IPS). Children with ASD showed reduced synchrony and smaller sway amplitude compared to typically developing children without ASD. They showed reduced cortical activation over the inferior frontal gyrus and superior temporal sulcus during IPS and did not show significant increase in cortical activation when more social information was provided. The cortical activation findings were significantly associated with IPS behaviors and social communication performance. The ASD-related neurobiomarkers identified in our study could be used as objective measures to evaluate intervention effects in children with ASD.

Increased interhemispheric somatomotor functional connectivity and mirror overflow in ADHD

•

Resting state fMRI study of the neurobiological basis of mirror overflow in ADHD.

•

Children with ADHD show greater interhemispheric motor functional connectivity.

•

Children with ADHD show greater mirror overflow during finger sequencing.

•

Interhemispheric functional connectivity correlates with mirror overflow in ADHD.

•

Greater interhemispheric motor connectivity may impede overflow inhibition in ADHD.

Mirror overflow is a developmental phenomenon defined as unintentional movements that mimic the execution of intentional movements in homologous muscles on the opposite side of the body. In children with attention-deficit/hyperactivity disorder (ADHD), mirror overflow is commonly excessive, abnormally persistent, and correlated with ADHD symptom severity. As such, it represents a promising clinical biomarker for disinhibited behavior associated with ADHD. Yet, the neural underpinnings of mirror overflow in ADHD remain unclear. Our objective was to test whether intrinsic interhemispheric functional connectivity between homologous regions of the somatomotor network (SMN) is associated with mirror overflow in school age children with and without ADHD using resting state functional magnetic resonance imaging. To this end, we quantified mirror overflow in 119 children (8–12 years old, 62 ADHD) during a finger sequencing task using finger twitch transducers affixed to the index and ring fingers. Group ICA was used to identify right- and left-lateralized SMNs and subject-specific back reconstructed timecourses were correlated to obtain a measure of SMN interhemispheric connectivity. We found that children with ADHD showed increased mirror overflow (p < 0.001; d = 0.671) and interhemispheric SMN functional connectivity (p = 0.023; d = 0.521) as compared to typically developing children. In children with ADHD, but not the typically developing children, there was a significant relationship between interhemispheric SMN functional connectivity and mirror overflow (t = 2.116; p = 0.039). Our findings of stronger interhemispheric functional connectivity between homologous somatomotor regions in children with ADHD is consistent with previous transcranial magnetic stimulation and diffusion-tractography imaging studies suggesting that interhemispheric cortical inhibitory mechanisms may be compromised in children with ADHD. The observed brain-behavior correlation further suggests that abnormally strong interhemispheric SMN connectivity in children with ADHD may diminish their ability to suppress overflow movements.

Supplementary and Premotor Aspects of the Corticospinal Tract Show Links with Restricted and Repetitive Behaviors in Middle-Aged Adults with Autism Spectrum Disorder

Individuals with autism spectrum disorder (ASD) show motor impairment into adulthood and risk decline during aging, but little is known about brain changes in aging adults with ASD. Few studies of ASD have directly examined the corticospinal tract (CST)-the major descending pathway in the brain responsible for voluntary motor behavior-outside its primary motor (M1) connections. In 26 middle-aged adults with ASD and 26 age-matched typical comparison participants, we used diffusion imaging to examine the microstructure and volume of CST projections from M1, dorsal premotor (PMd), supplementary motor area (SMA), and primary somatosensory (S1) cortices with respect to age. We also examined relationships between each CST sub-tract (-cst), motor skills, and autism symptoms. We detected no significant group or age-related differences in tracts extending from M1 or other areas. However, sub-tracts of the CST extending from secondary (but not primary) motor areas were associated with core autism traits. Increased microstructural integrity of left PMd-cst and SMA-cst were associated with less-severe restricted and repetitive behaviors (RRB) in the ASD group. These findings suggest that secondary motor cortical areas, known to be involved in selecting motor programs, may be implicated in cognitive motor processes underlying RRB in ASD.

Influence of Ideational Praxis on the Development of Play and Adaptive Behavior of Children with Autism Spectrum Disorder: A Comparative Analysis

BACKGROUND

Traditionally, assessment of praxis skills in children with ASD has focused on the evaluation of aspects related to the planning and execution of actions. This study aimed to evaluate the ideational abilities of children with ASD and analyze possible relationships with the development of play and adaptive behaviors.

METHODS

40 children between 4 to 6 years (TD = 20; ASD = 20) were evaluated with the Test of Ideational Praxis, the Revised Knox Preschool Play Scale, and the Adaptive Behavior Assessment System II.

RESULTS

Statistically significant relationships were obtained between ideational praxis and play skills development (r = 0.649; p = 0.01), adaptive leisure behavior (r = 0.338; p = 0.04) and social adaptive behavior (r = 0.319; p = 0.04). Results of multiple linear regression models found a linear relationship between ideational praxis and play development (p = 0.005) and adaptive leisure skills (p = 0.004), but not with social interaction skills (p > 0.05).

CONCLUSIONS

Objective evaluation with a specific ideational praxis assessment facilitates understanding of the ideational abilities and widens understanding of praxis skills and their impact on play and adaptive behaviors in children with ASD.

Imbalanced social-communicative and restricted repetitive behavior subtypes of autism spectrum disorder exhibit different neural circuitry

Social-communication (SC) and restricted repetitive behaviors (RRB) are autism diagnostic symptom domains. SC and RRB severity can markedly differ within and between individuals and may be underpinned by different neural circuitry and genetic mechanisms. Modeling SC-RRB balance could help identify how neural circuitry and genetic mechanisms map onto such phenotypic heterogeneity. Here, we developed a phenotypic stratification model that makes highly accurate (97-99%) out-of-sample SC = RRB, SC > RRB, and RRB > SC subtype predictions. Applying this model to resting state fMRI data from the EU-AIMS LEAP dataset (n = 509), we find that while the phenotypic subtypes share many commonalities in terms of intrinsic functional connectivity, they also show replicable differences within some networks compared to a typically-developing group (TD). Specifically, the somatomotor network is hypoconnected with perisylvian circuitry in SC > RRB and visual association circuitry in SC = RRB. The SC = RRB subtype show hyperconnectivity between medial motor and anterior salience circuitry. Genes that are highly expressed within these networks show a differential enrichment pattern with known autism-associated genes, indicating that such circuits are affected by differing autism-associated genomic mechanisms. These results suggest that SC-RRB imbalance subtypes share many commonalities, but also express subtle differences in functional neural circuitry and the genomic underpinnings behind such circuitry.

Neuropsychiatric disease classification using functional connectomics - results of the connectomics in neuroimaging transfer learning challenge

Large, open-source datasets, such as the Human Connectome Project and the Autism Brain Imaging Data Exchange, have spurred the development of new and increasingly powerful machine learning approaches for brain connectomics. However, one key question remains: are we capturing biologically relevant and generalizable information about the brain, or are we simply overfitting to the data? To answer this, we organized a scientific challenge, the Connectomics in NeuroImaging Transfer Learning Challenge (CNI-TLC), held in conjunction with MICCAI 2019. CNI-TLC included two classification tasks: (1) diagnosis of Attention-Deficit/Hyperactivity Disorder (ADHD) within a pre-adolescent cohort; and (2) transference of the ADHD model to a related cohort of Autism Spectrum Disorder (ASD) patients with an ADHD comorbidity. In total, 240 resting-state fMRI (rsfMRI) time series averaged according to three standard parcellation atlases, along with clinical diagnosis, were released for training and validation (120 neurotypical controls and 120 ADHD). We also provided Challenge participants with demographic information of age, sex, IQ, and handedness. The second set of 100 subjects (50 neurotypical controls, 25 ADHD, and 25 ASD with ADHD comorbidity) was used for testing. Classification methodologies were submitted in a standardized format as containerized Docker images through ChRIS, an open-source image analysis platform. Utilizing an inclusive approach, we ranked the methods based on 16 metrics: accuracy, area under the curve, F1-score, false discovery rate, false negative rate, false omission rate, false positive rate, geometric mean, informedness, markedness, Matthew's correlation coefficient, negative predictive value, optimized precision, precision, sensitivity, and specificity. The final rank was calculated using the rank product for each participant across all measures. Furthermore, we assessed the calibration curves of each methodology. Five participants submitted their method for evaluation, with one outperforming all other methods in both ADHD and ASD classification. However, further improvements are still needed to reach the clinical translation of functional connectomics. We have kept the CNI-TLC open as a publicly available resource for developing and validating new classification methodologies in the field of connectomics.

Atypical neural processing during the execution of complex sensorimotor behavior in autism

Stereotyped behavior is rhythmic, repetitive movement that is essentially invariant in form. Stereotypy is common in several clinical disorders, such as autism spectrum disorders (ASD), where it is considered maladaptive. However, it also occurs early in typical development (TD) where it is hypothesized to serve as the foundation on which complex, adaptive motor behavior develops. This transition from stereotyped to complex movement in TD is thought to be supported by sensorimotor integration. Stereotypy in clinical disorders may persist due to deficits in sensorimotor integration. The present study assessed whether differences in sensorimotor processing may limit the expression of complex motor behavior in individuals with ASD and contribute to the clinical stereotypy observed in this population. Adult participants with ASD and TD performed a computer-based stimulus-tracking task in the presence and absence of visual feedback. Electroencephalography was recorded during the task. Groups were compared on motor performance (root mean square error), motor complexity (sample entropy), and neural complexity (multiscale sample entropy of the electroencephalography signal) in the presence and absence of visual feedback. No group differences were found for motor performance or motor complexity. The ASD group demonstrated greater neural complexity and greater differences between feedback conditions than TD individuals, specifically in signals relevant to sensorimotor processing. Motor performance and motor complexity correlated with clinical stereotypy in the ASD group. These findings support the hypothesis that individuals with ASD have differences in sensorimotor processing when executing complex motor behavior and that stereotypy is associated with low motor complexity.

Could Gait Biomechanics Become a Marker of Atypical Neuronal Circuitry in Human Development?-The Example of Autism Spectrum Disorder

This perspective paper presents converging recent knowledge in neurosciences (motor neurophysiology, neuroimaging and neuro cognition) and biomechanics to outline the relationships between maturing neuronal network, behavior, and gait in human development. Autism Spectrum Disorder (ASD) represents a particularly relevant neurodevelopmental disorder (NDD) to study these convergences, as an early life condition presenting with sensorimotor and social behavioral alterations. ASD diagnosis relies solely on behavioral criteria. The absence of biological marker in ASD is a main challenge, and hampers correlations between behavioral development and standardized data such as brain structure alterations, brain connectivity, or genetic profile. Gait, as a way to study motor system development, represents a well-studied, early life ability that can be characterized through standardized biomechanical analysis. Therefore, developmental gait biomechanics might appear as a possible motor phenotype and biomarker, solid enough to be correlated to neuronal network maturation, in normal and atypical developmental trajectories—like in ASD.

Low-level, prediction-based sensory and motor processes are unimpaired in Autism

A new promising account of human brain function suggests that sensory cortices try to optimise information processing via predictions that are based on prior experiences. The brain is thus likened to a probabilistic prediction machine. There has been a growing - though inconsistent - literature to suggest that features of autism spectrum conditions (ASCs) are associated with a deficit in modelling the world through such prediction-based inference. However empirical evidence for differences in low-level sensorimotor predictions in autism is still lacking. One approach to examining predictive processing in the sensorimotor domain is in the context of self-generated (predictable) as opposed to externally-generated (less predictable) effects. We employed two complementary tasks - forcematching and intentional binding - which examine self-versus externally-generated action effects in terms of sensory attenuation and intentional binding respectively in adults with and without autism. The results show that autism was associated with normal levels of sensory attenuation of internally-generated force and with unaltered temporal attraction of voluntary actions and their outcomes. Thus, our results do not support a general deficit in predictive processing in autism.

Atypical Resting-State Functional Connectivity of Intra/Inter-Sensory Networks Is Related to Symptom Severity in Young Boys With Autism Spectrum Disorder

Autism spectrum disorder (ASD) has been reported to have altered brain connectivity patterns in sensory networks, assessed using resting-state functional magnetic imaging (rs-fMRI). However, the results have been inconsistent. Herein, we aimed to systematically explore the interaction between brain sensory networks in 3–7-year-old boys with ASD (N = 29) using independent component analysis (ICA). Participants were matched for age, head motion, and handedness in the MRI scanner. We estimated the between-group differences in spatial patterns of the sensory resting-state networks (RSNs). Subsequently, the time series of each RSN were extracted from each participant’s preprocessed data and associated estimates of interaction strength between intra- and internetwork functional connectivity (FC) and symptom severity in children with ASD. The auditory network (AN), higher visual network (HVN), primary visual network (PVN), and sensorimotor network (SMN) were identified. Relative to TDs, individuals with ASD showed increased FC in the AN and SMN, respectively. Higher positive connectivity between the PVN and HVN in the ASD group was shown. The strength of such connections was associated with symptom severity. The current study might suggest that the abnormal connectivity patterns of the sensory network regions may underlie impaired higher-order multisensory integration in ASD children, and be associated with social impairments.

Can a Community-Based Football Program Benefit Motor Ability in Children with Autism Spectrum Disorder? A Pilot Evaluation Considering the Role of Social Impairments

This non-randomised pilot study evaluated the impact of a community football program on motor ability in children aged 5-12 years with autism spectrum disorder. Sixteen children were evaluated at baseline-and-post attendance in a football program for a varied number of weeks and compared to 19 children engaging in treatment-as-usual. Primary analyses indicated a statistically significant increase in total MABC-2, aiming and catching, and balance scores for the intervention group, with no changes in scores in the comparison group. There were no changes in manual dexterity across either group. At a between group level, the changes in aiming and catching scores were significantly greater for the intervention group. Further analyses highlighted the potential importance of social impairments regarding aiming and catching.

Computerized Assessment of Motor Imitation as a Scalable Method for Distinguishing Children With Autism

BACKGROUND

Imitation deficits are prevalent in autism spectrum conditions (ASCs) and are associated with core autistic traits. Imitating others' actions is central to the development of social skills in typically developing populations, as it facilitates social learning and bond formation. We present a Computerized Assessment of Motor Imitation (CAMI) using a brief (1-min), highly engaging video game task.

METHODS

Using Kinect Xbox motion tracking technology, we recorded 48 children (27 with ASCs, 21 typically developing) as they imitated a model's dance movements. We implemented an algorithm based on metric learning and dynamic time warping that automatically detects and evaluates the important joints and returns a score considering spatial position and timing differences between the child and the model. To establish construct validity and reliability, we compared imitation performance measured by the CAMI method to the more traditional human observation coding (HOC) method across repeated trials and two different movement sequences.

RESULTS

Results revealed poorer imitation in children with ASCs than in typically developing children (ps < .005), with poorer imitation being associated with increased core autism symptoms. While strong correlations between the CAMI and HOC methods (rs = .69-.87) confirmed the CAMI's construct validity, CAMI scores classified the children into diagnostic groups better than the HOC scores (accuracyCAMI = 87.2%, accuracyHOC = 74.4%). Finally, by comparing repeated movement trials, we demonstrated high test-retest reliability of CAMI (rs = .73-.86).

CONCLUSIONS

Findings support the CAMI as an objective, highly scalable, directly interpretable method for assessing motor imitation differences, providing a promising biomarker for defining biologically meaningful ASC subtypes and guiding intervention.

Greater functional connectivity between sensory networks is related to symptom severity in toddlers with autism spectrum disorder

BACKGROUND

Symptoms of autism spectrum disorder (ASD) emerge in the first years of life. Yet, little is known about the organization and development of functional brain networks in ASD proximally to the symptom onset. Further, the relationship between brain network connectivity and emerging ASD symptoms and overall functioning in early childhood is not well understood.

METHODS

Resting-state fMRI data were acquired during natural sleep from 24 young children with ASD and 23 typically developing (TD) children, aged 17-45 months. Intrinsic functional connectivity (iFC) within and between resting-state functional networks was derived with independent component analysis (ICA).

RESULTS

Increased iFC between visual and sensorimotor networks was found in young children with ASD compared to TD participants. Within the ASD group, the degree of overconnectivity between visual and sensorimotor networks was associated with greater autism symptoms. Age-related weakening of the visual-auditory between-network connectivity was observed in the ASD but not the TD group.

CONCLUSIONS

Taken together, these results provide evidence for disrupted functional network maturation and differentiation, particularly involving visual and sensorimotor networks, during the first years of life in ASD. The observed pattern of greater visual-sensorimotor between-network connectivity associated with poorer clinical outcomes suggests that disruptions in multisensory brain circuitry may play a critical role for early development of behavioral skills and autism symptomatology in young children with ASD.

A Data Driven Approach Reveals That Anomalous Motor System Connectivity is Associated With the Severity of Core Autism Symptoms

This study examined whether disruptions in connectivity involving regions critical for learning, planning, and executing movements are relevant to core autism symptoms. Spatially constrained ICA was performed using resting-state fMRI from 419 children (autism spectrum disorder (ASD) = 105; typically developing (TD) = 314) to identify functional motor subdivisions. Comparing the spatial organization of each subdivision between groups, we found voxels that contributed significantly less to the right posterior cerebellar component in children with ASD versus TD (P <0.001). Next, we examined the effect of diagnosis on right posterior cerebellar connectivity with all other motor subdivisions. The model was significant (P = 0.014) revealing that right posterior cerebellar connectivity with bilateral dorsomedial primary motor cortex was, on average, stronger in children with ASD, while right posterior cerebellar connectivity with left-inferior parietal lobule (IPL), bilateral dorsolateral premotor cortex, and supplementary motor area was stronger in TD children (all P ≤0.02). We observed a diagnosis-by-connectivity interaction such that for children with ASD, elevated social-communicative and excessive repetitive-behavior symptom severity were both associated with right posterior cerebellar-left-IPL hypoconnectivity (P ≤0.001). Right posterior cerebellar and left-IPL are strongly implicated in visuomotor processing with dysfunction in this circuit possibly leading to anomalous development of skills, such as motor imitation, that are crucial for effective social-communication. LAY SUMMARY: This study examines whether communication between various brain regions involved in the control of movement are disrupted in children with autism spectrum disorder (ASD). We show communication between the right posterior cerebellum and left IPL, a circuit important for efficient visual-motor integration, is disrupted in children with ASD and associated with the severity of ASD symptoms. These results may explain observations of visual-motor integration impairments in children with ASD that are associated with ASD symptom severity.

Motor Impairment Increases in Children With Autism Spectrum Disorder as a Function of Social Communication, Cognitive and Functional Impairment, Repetitive Behavior Severity, and Comorbid Diagnoses: A SPARK Study Report

Eighty-seven percent of a large sample of children with autism spectrum disorder (ASD) are at risk for motor impairment (Bhat, Physical Therapy, 2020, 100, 633-644). In spite of the high prevalence for motor impairment in children with ASD, it is not considered among the diagnostic criteria or specifiers within DSM-V. In this article, we analyzed the SPARK study dataset (n = 13,887) to examine associations between risk for motor impairment using the Developmental Coordination Disorder-Questionnaire (DCD-Q), social communication impairment using the Social Communication Questionnaire (SCQ), repetitive behavior severity using the Repetitive Behaviors Scale - Revised (RBS-R), and parent-reported categories of cognitive, functional, and language impairments. Upon including children with ASD with cognitive impairments, 88.2% of the SPARK sample was at risk for motor impairment. The relative risk ratio for motor impairment in children with ASD was 22.2 times greater compared to the general population and that risk further increased up to 6.2 with increasing social communication (5.7), functional (6.2), cognitive (3.8), and language (1.6) impairments as well as repetitive behavior severity (5.0). Additionally, the magnitude of risk for motor impairment (fine- and gross-motor) increased with increasing severity of all impairment types with medium to large effects. These findings highlight the multisystem nature of ASD, the need to recognize motor impairments as one of the diagnostic criteria or specifiers for ASD, and the need for appropriate motor screening and assessment of children with ASD. Interventions must address not only the social communication and cognitive/behavioral challenges of children with ASD but also their motor function and participation. LAY ABSTRACT: Eighty-eight percent of the SPARK sample of children with ASD were at risk for motor impairment. The relative risk for motor impairment was 22.2 times greater in children with ASD compared to the general population and the risk increased with more social communication, repetitive behavior, cognitive, and functional impairment. It is important to recognize motor impairments as one of the diagnostic criteria or specifiers for ASD and there is a need to administer appropriate motor screening, assessment, and interventions in children with ASD.

Diagnostic Classification for Human Autism and Obsessive-Compulsive Disorder Based on Machine Learning From a Primate Genetic Model

OBJECTIVE

Psychiatric disorders commonly comprise comorbid symptoms, such as autism spectrum disorder (ASD), obsessive-compulsive disorder (OCD), and attention deficit hyperactivity disorder (ADHD), raising controversies over accurate diagnosis and overlap of their neural underpinnings. The authors used noninvasive neuroimaging in humans and nonhuman primates to identify neural markers associated with DSM-5 diagnoses and quantitative measures of symptom severity.

METHODS

Resting-state functional connectivity data obtained from both wild-type and methyl-CpG binding protein 2 (MECP2) transgenic monkeys were used to construct monkey-derived classifiers for diagnostic classification in four human data sets (ASD: Autism Brain Imaging Data Exchange [ABIDE-I], N=1,112; ABIDE-II, N=1,114; ADHD-200 sample: N=776; OCD local institutional database: N=186). Stepwise linear regression models were applied to examine associations between functional connections of monkey-derived classifiers and dimensional symptom severity of psychiatric disorders.

RESULTS

Nine core regions prominently distributed in frontal and temporal cortices were identified in monkeys and used as seeds to construct the monkey-derived classifier that informed diagnostic classification in human autism. This same set of core regions was useful for diagnostic classification in the OCD cohort but not the ADHD cohort. Models based on functional connections of the right ventrolateral prefrontal cortex with the left thalamus and right prefrontal polar cortex predicted communication scores of ASD patients and compulsivity scores of OCD patients, respectively.

CONCLUSIONS

The identified core regions may serve as a basis for building markers for ASD and OCD diagnoses, as well as measures of symptom severity. These findings may inform future development of machine-learning models for psychiatric disorders and may improve the accuracy and speed of clinical assessments.

Altered Inferior Parietal Functional Connectivity is Correlated with Praxis and Social Skill Performance in Children with Autism Spectrum Disorder

Children with autism spectrum disorder (ASD) have difficulties perceiving and producing skilled gestures, or praxis. The inferior parietal lobule (IPL) is crucial to praxis acquisition and expression, yet how IPL connectivity contributes to autism-associated impairments in praxis as well as social-communicative skill remains unclear. Using resting-state functional magnetic resonance imaging, we applied independent component analysis to test how IPL connectivity relates to praxis and social-communicative skills in children with and without ASD. Across all children (with/without ASD), praxis positively correlated with connectivity of left posterior-IPL with the left dorsal premotor cortex and with the bilateral posterior/medial parietal cortex. Praxis also correlated with connectivity of right central-IPL connectivity with the left intraparietal sulcus and medial parietal lobe. Further, in children with ASD, poorer praxis and social-communicative skills both correlated with weaker right central-IPL connectivity with the left cerebellum, posterior cingulate, and right dorsal premotor cortex. Our findings suggest that IPL connectivity is linked to praxis development, that contributions arise bilaterally, and that right IPL connectivity is associated with impaired praxis and social-communicative skills in autism. The findings underscore the potential impact of IPL connectivity and impaired skill acquisition on the development of a range of social-communicative and motor functions during childhood, including autism-associated impairments.

Differences in cortical activation patterns during action observation, action execution, and interpersonal synchrony between children with or without autism spectrum disorder (ASD): An fNIRS pilot study

Engaging in socially embedded actions such as imitation and interpersonal synchrony facilitates relationships with peers and caregivers. Imitation and interpersonal synchrony impairments of children with Autism Spectrum Disorder (ASD) might contribute to their difficulties in connecting and learning from others. Previous fMRI studies investigated cortical activation in children with ASD during finger/hand movement imitation; however, we do not know whether these findings generalize to naturalistic face-to-face imitation/interpersonal synchrony tasks. Using functional near infrared spectroscopy (fNIRS), the current study assessed the cortical activation of children with and without ASD during a face-to-face interpersonal synchrony task. Fourteen children with ASD and 17 typically developing (TD) children completed three conditions: a) Watch-observed an adult clean up blocks; b) Do-cleaned up the blocks on their own; and c) Together-synchronized their block clean up actions to that of an adult. Children with ASD showed lower spatial and temporal synchrony accuracies but intact motor accuracy during the Together/interpersonal synchrony condition. In terms of cortical activation, children with ASD had hypoactivation in the middle and inferior frontal gyri (MIFG) as well as middle and superior temporal gyri (MSTG) while showing hyperactivation in the inferior parietal cortices/lobule (IPL) compared to the TD children. During the Together condition, the TD children showed bilaterally symmetrical activation whereas children with ASD showed more left-lateralized activation over MIFG and right-lateralized activation over MSTG. Additionally, using ADOS scores, in children with ASD greater social affect impairment was associated with lower activation in the left MIFG and more repetitive behavior impairment was associated with greater activation over bilateral MSTG. In children with ASD better communication performance on the VABS was associated with greater MIFG and/or MSTG activation. We identified objective neural biomarkers that could be utilized as outcome predictors or treatment response indicators in future intervention studies.

Children with Autism Spectrum Disorder Show Impairments During Dynamic Versus Static Grip-force Tracking

Impairments in visuomotor integration (VMI) may contribute to anomalous development of motor, as well as social-communicative, skills in children with autism spectrum disorder (ASD). However, it is relatively unknown whether VMI impairments are specific to children with ASD versus children with other neurodevelopmental disorders. As such, this study addressed the hypothesis that children with ASD, but not those in other clinical control groups, would show greater deficits in high-VMI dynamic grip-force tracking versus low-VMI static presentation. Seventy-nine children, aged 7-17 years, participated: 22 children with ASD, 17 children with fetal alcohol spectrum disorder (FASD), 18 children with Attention-Deficit Hyperactivity Disorder (ADHD), and 22 typically developing (TD) children. Two grip-force tracking conditions were examined: (1) a low-VMI condition (static visual target) and (2) a high-VMI condition (dynamic visual target). Low-frequency force oscillations <0.5 Hz during the visuomotor task were also examined. Two-way ANCOVAs were used to examine group x VMI and group x frequency effects (α = 0.05). Children with ASD showed a difficulty, above that seen in the ADHD/FASD groups, tracking dynamic, but not static, visual stimuli as compared to TD children. Low-frequency force oscillations <0.25 Hz were also significantly greater in the ASD versus the TD group. This study is the first to report VMI deficits during dynamic versus static grip-force tracking and increased proportion of force oscillations <0.25 Hz during visuomotor tracking in the ASD versus TD group. Dynamic VMI impairments may be a core psychophysiologic feature that could contribute to impaired development of motor and social-communicative skills in ASD. LAY SUMMARY: Children with autism spectrum disorder (ASD) show difficulties using dynamic visual stimuli to guide their own movements compared to their typically developing (TD) peers. It is unknown whether children without a diagnosis of ASD, but with other neurological disorders, show similar difficulties processing dynamic visual stimuli. In this study, we showed that children with ASD show a difficulty using dynamic, but not static, visual stimuli to guide movement that may explain atypical development of motor and social skills.

Anomalous intrinsic connectivity within and between visual and auditory networks in major depressive disorder

OBJECTIVE

Major depressive disorder (MDD) is a ubiquitous mental illness with heterogeneous symptoms, however, the pathophysiology mechanisms are still not fully understood. Clinical and preclinical studies suggested that depression could cause disturbances in sensory perception systems, disruptions in auditory and visual functions may serve as an essential clinical features underlying MDD.

METHODS

The current study investigated the abnormal intrinsic connectivity within and between visual and auditory networks in 95 MDD patients and 97 age-, gender-, education level-matched healthy controls (HCs) by using resting-state functional magnetic resonance imaging (fMRI). One auditory network (AN) and three visual components including visual component 1 (VC1), VC2, and VC3 were identified by using independent component analysis method based on the fMRI networks during the resting state with the largest spatial correlations, combining with brain regions and specific network templates.

RESULTS

We found that MDD could be characterized by the following disrupted network model relative to HCs: (i) reduced within-network connectivity in the AN, VC2, and VC3; (ii) reduced between-network connectivity between the AN and the VC3. Furthermore, aberrant functional connectivity (FC) within the visual network was linked to the clinical symptoms.

CONCLUSIONS

Overall, our results demonstrated that abnormalities of FC in perception systems including intrinsic visual and auditory networks may explain neurobiological mechanisms underlying MDD and could serve as a potential effective biomarker.

Sensorimotor Research Utilising Immersive Virtual Reality: A Pilot Study with Children and Adults with Autism Spectrum Disorders

When learning and interacting with the world, people with Autism Spectrum Disorders (ASD) show compromised use of vision and enhanced reliance on body-based information. As this atypical profile is associated with motor and social difficulties, interventions could aim to reduce the potentially isolating reliance on the body and foster the use of visual information. To this end, head-mounted displays (HMDs) have unique features that enable the design of Immersive Virtual Realities (IVR) for manipulating and training sensorimotor processing. The present study assesses feasibility and offers some early insights from a new paradigm for exploring how children and adults with ASD interact with Reality and IVR when vision and proprioception are manipulated. Seven participants (five adults, two children) performed a self-turn task in two environments (Reality and IVR) for each of three sensory conditions (Only Proprioception, Only Vision, Vision + Proprioception) in a purpose-designed testing room and an HMD-simulated environment. The pilot indicates good feasibility of the paradigm. Preliminary data visualisation suggests the importance of considering inter-individual variability. The participants in this study who performed worse with Only Vision and better with Only Proprioception seemed to benefit from the use of IVR. Those who performed better with Only Vision and worse with Only Proprioception seemed to benefit from Reality. Therefore, we invite researchers and clinicians to consider that IVR may facilitate or impair individuals depending on their profiles.

Cortical Functional Connectivity during Praxis in Autism Spectrum Disorder

Abnormal functional connectivity was reported as one of the underlying characteristics of autism spectrum disorder (ASD). Considering the motor deficits in ASD, we utilized praxis to investigate the neural mechanisms of ASD during motor task. Since the previous functional connectivity studies reported divergent results, we explored the properties of the functional connectivity using graph metrics to address brain organization alterations of ASD. We proposed the use of eLORETA to investigate the cortical connectivity during praxis based on a cohort of 45 high-functioning ASD (HFA) children and 45 typically developing (TD) children. The between-group comparison revealed higher clustering coefficient and lower global efficiency for HFA relative to TD while the between-phase comparison suggested decreasing global efficiency, increasing characteristic path length for TD. Nodal metrics exhibited significant differences between groups in frontal and occipital regions. These regions also showed significant changes of nodal metrics and connection strengths between baseline and praxis execution for TD. However, there were no significant changes in global, nodal metrics and connection strengths between phases for HFA. Our study suggested that cortical connectivity in ASD exhibited lower overall efficiency and a deficit in reorganization, which deepens the understanding of abnormal brain organization in ASD.

A joint network optimization framework to predict clinical severity from resting state functional MRI data

We propose a novel optimization framework to predict clinical severity from resting state fMRI (rs-fMRI) data. Our model consists of two coupled terms. The first term decomposes the correlation matrices into a sparse set of representative subnetworks that define a network manifold. These subnetworks are modeled as rank-one outer-products which correspond to the elemental patterns of co-activation across the brain; the subnetworks are combined via patient-specific non-negative coefficients. The second term is a linear regression model that uses the patient-specific coefficients to predict a measure of clinical severity. We validate our framework on two separate datasets in a ten fold cross validation setting. The first is a cohort of fifty-eight patients diagnosed with Autism Spectrum Disorder (ASD). The second dataset consists of sixty three patients from a publicly available ASD database. Our method outperforms standard semi-supervised frameworks, which employ conventional graph theoretic and statistical representation learning techniques to relate the rs-fMRI correlations to behavior. In contrast, our joint network optimization framework exploits the structure of the rs-fMRI correlation matrices to simultaneously capture group level effects and patient heterogeneity. Finally, we demonstrate that our proposed framework robustly identifies clinically relevant networks characteristic of ASD.