Distinct Biological Motion Perception in Autism Spectrum Disorder: A Meta-Analysis

AbstractIf neurotypical people rely on specialized perceptual mechanisms when perceiving biological motion, then one would not expect an association between task performance and IQ. However, if those with ASD recruit higher order cognitive skills when solving biological motion tasks, performance may be predicted by IQ. In a meta-analysis that included 19 articles, we found an association between biological motion perception and IQ among observers with ASD but no significant relationship among typical observers. If the task required emotion perception, then there was an even stronger association with IQ in the ASD group.

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Correction to: Biological motion perception in autism spectrum disorder: a meta-analysis

Molecular Autism ◽

10.1186/s13229-021-00462-6 ◽

2021 ◽

Vol 12 (1) ◽

Author(s):

Greta Krasimirova Todorova ◽

Rosalind Elizabeth Mcbean Hatton ◽

Frank Earl Pollick

Keyword(s):

Autism Spectrum Disorder ◽

Motion Perception ◽

Biological Motion ◽

Meta Analysis ◽

Autism Spectrum ◽

Spectrum Disorder ◽

Biological Motion Perception

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Biological motion perception in autism spectrum disorder: a meta-analysis

Molecular Autism ◽

10.1186/s13229-019-0299-8 ◽

2019 ◽

Vol 10 (1) ◽

Cited By ~ 3

Author(s):

Greta Krasimirova Todorova ◽

Rosalind Elizabeth Mcbean Hatton ◽

Frank Earl Pollick

Keyword(s):

Autism Spectrum Disorder ◽

Motion Perception ◽

Biological Motion ◽

Meta Analysis ◽

Age Groups ◽

Autism Spectrum ◽

Spectrum Disorder ◽

Affective States ◽

Biological Motion Perception ◽

The Difference

Abstract Background Biological motion, namely the movement of others, conveys information that allows the identification of affective states and intentions. This makes it an important avenue of research in autism spectrum disorder where social functioning is one of the main areas of difficulty. We aimed to create a quantitative summary of previous findings and investigate potential factors, which could explain the variable results found in the literature investigating biological motion perception in autism. Methods A search from five electronic databases yielded 52 papers eligible for a quantitative summarisation, including behavioural, eye-tracking, electroencephalography and functional magnetic resonance imaging studies. Results Using a three-level random effects meta-analytic approach, we found that individuals with autism generally showed decreased performance in perception and interpretation of biological motion. Results additionally suggest decreased performance when higher order information, such as emotion, is required. Moreover, with the increase of age, the difference between autistic and neurotypical individuals decreases, with children showing the largest effect size overall. Conclusion We highlight the need for methodological standards and clear distinctions between the age groups and paradigms utilised when trying to interpret differences between the two populations.

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Effectiveness of Virtual/Augmented Reality–Based Therapeutic Interventions on Individuals With Autism Spectrum Disorder: A Comprehensive Meta-Analysis

Frontiers in Psychiatry ◽

10.3389/fpsyt.2021.665326 ◽

2021 ◽

Vol 12 ◽

Author(s):

Behnam Karami ◽

Roxana Koushki ◽

Fariba Arabgol ◽

Maryam Rahmani ◽

Abdol-Hossein Vahabie

Keyword(s):

Autism Spectrum Disorder ◽

Augmented Reality ◽

Cognitive Skills ◽

Meta Analysis ◽

Autism Spectrum ◽

Spectrum Disorder ◽

Therapeutic Interventions ◽

Vr Training ◽

Living Skills ◽

And Training

In recent years, the application of virtual reality (VR) for therapeutic purposes has escalated dramatically. Favorable properties of VR for engaging patients with autism, in particular, have motivated an enormous body of investigations targeting autism-related disabilities with this technology. This study aims to provide a comprehensive meta-analysis for evaluating the effectiveness of VR on the rehabilitation and training of individuals diagnosed with an autism spectrum disorder. Accordingly, we conducted a systematic search of related databases and, after screening for inclusion criteria, reviewed 33 studies for more detailed analysis. Results revealed that individuals undergoing VR training have remarkable improvements with a relatively large effect size with Hedges g of 0.74. Furthermore, the results of the analysis of different skills indicated diverse effectiveness. The strongest effect was observed for daily living skills (g = 1.15). This effect was moderate for other skills: g = 0.45 for cognitive skills, g = 0.46 for emotion regulation and recognition skills, and g = 0.69 for social and communication skills. Moreover, five studies that had used augmented reality also showed promising efficacy (g = 0.92) that calls for more research on this tool. In conclusion, the application of VR-based settings in clinical practice is highly encouraged, although their standardization and customization need more research.

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Global Motion Perception in Autism Spectrum Disorder: A Meta-Analysis

Journal of Autism and Developmental Disorders ◽

10.1007/s10803-019-04194-8 ◽

2019 ◽

Vol 49 (12) ◽

pp. 4901-4918 ◽

Cited By ~ 6

Author(s):

Ruth Van der Hallen ◽

Catherine Manning ◽

Kris Evers ◽

Johan Wagemans

Keyword(s):

Autism Spectrum Disorder ◽

Motion Perception ◽

Meta Analysis ◽

Autism Spectrum ◽

Spectrum Disorder ◽

Global Motion

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Effectiveness of Virtual Reality-based therapeutic interventions on individuals with autism spectrum disorder: A comprehensive meta-analysis

10.31234/osf.io/s2jvy ◽

2020 ◽

Cited By ~ 1

Author(s):

behnam karami ◽

Roxana Koushki ◽

Fariba Arabgol ◽

Maryam Rahmani ◽

AbdolHossein Vahabie

Keyword(s):

Autism Spectrum Disorder ◽

Virtual Reality ◽

Cognitive Skills ◽

Meta Analysis ◽

Autism Spectrum ◽

Spectrum Disorder ◽

Therapeutic Interventions ◽

Targeted Screening ◽

Vr Training ◽

Living Skills

We conducted a comprehensive meta-analysis for evaluating the effectiveness of Virtual Reality (VR) on the rehabilitation and training of individuals diagnosed with an autism spectrum disorder. After a systematic search and targeted screening, 33 articles were analyzed. Results revealed that individuals undergoing VR training have remarkable improvements with a large effect size (g= 0.76). Furthermore, the results of the analysis of different skills indicated diverse effectiveness. The strongest effect was observed for daily living skills (g=1.23). This effect was moderate for other skills: g=0.45 for Cognitive skills, g=0.47 for Emotion Recognition skills, and g=0.67 for Social and Communication skills. In conclusion, the application of VR-based settings in clinical practice is highly encouraged, though their standardization and customization need more research.

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ANOMALOUS PERCEPTION OF BIOLOGICAL MOTION IN AUTISM: A CONCEPTUAL REVIEW AND META-ANALYSIS

10.1101/530030 ◽

2019 ◽

Cited By ~ 1

Author(s):

Alessandra Federici ◽

Valentina Parma ◽

Michele Vicovaro ◽

Luca Radassao ◽

Luca Casartelli ◽

...

Keyword(s):

Autism Spectrum Disorder ◽

Biological Motion ◽

Temporal Dynamics ◽

Traditional Approach ◽

Meta Analysis ◽

Autism Spectrum ◽

Spectrum Disorder ◽

Wide Range ◽

Spatio Temporal ◽

Meta Analyses

AbstractDespite its popularity, the construct of biological motion (BM) and its putative anomalies in autism spectrum disorder (ASD) are not completely clarified. Here, we propose a new model describing distinct levels of BM processing, and we present a meta-analysis investigating BM perception in ASD. We screened 114 articles testing BM perception in ASD and typical developing peers. A general meta-analysis including all the selected studies (N=27) showed BM processing moderate deficit in ASD, but high heterogeneity. This heterogeneity was explored in different additional meta-analyses where studies were grouped according to different levels of BM processing (first-order/direct/instrumental) and the manipulation of low-level perceptual features (spatial/temporal). Results suggest that the most severe deficit in ASD is evident when perception of BM is serving a secondary purpose (e.g., inferring intentionality/action/emotion) and, interestingly, that temporal dynamics could be an important factor in determining BM processing anomalies in ASD. In conclusion, this work questions the traditional understanding of BM anomalies in ASD and claims for a paradigm shift that deconstructs BM into distinct levels of processing and specific spatio-temporal subcomponents.Public Significance statementSince the seminal study by Johansson (1973), the construct of “biological motion” (BM) has gained a considerable success in a wide range of disciplines. In particular, BM processing has been considered a putative marker for social difficulties in neurodevelopmental conditions such as autism spectrum disorder (ASD). Our work aims to quantitatively test the solidity of this view through a meta-analytic approach and also to better define anomalies in BM perception according to distinct levels of complexity and specific spatio-temporal features. Interestingly, we do it by challenging the traditional approach to the conception of BM. This novel conceptualization has intriguing clinical and theoretical insights.

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Supplemental Material for Memory in Autism Spectrum Disorder: A Meta-Analysis of Experimental Studies

Psychological Bulletin ◽

10.1037/bul0000225.supp ◽

2020 ◽

Keyword(s):

Autism Spectrum Disorder ◽

Meta Analysis ◽

Experimental Studies ◽

Autism Spectrum ◽

Spectrum Disorder

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Meta-Analysis of Neurological Foci and Cognitive Dysfunction Correlates in Autism Spectrum Disorder

PsycEXTRA Dataset ◽

10.1037/e659972012-001 ◽

2012 ◽

Author(s):

Peter D. Marle ◽

Alisa J. Estey ◽

Frederick L. Coolidge

Keyword(s):

Autism Spectrum Disorder ◽

Cognitive Dysfunction ◽

Meta Analysis ◽

Autism Spectrum ◽

Spectrum Disorder

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Diagnostic test accuracy for use of machine learning in diagnosis of autism spectrum disorder: A Systematic Review and Meta-Analysis (Preprint)

10.2196/preprints.14108 ◽

2019 ◽

Author(s):

Sun Jae Moon ◽

Jin Seub Hwang ◽

Rajesh Kana ◽

John Torous ◽

Jung Won Kim

Keyword(s):

Machine Learning ◽

Systematic Review ◽

Autism Spectrum Disorder ◽

Meta Analysis ◽

Learning Algorithms ◽

Structural Mri ◽

Autism Spectrum ◽

Machine Learning Algorithms ◽

Spectrum Disorder ◽

Test Accuracy

BACKGROUND Over the recent years, machine learning algorithms have been more widely and increasingly applied in biomedical fields. In particular, its application has been drawing more attention in the field of psychiatry, for instance, as diagnostic tests/tools for autism spectrum disorder. However, given its complexity and potential clinical implications, there is ongoing need for further research on its accuracy. OBJECTIVE The current study aims to summarize the evidence for the accuracy of use of machine learning algorithms in diagnosing autism spectrum disorder (ASD) through systematic review and meta-analysis. METHODS MEDLINE, Embase, CINAHL Complete (with OpenDissertations), PsyINFO and IEEE Xplore Digital Library databases were searched on November 28th, 2018. Studies, which used a machine learning algorithm partially or fully in classifying ASD from controls and provided accuracy measures, were included in our analysis. Bivariate random effects model was applied to the pooled data in meta-analysis. Subgroup analysis was used to investigate and resolve the source of heterogeneity between studies. True-positive, false-positive, false negative and true-negative values from individual studies were used to calculate the pooled sensitivity and specificity values, draw SROC curves, and obtain area under the curve (AUC) and partial AUC. RESULTS A total of 43 studies were included for the final analysis, of which meta-analysis was performed on 40 studies (53 samples with 12,128 participants). A structural MRI subgroup meta-analysis (12 samples with 1,776 participants) showed the sensitivity at 0.83 (95% CI-0.76 to 0.89), specificity at 0.84 (95% CI -0.74 to 0.91), and AUC/pAUC at 0.90/0.83. An fMRI/deep neural network (DNN) subgroup meta-analysis (five samples with 1,345 participants) showed the sensitivity at 0.69 (95% CI- 0.62 to 0.75), the specificity at 0.66 (95% CI -0.61 to 0.70), and AUC/pAUC at 0.71/0.67. CONCLUSIONS Machine learning algorithms that used structural MRI features in diagnosis of ASD were shown to have accuracy that is similar to currently used diagnostic tools.

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