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

2019 ◽  
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.

scholarly journals The Association between Autism Spectrum Disorder and Pre- and Postnatal Antibiotic Exposure in Childhood—A Systematic Review with Meta-Analysis

2019 ◽  
Vol 16 (20) ◽  
pp. 4042 ◽  
Author(s):  
Eunmi Lee ◽  
Jeonghyun Cho ◽  
Ka Young Kim
Keyword(s):  
Autism Spectrum Disorder ◽  
Developmental Disorder ◽  
Web Of Science ◽  
Meta Analysis ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Cochrane Library ◽  
Antibiotic Exposure ◽  
Children With Asd ◽  
Meta Analyses

Autism spectrum disorder (ASD) is a developmental disorder that begins in early childhood and has been associated with several environmental and genetic factors. We aimed to conduct two-side meta-analyses to determine the association between ASD and pre- and postnatal antibiotic exposure in childhood. We searched PubMed, Embase, Web of Science, and Cochrane Library for articles published up to February 2019. We evaluated observational studies that assessed the association between ASD and antibiotic exposure. Of 1459 articles, nine studies were used in the meta-analysis. We found that early antibiotic exposure, including pre- and postnatal, significantly increased the ASD risk in children. Furthermore, early antibiotic exposure, including pre- and postnatal, was significantly increased in children with ASD. Specifically, prenatal antibiotic exposure was significantly increased in children with ASD; however, postnatal antibiotic exposure was not. Our results indicate an association between ASD and early antibiotic exposure; specifically, that prenatal antibiotic exposure is an important risk factor of ASD in children.

scholarly journals Composition of Gut Microbiota in Children with Autism Spectrum Disorder: A Systematic Review and Meta-Analysis

Nutrients ◽  
2020 ◽  
Vol 12 (3) ◽  
pp. 792 ◽  
Author(s):  
Lucía Iglesias-Vázquez ◽  
Georgette Van Ginkel Riba ◽  
Victoria Arija ◽  
Josefa Canals
Keyword(s):  
Autism Spectrum Disorder ◽  
Gut Microbiota ◽  
Psychiatric Symptoms ◽  
Meta Analysis ◽  
Public Health Problem ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Lower Percentage ◽  
Children With Asd ◽  
Meta Analyses

Background: Autism spectrum disorder (ASD) is a public health problem and has a prevalence of 0.6%–1.7% in children. As well as psychiatric symptoms, dysbiosis and gastrointestinal comorbidities are also frequently reported. The gut–brain microbiota axis suggests that there is a form of communication between microbiota and the brain underlying some neurological disabilities. The aim of this study is to describe and compare the composition of gut microbiota in children with and without ASD. Methods: Electronic databases were searched as far as February 2020. Meta-analyses were performed using RevMan5.3 to estimate the overall relative abundance of gut bacteria belonging to 8 phyla and 17 genera in children with ASD and controls. Results: We included 18 studies assessing a total of 493 ASD children and 404 controls. The microbiota was mainly composed of the phyla Bacteroidetes, Firmicutes, and Actinobacteria, all of which were more abundant in the ASD children than in the controls. Children with ASD showed a significantly higher abundance of the genera Bacteroides, Parabacteroides, Clostridium, Faecalibacterium, and Phascolarctobacterium and a lower percentage of Coprococcus and Bifidobacterium. Discussion: This meta-analysis suggests that there is a dysbiosis in ASD children which may influence the development and severity of ASD symptomatology. Further studies are required in order to obtain stronger evidence of the effectiveness of pre- or probiotics in reducing autistic behaviors.

scholarly journals Comparative meta-analyses of brain structural and functional abnormalities during cognitive control in attention-deficit/hyperactivity disorder and autism spectrum disorder

2020 ◽  
Vol 50 (6) ◽  
pp. 894-919 ◽  
Author(s):  
Steve Lukito ◽  
Luke Norman ◽  
Christina Carlisi ◽  
Joaquim Radua ◽  
Heledd Hart ◽  
...  
Keyword(s):  
Autism Spectrum Disorder ◽  
Attention Deficit Hyperactivity Disorder ◽  
Cognitive Control ◽  
Attention Deficit ◽  
Meta Analysis ◽  
Gray Matter Volume ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Hyperactivity Disorder ◽  
Meta Analyses

AbstractBackgroundPeople with attention-deficit/hyperactivity disorder (ADHD) and autism spectrum disorder (ASD) have abnormalities in frontal, temporal, parietal and striato-thalamic networks. It is unclear to what extent these abnormalities are distinctive or shared. This comparative meta-analysis aimed to identify the most consistent disorder-differentiating and shared structural and functional abnormalities.MethodsSystematic literature search was conducted for whole-brain voxel-based morphometry (VBM) and functional magnetic resonance imaging (fMRI) studies of cognitive control comparing people with ASD or ADHD with typically developing controls. Regional gray matter volume (GMV) and fMRI abnormalities during cognitive control were compared in the overall sample and in age-, sex- and IQ-matched subgroups with seed-based d mapping meta-analytic methods.ResultsEighty-six independent VBM (1533 ADHD and 1295 controls; 1445 ASD and 1477 controls) and 60 fMRI datasets (1001 ADHD and 1004 controls; 335 ASD and 353 controls) were identified. The VBM meta-analyses revealed ADHD-differentiating decreased ventromedial orbitofrontal (z = 2.22, p < 0.0001) but ASD-differentiating increased bilateral temporal and right dorsolateral prefrontal GMV (zs ⩾ 1.64, ps ⩽ 0.002). The fMRI meta-analyses of cognitive control revealed ASD-differentiating medial prefrontal underactivation but overactivation in bilateral ventrolateral prefrontal cortices and precuneus (zs ⩾ 1.04, ps ⩽ 0.003). During motor response inhibition specifically, ADHD relative to ASD showed right inferior fronto-striatal underactivation (zs ⩾ 1.14, ps ⩽ 0.003) but shared right anterior insula underactivation.ConclusionsPeople with ADHD and ASD have mostly distinct structural abnormalities, with enlarged fronto-temporal GMV in ASD and reduced orbitofrontal GMV in ADHD; and mostly distinct functional abnormalities, which were more pronounced in ASD.

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

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

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

2019 ◽  
Vol 10 (1) ◽  
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.

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

2021 ◽  
Author(s):  
Victoria Foglia ◽  
Hasan Siddiqui ◽  
Zainab Khan ◽  
Stephanie Liang ◽  
M. D. Rutherford
Keyword(s):  
Autism Spectrum Disorder ◽  
Task Performance ◽  
Motion Perception ◽  
Significant Relationship ◽  
Cognitive Skills ◽  
Biological Motion ◽  
Meta Analysis ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Biological Motion Perception

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.

scholarly journals The Prevalence of Psychotic Experiences in Autism Spectrum Disorder and Autistic Traits: A Systematic Review and Meta-analysis

2020 ◽  
Vol 1 (1) ◽  
Author(s):  
Tomoki Kiyono ◽  
Masaya Morita ◽  
Ryo Morishima ◽  
Shinya Fujikawa ◽  
Syudo Yamasaki ◽  
...  
Keyword(s):  
Systematic Review ◽  
Autism Spectrum Disorder ◽  
Meta Analysis ◽  
Correlation Coefficients ◽  
Autistic Traits ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Psychotic Experiences ◽  
Meta Analyses ◽  
Statistical Results

Abstract Several reports have highlighted an association between psychotic experiences (PEs) and autism spectrum disorder/autistic traits; however, no systematic review of the evidence has been done. We searched PubMed, PsycINFO, Web of Science, and Cochrane database on November 20, 2018, for studies providing statistical results on the association between PEs and autism spectrum disorder/autistic traits. Meta-analyses were conducted for both the prevalence of PEs in autism spectrum disorder and the correlation coefficients between PEs and autistic traits. Subgroup analyses were conducted for each PE subtype. Among the 17 included studies, 9 had data about prevalence and 8 had data about correlation. The pooled prevalence of PEs in autism spectrum disorder was 24% (95% confidence interval [CI] 14%–34%). However, subanalyses found that prevalence varied between PE subtypes (hallucinations, 6% [95% CI 1%–11%] and delusions, 45% [95% CI 0%–99%]). Pooled results showed that PEs and autistic traits had a weak to medium correlation (r = .34 [95% CI 0.27–0.41]). Based on our meta-analysis, PEs seem to be more prevalent in individuals with autism spectrum disorder/autistic traits than in the general population, but this finding may vary according to the PE subtype. Future studies should focus on statistical results for each PE subtype separately. More studies should be conducted to clarify the relationship between autism spectrum disorder/autistic traits and PEs by subtype.

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

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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