scholarly journals Genotype-Based Deep Learning in Autism Spectrum Disorder: Diagnostic Classification and Prognostic Prediction Using Common Genetic Variants (Preprint)

2020 ◽  
Author(s):  
Haishuai Wang ◽  
Paul Avillach
Keyword(s):  
Machine Learning ◽  
Autism Spectrum Disorder ◽  
Deep Learning ◽  
The United States ◽  
Autism Spectrum ◽  
Repetitive Behaviors ◽  
Spectrum Disorder ◽  
Screening Tools ◽  
Common Variants ◽  
Autism Screening

BACKGROUND In the United States, about 3 million people have autism spectrum disorder (ASD), and around 1 out of 59 children are diagnosed with ASD. People with ASD have characteristic social communication deficits and repetitive behaviors. The causes of this disorder remain unknown; however, in up to 25% of cases, a genetic cause can be identified. Detecting ASD as early as possible is desirable because early detection of ASD enables timely interventions in children with ASD. Identification of ASD based on objective pathogenic mutation screening is the major first step toward early intervention and effective treatment of affected children. OBJECTIVE Recent investigation interrogated genomics data for detecting and treating autism disorders, in addition to the conventional clinical interview as a diagnostic test. Since deep neural networks perform better than shallow machine learning models on complex and high-dimensional data, in this study, we sought to apply deep learning to genetic data obtained across thousands of simplex families at risk for ASD to identify contributory mutations and to create an advanced diagnostic classifier for autism screening. METHODS After preprocessing the genomics data from the Simons Simplex Collection, we extracted top ranking common variants that may be protective or pathogenic for autism based on a chi-square test. A convolutional neural network–based diagnostic classifier was then designed using the identified significant common variants to predict autism. The performance was then compared with shallow machine learning–based classifiers and randomly selected common variants. RESULTS The selected contributory common variants were significantly enriched in chromosome X while chromosome Y was also discriminatory in determining the identification of autistic from nonautistic individuals. The ARSD, MAGEB16, and MXRA5 genes had the largest effect in the contributory variants. Thus, screening algorithms were adapted to include these common variants. The deep learning model yielded an area under the receiver operating characteristic curve of 0.955 and an accuracy of 88% for identifying autistic from nonautistic individuals. Our classifier demonstrated a significant improvement over standard autism screening tools by average 13% in terms of classification accuracy. CONCLUSIONS Common variants are informative for autism identification. Our findings also suggest that the deep learning process is a reliable method for distinguishing the diseased group from the control group based on the common variants of autism.

scholarly journals Machine Learning and Virtual Reality on Body Movements’ Behaviors to Classify Children with Autism Spectrum Disorder

2020 ◽  
Vol 9 (5) ◽  
pp. 1260 ◽  
Author(s):  
Mariano Alcañiz Raya ◽  
Javier Marín-Morales ◽  
Maria Eleonora Minissi ◽  
Gonzalo Teruel Garcia ◽  
Luis Abad ◽  
...  
Keyword(s):  
Machine Learning ◽  
Autism Spectrum Disorder ◽  
Virtual Reality ◽  
Quantitative Methods ◽  
Real Life ◽  
Autism Spectrum ◽  
Repetitive Behaviors ◽  
Spectrum Disorder ◽  
Body Movements ◽  
Children With Asd

Autism spectrum disorder (ASD) is mostly diagnosed according to behavioral symptoms in sensory, social, and motor domains. Improper motor functioning, during diagnosis, involves the qualitative evaluation of stereotyped and repetitive behaviors, while quantitative methods that classify body movements’ frequencies of children with ASD are less addressed. Recent advances in neuroscience, technology, and data analysis techniques are improving the quantitative and ecological validity methods to measure specific functioning in ASD children. On one side, cutting-edge technologies, such as cameras, sensors, and virtual reality can accurately detect and classify behavioral biomarkers, as body movements in real-life simulations. On the other, machine-learning techniques are showing the potential for identifying and classifying patients’ subgroups. Starting from these premises, three real-simulated imitation tasks have been implemented in a virtual reality system whose aim is to investigate if machine-learning methods on movement features and frequency could be useful in discriminating ASD children from children with typical neurodevelopment. In this experiment, 24 children with ASD and 25 children with typical neurodevelopment participated in a multimodal virtual reality experience, and changes in their body movements were tracked by a depth sensor camera during the presentation of visual, auditive, and olfactive stimuli. The main results showed that ASD children presented larger body movements than TD children, and that head, trunk, and feet represent the maximum classification with an accuracy of 82.98%. Regarding stimuli, visual condition showed the highest accuracy (89.36%), followed by the visual-auditive stimuli (74.47%), and visual-auditive-olfactory stimuli (70.21%). Finally, the head showed the most consistent performance along with the stimuli, from 80.85% in visual to 89.36% in visual-auditive-olfactory condition. The findings showed the feasibility of applying machine learning and virtual reality to identify body movements’ biomarkers that could contribute to improving ASD diagnosis.

scholarly journals A new machine learning model based on induction of rules for autism detection

2019 ◽  
Vol 26 (1) ◽  
pp. 264-286 ◽  
Author(s):  
Fadi Thabtah ◽  
David Peebles
Keyword(s):  
Machine Learning ◽  
Autism Spectrum Disorder ◽  
Autism Spectrum ◽  
Classification Systems ◽  
Repetitive Behaviors ◽  
Spectrum Disorder ◽  
Medical Psychology ◽  
Screening Methods ◽  
Autism Spectrum Quotient ◽  
New Machine

Autism spectrum disorder is a developmental disorder that describes certain challenges associated with communication (verbal and non-verbal), social skills, and repetitive behaviors. Typically, autism spectrum disorder is diagnosed in a clinical environment by licensed specialists using procedures which can be lengthy and cost-ineffective. Therefore, scholars in the medical, psychology, and applied behavioral science fields have in recent decades developed screening methods such as the Autism Spectrum Quotient and Modified Checklist for Autism in Toddlers for diagnosing autism and other pervasive developmental disorders. The accuracy and efficiency of these screening methods rely primarily on the experience and knowledge of the user, as well as the items designed in the screening method. One promising direction to improve the accuracy and efficiency of autism spectrum disorder detection is to build classification systems using intelligent technologies such as machine learning. Machine learning offers advanced techniques that construct automated classifiers that can be exploited by users and clinicians to significantly improve sensitivity, specificity, accuracy, and efficiency in diagnostic discovery. This article proposes a new machine learning method called Rules-Machine Learning that not only detects autistic traits of cases and controls but also offers users knowledge bases (rules) that can be utilized by domain experts in understanding the reasons behind the classification. Empirical results on three data sets related to children, adolescents, and adults show that Rules-Machine Learning offers classifiers with higher predictive accuracy, sensitivity, harmonic mean, and specificity than those of other machine learning approaches such as Boosting, Bagging, decision trees, and rule induction.

scholarly journals Polychlorinated Biphenyls (PCBs): Risk Factors for Autism Spectrum Disorder?

Toxics ◽  
2020 ◽  
Vol 8 (3) ◽  
pp. 70
Author(s):  
Harmanpreet Kaur Panesar ◽  
Conner L. Kennedy ◽  
Kimberly P. Keil Stietz ◽  
Pamela J. Lein
Keyword(s):  
Risk Factors ◽  
Autism Spectrum Disorder ◽  
Polychlorinated Biphenyls ◽  
Genetic Factors ◽  
The United States ◽  
Autism Spectrum ◽  
Repetitive Behaviors ◽  
Spectrum Disorder ◽  
Individual Risk ◽  
Social Reciprocity

Autism spectrum disorder (ASD) includes a group of multifactorial neurodevelopmental disorders defined clinically by core deficits in social reciprocity and communication, restrictive interests and repetitive behaviors. ASD affects one in 54 children in the United States, one in 89 children in Europe, and one in 277 children in Asia, with an estimated worldwide prevalence of 1–2%. While there is increasing consensus that ASD results from complex gene x environment interactions, the identity of specific environmental risk factors and the mechanisms by which environmental and genetic factors interact to determine individual risk remain critical gaps in our understanding of ASD etiology. Polychlorinated biphenyls (PCBs) are ubiquitous environmental contaminants that have been linked to altered neurodevelopment in humans. Preclinical studies demonstrate that PCBs modulate signaling pathways implicated in ASD and phenocopy the effects of ASD risk genes on critical morphometric determinants of neuronal connectivity, such as dendritic arborization. Here, we review human and experimental evidence identifying PCBs as potential risk factors for ASD and discuss the potential for PCBs to influence not only core symptoms of ASD, but also comorbidities commonly associated with ASD, via effects on the central and peripheral nervous systems, and/or peripheral target tissues, using bladder dysfunction as an example. We also discuss critical data gaps in the literature implicating PCBs as ASD risk factors. Unlike genetic factors, which are currently irreversible, environmental factors are modifiable risks. Therefore, data confirming PCBs as risk factors for ASD may suggest rational approaches for the primary prevention of ASD in genetically susceptible individuals.

scholarly journals The Alert Collector: Autism Spectrum Disorder: A Guide to the Latest Resources

2015 ◽  
Vol 55 (2) ◽  
pp. 113
Author(s):  
Michele Frasier-Robinson
Keyword(s):  
Autism Spectrum Disorder ◽  
Social Communication ◽  
Neurodevelopmental Disorders ◽  
The United States ◽  
Autism Spectrum ◽  
Repetitive Behaviors ◽  
Spectrum Disorder ◽  
Statistical Manual ◽  
Diagnostic And Statistical Manual ◽  
Dsm 5

Since the early 1990s there has been a steady escalation in the numbers of children diagnosed with autism spectrum disorder (ASD)—today it is considered the fastest growing developmental disability in the United States. In 2010, it was estimated that 1 in 68 children were affected by autism spectrum disorder. This is an increase of approximately 120 percent from the data collected ten years earlier. Identifying it as one of six neurodevelopmental disorders, the American Psychiatric Association’s Diagnostic and Statistical Manual of Mental Disorders (DSM-5) describes autism spectrum disorder as “a series of developmental disabilities characterized by impaired social communication and interaction skills, accompanied by the existence of repetitive behaviors or activities, such as rocking movements, hand clapping or obsessively arranging personal belongings.”

scholarly journals Identification of Autism Spectrum Disorder (ASD) using Autoencoder

2020 ◽  
Vol 9 (4) ◽  
pp. 945-948
Keyword(s):  
Machine Learning ◽  
Autism Spectrum Disorder ◽  
Deep Learning ◽  
Great Degree ◽  
Computational Models ◽  
Autism Spectrum ◽  
Benchmark Dataset ◽  
Spectrum Disorder ◽  
Learning Techniques ◽  
3D Representations

Deep Learning (DL) techniques are computational models based on representation learnings. They are demonstrated to be the best reasonable strategies to deal with information with various portrayals and with numerous degrees of reflection. Recognizable proof of ASD has been a test as there is no demonstrated reason for it. The issue has been tended to by numerous specialists with the utilization of fMRI. As MRI and its varieties have 3D representations, Machine Learning and Deep Learning techniques are appropriate to deal with and handle them. This paper extends the recognizable proof of ASD from fMRI pictures utilizing Autoencoder organize. The examinations are led on the benchmark dataset ABIDE II. Results uncover that DL strategies are bringing out better classifiers delivering a great degree of arrangement exactness.

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.

Emotion Dysregulation in Autism Spectrum Disorder

2018 ◽  
pp. 282-298
Author(s):  
Emily Neuhaus
Keyword(s):  
Autism Spectrum Disorder ◽  
Social Communication ◽  
Educational Outcomes ◽  
Emotion Dysregulation ◽  
Research Priorities ◽  
Theoretical Models ◽  
Autism Spectrum ◽  
Repetitive Behaviors ◽  
Spectrum Disorder

Autism spectrum disorder (ASD) is defined by deficits in social communication and interaction, and restricted and repetitive behaviors and interests. Although current diagnostic conceptualizations of ASD do not include emotional difficulties as core deficits, the disorder is associated with emotion dysregulation across the lifespan, with considerable implications for long-term psychological, social, and educational outcomes. The overarching goal of this chapter is to integrate existing knowledge of emotion dysregulation in ASD and identify areas for further investigation. The chapter reviews the prevalence and expressions of emotion dysregulation in ASD, discusses emerging theoretical models that frame emotion dysregulation as an inherent (rather than associated) feature of ASD, presents neurobiological findings and mechanisms related to emotion dysregulation in ASD, and identifies continuing controversies and resulting research priorities.

scholarly journals The Impact of COVID-19 on the Adaptive Functioning, Behavioral Problems, and Repetitive Behaviors of Italian Children with Autism Spectrum Disorder: An Observational Study

Children ◽  
2021 ◽  
Vol 8 (2) ◽  
pp. 96
Author(s):  
Martina Siracusano ◽  
Eugenia Segatori ◽  
Assia Riccioni ◽  
Leonardo Emberti Gialloreti ◽  
Paolo Curatolo ◽  
...  
Keyword(s):  
Autism Spectrum Disorder ◽  
Behavioral Problems ◽  
Adaptive Functioning ◽  
Children With Autism ◽  
Autism Spectrum ◽  
Repetitive Behaviors ◽  
Assessment System ◽  
Spectrum Disorder ◽  
Adaptive Skills ◽  
The Impact

Children with autism spectrum disorder (ASD) and their families have represented a fragile population on which the extreme circumstances of the COVID-19 outbreak may have doubly impaired. Interruption of therapeutical interventions delivered in-person and routine disruption constituted some of the main challenges they had to face. This study investigated the impact of the COVID-19 lockdown on adaptive functioning, behavioral problems, and repetitive behaviors of children with ASD. In a sample of 85 Italian ASD children (mean age 7 years old; 68 males, 17 females), through a comparison with a baseline evaluation performed during the months preceding COVID-19, we evaluated whether after the compulsory home confinement any improvement or worsening was reported by parents of ASD individuals using standardized instruments (Adaptive Behavior Assessment System (Second Edition), Achenbach Child Behavior Checklist, Repetitive Behavior Scale-Revised). No significant worsening in the adaptive functioning, problematic, and repetitive behaviors emerged after the compulsory home confinement. Within the schooler children, clinical stability was found in reference to both adaptive skills and behavioral aspects, whereas within preschoolers, a significant improvement in adaptive skills emerged and was related to the subsistence of web-delivered intervention, parental work continuance, and online support during the lockdown.

scholarly journals Local Protein Translation and RNA Processing of Synaptic Proteins in Autism Spectrum Disorder

2021 ◽  
Vol 22 (6) ◽  
pp. 2811
Author(s):  
Yuyoung Joo ◽  
David R. Benavides
Keyword(s):  
Autism Spectrum Disorder ◽  
Fragile X ◽  
Single Gene ◽  
Protein Translation ◽  
Autism Spectrum ◽  
Repetitive Behaviors ◽  
Spectrum Disorder ◽  
Synaptic Proteins ◽  
Local Translation ◽  
Translation Control

Autism spectrum disorder (ASD) is a heritable neurodevelopmental condition associated with impairments in social interaction, communication and repetitive behaviors. While the underlying disease mechanisms remain to be fully elucidated, dysfunction of neuronal plasticity and local translation control have emerged as key points of interest. Translation of mRNAs for critical synaptic proteins are negatively regulated by Fragile X mental retardation protein (FMRP), which is lost in the most common single-gene disorder associated with ASD. Numerous studies have shown that mRNA transport, RNA metabolism, and translation of synaptic proteins are important for neuronal health, synaptic plasticity, and learning and memory. Accordingly, dysfunction of these mechanisms may contribute to the abnormal brain function observed in individuals with autism spectrum disorder (ASD). In this review, we summarize recent studies about local translation and mRNA processing of synaptic proteins and discuss how perturbations of these processes may be related to the pathophysiology of ASD.

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