Spectrum Disorder
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2022 ◽  
Vol 12 (1) ◽  
pp. 1-11
Sanat Kumar Sahu ◽  
Pratibha Verma

In this paper, Feature Selection Technique (FST) namely Particle Swarm Optimization (PSO) has been used. The filter based PSO is a search method with Correlation-based Feature Selection (CBFS) as a fitness function. The FST has two key goals of improving classification efficiency and reducing feature counts. Artificial Neural Network (ANN) Based Multilayer Perceptron Network (MLP) and Deep Learning (DL) have been considered the classification methods on 2 benchmark Autistic Spectrum Disorder (ASD) dataset. The experimental result was compared to the non-reduced features and reduced feature of ASD datasets. The reduced feature give up enhanced results in both classifiers MLP and DL. In addition, an experimental study on the exhibitions of these methodologies has been conducted. Finally, a new trend of PSO-MLP and PSO-DL based classification model is proposed.

2021 ◽  
Vol 44 ◽  
Amelia Anderson ◽  
Selena Layden

School librarians work with students across their organizations, including those with disabilities such as autism spectrum disorder (ASD). However, little is known about how prepared school librarians are to serve these students. Using a mixed-methods survey, this study sought to explore training school librarians have taken about ASD and students with disabilities, as well as the effects of training on librarian confidence and library services. Based on results, librarians who received training through their school district or professional development outside of coursework reported being more confident in supporting students with ASD in the school library.

2021 ◽  
Ting Li ◽  
Martine Hoogman ◽  
Nina Roth Mota ◽  
Jan K. Buitelaar ◽  
Alejandro Arias Vasquez ◽  

2021 ◽  
Vol 11 (1) ◽  
Jürgen Germann ◽  
Flavia Venetucci Gouveia ◽  
Helena Brentani ◽  
Saashi A. Bedford ◽  
Stephanie Tullo ◽  

AbstractThe habenula is a small epithalamic structure with widespread connections to multiple cortical, subcortical and brainstem regions. It has been identified as the central structure modulating the reward value of social interactions, behavioral adaptation, sensory integration and circadian rhythm. Autism spectrum disorder (ASD) is characterized by social communication deficits, restricted interests, repetitive behaviors, and is frequently associated with altered sensory perception and mood and sleep disorders. The habenula is implicated in all these behaviors and results of preclinical studies suggest a possible involvement of the habenula in the pathophysiology of this disorder. Using anatomical magnetic resonance imaging and automated segmentation we show that the habenula is significantly enlarged in ASD subjects compared to controls across the entire age range studied (6–30 years). No differences were observed between sexes. Furthermore, support-vector machine modeling classified ASD with 85% accuracy (model using habenula volume, age and sex) and 64% accuracy in cross validation. The Social Responsiveness Scale (SRS) significantly differed between groups, however, it was not related to individual habenula volume. The present study is the first to provide evidence in human subjects of an involvement of the habenula in the pathophysiology of ASD.

2021 ◽  
Vol 12 ◽  
Clémence Bougeard ◽  
Françoise Picarel-Blanchot ◽  
Ramona Schmid ◽  
Rosanne Campbell ◽  
Jan Buitelaar

Objective: Individuals with autism spectrum disorder often present somatic and/or psychiatric co-morbid disorders. The DSM-5 allows for consideration of additional diagnoses besides ASD and may have impacted the prevalence of co-morbidities as well as being limited in capturing the true differences in prevalence observed between males and females. We describe the prevalence of ASD and frequently observed co-morbidities in children and adolescents (<18 years) in the United States and five European countries.Methods: Two systematic literature reviews were conducted in PubMed and Embase for the period 2014–2019 and focusing on the prevalence of ASD and nine co-morbidities of interest based on their frequency and/or severity: Attention Deficit Hyperactivity Disorder (ADHD), anxiety, depressive disorders, epilepsy, intellectual disability (ID), sleep disorders, sight/hearing impairment/loss, and gastro-intestinal syndromes (GI).Results: Thirteen studies on prevalence of ASD and 33 on prevalence of co-morbidities were included. Prevalence of ASD was 1.70 and 1.85% in US children aged 4 and 8 years respectively, while prevalence in Europe ranged between 0.38 and 1.55%. Additionally, current evidence is supportive of a global increase in ASD prevalence over the past years. Substantial heterogeneity in prevalence of co-morbidities was observed: ADHD (0.00–86.00%), anxiety (0.00–82.20%), depressive disorders (0.00–74.80%), epilepsy (2.80–77.50%), ID (0.00–91.70%), sleep disorders (2.08–72.50%), sight/hearing impairment/loss (0.00–14.90%/0.00–4.90%), and GI syndromes (0.00–67.80%). Studies were heterogeneous in terms of design and method to estimate prevalence. Gender appears to represent a risk factor for co-morbid ADHD (higher in males) and epilepsy/seizure (higher in females) while age is also associated with ADHD and anxiety (increasing until adolescence).Conclusion: Our results provide a descriptive review of the prevalence of ASD and its co-morbidities in children and adolescents. These insights can be valuable for clinicians and parents/guardians of autistic children. Prevalence of ASD has increased over time while co-morbidities bring additional heterogeneity to the clinical presentation, which further advocates for personalized approaches to treatment and support. Having a clear understanding of the prevalence of ASD and its co-morbidities is important to raise awareness among stakeholders.

F1000Research ◽  
2021 ◽  
Vol 10 ◽  
pp. 1087
Reham Moniem Ali ◽  
Deema Faisal Al-Saleh ◽  
Khadeeja M N Ansari ◽  
Hala A. El-Wakeel ◽  
Mai Ibrahim Shukri

Purpose: The primary objective of this research paper was to explore the current state-of-the-art research on autism spectrum disorder from a designer's perspective. An increasing number of scholarly publications in this discipline have urged researcher interest in this topic; however, there is still a lack of quantitative analysis. Therefore, this paper aims to analyze global research output on autism spectrum disorder from a designer's perspective during 1992–2021. Methodology: A bibliometric method was employed to analyze the published literature from 1992–2021. 812 papers were downloaded from the Web of Science core collection for analysis focused on annual growth of literature, prolific authors, authorship pattern, productive organizations, countries, international collaboration, literature trends by keyword analysis, and identifying the funding agencies. Various bibliometrics and scientometrics software were used to analyze the data, namely Bibexcel, Biblioshiny, and VOS viewer. Results: There were 812 research papers published in 405 sources during 1992–2021. 2019 was noted as the most productive year (NP=101), and 2014 received the highest number of citations (TC=6634). Researchers preferred to publish as journal articles (NP=538; TC=24922). The University of Toronto, Canada, was identified as a productive institution with 42 publications and 5358 citations. The USA was the leading producing country with 433 publications, and most of the researchers publish their work in the journal "Scientific Reports" (NP=16). The word "autism" (NP=257) and "architecture" (NP=165) were the most frequently used keywords in autism research.

2021 ◽  
Vol 12 ◽  
Johanna Waltereit ◽  
Charlotte Czieschnek ◽  
Katja Albertowski ◽  
Veit Roessner ◽  
Robert Waltereit

Background: Diagnosis of autism spectrum disorder (ASD) can be made early in childhood, but also later in adolescence or adulthood. In the latter cases, concerns about an individual's behavior typically lead to consultation of a mental health professional (MHP). As part of the initial clinical examination by the MHP, a clinical diagnostic interview is performed, in order to obtain the patient's history, and may lead to the hypothesis of ASD. We were here interested to study family and developmental history as key parts of the patient's history. The aim of the study was to investigate empirical differences between adolescents with ASD and adolescent control persons in family and developmental history.Method: Clinical diagnostic interview items addressing family and developmental history were adopted from their regular use at several university hospitals and in leading textbooks. Parents of male adolescents with normal intelligence and an ASD diagnosis (n = 67) and parents of male adolescents without psychiatric diagnosis (n = 51) between the age of 12 and 17 years were investigated. Data were operationalized into three categories: 0 = normal behavior, 1 = minor pathological behavior, and 2 = major pathological behavior. Differences were analyzed by multiple t-test of two-way ANOVA.Results: Adolescents with ASD expressed a profile of items significantly differing from control persons. Comparison of significant items with the empirical ASD literature indicated robust accordance.Conclusions: Our findings support the importance and feasibility of the clinical diagnostic interview of family and developmental history for initiation of the diagnostic process of ASD in adolescents.

2021 ◽  
Cooper J Mellema ◽  
Kevin P Nguyen ◽  
Alex Treacher ◽  
Albert Montillo

Autism spectrum disorder (ASD) is the fourth most common neurodevelopmental disorder, with a prevalence of 1 in 160 children. Accurate diagnosis relies on experts, but such individuals are scarce. This has led to increasing interest in the development of machine learning (ML) models that can integrate neuroimaging features from functional and structural MRI (fMRI and sMRI) to measure alterations manifest in ASD. We optimized and compared the performance of 12 of the most popular and powerful ML models. Each was separately trained using 15 different combinations of fMRI and sMRI features and optimized with an unbiased model search. Deep learning models predicted ASD with the highest diagnostic accuracy and generalized well to other MRI datasets. Our model achieves state-of-the-art 80% area under the ROC curve (AUROC) in diagnosis on test data from the IMPAC dataset; and 86% and 79% AUROC on the external ABIDE I and ABIDE II datasets. The highest performing models identified reproducible putative biomarkers for accurate ASD diagnosis in accord with known ASD markers as well as novel cerebellum biomarkers. Such reproducibility lends credence to their tremendous potential for defining and using a set of truly generalizable ASD biomarkers that will advance scientific understanding of neuronal changes in ASD.

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