Parental Beliefs About Causes of Autism Spectrum Disorder: An Investigation of a Research Measure Using Principal Component Analysis

2021 ◽  
Vol 87 ◽  
pp. 101825
Author(s):  
Christie M. Brewton ◽  
Sarah S. Mire ◽  
Tammy D. Tolar ◽  
Robin P. Goin-Kochel ◽  
Milena A. Keller-Margulis ◽  
...  
Keyword(s):  
Autism Spectrum Disorder ◽  
Principal Component Analysis ◽  
Principal Component ◽  
Component Analysis ◽  
Parental Beliefs ◽  
Autism Spectrum ◽  
Spectrum Disorder

scholarly journals Efficacy of the Feline Temperament Profile in evaluating sheltered cats for adoption into families of a child with Autism Spectrum Disorder

2020 ◽  
Vol 9 (2) ◽  
pp. 21-55
Author(s):  
Angélique Lamontagne ◽  
Rebecca Johnson ◽  
Gretchen Carlisle ◽  
Leslie Lyons ◽  
Jessica Bibbo ◽  
...  
Keyword(s):  
Autism Spectrum Disorder ◽  
Principal Component Analysis ◽  
Principal Component ◽  
Children With Autism ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Animal Shelters ◽  
Significant Difference ◽  
Research Questions ◽  
Similarities And Differences

This project was part of the Feline Friends Study, which matches shelter cats with families of children with Autism Spectrum Disorder (ASD) to study children’s social behaviour and cats’ stress. Cats were screened for calm temperament using the Feline Temperament Profile (FTP). The FTP consists of ten phases, with a list of ‘acceptable’ and ‘questionable’ behaviours under each phase. Our aim was to answer the following research questions: What items of the FTP best predict temperament in shelter cats? What are similarities and differences in temperament between cats who qualified or did not qualify for placement? Forty-four shelter cats were rejected for placement in a home with a child with ASD and twenty-six cats qualified. There was no difference according to sex; however, there was a significant difference in FTP scores across animal shelters. Principal Component Analysis showed that ‘vocalizations’ and ‘watches with no approach’ were most predictive of acceptable temperament. The findings show that the FTP can be shortened with no loss of reliability, facilitating shelter efforts to rehome cats successfully. The implementation of FTP could effectively reduce the amount of time cats spend in the shelter as well as the number returned to the shelter after adoption, resulting in improved welfare both at the shelter (shorter stay) and in their new home (decreased return).

Lack of mental flexibility as endophenotype in autism spectrum disorder and obsessive comulsive disorder families

2011 ◽  
Vol 26 (S2) ◽  
pp. 970-970
Author(s):  
V. Goussé ◽  
A. Hagi ◽  
J.-L. Stilgenbauer ◽  
R. Delorme
Keyword(s):  
Autism Spectrum Disorder ◽  
Executive Functions ◽  
Principal Component ◽  
Autism Spectrum ◽  
Repetitive Behaviors ◽  
Spectrum Disorder ◽  
Central Coherence ◽  
Obsessive Compulsive ◽  
Compulsive Disorder ◽  
First Degree Relatives

IntroductionResults from clinical and molecular genetic studies suggest that autism spectrum disorder (ASD) and obsessive compulsive disorder (OCD) could have a shared pattern of heritability. Among a large number of clinical variables evaluated, obsessive compulsive/repetitive behaviors have been found to be highly correlated among autism probands and their relatives. Empirical evidence from neuropsychological studies suggest that an appropriate model for repetitive behaviours is a deficit of executive functions specifically flexibility. Given the lack of flexibility observed in ASD and OCD probands, we hypothesised that it could represent a shared endophenotype in both families.MethodsSeven cognitive tests belonging to executive functions, central coherence and theory of mind were proposed to 58 unaffected first-degree relatives of probands with ASD and 61 unaffected first-degree relatives of OCD patients and compared with 34 healthy controls. A principal component analysis (PCA) was performed.ResultsASD relatives - specifically mothers - performed significantly worse on all the tests than OCD relatives and controls. Moreover, inside the OCD group, female individuals performed significantly worse than males. Finally, the PCA indicate that the cognitive profiles of the ASD relatives and the OCD relatives were similar but distinct from controls.ConclusionLack of cognitive flexibility is observed in ASD and OCD families and might constitute a shared intermediate cognitive phenotype. Our study constitutes an effort to clarify the relationship between ASD and OCD having implications for our nosological understanding of both disorders.

scholarly journals Proposal of a questionnaire to investigate social communication skills of children with typical development and communication disorders

Revista CEFAC ◽  
2021 ◽  
Vol 23 (4) ◽  
Author(s):  
Shelly Lagus ◽  
Fernanda Dreux Miranda Fernandes
Keyword(s):  
Autism Spectrum Disorder ◽  
Speech Production ◽  
Language Disorder ◽  
Principal Component ◽  
Communication Disorders ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Typical Development ◽  
Social Communication Skills ◽  
Student’S T

ABSTRACT Purpose: to verify the possibility of administering a simple questionnaire to family members who communicate with their children to identify communication functional characteristics of children with different manifestations of language development. Methods: 95 parents/guardians were individually interviewed. Their children were afterwards diagnosed with language disorder (LD), speech production disorder (SPD), autism spectrum disorder (ASD), and typical development (TD). The interviews were conducted with the Communicative Skills Questionnaire to characterize the pragmatic performance. The Student’s t-test and the principal component analysis were used in statistical analysis, considering significant p-values < 0.05. Results: the statistical analyses reveal that the questionnaire distinguished the groups of children diagnosed with autism spectrum disorder and language disorder from the groups of children with speech production disorders and typical development. Conclusion: the questionnaire proved to be capable of distinguishing and characterizing, from the pragmatic standpoint, the children with different manifestations of communication development, revealing the impaired pragmatic skills of children with autism spectrum disorders and language disorders.

scholarly journals Data Analysis and Classification of Autism Spectrum Disorder Using Principal Component Analysis

2020 ◽  
Vol 2020 ◽  
pp. 1-8
Author(s):  
Ammar I. Shihab ◽  
Faten A. Dawood ◽  
Ali H. Kashmar
Keyword(s):  
Autism Spectrum Disorder ◽  
Data Analysis ◽  
Attention Deficit Disorder ◽  
Component Analysis ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Data Set ◽  
Children With Asd ◽  
Adults And Children

Autism spectrum disorder (ASD) is an early developmental disorder characterized by mutation of enculturation associated with attention deficit disorder in the visual perception of emotional expressions. An estimated one in more than 100 people has autism. Autism affects almost four times as many boys than girls. Data analysis and classification of ASD is still challenging due to unsolved issues arising from many severity levels and range of signs and symptoms. To understanding the functions which involved in autism, neuroscience technology analyzed responses to stimuli of autistic audio and video. The study focuses on analyzing the data set of adults and children with ASD using practical component analysis method. To satisfy this aim, the proposed method consists of three main stages including: (1) data set preparation, (2) Data analysis, and (3) Unsupervised Classification. The experimental results were performed to classify adults and children with ASD. The classification results in adults give a sensitivity of 78.6% and specificity of 82.47%, while the classification results in children give a sensitivity of 87.5% and specificity of 95.7%.

scholarly journals Determinant Factors of Sensory in Creating Autism Learning Environment

2021 ◽  
Vol 6 (16) ◽  
pp. 113-118
Author(s):  
Roslinda Ghazali ◽  
Siti Rasidah Md Sakip ◽  
Ismail Samsuddin ◽  
Heba Samra
Keyword(s):  
Autism Spectrum Disorder ◽  
Learning Environment ◽  
Publishing House ◽  
Reliability And Validity ◽  
Principal Component ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Design Stage ◽  
Sensory Sensitivity ◽  
Conducive Environment

Children with Autism Spectrum Disorder (ASD) have difficulty sitting still, focusing, and attending the task. Creating a sensory environment would promote a conducive environment for them. Sensory sensitivity, sensory stimulation, sensory design, and physical learning environment are the key concepts contributing to the autism environment. The study goal is to explore the design criteria for creating a conducive environment for autism.  This article is to examine the reliability and validity of the four main constructs.  This is important to ensure the quality of achieving the data. The study's significance is to provide a guideline during the design stage and improve the autism learning environment. Keywords: Autism Spectrum Disorder; Principal Component Analysis, Autism Environment. eISSN: 2398-4287© 2021. The Authors. Published for AMER ABRA cE-Bs by e-International Publishing House, Ltd., UK. This is an open access article under the CC BYNC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Peer–review under responsibility of AMER (Association of Malaysian Environment-Behaviour Researchers), ABRA (Association of Behavioural Researchers on Asians/Africans/Arabians) and cE-Bs (Centre for Environment-Behaviour Studies), Faculty of Architecture, Planning & Surveying, Universiti Teknologi MARA, Malaysia. DOI: https://doi.org/10.21834/ebpj.v6i16.2696

scholarly journals Effective Implementation of Pre-Processing Techniques in Machine Learning for Autism Spectrum Disorder

2020 ◽  
Vol 9 (5) ◽  
pp. 2253-2257
Keyword(s):  
Machine Learning ◽  
Autism Spectrum Disorder ◽  
Random Forest ◽  
Learning Strategies ◽  
Principal Component ◽  
Processing Technique ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Machine Learning Techniques ◽  
Processing Techniques

Autism Spectrum disorder (ASD) is a neurobiological developmental disorder is symbolize by means of the impairment of social interaction, stereotypic behaviours, and communiqué lack. Early deduction of ASD will enhance the fine of lifestyles of the affected person. The objective of the paper is to focus on the application of various Machine Learning strategies applied for the autism dataset for diagnosing ASD. In this study, the effective pre-processing techniques One-hot encoding, Splitting and Scaling are used to standardize the dataset and the Principal Component Analysis (PCA) evaluator method is applied for the best feature selection. This technique is investigated with various Machine learning techniques like Random Forest, SVM, Logistic Regression, KNN, Naive Bayes. Comparatively, the effective Pre-Processing technique with Random Forest model shows the better accuracy of 92% in diagnosing ASD. When with other metrics such as accuracy, precision, recall, F1-score, ROC, error rate.

Sign in / Sign up

Export Citation Format

Share Document