Successfully Serving Students With ASD in the Schools: Let the Evidence Be Your Guide

2011 ◽  
Vol 12 (3) ◽  
pp. 84-90
Author(s):  
Catherine B. Zenko
Keyword(s):  
Autism Spectrum Disorder ◽  
Disease Control ◽  
Pervasive Developmental Disorder ◽  
Developmental Disorder ◽  
School Setting ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Speech Language Pathologist ◽  
Not Otherwise Specified ◽  
Centers For Disease Control

The caseload of a speech-language pathologist in the school setting consists of students with an array of abilities. The number of students with a diagnosis of autism spectrum disorder (ASD) is on the rise according to the most recent statistics: 1/110 children have an ASD (Centers for Disease Control, 2009). The diagnoses that fall under the ASD umbrella include autism, Asperger's syndrome, and pervasive developmental disorder not otherwise specified (PDD-NOS). Given these statistics, school clinicians will see an increase of students with ASD on their caseloads. Ways to effectively address the needs of children who fall under the ASD diagnostic umbrella will be discussed.

scholarly journals IDENTIFIKASI PERKEMBANGAN KOMUNIKASI ANAK PERVASIVE DEVELOPMENTAL DISORDER

2017 ◽  
Vol 32 (2) ◽  
Author(s):  
Muhammad Nurrohman Jauhari
Keyword(s):  
Autism Spectrum Disorder ◽  
Asperger Syndrome ◽  
Rett Syndrome ◽  
Pervasive Developmental Disorder ◽  
Developmental Disorder ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Not Otherwise Specified ◽  
Pervasive Development Disorder

Komunikasi merupakan bagian terpenting dalam hidup manusia, tanpa adanya komunikasi, manusia tidak dapat berinteraksi dengan manusia lainnya. Peran dasar komunikasi adalah jembatan untuk membangun interaksi sosial antara individu satu dengan individu lainnya. Untuk itu, komunikasi berfungsi sebagai medium bagi pembentukan dan pengembangan pribadi individu melalui kontak sosial Anak pervasive developmental disorder atau dapat disebut dengan ASD (Autism Spectrum Disorder) merupakan suatu gangguan atau ketidaknormalan pada seseorang yang ditandai dengan tidak berkembangnya kemampuan sosial dan komunikasi yang di iringi dengan perilaku repetitive dan restrictive (gangguan minat). Pervasive developmental disorder mempunyai empat klasifikasi, yaitu Autis Disorder, Asperger syndrome, Rett Syndrome, dan PDD-NOS (Pervasive Development Disorder-Not Otherwise Specified). Tujuan dalam penelitian ini adalah untuk mengidentiikasi dan mendeskripsikan tahapan komunikasi anak pervasive developmental disorder. Penelitian ini menggunakan pendekatan kualitatif, jenis penelitian kualitatif yang digunakan adalah studi kasus. Berdasarkan hasil observasi dan pengamatan menunjukan bahwa anak pervasive developmental disorder mempunyai perkembangan komunikasi yang berbeda-beda. Dari hasil penelitian dapat disimpulkan bahwa bahwa perkembangan komunikasi anak Autis Disorder, Asperger syndrome, Rett Syndrome, dan PDD-NOS (Pervasive Development Disorder-Not Otherwise Specified) mempunyai perbedaan berdasarkan karakteristik anak. Kata kunci : komunikasi, anak pervasive developmental disorder, identifikasi

scholarly journals Interpretable Machine Learning Reveals Dissimilarities Between Subtypes of Autism Spectrum Disorder

2021 ◽  
Vol 12 ◽  
Author(s):  
Mateusz Garbulowski ◽  
Karolina Smolinska ◽  
Klev Diamanti ◽  
Gang Pan ◽  
Khurram Maqbool ◽  
...  
Keyword(s):  
Machine Learning ◽  
Autism Spectrum Disorder ◽  
Developmental Disorder ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Case Control Studies ◽  
Bioinformatics Analyses ◽  
Not Otherwise Specified ◽  
Interpretable Machine Learning ◽  
Transcriptomics Data

Autism spectrum disorder (ASD) is a heterogeneous neuropsychiatric disorder with a complex genetic background. Analysis of altered molecular processes in ASD patients requires linear and nonlinear methods that provide interpretable solutions. Interpretable machine learning provides legible models that allow explaining biological mechanisms and support analysis of clinical subgroups. In this work, we investigated several case-control studies of gene expression measurements of ASD individuals. We constructed a rule-based learning model from three independent datasets that we further visualized as a nonlinear gene-gene co-predictive network. To find dissimilarities between ASD subtypes, we scrutinized a topological structure of the network and estimated a centrality distance. Our analysis revealed that autism is the most severe subtype of ASD, while pervasive developmental disorder-not otherwise specified and Asperger syndrome are closely related and milder ASD subtypes. Furthermore, we analyzed the most important ASD-related features that were described in terms of gene co-predictors. Among others, we found a strong co-predictive mechanism between EMC4 and TMEM30A, which may suggest a co-regulation between these genes. The present study demonstrates the potential of applying interpretable machine learning in bioinformatics analyses. Although the proposed methodology was designed for transcriptomics data, it can be applied to other omics disciplines.

scholarly journals Significant Changes in Plasma Alpha-Synuclein and Beta-Synuclein Levels in Male Children with Autism Spectrum Disorder

2018 ◽  
Vol 2018 ◽  
pp. 1-7 ◽  
Author(s):  
Wilaiwan Sriwimol ◽  
Pornprot Limprasert
Keyword(s):  
Autism Spectrum Disorder ◽  
Developmental Disorder ◽  
Children With Autism ◽  
Autism Spectrum ◽  
Alpha Synuclein ◽  
Spectrum Disorder ◽  
Enzyme Linked Immunosorbent Assay ◽  
Not Otherwise Specified ◽  
The Mean ◽  
Presynaptic Proteins

Alpha-synuclein (α-synuclein) and beta-synuclein (β-synuclein) are presynaptic proteins playing important roles in neuronal plasticity and synaptic vesicle regulation. To evaluate the association of these two proteins and autism spectrum disorder (ASD), we investigated the plasma α-synuclein and β-synuclein levels in 39 male children with ASD (2 subgroups: 25 autism and 14 pervasive developmental disorder-not otherwise specified (PDD-NOS)) comparing with 29 sex- and age-matched controls by using enzyme-linked immunosorbent assay (ELISA). We first determined the levels of these two proteins in the ASD subgroups and found that there were no significant differences in both plasma α-synuclein and β-synuclein levels in the autism and PDD-NOS groups. Thus, we could combine the 2 subgroups into one ASD group. Interestingly, the mean plasma α-synuclein level was significantly lower (P<0.001) in the ASD children (10.82±6.46 ng/mL) than in the controls (29.47±18.62 ng/mL), while the mean plasma β-synuclein level in the ASD children (1344.19±160.26 ng/mL) was significantly higher (P<0.05) than in the controls (1219.16±177.10 ng/mL). This is the first study examining the associations between α-synuclein and β-synuclein and male ASD patients. We found that alterations in the plasma α-synuclein and β-synuclein levels might be implicated in the association between synaptic abnormalities and ASD pathogenesis.

scholarly journals A novel automated autism spectrum disorder detection system

2021 ◽  
Author(s):  
Shu Lih Oh ◽  
V. Jahmunah ◽  
N. Arunkumar ◽  
Enas W. Abdulhay ◽  
Raj Gururajan ◽  
...  
Keyword(s):  
Autism Spectrum Disorder ◽  
Developmental Disorder ◽  
Detection System ◽  
Brain Activity ◽  
Limited Range ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Nonlinear Features ◽  
Genetic And Environmental Factors ◽  
Future Work

AbstractAutism spectrum disorder (ASD) is a neurological and developmental disorder that begins early in childhood and lasts throughout a person’s life. Autism is influenced by both genetic and environmental factors. Lack of social interaction, communication problems, and a limited range of behaviors and interests are possible characteristics of autism in children, alongside other symptoms. Electroencephalograms provide useful information about changes in brain activity and hence are efficaciously used for diagnosis of neurological disease. Eighteen nonlinear features were extracted from EEG signals of 40 children with a diagnosis of autism spectrum disorder and 37 children with no diagnosis of neuro developmental disorder children. Feature selection was performed using Student’s t test, and Marginal Fisher Analysis was employed for data reduction. The features were ranked according to Student’s t test. The three most significant features were used to develop the autism index, while the ranked feature set was input to SVM polynomials 1, 2, and 3 for classification. The SVM polynomial 2 yielded the highest classification accuracy of 98.70% with 20 features. The developed classification system is likely to aid healthcare professionals as a diagnostic tool to detect autism. With more data, in our future work, we intend to employ deep learning models and to explore a cloud-based detection system for the detection of autism. Our study is novel, as we have analyzed all nonlinear features, and we are one of the first groups to have uniquely developed an autism (ASD) index using the extracted features.

scholarly journals Prenatal Risk for Autism Spectrum Disorder (ASD): Fetal Cortisol Exposure Predicts Child ASD Symptoms

2018 ◽  
Vol 7 (2) ◽  
pp. 349-361 ◽  
Author(s):  
Sheena Ram ◽  
Mariann A. Howland ◽  
Curt A. Sandman ◽  
Elysia Poggi Davis ◽  
Laura M. Glynn
Keyword(s):  
Autism Spectrum Disorder ◽  
Developmental Disorder ◽  
Maternal Plasma ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Sexually Dimorphic ◽  
Fetal Exposure ◽  
Prospective Longitudinal Study ◽  
The Social ◽  
Prospective Longitudinal

The etiology of autism spectrum disorder (ASD) is multifactorial, complex, and likely involves interactions among genetic, epigenetic, and environmental factors. With respect to environmental influences, a growing literature implicates intrauterine experiences in the origin of this pervasive developmental disorder. In this prospective longitudinal study, we examined the hypothesis that fetal exposure to maternal cortisol may confer ASD risk. In addition, because ASD is four times more prevalent in males than in females, and because sexually dimorphic responses to intrauterine experiences are commonly observed, we examined whether or not any associations differ by fetal sex. Maternal plasma cortisol was measured at 15, 19, 25, 31, and 37 weeks’ gestation in a sample of 84 pregnant women. ASD symptoms were assessed in their 5-year-old children with the Social Communication Questionnaire (SCQ). Fetal exposure to lower levels of maternal cortisol was associated with higher levels of ASD symptoms only among boys. The observed hypocortisolemic profile exhibited by these mothers may indicate a risk factor that precedes the stress of caregiving for a child with ASD and may not be solely a consequence of the stress of caregiving, as previously thought. These findings confirm the value of examining prenatal hormone exposures as predictors of ASD risk and support the premise that altered prenatal steroid exposures may play a role in the etiology of ASD.

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