Performance of the Autism Spectrum Rating Scale and Social Responsiveness Scale in Identifying Autism Spectrum Disorder Among Cases of Intellectual Disability

Li, Chunpei; Zhou, Hao; Wang, Tianqi; Long, Shasha; Du, Xinyue; Xu, Xiu; Yan, Weili; Wang, Yi

doi:10.1007/s12264-018-0237-3

Cited by 15 publications

(13 citation statements)

References 36 publications

(55 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Machine learning is a promising tool for investigating the replicability of patterns across larger, more heterogeneous datasets [11][12][13]. For automated diagnosis of ASD, personal characteristic (PC) data, such as intelligence quotient (IQ) and Social Responsiveness Scale (SRS) score have been adopted in several studies [14][15][16]. In the study of ASD, IQ is a type of standard score that is derived from several standardized tests designed to assess human intelligence, and the SRS score includes a 65-item standardized questionnaire regarding behaviors that are associated with ASD [17].…”

Section: Introductionmentioning

confidence: 99%

Multichannel Deep Attention Neural Networks for the Classification of Autism Spectrum Disorder Using Neuroimaging and Personal Characteristic Data

et al. 2020

View full text Add to dashboard Cite

Autism spectrum disorder (ASD) is a developmental disorder that impacts more than 1.6% of children aged 8 across the United States. It is characterized by impairments in social interaction and communication, as well as by a restricted repertoire of activity and interests. The current standardized clinical diagnosis of ASD remains to be a subjective diagnosis, mainly relying on behavior-based tests. However, the diagnostic process for ASD is not only time consuming, but also costly, causing a tremendous financial burden for patients’ families. Therefore, automated diagnosis approaches have been an attractive solution for earlier identification of ASD. In this work, we set to develop a deep learning model for automated diagnosis of ASD. Specifically, a multichannel deep attention neural network (DANN) was proposed by integrating multiple layers of neural networks, attention mechanism, and feature fusion to capture the interrelationships in multimodality data. We evaluated the proposed multichannel DANN model on the Autism Brain Imaging Data Exchange (ABIDE) repository with 809 subjects (408 ASD patients and 401 typical development controls). Our model achieved a state-of-the-art accuracy of 0.732 on ASD classification by integrating three scales of brain functional connectomes and personal characteristic data, outperforming multiple peer machine learning models in a k-fold cross validation experiment. Additional k-fold and leave-one-site-out cross validation were conducted to test the generalizability and robustness of the proposed multichannel DANN model. The results show promise for deep learning models to aid the future automated clinical diagnosis of ASD.

show abstract

Section: Introductionmentioning

confidence: 99%

Multichannel Deep Attention Neural Networks for the Classification of Autism Spectrum Disorder Using Neuroimaging and Personal Characteristic Data

et al. 2020

View full text Add to dashboard Cite

show abstract

“…The parent-reported SRS is a widely used measure to screen social function in ASD individuals aged 4-18 years old (29). Previous studies found that SRS scores were highly related to ADI-R scores (30,31). The SRS is a 65-item 4point Likert scale, from 0 (never true) to 3 (almost true).…”

Section: Participantsmentioning

confidence: 93%

Neural Correlations Between Executive Function and Social Function in Autism Spectrum Disorder: A Mediation Analysis of fMRI

Zhang

Shi

Lin

et al. 2021

Preprint

View full text Add to dashboard Cite

Background: Autism spectrum disorder (ASD) is a set of neurodevelopmental disorders with high heterogeneity. The co-occurrence of social deficits and executive dysfunction is frequently reported in individuals with ASD. The present study evaluated the association between social deficit and executive dysfunction in ASD subjects and explored the underlying neural mechanisms that may mediate this association.Methods: A total of 186 patients with ASD, 5-18 years old (10.25 ± 2.72 years old), from the Autism Brain Imaging Data Exchange II database were enrolled in the final sample. Social function was evaluated by the parent-reported Social Responsiveness Scale (SRS), and executive function (EF) was measured by the parent-reported Behavior Rating Inventory of Executive Function (BRIEF). We selected bilateral amygdala, caudate and putamen as regions of interests, and used the region of interest-based resting-state functional connectivity (FC) analysis to explore the shared and specific brain mechanisms underlying EF and social function. The association analysis of EF, social deficits and FC was conducted using linear regression by controlling covariates, including age, gender, full IQ, handedness, and current medication. Results: The result showed that social functions in ASD children and adolescents were positively associated with EF (r = 0.612, p <0.01). Additionally, 14 and 21 FC links were associated with social deficits and executive dysfunction separately, in which five FC links (right amygdala and right inferior frontal gyrus opercular part, left caudate and right supramarginal gyrus, left putamen and right superior frontal gyrus medial orbital, right putamen and left gyrus rectus, and right putamen and left paracentral lobule) were simultaneously correlated with social function and EF in ASD subjects. We also found that connectivity between the right amygdala and right inferior frontal gyrus mediated the effects of EF on social deficit. Limitations: All the included participants in the present study were ASD patients and male participants, which might limit the generalization of our findings.Conclusion: We found a significant association between social deficits and executive dysfunction in ASD subjects. This association might be mediated by FC between the right amygdala and the right inferior frontal gyrus, which may further illustrate the underlying mechanism of ASD and co-occurrence of social deficits and executive dysfunction in this population.

show abstract

“…Teal & Wiebe, 1986), did not compare people with intellectual disability and ASD with people who only have intellectual disability (e.g. Li et al, 2018) or outlined a tool which was not a screening instrument for ASD per se, but instead screened for additional challenges people with ASD/intellectual disability may face (e.g. Matson, Fodstad, & Mahan, 2009), for further details of inclusion criteria see table 3.…”

Section: Search Strategymentioning

confidence: 99%

Screening tools for autism spectrum disorder, used with people with an intellectual disability: A systematic review

Metcalfe

McKenzie

McCarty

et al. 2020

Research in Autism Spectrum Disorders

View full text Add to dashboard Cite

Background: A diagnosis of autism spectrum disorder (ASD) can be beneficial in ensuring the person receives appropriate support. People with intellectual disability often have undiagnosed co-occurring ASD, due to the specific diagnostic challenges that having intellectual disability can present.Screening tools can be useful to indicate those who are likely to require full diagnostic assessment of ASD.Method: We conducted a systematic review of the literature. The databases ProQuest, PsycArticles, PubMed, and Web of Science were searched for articles published before July 2019. When duplicates were removed 3068 articles were retained. Articles were removed in stages and were retained if there was a possibility that the content was relevant. In total, 14 articles were reviewed fully.Results: The articles covered eight ASD screening instruments and were reviewed in respect of the quality of the available reliability and validity data when used with people with intellectual disability. Conclusion: A few tools have psychometric properties that indicate they have potential to screen for ASD in people with intellectual disability, but overall research with this group is limited, particularly in terms of reliability. The implications for screening and diagnosis of ASD in people with intellectual disability are discussed.

show abstract

Performance of the Autism Spectrum Rating Scale and Social Responsiveness Scale in Identifying Autism Spectrum Disorder Among Cases of Intellectual Disability

Cited by 15 publications

References 36 publications

Multichannel Deep Attention Neural Networks for the Classification of Autism Spectrum Disorder Using Neuroimaging and Personal Characteristic Data

Multichannel Deep Attention Neural Networks for the Classification of Autism Spectrum Disorder Using Neuroimaging and Personal Characteristic Data

Neural Correlations Between Executive Function and Social Function in Autism Spectrum Disorder: A Mediation Analysis of fMRI

Screening tools for autism spectrum disorder, used with people with an intellectual disability: A systematic review

Contact Info

Product

Resources

About