Expressive and Receptive Language in Russian Primary-School-Aged Children with Autism Spectrum Disorder

Non-Word Repetition in Arabic-speaking children with and without Autism Spectrum Disorder (ASD): A closer look into accuracy and error patterns

Non-Word Repetition (NWR) tasks effectively identify language impairments and assess phonological skills across diverse populations and languages, including Arabic. Prior research revealed heterogeneity of performance in children with Autism Spectrum Disorder (ASD) on NWR tasks. The current study is the first to evaluate phonological skills of Palestinian-Arabic-speaking children with and without ASD, employing a Palestinian-Arabic NWR task. A total of 142 Palestinian-Arabic-speaking children, aged 5-11 participated in the study, including 75 children with Typical Language Development (TLD) and 67 children with ASD. The NWR task included 18 non-words of varying length (1-4 syllables) and complexity (with and without consonant clusters). Quantitative analysis examined the effects of length and phonological complexity on the NWR accuracy scores in children with TLD and ASD. Error pattern analysis accounting for phoneme and syllable substitutions/deletions/additions and lexicalisations aimed to shed light on the phonological representations of children with and without ASD. Within the ASD group, two subgroups emerged: 72% exhibited age-appropriate NWR performance, while 28% showed performance at-risk for phonological impairment. Non-word length, rather than complexity, significantly influenced the children's performance. Consonant substitution was the most frequent error pattern across all groups. On the theoretical side, these findings extend cross-linguistic evidence of phonological skill heterogeneity in children with ASD to Arabic-speaking children. Additionally, they highlight an overall delayed but qualitatively similar pattern of phonological development in children with ASD. On the clinical side, results underscore the importance of comprehensive language assessment in children with ASD.

Occupational Therapy Observation Tool-Adjustment Support Details for autistic children: Investigation of content validity and clinical utility

INTRODUCTION

Standardised testing is crucial for autistic children to receive appropriate interventions, necessary services, and funding. Behaviours associated with autism can hinder children's test performance and participation. The 18-item, two-page Occupational Therapy Observation Tool-Adjustment Support Details (OTOT-ASD) is used in conjunction with standardised assessments. The OTOT-ASD enables the recording of autism-related behaviours and the reasonable assessment accommodations made to support participation in testing. The current study aims to investigate the content validity and clinical utility of the OTOT-ASD from occupational therapists' perspectives.

METHOD

A specifically designed online questionnaire was distributed Victoria-wide to occupational therapists with clinical experience with autistic children. The questionnaire was designed in line with the COnsensus-based Standards for selection of health Measurement INstruments (COSMIN), including relevance, comprehensiveness, and comprehensibility of items evaluated for content validity. Applicability and clinical usefulness were also investigated. Quantitative data were analysed using descriptive statistics.

RESULTS

Seventy-one occupational therapists responded to the questionnaire and 46 provided a full dataset. Over 96% of the respondents considered documenting behaviours and testing accommodations as important, yet less than 45% reported having resources to record this information. Ninety-five percent of the respondents perceived the OTOT-ASD to be useful in clinical practice. Over 70% agreed that OTOT-ASD items were significantly relevant to practice and comprehensive. To improve comprehensibility, identified items were altered following feedback.

CONCLUSION

The findings suggest that the OTOT-ASD is clinically useful and content validity is sound. Further research on other psychometric properties of the OTOT-ASD and users' qualitative experiences in utilising the tool is recommended.

Morphosyntactic skills in Arabic-speaking children with autism spectrum disorder: Evidence from error patterns in the sentence repetition task

Background and aims

Although autism spectrum disorder (ASD) has not traditionally been associated with morphosyntactic impairments, some children with ASD manifest significant difficulties in this domain. Sentence Repetition (SRep) tasks are highly reliable tools for detecting morphosyntactic impairment in different languages and across various populations, including children with ASD. This study is among the first to evaluate morphosyntactic abilities of Palestinian-Arabic (PA) speaking children using a PA SRep task.

Methods

A total of 142 PA-speaking children, aged 5-11, participated in the study: 75 children with typical language development (TLD) and 67 children with ASD. The PA SRep task targeted morphosyntactic structures of varying complexity (simple subject-verb-object [SVO] sentences, biclausal sentences, wh-questions, relative clauses). Children's accuracy scores were assessed across these structures and error patterns encompassing morphosyntactic and pragmatic aspects were analyzed.

Results

Two subgroups of ASD emerged: 43% showed age-appropriate language skills (ASD  +  NL) pairing up with TLD peers, while 57% showed signs of morphosyntactic impairment (ASD  +  LI). Children in both groups exhibited a higher frequency of morphosyntactic errors than pragmatic ones. Children with ASD  +  LI showed difficulties with producing complex morphosyntactic structures, such as relative clauses and object wh-questions. Error analysis revealed that children in the ASD  +  LI group produced sentence fragments and simplified constructions when complex structures were targeted.

Conclusions

The current study extends the cross-linguistic evidence of the heterogeneity of morphosyntactic profiles in children with ASD to Arabic-speaking children. Error analysis indicates that poor morphosyntax, rather than pragmatics, challenges children's performance on the SRep task.

Implications

Our results emphasize the importance of comprehensive language assessment in children with ASD and underscore the need for tailored intervention plans targeting impaired morphosyntactic structures in some children with ASD.

A Slip Between the Brain and the Lip: Working Memory and Cognitive-Communication Disorders

The relationship between working memory and speech has been a topic of intense research interest and investigation for many years. Memory studies have found that the active processing of working memory is required for language comprehension and speech production. Though there are studies that discuss the capacity of working memory, the processing of verbal stimuli into verbal memory remains unclear. Therefore, it is essential to understand the functioning of the working memory and how it processes verbal information. As working memory is intricately linked with communication, any deficits in working memory could cause communication disorders. Also, the disruption in the storage and retrieval of verbal memory could cause a disturbance in the speech pattern. To this point, this review elaborates on the active processing of working memory and its role in communication. Further, by studying the deficits in working memory that could cause cognitive-communication disorders such as apraxia of speech, dementia, and dysarthria, this article highlights the importance of verbal memory in speech.

Language in autism: domains, profiles and co-occurring conditions

This article reviews the current knowledge state on pragmatic and structural language abilities in autism and their potential relation to extralinguistic abilities and autistic traits. The focus is on questions regarding autism language profiles with varying degrees of (selective) impairment and with respect to potential comorbidity of autism and language impairment: Is language impairment in autism the co-occurrence of two distinct conditions (comorbidity), a consequence of autism itself (no comorbidity), or one possible combination from a series of neurodevelopmental properties (dimensional approach)? As for language profiles in autism, three main groups are identified, namely, (i) verbal autistic individuals without structural language impairment, (ii) verbal autistic individuals with structural language impairment, and (iii) minimally verbal autistic individuals. However, this tripartite distinction hides enormous linguistic heterogeneity. Regarding the nature of language impairment in autism, there is currently no model of how language difficulties may interact with autism characteristics and with various extralinguistic cognitive abilities. Building such a model requires carefully designed explorations that address specific aspects of language and extralinguistic cognition. This should lead to a fundamental increase in our understanding of language impairment in autism, thereby paving the way for a substantial contribution to the question of how to best characterize neurodevelopmental disorders.

A machine learning-based diagnostic model for children with autism spectrum disorders complicated with intellectual disability

Background

Early detection of children with autism spectrum disorder (ASD) and comorbid intellectual disability (ID) can help in individualized intervention. Appropriate assessment and diagnostic tools are lacking in primary care. This study aims to explore the applicability of machine learning (ML) methods in diagnosing ASD comorbid ID compared with traditional regression models.

Method

From January 2017 to December 2021, 241 children with ASD, with an average age of 6.41 ± 1.96, diagnosed in the Developmental Behavior Department of the Children's Hospital Affiliated with the Medical College of Zhejiang University were included in the analysis. This study trained the traditional diagnostic models of Logistic regression (LR), Support Vector Machine (SVM), and two ensemble learning algorithms [Random Forest (RF) and XGBoost]. Socio-demographic and behavioral observation data were used to distinguish whether autistic children had combined ID. The hyperparameters adjustment uses grid search and 10-fold validation. The Boruta method is used to select variables. The model's performance was evaluated using discrimination, calibration, and decision curve analysis (DCA).

Result

Among 241 autistic children, 98 (40.66%) were ASD comorbid ID. The four diagnostic models can better distinguish whether autistic children are complicated with ID, and the accuracy of SVM is the highest (0.836); SVM and XGBoost have better accuracy (0.800, 0.838); LR has the best sensitivity (0.939), followed by SVM (0.952). Regarding specificity, SVM, RF, and XGBoost performed significantly higher than LR (0.355). The AUC of ML (SVM, 0.835 [95% CI: 0.747-0.944]; RF, 0.829 [95% CI: 0.738-0.920]; XGBoost, 0.845 [95% CI: 0.734-0.937]) is not different from traditional LR (0.858 [95% CI: 0.770-0.944]). Only SVM observed a good calibration degree. Regarding DCA, LR, and SVM have higher benefits in a wider threshold range.

Conclusion

Compared to the traditional regression model, ML model based on socio-demographic and behavioral observation data, especially SVM, has a better ability to distinguish whether autistic children are combined with ID.