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A Modeling-Guided Case Study of Disordered Speech in Minimally Verbal Children With Autism Spectrum Disorder.

ABSTRACT: Purpose Understanding what limits speech development in minimally verbal (MV) children with autism spectrum disorder (ASD) is important for providing highly effective targeted therapies. This preliminary investigation explores the extent to which developmental speech deficits predicted by Directions Into Velocities of Articulators (DIVA), a computational model of speech production, exemplify real phenotypes. Method Implementing a motor speech disorder in DIVA predicted that speech would become highly variable within and between tokens, while implementing a motor speech plus an auditory processing disorder predicted that DIVA's speech would become highly centralized (schwa-like). Acoustic analyses of DIVA's output predicted that acoustically measured phoneme distortion would be similar between the two cases, but that in the former case, speech would show more within- and between-token variability than in the latter case. We tested these predictions quantitatively on the speech of children with MV ASD. In Study 1, we tested the qualitative predictions using perceptual analysis methods. Speech pathologists blinded to the purpose of the study tallied the signs of childhood apraxia of speech that appeared in the speech of 38 MV children with ASD. K-means clustering was used to create two clusters from the group of 38, and analysis of variance was used to determine whether the clusters differed according to perceptual features corresponding to within- and between-token variability. In Study 2, we employed acoustic analyses on the speech of the child from each cluster who produced the largest number of analyzable tokens to test the predictions of differences in within-token variability, between-token variability, and vowel space area. Results Clusters produced by k-means analysis differed by perceptual features that corresponded to within-token variability. Nonsignificant differences between clusters were found for features corresponding to between-token variability. Subsequent acoustic analyses of the selected cases revealed that the speech of the child from the high-variability cluster showed significantly more quantitative within- and between-token variability than the speech of the child from the low-variability cluster. The vowel space of the child from the low-variability cluster was more centralized than that of typical children and that of the child from the high-variability cluster. Conclusions Results provide preliminary evidence that subphenotypes of children with MV ASD may exist, characterized by (a) comorbid motor speech disorder and (b) comorbid motor speech plus auditory processing disorder. The results motivate testable predictions about how these comorbidities affect speech. Supplemental Material https://doi.org/10.23641/asha.14384432.

SUBMITTER: Chenausky KV

PROVIDER: S-EPMC8702870 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundMobile touchscreen devices are currently being used as speech-generating devices (SGDs) and have been shown to promote the communication skills, particularly the requesting skills of children with autism spectrum disorders (ASD) who have limited spoken language. However, no augmentative and alternative communication (AAC) mobile app has been developed and evaluated in the Chinese language in Mainland China.MethodsWe developed an AAC mobile app, which is the first in Mainland China, to our knowledge, named Yuudee (Chinese name (xiaoyudi)). Yuudee was developed using the Objective-C and Java programming languages. A five-phase training protocol for making requests using Yuudee was developed based on the Picture Exchange Communication System. We trained ten minimally verbal children with ASD to make requests using Yuudee and evaluated the effectiveness of the training.ResultsYuudee has a built-in library of over 400 pictures with corresponding spoken phrases that are divided into 39 categories ranging from making simple requests to expressing emotions. An additional important feature of Yuudee is its customization functions that allow a parent or trainer to easily select pictures and phrases to display, create new pictures and phrases, and change the layouts and orders of the pictures to fit the personal needs of each child. Yuudee is freely available in an iOS version from the iTunes App Store (https://itunes.apple.com/cn/app/xiao-yu-di/id794832934?mt=8) and in an Android version from Google Play (https://play.google.com/store/apps/details?id=com.supersuperstar.yuudee.vue) and domestic Chinese Android App stores. Three consecutive unprompted successful responses, which were defined as an initial training success, were achieved in at least three of the five phases for all ten of the evaluated children. The accuracy rate of a given phase was calculated for each child who achieved three consecutive unprompted successful responses in the phase. Seven children achieved at least 50% accuracy in at least two of the five phases. The other three children achieved at least 50% accuracy in only one phase. Two children achieved at least 50% accuracy in all of the phases in which they were trained.ConclusionsOur data suggest that Yuudee is a useful tool for helping minimally verbal children with ASD make requests.

Project description:There has been increasing attention to the assessment of minimally verbal (MV) children with autism spectrum disorder (ASD). Previous research has begun to examine the relationship between verbal abilities and emotional and behavioral problems (EBP). The current study compared parent-reported EBP in children of differing language levels on two instruments commonly used in ASD research and clinical practice, the Child Behavior Checklist (CBCL) and Aberrant Behavior Checklist (ABC). The study consisted of 1,937 6-18?years old children with ASD from the Simons Simplex Collection. Children were divided into three language groups, by ADOS module (Module 1 = MV, 2 = phrase speech (PS), and 3 = verbally fluent (VF)) and then compared on CBCL and ABC subscales. The ABC and CBCL showed different patterns of elevations across the language groups. MV children were reported to have more impairment than VF children on the ABC irritability, lethargy, and hyperactivity scales. Children with less language (MV and PS) exhibited less impairment on the CBCL internalizing domain than VF children, but did not differ on the externalizing domain. Post hoc comparisons showed that internalizing differences were driven by fewer children with less language exhibiting clinically elevated anxious/depressed scores compared to VF children. The present study underscores the significance of considering language when assessing EBP. Results have implications for the psychiatric screening of children with ASD, particularly those with language impairments. Researchers should exercise caution when applying EBP instruments designed for use with different populations and purposes to broad samples of children. Autism Res 2019, 12: 1367-1375. © 2019 International Society for Autism Research, Wiley Periodicals, Inc. LAY SUMMARY: Some emotional and behavioral problem (EBP) scales include items that may be inappropriate for children with ASD and limited language. The present study found that there was a tendency for children with language impairment to have lower internalizing scores on the Child Behavior Checklist, but higher scores lethargy and irritability scores on the Aberrant Behavior Checklist, relative to verbally fluent children. This suggests that each of these instruments may underestimate EBPs in certain subsets of children.

Project description:BackgroundNeuroimaging research on individuals who have autism spectrum disorder (ASD) has historically been limited primarily to those with age-appropriate cognitive and language performance. Children with limited abilities are frequently excluded from such neuroscience research given anticipated barriers like tolerating the loud sounds associated with magnetic resonance imaging and remaining still during data collection. To better understand brain function across the full range of ASD there is a need to (1) include individuals with limited cognitive and language performance in neuroimaging research (non-sedated, awake) and (2) improve data quality across the performance range. The purpose of this study was to develop, implement, and test the feasibility of a clinical/behavioral and technical protocol for obtaining magnetoencephalography (MEG) data. Participants were 38 children with ASD (8-12 years) meeting the study definition of minimally verbal/nonverbal language. MEG data were obtained during a passive pure-tone auditory task.ResultsBased on stakeholder feedback, the MEG Protocol for Low-language/cognitive Ability Neuroimaging (MEG-PLAN) was developed, integrating clinical/behavioral and technical components to be implemented by an interdisciplinary team (clinicians, behavior specialists, scientists, and technologists). Using MEG-PLAN, a 74% success rate was achieved for acquiring MEG data, with a 71% success rate for evaluable and analyzable data. Exploratory analyses suggested nonverbal IQ and adaptive skills were related to reaching the point of acquirable data. No differences in group characteristics were observed between those with acquirable versus evaluable/analyzable data. Examination of data quality (evaluable trial count) was acceptable. Moreover, results were reproducible, with high intraclass correlation coefficients for pure-tone auditory latency.ConclusionsChildren who have ASD who are minimally verbal/nonverbal, and often have co-occurring cognitive impairments, can be effectively and comfortably supported to complete an electrophysiological exam that yields valid and reproducible results. MEG-PLAN is a protocol that can be disseminated and implemented across research teams and adapted across technologies and neurodevelopmental disorders to collect electrophysiology and neuroimaging data in previously understudied groups of individuals.

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A Modeling-Guided Case Study of Disordered Speech in Minimally Verbal Children With Autism Spectrum Disorder.

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