Voice acoustics allow classifying autism spectrum disorder with high accuracy.

Early identification of children on the autism spectrum is crucial for early intervention with long-term positive effects on symptoms and skills. The need for improved objective autism detection tools is emphasized by the poor diagnostic power in current tools. Here, we aim to evaluate the classification performance of acoustic features of the voice in children with autism spectrum disorder (ASD) with respect to a heterogeneous control group (composed of neurotypical children, children with Developmental Language Disorder [DLD] and children with sensorineural hearing loss with Cochlear Implant [CI]). This retrospective diagnostic study was conducted at the Child Psychiatry Unit of Tours University Hospital (France). A total of 108 children, including 38 diagnosed with ASD (8.5 ± 0.25 years), 24 typically developing (TD; 8.2 ± 0.32 years) and 46 children with atypical development (DLD and CI; 7.9 ± 0.36 years) were enrolled in our studies. The acoustic properties of speech samples produced by children in the context of a nonword repetition task were measured. We used a Monte Carlo cross-validation with an ROC (Receiving Operator Characteristic) supervised k-Means clustering algorithm to develop a classification model that can differentially classify a child with an unknown disorder. We showed that voice acoustics classified autism diagnosis with an overall accuracy of 91% [CI95%, 90.40%-91.65%] against TD children, and of 85% [CI95%, 84.5%-86.6%] against an heterogenous group of non-autistic children. Accuracy reported here with multivariate analysis combined with Monte Carlo cross-validation is higher than in previous studies. Our findings demonstrate that easy-to-measure voice acoustic parameters could be used as a diagnostic aid tool, specific to ASD.

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.

Bilingualism effects on cognition in autistic children are not all-or-nothing: The role of socioeconomic status in intellectual skills in bilingual autistic children.

LAY ABSTRACT: Previous research has suggested that bilingualism may improve cognition in children with autism, and that this boost may stem from improvement in executive functions. The Wechsler Intelligence Scales for Children are considered to be reliable and valid measures of intelligence when administered to autistic children. These measures have so far revealed unusual psychometric properties in monolingual autistic children, notably distinctive patterns of strengths and weaknesses and low inter-correlation among verbal and nonverbal IQ subtests. The way bilingualism affects the intellectual functioning of autistic children has not been explored yet. Nor has there been a satisfactory factor structure that explains monolingual and bilingual autistic children's IQ performance in terms of individual factors, such as age and socioeconomic status. The current study examined the intelligence profiles of 316 bilingual and age- and gender-matched monolingual children with autism using the Wechsler Intelligence Scales for Children-Third Edition. The study applied clustering models to extract intelligence subtypes of autism, and mediation analyses to examine potential mediation effects of age and socioeconomic status on the children's verbal and nonverbal IQ performance. The results support the mediational role of the children's socioeconomic status in the association between bilingualism and intelligence. Low-socioeconomic status bilingual autistic children outperformed their monolingual peers on both verbal and nonverbal subtests, while the differences faded in medium-socioeconomic status and high-socioeconomic status children. The findings emphasize the positive effects of bilingualism on low-socioeconomic status autistic children's intelligence and also highlight high-socioeconomic status as a factor that may mitigate discrepant patterns of strengths and weaknesses in monolingual children's IQ performance.

Identifying Language and Cognitive Profiles in Children With ASD via a Cluster Analysis Exploration: Implications for the New ICD-11.

The new version of the International Classification of Diseases (ICD-11) mentions the existence of four different profiles in the verbal part of the Autism Spectrum Disorder (ASD), describing them as combinations of either spared or impaired functional language and intellectual abilities. The aim of the present study was to put ASD heterogeneity to the forefront by exploring whether clear profiles related to language and intellectual abilities emerge when investigation is extended to the entire spectrum, focusing on verbal children. Our study proposed a systematic investigation of both language (specifically, structural language abilities) and intellectual abilities (specifically, nonverbal cognitive abilities) in 51 6- to 12-year-old verbal children with ASD based on explicitly motivated measures. For structural language abilities, sentence repetition and nonword repetition tasks were selected; for nonverbal cognitive abilities, we chose Raven's Progressive Matrices, as well as Matrix Reasoning and Block Design from the Wechsler Scales. An integrative approach based on cluster analyses revealed five distinct profiles. Among these five profiles, all four logically possible combinations of structural language and nonverbal abilities mentioned in the ICD-11 were detected. Three profiles emerged among children with normal language abilities and two emerged among language-impaired children. Crucially, the existence of discrepant profiles of abilities suggests that children with ASD can display impaired language in presence of spared nonverbal intelligence or spared language in the presence of impaired nonverbal intelligence, reinforcing the hypothesis of the existence of a separate language module in the brain. Autism Res 2020, 13: 1155-1167. © 2020 International Society for Autism Research, Wiley Periodicals, Inc. LAY SUMMARY: The present work put Autism Spectrum Disorder heterogeneity to the forefront by exploring whether clear profiles related to language and cognitive abilities emerge when investigation is extended to the entire spectrum (focusing on verbal children). The use of explicitly motivated measures of both language and cognitive abilities and of an unsupervised machine learning approach, the cluster analysis, (a) confirmed the existence of all four logically possible profiles evoked in the new ICD-11, (b) evoked the existence of (at least) a fifth profile of language/cognitive abilities, and (c) reinforced the hypothesis of a language module in the brain.