Background: Studies estimate that 30% of individuals with autism are minimally verbal. Understanding what factors predict longer-term expressive development in children with language delays is critical to inform identification and treatment of those at-risk for persistent language impairments. The present study examined predictors of expressive language development in language-delayed preschoolers followed through later school-age and young adulthood.
Methods: Children using single words or less on the Autism Diagnostic Observation Schedule (ADOS) at approximately 3 years old were drawn from the Early Diagnosis (EDX) and Pathways in ASD longitudinal cohorts. Age-3 predictors of Age-19 ADOS language level were identified using Classification and Regression Trees (CART) in the EDX sample. Linear mixed models examined the effects of CART-identified predictors on Vineland expressive communication (VExp) trajectories from Age-3 to Age-19. The same linear mixed models were examined in the Pathways sample, identifying predictors of VExp from ages 3 to 10.5 years.
Results: Significantly delayed fine motor skills (T-score < 20) was the strongest CART predictor of Age-19 language. In the linear mixed models, time, Age-3 fine motor skills and initiation of joint attention (IJA) predicted VExp trajectories in the EDX sample, even when controlling for Age-3 visual receptive abilities. In the Pathways sample, time and Age-3 fine motor skills were significant predictors of VExp trajectories; IJA and cognitive skills were not significant predictors.
Conclusions: Marked deficits in fine motor skills may be a salient proxy marker for identifying language-delayed children with ASD who are at risk for persistent language impairments. This finding adds to the literature demonstrating a relation between motor and language development in ASD. Investigating individual skill areas (e.g., fine motor and nonverbal problem-solving skills), rather than broader indices of developmental level (e.g., nonverbal IQ) may provide important cues to understanding longer-term language outcomes that can be targeted in early intervention.
Download full-text PDF |
Source |
---|---|
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7028445 | PMC |
http://dx.doi.org/10.1111/jcpp.13117 | DOI Listing |
Front Psychol
December 2024
Institute for Logic, Language and Computation, University of Amsterdam, Amsterdam, Netherlands.
The key function of storytelling is a meeting of hearts: a resonance in the recipient(s) of the story narrator's emotion toward the story events. This paper focuses on the role of gestures in engendering emotional resonance in conversational storytelling. The paper asks three questions: Does story narrators' gesture expressivity increase from story onset to climax offset (RQ #1)? Does gesture expressivity predict specific EDA responses in story participants (RQ #2)? How important is the contribution of gesture expressivity to emotional resonance compared to the contribution of other predictors of resonance (RQ #3)? 53 conversational stories were annotated for a large number of variables including Protagonist, Recency, Group composition, Group size, Sentiment, and co-occurrence with quotation.
View Article and Find Full Text PDFNeural Netw
January 2025
State Key Laboratory of Public Big Data, Guizhou University, 550025, China; Engineering Research Center of Text Computing & Cognitive Intelligence, Ministry of Education, Guizhou University, 550025, China; College of Computer Science and Technology, Guizhou University, 550025, China. Electronic address:
Relation extraction independently verifies all entity pairs in a sentence to identify predefined relationships between named entities. Because these entity pairs share the same contextual features of a sentence, they lead to a complicated semantic structure. To distinguish semantic expressions between relation instances, manually designed rules or elaborate deep architectures are usually applied to learn task-relevant representations.
View Article and Find Full Text PDFInt J Lang Commun Disord
January 2025
Division of Communication Sciences and Disorders, University of Cape Town, Rondebosch, South Africa.
Background: There is a global need for synthetic speech development in multiple languages and dialects, as many children who cannot communicate using their natural voice struggle to find synthetic voices on high-technology devices that match their age, social and linguistic background.
Aims: To document multiple stakeholders' perspectives surrounding the quality, acceptability and utility of newly created synthetic speech in three under-resourced South African languages, namely South African English, Afrikaans and isiXhosa.
Methods & Procedures: A mixed methods research design was selected.
Dev Psychobiol
January 2025
Department of Psychology, University of Oregon, Eugene, Oregon, USA.
Early language is shaped by parent-child interactions and has been examined in relation to maternal psychopathology and parenting stress. Minimal work has examined the relation between maternal emotion dysregulation and toddler vocabulary development. This longitudinal study examined associations between maternal emotion dysregulation prenatally, maternal everyday stress at 7 months postpartum, and toddler vocabulary at 18 months.
View Article and Find Full Text PDFComput Methods Programs Biomed
January 2025
Laberit, Avda. de Catalunya, 9, València, 46020, Spain.
Background And Objective: Despite significant investments in the normalization and the standardization of Electronic Health Records (EHRs), free text is still the rule rather than the exception in clinical notes. The use of free text has implications in data reuse methods used for supporting clinical research since the query mechanisms used in cohort definition and patient matching are mainly based on structured data and clinical terminologies. This study aims to develop a method for the secondary use of clinical text by: (a) using Natural Language Processing (NLP) for tagging clinical notes with biomedical terminology; and (b) designing an ontology that maps and classifies all the identified tags to various terminologies and allows for running phenotyping queries.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!