Pragmatic Language Markers of Autism Diagnosis and Severity

The profile of social communication in Dravet syndrome

Dravet syndrome (DS) presents a multifaceted clinical picture marked by epilepsy, cognitive impairments and behavioral disorders that progresses throughout development. Behavioral disorders include impairments in social relationships and communication, with frequent diagnosis of autism spectrum disorder. This study focused on comprehensively evaluating and comparing social communication profiles among a group of 43 children with Dravet syndrome, 30 children with level 1 autism spectrum disorder, 36 with social (pragmatic) communication disorder, and 18 with intellectual disability. Using validated tools like the Childhood Autism Spectrum Test and Children's Communication Checklist, distinct patterns of social communication deficits were delineated. Our findings indicate that children with Dravet syndrome experience challenges in social relationships, primarily due to difficulties in use of pragmatic language. Areas such as range of interests and social interaction are less affected compared to those with ASD, emphasizing differing profiles between the conditions. While children with DS and ID may have similar intellectual functioning, the different social communication deficits in DS indicate their role in the DS phenotype beyond ID. These results underscore the unique social communication profile of DS and emphasizes the importance of tailored interventions and deep phenotyping efforts for effective DS management.

Factors Associated with Confirmed and Unconfirmed Autism Spectrum Disorder Diagnosis in Children Volunteering for Research

PURPOSE

Diagnostic accuracy of autism spectrum disorder (ASD) is crucial to track and characterize ASD, as well as to guide appropriate interventions at the individual level. However, under-diagnosis, over-diagnosis, and misdiagnosis of ASD are still prevalent.

METHODS

We describe 232 children (MAge = 10.71 years; 19% female) with community-based diagnoses of ASD referred for research participation. Extensive assessment procedures were employed to confirm ASD diagnosis before study inclusion. The sample was subsequently divided into two groups with either confirmed ASD diagnoses (ASD+) or unconfirmed/inaccurate diagnoses (ASD-). Clinical characteristics differentiating the groups were further analyzed.

RESULTS

47% of children with community-based ASD diagnoses did not meet ASD criteria by expert consensus. ASD + and ASD- groups did not differ in age, gender, ethnicity, or racial make-up. The ASD + group was more likely to have a history of early language delays compared to the ASD- group; however, no group differences in current functional language use were reported by caregivers. The ASD + group scored significantly higher on ADI-R scores and on the ADOS-2 algorithm composite scores and calibrated severity scores (CSSs). The ASD- group attained higher estimated IQ scores and higher rates of psychiatric disorders, including anxiety disorder, disruptive behavior, and mood disorder diagnoses. Broadly, caregiver questionnaires (SRS-2, CCC-2) did not differentiate groups.

CONCLUSION

Increased reported psychiatric disorders in the ASD- group suggests psychiatric complexity may contribute to community misdiagnosis and possible overdiagnosis of ASD. Clinician-mediated tools (ADI-R, ADOS-2) differentiated ASD + versus ASD- groups, whereas caregiver-reported questionnaires did not.

Consistency and reliability of automated language measures across expressive language samples in autism

Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder with substantial clinical heterogeneity, especially in language and communication ability. There is a need for validated language outcome measures that show sensitivity to true change for this population. We used Natural Language Processing to analyze expressive language transcripts of 64 highly-verbal children and young adults (age: 6-23 years, mean 12.8 years; 78.1% male) with ASD to examine the validity across language sampling context and test-retest reliability of six previously validated Automated Language Measures (ALMs), including Mean Length of Utterance in Morphemes, Number of Distinct Word Roots, C-units per minute, unintelligible proportion, um rate, and repetition proportion. Three expressive language samples were collected at baseline and again 4 weeks later. These samples comprised interview tasks from the Autism Diagnostic Observation Schedule (ADOS-2) Modules 3 and 4, a conversation task, and a narration task. The influence of language sampling context on each ALM was estimated using either generalized linear mixed-effects models or generalized linear models, adjusted for age, sex, and IQ. The 4 weeks test-retest reliability was evaluated using Lin's Concordance Correlation Coefficient (CCC). The three different sampling contexts were associated with significantly (P < 0.001) different distributions for each ALM. With one exception (repetition proportion), ALMs also showed good test-retest reliability (median CCC: 0.73-0.88) when measured within the same context. Taken in conjunction with our previous work establishing their construct validity, this study demonstrates further critical psychometric properties of ALMs and their promising potential as language outcome measures for ASD research.

"Um" and "Uh" Usage Patterns in Children with Autism: Associations with Measures of Structural and Pragmatic Language Ability

Pragmatic language difficulties, including unusual filler usage, are common among children with Autism Spectrum Disorder (ASD). This study investigated "um" and "uh" usage in children with ASD and typically developing (TD) controls. We analyzed transcribed Autism Diagnostic Observation Schedule (ADOS) sessions for 182 children (117 ASD, 65 TD), aged 4 to 15. Although the groups did not differ in "uh" usage, the ASD group used fewer "ums" than the TD group. This held true after controlling for age, sex, and IQ. Within ASD, social affect and pragmatic language scores did not predict filler usage; however, structural language scores predicted "um" usage. Lower "um" rates among children with ASD may reflect problems with planning or production rather than pragmatic language.