No abstract

Background Differences in responding to sensory stimuli, including sensory hyperreactivity (HYPER), hyporeactivity (HYPO), and sensory seeking (SEEK) have been observed in autistic individuals across sensory modalities, but few studies have examined the structure of these “supra-modal” traits in the autistic population. Methods Leveraging a combined sample of 3,868 autistic youth drawn from 12 distinct data sources (ages 3–18 years and representing the full range of cognitive ability), the current study used modern psychometric and meta-analytic techniques to interrogate the latent structure and correlates of caregiver-reported HYPER, HYPO, and SEEK within and across sensory modalities. Bifactor statistical indices were used to both evaluate the strength of a “general response pattern” factor for each supra-modal construct and determine the added value of “modality-specific response pattern” scores (e.g., Visual HYPER). Bayesian random-effects integrative data analysis models were used to examine the clinical and demographic correlates of all interpretable HYPER, HYPO and SEEK (sub)constructs. Results All modality-specific HYPER subconstructs could be reliably and validly measured, whereas certain modality-specific HYPO and SEEK subconstructs were psychometrically inadequate when measured using existing items. Bifactor analyses unambiguously supported the validity of a supra-modal HYPER construct (ωH = .800), whereas a coherent supra-modal HYPO construct was not supported (ωH = .611), and supra-modal SEEK models suggested a more limited version of the construct that excluded some sensory modalities (ωH = .799; 4/7 modalities). Within each sensory construct, modality-specific subscales demonstrated substantial added value beyond the supra-modal score. Meta-analytic correlations varied by construct, although sensory features tended to correlate most strongly with other domains of core autism features and co-occurring psychiatric symptoms. Certain subconstructs within the HYPO and SEEK domains were also associated with lower adaptive behavior scores. Limitations: Conclusions may not be generalizable beyond the specific pool of items used in the current study, which was limited to parent-report of observable behaviors and excluded multisensory items that reflect many “real-world” sensory experiences. Conclusion Psychometric issues may limit the degree to which some measures of supra-modal HYPO/SEEK can be interpreted. Depending on the research question at hand, modality-specific response pattern scores may represent a valid alternative method of characterizing sensory reactivity in autism.

No abstract

Background Individuals with Down syndrome (DS) appear to perform at a level that is commensurate with developmental expectations on simple tasks of selective attention. In this study, we examine how their selective attention is impacted by target changes that unfold over both time and space. This increased complexity reflects an attempt at greater ecological validity in an experimental task, as a steppingstone for better understanding attention among persons with DS in real-world environments. Methods A modified flanker task was used to assess visual temporal and spatial filtering among persons with DS (n = 14) and typically developing individuals (n = 14) matched on non-verbal mental age (mental age = 8.5 years). Experimental conditions included varying the stimulus onset asynchronies between the onset of the target and flankers, the distances between the target and flankers, and the similarity of the target and flankers. ResultsBoth the participants with DS and the typically developing participants showed slower reaction times and lower accuracy rates when the flankers appeared closer in time and/or space to the target. Conclusion No group differences were found on a broad level, but the findings suggest that dynamic stimuli may be processed differently by those with DS. Implications of the findings are discussed in relation to the developmental approach to intellectual disability originally articulated by Ed Zigler.

Data science advances in behavioral signal processing and machine learning hold the promise to automatically quantify clinically meaningful behaviors that can be applied to a large amount of data. The objective of this study was to identify an automated behavioral marker of treatment response in social communication in children with autism spectrum disorder (ASD). First, using an automated computational method, we successfully derived the amount of time it took for a child with ASD and an adult social partner (N pairs = 210) to respond to each other while they were engaged in conversation bits (“latency”) using recordings of brief, natural social interactions. Then, we measured changes in latency at pre- and post-interventions. Children with ASD who were receiving interventions showed significantly larger reduction in latency compared to those who were not receiving interventions. There was also a significant group difference in the changes in latency for adult social partners. Results suggest that the automated measure of latency derived from natural social interactions is a scalable and objective method to quantify treatment response in children with ASD.