Phenoscreening: a developmental approach to research domain criteria‐motivated sampling

Abstract

Background: To advance early identification efforts, we must detect and characterize neurodevelopmental sequelae of risk among population-based samples early in development. However, variability across the typical-to-atypical continuum and heterogeneity within and across early emerging psychiatric/neurodevelopmental disorders represent fundamental challenges to overcome. Identifying multidimensionally determined profiles of risk, agnostic to DSM categories, via data-driven computational approaches represents an avenue to improve early identification of risk. Methods: Factor mixture modeling (FMM) was used to identify subgroups and characterize phenotypic risk profiles, derived from multiple parent-report measures of typical and atypical behaviors common to autism spectrum disorder, in a community-based sample of 17-to 25-month-old toddlers (n = 1,570). To examine the utility of risk profile classification, a subsample of toddlers (n = 107) was assessed on a distal, independent outcome examining internalizing, externalizing, and dysregulation at approximately 30 months. Results: FMM results identified five asymmetrically sized subgroups. The putative high-and moderate-risk groups comprised 6% of the sample. Followup analyses corroborated the utility of the risk profile classification; the high-, moderate-, and low-risk groups were differentially stratified (i.e., HR > moderate-risk > LR) on outcome measures and comparison of high-and low-risk groups revealed large effect sizes for internalizing (d = 0.83), externalizing (d = 1.39), and dysregulation (d = 1.19). Conclusions: This data-driven approach yielded five subgroups of toddlers, the utility of which was corroborated by later outcomes. Data-driven approaches, leveraging multiple developmentally appropriate dimensional RDoC constructs, hold promise for future efforts aimed toward early identification of at-risk-phenotypes for a variety of early emerging neurodevelopmental disorders.

Infants who develop autism show smaller inventories of deictic and symbolic gestures at 12 months of age

“…There is mounting evidence that the MCDI–Words and Gestures captures delays in gesture use before ASD diagnoses are established; hence, future efforts should evaluate the measure as part of a suite of early screening measures. For example, Doyle et al (2021) recruited a large community sample of parents of toddlers and collected parent‐report measures of toddler repetitive behavior, reciprocal social communication, and word production and gestures (via the MCDI–Words and Gestures). Using a data‐driven, RDoC‐inspired approach, factor mixture models identified a five‐class solution, of which two classes were “high‐risk.” Follow‐up analyses indicated that these two classes also had elevated rates of externalizing symptoms and dysregulated behaviors.…”

“…evaluate the measure as part of a suite of early screening measures. For example,Doyle et al (2021) recruited a large community sample of parents of toddlers and collected parent-report measures of toddler repetitive behavior, reciprocal social communication, and word production and gestures (via the MCDI-Words and Ges-…”

Responding to joint attention as a developmental catalyst: Longitudinal associations with language and social responsiveness

“…The Video‐Reference Rating of Reciprocal Social Behavior (vrRSB; Marrus et al., 2015 , 2020 ) is a 48‐item parent‐report questionnaire for assessing social responsiveness and has been validated in 18‐ to 30‐month‐olds (Doyle et al., 2021 ; Hawks et al., 2018 ; Lasch et al., 2020 ; Marrus et al., 2015 , 2020 ). It includes 13 items that ask parents to directly compare their child's behavior to that of a 19‐month‐old exemplar in a short video.…”

Personality dynamics: Regulatory mechanisms and processes

“…The Cybernetic Big Five model (CB5T; DeYoung, 2015), as a dynamical, mechanistic explanation of human personality, pays attention to metatraits of Plasticity and Stability. Rogers and colleagues present a theory of autism as low Plasticity (the shared variance of Extraversion and Openness/Intellect) and extended CB5T to provide a cybernetic account of the origin of autistic traits (e.g., Baron‐Cohen et al, 1985; Doyle et al, 2021; Happé & Frith, 2006; Hill, 2004). They make predictions concerning the further developmental consequences of early low Plasticity on personality dynamics, and each prediction maps well onto autistic symptomatology, explaining the heterogeneity of autism.…”