Aaron James Kaat

More than 80% of health outcomes are determined by social determinants of health (SDoH). That social determinants of health affects physical health outcomes has been shown in previous studies of Duchenne Muscular Dystrophy (DMD), a progressive X-linked recessive genetic disease caused by variants in the DMD gene, in which males are affected whereas females are disease carriers. To date, however, whether community-level disadvantage affects brain health in Duchenne muscular dystrophy has not been investigated. We addressed this question by measuring cognitive performance of 65 males with Duchenne muscular dystrophy and their 60 biological mothers recruited from a national pool of study participants, hypothesizing that community disadvantage would negatively correlate with cognitive performance. The Social Vulnerability Index (SVI)—a geospatially-determined scale—was used to estimate the degree of community disadvantage experienced by each son-mother dyad by using their residential zip code. Total, crystallized, and fluid composite cognition scores were obtained from all study participants using age-appropriate battery of cognition with the National Institutes of Health Toolbox Cognition Battery (NIHTB-CB). The location of DMD variants from affected Duchenne muscular dystrophy males, and the DMD carrier status of their biological mothers were collected as part of study protocol. The mean age of the 65 males with Duchenne muscular dystrophy was 10.7 years [standard deviation [SD]] [3.6] and their 60 biological mothers was 42.0 years [SD 7.0]. Males with Duchenne muscular dystrophy scored lower than age-expected norms in fluid cognition (n = 57, mean = 82.7; SD = 22.8), and within age-expected range in crystallized and total cognition (n = 56, mean = 105.4, SD = 21.3; n = 55, mean = 93.5, SD = 24.9, respectively). As a group, biological mothers scored in the normative range, except in one fluid cognition measure, namely the Flanker Task of Inhibitory Control and Attention. Biological mothers who were DMD carriers scored lower than non-carrier mothers in total composite and fluid cognition scores. Amongst the fluid cognition scores, DMD carriers scores lower in the Flanker Task of Inhibitory Control and Attention and cognitive flexibility. Son-mother dyads from communities with higher SVI scored lower on fluid cognition measures, namely the Flanker Task of Inhibitory Control and Attention (r = -0.20), and the Dimensional Card Change Sort test of cognitive flexibility (r = -0.16), respectively. Males with Duchenne muscular dystrophy and their biological mothers from disadvantaged communities demonstrate vulnerability in cognitive measures that are critical for attention regulation and cognitive flexibility. Our data highlight the influence of experiential social environment in Duchenne muscular dystrophy.

Executive function (EF) refers to a set of cognitive processes that support goal-directed behaviors and higher-order skills such as reasoning, problem-solving, and planning (Diamond, 2013). Among t...

Background: Best practices in diagnosing autism spectrum disorder require an expert diagnostician to integrate multiple sources of information, including direct observations, interviews, and rating scales completed by knowledgeable informants. Machine learning methods are well‐positioned to integrate disparate information sources to improve classification. This study sought to evaluate whether a machine learning algorithm could support appropriately weighting multiple sources of diagnostically relevant information. Methods: Telehealth diagnostic assessments were conducted for 639 toddlers (age mean = 30.4, SD = 4.3 months; 29.7% female) already receiving early intervention (EI). The Toddler Autism Symptom Interview (TASI) and the TELE‐ASD‐PEDS (TAP) were completed during separate visits. Each child's caregivers and usual EI providers asynchronously completed rating scales of autistic characteristics developed for this study. Calibration and validation data subsets were created using a 2:1 split. The optimal algorithm was developed using elastic net regularized regression in the calibration dataset, which was then evaluated in the validation dataset. Statistical fairness criteria evaluated whether the proposed algorithm functioned similarly across multiple protected group statuses. Results: The prevalence of autism in the sample was 80.4%. The algorithm developed in the calibration dataset included both the caregiver‐ and EI provider‐completed rating scales, four items from the TASI, and six items from the TAP. Model performance was high in both the calibration and validation samples (sensitivity >0.90; specificity >0.75; kappa >0.60), but statistical fairness criteria varied. False positives were relatively rare, but were more common in advantaged groups, which may suggest systematic under‐classification by the algorithm in minoritized groups. Conclusion: Diagnostic practices for autism require integrating multiple sources of information. Machine learning can support diagnosticians in this effort, as demonstrated here utilizing multiple rating scales, the TASI, and the TAP. However, there is a risk for bias when using machine learning and as such, no algorithm should replace expert clinical judgment.

Purpose: Early interventions for children at risk for developmental language disorder (DLD) have not systematically targeted the transition from vocabulary to syntax. This randomized controlled trial examined short-term effects of the Enhanced Milieu Teaching–Sentence Focused (EMT-SF) intervention, an 18-month multiphase intervention, on vocabulary after 6 months (child age = 36 months) and grammar after 12 months (child age = 42 months). Method: Participants included 108 children at risk for DLD between 30 and 31 months of age and their caregivers. Participants spoke only English in the home and were primarily from White, non-Hispanic, college-educated families. Children were randomly assigned to the treatment or control group. Interventionists coached caregivers on EMT-SF strategies via telehealth. Following 6 months of intervention, a latent variable composed of Peabody Picture Vocabulary Test–Fifth Edition (PPVT-5) and Expressive Vocabulary Test–Third Edition scores characterized vocabulary outcomes. Following 12 months of intervention, a latent variable composed of scores on the Structured Photographic Expressive Language Test–Preschool 2 and the Test of Early Grammatical Impairment characterized grammar outcomes. Results: At 36 months, mean vocabulary scores for both groups fell within the average range. Structural equation modeling (SEM) revealed no significant short-term treatment effect on the vocabulary latent variable ( p = .458); however, post hoc analyses indicated a significant effect on PPVT-5 scores ( p = .042). At 42 months, mean grammar scores for both groups fell below the average range. SEM revealed a significant short-term treatment effect on the grammar latent variable ( p = .047). Conclusions: The EMT-SF treatment group demonstrated significantly higher single-word receptive vocabulary scores at 36 months and grammar scores at 42 months than the control group. Grammatical difficulties persisted for most children in both groups at 42 months, further documenting that grammar is a particularly vulnerable language domain for children at risk for DLD. Supplemental Material: https://doi.org/10.23641/asha.32581677

Young children’s symbolic play provides insight into their language and cognitive development. However, little is known about the development of symbolic play in multilingual learners. Recent evidence suggests that multilingual environments can lead to certain cognitive adaptations, including in processes that have been linked to symbolic play. In the current study, we observed a U.S.-based sample of 15- to 20-month-old multilingual and monolingual learners in a parent-child interaction from which we coded their symbolic play (47 multilingual, 48 monolingual). We found no differences between the monolingual and multilingual learners in early emerging levels of symbolic play (e.g., drinking from an empty cup). However, we found significant differences in later emerging levels of symbolic play (e.g., using a piece of cloth as a blanket), with multilingual learners showing greater engagement in these more advanced levels of symbolic play than monolingual learners. This is the first study to show that multilingual learners engage in more abstract levels of symbolic play than age-matched monolingual learners. These findings support the hypothesis that early exposure to multiple languages may contribute to cognitive adaptations with diverse behavioral manifestations.

Naturalistic developmental behavior interventions (NDBIs) are commonly taught to caregivers to target social communication in autistic children. In addition to social communication challenges, 50% of autistic children engage in disruptive behaviors. This study aims to evaluate the impact of responsive and directive NDBI social communication strategies on autistic children’s disruptive behavior. At baseline, mother–child dyads were randomized to receive 8 weeks of a caregiver-mediated intervention focused on either responsive or directive NDBI communication strategies ( n = 73, M _age = 33.49, standard deviation [ SD ] = 6.37, directive n = 36, responsive n = 37). Before and after intervention, 10-min mother–child interactions were video-recorded and coded using an event-based disruptive behavior code. A linear model revealed that children in the directive condition were more likely to display disruptive behavior as compared to the responsive condition. This study’s findings can be used by providers and caregivers when engaging in shared decision-making to identify NDBI strategies that best fit the family’s priorities. Additional research is needed to examine (a) disruptive behavior over time and across contexts and (b) the effects of directive and responsive strategies implemented in tandem on disruptive behavior. The original clinical trial was prospectively registered in a publicly accessible database (NCT02632773, clinicaltrials.gov).

ObjectiveTo evaluate the diagnostic accuracy of an Early Intervention (EI) autism diagnostic pathway, which included: (a) a multi-disciplinary team, (b) direct child observation and parent interview, and (c) a feedback session to explain the diagnosis.MethodsChildren between 15 and 36 months were referred to this prospective double masked diagnostic study. The primary outcome was agreement on autism diagnosis between the EI (index diagnosis) and the expert diagnostic team (reference diagnosis). One observational assessment (TELE-ASD-PEDS) and one caregiver interview (Toddler Autism Symptom Inventory) were used to inform the diagnosis. ResultsIn a sample of 639 children (mean age: 30 months; 70% male; 15% Hispanic, 30% Black, 32% White), we found 93.2% [95% CI, 91.0%-95.2%] agreement between EI teams and the expert team across conclusive diagnoses. Diagnostic accuracy indices related to a positive autism diagnosis (i.e., specificity, PPV) were higher (96.7% [95% CI, 95.0%-98.2] and 95.6% [95% CI, 93.7%-97.2%] respectively) than indices related to a negative autism diagnosis (i.e., sensitivity [67.2%; 95% CI, 56.1%-78.2%], NPV [73.8%; 95% CI 61.9%-84.1%]). The overall rate of autism diagnosis was 80%. Parents also reported high satisfaction. ConclusionEI teams are very accurate at diagnosing autism in toddlers enrolled in EI without any concomitant medical conditions. In the current study, 80% of children could be triaged to an EI diagnostic pathway, such that the limited number of experts could be reserved for more complex or ambiguous cases. These findings suggest that this pathway has the potential to markedly reduce delays in autism identification

The NIH Toolbox ^® for Assessment of Neurological and Behavioral Function Cognition Battery (NIHTB-CB) is a brief neuropsychological assessment tool created as part of the NIH Neuroscience Blueprint. Since its inception, NIHTB-CB has been widely used in a variety of clinical and research settings, including large-scale epidemiological studies. The NIHTB-CB was recently updated and re-normed to Version 3 (V3). We describe the approach to establish normative reference values. The NIHTB-CB tests were administered to a large English-speaking sample of n = 3,904 (average age = 25.7 years, 52.1% female) individuals from the U.S. population, stratified by age, race and ethnicity, sex assigned at birth, and education level within the four U.S. census regions. Normative data were raked via iterative proportional fitting (e.g., by sex assigned at birth, race, ethnicity, and educational attainment nested within geographic region) to derive sampling weights that match the demographic proportions from the U.S. Census targets. Through regression-based continuous norming and bootstrap techniques, age-adjusted and age-and-education-adjusted normative scores were created for individual measure-level and composite scores.