Examining the validity of the use of ratio IQs in psychological assessments | IQ tests are amongst the most used psychological assessments, both in research and clinical settings. For participants who cannot complete IQ tests normed for their age, ratio IQ scores (RIQ) are routinely computed and used as a proxy of IQ, especially in large research databases to avoid missing data points. However, because it has never been scientifically validated, this practice is questionable. In the era of big data, it is important to examine the validity of this widely used practice. In this paper, we use the case of autism to examine the differences between standard full-scale IQ (FSIQ) and RIQ. Data was extracted from four databases in which ages, FSIQ scores and subtests raw scores were available for autistic participants between 2 and 17 years old. The IQ tests included were the MSEL (N=12033), DAS-II early years (N=1270), DAS-II school age (N=2848), WISC-IV (N=471) and WISC-V (N=129). RIQs were computed for each participant as well as the discrepancy (DSC) between RIQ and FSIQ. We performed two linear regressions to respectively assess the effect of FSIQ and of age on the DSC for each IQ test, followed by additional analyses comparing age subgroups as well as FSIQ subgroups on DSC. Participants at the extremes of the FSIQ distribution tended to have a greater DSC than participants with average FSIQ. Furthermore, age significantly predicted the DSC, with RIQ superior to FSIQ for younger participants while the opposite was found for older participants. These results question the validity of this widely used alternative scoring method, especially for individuals at the extremes of the normal distribution, with whom RIQs are most often employed. | 98/17423 | Secondary Analysis | Shared |
Controls for SCCRIP | To establish a well characterized cohort for pediatric patients living with sickle cell disease | 5/11185 | Secondary Analysis | Private |
The importance of low IQ to early diagnosis of autism | Some individuals can flexibly adapt to life’s changing demands while others, in particular those with Autism Spectrum Disorder (ASD), find it challenging. The origin of early individual differences in cognitive abilities, the putative tools with which to navigate novel information in life, including in infants later diagnosed with ASD remains unexplored. Moreover, the role of intelligence quotient (IQ) vis-à-vis core features of autism remains debated. We systematically investigate the contribution of early IQ in future autism outcomes in an extremely large, population-based study of 8,000 newborns, infants, and toddlers from the US between 2 and 68 months with over 15,000 cross-sectional and longitudinal assessments, and for whom autism outcomes are ascertained or ruled out by about 2-4 years. This population is representative of subjects involved in the National Institutes of Health (NIH)-funded research, mainly on atypical development, in the US. Analyses using predetermined age bins showed that IQ scores are consistently lower in ASD relative to TD at all ages (p<0.001), and IQ significantly correlates with calibrated severity scores (total CSS, as well as non-verbal and verbal CSS) on the ADOS. Note, VIQ is no better than the full-scale IQ to predict ASD cases. These findings raise new, compelling questions about potential atypical brain circuitry affecting performance in both verbal and nonverbal abilities and that precede an ASD diagnosis. This study is the first to establish prospectively that low early IQ is a major feature of ASD in early childhood. | 98/6323 | Secondary Analysis | Shared |
The effect of compensatory mechanisms during and after pregnancy on a child's development | Early childhood involves rapid processes of human growth leading to different trajectories in physical, cognitive, social, and emotional development (Graignic-Philippe et al., 2014). These processes are influenced by a wide variety of factors such as maternal health, environmental stressors, and early childhood experiences. Current literature has shown how exposure to both acute and chronic stress during pregnancy have a pathogenetic effect throughout childhood (Kim & Leventhal, 2015; Rice, et al, 2010), leading to neurotypical or atypical development. Studies have shown how these stressors are linked neurodevelopmental disorders such Autism Spectrum Disorders (Zerbo et al., 2015; Atladóttir et al., 2012) or Attention Deficit Hyperactivity Disorder (Rosenqvist et al., 2019).
In recent years, there has been a shift from traditional diagnostic research models to synthesis of different scientific fields to map lifecourse development in order for rapid translation into healthcare practices (Halfon et al., 2014). Whilst there are studies showing links between stress and atypical developmental outcomes, there is still very limited literature on compensatory mechanisms found pre- and post-pregnancy, which illustrate development of protective factors (such as presence of self-regulation, high verbal intelligence, sociability, adept social communication) against atypical developmental outcomes. This study aims to identify and measure the presence of these protective factors that appear to guard against or mitigate the emergence of neurodevelopmental disorders. Therefore, nationwide and longitudinal data are needed in order to accurately create risk models in order to map developmental trajectories.
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Autism Sensory Research Consortium Cross-lab Integrative Data Analysis | Since 2013, when sensory features were officially added to the diagnostic criteria for autism, research into the sensory manifestations of the condition has increased dramatically. However, the majority of this research has primarily been conducted using small laboratory-based samples of children on the autism spectrum, substantially limiting the hypotheses that can be tested in any one dataset and the generalizability of results to the wider autistic population. The Autism Sensory Research Consortium (ASRC), funded by the Nancy Lurie Marks Family Foundation, represents the first major international collaboration of over a dozen research groups that study sensory functioning in autism. As a major thrust of this collaboration, the ASRC has begun a data sharing initiative, in which all participating labs can contribute existing data from their past and present research studies to a centralized database. These “Big Data” can then be systematically examined using powerful large-sample statistical techniques such as structural equation modeling and item response theory, which will allow researchers to test more complex hypotheses regarding the nature of sensory differences in autism and their relationships with sociodemographic and non-sensory clinical features.
Once data from all sites has been pooled, it will be analyzed using a method called integrative data analysis, which is specially designed to derive insights from large and heterogeneous samples. One major advantage of this methodology is the ability to construct and test measurement models of sensory symptoms, determining the most appropriate set of questions for assessing each construct and making sure that the scales do not produce biased comparisons when they are examined across diagnostic groups or subsets of the autistic population. Furthermore, measurement models can be constructed to bridge multiple questionnaires, allowing for the calculation of robust composite scores that can be compared between studies that only administered items from one of the contributing questionnaires. These models can further facilitate pooling of data across studies, allowing us to amass even larger datasets to answer questions about sensory function in the autistic population. Furthermore, moving forward, the composite sensory measures from the integrative data analysis can be employed in other studies, providing investigators in sensory autism research with a suite of reliable and valid behavioral measures that can be used as outcomes in trials of interventions targeting these symptoms.
In the long term, this project has the potential to help us better understand the nature of sensory function in persons on the spectrum, as well as how sensory alterations relate to broader features of the condition—specifically, for whom and/or at what point in development sensory features are most predictive of core autism behaviors or other meaningful clinical outcomes such as language acquisition and adaptive behavior. Incorporation of neuroscientific data collected within the ASRC can also possibly shed some light on the neural basis of sensory disruptions in the autistic population. All of this will help to lay a foundation for future work testing the efficacy of candidate interventions aimed at improving sensory function and more distal skills in autistic individuals. | 7/2110 | Secondary Analysis | Private |
Personalized Autism Symptom Assessment with the Youth Top Problems Scale: Observational and Parent-Report Formats for Clinical Trials Applications | To date, few measures of comorbid psychiatric symptoms in the context of autism spectrum disorder (ASD) have been established as both psychometrically robust and sensitive to the effects of treatment. Therefore, I propose to conduct an item response theory (IRT) analysis for this study using the Child and Adolescent Symptom Inventory (CASI) data from the National Database for Autism Research. Item parameters will be obtained through an IRT calibration of CASI items using flexMIRT.3 (Cai, 2016). In order to conduct IRT, the CASI calibration sample will include both these NDAR participants and a sample of 68 children with diagnoses of ASD and IQ>70 (ages 6-13 years) who participated in our recent NIMH-funded clinical trial of cognitive behavioral therapy, which will create a sample large enough for the IRT analysis. I plan to publish this data as part of a broader psychometric study of children with autism. | 2/746 | Secondary Analysis | Private |
Word Learning and Word Features | Vocabulary composition and word-learning biases are closely interrelated in typical development. Learning new words involves attending to certain properties to facilitate word learning. Such word-learning biases are influenced by perceptually and conceptually salient word features, including high imageability, concreteness, and iconicity. This study examined the association of vocabulary knowledge and word features in young children with ASD (n = 280) and typically developing (TD) toddlers (n = 1,054). Secondary analyses were conducted using data from the National Database for Autism Research and the Wordbank database. Expressive vocabulary was measured using the MacArthur-Bates Communicative Development Inventory. Although the trajectories for concreteness, iconicity, and imageability are similar between children with ASD and TD toddlers, divergences were observed. Differences in imageability are seen early but resolve to a common trajectory; differences in iconicity are small but consistent; and differences in concreteness only emerge after both groups reach a simultaneous peak, before converging again. This study reports unique information about the nonlinear growth patterns associated with each word feature for and distinctions in these growth patterns between the groups. | 1/280 | Primary Analysis | Shared |
Examining the Shape Bias in Young Autistic Children: A Vocabulary Composition Analysis | Shape is a salient object property and one of the first that children use to categorize objects under one label. Colunga and Sims (2017) suggest that noun vocabulary composition and word learning biases are closely interrelated in typical development. The current study examined the association between noun vocabulary knowledge and perceptual word features, specifically shape and material features. Participants included 249 autistic children and 1,245 non-autistic toddlers who were matched on expressive noun vocabulary size and gender. Nouns were categorized using the Samuelson and Smith (1999) noun feature database. A simple group comparison revealed no group differences in shape bias; both groups evidenced developing noun vocabularies that favored shape+solid and nonsolid+material nouns. However, the trajectory of evidence of shape bias as a function of vocabulary size differed between the groups, with autistic children
demonstrating a reduced shape-bias initially. Future work should examine how children’s learning biases shift over development and whether the shape bias promotes lexical development to the same degree across groups. | 1/249 | Secondary Analysis | Private |
Deviant vocabulary development in children with Autism Spectrum Disorder | Children diagnosed with autism spectrum disorder (ASD) have core impairments in social communication and have restricted interests and repetitive behaviors. Additionally, the majority of young children with ASD have early language delays. Although these early delays are well-documented, it is remains unclear whether language skills are simply delayed or if they are deviant. The current study aimed to expand on previous studies (e.g., Charman et al., 2003; Luyster, Lopez, & Lord, 2007; Rescorla & Safyer, 2012) to provide a large-scale comparison of early language profiles between typically developing (TD) toddlers and young children with ASD. Specifically, we sought to examine the composition of word classes (i.e., nouns, predicates, and close classed words) and semantic categories (i.e., games and routines, sound effects and animal noises) in the early TD and ASD language profiles. A series of linear regression analyses revealed that children with ASD produced a smaller percentage of nouns, and that the percentage of nouns in a vocabulary decreased as children learned more words, but that this reduction was less steep in the ASD group. When examining predicates, we found that children with ASD produced a significantly higher percentage of predicates. Also, as vocabulary size increased, the percentage of predicates increased; however, the slope was less steep for children with ASD. Lastly, children with SD produced a significantly higher percentage of closed class words and the trajectory of growth of the percentage of closed class words differed between groups. The current findings suggest that children with ASD may employ different word-learning strategies during early lexical development. | 1/247 | Secondary Analysis | Shared |
EVIDENCE FOR THE DIMENSIONAL AND CATEGORIAL ACCOUNTS OF LANGUAGE DEVELOPMENT | This study compared the lexical composition of 216 children with ASD aged 11 to 173 months with that of 7,287 typically developing toddlers with and without language delay aged 8 to 30 months. The children with ASD and late talkers produced a lower proportion of nouns and a higher proportion of predicates than typical talkers. The ASD group produced a higher proportion of action words and place words as well as a lower proportion of sound words than the neurotypical groups. We found that children with ASD produced fewer high-social verbs as rated by adults. We discuss how these differences might be associated with features of ASD in a way that supports the categorical view of language development. | 1/216 | Secondary Analysis | Shared |
Semantic modeling 2023 | Although it is well documented that children with ASD are slower to develop their lexicons, we still have a limited understanding of the structure of early lexical knowledge in children with ASD. The current study uses network analysis and differential item functioning anlaysis to examine the structure of semantic knowledge, which may provide insight into the learning processes that influence early word learning. | 1/208 | Secondary Analysis | Private |
Semantic Network Modeling | Although it is well documented that children with ASD are slower to develop their lexicons, we still have a limited understanding of the structure of early lexical knowledge in children with ASD. The current study uses network analysis to examine the structure of semantic knowledge, which may provide insight into the learning processes that influence early word learning. | 1/200 | Secondary Analysis | Shared |