| 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. | 202/17423 | Secondary Analysis | Shared |
| Working Title: Differentiating Core Autism Symptomatology | Autism spectrum disorder (ASD) is characterized by persistent deficits in social communication and social interaction, social-emotional reciprocity, and repetitive behavior or restricted interest (American Psychiatric Association [APA], 2013). This study extends the existing literature by clarifying the extent to which mental health disorder symptoms differentially converge with autism symptoms related to social communication and restricted and repetitive behavior, as well as the extent to which mental health symptoms are empirically differentiated from the core autism symptom domains. Although there is a well-documented correlation between the severity of core ASD symptoms and the presence of mental health disorder symptoms, such as anxiety and irritability, the nature of this linkage remains poorly understood. In this project, the National Database for Autism Research (NDAR) and Research Domain Criteria Database (RDoCdb) were used to observe continuous symptom measures such as the Social Responsiveness Scale (SRS) and the Child Behavior Checklist (CBCL) to examine correlation matrices as well as factor structure models to examine these patterns of association. The SRS “social communication” and “repetitive restricted” subscales were correlated with the CBCL externalizing, internalizing, attention, conduct, aggression, psychosomatic, and withdrawn subscales. We hypothesized that “repetitive and restricted” behaviors would be more correlated with the CBCL scales than would the “social communication” scale. These results were also interpreted according to age and IQ. In conclusion, this study may elucidate ongoing questions about the centrality of mental health symptoms like anxiety to aspects of ASD taxonomy. | 188/11144 | Secondary Analysis | Private |
| Characterizing Auditory Hyperreactivity in Autism | Objective: To answer the following research questions: 1) What is the prevalence of auditory hyper-reactivity in ASD? 2) Does auditory hyper-reactivity severity change with age? and 3) What are the most common auditory stimuli reported to be bothersome?
Research Design: Primarily descriptive secondary data analysis.
Methods: Type of data: Questionnaire items regarding auditory hyper-reactivity will be filtered from: Autism Diagnostic Interview-Revised, Sensory Profile (all forms), Sensory Over-Responsivity Scale, and Sensory Experiences Questionnaire in addition to demographics (i.e., age, race, ethnicity, diagnoses).
Analysis Plan: Descriptive statistics, tables and figures will be used to summarize the prevalence and severity of auditory hyper-reactivity by age. Linear regression modeling will be used to evaluate changes in auditory hyper-reactivity by age. If data is available for control subjects, statistical analyses will be conducted for means comparison (ASD vs. non-ASD).
| 159/7001 | 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. | 183/6323 | 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.
| 97/5717 | Secondary Analysis | Private |
| Investigating autism etiology and heterogeneity by decision tree algorithm | Autism spectrum disorder (ASD) is a neurodevelopmental disorder that causes deficits in cognition, communication and social skills. ASD, however, is a highly heterogeneous disorder. This heterogeneity has made identifying the etiology of ASD a particularly difficult challenge, as patients exhibit a wide spectrum of symptoms without any unifying genetic or environmental factors to account for the disorder. For better understanding of ASD, it is paramount to identify potential genetic and environmental risk factors that are comorbid with it. Identifying such factors is of great importance to determine potential causes for the disorder, and understand its heterogeneity. Existing large-scale datasets offer an opportunity for computer scientists to undertake this task by utilizing machine learning to reliably and efficiently obtain insight about potential ASD risk factors, which would in turn assist in guiding research in the field. In this study, decision tree algorithms were utilized to analyze related factors in datasets obtained from the National Database for Autism Research (NDAR) consisting of nearly 3000 individuals. We were able to identify 15 medical conditions that were highly associated with ASD diagnoses in patients; furthermore, we extended our analysis to the family medical history of patients and we report six potentially hereditary medical conditions associated with ASD. Associations reported had a 90% accuracy. Meanwhile, gender comparisons highlighted conditions that were unique to each gender and others that overlapped. Those findings were validated by the academic literature, thus opening the way for new directions for the use of decision tree algorithms to further understand the etiology of autism.
| 200/3382 | Secondary Analysis | Shared |
| Imbalanced social-communicative and restricted repetitive behavior subtypes in autism spectrum disorder exhibit different neural circuitry | Social-communication (SC) and restricted repetitive behaviors (RRB) are autism diagnostic symptom domains. SC and RRB severity can markedly differ within and between individuals and may be underpinned by different neural circuitry and genetic mechanisms. Modeling SC-RRB balance could help identify how neural circuitry and genetic mechanisms map onto such phenotypic heterogeneity. Here, we developed a phenotypic stratification model that makes highly accurate (97–99%) out-of-sample SC = RRB, SC > RRB, and RRB > SC subtype predictions. Applying this model to resting state fMRI data from the EU-AIMS LEAP dataset (n = 509), we find that while the phenotypic subtypes share many commonalities in terms of intrinsic functional connectivity, they also show replicable differences within some networks compared to a typically-developing group (TD). Specifically, the somatomotor network is hypoconnected with perisylvian circuitry in SC > RRB and visual association circuitry in SC = RRB. The SC = RRB subtype show hyperconnectivity between medial motor and anterior salience circuitry. Genes that are highly expressed within these networks show a differential enrichment pattern with known autism-associated genes, indicating that such circuits are affected by differing autism-associated genomic mechanisms. These results suggest that SC-RRB imbalance subtypes share many commonalities, but also express subtle differences in functional neural circuitry and the genomic underpinnings behind such circuitry. | 51/1708 | Secondary Analysis | Shared |
| 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. | 94/280 | 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. | 90/249 | 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. | 97/247 | 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. | 83/216 | 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. | 81/208 | 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. | 64/200 | Secondary Analysis | Shared |
| Structural MRI scans in autism during early childhood in BIDS format | This repository contains defaced standardized structural MRI data from multiple time points with a total of 102 children with autism spectrum disorder (ASD), 18 children with a non-autism developmental delay (DD), and 53 typical controls (see BIDS participants.tsv file for age and sex distribution in each subject’s first MRI session). These data were used and described in detail in Raznahan et al. 2013 (https://doi.org/10.1016/j.nicl.2012.10.005) and Smith et al. 2016 (https://doi.org/10.1002/hbm.23195) among other publications. The data have been formatted in the brain imaging data structure (BIDS) for easier re-use. The full dataset in this study is less than 5 GB in size.
The study GitHub repository with a viewable README file and scripts used for curation can be found at https://github.com/nimh-dsst/nda-study-1887/ . | 173/173 | Primary Analysis | Shared |
| Critical test items to differentiate individuals with SPCD from those with ASD and typical controls | Social (pragmatic) communication disorder (SPCD) is a new category in the DSM-5. This study used IRT modelling to analyze archive data of item responses to the Social Communication Question-Lifetime (SCQ) from the National Database of Autism Research (NDAR), to select critical test items that could efficiently differentiate SPCD from ASD and TD.
Methods: The SCQ records were downloaded from the NDAR. The item difficulty values and participants ability in the social communication and repetitive behavior and restricted interests were estimated through Winsteps. The items with difficulty values mostly matching the participants ability at the cut-off zones among three groups were selected.
Result: The eight test items were identified for screening SPCD with 75% sensitivity. The specificity for differentiating SPCD from TD and ASD is 86.27% and 68.9% respectively.
Conclusion: This study provides a short list of critical items that could be used to screen SPCD from TD and ASD.
| 7/151 | Secondary Analysis | Private |
| Identifying Areas of Overlap and Distinction in Early Lexical Profiles of Children with Autism Spectrum Disorder, Late Talkers, and Typical Talkers | This study compares the lexical composition of 11827 children with autism spectrum disorder (ASD) aged 121 to 84173 months with 4,626 vocabulary-matched typically developing toddlers with and without language delay, aged 8 to 30 months. Children with ASD produced a higher proportion of verbs than typical and late talkers, but a similar number of nouns. Additionally, differences were identified in five four semantic categories, four three of them related to play. Most differences appear to reflect the extent of the language delay between the groups. However, children with ASD produced fewer high-social verbs than neurotypical children. We discuss how these lexical differences might be associated with ASD features and language delay, providing partial support for a categorical view of language delay. | 42/118 | Secondary Analysis | Shared |
| Do children with Autism Spectrum Disorder learn words differently? | Children with ASD often are late to start to produce words. However, despite the importance of language abilities for child outcomes in children with ASD, we still have only scratched the surface of understanding these children's early lexicons. Therefore, in the current study we examined the semantic networks of the words that children with ASD have been reported to produce and compared them to typically developing children. | 39/82 | Secondary Analysis | Shared |
| Investigating Vocabulary Profiles in Preverbal and Minimally Verbal Children with Autism Spectrum Disorder | The majority of children with autism spectrum disorder (ASD) are delayed in producing their first words and approximately 30% continue to be minimally verbal across childhood. The current study examined the syntactic and semantic features of the early words that 64 preverbal and minimally verbal children with ASD produced and compared them to 682 typically developing (TD) toddlers who produced 1-10 words. Word-level responses that were reported on the MacArthur-Bates Communicative Development Inventory were examined. Children with ASD produced a greater proportion of predicates, relative to the TD group. Also, there were group differences in the following semantic categories: action words, people, sound effects, and animals. Of these, children with ASD produced more action words. We further examined the action words by assigning them social scores, with action words that typically involve people having higher social scores. TD toddlers produced action words that were slightly more social than children with ASD. These findings suggest that future studies should examine early verb learning and processing in children with ASD. | 34/64 | Secondary Analysis | Shared |
| Vocabulary Comprehension in MV Children with ASD | The majority of children with autism spectrum disorder (ASD) experience early language delays; approximately 30% continue to be minimally verbal throughout childhood. The current study examined the syntactic and semantic features of the early words that 31 minimally verbal (MV) children with ASD were reported to understand. This receptive vocabulary profile was compared to 124 TD toddlers who were matched on expressive vocabulary and 124 TD toddlers who were matched on receptive vocabulary. Word-level responses that were reported on the MacArthur-Bates Communicative Development Inventory were examined. Children with ASD understood a greater proportion of verbs, relative to both the TD groups. Numerous additional differences existed between the MV-ASD group and the TD expressive vocabulary-matched group. In contrast, with a few exceptions, MV children with ASD displayed a similar receptive vocabulary profile to TD toddlers who were matched on receptive vocabulary abilities. These similarities existed despite large differences in expressive vocabulary knowledge, chronological age, and mental age. These findings suggest that future studies should examine early verb learning and processing in MV children with ASD. | 23/31 | Secondary Analysis | Shared |
