| 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. | 157/17423 | Secondary Analysis | Shared |
| Controls for SCCRIP | To establish a well characterized cohort for pediatric patients living with sickle cell disease | 2/11185 | 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).
| 158/7001 | 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. | 145/6323 | 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.
| 126/5717 | Secondary Analysis | Private |
| 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. | 1/2110 | Secondary Analysis | Private |
| Unravelling the Collective Diagnostic Power Behind the Features in the Autism Diagnostic Observation Schedule | Background: Autism is a group of heterogeneous disorders defined by deficits in social interaction and communication. Typically, diagnosis depends on the results of a behavioural examination called the Autism Diagnostic Observation Schedule (ADOS). Unfortunately, administration of the ADOS exam is time-consuming and requires a significant amount of expert intervention, leading to delays in diagnosis and access to early intervention programs. The diagnostic power of each feature in the ADOS exam is currently unknown. Our hypothesis is that certain features could be removed from the exam without a significant reduction in diagnostic accuracy, sensitivity or specificity.
Objective: Determine the smallest subset of predictive features in ADOS module-1 (an exam variant for patients with minimal verbal skills).
Methodology: ADOS module-1 datasets were acquired from the Autism Genetic Resource Exchange and the National Database for Autism Research. The datasets contained 2572 samples with the following labels: autism (1763), autism spectrum (513), and non-autism (296). The datasets were used as input to 4 different cost-sensitive classifiers in Weka (functional trees, LADTree, logistic model trees, and PART). For each classifier, a 10-fold cross validation was preformed and the number of predictive features, accuracy, sensitivity, and specificity was recorded.
Results & Conclusion: Each classifier resulted in a reduction of the number of ADOS features required for autism diagnosis. The LADtree classifier was able to obtain the largest reduction, utilizing only 10 of 29 ADOS module-1 features (96.8% accuracy, 96.9% sensitivity, and 95.9% specificity). Overall, these results are a step towards a more efficient behavioural exam for autism diagnosis.
| 126/1832 | 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. | 66/1708 | Secondary Analysis | Shared |
| Automated Autism Diagnosis using Phenotypic and Genotypic Attributes: Phase I | The ultimate goal of this project is to develop a predictive system that can automate the diagnosis process for autism using phenotypic and genotypic attributes for classification. At this time, only a first phase is being pursued: starting with scores from Autism Diagnostic Observation Schedule (ADOS) reports, use data-mining techniques to select the smallest set of the most informative evaluation points that can lead to similar behavioral diagnoses as using all report features. The effort began in March, 2016 after data access to NDAR was granted. This report describes the results from that date through the end of December 2016. | 1/1045 | Secondary Analysis | Shared |
| Gender as a Moderator of the Association between Social Responsiveness and Cognitive Ability for Children with Autism | | 5/977 | Secondary Analysis | Private |
| Gender differences in restricted and repetitive behaviors and interests in autism | Background: The female autism phenotype has been defined by differences in core autism spectrum disorder (ASD) symptomology related to reciprocal social communication and restricted and repetitive behaviors and interests (RRBI). Previous research on RRBI in ASD has found that affected boys have increased stereotyped and restricted behaviors compared to girls with ASD (Hiller, Young, & Weber, 2014; Mandy et al., 2012). Other domains of RRBI (i.e., self-injurious, compulsive, and insistence on sameness behaviors), which contribute to DSM-5 diagnosis, are less studied and have not been examined across gender. To date, no studies have examined gender differences using a comprehensive RRBI measure, which spans stereotyped, self-injurious, compulsive, insistence on sameness, and restricted behavior domains.
Objectives: To investigate whether symptoms of RRBI (i.e., stereotyped, self-injurious, ritualistic, compulsive, insistence on sameness, and restricted behavior), as measured by item-level data on the Repetitive Behavior Scale-Revised (RBS-R), can classify males versus females with ASD.
Methods: Participants included 615 youth with ASD (507 males; 82.4%), between 3 and 18 years of age (M=10.26, SD=4.20), who agreed to share data with the National Database for Autism Research (NDAR). A stepwise discriminant function analysis (DFA) was used to predict the degree RBS-R data could correctly classify gender in a large sample of individuals with ASD. Standardized canonical function coefficients (SCFC) from the DFA represent the contribution of each variable to the discrimination between groups, with greater SCFC indicating greater discrimination.
Results: DFA results suggest that RBS-R items significantly differentiate girls versus boys with ASD, Wilks’ λ=0.89, χ2=70.79, p<0.001. Of note, gender was classified based on a set of 8 items (see table 1). Interestingly, the items that differentiated boys from girls did not solely consist of higher stereotyped and restricted behavior in boys (as indicated by negative SCFC scores). Half of the items that differentiated gender were higher in females with ASD (as indicated by positive SCFC scores) and from the self-injurious, compulsive, and insistence on sameness domains. This set of RBS-R items had greater success in correctly classifying boys with ASD (67.90%) than in correctly classifying affected girls (61.00%).
Conclusions: This study extends findings of gender differences in RRBI for ASD, demonstrating that girls with ASD may demonstrate higher self-injurious, compulsive, and insistence on sameness behavior than affected boys. It is important for future research to disentangle whether these elevated rates of RRBI in girls with ASD are central to the female autism phenotype or an epiphenomenon of the high rates of co-occurring disorders (e.g., anxiety) noted in affected girls.
| 1/612 | Secondary Analysis | Private |
| Identifying Sex-Specific Cognitive and Diagnostic Profiles for Children on the Autism Spectrum | | 4/252 | Secondary Analysis | Private |
| Data-Driven Generation of Synthetic Behavioral Feature Vectors Modeling Children with Autism Spectrum Disorders | Behavioral data on children with Autism Spectrum Disorders (ASD) are available thanks to standardized diagnostic tools, such as the Autism Diagnostic Observation Schedule (ADOS). This data can be of great use to enhance the learning and reasoning of agents interacting with children with ASD. However, the amount of such available data is limited and may not prove useful by itself to inform the algorithms of complex agents. To address this data scarcity problem, we present a method for generating synthetic behavioral data in the form of feature vectors characterizing a wide range of children with ASD. Our method relies on a thorough analysis and partition of the feature space based on a real dataset containing the ADOS scores of 279 children. We first analyze the real dataset using dimensionality reduction techniques, then introduce data-driven descriptors that partition the feature space into regions naturally arising from the data. We end by presenting a descriptor-based sampling method to generate synthetic feature vectors that successfully preserves the correlation structure of the real dataset. | 1/173 | Secondary 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.
| 3/151 | Secondary Analysis | Private |
