[{"collection_subject_count":1,"data_usage":"Secondary Analysis","description":"A major goal of precision medicine is to predict prognosis based on individualized information at the earliest possible points in development. Using early snapshots of adaptive functioning and unsupervised data- driven discovery methods, we uncover highly stable early autism subtypes that yield information relevant to later prognosis. Data from the National Institute of Mental Health Data Archive (NDA) (n = 1,098) was used to uncover three early subtypes (<72 months) that generalize with 96% accuracy. Outcome data from NDA (n = 2,561; mean age, 13 years) also reproducibly clusters into three subtypes with 99% generalization accuracy. Early snapshot subtypes predict developmental trajectories in non-verbal cognitive, language and motor domains and are predictive of membership in different adaptive functioning outcome subtypes. Robust and prognosis- relevant subtyping of autism based on early snapshots of adaptive functioning may aid future research work via prediction of these subtypes with our reproducible stratification model.","doi":"10.15154/0z1c-1d37","id":2396,"state":"Shared","study_subject_count":3517,"title":"Prognostic early snapshot stratification of autism based on adaptive functioning"},{"collection_subject_count":195,"data_usage":"Secondary Analysis","description":"The Social Communication Questionnaire (SCQ) was developed as a screener of Autism Spectrum Disorder (ASD). To date, the majority of the SCQ utility studies focused on its external validity (e.g., ROC curve analyses), but very few have addressed the internal validity issues. With samples consisting of 2,134 individuals available from the National Database for Autism Research (NDAR), the current study examined the factor structure, item-level characteristics, and measurement equivalence of the SCQ forms (i.e., Lifetime form and Current form) using both the classical true score theory and the item response theory (IRT). While our findings indicate sufficient psychometric properties of the SCQ Lifetime form, measurement issues emerged with respect to the SCQ Current form. These issues include lower internal consistencies, a weaker factor structure, lower item discriminations, significant pseudo-guessing effects, and subscale-level measurement bias. Thus, we caution researchers and clinicians about the use of the SCQ Current form. In particular, it seems inappropriate to use the Current form as an alternative to the Lifetime form among children younger than 5 years old or under other special situations (e.g., teacher-report data), although such practices were advised by the publisher of the SCQ. Instead, we recommend modifying the wording of the Lifetime form items rather than switching to the Current form where a 3-month timeframe is specified for responding to SCQ items. Future studies may consider investigating the association between the temporality of certain behaviors and the individual’s potential for being diagnosed with ASD, as well as the age neutrality of the SCQ.","doi":"10.15154/1158938","id":358,"state":"Shared","study_subject_count":2054,"title":"Psychometric Analysis of the Social Communication Questionnaire Using an Item-Response Theory Framework: Implications for the Use of the Lifetime and Current Forms"},{"collection_subject_count":1,"data_usage":"Secondary Analysis","description":"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. \n\nObjective: Determine the smallest subset of predictive features in ADOS module-1 (an exam variant for patients with minimal verbal skills).\n\nMethodology: 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.\n\nResults & 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. \n","doi":"10.15154/1421921","id":513,"state":"Shared","study_subject_count":1832,"title":"Unravelling the Collective Diagnostic Power Behind the Features in the Autism Diagnostic Observation Schedule"},{"collection_subject_count":117,"data_usage":"Secondary Analysis","description":"An automated pipeline was developed to reference Neuroimages hosted by the National Database for Autism Research (NDAR) and derive volumes for distinct brain structures using Advanced Normalization Tools (ANTs) and the Configurable-Pipeline for the Analysis of Connectomes (C-PAC) platform. This pipeline utilized the ANTs cortical thickness methodology discuessed in \"Large-Scale Evaluation of ANTs and Freesurfer Cortical Tchickness Measurements\" [http://www.ncbi.nlm.nih.gov/pubmed/24879923] to extract a cortical thickness volume from T1-weighted anatomical MRI data gathered from the NDAR database. This volume was then registered to an stereotaxic-space anatomical template (OASIS-30 Atropos Template) which was acquired from the Mindboggle Project webpage [http://mindboggle.info/data.html]. After registration, the mean cortical thickness was calculated at 31 ROIs on each hemisphere of the cortex and using the Desikan-Killiany-Tourville (DKT-31) cortical labelling protocol [http://mindboggle.info/faq/labels.html] over the OASIS-30 template.\r\n\r\n**NOTE: This study is ongoing; additional data my be available in the future.**\r\n\r\nAs a result, each subject that was processed has a cortical thickness volume image and a text file with the mean thickness ROIs (in mm) stored in Amazon Web Services (AWS) Simple Storage Service (S3). Additionally, these results were tabulated in an AWS-hosted database (through NDAR) to enable simple, efficient querying and data access.\r\n\r\nAll of the code used to perform this analysis is publicly available on Github [https://github.com/FCP-INDI/ndar-dev]. Additionally, as a computing platform, we developed an Amazon Machine Image (AMI) that comes fully equipped to run this pipeline on any dataset. Using AWS Elastic Cloud Computing (EC2), users can launch our publicly available AMI (\"C-PAC with benchmark\", AMI ID: \"ami-fee34296\", N. Virginia region) and run the ANTs cortical thickness pipeline. The AMI is fully compatible with Sun Grid Engine as well; this enables users to perform many pipeline runs in parallel over a cluster-computing framework.","doi":"10.15154/1165646","id":342,"state":"Shared","study_subject_count":1428,"title":"Derivation of Brain Structure Volumes from MRI Neuroimages hosted by NDAR using C-PAC pipeline and ANTs"},{"collection_subject_count":1,"data_usage":"Secondary Analysis","description":"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.","doi":"10.15154/1343559","id":441,"state":"Shared","study_subject_count":1045,"title":"Automated Autism Diagnosis using Phenotypic and Genotypic Attributes: Phase I"},{"collection_subject_count":8,"data_usage":"Secondary Analysis","description":"In analyzing data from the National Database for Autism Research, we examine revising the Social Communication Questionnaire (SCQ), a commonly used screening instrument for Autism Spectrum Disorder. A combination of Item Response Theory and Mokken scaling techniques were utilized to achieve this and abbreviated scoring of the SCQ is suggested. The psychometric sensitivity of this abbreviated SCQ was examined via bootstrapped Receiver Operator Characteristic (ROC) curve analyses. Additionally, we examined the sensitivity of the abbreviated and total scaled SCQ as it relates to a potential diagnosis of Social (Pragmatic) Communication Disorder (SCD). As SCD is a new disorder introduced with the fifth edition of the Diagnostic and Statistical Manual (DSM-5), we identified individuals with potential diagnosis of SCD among individuals with ASD via mixture modeling techniques using the same NDAR data. These analyses revealed two classes or clusters of individuals when considering the two core areas of impairment among individuals with ASD: social communication and restricted, repetitive patterns of behavior. ","doi":"10.15154/1195993","id":376,"state":"Shared","study_subject_count":889,"title":"Revising the Social Communication Questionnaire scoring procedures for Autism Spectrum Disorder and potential Social Communication Disorder"},{"collection_subject_count":112,"data_usage":"Secondary Analysis","description":"LONI utilized de-identified data from NDAR's cloud and a LONI Pipeline (pipeline.loni.usc.edu) processing workflow to perform a secondary structural MRI examination. The workflow used in this study pulls data from and provided by NDAR to an instance on the LONI compute cluster, aligns data to a standard orientation using FSLreorient2stsd, and undergoes further image processing to eventually identify, extract, and analyze cortical and sub-cortical structures in different MRI brain volumes. Two methods were used for this image processing: the first uses Freesurfer Recon_All to extract brain cortical parcellation and surfaces, align the data to an atlas, and identify, and analyze regions of interest; the second uses FSL to extract the brain (BET), align the data to an atlas, and extract ROIs including sub-cortical regions using FSL FIRST. The second method also uses Freesurfer (mri_segstats) to perform statistical analysis of these ROIs. Lastly, the LONI Pipeline workflow updates and returns the data processed and extracted by Freesurfer and FSL as a miNDAR back to NDAR's cloud storage instance.  These results can be used to assess quality control or be used to perform post-hoc comparisons of cortical and sub-cortical brain architecture between subject types.  See also, Torgerson et al. (2015) Brain Imaging and Behavior, for additional details on using LONI Pipeline to access and process NDAR data.","doi":"10.15154/1169189","id":333,"state":"Shared","study_subject_count":740,"title":"Derivation of Brain Structure Volumes from MRI Neuroimages hosted  by NDAR using LONI Workflows"},{"collection_subject_count":117,"data_usage":"Secondary Analysis","description":"Using the National Database for Autism Research cloud platform, MRI data were analyzed using neuroimaging pipelines that included packages available as part of the Neuroimaging Informatics Tools and Resources Clearinghouse (NITRC) Computational Environment to derive standardized measures of MR image quality.  Structural QA was performed according to Haselgrove, et al (http://journal.frontiersin.org/Journal/10.3389/fninf.2014.00052/abstract) to provide values for Signal to Noise (SNR) and Contrast to Noise (CNR) Ratios that can be compared between subjects within NDAR and between other public data releases.","doi":"10.15154/1149599","id":331,"state":"Shared","study_subject_count":423,"title":"Derivation of Quality Measures for Structural Images by Neuroimaging Pipelines"},{"collection_subject_count":117,"data_usage":"Secondary Analysis","description":"A draft publication is in progress.\r\n\r\nGitHub repository with code for working with NDAR Data is available here: https://github.com/chaselgrove/ndar\r\n\r\n**Note this study is ongoing; additional may be added.**","doi":"10.15154/1149600","id":332,"state":"Shared","study_subject_count":356,"title":"Derivation of Brain Structure Volumes from MRI Neuroimages hosted by NDAR using NITRC-CE"},{"collection_subject_count":111,"data_usage":"Secondary Analysis","description":"Using the National Database for Autism Research cloud platform, MRI data were analyzed using neuroimaging pipelines that included packages available as part of the Neuroimaging Informatics Tools and Resources Clearinghouse (NITRC) Computational Environment to derive standardized measures of MR image quality.  Time series QA was performed according to Friedman, et al. (http://www.ncbi.nlm.nih.gov/pubmed/16952468) providing values for Signal to Noise Ratio that can be compared to other subjects.","doi":"10.15154/1149598","id":350,"state":"Shared","study_subject_count":356,"title":"Derivation of Quality Measures for Time-Series Images by Neuroimaging Pipelines"},{"collection_subject_count":80,"data_usage":"Secondary Analysis","description":"Scientific Abstract\nThe Social Communication Questionnaire (SCQ) assesses communication skills and social functioning in screening for symptoms of autism-spectrum disorder (ASD). The SCQ is recommended for individuals between 4 to 40 years with a cutoff score of 15 for referral. Mixed findings have been reported regarding the recommended cutoff score’s ability to accurately classify an individual as at-risk for ASD (sensitivity) versus an individual as not at-risk for ASD (specificity). Based on a sample from the National Database for Autism Research (n=344; age: 1.58 to 25.92 years old), the present study examined the SCQ’s sensitivity versus specificity across a range of ages. We recommend that the cutoff scores for the SCQ be re-evaluated with age as a consideration.\nLay Abstract\nThe age neutrality of the Social Communication Questionnaire (SCQ) was examined as a common screener for ASD. Mixed findings have been reported regarding the recommended cutoff score’s ability to accurately classify an individual as at-risk for ASD (sensitivity) versus accurately classifying an individual as not at-risk for ASD (specificity). With a sample from the National Database for Autism Research, the present study examined the SCQ’s sensitivity versus specificity. Analyses indicated that the actual sensitivity and specificity scores were lower than initially reported by the creators of the SCQ.","doi":"10.15154/1195992","id":375,"state":"Shared","study_subject_count":339,"title":"The Sensitivity and Specificity of the Social Communication Questionnaire for  Autism Spectrum Disorder with Respect to Age"},{"collection_subject_count":43,"data_usage":"Secondary Analysis","description":"Autism spectrum disorder (ASD), a neurodevelopmental disorder affecting nearly 1 in 88 children, is thought to result from aberrant brain connectivity. Remarkably, there have been no systematic attempts to characterize whole-brain connectivity in children with ASD. Here, we use neuroimaging to show that there are more instances of greater functional connectivity in the brains of children with ASD in comparison to those of typically developing children. Hyperconnectivity in ASD was observed at the whole-brain and subsystems levels, across long- and short-range connections, and was associated with higher levels of fluctuations in regional brain signals. Brain hyperconnectivity predicted symptom severity in ASD, such that children with greater functional connectivity exhibited more severe social deficits. We replicated these findings in two additional independent cohorts, demonstrating again that at earlier ages, the brain of children with ASD is largely functionally hyperconnected in ways that contribute to social dysfunction. Our findings provide unique insights into brain mechanisms underlying childhood autism.","doi":"10.15154/1223791","id":269,"state":"Shared","study_subject_count":43,"title":"Brain Hyperconnectivity in Children with Autism and its Links to Social Deficits "}]