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NDAR provides a single access to de-identified autism research data. For permission to download data, you will need an NDAR account with approved access to NDAR or a connected repository (AGRE, IAN, or the ATP). For NDAR access, you need to be a research investigator sponsored by an NIH recognized institution with federal wide assurance. See Request Access for more information.

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1 Numbers reported are subjects by age
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Format should be in the following format: Activity Code, Institute Abbreviation, and Serial Number. Grant Type, Support Year, and Suffix should be excluded. For example, grant 1R01MH123456-01A1 should be entered R01MH123456

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Collection Summary Collection Charts
Collection Title Collection Investigators Collection Description
Identifying Brain-Based Biomarkers for ASD and their Biological Subtypes
Bradley S. Peterson 
This is a multi-modal brain imaging study of Autism Spectrum Disorders to define biomarkers and subtypes of the disorder.--------------------------------------------------------------------No gradient tables provided for DTI data files. fMRI files headers were cleaned - DOB removed; Spectroscopy files were cleaned - DOB and Physician names removed.
NDAR
Closed
Shared
$2,431,811.00
196
200
35
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NIH - Extramural None


R01MH089582-01 Identifying Brain-Based Biomarkers for ASD & their Biological Subtypes 09/30/2009 08/31/2011 200 35 NEW YORK STATE PSYCHIATRIC INSTITUTE $2,431,811.00

Collection Owners and those with Collection Administrator permission, may edit a collection. The following is currently available for Edit on this page:

Experiments

To create a new Omics, eye tracking, fMRI, or EEG experiment, press the "+ New Experiment" button. Once an experiment is created, then raw files for these types of experiments should be provided, associating the experiment – through Experiment_ID – with the metadata defined in the experiments interface.

IDNameCreated DateStatusType
No records found.

Collection Owners and those with Collection Administrator permission, may edit a collection. The following is currently available for Edit on this page:

Shared Data

Data structures with the number of subjects submitted and shared are provided.

Autism Diagnostic Interview, Revised (ADI-R) Clinical Assessments 59
Autism Diagnostic Observation Schedule (ADOS) - Module 4 Clinical Assessments 35
Autism Diagnostic Observation Schedule (ADOS)- Module 1 Clinical Assessments 4
Autism Diagnostic Observation Schedule (ADOS)- Module 2 Clinical Assessments 8
Autism Diagnostic Observation Schedule (ADOS)- Module 3 Clinical Assessments 35
Autism Spectrum Quotient (AQ) Clinical Assessments 57
CU/NYSPI Neuropsych Assessment Clinical Assessments 163
Children's Memory Scale (CMS) - Ages 5 to 8 Clinical Assessments 21
Children's Memory Scale (CMS) - Ages 9 to 16 Clinical Assessments 22
Clock Test (1997) Clinical Assessments 140
Image Imaging 194
Judgment of Line Orientation - Form H Clinical Assessments 138
Social Communication Questionnaire (SCQ) - Lifetime Clinical Assessments 63
Social Responsiveness Scale (SRS) Clinical Assessments 71
Social Responsiveness Scale (SRS) - Adult Version Clinical Assessments 29
Social Responsiveness Scale (SRS) - Adult/Self Version Clinical Assessments 49
Spectroscopy Form Imaging 170
Water Jar Task (1991) Clinical Assessments 162
Wechsler Abbreviated Scale of Intelligence (WASI) Clinical Assessments 150
Wechsler Memory Scale, Third Edition (WMS-III) Clinical Assessments 42

Collection Owners and those with Collection Administrator permission, may edit a collection. The following is currently available for Edit on this page:

Publications

Publications relevant to NDAR data are listed below. Most displayed publications have been associated with the grant within Pubmed. Use the "+ New Publication" button to add new publications. Publications relevant/not relevant to data expected are categorized. Relevant publications are then linked to the underlying data by selecting the Create Study link. Study provides the ability to define cohorts, assign subjects, define outcome measures and lists the study type, data analysis and results. Analyzed data and results are expected in this way.

PubMed IDStudyTitleJournalAuthorsDateStatus
28239517Create StudyCortical interactions during the resolution of information processing demands in autism spectrum disorders.Brain and behaviorDenisova K, Zhao G, Wang Z, Goh S, Huo Y, Peterson BSFebruary 2017Not Determined
26526072Create StudyDifferences in neural activity when processing emotional arousal and valence in autism spectrum disorders.Human brain mappingTseng A, Wang Z, Huo Y, Goh S, Russell JA, Peterson BSFebruary 2016Not Determined
25620520Create StudyA highly accurate symmetric optical flow based high-dimensional nonlinear spatial normalization of brain images.Magnetic resonance imagingWen, Ying; Hou, Lili; He, Lianghua; Peterson, Bradley S; Xu, DongrongMay 2015Not Relevant
24920613Create StudyDeficits in predictive coding underlie hallucinations in schizophrenia.The Journal of neuroscience : the official journal of the Society for NeuroscienceHorga G, Schatz KC, Abi-Dargham A, Peterson BSJune 11, 2014Not Determined
24718932Create StudyMitochondrial dysfunction as a neurobiological subtype of autism spectrum disorder: evidence from brain imaging.JAMA psychiatryGoh S, Dong Z, Zhang Y, DiMauro S, Peterson BSJune 2014Not Determined
24286520Create StudyAnnual research review: The neurobehavioral development of multiple memory systems--implications for childhood and adolescent psychiatric disorders.Journal of child psychology and psychiatry, and allied disciplinesGoodman J, Marsh R, Peterson BS, Packard MGJune 2014Not Determined
24055410Create StudyThe effects of changing water content, relaxation times, and tissue contrast on tissue segmentation and measures of cortical anatomy in MR images.Magnetic resonance imagingBansal R, Hao X, Liu F, Xu D, Liu J, Peterson BSDecember 2013Not Determined
23986248Create StudyUsing IQ discrepancy scores to examine the neural correlates of specific cognitive abilities.The Journal of neuroscience : the official journal of the Society for NeuroscienceMargolis A, Bansal R, Hao X, Algermissen M, Erickson C, Klahr KW, Naglieri JA, Peterson BSAugust 28, 2013Not Determined
22835646Create StudyImproving the correction of eddy current-induced distortion in diffusion-weighted images by excluding signals from the cerebral spinal fluid.Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging SocietyLiu W, Liu X, Yang G, Zhou Z, Zhou Y, Li G, Dubin M, Bansal R, Peterson BS, Xu DOctober 2012Not Determined
22394424Create StudyAnnual research review: progress in using brain morphometry as a clinical tool for diagnosing psychiatric disorders.Journal of child psychology and psychiatry, and allied disciplinesHaubold A, Peterson BS, Bansal RMay 2012Not Determined
22076792Create StudyMultimodal magnetic resonance imaging: The coordinated use of multiple, mutually informative probes to understand brain structure and function.Human brain mappingHao X, Xu D, Bansal R, Dong Z, Liu J, Wang Z, Kangarlu A, Liu F, Duan Y, Shova S, Gerber AJ, Peterson BSFebruary 2013Not Determined
22072672Create StudyAdaptation to conflict via context-driven anticipatory signals in the dorsomedial prefrontal cortex.The Journal of neuroscience : the official journal of the Society for NeuroscienceHorga G, Maia TV, Wang P, Wang Z, Marsh R, Peterson BSNovember 9, 2011Not Determined
21144708Create StudyAn improved representation of regional boundaries on parcellated morphological surfaces.Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging SocietyHao X, Xu D, Bansal R, Liu J, Peterson BSApril 2011Not Determined
21129881Create StudyAutomated artifact detection and removal for improved tensor estimation in motion-corrupted DTI data sets using the combination of local binary patterns and 2D partial least squares.Magnetic resonance imagingZhou Z, Liu W, Cui J, Wang X, Arias D, Wen Y, Bansal R, Hao X, Wang Z, Peterson BS, Xu DFebruary 2011Not Determined
20494263Create StudyForm determines function: new methods for identifying the neuroanatomical loci of circuit-based disturbances in childhood disorders.Journal of the American Academy of Child and Adolescent PsychiatryPeterson BSJune 2010Not Determined
help.tab.dataexpected

Relevant Publications
PubMed IDStudyTitleJournalAuthorsDate
No records found.
help.tab.dataexpected.addnew
Data Expected
Data ExpectedTargeted EnrollmentInitial SubmissionSubjects SharedStatus
Benton Judgment of Line Orientation Test (BJLOT) info iconApproved
Wechsler Abbreviated Scale of Intelligence (WASI) info iconApproved
Water Jar Task info iconApproved
Wechsler Memory Scale, Third Edition (WMS-III) info iconApproved
Imaging (Structural, fMRI, DTI, PET, microscopy) info iconApproved
ADI-R info iconApproved
ADOS info iconApproved
Autistic Spectrum Quotient info iconApproved
Clock Test (1997) info iconApproved
Childrens Memory Scale (CMS) info iconApproved
Spectroscopy info iconApproved
Neuropsychological Assessment info iconApproved
Social Communication Questionnaire (SCQ) info iconApproved
Social Responsiveness Scale (SRS) info iconApproved
NEPSY info iconApproved
Structure not yet defined

Collection Owners and those with Collection Administrator permission, may edit a collection. The following is currently available for Edit on this page:

Associated Studies

Studies that have been defined using data from a Collection are important criteria to determine the value of data shared. The number of subjects column displays the counts from this Collection that are included in a Study, out of the total number of subjects in that study. The Data Use column represents whether or not the study is a primary analysis of the data or a secondary analysis. State indicates whether the study is private or shared with the research community.

Study Name Description Number of Subjects
Collection / Total
Data Use State
Revising the Social Communication Questionnaire scoring procedures for Autism Spectrum Disorder and potential Social Communication Disorder 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. 35 / 1021 Secondary Analysis Shared
Derivation of Quality Measures for Structural Images by Neuroimaging Pipelines 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. 64 / 425 Secondary Analysis Shared
Derivation of Brain Structure Volumes from MRI Neuroimages hosted by NDAR using C-PAC pipeline and ANTs 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. **NOTE: This study is ongoing; additional data my be available in the future.** As 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. All 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. 62 / 1540 Secondary Analysis Shared
Derivation of Brain Structure Volumes from MRI Neuroimages hosted by NDAR using LONI Workflows 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. 62 / 780 Secondary Analysis Shared
The Sensitivity and Specificity of the Social Communication Questionnaire for Autism Spectrum Disorder with Respect to Age Scientific Abstract The 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. Lay Abstract The 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. 27 / 339 Secondary Analysis Shared
Psychometric Analysis of the Social Communication Questionnaire Using an Item-Response Theory Framework: Implications for the Use of the Lifetime and Current Forms 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. 63 / 2134 Secondary Analysis Shared
* Data not on individual level
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