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1 Numbers reported are subjects by age
New Trial
New Project

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

Please select an experiment type below

Collection - Use Existing Experiment
To associate an experiment to the current collection, just select an axperiment from the table below then click the associate experiment button to persist your changes (saving the collection is not required). Note that once an experiment has been associated to two or more collections, the experiment will not longer be editable.

The table search feature is case insensitive and targets the experiment id, experiment name and experiment type columns. The experiment id is searched only when the search term entered is a number, and filtered using a startsWith comparison. When the search term is not numeric the experiment name is used to filter the results.
SelectExperiment IdExperiment NameExperiment Type
Created On
24HI-NGS_R1Omics02/16/2011
475MB1-10 (CHOP)Omics06/07/2016
490Illumina Infinium PsychArray BeadChip AssayOmics07/07/2016
501PharmacoBOLD Resting StatefMRI07/27/2016
506PVPREFOmics08/05/2016
509ABC-CT Resting v2EEG08/18/2016
13Comparison of FI expression in Autistic and Neurotypical Homo SapiensOmics12/28/2010
18AGRE/Broad Affymetrix 5.0 Genotype ExperimentOmics01/06/2011
22Stitching PCR SequencingOmics02/14/2011
26ASD_MethylationOmics03/01/2011
29Microarray family 03 (father, mother, sibling)Omics03/24/2011
37Standard paired-end sequencing of BCRsOmics04/19/2011
38Illumina Mate-Pair BCR sequencingOmics04/19/2011
39Custom Jumping LibrariesOmics04/19/2011
40Custom CapBPOmics04/19/2011
41ImmunofluorescenceOmics05/11/2011
43Autism brain sample genotyping, IlluminaOmics05/16/2011
47ARRA Autism Sequencing Collaboration at Baylor. SOLiD 4 SystemOmics08/01/2011
53AGRE Omni1-quadOmics10/11/2011
59AGP genotypingOmics04/03/2012
60Ultradeep 454 sequencing of synaptic genes from postmortem cerebella of individuals with ASD and neurotypical controlsOmics06/23/2012
63Microemulsion PCR and Targeted Resequencing for Variant Detection in ASDOmics07/20/2012
76Whole Genome Sequencing in Autism FamiliesOmics01/03/2013
519RestingfMRI11/08/2016
90Genotyped IAN SamplesOmics07/09/2013
91NJLAGS Axiom Genotyping ArrayOmics07/16/2013
93AGP genotyping (CNV)Omics09/06/2013
106Longitudinal Sleep Study. H20 200. Channel set 2EEG11/07/2013
107Longitudinal Sleep Study. H20 200. Channel set 3EEG11/07/2013
108Longitudinal Sleep Study. AURA 200EEG11/07/2013
105Longitudinal Sleep Study. H20 200. Channel set 1EEG11/07/2013
109Longitudinal Sleep Study. AURA 400EEG11/07/2013
116Gene Expression Analysis WG-6Omics01/07/2014
131Jeste Lab UCLA ACEii: Charlie Brown and Sesame Street - Project 1Eye Tracking02/27/2014
132Jeste Lab UCLA ACEii: Animacy - Project 1Eye Tracking02/27/2014
133Jeste Lab UCLA ACEii: Mom Stranger - Project 2Eye Tracking02/27/2014
134Jeste Lab UCLA ACEii: Face Emotion - Project 3Eye Tracking02/27/2014
145AGRE/FMR1_Illumina.JHUOmics04/14/2014
146AGRE/MECP2_Sanger.JHUOmics04/14/2014
147AGRE/MECP2_Junior.JHUOmics04/14/2014
151Candidate Gene Identification in familial AutismOmics06/09/2014
152NJLAGS Whole Genome SequencingOmics07/01/2014
154Math Autism Study - Vinod MenonfMRI07/15/2014
155RestingfMRI07/25/2014
156SpeechfMRI07/25/2014
159EmotionfMRI07/25/2014
160syllable contrastEEG07/29/2014
167School-age naturalistic stimuliEye Tracking09/19/2014
44AGRE/Broad Affymetrix 5.0 Genotype ExperimentOmics06/27/2011
45Exome Sequencing of 20 Sporadic Cases of Autism Spectrum DisorderOmics07/15/2011
Collection - Add Experiment
Add Supporting Documentation
Select File

To add an existing Data Structure, enter its title in the search bar. If you need to request changes, select the indicator "No, it requires changes to meet research needs" after selecting the Structure, and upload the file with the request changes specific to the selected Data Structure. Your file should follow the Request Changes Procedure. If the Data Structure does not exist, select "Request New Data Structure" and upload the appropriate zip file.

Request Submission Exemption
Characters Remaining:
Not Eligible

The Data Expected list for this Collection shows some raw data as missing. Contact the NDA Help Desk with any questions.

Please confirm that you will not be enrolling any more subjects and that all raw data has been collected and submitted.

Collection Updated

Your Collection is now in Data Analysis phase and exempt from biannual submissions. Analyzed data is still expected prior to publication or no later than the project end date.

[CMS] Attention
[CMS] Please confirm that you will not be enrolling any more subjects and that all raw data has been collected and submitted.
[CMS] Error

[CMS]

Unable to change collection phase where targeted enrollment is less than 90%

Delete Submission Exemption
Are you sure you want to delete this submission exemption?
You have requested to move the sharing dates for the following assessments:
Data Expected Item Original Sharing Date New Sharing Date

Please provide a reason for this change, which will be sent to the Program Officers listed within this collection:

Explanation must be between 20 and 200 characters in length.

Please press Save or Cancel
Add New Email Address - Dialog
New Email Address
Collection Summary Collection Charts
Collection Title Collection Investigators Collection Description
Clinical and Immunological Investigations of Subtypes of Autism
Susan Swedo, Audrey Thurm  
This was a longitudinal observational study of children with autistic disorder, non-autism developmental delay, or typical development.
NIMH Data Archive
03/31/2016
Funding Completed
Close Out
No
204
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NIH - Intramural None




helpcenter.collection.general-tab

NDA Help Center

Collection - General Tab

Fields available for edit on the top portion of the page include:

  • Collection Title
  • Investigators
  • Collection Description
  • Collection Phase
  • Funding Source
  • Clinical Trials

Collection Phase: The current status of a research project submitting data to an NDA Collection, based on the timing of the award and/or the data that have been submitted.

  • Pre-Enrollment: The default entry made when the NDA Collection is created.
  • Enrolling: Data have been submitted to the NDA Collection or the NDA Data Expected initial submission date has been reached for at least one data structure category in the NDA Collection.
  • Data Analysis: Subject level data collection for the research project is completed and has been submitted to the NDA Collection. The NDA Collection owner or the NDA Help Desk may set this phase when they’ve confirmed data submission is complete and submitted subject counts match at least 90% of the target enrollment numbers in the NDA Data Expected. Data submission reminders will be turned off for the NDA Collection.
  • Funding Completed: The NIH grant award (or awards) associated with the NDA Collection has reached its end date. NDA Collections in Funding Completed phase are assigned a subphase to indicate the status of data submission.
    • The Data Expected Subphase indicates that NDA expects more data will be submitted
    • The Closeout Subphase indicates the data submission is complete.
    • The Sharing Not Met Subphase indicates that data submission was not completed as expected.

Blinded Clinical Trial Status:

  • This status is set by a Collection Owner and indicates the research project is a double blinded clinical trial. When selected, the public view of Data Expected will show the Data Expected items and the Submission Dates, but the targeted enrollment and subjects submitted counts will not be displayed.
  • Targeted enrollment and subjects submitted counts are visible only to NDA Administrators and to the NDA Collection or as the NDA Collection Owner.
  • When an NDA Collection that is flagged Blinded Clinical Trial reaches the maximum data sharing date for that Data Repository (see https://nda.nih.gov/nda/sharing-regimen.html), the embargo on Data Expected information is released.

Funding Source

The organization(s) responsible for providing the funding is listed here.

Supporting Documentation

Users with Submission privileges, as well as Collection Owners, Program Officers, and those with Administrator privileges, may upload and attach supporting documentation. By default, supporting documentation is shared to the general public, however, the option is also available to limit this information to qualified researchers only.

Grant Information

Identifiable details are displayed about the Project of which the Collection was derived from. You may click in the Project Number to view a full report of the Project captured by the NIH.

Clinical Trials

Any data that is collected to support or further the research of clinical studies will be available here. Collection Owners and those with Administrator privileges may add new clinical trials.

Frequently Asked Questions

  • How does the NIMH Data Archive (NDA) determine which Permission Group data are submitted into?
    During Collection creation, NDA staff determine the appropriate Permission Group based on the type of data to be submitted, the type of access that will be available to data access users, and the information provided by the Program Officer during grant award.
  • How do I know when a NDA Collection has been created?
    When a Collection is created by NDA staff, an email notification will automatically be sent to the PI(s) of the grant(s) associated with the Collection to notify them.
  • Is a single grant number ever associated with more than one Collection?
    The NDA system does not allow for a single grant to be associated with more than one Collection; therefore, a single grant will not be listed in the Grant Information section of a Collection for more than one Collection.
  • Why is there sometimes more than one grant included in a Collection?
    In general, each Collection is associated with only one grant; however, multiple grants may be associated if the grant has multiple competing segments for the same grant number or if multiple different grants are all working on the same project and it makes sense to hold the data in one Collection (e.g., Cooperative Agreements).

Glossary

  • Administrator Privilege
    A privilege provided to a user associated with an NDA Collection or NDA Study whereby that user can perform a full range of actions including providing privileges to other users.
  • Collection Owner
    Generally, the Collection Owner is the contact PI listed on a grant. Only one NDA user is listed as the Collection owner. Most automated emails are primarily sent to the Collection Owner.
  • Collection Phase
    The Collection Phase provides information on data submission as opposed to grant/project completion so while the Collection phase and grant/project phase may be closely related they are often different. Collection users with Administrative Privileges are encouraged to edit the Collection Phase. The Program Officer as listed in eRA (for NIH funded grants) may also edit this field. Changes must be saved by clicking the Save button at the bottom of the page. This field is sortable alphabetically in ascending or descending order. Collection Phase options include:
    • Pre-Enrollment: A grant/project has started, but has not yet enrolled subjects.
    • Enrolling: A grant/project has begun enrolling subjects. Data submission is likely ongoing at this point.
    • Data Analysis: A grant/project has completed enrolling subjects and has completed all data submissions.
    • Funding Completed: A grant/project has reached the project end date.
  • Collection Title
    An editable field with the title of the Collection, which is often the title of the grant associated with the Collection.
  • Grant
    Provides the grant number(s) for the grant(s) associated with the Collection. The field is a hyperlink so clicking on the Grant number will direct the user to the grant information in the NIH Research Portfolio Online Reporting Tools (RePORT) page.
  • Supporting Documentation
    Various documents and materials to enable efficient use of the data by investigators unfamiliar with the project and may include the research protocol, questionnaires, and study manuals.
  • NIH Research Initiative
    NDA Collections may be organized by scientific similarity into NIH Research Initiatives, to facilitate query tool user experience. NIH Research Initiatives map to one or multiple Funding Opportunity Announcements.
  • Permission Group
    Access to shared record-level data in NDA is provisioned at the level of a Permission Group. NDA Permission Groups consist of one or multiple NDA Collections that contain data with the same subject consents.
  • Planned Enrollment
    Number of human subject participants to be enrolled in an NIH-funded clinical research study. The data is provided in competing applications and annual progress reports.
  • Actual Enrollment
    Number of human subjects enrolled in an NIH-funded clinical research study. The data is provided in annual progress reports.
  • NDA Collection
    A virtual container and organization structure for data and associated documentation from one grant or one large project/consortium. It contains tools for tracking data submission and allows investigators to define a wide array of other elements that provide context for the data, including all general information regarding the data and source project, experimental parameters used to collect any event-based data contained in the Collection, methods, and other supporting documentation. They also allow investigators to link underlying data to an NDA Study, defining populations and subpopulations specific to research aims.
  • Data Use Limitations
    Data Use Limitations (DULs) describe the appropriate secondary use of a dataset and are based on the original informed consent of a research participant. NDA only accepts consent-based data use limitations defined by the NIH Office of Science Policy.
  • Total Subjects Shared
    The total number of unique subjects for whom data have been shared and are available for users with permission to access data.
IDNameCreated DateStatusType
No records found.
helpcenter.collection.experiments-tab

NDA Help Center

Collection - Experiments

The number of Experiments included is displayed in parentheses next to the tab name. You may download all experiments associated with the Collection via the Download button. You may view individual experiments by clicking the Experiment Name and add them to the Filter Cart via the Add to Cart button.

Collection Owners, Program Officers, and users with Submission or Administrative Privileges for the Collection may create or edit an Experiment.

Please note: The creation of an NDA Experiment does not necessarily mean that data collected, according to the defined Experiment, has been submitted or shared.

Frequently Asked Questions

  • Can an Experiment be associated with more than one Collection?

    Yes -see the “Copy” button in the bottom left when viewing an experiment. There are two actions that can be performed via this button:

    1. Copy the experiment with intent for modifications.
    2. Associate the experiment to the collection. No modifications can be made to the experiment.

Glossary

  • Experiment Status
    An Experiment must be Approved before data using the associated Experiment_ID may be uploaded.
  • Experiment ID
    The ID number automatically generated by NDA which must be included in the appropriate file when uploading data to link the Experiment Definition to the subject record.
Autism Diagnostic Interview, Rev (ADI-R) Toddler 2004 Clinical Assessments 87
Autism Diagnostic Interview, Revised (ADI-R) Clinical Assessments 159
Autism Diagnostic Observation Schedule, 2nd Edition (ADOS-2) - Module 1 Clinical Assessments 130
Autism Diagnostic Observation Schedule, 2nd Edition (ADOS-2) - Module 2 Clinical Assessments 82
Autism Diagnostic Observation Schedule, 2nd Edition (ADOS-2) - Module 3 Clinical Assessments 74
Autism Diagnostic Observation Schedule, 2nd Edition (ADOS-2) - Toddler Module Clinical Assessments 15
DAS-II: Differential Ability Scales 2nd Ed. School Age Clinical Assessments 22
DAS-II:Differential Ability Scales 2nd Ed. Early Years Clinical Assessments 122
DSM-IV Checklist (Early Steps; SOFAS) Clinical Assessments 203
Expressive One-Word Picture Vocabulary Test (2000) Clinical Assessments 156
M-CHAT Clinical Assessments 65
MacArthur-Bates CDI - Words and Gestures Form Clinical Assessments 114
MacArthur-Bates CDI - Words and Sentences Form Clinical Assessments 125
Mullen Scales of Early Learning Clinical Assessments 196
Peabody Picture Vocabulary Test, Third Edition (Form A) Clinical Assessments 149
Peabody Picture Vocabulary Test, Third Edition (Form B) Clinical Assessments 134
Processed MRI Data Imaging 173
Repetitive Behavior Scale - Revised (RBS-R) Clinical Assessments 199
Research Subject Clinical Assessments 202
Social Communication Questionnaire (SCQ) - Current Form Clinical Assessments 200
Social Responsiveness Scale (SRS) Clinical Assessments 188
Social Responsiveness Scale (SRS) - Preschool Version Clinical Assessments 15
Vineland-II - Survey Form (2005) Clinical Assessments 203
helpcenter.collection.shared-data-tab

NDA Help Center

Collection - Shared Data

This tab provides a quick overview of the Data Structure title, Data Type, and Number of Subjects that are currently Shared for the Collection. The information presented in this tab is automatically generated by NDA and cannot be edited. If no information is visible on this tab, this would indicate the Collection does not have shared data or the data is private.

The shared data is available to other researchers who have permission to access data in the Collection's designated Permission Group(s). Use the Download button to get all shared data from the Collection to the Filter Cart.

Frequently Asked Questions

  • How will I know if another researcher uses data that I shared through the NIMH Data Archive (NDA)?
    To see what data your project have submitted are being used by a study, simply go the Associated Studies tab of your collection. Alternatively, you may review an NDA Study Attribution Report available on the General tab.
  • Can I get a supplement to share data from a completed research project?
    Often it becomes more difficult to organize and format data electronically after the project has been completed and the information needed to create a GUID may not be available; however, you may still contact a program staff member at the appropriate funding institution for more information.
  • Can I get a supplement to share data from a research project that is still ongoing?
    Unlike completed projects where researchers may not have the information needed to create a GUID and/or where the effort needed to organize and format data becomes prohibitive, ongoing projects have more of an opportunity to overcome these challenges. Please contact a program staff member at the appropriate funding institution for more information.

Glossary

  • Data Structure
    A defined organization and group of Data Elements to represent an electronic definition of a measure, assessment, questionnaire, or collection of data points. Data structures that have been defined in the NDA Data Dictionary are available at https://nda.nih.gov/general-query.html?q=query=data-structure
  • Data Type
    A grouping of data by similar characteristics such as Clinical Assessments, Omics, or Neurosignal data.
  • Shared
    The term 'Shared' generally means available to others; however, there are some slightly different meanings based on what is Shared. A Shared NDA Study is viewable and searchable publicly regardless of the user's role or whether the user has an NDA account. A Shared NDA Study does not necessarily mean that data used in the NDA Study have been shared as this is independently determined. Data are shared according the schedule defined in a Collection's Data Expected Tab and/or in accordance with data sharing expectations in the NDA Data Sharing Terms and Conditions. Additionally, Supporting Documentation uploaded to a Collection may be shared independent of whether data are shared.

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 NDA 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
34518555Create StudyThe contribution of platelets to peripheral BDNF elevation in children with autism spectrum disorder.Scientific reportsFarmer CA, Thurm AE, Honnekeri B, Kim P, Swedo SE, Han JCSeptember 13, 2021Relevant
29875224Create StudySpindle activity in young children with autism, developmental delay, or typical development.NeurologyFarmer CA, Chilakamarri P, Thurm AE, Swedo SE, Holmes GL, Buckley AWJuly 10, 2018Relevant
27178705Create StudyCSF concentrations of 5-methyltetrahydrofolate in a cohort of young children with autism.NeurologyShoffner J, Trommer B, Thurm A, Farmer C, Langley WA, Soskey L, Rodriguez AN, D'Souza P, Spence SJ, Hyland K, Swedo SEJune 14, 2016Not Determined
27061356Create StudyCortical thickness change in autism during early childhood.Human brain mappingSmith E, Thurm A, Greenstein D, Farmer C, Swedo S, Giedd J, Raznahan AJuly 2016Not Determined
26844269Create StudyState-Dependent Differences in Functional Connectivity in Young Children With Autism Spectrum Disorder.EBioMedicineBuckley AW, Scott R, Tyler A, Mahoney JM, Thurm A, Farmer C, Swedo S, Burroughs SA, Holmes GLDecember 2015Not Determined
26366376Create StudyNo Evidence of Antibodies against GAD65 and Other Specific Antigens in Children with Autism.BBA clinicalKalra S, Burbelo PD, Bayat A, Ching KH, Thurm A, Iadarola MJ, Swedo SEDecember 1, 2015Not Determined
24274034Create StudyPatterns of skill attainment and loss in young children with autism.Development and psychopathologyThurm A, Manwaring SS, Luckenbaugh DA, Lord C, Swedo SEFebruary 2014Not Determined
24179764Create StudyMapping cortical anatomy in preschool aged children with autism using surface-based morphometry.NeuroImage. ClinicalRaznahan A, Lenroot R, Thurm A, Gozzi M, Hanley A, Spence SJ, Swedo SE, Giedd JN2012Not Determined
24042076Create StudyDietary adequacy of children with autism compared with controls and the impact of restricted diet.Journal of developmental and behavioral pediatrics : JDBPGraf-Myles J, Farmer C, Thurm A, Royster C, Kahn P, Soskey L, Rothschild L, Swedo SSeptember 2013Not Determined
22906515Create StudyDiffusion tensor imaging in young children with autism: biological effects and potential confounds.Biological psychiatryWalker L, Gozzi M, Lenroot R, Thurm A, Behseta B, Swedo S, Pierpaoli CDecember 15, 2012Not Determined
22386453Create StudyA magnetization transfer imaging study of corpus callosum myelination in young children with autism.Biological psychiatryGozzi M, Nielson DM, Lenroot RK, Ostuni JL, Luckenbaugh DA, Thurm AE, Giedd JN, Swedo SEAugust 1, 2012Not Determined
21041596Create StudyRapid eye movement sleep percentage in children with autism compared with children with developmental delay and typical development.Archives of pediatrics & adolescent medicineBuckley AW, Rodriguez AJ, Jennison K, Buckley J, Thurm A, Sato S, Swedo SNovember 2010Not Determined
helpcenter.collection.publications-tab

NDA Help Center

Collection - Publications

The number of Publications is displayed in parentheses next to the tab name. Clicking on any of the Publication Titles will open the Publication in a new internet browsing tab.

Collection Owners, Program Officers, and users with Submission or Administrative Privileges for the Collection may mark a publication as either Relevant or Not Relevant in the Status column.

Frequently Asked Questions

  • How can I determine if a publication is relevant?
    Publications are considered relevant to a collection when the data shared is directly related to the project or collection.
  • Where does the NDA get the publications?
    PubMed, an online library containing journals, articles, and medical research. Sponsored by NiH and National Library of Medicine (NLM).

Glossary

  • Create Study
    A link to the Create an NDA Study page that can be clicked to start creating an NDA Study with information such as the title, journal and authors automatically populated.
  • Not Determined Publication
    Indicates that the publication has not yet been reviewed and/or marked as Relevant or Not Relevant so it has not been determined whether an NDA Study is expected.
  • Not Relevant Publication
    A publication that is not based on data related to the aims of the grant/project associated with the Collection or not based on any data such as a review article and, therefore, an NDA Study is not expected to be created.
  • PubMed
    PubMed provides citation information for biomedical and life sciences publications and is managed by the U.S. National Institutes of Health's National Library of Medicine.
  • PubMed ID
    The PUBMed ID is the unique ID number for the publication as recorded in the PubMed database.
  • Relevant Publication
    A publication that is based on data related to the aims of the grant/project associated with the Collection and, therefore, an NDA Study is expected to be created.
Data Expected List: Mandatory Data Structures

These data structures are mandatory for your NDA Collection. Please update the Targeted Enrollment number to accurately represent the number of subjects you expect to submit for the entire study.

For NIMH HIV-related research that involves human research participants: Select the dictionary or dictionaries most appropriate for your research. If your research does not require all three data dictionaries, just ignore the ones you do not need. There is no need to delete extra data dictionaries from your NDA Collection. You can adjust the Targeted Enrollment column in the Data Expected tab to “0” for those unnecessary data dictionaries. At least one of the three data dictionaries must have a non-zero value.

Data ExpectedTargeted EnrollmentInitial SubmissionSubjects SharedStatus
Research Subject and Pedigree info icon
20107/15/2016
202
Approved
To create your project's Data Expected list, use the "+New Data Expected" to add or request existing structures and to request new Data Structures that are not in the NDA Data Dictionary.

If the Structure you need already exists, locate it and specify your dates and enrollment when adding it to your Data Expected list. If you require changes to the Structure you need, select the indicator stating "No, it requires changes to meet research needs," and upload a file containing your requested changes.

If the structure you need is not yet defined in the Data Dictionary, you can select "Upload Definition" and attach the necessary materials to request its creation.

When selecting the expected dates for your data, make sure to follow the standard Data Sharing Regimen and choose dates within the date ranges that correspond to your project start and end dates.

Please visit the Completing Your Data Expected Tutorial for more information.
Data Expected List: Data Structures per Research Aims

These data structures are specific to your research aims and should list all data structures in which data will be collected and submitted for this NDA Collection. Please update the Targeted Enrollment number to accurately represent the number of subjects you expect to submit for the entire study.

Data ExpectedTargeted EnrollmentInitial SubmissionSubjects SharedStatus
Mullen Scales of Early Learning info icon
20104/11/2016
196
Approved
ADOS info icon
20104/11/2016
202
Approved
Expressive One-Word Picture Vocabulary Test (2000) info icon
20104/11/2016
156
Approved
ADI-R info icon
20104/11/2016
190
Approved
Social Responsiveness Scale (SRS) info icon
20104/11/2016
188
Approved
Social Communication Questionnaire (SCQ) info icon
20104/11/2016
200
Approved
Demographics info icon
20104/11/2016
0
Approved
M-CHAT info icon
20104/11/2016
65
Approved
Processed MRI Data info icon
20105/12/2022
173
Approved
DAS-II: Differential Ability Scales info icon
20104/11/2016
126
Approved
MacArthur Bates Communicative Development Inventory info icon
20107/15/2016
184
Approved
Peabody Picture Vocabulary Test, Fourth Edition info icon
20104/11/2016
165
Approved
Repetitive Behavior Scale - Revised (RBS-R) info icon
20104/11/2016
199
Approved
DSM IV Criteria info icon
20104/11/2016
203
Approved
Vineland (Parent and Caregiver) info icon
20104/11/2016
203
Approved
Nutrition Data System for Research info icon
20111/21/2022
0
Approved
Structure not yet defined
No Status history for this Data Expected has been recorded yet
helpcenter.collection.data-expected-tab

NDA Help Center

Collection - Data Expected

The Data Expected tab displays the list of all data that NDA expects to receive in association with the Collection as defined by the contributing researcher, as well as the dates for the expected initial upload of the data, and when it is first expected to be shared, or with the research community. Above the primary table of Data Expected, any publications determined to be relevant to the data within the Collection are also displayed - members of the contributing research group can use these to define NDA Studies, connecting those papers to underlying data in NDA.

The tab is used both as a reference for those accessing shared data, providing information on what is expected and when it will be shared, and as the primary tracking mechanism for contributing projects. It is used by both contributing primary researchers, secondary researchers, and NIH Program and Grants Management staff.

Researchers who are starting their project need to update their Data Expected list to include all the Data Structures they are collecting under their grant and set their initial submission and sharing schedule according to the NDA Data Sharing Regimen.

To add existing Data Structures from the Data Dictionary, to request new Data Structure that are not in the Dictionary, or to request changes to existing Data Structures, click "+New Data Expected".

For step-by-step instructions on how to add existing Data Structures, request changes to an existing Structure, or request a new Data Structure, please visit the Completing Your Data Expected Tutorial.

If you are a contributing researcher creating this list for the first time, or making changes to the list as your project progress, please note the following:

  • Although items you add to the list and changes you make are displayed, they are not committed to the system until you Save the entire page using the "Save" button at the bottom of your screen. Please Save after every change to ensure none of your work is lost.
  • If you attempt to add a new structure, the title you provide must be unique - if another structure exists with the same name your change will fail.
  • Adding a new structure to this list is the only way to request the creation of a new Data Dictionary definition.

Frequently Asked Questions

  • What is an NDA Data Structure?
    An NDA Data Structure is comprised of multiple Data Elements to make up an electronic definition of an assessment, measure, questionnaire, etc will have a corresponding Data Structure.
  • What is the NDA Data Dictionary?
    The NDA Data Dictionary is comprised of electronic definitions known as Data Structures.

Glossary

  • Analyzed Data
    Data specific to the primary aims of the research being conducted (e.g. outcome measures, other dependent variables, observations, laboratory results, analyzed images, volumetric data, etc.) including processed images.
  • Data Item
    Items listed on the Data Expected list in the Collection which may be an individual and discrete Data Structure, Data Structure Category, or Data Structure Group.
  • Data Structure
    A defined organization and group of Data Elements to represent an electronic definition of a measure, assessment, questionnaire, or collection of data points. Data structures that have been defined in the NDA Data Dictionary are available at https://nda.nih.gov/general-query.html?q=query=data-structure
  • Data Structure Category
    An NDA term describing the affiliation of a Data Structure to a Category, which may be disease/disorder or diagnosis related (Depression, ADHD, Psychosis), specific to data type (MRI, eye tracking, omics), or type of data (physical exam, IQ).
  • Data Structure Group
    A Data Item listed on the Data Expected tab of a Collection that indicates a group of Data Structures (e.g., ADOS or SCID) for which data may be submitted instead of a specific Data Structure identified by version, module, edition, etc. For example, the ADOS Data Structure Category includes every ADOS Data Structure such as ADOS Module 1, ADOS Module 2, ADOS Module 1 - 2nd Edition, etc. The SCID Data Structure Group includes every SCID Data Structure such as SCID Mania, SCID V Mania, SCID PTSD, SCID-V Diagnosis, and more.
  • Evaluated Data
    A new Data Structure category, Evaluated Data is analyzed data resulting from the use of computational pipelines in the Cloud and can be uploaded directly back to a miNDAR database. Evaluated Data is expected to be listed as a Data Item in the Collection's Data Expected Tab.
  • Imaging Data
    Imaging+ is an NDA term which encompasses all imaging related data including, but not limited to, images (DTI, MRI, PET, Structural, Spectroscopy, etc.) as well as neurosignal data (EEG, fMRI, MEG, EGG, eye tracking, etc.) and Evaluated Data.
  • Initial Share Date
    Initial Submission and Initial Share dates should be populated according to the NDA Data Sharing Terms and Conditions. Any modifications to these will go through the approval processes outlined above. Data will be shared with authorized users upon publication (via an NDA Study) or 1-2 years after the grant end date specified on the first Notice of Award, as defined in the applicable Data Sharing Terms and Conditions.
  • Initial Submission Date
    Initial Submission and Initial Share dates should be populated according to these NDA Data Sharing Terms and Conditions. Any modifications to these will go through the approval processes outlined above. Data for all subjects is not expected on the Initial Submission Date and modifications may be made as necessary based on the project's conduct.
  • Research Subject and Pedigree
    An NDA created Data Structure used to convey basic information about the subject such as demographics, pedigree (links family GUIDs), diagnosis/phenotype, and sample location that are critical to allow for easier querying of shared data.
  • Submission Cycle
    The NDA has two Submission Cycles per year - January 15 and July 15.
  • Submission Exemption
    An interface to notify NDA that data may not be submitted during the upcoming/current submission cycle.

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 NameAbstractCollection/Study SubjectsData UsageState
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.202/17423Secondary AnalysisShared
The importance of low IQ to early diagnosis of autismSome 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. 183/6323Secondary AnalysisShared
Examining Diagnostic Trends and Gender Differences in the ADOS-IIApproximately 3–4 boys for every girl meet the clinical criteria for autism in studies of community diagnostic patterns and studies of autism using samples of convenience. However, girls with autism have been hypothesized to be underdiagnosed, possibly because they may present with differing symptom profiles as compared to boys. This secondary data analysis used the National Database of Autism Research (NDAR) to examine how gender and symptom profiles are associated with one another in a gold standard assessment of autism symptoms, the Autism Diagnostic Observation Schedule II (ADOS-II; Lord, C., Luyster, R., Guthrie, W., & Pickles A. (2012a). Patterns of developmental trajectories in toddlers with autism spectrum disorder. Journal of Consulting and Clinical Psychology, 80(3):477–489. https://doi.org/10.1037/a0027214. Epub 2012 Apr 16. PMID: 22506796, PMCID: PMC3365612). ADOS-II scores from 6183 children ages 6–14 years from 78 different studies in the NDAR indicated that gender was a significant predictor of total algorithm, restrictive and repetitive behavioral, and social communicative difficulties composite severity scores. These findings suggest that gender differences in ADOS scores are common in many samples and may reflect on current diagnostic practices.122/5615Secondary AnalysisShared
Gender Differences: Confirmatory Factor Analysis of the ADOS-IIPurpose Recent research has suggested that autism may present differently in girls compared to boys, encouraging the exploration of a sex-differential diagnostic criteria. Gender differences in diagnostic assessments have been shown on the ADOS-II, such that, on average, females score significantly lower than males on all scales and are less likely to show atypicality on most items related to social communicative difficulties. Yet, gender differences in the latent structure of instruments like the ADOS-II have not been examined systematically. Methods As such, this secondary data analysis examined 4,100 youth diagnosed with autism (Mage = 9.9, 813 female & 3287 male) examined item response trends by gender on the ADOS-II Module 3. Results Multi-Group Confirmatory Factor Analysis results show that the factor loadings of four ADOS-II items differ across the genders. One SCD item and one RRB item are strongly related to the latent factor in the female group, while two RRB items have larger factor loadings in the male group. Conclusion The assumption of an identical latent factor structure for the ADOS-II Module 3 for males and females might not be justifiable. Possible diagnostic implications are discussed.122/5615Secondary AnalysisShared
Prognostic early snapshot stratification of autism based on adaptive functioningA 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.106/3517Secondary AnalysisShared
Investigating autism etiology and heterogeneity by decision tree algorithmAutism 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. 200/3382Secondary AnalysisShared
Imbalanced social-communicative and restricted repetitive behavior subtypes in autism spectrum disorder exhibit different neural circuitrySocial-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.51/1708Secondary AnalysisShared
Word Learning and Word FeaturesVocabulary 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.94/280Primary AnalysisShared
Examining the Shape Bias in Young Autistic Children: A Vocabulary Composition AnalysisShape 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.90/249Secondary AnalysisShared
Deviant vocabulary development in children with Autism Spectrum DisorderChildren 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.97/247Secondary AnalysisShared
Modeling Vocabulary Growth in Autistic and Non-Autistic ChildrenWe assessed the goodness of fit of three models of vocabulary growth, with varying sensitivity to the structure of the environment and the learner’s internal state, to estimated vocabulary growth trajectories in autistic and non-autistic children. We first computed word-level acquisition norms that indicate the vocabulary size at which individual words tend to be learned by each group. We then evaluated how well network growth models based on natural language co-occurrence structure and word associations account for variance in the autistic and non-autistic acquisition norms. In addition to replicating key observations from prior work and observing that the growth models explained similar amounts of variance in each group, we found that autistic vocabulary growth also exhibits growth consistent with “the lure of the associates” model. Thus, both groups leverage semantic structure in the learning environment for vocabulary development, but autistic vocabulary growth is also strongly influenced by existing vocabulary knowledge.91/247Secondary AnalysisShared
EVIDENCE FOR THE DIMENSIONAL AND CATEGORIAL ACCOUNTS OF LANGUAGE DEVELOPMENTThis 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.83/216Secondary AnalysisShared
Semantic modeling 2023Although 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.81/208Secondary AnalysisShared
Semantic Network ModelingAlthough 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.64/200Secondary AnalysisShared
Semantic Network Modeling in Young Autistic ChildrenBackground: Most young autistic children have delayed vocabulary growth relative to their non-autistic peers. Additionally, previous studies have revealed that autistic children are less likely to encode associated features of novel objects, suggesting inefficient encoding or different processes for acquiring semantic information about words. Recent network analyses of vocabulary growth revealed important relationships between early vocabulary acquisition and the structure of the sematic environment. Methods: We studied the expressive vocabularies of 970 non-autistic toddlers (Mage = 20.82 months) and 194 autistic children (Mage = 54.58 months) in two studies. The groups were vocabulary-matched (words produced: MAutistic = 213.60, MNon-autistic = 213.72). In study 1, we estimated their trajectories of semantic development using network analyses. Network structure was based on child-oriented adult-generated word associations. We compared child semantic networks according to indegree, average shortest path length, and clustering coefficient (features that holistically contribute to well-connected network structure). Then, in study 2, we attempted to relate vocabulary-level effects to word-level learning biases. Results: Study 1 revealed that autistic and non-autistic children are sensitive to the structure of their semantic environment. Both groups demonstrated nonlinear vocabulary trajectories that differed from random acquisition networks. Despite similarities, group differences were observed for each network metric. Differences were most pronounced for clustering coefficient (how closely connected groups of words are), with earlier peaks for autistic children. Study 2 demonstrated that many words differ in their expected vocabulary size of acquisition. Conclusions: Group differences at the vocabulary- and word-levels indicate that, although autistic children are learning from their semantic environment, they may be processing their semantic environment differently. These deviations indicate that autistic children have distinctive learning biases that may align with core autism features. 82/194Secondary AnalysisShared
Structural MRI scans in autism during early childhood in BIDS formatThis 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/ .173/173Primary AnalysisShared
Identifying Areas of Overlap and Distinction in Early Lexical Profiles of Children with Autism Spectrum Disorder, Late Talkers, and Typical TalkersThis 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.42/118Secondary AnalysisShared
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. 39/82Secondary AnalysisShared
Investigating Vocabulary Profiles in Preverbal and Minimally Verbal Children with Autism Spectrum DisorderThe 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.34/64Secondary AnalysisShared
Vocabulary Comprehension in MV Children with ASDThe 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.23/31Secondary AnalysisShared
* Data not on individual level
helpcenter.collection.associated-studies-tab

NDA Help Center

Collection - Associated Studies

Clicking on the Study Title will open the study details in a new internet browser tab. The Abstract is available for viewing, providing the background explanation of the study, as provided by the Collection Owner.

Primary v. Secondary Analysis: The Data Usage column will have one of these two choices. An associated study that is listed as being used for Primary Analysis indicates at least some and potentially all of the data used was originally collected by the creator of the NDA Study. Secondary Analysis indicates the Study owner was not involved in the collection of data, and may be used as supporting data.

Private v. Shared State: Studies that remain private indicate the associated study is only available to users who are able to access the collection. A shared study is accessible to the general public.

Frequently Asked Questions

  • How do I associate a study to my collection?
    Studies are associated to the Collection automatically when the data is defined in the Study.

Glossary

  • Associated Studies Tab
    A tab in a Collection that lists the NDA Studies that have been created using data from that Collection including both Primary and Secondary Analysis NDA Studies.
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