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NDA Help Center

Filter Cart

Viewable at the top right of NDA pages, the Filter Cart is a temporary holder for filters and data they select. Filters are added to the Workspace first, before being submitted to The Filter Cart. Data selected by filters in the Filter Cart can be added to a Data Package or an NDA Study from the Data Packaging Page, by clicking the 'Create Data Package / Add Data to Study' button.

The filter cart supports combining multiple filters together, and depending on filter type will use "AND" or "OR" when combining filters.

Multiple selections from the same filter type will result in those selections being applied with an ‘OR’ condition. For example, if you add an NDA Collection Filter with selections for both collections 2112 and 2563 to an empty Workspace, the subjects from NDA Collection 2112 ‘OR’ NDA Collection 2563 will be added to your Workspace even if a subject is in both NDA Collections. You can then add other NDA Collections to your Workspace which further extends the ‘OR’ condition.

If a different filter type is added to your Workspace, or a filter has already been submitted to the Filter Cart, the operation then performs a logical ‘AND’ operation. This means that given the subjects returned from the first filter, only those subjects that matched the first filter are returned by the second filter (i.e., subjects that satisfied both filters).

When combining other filters with the GUID filter, please note the GUID filter should be added last. Otherwise, preselected data may be lost. For example, a predefined filter from Featured Datasets may select a subset of data available for a subject. When combined with a GUID filter for the same subject, the filter cart will contain all data available from that subject, data structure, and dataset; this may be more data than was selected in the predefined filter for that subject. Again, you should add the GUID Filter as the last filter to your cart. This ensures 'AND' logic between filters and will limit results to the subjects, data structures, and datasets already included in your filter cart.

Note that only the subjects specific to your filter will be added to your Filter Cart and only on data shared with the research community. Other data for those same subjects may exist (i.e., within another NDA Collection, associated with a data structure that was not requested in the query, etc.). So, users should select ‘Find all Subjects Data’ to identify all data for those specific subjects.

Additional Tips:

  • You may query the data without an account, but to gain access you will need to create an NDA user account and apply for access. Most data access requires that you or your lab are sponsored by an NIH recognized institution with Federal Wide Assurance (FWA). Without access, you will not be able to obtain individual-level data.

Once you have selected data of interest you can:

  • Create a data package - This allows you to specify format for access/download
  • Assign to Study Cohort - Associate the data to an NDA Study allowing for a DOI to be generated and the data to be linked directly to a finding, publication, or data release.
  • Find All Subject Data - Depending on filter types being used, not all data associated with a subject will be selected. Data may be restricted by data structure, NDA Collection, or outcome variables (e.g., NDA Study). ‘Find All Data’ expands the filter criteria by replacing all filters in your Filter Cart with a single Query by GUID filter for all subjects selected by those filters.

Please Note:

  • When running a query, it may take a moment to populate the Filter Cart. Queries happen in the background so you can define other queries during this time.
  • When you add your first filter, all data associated with your query will be added to the Filter Cart (e.g., a Concept, an NDA Collection, a Data Structure/Element, etc.). As you add additional filters, they will also display in the Filter Cart. Only the name of filter will be shown in the Filter Cart, not the underlying structures.
  • Information about the contents of the Filter Cart can be seen by clicking "Edit”.
  • Once your results appear in the Filter Cart, you can create a data package or assign subjects to a study by selecting the 'Package/Assign to Study' option. You can also 'Edit' or 'Clear' filters.

Frequently Asked Questions

  • What is a Filter Cart?
    Viewable at the top right of NDA pages, the Filter Cart is a temporary holder of data identified by the user, through querying or browsing, as being of some potential interest. The Filter Cart is where you send the data from your Workspace after it has been filtered.
  • What do I do after filters are added to the Filter Cart?
    After filters are added to the Filter Cart, users have options to ‘Create a Package’ for download, ‘Associate to Study Cohort’, or ‘Find All Subject Data’. Selecting ‘Find All Subject Data’ identifies and pulls all data for the subjects into the Filter Cart. Choosing ‘Create a Package’ allows users to package and name their query information for download. Choosing ‘Associate to Study Cohort’ gives users the opportunity to choose the Study Cohort they wish to associate this data.
  • Are there limitations on the amount of data a user can download?

    NDA limits the rate at which individual users can transfer data out of Amazon Web Services (AWS) S3 Object storage to non-AWS internet addresses. All users have a download limit of 20 Terabytes. This limit applies to the volume of data an individual user can transfer within a 30-day window. Only downloads to non-AWS internet addresses will be counted against the limit.

  • How does Filter Cart Boolean logic work?

    The Filter Cart currently employs basic AND/OR Boolean logic. A single filter may contain multiple selections for that filter type, e.g., a single NDA Study filter might contain NDA Study 1 and NDA Study 2. A subject that is in EITHER 1 OR 2 will be returned. Adding multiple filters to the cart, regardless of type, will AND the result of each filter. If NDA Study 1 and NDA Study 2 are added as individual filters, data for a subject will only be selected if the subject is included in BOTH 1 AND 2.

    When combining other filters with the GUID filter, please note the GUID filter should be added last. Otherwise, preselected data may be lost. For example, a predefined filter from Featured Datasets may select a subset of data available for a subject. When combined with a GUID filter for the same subject, the filter cart will contain all data available from that subject, data structure, and dataset; this may be more data than was selected in the predefined filter for that subject. Again, you should add the GUID Filter as the last filter to your cart. This ensures 'AND' logic between filters and will limit results to the subjects, data structures, and datasets already included in your filter cart.


  • Workspace
    The Workspace within the General Query Tool is a holding area where you can review your pending filters prior to adding them to Filter Cart. Therefore, the first step in accessing data is to select one or more items and move it into the Workspace.
  • Filter Cart
    Viewable at the top right of NDA pages, the Filter Cart is a temporary holder of data identified by the user through querying or browsing as being of some potential interest. The Filter Cart adds data using an AND condition. The opportunity to further refine data to determine what will be downloaded or sent to a miNDAR is available on the Data Packaging Page, the next step after the Filter Cart. Subsequent access to data is restricted by User Permission or Privilege; however Filter Cart use is not.
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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
475MB1-10 (CHOP)Omics06/07/2016
490Illumina Infinium PsychArray BeadChip AssayOmics07/07/2016
501PharmacoBOLD Resting StatefMRI07/27/2016
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
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
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
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
151Candidate Gene Identification in familial AutismOmics06/09/2014
152NJLAGS Whole Genome SequencingOmics07/01/2014
154Math Autism Study - Vinod MenonfMRI07/15/2014
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

Please enter the name of the data structure to search or if your definition does not exist, please upload that definition so that it can be appropriately defined for submission. Multiple data structures may be associated with a single Data Expected entry. Please add only one data structure per assessment.

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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.

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[CMS] Please confirm that you will not be enrolling any more subjects and that all raw data has been collected and submitted.
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Unable to change collection phase where targeted enrollment is less than 90%

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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

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Explanation must be between 20 and 200 characters in length.

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Collection Summary Collection Charts
Collection Title Collection Investigators Collection Description
Early Autism Risk Longitudinal Investigation (EARLI) Network
Craig Newschaffer 
Descriptive data for EARLI (probands and sibling) ADOS, Vineland, SRS, SCQ
NIMH Data Archive
Autism Centers of Excellence (ACE)
Funding Completed
Close Out
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NIH - Extramural None

Dietary History Questionnaire codebooks.zip Other Dietary History Codebooks Jan 15, 2017 submission Qualified Researchers
EARLIFirstMaternalInterview_20170114.zip Other First Maternal Interview Data for Jan 15, 2017 submission Qualified Researchers
EARLI_DietaryHistoryQuestionnaires_20170114.zip Other Dietary History Data Jan 15, 2017 submission Qualified Researchers

R01ES016443-01 Early Autism Risk Longitudinal Investigation (EARLI) Network 04/01/2008 03/31/2013 4315 763 DREXEL UNIVERSITY $6,483,811.00


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 Status: The visibility status of an NDA Collection. Collection Status can be Shared or Private. Collections in Shared status are visible to all users and can be searched in the NDA Query Tool. Private Collections are not visible to NDA users. The Status of an NDA Collection only affects the visibility of information about the Collection (metadata) and does not relate to the status of the record-level research data in the NDA Collection.

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 and marked as Shared, 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).


  • 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 State
    The Collection State indicates whether the Collection is viewable and searchable. Collections can be either Private, Shared, or an Ongoing Study. A Collection that is shared does not necessarily have shared data as the Collection State and state of data are independent of each other. This field can be edited by Collection users with Administrative Privileges and the Program Officer as listed in eRA (for NIH funded grants). Changes must be saved by clicking the Save button at the bottom of the page.
  • 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
347EARLI Sperm DNAm07/09/2015ApprovedOmics
924EARLI Placenta WGBS05/24/2018ApprovedOmics

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.


  • 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.

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 Observation Schedule (ADOS) - Module 4 Clinical Assessments 5
Autism Diagnostic Observation Schedule (ADOS)- Module 1 Clinical Assessments 91
Autism Diagnostic Observation Schedule (ADOS)- Module 2 Clinical Assessments 89
Autism Diagnostic Observation Schedule (ADOS)- Module 3 Clinical Assessments 55
Autism Diagnostic Observation Schedule, 2nd Edition (ADOS-2) - Module 1 Clinical Assessments 45
Autism Diagnostic Observation Schedule, 2nd Edition (ADOS-2) - Module 2 Clinical Assessments 89
Autism Observation Scale for Infants Clinical Assessments 212
CSBS DP Infant-Toddler Checklist Clinical Assessments 177
Child Behavior Checklist (CBCL) 1-5 Clinical Assessments 291
Child Behavior Checklist (CBCL) 6-18 Clinical Assessments 82
Depression Survey Clinical Assessments 213
Dysmorphology Survey Clinical Assessments 365
EARLI Intervention History Clinical Assessments 190
Genomics Sample Genomics 176
Genomics Subject Genomics 176
Home Walk-Through Survey Clinical Assessments 212
MacArthur-Bates CDI - Words and Gestures Form Clinical Assessments 195
Mullen Scales of Early Learning Clinical Assessments 245
Placental Morphology Clinical Assessments 129
Research Subject Clinical Assessments 403
Sensory Experiences Questionnaire Clinical Assessments 204
Social Communication Questionnaire (SCQ) - Lifetime Clinical Assessments 360
Social Responsiveness Scale (SRS) Clinical Assessments 138
Stress Survey Clinical Assessments 213
Vineland-II - Parent and Caregiver Rating Form (2005) Clinical Assessments 403
Weekly Pregnancy Diary Clinical Assessments 182

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.


  • 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 Collection or NDA Study is viewable and searchable publicly regardless of the user's role or whether the user has an NDA account. A Shared Collection or NDA Study does not necessarily mean that data submitted to the Collection or 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, but will only be viewable and accessible if the Collection is Shared.

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


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
37140384Create StudyPrepregnancy BMI, gestational weight gain, and susceptibility to autism-related traits: the EARLI and HOME studies.Obesity (Silver Spring, Md.)Patti, Marisa A; Croen, Lisa A; Chen, Aimin; Fallin, M Daniele; Khoury, Jane; Lyall, Kristen; Newschaffer, Craig; Hertz-Picciotto, Irva; Schmidt, Rebecca J; Yolton, Kimberly; Braun, Joseph MMay 1, 2023Not Determined
37116678Create StudyAssociations of prenatal exposure to a mixture of persistent organic pollutants with social traits and cognitive and adaptive function in early childhood: Findings from the EARLI study.Environmental researchSong, Ashley Y; Kauffman, Elizabeth M; Hamra, Ghassan B; Dickerson, Aisha S; Croen, Lisa A; Hertz-Picciotto, Irva; Schmidt, Rebecca J; Newschaffer, Craig J; Fallin, M Daniele; Lyall, Kristen; Volk, Heather EJuly 15, 2023Not Determined
37019542Create StudySelecting a dietary supplement with appropriate dosing for 6 key nutrients in pregnancy.The American journal of clinical nutritionSauder, Katherine A; Couzens, G Lance; Bailey, Regan L; Hockett, Christine W; Switkowski, Karen M; Lyall, Kristen; Kerver, Jean M; Dabelea, Dana; Maldonado, Luis E; O'Connor, Thomas G; Deoni, Sean Cl; Glueck, Deborah H; Catellier, Diane J; program collaborators for Environmental influences on Child Health OutcomesApril 1, 2023Not Determined
36189953Create StudyAssociations between accelerated parental biologic age, autism spectrum disorder, social traits, and developmental and cognitive outcomes in their children.Autism research : official journal of the International Society for Autism ResearchSong, Ashley Y; Bakulski, Kelly; Feinberg, Jason I; Newschaffer, Craig; Croen, Lisa A; Hertz-Picciotto, Irva; Schmidt, Rebecca J; Farzadegan, Homayoon; Lyall, Kristen; Fallin, M Daniele; Volk, Heather E; Ladd-Acosta, ChristineDecember 1, 2022Not Determined
35918756Create StudyPrenatal vitamin intake in first month of pregnancy and DNA methylation in cord blood and placenta in two prospective cohorts.Epigenetics & chromatinDou, John F; Middleton, Lauren Y M; Zhu, Yihui; Benke, Kelly S; Feinberg, Jason I; Croen, Lisa A; Hertz-Picciotto, Irva; Newschaffer, Craig J; LaSalle, Janine M; Fallin, Daniele; Schmidt, Rebecca J; Bakulski, Kelly MAugust 2, 2022Not Determined
35870502Create StudyThe associations between prenatal phthalate exposure measured in child meconium and cognitive functioning of 12-month-old children in two cohorts at elevated risk for adverse neurodevelopment.Environmental researchMathew, Leny; Snyder, Nathaniel W; Lyall, Kristen; Lee, Brian K; McClure, Leslie A; Elliott, Amy J; Newschaffer, Craig J; program collaborators for Environmental influences on Child Health OutcomesNovember 1, 2022Not Determined
35807909Create StudyMaternal Dietary Patterns during Pregnancy and Child Autism-Related Traits: Results from Two US Cohorts.NutrientsVecchione, Rachel; Wang, Siwen; Rando, Juliette; Chavarro, Jorge E; Croen, Lisa A; Fallin, M Daniele; Hertz-Picciotto, Irva; Newschaffer, Craig J; Schmidt, Rebecca J; Lyall, KristenJune 30, 2022Not Determined
35764940Create StudyPlacental morphology in association with autism-related traits in the EARLI study.BMC pregnancy and childbirthZhong, Caichen; Shah, Ruchit; Rando, Juliette; Park, Bo; Girardi, Theresa; Walker, Cheryl K; Croen, Lisa A; Fallin, M Daniele; Hertz-Picciotto, Irva; Lee, Brian K; Schmidt, Rebecca J; Volk, Heather E; Newschaffer, Craig J; Salafia, Carolyn M; Lyall, KristenJune 28, 2022Not Determined
35678944Create StudyExamining associations between prenatal biomarkers of oxidative stress and ASD-related outcomes using quantile regression.Journal of autism and developmental disordersCarey, Meghan E; Rando, Juliette; Melnyk, Stepan; James, S Jill; Snyder, Nathaniel; Salafia, Carolyn; Croen, Lisa A; Fallin, M Daniele; Hertz-Picciotto, Irva; Volk, Heather; Newschaffer, Craig; Lyall, KristenJune 9, 2022Not Determined
35261202Create StudyPrenatal exposure to pesticide residues in the diet in association with child autism-related traits: Results from the EARLI study.Autism research : official journal of the International Society for Autism ResearchJoyce, Emily E; Chavarro, Jorge E; Rando, Juliette; Song, Ashley Y; Croen, Lisa A; Fallin, M Daniele; Hertz-Picciotto, Irva; Schmidt, Rebecca J; Volk, Heather; Newschaffer, Craig J; Lyall, KristenMay 1, 2022Not Determined
34494118Create StudyDisparities in Risks of Inadequate and Excessive Intake of Micronutrients during Pregnancy.The Journal of nutritionSauder, Katherine A; Harte, Robyn N; Ringham, Brandy M; Guenther, Patricia M; Bailey, Regan L; Alshawabkeh, Akram; Cordero, José F; Dunlop, Anne L; Ferranti, Erin P; Elliott, Amy J; Mitchell, Diane C; Hedderson, Monique M; Avalos, Lyndsay A; Zhu, Yeyi; Breton, Carrie V; Chatzi, Leda; Ran, Jin; Hertz-Picciotto, Irva; Karagas, Margaret R; Sayarath, Vicki; Hoover, Joseph; MacKenzie, Debra; Lyall, Kristen; Schmidt, Rebecca J; O'Connor, Thomas G; Barrett, Emily S; Switkowski, Karen M; Comstock, Sarah S; Kerver, Jean M; Trasande, Leonardo; Tylavsky, Frances A; Wright, Rosalind J; Kannan, Srimathi; Mueller, Noel T; Catellier, Diane J; Glueck, Deborah H; Dabelea, Dana; Program Collaborators for Environmental influences on Child Health Outcomes (ECHO)November 2, 2021Not Determined
34280640Create StudyPrenatal phthalate exposure measurement: A comparison of metabolites quantified in prenatal maternal urine and newborn''s meconium.The Science of the total environmentMathew, Leny; Snyder, Nathaniel W; Lyall, Kristen; Lee, Brian K; McClure, Leslie A; Elliott, Amy J; Newschaffer, Craig JNovember 20, 2021Not Determined
34110557Create StudyThe Association of Prenatal Vitamins and Folic Acid Supplement Intake with Odds of Autism Spectrum Disorder in a High-Risk Sibling Cohort, the Early Autism Risk Longitudinal Investigation (EARLI).Journal of autism and developmental disordersBrieger, Katharine K; Bakulski, Kelly M; Pearce, Celeste L; Baylin, Ana; Dou, John F; Feinberg, Jason I; Croen, Lisa A; Hertz-Picciotto, Irva; Newschaffer, Craig J; Fallin, M Daniele; Schmidt, Rebecca JJune 1, 2022Not Determined
33794742Create StudyMaternal blood metal concentrations and whole blood DNA methylation during pregnancy in the Early Autism Risk Longitudinal Investigation (EARLI).EpigeneticsAung, Max T; M Bakulski, Kelly; Feinberg, Jason I; F Dou, John; D Meeker, John; Mukherjee, Bhramar; Loch-Caruso, Rita; Ladd-Acosta, Christine; Volk, Heather E; Croen, Lisa A; Hertz-Picciotto, Irva; Newschaffer, Craig J; Fallin, M DanieleMarch 1, 2022Not Determined
33573264Create StudyGestational Exposure to Phthalates and Social Responsiveness Scores in Children Using Quantile Regression: The EARLI and HOME Studies.International journal of environmental research and public healthPatti, Marisa A; Newschaffer, Craig; Eliot, Melissa; Hamra, Ghassan B; Chen, Aimin; Croen, Lisa A; Fallin, M Daniele; Hertz-Picciotto, Irva; Kalloo, Geetika; Khoury, Jane C; Lanphear, Bruce P; Lyall, Kristen; Yolton, Kimberly; Braun, Joseph MJanuary 30, 2021Not Determined
33449933Create StudyAssociation between self-reported caffeine intake during pregnancy and social responsiveness scores in childhood: The EARLI and HOME studies.PloS onePatti, Marisa A; Li, Nan; Eliot, Melissa; Newschaffer, Craig; Yolton, Kimberly; Khoury, Jane; Chen, Aimin; Lanphear, Bruce P; Lyall, Kristen; Hertz-Picciotto, Irva; Fallin, Margaret Daniele; Croen, Lisa A; Braun, Joseph MJanuary 1, 2021Not Determined
33317014Create StudyPrenatal Multivitamin Use and MTHFR Genotype Are Associated with Newborn Cord Blood DNA Methylation.International journal of environmental research and public healthBakulski, Kelly M; Dou, John F; Feinberg, Jason I; Brieger, Katharine K; Croen, Lisa A; Hertz-Picciotto, Irva; Newschaffer, Craig J; Schmidt, Rebecca J; Fallin, M DanieleDecember 9, 2020Not Determined
33228808Create StudyMeconium androgens are correlated with ASD-related phenotypic traits in early childhood in a familial enriched risk cohort.Molecular autismTerloyeva, Dina; Frey, Alexander J; Park, Bo Y; Kauffman, Elizabeth M; Mathew, Leny; Bostwick, Anna; Varner, Erika L; Lee, Brian K; Croen, Lisa A; Fallin, Margaret D; Hertz-Picciotto, Irva; Newschaffer, Craig J; Lyall, Kristen; Snyder, Nathaniel WNovember 23, 2020Not Determined
33054850Create StudyCord blood DNA methylome in newborns later diagnosed with autism spectrum disorder reflects early dysregulation of neurodevelopmental and X-linked genes.Genome medicineMordaunt, Charles E; Jianu, Julia M; Laufer, Benjamin I; Zhu, Yihui; Hwang, Hyeyeon; Dunaway, Keith W; Bakulski, Kelly M; Feinberg, Jason I; Volk, Heather E; Lyall, Kristen; Croen, Lisa A; Newschaffer, Craig J; Ozonoff, Sally; Hertz-Picciotto, Irva; Fallin, M Daniele; Schmidt, Rebecca J; LaSalle, Janine MOctober 14, 2020Not Determined
32519188Create StudyThe Association Between Maternal Prenatal Fish Intake and Child Autism-Related Traits in the EARLI and HOME Studies.Journal of autism and developmental disordersVecchione, Rachel; Vigna, Chelsea; Whitman, Casey; Kauffman, Elizabeth M; Braun, Joseph M; Chen, Aimin; Xu, Yingying; Hamra, Ghassan B; Lanphear, Bruce P; Yolton, Kimberly; Croen, Lisa A; Fallin, M Daniele; Irva Hertz-Picciotto; Newschaffer, Craig J; Lyall, KristenFebruary 1, 2021Not Determined
32314879Create StudyThe Association Between Parental Age and Autism-Related Outcomes in Children at High Familial Risk for Autism.Autism research : official journal of the International Society for Autism ResearchLyall, Kristen; Song, Lanxin; Botteron, Kelly; Croen, Lisa A; Dager, Stephen R; Fallin, M Daniele; Hazlett, Heather C; Kauffman, Elizabeth; Landa, Rebecca; Ladd-Acosta, Christine; Messinger, Daniel S; Ozonoff, Sally; Pandey, Juhi; Piven, Joseph; Schmidt, Rebecca J; Schultz, Robert T; Stone, Wendy L; Newschaffer, Craig J; Volk, Heather EJune 2020Not Determined
30826615Create StudyEpigenetic marks of prenatal air pollution exposure found in multiple tissues relevant for child health.Environment internationalLadd-Acosta, Christine; Feinberg, Jason I; Brown, Shannon C; Lurmann, Frederick W; Croen, Lisa A; Hertz-Picciotto, Irva; Newschaffer, Craig J; Feinberg, Andrew P; Fallin, M Daniele; Volk, Heather EMay 2019Not Determined
29451060Create StudyCord blood buffy coat DNA methylation is comparable to whole cord blood methylation.EpigeneticsDou, John; Schmidt, Rebecca J; Benke, Kelly S; Newschaffer, Craig; Hertz-Picciotto, Irva; Croen, Lisa A; Iosif, Ana-Maria; LaSalle, Janine M; Fallin, M Daniele; Bakulski, Kelly MJanuary 2018Not Determined
29025028Create StudyCohort Profile: Pregnancy And Childhood Epigenetics (PACE) Consortium.International journal of epidemiologyFelix, Janine F; Joubert, Bonnie R; Baccarelli, Andrea A; Sharp, Gemma C; Almqvist, Catarina; Annesi-Maesano, Isabella; Arshad, Hasan; Baïz, Nour; Bakermans-Kranenburg, Marian J; Bakulski, Kelly M; Binder, Elisabeth B; Bouchard, Luigi; Breton, Carrie V; Brunekreef, Bert; Brunst, Kelly J; Burchard, Esteban G; Bustamante, Mariona; Chatzi, Leda; Cheng Munthe-Kaas, Monica; Corpeleijn, Eva; Czamara, Darina; Dabelea, Dana; Davey Smith, George; De Boever, Patrick; Duijts, Liesbeth; Dwyer, Terence; Eng, Celeste; Eskenazi, Brenda; Everson, Todd M; Falahi, Fahimeh; Fallin, M Daniele; Farchi, Sara; Fernandez, Mariana F; Gao, Lu; Gaunt, Tom R; Ghantous, Akram; Gillman, Matthew W; Gonseth, Semira; Grote, Veit; Gruzieva, Olena; Håberg, Siri E; Herceg, Zdenko; Hivert, Marie-France; Holland, Nina; Holloway, John W; Hoyo, Cathrine; Hu, Donglei; Huang, Rae-Chi; Huen, Karen; Järvelin, Marjo-Riitta; Jima, Dereje D; Just, Allan C; Karagas, Margaret R; Karlsson, Robert; Karmaus, Wilfried; Kechris, Katerina J; Kere, Juha; Kogevinas, Manolis; Koletzko, Berthold; Koppelman, Gerard H; Küpers, Leanne K; Ladd-Acosta, Christine; Lahti, Jari; Lambrechts, Nathalie; Langie, Sabine A S; Lie, Rolv T; Liu, Andrew H; Magnus, Maria C; Magnus, Per; Maguire, Rachel L; Marsit, Carmen J; McArdle, Wendy; Melén, Erik; Melton, Phillip; Murphy, Susan K; Nawrot, Tim S; Nisticò, Lorenza; Nohr, Ellen A; Nordlund, Björn; Nystad, Wenche; Oh, Sam S; Oken, Emily; Page, Christian M; Perron, Patrice; Pershagen, Göran; Pizzi, Costanza; Plusquin, Michelle; Raikkonen, Katri; Reese, Sarah E; Reischl, Eva; Richiardi, Lorenzo; Ring, Susan; Roy, Ritu P; Rzehak, Peter; Schoeters, Greet; Schwartz, David A; Sebert, Sylvain; Snieder, Harold; Sørensen, Thorkild I A; Starling, Anne P; Sunyer, Jordi; Taylor, Jack A; Tiemeier, Henning; Ullemar, Vilhelmina; Vafeiadi, Marina; Van Ijzendoorn, Marinus H; Vonk, Judith M; Vriens, Annette; Vrijheid, Martine; Wang, Pei; Wiemels, Joseph L; Wilcox, Allen J; Wright, Rosalind J; Xu, Cheng-Jian; Xu, Zongli; Yang, Ivana V; Yousefi, Paul; Zhang, Hongmei; Zhang, Weiming; Zhao, Shanshan; Agha, Golareh; Relton, Caroline L; Jaddoe, Vincent W V; London, Stephanie JFebruary 2018Not Determined
28163867Create StudyUmbilical cord blood androgen levels and ASD-related phenotypes at 12 and 36 months in an enriched risk cohort study.Molecular autismPark BY, Lee BK, Burstyn I, Tabb LP, Keelan JA, Whitehouse AJ, Croen LA, Fallin MD, Hertz-Picciotto I, Montgomery O, Newschaffer CJJanuary 2017Not Determined
27871978Create StudyDifferences in testosterone and its precursors by sex of the offspring in meconium.The Journal of steroid biochemistry and molecular biologyFrey AJ, Park BY, Schriver ER, Feldman DR, Parry S, Croen LA, Fallin DM, Hertz-Picciotto I, Newschaffer CJ, Snyder NWNovember 2016Not Relevant
25878217Create StudyPaternal sperm DNA methylation associated with early signs of autism risk in an autism-enriched cohort.International journal of epidemiologyFeinberg, Jason I; Bakulski, Kelly M; Jaffe, Andrew E; Tryggvadottir, Rakel; Brown, Shannon C; Goldman, Lynn R; Croen, Lisa A; Hertz-Picciotto, Irva; Newschaffer, Craig J; Fallin, M Daniele; Feinberg, Andrew PAugust 2015Not Determined
25455579Create StudyComplementary and alternative medicine treatments for children with autism spectrum disorders.Child and adolescent psychiatric clinics of North AmericaLevy, Susan E; Hyman, Susan LJanuary 2015Not Relevant
24118221Create StudyDetermining source strength of semivolatile organic compounds using measured concentrations in indoor dust.Indoor airShin HM, McKone TE, Nishioka MG, Fallin MD, Croen LA, Hertz-Picciotto I, Newschaffer CJ, Bennett DHJune 2014Not Relevant
19819542Create StudyAutism.Lancet (London, England)Levy, Susan E; Mandell, David S; Schultz, Robert TNovember 7, 2009Not Determined

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).


  • 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.

Data ExpectedTargeted EnrollmentInitial SubmissionSubjects SharedStatus
Research Subject and Pedigree info icon
You can use "Add New Data Expected" to add exsiting structures and create your project's list. However, this is also the method you can use to request new structures be created for your project. When adding the Data Expected item, if the structure already exists you can locate it and specify your dates and enrollment. To add a new structure and request it be defined in the Data Dictionary, select Upload Definition and attach the definition or material needed to create it, including manual, codebooks, forms, etc. If you have multiple files, please upload a zipped archive containing them all.

Expected dates should be selected based on the standard Data Sharing Regimen and are restricted to within date ranges based on the project start and end dates.
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
Genomics/omics info icon
ADOS info icon
Social Responsiveness Scale (SRS) info icon
Sensory Experiences Questionnaire (SEQ) info icon
Social Communication Questionnaire (SCQ) info icon
Child Behavior Checklist (CBCL) info icon
Enviromental Evaluation info icon
Autism Observation Scale for Infants (AOSI) info icon
Communication and Symbolic Behavior Scales (CSBS) info icon
Diet/Food Diary/Log info icon
MacArthur Bates Communicative Development Inventory info icon
Intervention History info icon
Physical Exam info icon
Depression Questionnaire info icon
Stress Questionnaires info icon
Exposure Diary info icon
Dysmorphology Survey info icon
Vineland (Parent and Caregiver) info icon
Structure not yet defined
No Status history for this Data Expected has been recorded yet

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.

Contributing researchers just getting started on their project will need to define this list by adding all of the items they are collecting under their grant and setting their schedule according to the NDA Data Sharing Regimen. If you fall into this category, you can begin by clicking "Add New Data Expected" and selecting which data structures you will be using, saving the page after each change, or requesting new structures by adding and naming a new item, providing any materials NDA Data Dictionary Curators can use to help define your structure. For more information see the tutorial on creating Data Expected.

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.


  • 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.245/17423Secondary AnalysisShared
Controls for SCCRIPTo establish a well characterized cohort for pediatric patients living with sickle cell disease2/11185Secondary AnalysisPrivate
Working Title: Differentiating Core Autism SymptomatologyAutism spectrum disorder (ASD) is characterized by persistent deficits in social communication and social interaction, social-emotional reciprocity, and repetitive behavior or restricted interest (American Psychiatric Association [APA], 2013). This study extends the existing literature by clarifying the extent to which mental health disorder symptoms differentially converge with autism symptoms related to social communication and restricted and repetitive behavior, as well as the extent to which mental health symptoms are empirically differentiated from the core autism symptom domains. Although there is a well-documented correlation between the severity of core ASD symptoms and the presence of mental health disorder symptoms, such as anxiety and irritability, the nature of this linkage remains poorly understood. In this project, the National Database for Autism Research (NDAR) and Research Domain Criteria Database (RDoCdb) were used to observe continuous symptom measures such as the Social Responsiveness Scale (SRS) and the Child Behavior Checklist (CBCL) to examine correlation matrices as well as factor structure models to examine these patterns of association. The SRS “social communication” and “repetitive restricted” subscales were correlated with the CBCL externalizing, internalizing, attention, conduct, aggression, psychosomatic, and withdrawn subscales. We hypothesized that “repetitive and restricted” behaviors would be more correlated with the CBCL scales than would the “social communication” scale. These results were also interpreted according to age and IQ. In conclusion, this study may elucidate ongoing questions about the centrality of mental health symptoms like anxiety to aspects of ASD taxonomy. 138/11144Secondary AnalysisPrivate
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. 242/6323Secondary AnalysisShared
The effect of compensatory mechanisms during and after pregnancy on a child's developmentEarly childhood involves rapid processes of human growth leading to different trajectories in physical, cognitive, social, and emotional development (Graignic-Philippe et al., 2014). These processes are influenced by a wide variety of factors such as maternal health, environmental stressors, and early childhood experiences. Current literature has shown how exposure to both acute and chronic stress during pregnancy have a pathogenetic effect throughout childhood (Kim & Leventhal, 2015; Rice, et al, 2010), leading to neurotypical or atypical development. Studies have shown how these stressors are linked neurodevelopmental disorders such Autism Spectrum Disorders (Zerbo et al., 2015; Atladóttir et al., 2012) or Attention Deficit Hyperactivity Disorder (Rosenqvist et al., 2019). In recent years, there has been a shift from traditional diagnostic research models to synthesis of different scientific fields to map lifecourse development in order for rapid translation into healthcare practices (Halfon et al., 2014). Whilst there are studies showing links between stress and atypical developmental outcomes, there is still very limited literature on compensatory mechanisms found pre- and post-pregnancy, which illustrate development of protective factors (such as presence of self-regulation, high verbal intelligence, sociability, adept social communication) against atypical developmental outcomes. This study aims to identify and measure the presence of these protective factors that appear to guard against or mitigate the emergence of neurodevelopmental disorders. Therefore, nationwide and longitudinal data are needed in order to accurately create risk models in order to map developmental trajectories. 258/5717Secondary AnalysisPrivate
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. 134/3382Secondary AnalysisShared
Autism Sensory Research Consortium Cross-lab Integrative Data Analysis Since 2013, when sensory features were officially added to the diagnostic criteria for autism, research into the sensory manifestations of the condition has increased dramatically. However, the majority of this research has primarily been conducted using small laboratory-based samples of children on the autism spectrum, substantially limiting the hypotheses that can be tested in any one dataset and the generalizability of results to the wider autistic population. The Autism Sensory Research Consortium (ASRC), funded by the Nancy Lurie Marks Family Foundation, represents the first major international collaboration of over a dozen research groups that study sensory functioning in autism. As a major thrust of this collaboration, the ASRC has begun a data sharing initiative, in which all participating labs can contribute existing data from their past and present research studies to a centralized database. These “Big Data” can then be systematically examined using powerful large-sample statistical techniques such as structural equation modeling and item response theory, which will allow researchers to test more complex hypotheses regarding the nature of sensory differences in autism and their relationships with sociodemographic and non-sensory clinical features. Once data from all sites has been pooled, it will be analyzed using a method called integrative data analysis, which is specially designed to derive insights from large and heterogeneous samples. One major advantage of this methodology is the ability to construct and test measurement models of sensory symptoms, determining the most appropriate set of questions for assessing each construct and making sure that the scales do not produce biased comparisons when they are examined across diagnostic groups or subsets of the autistic population. Furthermore, measurement models can be constructed to bridge multiple questionnaires, allowing for the calculation of robust composite scores that can be compared between studies that only administered items from one of the contributing questionnaires. These models can further facilitate pooling of data across studies, allowing us to amass even larger datasets to answer questions about sensory function in the autistic population. Furthermore, moving forward, the composite sensory measures from the integrative data analysis can be employed in other studies, providing investigators in sensory autism research with a suite of reliable and valid behavioral measures that can be used as outcomes in trials of interventions targeting these symptoms. In the long term, this project has the potential to help us better understand the nature of sensory function in persons on the spectrum, as well as how sensory alterations relate to broader features of the condition—specifically, for whom and/or at what point in development sensory features are most predictive of core autism behaviors or other meaningful clinical outcomes such as language acquisition and adaptive behavior. Incorporation of neuroscientific data collected within the ASRC can also possibly shed some light on the neural basis of sensory disruptions in the autistic population. All of this will help to lay a foundation for future work testing the efficacy of candidate interventions aimed at improving sensory function and more distal skills in autistic individuals.4/2110Secondary AnalysisPrivate
Psychometric Analysis of the Social Communication Questionnaire Using an Item-Response Theory Framework: Implications for the Use of the Lifetime and Current FormsThe 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.192/2054Secondary AnalysisShared
Unravelling the Collective Diagnostic Power Behind the Features in the Autism Diagnostic Observation ScheduleBackground: Autism is a group of heterogeneous disorders defined by deficits in social interaction and communication. Typically, diagnosis depends on the results of a behavioural examination called the Autism Diagnostic Observation Schedule (ADOS). Unfortunately, administration of the ADOS exam is time-consuming and requires a significant amount of expert intervention, leading to delays in diagnosis and access to early intervention programs. The diagnostic power of each feature in the ADOS exam is currently unknown. Our hypothesis is that certain features could be removed from the exam without a significant reduction in diagnostic accuracy, sensitivity or specificity. Objective: Determine the smallest subset of predictive features in ADOS module-1 (an exam variant for patients with minimal verbal skills). Methodology: ADOS module-1 datasets were acquired from the Autism Genetic Resource Exchange and the National Database for Autism Research. The datasets contained 2572 samples with the following labels: autism (1763), autism spectrum (513), and non-autism (296). The datasets were used as input to 4 different cost-sensitive classifiers in Weka (functional trees, LADTree, logistic model trees, and PART). For each classifier, a 10-fold cross validation was preformed and the number of predictive features, accuracy, sensitivity, and specificity was recorded. Results & Conclusion: Each classifier resulted in a reduction of the number of ADOS features required for autism diagnosis. The LADtree classifier was able to obtain the largest reduction, utilizing only 10 of 29 ADOS module-1 features (96.8% accuracy, 96.9% sensitivity, and 95.9% specificity). Overall, these results are a step towards a more efficient behavioural exam for autism diagnosis. 91/1832Secondary AnalysisShared
Computer-Based Testing to Shorten the Social Communication Questionnaire (SCQ): A Proof-of-Principle Study of the Lifetime and Current FormsThe Social Communication Questionnaire (SCQ) is a 40-item instrument designed to screen children at risk for Autism Spectrum Disorder (ASD). Both Lifetime and Current forms of the scale are available. Although these forms are manageable for many respondents, their use may result in substantial respondent and administrative burden, particularly among individuals who have difficulty reading, have physical illness, and/or are asked to take multiple questionnaires. The objective of this research was to examine the potential of two stopping rules for computer-based testing (namely, curtailment and stochastic curtailment) to shorten the SCQ without compromising its screening properties. A retrospective analysis was conducted using data from the National Database for Autism Research (NDAR); responses regarding 1236 at-risk individuals from the SCQ Lifetime and 709 at-risk individuals from the SCQ Current were analyzed. In post-hoc simulation, curtailment reduced mean test lengths by 29% to 44% compared to the full-length Lifetime form, and by 25% to 39% compared to the full-length Current form, while providing the same screening result as the corresponding full-length form in 100% of cases. Stochastic curtailment made further reductions in test length, but was not always concordant with the full-length form’s screening result. These findings suggest that curtailment has potential to improve the efficiency of the SCQ in computer-based administrations and should be tested prospectively.131/1820Secondary AnalysisPrivate
Development of a Short Form of the SRS: An Application of IRTBackground: Research and practice in autism spectrum disorder (ASD) rely on quantitative measures, such as the Social Responsiveness Scale (SRS), for characterization and diagnosis. Like many ASD diagnostic measures, SRS scores are influenced by factors unrelated to ASD core features. This study further interrogates the psychometric properties of the SRS using item response theory (IRT), and demonstrates a strategy to enhance measure specificity by applying IRT results. Methods: SRS analyses were conducted on a large sample (N=21,426) of youth from four ASD databases. Items were subjected to item factor analyses and evaluation of item bias by gender, age, and expressive language level. Results: Item selection based on dimensionality and DIF analyses produced a reduced item SRS subscale that was unidimensional in structure, highly reliable (α=.96), and free of gender, age, expressive language, and non-verbal IQ influence. The subscale also showed strong relationships with established measures of autism symptom severity (ADOS, ADI-R, Vineland). Degree of association between all measures varied as a function of expressive language. Conclusions: Results identified specific SRS items that are more vulnerable to non-ASD-related traits. The resultant 16-item SRS subscale may possess superior psychometric properties compared to the original scale and emerge as a more precise measure of ASD core symptom severity, facilitating research and practice. Future research using IRT is needed to further refine existing measures of autism symptomatology. 138/1478Secondary AnalysisPrivate
Automated Autism Diagnosis using Phenotypic and Genotypic Attributes: Phase IThe 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 2016.40/1045Secondary AnalysisShared
Gender as a Moderator of the Association between Social Responsiveness and Cognitive Ability for Children with Autism24/977Secondary AnalysisPrivate
Revising the Social Communication Questionnaire scoring procedures for Autism Spectrum Disorder and potential Social Communication DisorderIn 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. 64/889Secondary AnalysisShared
The Sensitivity and Specificity of the Social Communication Questionnaire for Autism Spectrum Disorder with Respect to AgeScientific 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.62/339Secondary AnalysisShared
Does Anxiety Mediate the Relationship between Sensory Hyperresponsiveness and Restricted, Repetitive Behaviors during Early Development?Sensory hyperresponsiveness, anxiety, and restricted, repetitive behaviors are known to be associated with one another, especially in autistic youth, and may be important to the development and presentation of autism over time. Few studies, however, have studied the nature of this three-way relationship prospectively, or in young children at elevated likelihood for autism. The goals of the current study were to gain greater insight into the development of autism from a symptom level before a diagnosis can be made, and specifically, to examine the relationship between sensory hyperresponsiveness, anxiety, and restricted, repetitive behaviors across time during early development in children at elevated likelihood for autism. Extant longitudinal data for a group of children at elevated likelihood for autism (N = 147) were used to conduct path analyses for two mediation model configurations, which included measures of sensory hyperresponsiveness at baseline, and anxiety and restricted and repetitive behaviors at follow-up. Results did not indicate mediating effects for either model; however, higher levels of sensory hyperresponsiveness at baseline were significantly associated with higher levels of anxiety symptoms at follow-up (b = 0.09, SE = 0.04, β = 0.24, p = 0.005, 95% CIs [0.07, 0.40]). Findings suggest that sensory hyperresponsiveness during early development later predicts anxiety symptoms in children at elevated likelihood for autism, which is consistent with prior findings in both autistic (Green et al., 2012) and non-autistic children (Carpenter et al., 2019). Although we are unable to determine whether this is a unidirectional or bidirectional relationship in the current study given the lack of concurrent data on anxiety symptoms at baseline, this result adds to emerging research suggesting that sensory hyperresponsiveness may be a risk factor for later developing anxiety. 159/159Secondary AnalysisShared
Placenta DNA methylation at ZNF300 is associated with fetal sex and with placental morphologyFetal sex-specific differences in placental morphology and physiology have been associated with sexually dimorphic health outcomes. However, the molecular mechanisms underlying these sex differences are not well understood. We performed whole genome bisulfite sequencing in 133 placenta samples and discovered a significant difference in DNA methylation (DNAm) at the ZNF300 gene locus between male and female offspring and replicated this result in 6 independent datasets. Additionally, the sex-specific pattern appears to be placenta-specific, is robust to a wide range of gestational ages and adverse health outcomes and is present in sorted placenta villous cytotrophoblast cells. Integration of DNAm, genetic, and placental morphology data from the same individuals revealed ZNF300 methylation is also associated with placenta area, perimeter, and max diameter, genetic variants on chromosomes 5 and X, and may mediate the effects of genetic variation on placental area.132/132Primary AnalysisPrivate
Paternal sperm DNA methylation associated with early signs of autism risk in an autism-enriched cohortEpigenetic mechanisms such as altered DNA methylation have been suggested to play a role in autism, beginning with the classical association of Prader-Willi syndrome, an imprinting disorder, with autistic features. Here we tested for the relationship of paternal sperm DNA methylation with autism risk in offspring, examining an enriched-risk cohort of fathers of autistic children. We examined genome-wide DNA methylation (DNAm) in paternal semen biosamples obtained from an autism spectrum disorder (ASD) enriched-risk pregnancy cohort, the Early Autism Risk Longitudinal Investigation (EARLI) cohort, to estimate associations between sperm DNAm and prospective ASD development, using a 12-month ASD symptoms assessment, the Autism Observation Scale for Infants (AOSI). We analysed methylation data from 44 sperm samples run on the CHARM 3.0 array, which contains over 4 million probes (over 7 million CpG sites), including 30 samples also run on the Illumina Infinium HumanMethylation450 (450K) BeadChip platform (∼485 000 CpG sites). We also examined associated regions in an independent sample of post-mortem human brain ASD and control samples for which Illumina 450K DNA methylation data were available. Using region-based statistical approaches, we identified 193 differentially methylated regions (DMRs) in paternal sperm with a family-wise empirical P-value [family-wise error rate (FWER)] <0.05 associated with performance on the Autism Observational Scale for Infants (AOSI) at 12 months of age in offspring. The DMRs clustered near genes involved in developmental processes, including many genes in the SNORD family, within the Prader-Willi syndrome gene cluster. These results were consistent among the 75 probes on the Illumina 450K array that cover AOSI-associated DMRs from CHARM. Further, 18 of 75 (24%) 450K array probes showed consistent differences in the cerebellums of autistic individuals compared with controls. These data suggest that epigenetic differences in paternal sperm may contribute to autism risk in offspring, and provide evidence that directionally consistent, potentially related epigenetic mechanisms may be operating in the cerebellum of individuals with autism.44/44Primary AnalysisShared
Placental gross shape differences in a high autism risk cohort and the general population.A growing body of evidence suggests that prenatal environment is important in Autism Spectrum Disorder (ASD) etiology. In this study, we compare placental shape features in younger siblings of children with ASD, who themselves are at high ASD risk, to a sample of low risk peers. Digital photographs of the fetal placenta surface and of the sliced placental disk from 129 high ASD risk newborns and from 267 newborns in the National Children’s Study Vanguard pilot were analysed to extract comparable measures of placental chorionic surface shape, umbilical cord displacement and disk thickness. Placental thickness measures were moderately higher in siblings of ASD cases. The placentas of ASD-case siblings were also rounder and more regular in perimeter than general population placentas. After stratification by sex, these across-group differences persisted for both sexes but were more pronounced in females. No significant differences were observed in cord insertion measures. Variations in placental shape features are generally considered to reflect flexibility in placental growth in response to changes in intrauterine environment as the placenta establishes and matures. Reduced placental shape variability observed in high ASD risk siblings compared to low-risk controls may indicate restricted ability to compensate for intrauterine changes.30/30Primary AnalysisShared
* Data not on individual level

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