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/about/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

  • 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.

  • 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.

  • 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.

  • 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

  • Number of human subjects enrolled in an NIH-funded clinical research study. The data is provided in annual progress reports.

  • 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. 

  • 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.

  • 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.
  • 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.

  • An editable field with the title of the Collection, which is often the title of the grant associated with the Collection.

  • 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.

  • 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.

  • 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. 

  • 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. 

  • 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.

  • 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.

  • 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.  

  • The total number of unique subjects for whom data have been shared and are available for users with permission to access data.

NDA Help Center

Collection - Shared Data Tab

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

  • 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.  

  • 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.

  • 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

  • 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

  • A grouping of data by similar characteristics such as Clinical Assessments, Omics, or Neurosignal data.

  • 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.

NDA Help Center

fMRi

fMRI stands for functional magnetic resonance imaging. fMRI tests measure blood flow, providing detailed functional images of the brain or body. 

Acquisition
The Acquisition parameters needed for an experiment include the following:

The name of the experiment is required. Please be concise and specific as possible.
Following experiment name, selection boxes are provided for the Equipment, Software, or other items specific to the experiment type. At least one selection is required for each. If NDAR does not have the appropriate listing, select Add New to add the information provided. Following the selection boxes, provide additional information may be required depending on the experiment type. Any required items are denoted by an asterisk (*).

Block/Event Design
At least one block/event is required. Note that any fields denoted with an asterisk (*) are required. All data must be devoid of personally identifiable data, including the contents of any files attached to the experiment.

Note: To simplify the definition of multiple events, we provide an Import from XML function. This function supports importing data from all three experiment sections (Acquisition, Block/Event Design, and Post Processing), at this time files cannot be uploaded from XML A test format is provided here and our XML Schema Definition (xsd) can be found here.

Post Processing
If you have completed any post-processing on your data, please choose 'Yes' for Has Postprocessing? If not, select 'No'. Depending on this selection the remaining post-processing fields will be enabled (some of which will be required). If you are initially providing data you can select 'No', then return to the experiment to add post-processing steps at a later date when the data are being provided.

Please provide information about post-processing manipulations, i.e. artifact detection algorithms, segmentation used for post data collection, items denoted with an asterisk (*) are required.

Frequently Asked Questions

Glossary

  • This button will add all selections to the Filter Cart. 

  • This button will allow you to copy all of the Experiment details as a template for a new experiment. 

  • Adds all data from the current selections in a Collection or NDA Study to the Filter Cart.

  • This button will allow you to return to the Experiments tab. 

NDA Help Center

Collection - Submissions Tab

Users with permission to access Shared data in the Collection’s assigned Permission Group may use this tab. 

Here, you can:

  • Review your uploads to your Collection, monitor their status, and download them individually to verify their contents.
  • Download individual datasets as a secondary user of the data approved for access.
  • Identify and download datasets containing errors identified by NDA's QA/QC process for review and resolution.
  • Report suspected or discovered Personally Identifiable Information in a submission via the Actions column.

Frequently Asked Questions

Glossary

  • The default view of Datasets within a Collection's Submission tab.

  • A Submission Loading Status on a Collection's Submission Tab that indicates that an issue has prevented the successful loading of the submission.  Users should contact the NDA Help Desk for assistance at NDAHelp@mail.nih.gov.

  • The NDA has two Submission Cycles per year - January 15 and July 15.

  • An interface to notify NDA that data may not be submitted during the upcoming/current submission cycle.  

  • The unique and sequentially assigned ID for a submission (e.g. a discrete upload via the Validation and Upload Tool), which may contain any number of datafiles, Data Structures and/or Data Types, regardless of the Submission Loading Status. A single submission may be divided into multiple Datasets, which are based on Data Type.

  • The total number of unique subjects for whom data have been shared and are available for users with permission to access data.

  • The total number of unique subjects for whom data have been submitted, which includes data in both a Private State and a Shared State.

NDA Help Center

Collection - Publications Tab

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

  • Publications are considered relevant to a collection when the data shared is directly related to the project or collection.

  • PubMed, an online library containing journals, articles, and medical research. Sponsored by NiH and National Library of Medicine (NLM). 

Glossary

  • 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.

  • 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.

  • 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 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.

  • The PUBMed ID is the unique ID number for the publication as recorded in the PubMed database.  

  • 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.

NDA Help Center

EEG

EEG stands for electroencencephalogram and is a test used to measure electrical activity in the brain.

Acquisition
The Acquisition parameters needed for an experiment include the following:

Name of the experiment is required. Please be concise and specific as possible.
Following experiment name, selection boxes are provided for the Equipment, Software, or other items specific to experiment type. At least one selection is required for each. If NDAR does not have the appropriate listing, select Add New to add the information provided. Following the selection boxes, provide additional information may be required depending on experiment type. Any required items are denoted by an asterisk (*).

Block/Event Design
At least one block/event is required. Note that any fields denoted with an asterisk (*) are required. All data must be devoid of personally identifiable data, including the contents of any files attached to the experiment.

Note: To simplify definition of multiple events, we provide an Import from XML function. This function supports importing data from all three experiment sections (Acquisition, Block/Event Design, and Post Processing), at this time files cannot be uploaded from XML A test format is provided here and our XML Schema Definition (xsd) can be found here.

Post Processing
If you have completed any post processing on your data, please choose 'Yes' for Has Postprocessing? If not, select 'No'. Depending on this selection the remaining post processing fields will be enabled (some of which will be required). If you are initially providing data you can select 'No', then return to the experiment to add post processing steps at a later date when the data are being provided.

Please provide information about post-processing manipulations, i.e. artifact detection algorithms, segmentation used for post data collection, items denoted with an asterisk (*) are required.

Frequently Asked Questions

Glossary

  • This button will add all selections to the Filter Cart. 

  • This button will allow you to copy all of the Experiment details as a template for a new experiment. 

  • Adds all data from the current selections in a Collection or NDA Study to the Filter Cart.

  • This button will allow you to return to the Experiments 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.

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

  • 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.

  • The NDA Data Dictionary is comprised of electronic definitions known as Data Structures.

Glossary

  • 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.

  • 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.

  • 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

  • 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).

  • 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. 

  • 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+ 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 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 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.

  • 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.

  • The NDA has two Submission Cycles per year - January 15 and July 15.

  • An interface to notify NDA that data may not be submitted during the upcoming/current submission cycle.  

NDA Help Center

Collection - Permissions Tab

Collection Owners, Program Officers, and users with Administrator privileges may view this tab.

The available permission groups include:

  • Query: This read-only access is generally for NIH Program Officers
  • Submission: This will grant read access and allow the user to upload data and create experiment definitions. This is for the typical contributing personnel member.
  • Administrator: In addition to the access provided to Query and Submission users, Admins can also edit the Collection itself, create or edit the Data Expected list, and edit user permissions. This access is for the PI, data managers, and anyone they wish to delegate this to.

The PI has a special designation as the Collection Owner in addition to administrator access.

Frequently Asked Questions

  • Collection Owners and Admins may assign Collection Privileges to anyone.

  • Yes, you can assign various Privileges to other users with an NDA account.

  • If you are the Collection Owner or have Admin privileges, you can view and make changes to the list of individuals who have access to the Collection on the Collection's Permissions tab.  Information on users who have access to data Shared in your Collection because they were granted access to a Permission Group is not available.

  • Staff/collaborators who are working submitting data to the Collection, checking the quality of the data, and/or analyzing data should have access for the duration of the project until all data have been submitted, NDA Studies have been created for data used in publications, and/or a collaborative relationship with the user exists.  

  • The individual listed as an Investigator on the General tab of the NDA Collection will generally be able to provide a user access to the NDA Collection.  Additional users may also have this ability if granted Administrator access to an NDA Collection; however, these users are not viewable unless your account has access to the NDA Collection.  Given this, it is best to contact the Investigator to request access to the Collection.

  • Privileges that can be assigned to a user include:
    Submission allows a user to submit data to Collection
    Query allows the user to download data from Collection even when in a Private state
    Admin is both the Submission and Query Privilege + the ability to give privileges to other users.

  • You may have staff who are working on the submission of data or other activities associated with data sharing such as the definition of the Data Expected list or NDA Experiment creation.  Also, many projects have multiple performance sites and wish to share data among the site PIs.  Submitting to the NDA facilitates access by all investigators working on a project even before data have been shared with other users.  You can control who gets access to data while in a Private state.

Glossary

  • 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. 

  • 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.

NDA Help Center

Eye Tracking

EyeTracking tests follow the movement of the eye. The visual trajectory or focus can help determine predictions and assist in diagnoses. 

Acquisition
The Acquisition parameters needed for an experiment include the following:

The name of the experiment is required. Please be concise and specific as possible.
Following experiment name, selection boxes are provided for the Equipment, Software, or other items specific to the experiment type. At least one selection is required for each. If NDAR does not have the appropriate listing, select Add New to add the information provided. Following the selection boxes, provide additional information may be required depending on the experiment type. Any required items are denoted by an asterisk (*).

Block/Event Design
At least one block/event is required. Note that any fields denoted with an asterisk (*) are required. All data must be devoid of personally identifiable data, including the contents of any files attached to the experiment.

Note: To simplify the definition of multiple events, we provide an Import from XML function. This function supports importing data from all three experiment sections (Acquisition, Block/Event Design, and Post Processing), at this time files cannot be uploaded from XML A test format is provided here and our XML Schema Definition (xsd) can be found here.

Post Processing
If you have completed any post-processing on your data, please choose 'Yes' for Has Postprocessing? If not, select 'No'. Depending on this selection the remaining post-processing fields will be enabled (some of which will be required). If you are initially providing data you can select 'No', then return to the experiment to add post-processing steps at a later date when the data are being provided.

Please provide information about post-processing manipulations, i.e. artifact detection algorithms, segmentation used for post data collection, items denoted with an asterisk (*) are required.

Frequently Asked Questions

Glossary

  • This button will add all selections to the Filter Cart. 

  • This button will allow you to copy all of the Experiment details as a template for a new experiment. 

  • Adds all data from the current selections in a Collection or NDA Study to the Filter Cart.

  • This button will allow you to return to the Experiments tab. 

NDA Help Center

Collection - Experiments Tab

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

  • 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

  • An Experiment must be Approved before data using the associated Experiment_ID may be uploaded.

  • 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.

NDA Help Center

Omics

Omics is a collective group of technologies, related to a field of study in Biology such as Genomics or proteomics. 

Experiment Parameters

To define an Omics experiment, provide a meaningful name and select a single molecule. The standard molecules are listed. However, if you are doing proteomic or environmental experiments, simply “Add New” and the new selection will be created. Only one value for molecule is permitted.

Next the technology (box 2) associated with the molecule will be presented along with its application. Again, only one selection is possible. If you wish to see all of NDAR’s options for any one box, Select “Show All”.

Platform

Continue to select the Platform (box 3).

Extraction

Next, the Extraction Protocol (box 4) and Kits (box 5) are presented based upon the Molecule selected and the Processing Protocol (box 6) and Kits (box 7) are presented based upon the Molecule and Technology Application (Box 1 and 2)

Processing

Note that for each of these (boxes 4, 5, 6, and 7) multiple selections are possible.

Additional Information

Lastly, the Software (box 8) and Equipment (box 9) is expected.

 

Once saved, the experiment will be associated with the Collection and by using the returned Experiment_ID, the NDA makes it possible to associate the experiment meta data directly with the data from the experiment.

Frequently Asked Questions

Glossary

  • This button will add all selections to the Filter Cart. 

  • This button will allow you to copy all of the Experiment details as a template for a new experiment. 

  • Adds all data from the current selections in a Collection or NDA Study to the Filter Cart.

  • This button will allow you to return to the Experiments 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

  • Studies are associated to the Collection automatically when the data is defined in the Study. 

Glossary

  • 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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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
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Collection Summary Collection Charts
Collection Title Collection Investigators Collection Description
ABCD Neurocognitive Prediction Challenge 2019
Kilian Pohl and Wes Thompson; Ehsan Adeli, Stanford University; Bennett A. Landman, Vanderbilt University; Marius G. Linguraru, Children’s National Health System; Susan F. Tapert, University of California – San Diego 
Phenotype data were derived from the Adolescent Brain Cognitive Development 1.1 Release (http://dx.doi.org/10.15154/1460410, accessed on or before November 15, 2018). Raw imaging data were retrieved from the Fast Track DICOM share in the Adolescent Brain Cognitive Development Study Collection 2573 https://ndar.nih.gov/edit_collection.html?id=2573, accessed on or before November 15, 2018). The individual-level imaging phenotype data in this Collection was computed by a custom processing pipeline developed by the organizers of the ABCD Prediction Challenge. The imaging phenotype data may therefore differ from the values shared by the ABCD Study investigators in Release 1.1 or future releases. The website provides a detailed description of the processing. When using the data in publications, the Data Supplement of "Pfefferbaum et al., Altered Brain Developmental Trajectories in Adolescents After Initiating Drinking. Am J Psychiatry, 175(4), pp. 370-380, 2018" should be cited as a description of the processing pipeline.
Adolescent Brain Cognitive Development
12/11/2018
Enrolling
Shared
No
8,670
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NIH - Extramural None

https://sibis.sri.com/abcd-np-challenge Methods ABCD NP Challenge site General Public
5U24DA041123-04 Findings ABCD-USA CONSORTIUM: DATA ANALYSIS CENTER General Public


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NDA Help Center

Collection - General Tab

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

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

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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.

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  • 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.
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    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.
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    Number of human subjects enrolled in an NIH-funded clinical research study. The data is provided in annual progress reports.
IDNameCreated DateStatusType
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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.

Brain Tissue Segmentaion Volumetrix Imaging 8670
Processed MRI Data Imaging 8670

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
31520123Create StudyDiscovery of shared genomic loci using the conditional false discovery rate approach.Human geneticsSmeland OB, Frei O, Shadrin A, O'Connell K, Fan CC, Bahrami S, Holland D, Djurovic S, Thompson WK, Dale AM, Andreassen OASeptember 2019Not Determined
31464996Create StudyThe emerging pattern of shared polygenic architecture of psychiatric disorders, conceptual and methodological challenges.Psychiatric geneticsSmeland, Olav B; Frei, Oleksandr; Fan, Chun-Chieh; Shadrin, Alexey; Dale, Anders M; Andreassen, Ole AOctober 2019Not Determined
30610197Create StudyGenome-wide analysis reveals extensive genetic overlap between schizophrenia, bipolar disorder, and intelligence.Molecular psychiatrySmeland, Olav B; Bahrami, Shahram; Frei, Oleksandr; Shadrin, Alexey; O'Connell, Kevin; Savage, Jeanne; Watanabe, Kyoko; Krull, Florian; Bettella, Francesco; Steen, Nils Eiel; Ueland, Torill; Posthuma, Danielle; Djurovic, Srdjan; Dale, Anders M; Andreassen, Ole AApril 2020Not Determined
30578952Create StudySexual minority children: Mood disorders and suicidality disparities.Journal of affective disordersBlashill, Aaron J; Calzo, Jerel PMarch 2019Not Determined
30339913Create StudyScreen media activity and brain structure in youth: Evidence for diverse structural correlation networks from the ABCD study.NeuroImagePaulus, Martin P; Squeglia, Lindsay M; Bagot, Kara; Jacobus, Joanna; Kuplicki, Rayus; Breslin, Florence J; Bodurka, Jerzy; Morris, Amanda Sheffield; Thompson, Wesley K; Bartsch, Hauke; Tapert, Susan FJanuary 2019Not Determined
30268792Create StudyAssociations between 24 hour movement behaviours and global cognition in US children: a cross-sectional observational study.The Lancet. Child & adolescent healthWalsh JJ, Barnes JD, Cameron JD, Goldfield GS, Chaput JP, Gunnell KE, Ledoux AA, Zemek RL, Tremblay MSNovember 2018Not Determined
29874361Create StudyAssessment of the Prodromal Questionnaire-Brief Child Version for Measurement of Self-reported Psychoticlike Experiences in Childhood.JAMA psychiatryKarcher, Nicole R; Barch, Deanna M; Avenevoli, Shelli; Savill, Mark; Huber, Rebekah S; Simon, Tony J; Leckliter, Ingrid N; Sher, Kenneth J; Loewy, Rachel LAugust 2018Not Determined
29627333Create StudyAssessment of culture and environment in the Adolescent Brain and Cognitive Development Study: Rationale, description of measures, and early data.Developmental cognitive neuroscienceZucker RA, Gonzalez R, Feldstein Ewing SW, Paulus MP, Arroyo J, Fuligni A, Morris AS, Sanchez M, Wills TAugust 2018Not Determined
29567376Create StudyThe Adolescent Brain Cognitive Development (ABCD) study: Imaging acquisition across 21 sites.Developmental cognitive neuroscienceCasey BJ, Cannonier T, Conley MI, Cohen AO, Barch DM, Heitzeg MM, Soules ME, Teslovich T, Dellarco DV, Garavan H, Orr CA, Wager TD, Banich MT, Speer NK, Sutherland MT, Riedel MC, Dick AS, Bjork JM, Thomas KM, Chaarani B, Mejia MH, Hagler DJ, Daniela Cornejo M, Sicat CS, Harms MP, et al.August 2018Not Determined
29556250Create StudyDetermining Genetic Causal Variants Through Multivariate Regression Using Mixture Model Penalty.Frontiers in geneticsSundar, V S; Fan, Chun-Chieh; Holland, Dominic; Dale, Anders MJanuary 2018Not Determined
29437252Create StudyHigh temporal resolution motion estimation using a self-navigated simultaneous multi-slice echo planar imaging acquisition.Journal of magnetic resonance imaging : JMRITeruel, Jose R; Kuperman, Joshua M; Dale, Anders M; White, Nathan SFebruary 2018Not Determined
28828560Create StudyLinking tuberous sclerosis complex, excessive mTOR signaling, and age-related neurodegeneration: a new association between TSC1 mutation and frontotemporal dementia.Acta neuropathologicaOlney, Nicholas T; Alquezar, Carolina; Ramos, Eliana Marisa; Nana, Alissa L; Fong, Jamie C; Karydas, Anna M; Taylor, Joanne B; Stephens, Melanie L; Argouarch, Andrea R; Van Berlo, Victoria A; Dokuru, Deepika R; Sherr, Elliott H; Jicha, Gregory A; Dillon, William P; Desikan, Rahul S; De May, Mary; Seeley, William W; Coppola, Giovanni; Miller, Bruce L; Kao, Aimee WNovember 2017Not Determined
28803940Create StudyReal-time motion analytics during brain MRI improve data quality and reduce costs.NeuroImageDosenbach NUF, Koller JM, Earl EA, Miranda-Dominguez O, Klein RL, Van AN, Snyder AZ, Nagel BJ, Nigg JT, Nguyen AL, Wesevich V, Greene DJ, Fair DANovember 2017Not Determined
28716389Create StudyBiomedical ethics and clinical oversight in multisite observational neuroimaging studies with children and adolescents: The ABCD experience.Developmental cognitive neuroscienceClark, Duncan B; Fisher, Celia B; Bookheimer, Susan; Brown, Sandra A; Evans, John H; Hopfer, Christian; Hudziak, James; Montoya, Ivan; Murray, Margaret; Pfefferbaum, Adolf; Yurgelun-Todd, DeborahAugust 2018Not Determined
28279988Create StudyEntorhinal Cortex: Antemortem Cortical Thickness and Postmortem Neurofibrillary Tangles and Amyloid Pathology.AJNR. American journal of neuroradiologyThaker, A A; Weinberg, B D; Dillon, W P; Hess, C P; Cabral, H J; Fleischman, D A; Leurgans, S E; Bennett, D A; Hyman, B T; Albert, M S; Killiany, R J; Fischl, B; Dale, A M; Desikan, R SMay 2017Not Determined
28271184Create StudyShared genetic risk between corticobasal degeneration, progressive supranuclear palsy, and frontotemporal dementia.Acta neuropathologicaYokoyama JS, Karch CM, Fan CC, Bonham LW, Kouri N, Ross OA, Rademakers R, Kim J, Wang Y, Höglinger GU, Müller U, Ferrari R, Hardy J, Momeni P, Sugrue LP, Hess CP, James Barkovich A, Boxer AL, Seeley WW, Rabinovici GD, Rosen HJ, Miller BL, Schmansky NJ, Fischl B, et al.May 2017Not Determined
27899424Create StudyGenetic architecture of sporadic frontotemporal dementia and overlap with Alzheimer''s and Parkinson''s diseases.Journal of neurology, neurosurgery, and psychiatryFerrari, Raffaele; Wang, Yunpeng; Vandrovcova, Jana; Guelfi, Sebastian; Witeolar, Aree; Karch, Celeste M; Schork, Andrew J; Fan, Chun C; Brewer, James B; International FTD-Genomics Consortium (IFGC),; International Parkinson's Disease Genomics Consortium (IPDGC),; International Genomics of Alzheimer's Project (IGAP),; Momeni, Parastoo; Schellenberg, Gerard D; Dillon, William P; Sugrue, Leo P; Hess, Christopher P; Yokoyama, Jennifer S; Bonham, Luke W; Rabinovici, Gil D; Miller, Bruce L; Andreassen, Ole A; Dale, Anders M; Hardy, John; Desikan, Rahul SFebruary 2017Not Determined
27862206Create StudyMalformations of cortical development.Annals of neurologyDesikan RS, Barkovich AJDecember 2016Not Determined

Relevant Publications
PubMed IDStudyTitleJournalAuthorsDate
No records found.

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
Data ExpectedTargeted EnrollmentInitial SubmissionSubjects SharedStatus
Processed MRI Data info icon
3,00012/15/2018
8,670
Approved
Evaluated Data info icon
3,00012/13/2018
8,670
Approved
Structure not yet defined
No Status history for this Data Expected has been recorded yet

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
Environmental Risk Factors and Psychotic-like Symptoms in Children Aged 9-11Objective: Research implicates environmental risk factors, including correlates of urbanicity, deprivation, and environmental toxins, in psychotic-like experiences (PLEs). The current study examined associations between several types of environmental risk factors and PLEs in school-age children, whether these associations were specific to PLEs or generalized to other psychopathology, and examined possible neural mechanisms for significant associations. Method: The current study used data from 10,328 9-11-year-olds from the Adolescent Brain Cognitive Development (ABCD) study. Hierarchical linear models examined associations between PLEs and geocoded environmental risk factors, and whether associations generalized to internalizing/externalizing symptoms. Mediation models examined whether structural MRI abnormalities (e.g., intracranial volume) mediated associations between PLEs and environmental risk factors. Results: The results found specific types of environmental risk factors, namely measures of urbanicity (i.e., drug offense exposure, less perception of neighborhood safety), deprivation (including overall deprivation, rate of poverty, fewer years at residence), and lead exposure risk, were associated with PLEs. These associations showed evidence of stronger associations with PLEs than internalizing/externalizing symptoms (especially overall deprivation, poverty, drug offense exposure, and lead exposure risk). There was evidence that brain volume mediated between 11-25% of the associations between poverty, perception of neighborhood safety, and lead exposure risk with PLEs. Conclusions: These results are the first to find support for neural measures partially mediating the association between PLEs and environmental exposures. Furthermore, the current study replicated and extended recent findings of the association between PLEs and environmental exposures, finding evidence for specific associations with correlates of urbanicity, deprivation, and lead exposure risk. 8669/11898Secondary AnalysisPrivate
P Factor Resting StateBACKGROUND Convergent research identifies a general factor (“P factor”) that confers transdiagnostic risk for psychopathology. However, brain functional connectivity patterns that underpin the P factor remain poorly understood, especially at the transition to adolescence when many serious mental disorders have their onset. OBJECTIVE: Identify a distributed connectome-wide neurosignature of the P factor and assess the generalizability of this neurosignature in held out samples. DESIGN, SETTING, AND PARTICIPANTS This study used data from the full baseline wave of the Adolescent Brain and Cognitive Development (ABCD) national consortium study, a prospective, population-based study of 11,875 9- and 10-year olds. Data for this study were collected from September 1, 2016 to November 15, 2018 at 21 research sites across the United States. MAIN OUTCOMES AND MEASURES We produced whole brain functional connectomes for 5,880 youth with high quality resting state scans. We then constructed a low rank basis set of 250 components that captures interindividual connectomic differences. Multi-level regression modeling was used to link these components to the P factor, and leave-one-site-out cross-validation was used to assess generalizability of P factor neurosignatures to held out subjects across 19 ABCD sites. RESULTS The set of 250 connectomic components was highly statistically significantly related to the P factor, over and above nuisance covariates alone (ANOVA nested model comparison, incremental R-squared 6.05%, χ2(250) = 412.1, p<4.6x10-10). In addition, two individual connectomic components were statistically significantly related to the P factor after Bonferroni correction for multiple comparisons (t(5511)= 4.8, p<1.4x10-06; t(5121)= 3.9, p<9.7x10-05). Functional connections linking control networks and default mode network were prominent in the P factor neurosignature. In leave-one-site-out cross-validation, the P factor neurosignature generalized to held out subjects (average correlation between actual and predicted P factor scores across 19 held out sites=0.13; pPERMUTATION<0.0001). Additionally, results remained significant after a number of robustness checks. CONCLUSIONS AND RELEVANCE: The general factor of psychopathology is associated with connectomic alterations involving control networks and default mode network. Brain imaging combined with network neuroscience can identify distributed and generalizable signatures of transdiagnostic risk for psychopathology during emerging adolescence. 4799/11878Secondary AnalysisPrivate
ABCD Dissertation StudyAssessing longitudinal trajectories of resting state functional connectivity and psychopathology, as they relate to social support. 8661/11876Secondary AnalysisPrivate
Structural alterations in the frontal lobe mediate the impact of snoring and associated symptoms on childhood behaviorParents frequently report behavioral problems among children who snore. Our understanding of the relationship between symptoms of obstructive sleep disordered breathing (oSDB)—e.g. snoring—and childhood behavioral problems attributable to brain structural alterations is limited. Therefore, we examined the relationships among oSDB symptoms, problem behaviors and brain morphometry in a diverse dataset comprising 10,140 preadolescents. We demonstrate that the symptoms of oSDB strongly predicted composite and domain-specific behavioral measures. Cortical morphometric alterations demonstrating the strongest negative associations with oSDB symptoms were most pronounced within the frontal lobe. The relationships between oSDB symptoms and behavioral measures were mediated by significantly smaller volumes of multiple frontal lobe regions. These results provide population-level evidence for regional structural alterations in cortical gray matter accompanying problem behaviors in children with oSDB. Timely recognition and treatment of oSDB may ameliorate these changes and the associated neurobehavioral morbidity while the frontal lobe still retains age-dependent plasticity.6904/11752Secondary AnalysisPrivate
The Emotional Word-Emotional Face Stroop task in the ABCD study: Psychometric validation and associations with measures of cognition and psychopathologyCharacterizing the interactions among attention, cognitive control, and emotion during adolescence may provide important insights into why this critical developmental period coincides with a dramatic increase in risk for psychopathology. However, it has proven challenging to develop a single neurobehavioral task that simultaneously engages and differentially measures these diverse domains. In the current study, we describe properties of performance on the Emotional Word-Emotional Face Stroop (EWEFS) task in the Adolescent Brain Cognitive Development (ABCD) Study, a task that allows researchers to concurrently measure processing speed/attentional vigilance (i.e., performance on congruent trials), inhibitory control (i.e., Stroop interference effect), and emotional information processing (i.e., difference in performance on trials with happy as compared to angry distracting faces). We first demonstrate that the task manipulations worked as designed and that Stroop performance is associated with multiple cognitive constructs derived from different measures at a prior time point. We then show that Stroop metrics tapping these three domains are preferentially associated with aspects of externalizing psychopathology and inattention. These results highlight the potential of the EWEFS task to help elucidate the longitudinal dynamics of attention, inhibitory control, and emotion across adolescent development, dynamics which may be altered by level of psychopathology.8229/11234Secondary AnalysisPrivate
Neuroanatomical correlates of impulsive traits in children aged 9 to 10Impulsivity refers to a set of traits that are generally negatively related to critical domains of adaptive functioning and are core features of numerous psychiatric disorders. The current study examined the gray and white matter correlates of five impulsive traits measured using an abbreviated version of the UPPS-P (Urgency, (lack of) Premeditation, (lack of) Perseverance, Sensation-Seeking, Positive Urgency) impulsivity scale in children aged 9 to 10 (N = 11,052) from the Adolescent Brain and Cognitive Development (ABCD) study. Linear mixed effect models and elastic net regression were used to examine features of regional gray matter and white matter tractography most associated with each UPPS-P scale; intraclass correlations were computed to examine the similarity of the neuroanatomical correlates among the scales. Positive Urgency showed the most robust association with neuroanatomy, with similar but less robust associations found for Negative Urgency. Perseverance showed little association with neuroanatomy. Premeditation and Sensation Seeking showed intermediate associations with neuroanatomy. Critical regions across measures include the dorsolateral prefrontal cortex, lateral temporal cortex, and orbitofrontal cortex; critical tracts included the superior longitudinal fasciculus and inferior fronto-occipital fasciculus. Negative Urgency and Positive Urgency showed the greatest neuroanatomical similarity. Some UPPS-P traits share neuroanatomical correlates, while others have distinct correlates or essentially no relation to neuroanatomy. Neuroanatomy tended to account for relatively little variance in UPPS-P traits (i.e., Model R2 < 1%) and effects were spread throughout the brain, highlighting the importance of well powered samples.8253/11051Secondary AnalysisShared
Differentiating distinct and converging neural correlates of types of systemic environmental exposuresBackground: Systemic environmental disadvantage relates to a host of health and functional outcomes. Specific structural factors have seldom been linked to neural structure, however, clouding understanding of putative mechanisms. Examining relations during childhood/preadolescence, a dynamic period of neurodevelopment, could aid bridge this gap. Methods: A total of 10,213 youth were recruited from the Adolescent Brain and Cognitive Development study. Self-report and objective measures (Census and Federal bureau of investigation metrics extracted using geocoding) of environmental exposures were used, including stimulation indexing lack of safety and high attentional demands, discrepancy indexing social exclusion/lack of belonging, and deprivation indexing lack of environmental enrichment. Environmental measures were related to cortical thickness, surface area and subcortical volume regions, controlling for other environmental exposures and accounting for other brain regions. Results: Self-report (|β|=0.04-0.09) and objective (|β|=0.02-0.06) environmental domains related to area/thickness in overlapping (e.g. insula, caudal anterior cingulate), and unique regions (e.g. for discrepancy, rostral anterior and isthmus cingulate, implicated in socioemotional functions; for stimulation, precuneus, critical for cue reactivity and integration of environmental cues, and for deprivation, superior frontal, integral to executive functioning). For stimulation and discrepancy exposures, self-report and objective measures showed similarities in correlate regions, while deprivation exposures evidenced distinct correlates for self-report and objective measures. Conclusions: Results represent a necessary step toward broader work aimed at establishing mechanisms and correlates of structural disadvantage, highlighting the relevance of going beyond aggregate models by considering types of environmental factors, and the need to incorporate both subjective and objective measurements in these efforts. 6961/9043Primary AnalysisShared
Microstructural development from 9 to 14 years: Evidence from the ABCD StudyDuring late childhood behavioral changes, such as increased risk-taking and emotional reactivity, have been associated with the maturation of cortico-cortico and cortico-subcortical circuits. Understanding microstructural changes in both white matter and subcortical regions may aid our understanding of how individual differences in these behaviors emerge. Restriction spectrum imaging (RSI) is a framework for modelling diffusion-weighted imaging that decomposes the diffusion signal from a voxel into hindered, restricted, and free compartments. This yields greater specificity than conventional methods of characterizing diffusion. Using RSI, we quantified voxelwise restricted diffusion across the brain and measured age associations in a large sample (n = 8086) from the Adolescent Brain and Cognitive Development (ABCD) study aged 9–14 years. Older participants showed a higher restricted signal fraction across the brain, with the largest associations in subcortical regions, particularly the basal ganglia and ventral diencephalon. Importantly, age associations varied with respect to the cytoarchitecture within white matter fiber tracts and subcortical structures, for example age associations differed across thalamic nuclei. This suggests that age-related changes may map onto specific cell populations or circuits and highlights the utility of voxelwise compared to ROI-wise analyses. Future analyses will aim to understand the relevance of this microstructural developmental for behavioral outcomes.7288/9040Secondary AnalysisPrivate
Individual Differences in Cognitive Performance Are Better Predicted by Global Rather Than Localized BOLD Activity Patterns Across the CortexDespite its central role in revealing the neurobiological mechanisms of behavior, neuroimaging research faces the challenge of producing reliable biomarkers for cognitive processes and clinical outcomes. Statistically significant brain regions, identified by mass univariate statistical models commonly used in neuroimaging studies, explain minimal phenotypic variation, limiting the translational utility of neuroimaging phenotypes. This is potentially due to the observation that behavioral traits are influenced by variations in neuroimaging phenotypes that are globally distributed across the cortex and are therefore not captured by thresholded, statistical parametric maps commonly reported in neuroimaging studies. Here, we developed a novel multivariate prediction method, the Bayesian polyvertex score, that turns a unthresholded statistical parametric map into a summary score that aggregates the many but small effects across the cortex for behavioral prediction. By explicitly assuming a globally distributed effect size pattern and operating on the mass univariate summary statistics, it was able to achieve higher out-of-sample variance explained than mass univariate and popular multivariate methods while still preserving the interpretability of a generative model. Our findings suggest that similar to the polygenicity observed in the field of genetics, the neural basis of complex behaviors may rest in the global patterning of effect size variation of neuroimaging phenotypes, rather than in localized, candidate brain regions and networks.6626/8892Primary AnalysisPrivate
Predicting Fluid Intelligence of Children using T1-weighted MR Images and a StackNetIn this work, we utilize T1-weighted MR images and StackNet to predict fluid intelligence in adolescents. Our framework includes feature extraction, feature normalization, feature denoising, feature selection, training a StackNet, and predicting fluid intelligence. The extracted feature is the distribution of different brain tissues in different brain parcellation regions. The proposed StackNet consists of three layers and 11 models. Each layer uses the predictions from all previous layers including the input layer. The proposed StackNet is tested on a public benchmark Adolescent Brain Cognitive Development Neurocognitive Prediction Challenge 2019 and achieves a mean squared error of 82.42 on the combined training and validation set with 10-fold cross-validation. In addition, the proposed StackNet also achieves a mean squared error of 94.25 on the testing data. The source code is available on GitHub.8670/8670Secondary AnalysisShared
Multimethod investigation of the neurobiological basis of ADHD symptomatology in children aged 9-10: baseline data from the ABCD studyAttention deficit/hyperactivity disorder is associated with numerous neurocognitive deficits, including poor working memory and difficulty inhibiting undesirable behaviors that cause academic and behavioral problems in children. Prior work has attempted to determine how these differences are instantiated in the structure and function of the brain, but much of that work has been done in small samples, focused on older adolescents or adults, and used statistical approaches that were not robust to model overfitting. The current study used cross-validated elastic net regression to predict a continuous measure of ADHD symptomatology using brain morphometry and activation during tasks of working memory, inhibitory control, and reward processing, with separate models for each MRI measure. The best model using activation during the working memory task to predict ADHD symptomatology had an out-of-sample R2 = 2% and was robust to residualizing the effects of age, sex, race, parental income and education, handedness, pubertal status, and internalizing symptoms from ADHD symptomatology. This model used reduced activation in task positive regions and reduced deactivation in task negative regions to predict ADHD symptomatology. The best model with morphometry alone predicted ADHD symptomatology with an R2 = 1% but this effect dissipated when including covariates. The inhibitory control and reward tasks did not yield generalizable models. In summary, these analyses show, with a large and well-characterized sample, that the brain correlates of ADHD symptomatology are modest in effect size and captured best by brain morphometry and activation during a working memory task.5837/7999Secondary AnalysisShared
Stability of polygenic scores across discovery genome-wide association studiesPolygenic scores (PGS) are commonly evaluated in terms of their predictive accuracy at the population level by the proportion of phenotypic variance they explain. To be useful for precision medicine applications, they also need to be evaluated at the individual level when phenotypes are not necessarily already known. We investigated the stability of PGS in European American (EUR) and African American (AFR)-ancestry individuals from the Philadelphia Neurodevelopmental Cohort and the Adolescent Brain Cognitive Development study using different discovery genome-wide association study (GWAS) results for post-traumatic stress disorder (PTSD), type 2 diabetes (T2D), and height. We found that pairs of EUR-ancestry GWAS for the same trait had genetic correlations >0.92. However, PGS calculated from pairs of same-ancestry and different-ancestry GWAS had correlations that ranged from <0.01 to 0.74. PGS stability was greater for height than for PTSD or T2D. A series of height GWAS in the UK Biobank suggested that correlation between PGS is strongly dependent on the extent of sample overlap between the discovery GWAS. Focusing on the upper end of the PGS distribution, different discovery GWAS do not consistently identify the same individuals in the upper quantiles, with the best case being 60% of individuals above the 80th percentile of PGS overlapping from one height GWAS to another. The degree of overlap decreases sharply as higher quantiles, less heritable traits, and different-ancestry GWAS are considered. PGS computed from different discovery GWAS have only modest correlation at the individual level, underscoring the need to proceed cautiously with integrating PGS into precision medicine applications.4415/5962Secondary AnalysisShared
ABCD Neurocognitive Prediction Challenge 2019: Test SetThe test data set for the ABCD Neurocognitive Prediction Challenge 2019 contains skull stripped and segmented T1-weighted MRIs, and volumetric brain measures of 3648 participants of the ABCD study. https://sibis.sri.com/abcd-np-challenge provides a detailed description about the processing. When using the data in publications, the Data Supplement of "Pfefferbaum et al., Altered Brain Developmental Trajectories in Adolescents After Initiating Drinking. Am J Psychiatry, 175(4), pp. 370-380, 2018" for should be cited as description of the processing pipeline. The data in this Study were derived from the Adolescent Brain Cognitive Development 1.1 Release (http://dx.doi.org/10.15154/1460410, accessed on or before November 15, 2018) and the Fast Track DICOM share in the Adolescent Brain Cognitive Development Study Collection 2573 (https://ndar.nih.gov/edit_collection.html?id=2573, accessed on or before November 15, 2018). The individual-level imaging phenotype data in this Collection was computed by a custom processing pipeline developed by the organizers of the ABCD Prediction Challenge. The imaging phenotype data may therefore differ from the values shared by the ABCD Study investigators in Release 1.1 or future releases4515/4516Secondary AnalysisShared
Associations among household and neighborhood socioeconomic disadvantages, resting-state frontoamygdala connectivity, and internalizing symptoms in youthExposure to socioeconomic disadvantages (SED) can have negative impacts on mental health, yet SED is a multifaceted construct and the precise processes by which SED confer deleterious effects are less clear. Using a large and diverse sample of preadolescents (ages 9-10 at baseline; N = 4,038; 49% female) from the Adolescent Brain Cognitive Development Study, we examined associations among SED at both household (i.e., income-to-needs and material hardship) and neighborhood (i.e., area deprivation and neighborhood unsafety) levels, frontoamygdala resting-state functional connectivity, and internalizing symptoms at baseline and 1-year follow-up. SED were positively associated with internalizing symptoms at baseline, and indirectly predicted symptoms one year later through elevated symptoms at baseline. At the household level, youth in households characterized by higher disadvantage (i.e., lower income-to-needs ratio) exhibited more strongly negative frontoamygdala coupling, particularly between the bilateral amygdala and medial orbitofrontal (mOFC) regions within the Frontoparietal Network. While more strongly positive amygdala-mOFC coupling was associated with higher levels of internalizing symptoms at baseline and 1-year follow-up, it did not mediate the association between income-to-needs ratio and internalizing symptoms. However, at the neighborhood level, amygdala-mOFC functional coupling moderated the effect of neighborhood deprivation on internalizing symptoms. Specifically, higher neighborhood deprivation was associated with higher internalizing symptoms for youth with more strongly positive connectivity, but not for youth with more strongly negative connectivity, suggesting a potential buffering effect. Findings highlight the importance of capturing multileveled socioecological contexts in which youth develop to identify youth who are most likely to benefit from early interventions. Exposure to socioeconomic disadvantages (SED) can have negative impacts on mental health, yet SED is a multifaceted construct and the precise processes by which SED confer deleterious effects are less clear. Using a large and diverse sample of preadolescents (ages 9-10 at baseline; N = 4,038; 49% female) from the Adolescent Brain Cognitive Development Study, we examined associations among SED at both household (i.e., income-to-needs and material hardship) and neighborhood (i.e., area deprivation and neighborhood unsafety) levels, frontoamygdala resting-state functional connectivity, and internalizing symptoms at baseline and 1-year follow-up. SED were positively associated with internalizing symptoms at baseline, and indirectly predicted symptoms one year later through elevated symptoms at baseline. At the household level, youth in households characterized by higher disadvantage (i.e., lower income-to-needs ratio) exhibited more strongly negative frontoamygdala coupling, particularly between the bilateral amygdala and medial orbitofrontal (mOFC) regions within the Frontoparietal Network. While more strongly positive amygdala-mOFC coupling was associated with higher levels of internalizing symptoms at baseline and 1-year follow-up, it did not mediate the association between income-to-needs ratio and internalizing symptoms. However, at the neighborhood level, amygdala-mOFC functional coupling moderated the effect of neighborhood deprivation on internalizing symptoms. Specifically, higher neighborhood deprivation was associated with higher internalizing symptoms for youth with more strongly positive connectivity, but not for youth with more strongly negative connectivity, suggesting a potential buffering effect. Findings highlight the importance of capturing multileveled socioecological contexts in which youth develop to identify youth who are most likely to benefit from early interventions. 4014/4163Secondary AnalysisPrivate
Fluid Intelligence Classification Based On Cortical WM/GM Contrast, Cortical Thickness and VolumetryFluid intelligence refers to the ability of solving and reasoning problems. The recent Neurocognitive Prediction Challenge (ABCD-NP-Challenge 2019) demonstrated that predicting residual fluid intelligence from structural MR images is indeed challenging; the correlation between predicted and actual intelligence scores was extremely weak. The correlation was low for all entries including the winner (r = 0.03). In order to better understand this apparent non-relationship we (i) considered a simplified version of the prediction problem by grouping the top and bottom 10% of children on fluid intelligence scores and attempting to classify these two groups; (ii) tested different classification methods on this problem; and (iii) investigated the role that scanner heterogeneity might be playing in producing these poor predictions by using either data from all scanners or a single scanner.4153/4153Secondary AnalysisPrivate
Childhood obesity, cortical structure and executive function in healthy childrenThe development of executive function is linked to maturation of prefrontal cortex in childhood. Childhood obesity has been associated with changes in brain structure, particularly in prefrontal cortex, as well as deficits in executive functions. We aimed to determine whether differences in cortical structure mediate the relationship between executive function and childhood obesity. We analysed MR-derived measures of cortical thickness for 2,700 children between the ages of 9-11 years, recruited as part of the NIH ABCD study. We related our findings to measures of executive function and body mass index (BMI). In our analysis, increased BMI was associated with significantly reduced mean cortical thickness, as well as specific bilateral reduced cortical thickness in prefrontal cortical regions. This relationship remained after accounting for age, sex, race, parental education, household income, birth-weight and in-scanner motion. Increased BMI was also associated with lower executive function. Reduced cortical thickness was found to mediate the relationship between BMI and executive function such that reduced thickness in the rostral medial and superior frontal cortex, the inferior frontal gyrus and the lateral orbitofrontal cortex accounted for partial reductions in executive function. These results suggest that childhood obesity is associated with compromised executive function. This relationship may be partly explained by BMI-associated reduced cortical thickness in the prefrontal cortex. 3766/3921Secondary AnalysisShared
ABCD Neurocognitive Prediction Challenge 2019: Training SetTraining data set for the ABCD Neurocognitive Prediction Challenge 2019 containing skull stripped and segmented T1-weighted MRIs, volumetric brain measures, and residual fluid intelligence scores of 3739 participants of the ABCD study. https://sibis.sri.com/abcd-np-challenge provides a detailed description about the processing. When using the data in publications, the Data Supplement of "Pfefferbaum et al., Altered Brain Developmental Trajectories in Adolescents After Initiating Drinking. Am J Psychiatry, 175(4), pp. 370-380, 2018" for should be cited as description of the processing pipeline. The data in this Study were derived from the Adolescent Brain Cognitive Development 1.1 Release (http://dx.doi.org/10.15154/1460410, accessed on or before November 15, 2018) and the Fast Track DICOM share in the Adolescent Brain Cognitive Development Study Collection 2573 (https://ndar.nih.gov/edit_collection.html?id=2573, accessed on or before November 15, 2018). The individual-level imaging phenotype data in this Collection was computed by a custom processing pipeline developed by the organizers of the ABCD Prediction Challenge. The imaging phenotype data may therefore differ from the values shared by the ABCD Study investigators in Release 1.1 or future releases3739/3739Secondary AnalysisShared
What Is the Link Between Attention-Deficit/Hyperactivity Disorder and Sleep Disturbance? A Multimodal Examination of Longitudinal Relationships and Brain Structure Using Large-Scale Population-Based CohortsBackground: Attention-deficit/hyperactivity disorder (ADHD) comorbid with sleep disturbances can produce profound disruption in daily life and negatively impact quality of life of both the child and the family. However, the temporal relationship between ADHD and sleep impairment is unclear, as are underlying common brain mechanisms. Methods: This study used data from the Quebec Longitudinal Study of Child Development (n = 1601, 52% female) and the Adolescent Brain Cognitive Development Study (n = 3515, 48% female). Longitudinal relationships between symptoms were examined using cross-lagged panel models. Gray matter volume neural correlates were identified using linear regression. The transcriptomic signature of the identified brain-ADHD-sleep relationship was characterized by gene enrichment analysis. Confounding factors, such as stimulant drugs for ADHD and socioeconomic status, were controlled for. Results: ADHD symptoms contributed to sleep disturbances at one or more subsequent time points in both cohorts. Lower gray matter volumes in the middle frontal gyrus and inferior frontal gyrus, amygdala, striatum, and insula were associated with both ADHD symptoms and sleep disturbances. ADHD symptoms significantly mediated the link between these structural brain abnormalities and sleep dysregulation, and genes were differentially expressed in the implicated brain regions, including those involved in neurotransmission and circadian entrainment. Conclusions: This study indicates that ADHD symptoms and sleep disturbances have common neural correlates, including structural changes of the ventral attention system and frontostriatal circuitry. Leveraging data from large datasets, these results offer new mechanistic insights into this clinically important relationship between ADHD and sleep impairment, with potential implications for neurobiological models and future therapeutic directions.2974/3075Secondary AnalysisShared
Investigation of Psychiatric and Neuropsychological Correlates of Default Mode Network and Dorsal Attention Network Anticorrelation in Children.The default mode network (DMN) and dorsal attention network (DAN) demonstrate an intrinsic "anticorrelation" in healthy adults, which is thought to represent the functional segregation between internally and externally directed thought. Reduced segregation of these networks has been proposed as a mechanism for cognitive deficits that occurs in many psychiatric disorders, but this association has rarely been tested in pre-adolescent children. The current analysis used data from the Adolescent Brain Cognitive Development study to examine the relationship between the strength of DMN/DAN anticorrelation and psychiatric symptoms in the largest sample to date of 9- to 10-year-old children (N = 6543). The relationship of DMN/DAN anticorrelation to a battery of neuropsychological tests was also assessed. DMN/DAN anticorrelation was robustly linked to attention problems, as well as age, sex, and socioeconomic factors. Other psychiatric correlates identified in prior reports were not robustly linked to DMN/DAN anticorrelation after controlling for demographic covariates. Among neuropsychological measures, the clearest correlates of DMN/DAN anticorrelation were the Card Sort task of executive function and cognitive flexibility and the NIH Toolbox Total Cognitive Score, although these did not survive correction for socioeconomic factors. These findings indicate a complicated relationship between DMN/DAN anticorrelation and demographics, neuropsychological function, and psychiatric problems.2201/3004Secondary AnalysisShared
Neurocognition ABCD 1.1Difficulties with higher-order cognitive functions in youth are a potentially important vulnerability factor for the emergence of problematic behaviors and a range of psychopathologies. This study examined 2,0139-10 year olds in the first data release from theAdolescent Brain Cognitive Development21-site consortium study inorder to identify resting state functional connectivity patterns that predict individual-differences in three domainsof higher-order cognitive functions:General Ability, Speed/Flexibility, and Learning/Memory.For General Ability scores in particular, we observed consistent cross-site generalizability, with statistically significant predictions in 14outof 15held-outsites.These resultssurvived several tests forrobustness includingreplication in split half analysis and in a low head motion subsample.Weadditionallyfound that connectivity patterns involving task control networks and defaultmode network were prominently implicated in predicting differencesinGeneral Abilityacrossparticipants. These findings demonstrate that restingstate connectivity can be leveraged to produce generalizable markers of neurocognitive functioning. Additionally, they highlight the importance of task control-default mode network interconnectionsas a major locus of individual differences in cognitive functioning in early adolescence.2122/2206Secondary AnalysisPrivate
Resting State Cortical Hub Nodes in Youthsabstract here407/500Primary AnalysisPrivate
ABCD Neurocognitive Prediction Challenge 2019: Validation setValidation data set for the ABCD Neurocognitive Prediction Challenge 2019 containing skull stripped and segmented T1-weighted MRIs, volumetric brain measures, and residual fluid intelligence scores of 415 participants of the ABCD study. https://sibis.sri.com/abcd-np-challenge provides a detailed description about the processing. When using the data in publications, the Data Supplement of "Pfefferbaum et al., Altered Brain Developmental Trajectories in Adolescents After Initiating Drinking. Am J Psychiatry, 175(4), pp. 370-380, 2018" for should be cited as description of the processing pipeline. The data in this Study were derived from the Adolescent Brain Cognitive Development 1.1 Release (http://dx.doi.org/10.15154/1460410, accessed on or before November 15, 2018) and the Fast Track DICOM share in the Adolescent Brain Cognitive Development Study Collection 2573 (https://ndar.nih.gov/edit_collection.html?id=2573, accessed on or before November 15, 2018). The individual-level imaging phenotype data in this Collection was computed by a custom processing pipeline developed by the organizers of the ABCD Prediction Challenge. The imaging phenotype data may therefore differ from the values shared by the ABCD Study investigators in Release 1.1 or future releases415/415Secondary AnalysisShared
Parsing Concussion HeterogeneityConcussions have a high incidence rate, especially in children and adolescents. Despite considerable time and money invested in research, no clinical trials have been successful in advancing concussion pharmacotherapy. The main factor underlying this stagnation is heterogeneity in pre-injury and injury-related factors, leading to an array of varied neuropathological and clinical presentations. In contrast, most prior concussion neuroimaging research has employed conventional group comparison approaches to average out heterogeneity in order to define a putative concussion biomarker. In this study, we used a double multivariate approach to find patterns in, rather than average out, heterogeneity in white matter structure and symptoms in children within the ABCD Study who had previously sustained concussions. We processed diffusion MRI images using a novel algorithm called Tractoflow, extracted conventional and emerging diffusion measures, and used principal components analysis to combine these measures into biologically-interpretable indices of white matter microstructure. We then used partial least squares correlation analysis on these white matter measures as well as 19 symptom measures to delineate linear combinations of white matter features that maximally covaried with linear combinations of symptoms. We called these hidden relationships "multi-tract multi-symptom pairs". We found highly informative relationships which were averaged out when analyses were performed using conventional techniques. Further, the expression of these multi-tract multi-symptom pairs predicted adverse psychiatric outcomes in an unseen subset of the data. This study introduces a fundamentally novel way of studying concussions by leveraging heterogeneity instead of averaging out.297/345Primary AnalysisPrivate
* Data not on individual level
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