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

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

  • 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. 

  • Typically considered Descriptive/Raw Data unless related to the primary aims of a study, Clinical Data includes diagnostic assessments, clinical measures, medical histories, demographic data, questionnaires, etc. Each set of clinical data is submitted to the NDA using a corresponding Data Structure in the NDA Data Dictionary.

  • 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 raw 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.

  • 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.

  • The NDA has data grouped data into Collections which are associated with a Permission Groups (e.g., ABCD, NDAR, NDCT, PedsMRI, RDoCdb, OAI) so that access requests are made for a Permission Group instead of individual Collections. While each Permission Group has it's own identity, all data included are in the NIMH Data Archive regardless of Permission Group.

  • 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.

  • 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. 

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://ndar.nih.gov/data_dictionary.html. 

  • 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:

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 raw, non-analyzed data you can select 'No', then return to the experiment to add post processing steps at a later date when the analyzed 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 raw, non-analyzed data you can select 'No', then return to the experiment to add post processing steps at a later date when the analyzed 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

  • 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.

  • 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://ndar.nih.gov/data_dictionary.html. 

  • 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. 

  • Typically not related to te primary aims of a study, Descriptive/raw data are data used to characterize a research subject, including data from standard diagnostic assessments, standard clinical measures, family/subject medical history, demographic data, raw unprocessed images, -omics (e.g. proteomics, genomics, metabolomics) data, raw neurosignal recordings, and genetic test results that are being collected in the course of the supported research. Descriptive/raw data are expected to be submitted to NDA on a semi-annual basis (on or before January 15 and July 15). Cumulative submission of clinical data is expected during each submission cycle to enable data corrections throughout the duration of the award. Raw -omic, EEG, and neuroimaging data are expected to be submitted only once.  Descriptive/raw data are Shared within 4 months after submission.

  • 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.

  • The earliest date on which the data related to the Data Item may expect to be Shared based on whether the data are considered Descriptive/Raw or Analyzed.  Descriptive/raw data are shared within 4 months after submission (on May 15 for data submitted during the January 15 Submission Cycle or on November 15 for data submitted during the July 15 Submission Cycle).  Analyzed data are expected to be Shared at the time a publication is released  through an NDA Study or one year after the original project completion, whichever comes first.  The Initial Share Date is used by the NDA as a trigger to automatically share data.

  • The earliest date on which the data related to the Data Item may expect to start being submitted based on whether the data are considered Descriptive/Raw or Analyzed and based on the project's data collection timeline.  Descriptive/raw data are expected to be submitted every 6 months (January 15 and July 15) while Analyzed data are expected to be submitted no later than the time a manuscript is accepted.  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. 

  • The NDA has data grouped data into Collections which are associated with a Permission Groups (e.g., ABCD, NDAR, NDCT, PedsMRI, RDoCdb, OAI) so that access requests are made for a Permission Group instead of individual Collections. While each Permission Group has it's own identity, all data included are in the NIMH Data Archive regardless of Permission Group.

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:

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 raw, non-analyzed data you can select 'No', then return to the experiment to add post processing steps at a later date when the analyzed 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

Filter Cart

The Filter Cart provides a powerful way to query and access data for which you may be interested.  

A few points related to the filter cart are important to understand with the NDA Query/Filter implementation: 

First, the filter cart is populated asyncronously.  So, when you run a query, it may take a moment to populate but this will happen in the background so you can define other queries during this time.  

When you are adding your first filter, all data associated with your query will be added to the filter cart (whether it be a collection, a concept, a study, a data structure/elment or subjects). Not all data structures or collections will necessarily be displayed.  For example, if you select the NDA imaging structure image03, and further restrict that query to scan_type fMRI, only fMRI images will appear and only the image03 structure will be shown.  To see other data structures, select "Find All Subject Data" which will query all data for those subjects. When a secord or third filter is applied, an AND condition is used.  A subject must exist in all filters.  If the subject does not appear in any one filter, that subjects data will not be included in your filter cart. If that happens, clear your filter cart, and start over.  

It is best to package more data than you need and access those data using other tools, independent of the NDA (e.g. miNDAR snapshot), to limit the data selected.  If you have any questions on data access, are interested in using avaialble web services, or need help accessing data, please contact us for assistance.  

Frequently Asked Questions

Glossary

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Frequently Asked Questions

Glossary

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

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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
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  • EEG
  • EGG
  • Eye Tracking
  • Omics
  • fMRI
Created On
1575Resting StatefMRI07/02/2020
1574NavonEye Tracking06/30/2020
1573KICEye Tracking06/30/2020
1572Speech Discrimination TaskfMRI06/30/2020
1571fMRI - Multimodal Approaches to Neurobiology of Traumatic DissociationfMRI06/29/2020
1570Shifted-attention Emotion Appraisal Task (SEAT)fMRI06/25/2020
1569Fear Conditioning TaskfMRI06/25/2020
1568resting statefMRI06/23/2020
1567Category-specific Cortex LocalizerfMRI06/17/2020
1566Fear Extinction - Animals/ToolsfMRI06/17/2020
1565Fear Conditioning - Animals/ToolsfMRI06/17/2020
1564Passive FacesfMRI06/17/2020
1563Resting StatefMRI06/03/2020
1562P1_language_CopyTesting fMRI06/02/2020
1561Jeste Lab UCLA ACEii: Animacy - Project 1Eye Tracking06/02/2020
1560Live Interaction (Experimenter-Infant)EEG06/02/2020
1559ICANEEG05/28/2020
1558Food Choice Task GEfMRI05/27/2020
1557Taste_fMRI GEfMRI05/27/2020
1556Passive Viewing TaskEye Tracking05/27/2020
1555Auditory Pitch Mismatch NegativityEEG05/22/2020
1554pcASLfMRI05/21/2020
1553Single Session Depressive Episode (SSDE)EEG05/14/2020
1551Emotion StroopfMRI05/06/2020
1550Emotion Regulation TaskfMRI05/06/2020
1549Parametric Go Stop GofMRI05/06/2020
1548CCEPEEG05/03/2020
1547Monetary Incentive Delay TaskfMRI04/28/2020
1546Social Incentive Delay TaskfMRI04/28/2020
1545DNA_methylation_hippocampusOmics04/24/2020
1544WONDER ETEye Tracking04/16/2020
1543Effort-related choices/effort discountingEye Tracking04/14/2020
1542Statistical LearningfMRI03/29/2020
1541Live Interaction (Experimenter-Infant)EEG03/12/2020
1540ABC-CT Social/Nonsocial (modified from 479)EEG03/12/2020
1539restHIP-2fMRI03/11/2020
1538restHIP-1fMRI03/11/2020
1537restWBfMRI03/11/2020
1535iPSC bulk DNA whole genome sequencing dataOmics02/25/2020
1534Single cell DNA whole genome sequencing high coverage dataOmics02/25/2020
1533Single cell DNA whole genome sequencing low coverage dataOmics02/25/2020
1532Resting State fMRIfMRI02/24/2020
1531Resting State fMRIfMRI02/24/2020
1530Resting State fMRI fMRI02/24/2020
1529Emotional Go No Go TaskfMRI02/21/2020
1528RestingfMRI02/21/2020
1527Pre- and Post-Treatment Reversal LearningfMRI02/12/2020
1526Brain-to-brain storytellingfMRI02/11/2020
1525Brain-to-brain teachingfMRI02/09/2020
1524Emotional Faces Assessment TaskfMRI02/06/2020
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Collection Summary Collection Charts
Collection Title Collection Investigators Collection Description
Adolescent Brain Cognitive Development Study (ABCD)
Terry L. Jernigan 
The ABCD Study is designed to permit the scientific community to answer important questions about the relationships among physical and mental health, cognition, substance use (SU), culture and environment, genetics, environmental exposures and brain development of adolescents. The ABCD Study is a nationwide study of more than 10,000 9-10 year-olds conducted at 21 sites (29% of the US population lives within 50 miles of our geographically spread sites), that, uniquely, can provide a representative sample and a large twin sample that together can help distinguish environmental, sociocultural, and genetic factors relevant to adolescent health and brain development. We ensure cohesion and standardization with a recruitment strategy designed by a professional survey company (experience with Monitoring the Future), standardized environmental, neurocognitive and mental health assessments, MRI assessments with scanners using modified harmonized Human Connectome Project procedures, and computerized data collection with real-time quality control. Developmentally tailored assessments will have stable sensitivity and construct validity across childhood and adolescence, capture even subtle changes in SU, mental health, neurocognition, development, and environment, and employ novel bioassays and passive data collection from mobile devices. The retention plan builds on the experience of our investigators to ensure success.
Adolescent Brain Cognitive Development (ABCD) / Connectome Coordination Facility (CCF)
Adolescent Brain Cognitive Development (ABCD)
Enrolling
Shared
No
$188,004,890.00
47949
58242
11,890
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NIH - Extramural None

3a. NDA 2.0.1 Changes and Known Issues Fix Release 2.0.1.pdf Background 3a. NDA 2.0.1 Changes and Known Issues Fix Release 2.0.1 Qualified Researchers
ABCD_Diffusion_Tables.zip Background Diffusion tables for Fast Track dMRI Qualified Researchers
22. ABCD_Release_2.0_mapping_r.csv Background Mapping of ABCD instruments to NDA structures for Release 2.0 Qualified Researchers
111.incorrect.genetic.subjectid.csv Background 111.incorrect.genetic.subjectid Qualified Researchers
ABCD_dMRI_fMRI_Slicetiming.pdf Background MRI image acquisition parameters for dMRI and fMRI slice timing Qualified Researchers
ABCD Release 2.0 Release Notes_Non_public.zip Background Please Read - Updated Release 2.0 Notes (June 2019 Update) Qualified Researchers
ABCD Release 2.0 Release Notes_public.zip Background Please Read - Updated Release 2.0 Notes (June 2019 Update) General Public
Fix Release Notes 2.0.1_Not_Public.zip Background Please Read - Release Notes: Adolescent Brain Cognitive Development (ABCD) Fix Release 2.0.1 Qualified Researchers
Fix Release Notes 2.0.1_Public.zip Background Please Read - Release Notes: Adolescent Brain Cognitive Development (ABCD) Fix Release 2.0.1 General Public
3a. NDA 2.0 Changes between Release 1.1 and 2.0_Known Issues Release 2.0.pdf Background Release 2.0 Known Issues and changes between Releases 1.1 and 2.0 (June 2019 Update) Qualified Researchers
3b. ABCD Release 2.0 Family History_issues.pdf Background Release 2.0 Family History Issues Qualified Researchers
ABCD Fast Track DICOM Sharing.pdf Background ABCD Fast Track DICOM Sharing (Jan 2020) Qualified Researchers

U01DA041089-01 ABCD-USA Consortium: Research Project 09/30/2015 03/31/2027 0 21852 UNIVERSITY OF CALIFORNIA, SAN DIEGO $18,871,688.00
U24DA041123-01 ABCD-USA Consortium: Data Analysis Center 09/30/2015 03/31/2027 0 0 UNIVERSITY OF CALIFORNIA, SAN DIEGO $16,937,518.00
U01DA041117-01 Adolescent Brain Cognitive Development (ABCD) Prospective Research in Studies of Maturation (PRISM) Consortium 09/30/2015 03/31/2027 1970 1210 UNIVERSITY OF MARYLAND BALTIMORE $8,130,355.00
U01DA041022-01 ABCD-USA Consortium: Research Project 09/30/2015 03/31/2027 350 354 SRI INTERNATIONAL $5,211,982.00
U01DA041148-01 ABCD-USA Consortium: Research Project 09/30/2015 03/31/2027 4724 2330 OREGON HEALTH & SCIENCE UNIVERSITY $12,436,525.00
U01DA041106-01 ABCD-USA Consortium: Research Project 09/30/2015 03/31/2027 4300 7050 UNIVERSITY OF MICHIGAN AT ANN ARBOR $12,503,946.00
U01DA041028-01 ABCD-USA Consortium:Research Project 09/30/2015 03/31/2027 450 455 UNIVERSITY OF PITTSBURGH AT PITTSBURGH $5,908,888.00
U01DA041048-01 ABCD-USA Consortium: Research Project 09/30/2015 03/31/2027 23922 10616 CHILDRENS HOSPITAL OF LOS ANGELES $10,297,865.00
U24DA041147-01 ABCD-USA Consortium: Coordinating Center 09/30/2015 03/31/2027 0 0 UNIVERSITY OF CALIFORNIA, SAN DIEGO $19,483,164.00
U01DA041156-01 FIU-ABCD: Pathways and Mechanisms to Addiction in the Latino Youth of South Florida 09/30/2015 03/31/2027 1200 1264 FLORIDA INTERNATIONAL UNIVERSITY $10,822,181.00
U01DA041134-01 Prospective Research Studies of Maturation (PRISM)- Research Project 09/30/2015 03/31/2027 1900 4008 UNIVERSITY OF UTAH $12,789,251.00
U01DA041174-01 ABCD-USA: NYC Research Project 09/30/2015 03/31/2027 1725 635 YALE UNIVERSITY $9,301,963.00
U01DA041120-01 ABCD-USA Consortium: Twin Research Project 09/30/2015 03/31/2027 4760 7287 UNIVERSITY OF MINNESOTA $24,573,975.00
U01DA041093-01 13/13 ABCD-USA Consortium: Research Project 07/01/2017 03/31/2027 2140 796 MEDICAL UNIVERSITY OF SOUTH CAROLINA $3,833,398.00
U01DA041025-01 ABCD-USA Consortium: UWM SIte 07/15/2017 03/31/2027 508 385 UNIVERSITY OF WISCONSIN MILWAUKEE $4,100,427.00
U01DA050989-01 15/21 ABCD-USA Consortium: Research Project Site at LIBR 04/15/2020 03/31/2027 Not Reported Not Reported LAUREATE INSTITUTE FOR BRAIN RESEARCH $2,138,910.00
U01DA051039-01 19/21 ABCD-USA CONSORTIUM: RESEARCH PROJECT SITE AT UVM 04/15/2020 03/31/2027 Not Reported Not Reported UNIVERSITY OF VERMONT & ST AGRIC COLLEGE $1,675,732.00
U01DA051016-01 18/21 ABCD-USA CONSORTIUM: RESEARCH PROJECT SITE AT THE UNIVERSITY OF FLORIDA 04/15/2020 03/31/2027 Not Reported Not Reported UNIVERSITY OF FLORIDA $1,331,959.00
U01DA051037-01 20/21 ABCD-USA CONSORTIUM: RESEARCH PROJECT SITE AT VCU 04/15/2020 03/31/2027 Not Reported Not Reported VIRGINIA COMMONWEALTH UNIVERSITY $1,677,928.00
U01DA050987-01 17/21 ABCD-USA CONSORTIUM: RESEARCH PROJECT SITE AT UCLA 04/15/2020 03/31/2027 Not Reported Not Reported UNIVERSITY OF CALIFORNIA LOS ANGELES $1,311,305.00
U01DA051018-01 14/21 ABCD-USA Consortium: Research Project Site at CU Boulder 04/15/2020 03/31/2027 Not Reported Not Reported UNIVERSITY OF COLORADO $1,635,016.00
U01DA051038-01 21/21 ABCD-USA CONSORTIUM: RESEARCH PROJECT SITE AT WUSTL 04/15/2020 03/31/2027 Not Reported Not Reported WASHINGTON UNIVERSITY $1,994,241.00
U01DA050988-01 16/21 ABCD-USA CONSORTIUM: RESEARCH PROJECT SITE AT UNIVERSITY OF ROCHESTER 04/15/2020 03/31/2027 Not Reported Not Reported UNIVERSITY OF ROCHESTER $1,036,673.00

IDNameCreated DateStatusType
648ABCD MID 04/17/2017ApprovedfMRI
649ABCD REST04/18/2017ApprovedfMRI
650ABCD SST 04/18/2017ApprovedfMRI
651ABCD NBACK 04/18/2017ApprovedfMRI
1194ABCD Smokescreen genotyping01/17/2019ApprovedOmics

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.

ABCD ACS Post Stratification Weights Clinical Assessments 11875
ABCD Brief Problem Monitor-Teacher Form For Ages 6-18 (BPMT) Clinical Assessments 11875
ABCD Cash Choice Task Clinical Assessments 11875
ABCD Child Nutrition Assessment Clinical Assessments 4951
ABCD Children's Report of Parental Behavioral Inventory Clinical Assessments 11875
ABCD Developmental History Questionnaire Clinical Assessments 11875
ABCD Family History Assessment Part 1 Clinical Assessments 11875
ABCD Family History Assessment Part 2 Clinical Assessments 11875
ABCD Fasttrack QC Instrument Imaging 11769
ABCD Hormone Saliva Salimetric Scores Clinical Assessments 11875
ABCD Irma Substudy Child Clinical Assessments 11875
ABCD Irma Substudy Parent Clinical Assessments 11875
ABCD Little Man Task Summary Scores Clinical Assessments 11873
ABCD Longitudinal Parent Demographics Survey Clinical Assessments 4951
ABCD Longitudinal Parent Diagnostic Interview for DSM-5 Background Items Full (KSAD) Clinical Assessments 4951
ABCD Longitudinal Parent Medical History Questionnaire Clinical Assessments 4951
ABCD Longitudinal Parent Ohio State Traumatic Brain Injury Screen-Short Modified (OTBI) Clinical Assessments 4951
ABCD Longitudinal Parent Sports and Activities Involvement Questionnaire (SAIQ) Clinical Assessments 4951
ABCD Longitudinal Summary Scores Medical History Clinical Assessments 4951
ABCD Longitudinal Summary Scores Sports Activity Clinical Assessments 4951
ABCD Longitudinal Summary Scores Traumatic Brain Injury Clinical Assessments 4951
ABCD Longitudinal Tracking Clinical Assessments 11875
ABCD MRI Info Imaging 11752
ABCD Other Resilience Clinical Assessments 11875
ABCD Parent Acculturation Survey Modified from PhenX (ACC) Clinical Assessments 11875
ABCD Parent Adult Self Report Raw Scores Aseba (ASR) Clinical Assessments 11875
ABCD Parent Adult Self Report Scores Aseba (ASR) Clinical Assessments 11875
ABCD Parent Child Behavior Checklist Raw Scores Aseba (CBCL) Clinical Assessments 11875
ABCD Parent Child Behavior Checklist Scores Aseba (CBCL) Clinical Assessments 11875
ABCD Parent Community Risk and Protective Factors (CRPF) Clinical Assessments 11875
ABCD Parent Demographics Survey Clinical Assessments 11875
ABCD Parent Diagnostic Interview for DSM-5 (KSADS) Traumatic Events Clinical Assessments 11860
ABCD Parent Diagnostic Interview for DSM-5 Background Items Full (KSADS-5) Clinical Assessments 11875
ABCD Parent Diagnostic Interview for DSM-5 Full (KSADS-5) Clinical Assessments 11867
ABCD Parent Family Environment Scale-Family Conflict Subscale Modified from PhenX (FES) Clinical Assessments 11875
ABCD Parent Fitbit Baseline Clinical Assessments 165
ABCD Parent Fitbit Followup Clinical Assessments 165
ABCD Parent Gender Identity Clinical Assessments 4951
ABCD Parent KSADS Conduct Disorder Clinical Assessments 11868
ABCD Parent Life Events Clinical Assessments 4951
ABCD Parent Medical History Questionnaire (MHX) Clinical Assessments 11875
ABCD Parent Medications Survey Inventory Modified from PhenX (PMP) Clinical Assessments 11875
ABCD Parent Mexican American Cultural Values Scale Modified (MACV) Clinical Assessments 11875
ABCD Parent Multi-Group Ethnic Identity-Revised Survey (MEIM) Clinical Assessments 11875
ABCD Parent Neighborhood Safety/Crime Survey Modified from PhenX (NSC) Clinical Assessments 11875
ABCD Parent Ohio State Traumatic Brain Injury Screen-Short Modified (OTBI) Clinical Assessments 11875
ABCD Parent Parent General Behavior Inventory-Mania (PGBI) Clinical Assessments 11875
ABCD Parent Participant Last Use Survey Day 2 3 4 (PLUS) Clinical Assessments 11875
ABCD Parent Pubertal Development Scale and Menstrual Cycle Survey History (PDMS) Clinical Assessments 11875
ABCD Parent Screen Time Survey (STQ) Clinical Assessments 11875
ABCD Parent Short Social Responsiveness Scale Clinical Assessments 4951
ABCD Parent Sleep Disturbance Scale for Children (SDS) Clinical Assessments 11875
ABCD Parent Sports and Activities Involvement Questionnaire (SAIQ) Clinical Assessments 11875
ABCD Parent Vancouver Index of Acculturation-Short Survey (VIA) Clinical Assessments 11875
ABCD Parental Monitoring Survey Clinical Assessments 11875
ABCD Parental Rules on Substance Use Clinical Assessments 11875
ABCD Pearson Scores Clinical Assessments 11871
ABCD Prodromal Psychosis Scale Clinical Assessments 11875
ABCD Pubertal Hormone Saliva Clinical Assessments 11875
ABCD RA Scanning Checklist and Notes Clinical Assessments 11875
ABCD School Risk and Protective Factors Survey Clinical Assessments 11875
ABCD Screener Clinical Assessments 11875
ABCD Sum Scores Culture & Environment Parent Clinical Assessments 11875
ABCD Sum Scores Culture & Environment Youth Clinical Assessments 11875
ABCD Sum Scores Mobil Tech Youth Clinical Assessments 11875
ABCD Sum Scores Physical Health Parent Clinical Assessments 11875
ABCD Sum Scores Physical Health Youth Clinical Assessments 11875
ABCD Sum Scores Traumatic Brain Injury Clinical Assessments 11875
ABCD Summary Scores Brief Problem Monitor-Teacher Form for Ages 6-18 Clinical Assessments 11875
ABCD Summary Scores Developmental History Clinical Assessments 11875
ABCD Summary Scores Medical History Clinical Assessments 11875
ABCD Summary Scores Sports Activity Clinical Assessments 11875
ABCD Summary Scores Substance Use Clinical Assessments 11875
ABCD TBX Demo Clinical Assessments 11873
ABCD Task fMRI MID Average Beta Weights Destrieux Parcellations Part 1 Imaging 9112
ABCD Task fMRI MID Average Beta Weights Destrieux Parcellations Part 2 Imaging 9112
ABCD Task fMRI MID Average Beta Weights Part 1 Imaging 9112
ABCD Task fMRI MID Average Beta Weights Part 2 Imaging 9112
ABCD Task fMRI MID Average SEM Destrieux Parcellations Part 1 Imaging 9112
ABCD Task fMRI MID Average SEM Destrieux Parcellations Part 2 Imaging 9112
ABCD Task fMRI MID Average Standard Error of the Mean Part 1 Imaging 9112
ABCD Task fMRI MID Average Standard Error of the Mean Part 2 Imaging 9112
ABCD Task fMRI MID Behavior Clinical Assessments 9801
ABCD Task fMRI MID Run 1 Beta Weights Destrieux Parcellations Part 1 Imaging 9112
ABCD Task fMRI MID Run 1 Beta Weights Destrieux Parcellations Part 2 Imaging 9112
ABCD Task fMRI MID Run 1 Beta Weights Part 1 Imaging 9112
ABCD Task fMRI MID Run 1 Beta Weights Part 2 Imaging 9112
ABCD Task fMRI MID Run 1 SEM Destrieux Parcellations Part 1 Imaging 9112
ABCD Task fMRI MID Run 1 SEM Destrieux Parcellations Part 2 Imaging 9112
ABCD Task fMRI MID Run 1 Standard Error of the Mean Part 1 Imaging 9112
ABCD Task fMRI MID Run 1 Standard Error of the Mean Part 2 Imaging 9112
ABCD Task fMRI MID Run 2 Beta Weights Destrieux Parcellations Part 1 Imaging 9036
ABCD Task fMRI MID Run 2 Beta Weights Destrieux Parcellations Part 2 Imaging 9036
ABCD Task fMRI MID Run 2 Beta Weights Part 1 Imaging 9036
ABCD Task fMRI MID Run 2 Beta Weights Part 2 Imaging 9036
ABCD Task fMRI MID Run 2 SEM Destrieux Parcellations Part 1 Imaging 9036
ABCD Task fMRI MID Run 2 SEM Destrieux Parcellations Part 2 Imaging 9036
ABCD Task fMRI MID Run 2 Standard Error of the Mean Part 1 Imaging 9036
ABCD Task fMRI MID Run 2 Standard Error of the Mean Part 2 Imaging 9036
ABCD Task fMRI REC Behavior Clinical Assessments 8976
ABCD Task fMRI SST Average Beta Weights Imaging 8918
ABCD Task fMRI SST Average Beta Weights Destrieux Parcellations Part 1 Imaging 8918
ABCD Task fMRI SST Average Beta Weights Destrieux Parcellations Part 2 Imaging 8918
ABCD Task fMRI SST Average SEM Destrieux Parcellations Part 1 Imaging 8918
ABCD Task fMRI SST Average SEM Destrieux Parcellations Part 2 Imaging 8918
ABCD Task fMRI SST Average Standard Error of the Mean Imaging 8918
ABCD Task fMRI SST Behavior Clinical Assessments 9598
ABCD Task fMRI SST Run 1 Beta Weights Imaging 8918
ABCD Task fMRI SST Run 1 Beta Weights Destrieux Parcellations Part 1 Imaging 8918
ABCD Task fMRI SST Run 1 Beta Weights Destrieux Parcellations Part 2 Imaging 8918
ABCD Task fMRI SST Run 1 SEM Destrieux Parcellations Part 1 Imaging 8918
ABCD Task fMRI SST Run 1 SEM Destrieux Parcellations Part 2 Imaging 8918
ABCD Task fMRI SST Run 1 Standard Error of the Mean Imaging 8918
ABCD Task fMRI SST Run 2 Beta Weights Imaging 8795
ABCD Task fMRI SST Run 2 Beta Weights Destrieux Parcellations Part 1 Imaging 8795
ABCD Task fMRI SST Run 2 Beta Weights Destrieux Parcellations Part 2 Imaging 8795
ABCD Task fMRI SST Run 2 SEM Destrieux Parcellations Part 1 Imaging 8795
ABCD Task fMRI SST Run 2 SEM Destrieux Parcellations Part 2 Imaging 8795
ABCD Task fMRI SST Run 2 Standard Error of the Mean Imaging 8795
ABCD Task fMRI nBack Average Beta Weights Imaging 8854
ABCD Task fMRI nBack Average Beta Weights Destrieux Parcellations Part 1 Imaging 8854
ABCD Task fMRI nBack Average Beta Weights Destrieux Parcellations Part 2 Imaging 8854
ABCD Task fMRI nBack Average SEM Destrieux Parcellations Part 1 Imaging 8854
ABCD Task fMRI nBack Average SEM Destrieux Parcellations Part 2 Imaging 8854
ABCD Task fMRI nBack Average Standard Error of the Mean Imaging 8854
ABCD Task fMRI nBack Behavior Clinical Assessments 9468
ABCD Task fMRI nBack Run 1 Beta Weights Imaging 8854
ABCD Task fMRI nBack Run 1 Beta Weights Destrieux Parcellations Part 1 Imaging 8854
ABCD Task fMRI nBack Run 1 Beta Weights Destrieux Parcellations Part 2 Imaging 8854
ABCD Task fMRI nBack Run 1 SEM Destrieux Parcellations Part 1 Imaging 8854
ABCD Task fMRI nBack Run 1 SEM Destrieux Parcellations Part 2 Imaging 8854
ABCD Task fMRI nBack Run 1 Standard Error of the Mean Imaging 8854
ABCD Task fMRI nBack Run 2 Beta Weights Imaging 8823
ABCD Task fMRI nBack Run 2 Beta Weights Destrieux Parcellations Part 1 Imaging 8823
ABCD Task fMRI nBack Run 2 Beta Weights Destrieux Parcellations Part 2 Imaging 8823
ABCD Task fMRI nBack Run 2 SEM Destrieux Parcellations Part 1 Imaging 8823
ABCD Task fMRI nBack Run 2 SEM Destrieux Parcellations Part 2 Imaging 8823
ABCD Task fMRI nBack Run 2 Standard Error of the Mean Imaging 8823
ABCD Timeline Follow-back Survey Calendar Scores (TLFB) Clinical Assessments 11875
ABCD Youth 10 Item Delinquency Scale Clinical Assessments 4951
ABCD Youth 7-Up Mania Items Clinical Assessments 4951
ABCD Youth Acculturation Survey Modified from PhenX (ACC) Clinical Assessments 11875
ABCD Youth Alcohol Measures Clinical Assessments 4951
ABCD Youth Alcohol Screen Clinical Assessments 11875
ABCD Youth Anthropometrics Modified From PhenX (ANT) Clinical Assessments 11875
ABCD Youth Behavioral Inhibition/Behavioral Approach System Scales Modified from PhenX (BIS/BAS) Clinical Assessments 11875
ABCD Youth Brief Problem Monitor Clinical Assessments 8720
ABCD Youth Delay Discounting Sum Scores Clinical Assessments 4950
ABCD Youth Diagnostic Interview for DSM-5 5 (KSADS-5) Clinical Assessments 11871
ABCD Youth Diagnostic Interview for DSM-5 Background Items 5 (KSADS-5) Clinical Assessments 11875
ABCD Youth Discrimination Measure Clinical Assessments 4951
ABCD Youth Edinburgh Handedness Inventory Short Form (EHIS) Clinical Assessments 11875
ABCD Youth Emotional Stroop Task Clinical Assessments 4949
ABCD Youth Family Environment Scale-Family Conflict Subscale Modified from PhenX (FES) Clinical Assessments 11875
ABCD Youth Fitbit Baseline Clinical Assessments 165
ABCD Youth Fitbit Followup Clinical Assessments 165
ABCD Youth Gender Identity Clinical Assessments 4950
ABCD Youth Genetic Blood (RUCDR) Clinical Assessments 11875
ABCD Youth Genetic Saliva (RUCDR) Clinical Assessments 11875
ABCD Youth Hair Results Clinical Assessments 11875
ABCD Youth Hair Sample Clinical Assessments 11875
ABCD Youth Life Events Clinical Assessments 4951
ABCD Youth Marijuana Illicit Drug Measures Clinical Assessments 4951
ABCD Youth Mid Year Phone Interview Substance Use Clinical Assessments 8649
ABCD Youth Monetary Incentive Delay Task Survey Post Scan Questionnaire Clinical Assessments 11875
ABCD Youth NIH TB Summary Scores Clinical Assessments 11873
ABCD Youth NIH Toolbox Positive Affect Items Clinical Assessments 8720
ABCD Youth Neighborhood Safety/Crime Survey Modified from PhenX (NSC) Clinical Assessments 11875
ABCD Youth Nicalert Clinical Assessments 4951
ABCD Youth Nicotine Measures Clinical Assessments 4951
ABCD Youth Participant Last Use Survey Day 1 2 3 4 (PLUS) Clinical Assessments 11875
ABCD Youth Post Scan Questionnaire 2 Clinical Assessments 11875
ABCD Youth Post Scan Questionnaire 1 Clinical Assessments 11875
ABCD Youth Pre Scan Questionnaire 1 Clinical Assessments 11875
ABCD Youth Pre Scan Questionnaire 2 Clinical Assessments 11875
ABCD Youth Pubertal Development Scale and Menstrual Cycle Survey History (PDMS) Clinical Assessments 11875
ABCD Youth Rescan Monetary Incentive Delay Task Survey Post Scan Questionnaire Clinical Assessments 11875
ABCD Youth Screen Time Survey (STQ) Clinical Assessments 11875
ABCD Youth Snellen Vision Screener (SVS) Clinical Assessments 11875
ABCD Youth Substance Use Attitudes Clinical Assessments 4951
ABCD Youth Substance Use Interview Clinical Assessments 11875
ABCD Youth Substance Use Introduction and Patterns Clinical Assessments 4951
ABCD Youth Summary Scores BPM and POA Clinical Assessments 8720
ABCD Youth Toxicology Test Clinical Assessments 11875
ABCD Youth Wills Problem Solving Scale Clinical Assessments 4951
ABCD Youth Youth Risk Behavior Survey Exercise Physical Activity (YRB) Clinical Assessments 11875
ABCD dMRI DTI Destrieux Parcellations Part 1 Imaging 11400
ABCD dMRI DTI Destrieux Parcellations Part 2 Imaging 11400
ABCD dMRI DTI Full Destrieux Parcellation Part 1 Imaging 11400
ABCD dMRI DTI Full Destrieux Parcellation Part 2 Imaging 11400
ABCD dMRI DTI Full Part 1 Imaging 11400
ABCD dMRI DTI Full Part 2 Imaging 11400
ABCD dMRI DTI Part 1 Imaging 11400
ABCD dMRI DTI Part 2 Imaging 11400
ABCD dMRI Post Processing QC Imaging 3733
ABCD dMRI RSI Destrieux Parcellation Part 1 Imaging 11400
ABCD dMRI RSI Destrieux Parcellation Part 2 Imaging 11400
ABCD dMRI RSI Destrieux Parcellation Part 3 Imaging 11400
ABCD dMRI RSI Part 1 Imaging 11400
ABCD dMRI RSI Part 2 Imaging 11400
ABCD rsfMRI Destrieux Imaging 10966
ABCD rsfMRI Gordon Network Correlations Imaging 10966
ABCD rsfMRI Network to Subcortical ROI Correlations Imaging 10966
ABCD rsfMRI Temporal Variance Imaging 10966
ABCD sMRI Destrieux Parcellation Part 1 Imaging 11534
ABCD sMRI Destrieux Parcellation Part 2 Imaging 11534
ABCD sMRI Part 1 Imaging 11534
ABCD sMRI Part 2 Imaging 11534
FreeSurfer QC Imaging 11556
Genomics Sample Genomics 10659
Image Imaging 11808
MR Findings Clinical Assessments 11875
MRI QC Raw Part 1 Imaging 11871
MRI QC Raw Part 2 Imaging 11871
MRI QC Raw Part 3 Imaging 11871
Mobile Data Imaging 150
Parent Prosocial Behavior Survey Clinical Assessments 11875
Processed MRI Data Imaging 11462
Residential History Derived Scores Clinical Assessments 11875
Sum Scores Mental Health Parent Clinical Assessments 11875
Sum Scores Mental Health Youth Clinical Assessments 11875
UPPS-P for Children Short Form (ABCD-version) Clinical Assessments 11875
Youth Prosocial Behavior Survey Clinical Assessments 11875

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 OB, Frei O, Fan CC, Shadrin A, Dale AM, Andreassen OAOctober 2019Not Determined
30780067Create StudyTesting helping behavior and its relationship to antisocial personality and psychopathic traits.Psychiatry researchSakai JT, Raymond KM, Mcwilliams SK, Mikulich-Gilbertson SKApril 2019Not Determined
30760808Create StudyBasic Units of Inter-Individual Variation in Resting State Connectomes.Scientific reportsSripada C, Angstadt M, Rutherford S, Kessler D, Kim Y, Yee M, Levina EFebruary 2019Not Determined
30610197Create StudyGenome-wide analysis reveals extensive genetic overlap between schizophrenia, bipolar disorder, and intelligence.Molecular psychiatrySmeland OB, Bahrami S, Frei O, Shadrin A, O'Connell K, Savage J, Watanabe K, Krull F, Bettella F, Steen NE, Ueland T, Posthuma D, Djurovic S, Dale AM, Andreassen OAJanuary 2019Not Determined
30578952Create StudySexual minority children: Mood disorders and suicidality disparities.Journal of affective disordersBlashill AJ, Calzo JPMarch 2019Not Determined
30573013Create StudyAdolescent Brain Surface Area Pre- and Post-Cannabis and Alcohol Initiation.Journal of studies on alcohol and drugsInfante MA, Courtney KE, Castro N, Squeglia LM, Jacobus JNovember 2018Not Determined
30481666Create StudyThe frontal aslant tract (FAT) and its role in speech, language and executive function.Cortex; a journal devoted to the study of the nervous system and behaviorDick AS, Garic D, Graziano P, Tremblay PFebruary 2019Not Determined
30474579Create StudyAerobic Fitness Level Moderates the Association Between Cannabis Use and Executive Functioning and Psychomotor Speed Following Abstinence in Adolescents and Young Adults.Journal of the International Neuropsychological Society : JINSWade NE, Wallace AL, Swartz AM, Lisdahl KMNovember 2018Not Determined
30382511Create StudyEditors' Commentary for Special Issue: The 2017 CALDAR Summer Institute and International Conference Promoting Global Health-Precision Research in Substance Abuse, HIV, and Care.Journal of neuroimmune pharmacology : the official journal of the Society on NeuroImmune PharmacologyChang L, Li MD, Hser YIDecember 2018Not Determined
30347017Create StudyGenetic Predisposition vs Individual-Specific Processes in the Association Between Psychotic-like Experiences and Cannabis Use.JAMA psychiatryKarcher NR, Barch DM, Demers CH, Baranger DAA, Heath AC, Lynskey MT, Agrawal AJanuary 2019Not Determined
30343458Create StudyCognitive Deficits in Psychotic Disorders: A Lifespan Perspective.Neuropsychology reviewSheffield JM, Karcher NR, Barch DMDecember 2018Not Determined
30339913Create StudyScreen media activity and brain structure in youth: Evidence for diverse structural correlation networks from the ABCD study.NeuroImagePaulus MP, Squeglia LM, Bagot K, Jacobus J, Kuplicki R, Breslin FJ, Bodurka J, Morris AS, Thompson WK, Bartsch H, Tapert SFJanuary 2019Not Determined
30295694Create StudyEffects of Cannabis Use and Subclinical ADHD Symptomology on Attention Based Tasks in Adolescents and Young Adults.Archives of clinical neuropsychology : the official journal of the National Academy of NeuropsychologistsWallace AL, Wade NE, Hatcher KF, Lisdahl KMOctober 2018Not 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
30159951Create StudyLaterality of the frontal aslant tract (FAT) explains externalizing behaviors through its association with executive function.Developmental scienceGaric D, Broce I, Graziano P, Mattfeld A, Dick ASMarch 2019Not Determined
30025312Create StudyOrbitofrontal connectivity is associated with depression and anxiety in marijuana-using adolescents.Journal of affective disordersSubramaniam P, Rogowska J, Dimuzio J, Lopez-Larson M, Mcglade E, Yurgelun-Todd DOctober 2018Not Determined
29977984Create StudySex Differences in the Developmental Neuroscience of Adolescent Substance Use Risk.Current opinion in behavioral sciencesHeitzeg MM, Hardee JE, Beltz AMOctober 2018Not Determined
29946511Create StudyAbnormal cortical gyrification in criminal psychopathy.NeuroImage. ClinicalMiskovich TA, Anderson NE, Harenski CL, Harenski KA, Baskin-Sommers AR, Larson CL, Newman JP, Hanson JL, Stout DM, Koenigs M, Shollenbarger SG, Lisdahl KM, Decety J, Kosson DS, Kiehl KAJanuary 2018Not Determined
29944961Create StudyMeta-analytic evidence for a core problem solving network across multiple representational domains.Neuroscience and biobehavioral reviewsBartley JE, Boeving ER, Riedel MC, Bottenhorn KL, Salo T, Eickhoff SB, Brewe E, Sutherland MT, Laird ARSeptember 2018Not Determined
29910020Create StudyThe racially diverse affective expression (RADIATE) face stimulus set.Psychiatry researchConley MI, Dellarco DV, Rubien-Thomas E, Cohen AO, Cervera A, Tottenham N, Casey BJDecember 2018Not Determined
29884281Create StudyInteroception and Mental Health: A Roadmap.Biological psychiatry. Cognitive neuroscience and neuroimagingKhalsa SS, Adolphs R, Cameron OG, Critchley HD, Davenport PW, Feinstein JS, Feusner JD, Garfinkel SN, Lane RD, Mehling WE, Meuret AE, Nemeroff CB, Oppenheimer S, Petzschner FH, Pollatos O, Rhudy JL, Schramm LP, Simmons WK, Stein MB, Stephan KE, Van Den Bergh O, Van Diest I, Von Leupoldt A, Paulus MP, June 2018Not Determined
29879391Create StudyNeuroimaging Impaired Response Inhibition and Salience Attribution in Human Drug Addiction: A Systematic Review.NeuronZilverstand A, Huang AS, Alia-Klein N, Goldstein RZJune 2018Not Determined
29874361Create StudyAssessment of the Prodromal Questionnaire-Brief Child Version for Measurement of Self-reported Psychoticlike Experiences in Childhood.JAMA psychiatryKarcher NR, Barch DM, Avenevoli S, Savill M, Huber RS, Simon TJ, Leckliter IN, Sher KJ, Loewy RLAugust 2018Not Determined
29773510Create StudyImplications of the ABCD study for developmental neuroscience.Developmental cognitive neuroscienceFeldstein Ewing SW, Bjork JM, Luciana MAugust 2018Not Determined
29706313Create StudyA description of the ABCD organizational structure and communication framework.Developmental cognitive neuroscienceAuchter AM, Hernandez Mejia M, Heyser CJ, Shilling PD, Jernigan TL, Brown SA, Tapert SF, Dowling GJAugust 2018Not Determined
29680211Create StudyOutreach and innovation: Communication strategies for the ABCD Study.Developmental cognitive neuroscienceHoffman EA, Howlett KD, Breslin F, Dowling GJAugust 2018Not Determined
29679914Create StudyA multi-site proof-of-concept investigation of computerized approach-avoidance training in adolescent cannabis users.Drug and alcohol dependenceJacobus J, Taylor CT, Gray KM, Meredith LR, Porter AM, Li I, Castro N, Squeglia LMJune 2018Not Determined
29655614Create StudyA brief validated screen to identify boys and girls at risk for early marijuana use.Developmental cognitive neuroscienceLoeber R, Clark DB, Ahonen L, Fitzgerald D, Trucco EM, Zucker RAAugust 2018Not Determined
29636283Create StudyCurrent, future and potential use of mobile and wearable technologies and social media data in the ABCD study to increase understanding of contributors to child health.Developmental cognitive neuroscienceBagot KS, Matthews SA, Mason M, Squeglia LM, Fowler J, Gray K, Herting M, May A, Colrain I, Godino J, Tapert S, Brown S, Patrick KMarch 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
29606560Create StudyBiospecimens and the ABCD study: Rationale, methods of collection, measurement and early data.Developmental cognitive neuroscienceUban KA, Horton MK, Jacobus J, Heyser C, Thompson WK, Tapert SF, Madden PAF, Sowell ER, August 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.March 2018Not Determined
29559216Create StudyAdolescent brain cognitive development (ABCD) study: Overview of substance use assessment methods.Developmental cognitive neuroscienceLisdahl KM, Sher KJ, Conway KP, Gonzalez R, Feldstein Ewing SW, Nixon SJ, Tapert S, Bartsch H, Goldstein RZ, Heitzeg MFebruary 2018Not Determined
29556250Create StudyDetermining Genetic Causal Variants Through Multivariate Regression Using Mixture Model Penalty.Frontiers in geneticsSundar VS, Fan CC, Holland D, Dale AMJanuary 2018Not Determined
29527590Create StudyChronic Stress in Adolescents and Its Neurobiological and Psychopathological Consequences: An RDoC Perspective.Chronic stress (Thousand Oaks, Calif.)Sheth C, Mcglade E, Yurgelun-Todd D2017 Jan-DecNot Determined
29525452Create StudyAdolescent neurocognitive development and impacts of substance use: Overview of the adolescent brain cognitive development (ABCD) baseline neurocognition battery.Developmental cognitive neuroscienceLuciana M, Bjork JM, Nagel BJ, Barch DM, Gonzalez R, Nixon SJ, Banich MTFebruary 2018Not Determined
29484767Create StudyDissociable meta-analytic brain networks contribute to coordinated emotional processing.Human brain mappingRiedel MC, Yanes JA, Ray KL, Eickhoff SB, Fox PT, Sutherland MT, Laird ARJune 2018Not Determined
29467408Create StudyPrediction complements explanation in understanding the developing brain.Nature communicationsRosenberg MD, Casey BJ, Holmes AJFebruary 2018Not Determined
29460349Create StudyAdolescent Brain Development: Implications for Understanding Risk and Resilience Processes Through Neuroimaging Research.Journal of research on adolescence : the official journal of the Society for Research on AdolescenceMorris AS, Squeglia LM, Jacobus J, Silk JSMarch 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 JR, Kuperman JM, Dale AM, White NSFebruary 2018Not Determined
29337280Create StudyBehavioral interventions for reducing head motion during MRI scans in children.NeuroImageGreene DJ, Koller JM, Hampton JM, Wesevich V, Van AN, Nguyen AL, Hoyt CR, Mcintyre L, Earl EA, Klein RL, Shimony JS, Petersen SE, Schlaggar BL, Fair DA, Dosenbach NUFJanuary 2018Not Determined
29198276Create StudyDoes Cannabis Use Cause Declines in Neuropsychological Functioning? A Review of Longitudinal Studies.Journal of the International Neuropsychological Society : JINSGonzalez R, Pacheco-Colón I, Duperrouzel JC, Hawes SWOctober 2017Not Determined
29197573Create StudyDevelopment of the emotional brain.Neuroscience lettersCasey BJ, Heller AS, Gee DG, Cohen AODecember 2017Not Determined
29182012Create StudyValidating Online Measures of Cognitive Ability in Genes for Good, a Genetic Study of Health and Behavior.AssessmentLiu M, Rea-Sandin G, Foerster J, Fritsche L, Brieger K, Clark C, Li K, Pandit A, Zajac G, Abecasis GR, Vrieze SNovember 2017Not Determined
29150307Create StudyApproaching Retention within the ABCD Study.Developmental cognitive neuroscienceFeldstein Ewing SW, Chang L, Cottler LB, Tapert SF, Dowling GJ, Brown SANovember 2017Not Determined
29113758Create StudyDemographic, physical and mental health assessments in the adolescent brain and cognitive development study: Rationale and description.Developmental cognitive neuroscienceBarch DM, Albaugh MD, Avenevoli S, Chang L, Clark DB, Glantz MD, Hudziak JJ, Jernigan TL, Tapert SF, Yurgelun-Todd D, Alia-Klein N, Potter AS, Paulus MP, Prouty D, Zucker RA, Sher KJNovember 2017Not Determined
29107609Create StudyThe utility of twins in developmental cognitive neuroscience research: How twins strengthen the ABCD research design.Developmental cognitive neuroscienceIacono WG, Heath AC, Hewitt JK, Neale MC, Banich MT, Luciana MM, Madden PA, Barch DM, Bjork JMSeptember 2017Not Determined
29038777Create StudyThe ABCD study of neurodevelopment: Identifying neurocircuit targets for prevention and treatment of adolescent substance abuse.Current treatment options in psychiatryBjork JM, Straub LK, Provost RG, Neale MCJune 2017Not Determined
28900686Create StudyChanges in marijuana use symptoms and emotional functioning over 28-days of monitored abstinence in adolescent marijuana users.PsychopharmacologyJacobus J, Squeglia LM, Escobar S, Mckenna BM, Hernandez MM, Bagot KS, Taylor CT, Huestis MADecember 2017Not Determined
28868337Create StudyThe adolescent brain at risk for substance use disorders: a review of functional MRI research on motor response inhibition.Current opinion in behavioral sciencesKoyama MS, Parvaz MA, Goldstein RZFebruary 2017Not Relevant
28838468Create StudyComputational Dysfunctions in Anxiety: Failure to Differentiate Signal From Noise.Biological psychiatryHuang H, Thompson W, Paulus MPSeptember 2017Not Determined
28828560Create StudyLinking tuberous sclerosis complex, excessive mTOR signaling, and age-related neurodegeneration: a new association between TSC1 mutation and frontotemporal dementia.Acta neuropathologicaOlney NT, Alquezar C, Ramos EM, Nana AL, Fong JC, Karydas AM, Taylor JB, Stephens ML, Argouarch AR, Van Berlo VA, Dokuru DR, Sherr EH, Jicha GA, Dillon WP, Desikan RS, De May M, Seeley WW, Coppola G, Miller BL, Kao AWNovember 2017Not Relevant
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
28779616Create StudyPhenotypic and familial associations between childhood maltreatment and cannabis initiation and problems in young adult European-American and African-American women.Drug and alcohol dependenceGrant JD, Agrawal A, Werner KB, Mccutcheon VV, Nelson EC, Madden PAF, Bucholz KK, Heath AC, Sartor CEOctober 2017Not Relevant
28716389Create StudyBiomedical ethics and clinical oversight in multisite observational neuroimaging studies with children and adolescents: The ABCD experience.Developmental cognitive neuroscienceClark DB, Fisher CB, Bookheimer S, Brown SA, Evans JH, Hopfer C, Hudziak J, Montoya I, Murray M, Pfefferbaum A, Yurgelun-Todd DJune 2017Not Determined
28714184Create StudyResearch Review: What have we learned about adolescent substance use?Journal of child psychology and psychiatry, and allied disciplinesGray KM, Squeglia LMJuly 2017Not Determined
28641131Create StudyChildren's brain activation during risky decision-making: A contributor to substance problems?Drug and alcohol dependenceCrowley TJ, Dalwani MS, Sakai JT, Raymond KM, Mcwilliams SK, Banich MT, Mikulich-Gilbertson SKJune 2017Not Relevant
28438513Create StudyRapid-Response Impulsivity Predicts Depression and Posttraumatic Stress Disorder Symptomatology at 1-Year Follow-Up in Blast-Exposed Service Members.Archives of physical medicine and rehabilitationBjork JM, Burroughs TK, Franke LM, Pickett TC, Johns SE, Moeller FG, Walker WCAugust 2017Not Determined
28279988Create StudyEntorhinal Cortex: Antemortem Cortical Thickness and Postmortem Neurofibrillary Tangles and Amyloid Pathology.AJNR. American journal of neuroradiologyThaker AA, Weinberg BD, Dillon WP, Hess CP, Cabral HJ, Fleischman DA, Leurgans SE, Bennett DA, Hyman BT, Albert MS, Killiany RJ, Fischl B, Dale AM, Desikan RSMarch 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.March 2017Not Relevant
28161313Create StudyDevelopment of large-scale functional networks from birth to adulthood: A guide to the neuroimaging literature.NeuroImageGrayson DS, Fair DAFebruary 2017Not Determined
28018986Create StudyA Roadmap for the Development of Applied Computational Psychiatry.Biological psychiatry : cognitive neuroscience and neuroimagingPaulus MP, Huys QJ, Maia TVSeptember 2016Not Relevant
27995817Create StudyPsychophysiological endophenotypes to characterize mechanisms of known schizophrenia genetic loci.Psychological medicineLiu M, Malone SM, Vaidyanathan U, Keller MC, Abecasis G, Mcgue M, Iacono WG, Vrieze SIDecember 2016Not Relevant
27899424Create StudyGenetic architecture of sporadic frontotemporal dementia and overlap with Alzheimer's and Parkinson's diseases.Journal of neurology, neurosurgery, and psychiatryFerrari R, Wang Y, Vandrovcova J, Guelfi S, Witeolar A, Karch CM, Schork AJ, Fan CC, Brewer JB, Momeni P, Schellenberg GD, Dillon WP, Sugrue LP, Hess CP, Yokoyama JS, Bonham LW, Rabinovici GD, Miller BL, Andreassen OA, Dale AM, Hardy J, Desikan RSFebruary 2017Not Relevant
27862206Create StudyMalformations of cortical development.Annals of neurologyDesikan RS, Barkovich AJDecember 2016Not Relevant
27774503Create StudyIs biological aging accelerated in drug addiction?Current opinion in behavioral sciencesBachi K, Sierra S, Volkow ND, Goldstein RZ, Alia-Klein NFebruary 2017Not Relevant
27739397Create StudyNeural predictors of alcohol use and psychopathology symptoms in adolescents.Development and psychopathologyBrumback TY, Worley M, Nguyen-Louie TT, Squeglia LM, Jacobus J, Tapert SFNovember 2016Not Determined
27539487Create StudyNeural Predictors of Initiating Alcohol Use During Adolescence.The American journal of psychiatrySqueglia LM, Ball TM, Jacobus J, Brumback T, Mckenna BS, Nguyen-Louie TT, Sorg SF, Paulus MP, Tapert SFAugust 2016Not Determined
27503447Create StudyEffects of Marijuana Use on Brain Structure and Function: Neuroimaging Findings from a Neurodevelopmental Perspective.International review of neurobiologyBrumback T, Castro N, Jacobus J, Tapert SJanuary 2016Not Relevant
27408790Create StudyRecreational marijuana use impacts white matter integrity and subcortical (but not cortical) morphometry.NeuroImage. ClinicalOrr JM, Paschall CJ, Banich MTJanuary 2016Not Determined
27288319Create StudyNeuroimaging cognitive reappraisal in clinical populations to define neural targets for enhancing emotion regulation. A systematic review.NeuroImageZilverstand A, Parvaz MA, Goldstein RZJune 2016Not Relevant
27175326Create StudyComorbid Cannabis and Tobacco Use in Adolescents and Adults.Current addiction reportsSubramaniam P, Mcglade E, Yurgelun-Todd DJune 2016Not Relevant
27001846Create StudyIndividual differences in frontolimbic circuitry and anxiety emerge with adolescent changes in endocannabinoid signaling across species.Proceedings of the National Academy of Sciences of the United States of AmericaGee DG, Fetcho RN, Jing D, Li A, Glatt CE, Drysdale AT, Cohen AO, Dellarco DV, Yang RR, Dale AM, Jernigan TL, Lee FS, Casey BJ, April 2016Not Determined
2605997Create StudyEthanol's effects on auditory thresholds and reaction times during the acquisition of chronic ethanol self-administration in baboons.Drug and alcohol dependenceHienz RD, Brady JV, Bowers DA, Ator NADecember 1989Not Relevant

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
Genomics/omics info icon
10,69904/24/2017
10,659
Approved
Processed MRI Data info icon
11,50001/15/2018
11,462
Approved
Evaluated Data info icon
11,50001/15/2018
11,880
Approved
Imaging (Structural, fMRI, DTI, PET, microscopy) info icon
11,50004/24/2017
11,808
Approved
Wearable Data info icon
15004/24/2017
150
Approved
Task Based info icon
4,52401/15/2018
0
Approved
ABCD Med History Measures info icon
11,50001/15/2018
11,875
Approved
ABCD Questionnaire Measures info icon
11,50001/15/2018
11,875
Approved
ABCD Task Based Measures info icon
11,50001/15/2018
11,875
Approved
ABCD Substance Use Measures info icon
11,50001/15/2018
11,875
Approved
ABCD Social Adjustment Measures info icon
11,50001/15/2018
11,875
Approved
ABCD Phys Characteristics Measures info icon
11,50001/15/2018
11,875
Approved
ABCD Phys Exam Measures info icon
11,50001/15/2018
11,875
Approved
ABCD Diagnostic Measures info icon
11,50001/15/2018
11,875
Approved
ABCD PTSD Measures info icon
11,50001/15/2018
11,860
Approved
ABCD Sleep Measures info icon
11,50001/15/2018
11,875
Approved
ABCD Activity Measures info icon
11,50001/15/2018
11,875
Approved
ABCD Socioeconomic Measures info icon
11,50001/15/2018
11,875
Approved
ABCD Personality Measures info icon
11,50001/15/2018
11,875
Approved
ABCD Behavior Measures info icon
11,50001/15/2018
11,875
Approved
ABCD Parenting Measures info icon
11,50001/15/2018
11,875
Approved
ABCD Trauma Measures info icon
11,50001/15/2018
11,875
Approved
ABCD Demographics Measures info icon
11,50001/15/2018
11,875
Approved
ABCD Cognitive Measures info icon
11,50001/15/2018
11,875
Approved
ABCD Summary Measures info icon
11,50001/15/2018
11,875
Approved
ABCD Treatment Measures info icon
11,50001/15/2018
11,875
Approved
ABCD Psychosis Measures info icon
11,50001/15/2018
11,875
Approved
Structure not yet defined

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

Associated Studies

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

Study NameAbstractCollection/Study SubjectsData UsageState
ABCD LPABackground: Predominant theories of addiction (and other disorders) emphasize the ‘developmental mismatch’ between cortical and subcortical structures as a key mechanism for heightened risk-seeking behaviors in early adolescence. Similarly, individuals at particular risk for substance-use initiation are hypothesized to have a greater maturational mismatch (i.e., faster subcortical relative to cortical development) than same age peers. Within this context, it is likely that, at a population level, (1) there exist subgroups of individuals that share similar patterns of neural function (e.g., increased striatal response during reward + decreased amygdala during emotion processing) and (2) that these subgroups may differ with respect to other common risk factors (e.g., internalizing and externalizing symptoms) and longer term outcomes (e.g., substance use initiation). However, this has not been tested previously in a sample such as ABCD. Approach: Use latent profile analysis (LPA) of ABCD fMRI data to identify subgroups of individuals based on neural functional responses at age 10 during performance of ABCD's fMRI tasks: (1) stop-signal (response inhibition) task; (2) affective n-back (working memory w/ emotional faces) task; (3) monetary incentive delay (reward) task. LPA’s will need to be conducted for right and left hemispheres separately. Sarah & Sarah will come up w/ 3 ROIs per task (total of 9 ROIs per hemisphere) for initial LPA. LPA's will be conducted using a split-half validation approach, in which the LPA is generated using half of the ABCD data set (n approximately 5,000) and tested/validated in the other half of the data (n approximately 5,000). 11875/11875Secondary AnalysisPrivate
Adolescent Brain Cognitive Development DEAP Study (ABCD)The purpose of the RDS file is for the implementation of DEAP for the most current release of ABCD Study data (Data Release 2.0.1). The variable names in DEAP have been modified from the official NDA variable names to make them easier to search using the data ontology implemented in the Explore module in DEAP. These DEAP names are listed as aliases in the NDA 2.0.1 release files. RDS 2.0.1 includes 218 tables, 129 of which are from the original ABCD Data Release 2.0 and 89 are from ABCD Data Release 2.0.1. Details are in the official Data Release 2.0.1 release notes.11875/11875Secondary AnalysisShared
Adolescent Brain Cognitive Development DEAP Study (ABCD) release 2.0.1 updateThe purpose of the RDS file is for the implementation of DEAP for the most current release of ABCD Study data (Data Release 2.0.1). The variable names in DEAP have been modified from the official NDA variable names to make them easier to search using the data ontology implemented in the Explore module in DEAP. These DEAP names are listed as aliases in the NDA 2.0.1 release files. RDS 2.0.1 includes 218 tables, 129 of which are from the original ABCD Data Release 2.0 and 89 are from ABCD Data Release 2.0.1. Details are in the official Data Release 2.0.1 release update notes.11875/11875Secondary AnalysisShared
Adolescent Brain Cognitive Development Study (ABCD) - Annual Release 2.0The ABCD Curated Annual Release 2.0 includes high quality baseline data from ~11,800 research participants, including minimally processed brain image volumes and tabulated structural MRI, diffusion MRI, resting-state fMRI and task fMRI results, as well as all non-imaging assessment data from the genetics, mental health, physical health, neurocognition, substance use, mobile technology, and culture & environment domains. All personally identifiable information is removed from the data to ensure participant confidentiality and anonymity. For a detailed description of all the measures included in this release, download the Curated Annual Release 2.0 Summary document. Problems have been identified with imaging data tables and associated data dictionaries for the following instruments: abcd_dti_p101, abcd_dti_p201, abcd_ddtidp101, abcd_ddtidp201, abcd_dmdtifp201, abcd_midasemdp201, abcd_midr1bwdp201, abcd_tr2bwdp201, abcd_midabwdp201, abcd_tmidr1semdp201, abcd_tr2semdp201. Corrected files will be available soon. An error was also discovered in imaging data collected from Siemens scanners between September 2017 and December 2017 where structural images are flipped left-right. These data will be updated in a patch release later this year. 11875/11875Primary AnalysisShared
Adolescent Brain Cognitive Development Study (ABCD) 2.0.1 releaseDue to reporting compilation and processing errors in 2.0 Data Release, a 2.0.1 Fix Release has been issued. Please ensure curated data (datasheets, minimally processed data) from the original Data Release 2.0 are replaced with data from 2.0.1 Fix Release. The following release notes were updated to reflect these changes: NDA 2.0.1 Release Notes ABCD README FIRST NDA 2.0.1 Release Notes Imaging Instruments NDA 2.0.1 Changes and Known Issues Fix Release 2.0.1 NDA 2.0.1 Diffusion Magnetic Resonance Imaging NDA 2.0.1 Task-Based Functional Magnetic Resonance Imaging NDA 2.0.1 Mental Health NDA 2.0.1 Genetics Registered users can obtain more information from https://nda.nih.gov/study.html?id=721, and access updated data via Option One or Option Two using the NDA Query Tool - https://nda.nih.gov/general-query.html.. For downloading only updated minimally processed imaging data, add the Minimally Processed "Release 2.0.1" to the Workspace via Option Two. 11875/11875Primary AnalysisShared
An IRT Analysis of the Prodromal Questionnaire-Brief Child Version: Developing a Screening Form that Informs Understanding of Self-Reported Psychotic-Like Experiences in ChildhoodThe Prodromal Questionnaire-Brief Child Version (PQ-BC) has been developed as a tool for identifying psychotic-like experiences (PLEs) in school-age children. The current study examined the psychometric properties of the PQ-BC, examined how well the PQ-BC estimates the latent construct of PLEs (θ ̂), and began the process of developing a screening form informed by item response theory (IRT). Utilizing the baseline (n=11,129) sample from the Adolescent Brain Cognitive Brain (ABCD) study, we examined which PQ-BC items provide the most information and best discriminate individuals experiencing PLEs. Using hierarchical linear models (HLMs), we found that θ ̂ scores were significantly associated with several previously identified predictors of psychosis spectrum symptoms (i.e., history of psychosis, internalizing symptoms, cognitive impairments, developmental milestone delays, and resting-state functional connectivity impairments) at baseline and year 1 (n=5,532). Using item level information and discrimination parameters of the PQ-BC from the baseline sample, we created a seven-item screening form. HLMs generally found significant associations between screening form scores for both baseline and year 1 with the aforementioned predictors. The analyses provide evidence for the validity of a screening form derived from the PQ-BC using IRT derived parameters. This screening form could prove useful when the full measure is not feasible. 11875/11875Secondary AnalysisShared
M145: Structural and Resting State Neural Correlates of Pediatric Obsessive-Compulsive Symptoms in the Adolescent Brain and Cognitive Development StudyBackground: Subclinical Obsessive-Compulsive symptoms (OCS) in childhood increase risk for later onset of Obsessive-Compulsive Disorder (OCD) and related impairment. Studying the neural circuits underlying subclinical OCS may facilitate the identification of neural markers of risk for later OCD as well as potential targets for novel mechanism-based interventions and prevention strategies. Yet, the neural mechanisms underlying OCS and their trajectories over development are poorly understood at present, though are hypothesized to involve differential engagement of task control circuits that underlie attentional and cognitive control processes (e.g. Maia et al., 2008). Dysfunction in these circuits and processes likely contributes to the repetitive thoughts and inappropriate actions that characterize OCS. While a growing literature has probed the neural underpinnings of OCD in children, including ENIGMA mega-analytic findings suggesting larger thalamic volumes in pediatric OCD (Boedhoe et al., 2017), few studies have examined subclinical OCS. One relatively larger study noted associations between OCS and altered gray and white matter volume in healthy children (Suñol et a., 2018). The Adolescent Brain and Cognitive Development (ABCD) provides an opportunity to examine associations between OCS and brain structure in the largest sample of children to date as well as to provide novel insight into associations with resting state connectivity of task control circuits. Methods: Data from the 2.0.1 release (July 2019) of baseline data from the ABCD Study were examined. These data include clinical interviews, cognitive testing, questionnaires, and MRI assessments from a nationally representative sample of N = 11,876 9-10-year-old children. An 8-item subscale for OCS severity (Hudziak et al., 2006) was ascertained from parent report on the Child Behavior Checklist (CBCL). Diagnosis of OCD was based on parent report on the Kiddie-Schedule for Affective Disorders and Schizophrenia for School-Age Children (KSADS). Cognitive performance was assessed using the NIH Toolbox. Of these children, n = 10,585 successfully completed T1 structural imaging that were analyzed using FreeSurfer and that passed ABCD quality control procedures. Resting state data was also collected and analyzed with the ABCD pipelines; n = 8,341 children had  >5 minutes of data retained after quality control. Within and between network connectivity was extracted from regions/networks defined in the Gordon et al., 2016 atlas. Linear mixed effects models were used to examine whether CBCL OCS related to cognitive performance, subcortical volumes, cortical thickness, or resting state connectivity of default mode and task control circuits. Results: N = 5,257 children (44.30%) exhibited non-zero CBCL OCS scores and, as expected, scores were elevated among the N = 898 children who met KSADS criteria for current OCD (b = 2.30, t = 36.82, p < .001, d = 1.35). CBCL OCS associated with worse performance on NIH Toolbox measures of inhibitory control, executive function, and working memory (all t < −2.2, p < .05). No associations between CBCL OCS and brain structure passed correction for multiple comparisons. CBCL OCS associated positively with resting state connectivity between the dorsal attention and default mode networks, the dorsal and ventral attention networks, and ventral attention and cingulo-parietal networks (all t > −2.79, p < .005). CBCL OCS associated negatively with connectivity within the dorsal attention network (t = −2.95, p = .003).11875/11875Secondary AnalysisShared
Neural correlates of response inhibition and executive control in children with extensive screen timePreliminary fMRI data reveals that total weekly screen-time interacts with sex in marginally predicting BOLD signal during an emotional N-Back task across frontal and parietal regions and significantly predicts stop signal failed stop performance in the right superior frontal region. However, given the few regions influenced and small effect-sizes, it appears the total screen-time is not likely an important predictor of neural functioning. Thus, fears regarding total screen-time may be overstated. Future research should focus on how screens are used, rather than total amount of time used.11875/11875Secondary AnalysisPrivate
Parental and Social Factors in relation to Child Psychopathology, Behavior, and Cognitive FunctionParental and social factors have long-term impact on the neurodevelopment of offspring, but tend to highly covary with each other. Thus, it is difficult to parse out which parental and social factor contributes most to neurodevelopmental outcomes. This study aimed to assess clusters of parental and social factors associated with child psychopathology, behavioral problems, and cognition. This study employed the data of 11,875 children (9-to-11 years) from the Adolescent Brain Cognitive Development (ABCD) study. Principal component analysis (PCA) was performed on 39 environmental measures and 30 child behavior and cognitive measures separately to identify clusters of parental and social factors and clusters of child psychopathology, behaviour, and cognition. Regression analysis was used to examine independent effects of each cluster of parental and social factors on child psychopathology, behavioral problems, and cognition. Greater Parent Psychopathology cluster was associated with greater Child Psychopathology cluster. Moreover, greater Socioeconomic Status cluster was associated with greater child General Cognition and Executive Function but less Behavioral Inhibition clusters. Greater Proximal Social Environment and Interaction cluster were associated with less child Impulsive Behavior and Behavioral Inhibition, but greater Behavioral Activation cluster. The environmental clusters related to birth outcomes, maternal tobacco, and drug use were not significantly related to child psychopathology, behavior, and cognition. Our findings suggest that socioeconomic status, parental psychopathology, and social environment and interactions are the strongest risks for behavioral problems and cognitive performance in a general child population. Intervention programs should target modifiable factors within these domains.11875/11875Secondary AnalysisPrivate
Screen Time and Other Determinants of Mental Health Predicting Emerging Psychotic-like Experiences in 9-10 Year Old ChildrenIMPORTANCE: Worsening child psychotic-like experiences (PLEs) are risk factors of poor future mental health including full blown psychotic illness. Identifying readily assessable indicators for worsening PLEs are therefore of great interest. OBJECTIVE: To examine relationships between a set of indicators (behaviors and stressors) previously associated with mental health (screen time, school environment, neighborhood safety, family conflict, parental (caregiver) acceptance, and sleep habits) and worsening PLE severity. DESIGN, SETTING, AND PARTICIPANTS: This prospective study included 4296 children (mean age 10 years (standard deviation = 0.7)), 52% boys) from the Adolescent Brain and Cognitive Development study. MAIN OUTCOMES AND MEASURES: The primary outcome was 12-month distress score of the Prodromal-Questionnaire Brief-Child (PQ-BC) Version, modeled after controlling for age, sex, race, ethnicity, parental marital status/education, and baseline PQ-BC severity. RESULTS: Significant baseline indicators of worsening PLE severity by 12-month follow-up included high screen time (46% increase for ≥ 4.6 hours/day (vs. 0 to 1.5 hours/day), 95% CI = 28% to 66%), living in an unsafe neighborhood (30% increase in most unsafe (vs. safest), 95% CI = 6% to 61%), high family conflict (30% increase with 2-3 and 16% increase with ≥ 4 "yes" responses to questions re: conflict (vs. none), 95% CIs = 16% to 46% and 2 to 32%, respectively), lack of parental acceptance (13% decrease in group with highest possible acceptance (vs. low group), 95% CI = -24% to -2%), and sleeping < 9 hours/night (16% increase (vs. sleeping 9-11 hours/night), 95% CI = 7% to 27%). Examining screen time variants, significant associations were observed with time watching videos (33% increase for ≥ 1.2 hours/day (vs. 0 to 0.1 hours/day), 95% CI = 16% to 52%) and time texting (29% decrease when texting 0.1-0.2 hours/day (vs. none), 95% CI = -41% to -15%). An interaction between sex and video chatting was also observed, as video chatting was a negative indicator in girls but not boys. CONCLUSIONS AND RELEVANCE: Screen time, crime, and family environment were identified as future indicators of mental health risk. These results suggest the value of prospective evaluation of behavior and stressors in identifying at risk groups. 11875/11875Primary AnalysisShared
Obesity and Eating Disorder Disparities among Sexual and Gender Minority YouthObesity and eating disorders (EDs) in youth are prevalent, associated with medical and psychosocial consequences, and may persist into adulthood; therefore identifying subgroups of youth vulnerable to one or both conditions is critical. One group that may be at-risk for obesity and disordered eating is sexual and gender minorities (SGM; those who identify as lesbian, gay, bisexual, and/or transgender or whose sexual orientation or gender identity/expression do not conform to societal conventions). Though SGM identities may begin in childhood and early adolescence, many studies assess older adolescents and adults, and rely upon self-reported weight and eating pathology. Given the adverse sequelae of obesity and EDs, the identification of disparities among SGM youth has implications for clinical practice and public health.11852/11852Secondary AnalysisShared
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Prevalence and Correlates of Concussion in Children: Data from the Adolescent Brain Cognitive Development StudyIntroduction: Concussions are one of the most common causes for emergency room use in the United States (US) among youth and adolescents; however, prevalence data on concussion in this population are inconsistent. A growing body of literature has explored associations of a range of variables with pediatric concussion, but they have not been explored simultaneously in a well-powered sample in the United States. The present study aimed to present lifetime concussion prevalence, evaluate demographic, psychological, and cognitive correlates of concussion, and assess for differences across these variables based on age of first concussion in a large sample of US children. Methods: We analyzed the Adolescent Brain Cognitive Development (ABCD) sample, which monitors biopsychosocial development in 11,875 children at 21 sites across the US between ages 9 and 10. Along with presenting rates of concussion, we also evaluated the association of demographics, sleep disturbance, cognitive functioning, and externalizing and internalizing symptoms with concussion history using backwards binary logistic regression. We further conducted univariate comparisons of all variables between those who experienced their first concussion before and after age 5. Significance was based on α = .02, with Benjamini-Hochberg FDR adjustments for multiple comparisons. Results: We found approximately 4% of the sample had experienced a concussion, and significant correlates of experiencing a concussion were male sex, increased family income, and higher somatic symptoms after FDR correction. Symptoms of ADHD were also noted as nominally significant. No differences based on age of first concussion were found. Discussion: Our analyses provided updated prevalence estimates of pediatric concussion in the US that aligns with many hospital records-based studies. The strongest correlates we found largely mirrored those in the literature with the exception of somatic symptoms. Limitations of findings and implications of individual findings are discussed.11655/11655Secondary AnalysisPrivate
35.2 Probing Structural and Functional Subcortical Regions Implicated in Youth DepressionBackground: Prior structural and functional neuroimaging research in adolescent major depressive disorder (MDD) has consistently implicated abnormalities in subcortical regions (Auerbach et at., 2014; Luking et al., 2016). However, research has often relied on sample sizes that limit power to detect effects that are presumed to be small. Additionally, heterogeneity in disease course and treatment history undoubtedly affects the reliable identification of structural and functional abnormalities among unaffected, high-risk youth as well as youth diagnosed with MDD. To reconcile inconsistent structural and functional neuroimaging findings, the presentation will leverage data from the Adolescent Brain and Cognitive Development (ABCD) Study and Boston Adolescent Neuroimaging of Depression and Anxiety (BANDA). ABCD is a multi-site project that was designed to assess normal variability in adolescent brain and cognitive development among 9-10-year-old children. By contrast, BANDA is a human connectome project that aims to characterize neural circuitry underlying depression and anxiety in adolescents ages 14-16 years. Collectively, these projects afford a unique opportunity to probe subcortical abnormalities in at-risk and currently depressed youth. Methods: The ABCD Study acquired structural MRI data from 9-10-year-old children (n = 4,521). Of these children, 29.7% (n = 1,343) had a parental depressive history. Secondary analyses also tested whether subcortical brain differences were present in youth with a lifetime depressive disorder history. For BANDA, adolescents (n = 141) completed an incentive processing task while fMRI data were collected. Primary analyses probed differences in subcortical activation, and secondary analyses will test whether blunted activation within striatal regions related to anhedonia and a history of suicidal thoughts and behaviors. Results: Several findings emerged. Within ABCD, relative to low-risk youth, high-risk participants with a maternal, but not paternal, depression history exhibited smaller volumes of the right putamen, right accumbens, and left pallidum (FDR-corrected p < 0.05, p < 0.002, t < −2.57) as well as smaller left amygdala volumes (this latter finding did not pass FDR correction). As expected, depressive disorders were more common among those with a parental history of depression (15.96% [parental depressive history] vs. 8.72% [no parental depressive history]; χ2(1) = 47.36, p = 5.90 x 10–12), but there were no significant associations (after FDR correction) between subcortical volumes and children’s depressive disorder history. Among all BANDA participants, there was greater activation in the nucleus accumbens for reward versus loss (t(140) = 10.00, p < 0.001). Preliminary analyses showed that the reward-loss contrast activation was blunted in adolescents with depression and anxiety (B = −0.47, t = −2.29, p = 0.02). For adolescents with depression and anxiety, incentive-related activation was altered in a number of other regions in whole brain analyses, including reduced anterior insula and anterior cingulate activation as well as increased activation in the mPFC and posterior cingulate.11534/11534Secondary AnalysisShared
A Family-Built Brain: Associations between family environment and child brain function and structureThis project examines the relation between family environment (FE) and brain functioning and structure. We hypothesize that an unsupportive FE accelerates brain development, and will examine whether pubertal status mediates the relation between FE and brain functioning. FE will be measured by a latent construct combining questionnaire data on family relationships and demographical information, such as socioeconomic status and parental marital status, using structural equation modeling in MPlus. Pubertal status will be measured by the Pubertal Development Scale and sex hormones. Brain function and structure will be assessed using task-based fMRI (monetary incentive delay task), resting-state fMRI, DTI and T1 weighted scans. 10966/10966Secondary AnalysisPrivate
Neighborhood deprivation, prefrontal structure, and cognitive function BACKGROUND: Neighborhood deprivation adversely effects neurodevelopment and cognitive function; however, mechanisms remain unexplored. Neighborhood deprivation could be particularly impactful in late childhood/early adolescence, in neural regions with protracted developmental trajectories, e.g., prefrontal cortex (PFC). METHODS: The Adolescent Brain Cognitive Development (ABCD) study recruited 10,205 youth. Geocoded residential history was used to extract individual neighborhood characteristics. A general cognitive ability index and MRI scans were completed. Associations with neurocognition were examined. The relation of PFC surface area and cortical thickness to neighborhood deprivation was tested. PFC subregions and asymmetry, with putative differential environmental susceptibility during key developmental periods, were explored. Analyses tested PFC area as a possible mediating mechanism. RESULTS: Neighborhood deprivation predicted neurocognitive performance (β = - 0.11), even after accounting for parental education and household income (β = -0.07). Higher neighborhood deprivation related to greater overall PFC surface area (η p 2 = 0.003), and differences in leftward asymmetry were observed for area (η p 2 = 0.001), and thickness (η p 2 = 0.003). Subregion analyses highlighted differences among critical areas that are actively developing in late childhood/early adolescence and are essential to modulating high order cognitive function. These included orbitofrontal, superior frontal, rostral middle frontal, and frontal pole regions (Cohen’s d = 0.03-0.09). PFC surface area partially mediated the relation between neighborhood deprivation and neurocognition. DISCUSSION: Neighborhood deprivation related to cognitive function (a foundational skill tied to a range of lifetime outcomes) and PFC morphology, with evidence found for partial mediation of PFC on neurocognitive function. Results inform public health conceptualizations of development and environmental vulnerability.10204/10204Secondary AnalysisShared
Neighborhood deprivation shapes motivational neurocircuit recruitment in childrenImplementing motivated behaviors based on prior reward is central to adaptive human functioning, but aberrant reward-motivated behavior is a core feature of neuropsychiatric illness. Children from disadvantaged neighborhoods have decreased access to rewards, which may shape motivational neurocircuits and risk for psychopathology. Here, we leverage the unprecedented neuroimaging data from the Adolescent Brain Cognitive Development (ABCD) study to test the hypothesis that neighborhood socioeconomic disadvantage shapes the functional recruitment of motivational neurocircuits in children. Specifically, via ABCD’s Monetary Incentive Delay task (N=6,396 9-10 year old children), we find that children from zip codes with a high Area Deprivation Index (ADI) demonstrate blunted recruitment of striatum (dorsal and ventral nuclei) and pallidum during reward anticipation. In fact, blunted dorsal striatal recruitment during reward anticipation mediated the association between ADI and increased attention problems. These data reveal a candidate mechanism driving elevated risk for psychopathology in children from socioeconomically disadvantaged neighborhoods. 6396/6396Secondary AnalysisShared
Adolescent Brain Cognitive Development Study (ABCD) - Annual Release 1.0The ABCD Curated Annual Release 1.0 includes high quality baseline data from the first ~4,500 research participants, including minimally processed brain image volumes and tabulated structural MRI, diffusion MRI, resting-state fMRI and task fMRI results, as well as all non-imaging assessment data from the physical & mental health, neurocognition, substance use, biospecimens and culture & environment domains. All personally identifiable information is removed from the data to ensure participant confidentiality and anonymity. For a detailed description of all the measures included in this release, download the Curated Annual Release 1.0 Summary document.4521/4521Primary AnalysisShared
Adolescent Brain Cognitive Development Study (ABCD) - Annual Release 1.1The ABCD Curated Annual Release 1.1 includes high quality baseline data from the first ~4,500 research participants, including minimally processed brain image volumes and tabulated structural MRI, diffusion MRI, resting-state fMRI and task fMRI results, as well as all non-imaging assessment data from the genetics, physical & mental health, neurocognition, substance use, biospecimens and culture & environment domains. All personally identifiable information is removed from the data to ensure participant confidentiality and anonymity. For a detailed description of all the measures included in this release, download the Curated Annual Release 1.1 Summary document.4521/4521Primary AnalysisShared
Demographic, Psychological, Behavioral, and Cognitive Correlates of BMI in Youth: Findings from the Adolescent Brain Cognitive Development (ABCD) StudyBackground: Previous research has implicated demographic, psychological, behavioral, and cognitive variables in the onset and maintenance of pediatric overweight/obesity. No adequately-powered study has simultaneously modeled these variables to assess their relative associations with body mass index (BMI; kg/m2) in a nationally representative sample of youth. Methods: Multiple machine learning regression approaches were employed to estimate the relative importance of 43 demographic, psychological, behavioral, and cognitive variables previously associated with BMI in youth to elucidate the associations of both fixed (e.g., demographics) and potentially modifiable (e.g., psychological/behavioral) variables with BMI in a diverse representative sample of youth. The primary analyses consisted of 9-10 year olds divided into a training (n = 2724) and test (n = 1123) sets. Secondary analyses were conducted by sex, ethnicity, and race. Results: The full sample model captured 12% of the variance in both the training and test sets, suggesting good generalizability. Stimulant medications and demographic factors were most strongly associated with BMI. Lower attention problems and matrix reasoning (i.e., nonverbal abstract problem solving and inductive reasoning) and higher social problems and screen time were robust positive correlates in the primary analyses and in analyses separated by sex. Conclusions: Beyond demographics and stimulant use, this study highlights abstract reasoning as an important cognitive variable and reaffirms social problems and screen time as significant correlates of BMI and as modifiable therapeutic targets. Prospective data are needed to understand the predictive power of these variables for BMI gain.4521/4521Secondary AnalysisShared
Association Between Childhood Anhedonia and Alterations in Large-scale Resting-State Networks and Task-Evoked ActivationIMPORTANCE: Anhedonia can present in children and predict detrimental clinical outcomes. OBJECTIVE: To map anhedonia in children onto changes in intrinsic large-scale connectivity and task-evoked activation and to probe the specificity of these changes in anhedonia against other clinical phenotypes (low mood, anxiety, and attention-deficit/hyperactivity disorder [ADHD]). DESIGN, SETTING, AND PARTICIPANTS: Functional magnetic resonance imaging (fMRI) data were from the first annual release of the Adolescent Brain Cognitive Development study, collected between September 2016 and September 2017 and analyzed between April and September 2018. Cross-sectional data of children aged 9 to 10 years from unreferred, community samples during rest (n = 2878) and during reward anticipation (n = 2874) and working memory (n = 2745) were analyzed. MAIN OUTCOMES AND MEASURES: Alterations in fMRI data during rest, reward anticipation, and working memory were examined, using both frequentist and Bayesian approaches. Functional MRI connectivity within large-scale networks, between networks, and between networks and subcortical regions were examined during rest. Functional MRI activation were examined during reward anticipation and working memory using the monetary incentive delayed and N-back tasks, respectively. RESULTS: Among 2878 children with adequate-quality resting-state fMRI data (mean [SD] age, 10.03 [0.62] years; 1400 girls [48.6%]), children with anhedonia (261 [9.1%]), compared with those without anhedonia (2617 [90.9%]), showed hypoconnectivity among various large-scale networks and subcortical regions, including between the arousal-related cingulo-opercular network and reward-related ventral striatum area (mean [SD] with anhedonia, 0.08 [0.10] vs without anhedonia, 0.10 [0.10]; t2,876 = 3.33; P < .001; q[false discovery rate] = 0.03; ln[Bayes factor10] = 2.85). Such hypoconnectivity did not manifest among children with low mood (277 of 2878 [9.62%]), anxiety (109 of 2878 [3.79%]), or ADHD (459 of 2878 [15.95%]), suggesting specificity. Similarly, among 2874 children (mean [SD] age, 10.03 [0.62] years; 1414 girls [49.2%]) with high-quality task-evoked fMRI data, children with anhedonia (248 of 2874 [8.63%]) demonstrated hypoactivation during reward anticipation in various areas, including the dorsal striatum and areas of the cingulo-opercular network. This hypoactivity was not found among children with low mood (268 of 2874 [9.32%]), anxiety (90 of 2874 [3.13%]), or ADHD (473 of 2874 [16.46%]). Moreover, we also found context- and phenotype-specific double dissociations; while children with anhedonia showed altered activation during reward anticipation (but not working memory), those with ADHD showed altered activation during working memory (but not reward anticipation). CONCLUSIONS AND RELEVANCE: Using the Adolescent Brain Cognitive Development study data set, phenotype-specific alterations were found in intrinsic large-scale connectivity and task-evoked activation in children with anhedonia. The hypoconnectivity at rest and hypoactivation during reward anticipation complementarily map anhedonia onto aberrations in neural-cognitive processes: lack of intrinsic reward-arousal integration during rest and diminishment of extrinsic reward-arousal activity during reward anticipation. These findings help delineate the pathophysiological underpinnings of anhedonia in children.4520/4520Secondary AnalysisShared
Differential Relationships of Child Anxiety and Depression to Child Report and Parent Report of Electronic Media UseChild depression and anxiety have been associated with electronic media use, but the comorbidity between the two has rarely been accounted for in analyses. We examined both child and parent reports of electronic media use in relation to parent-reported child depression and anxiety. Using survey and interview data collected for 9- to 11-year-olds from the 21-site Adolescent Brain Cognitive Development Study, we conducted generalized linear mixed models. Our results demonstrated that electronic media use was more strongly associated with depression than anxiety, and that accounting for depression significantly reduced the relationship between electronic media use and anxiety. Different categories of electronic media showed differential relationships to anxiety and depression, with video gaming and video chatting related to anxiety, but video watching related to depression. These findings provide important data to ground theories of the mechanisms that contribute to these associations.4520/4520Primary 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 releases4516/4516Secondary AnalysisShared
Assessment of the Prodromal Questionnaire-Brief Child Version for Measurement of Self-Reported Psychoticlike Experiences in ChildhoodIMPORTANCE: Childhood psychoticlike experiences (PLEs) are associated with greater odds of a diagnosis of a psychotic disorder during adulthood. However, no known, well-validated self-report tools have been designed to measure childhood PLEs. OBJECTIVE: To examine the construct validity and psychometric properties of a measure of PLEs, the Prodromal Questionnaire-Brief Child Version (PQ-BC). DESIGN, SETTING, AND PARTICIPANTS: This validation study used data from the first wave of the Adolescent Brain and Cognitive Development (ABCD) Study, a prospective longitudinal study aimed at assessing risk factors associated with adverse physical and mental health outcomes from ages 9 to 10 years into late adolescence and early adulthood. The population-based sample of 3984 children within the ABCD data set was recruited from 20 research sites across the United States. Data for this study were collected from June 1, 2016, through August 31, 2017. MAIN OUTCOMES AND MEASURES: The PQ-BC Total and Distress scores were analyzed for measurement invariance across race/ethnicity and sex, their associations with measures of PLEs, and their associations with known correlates of PLEs, including internalizing and externalizing symptoms, neuropsychological test performance, and developmental milestones. RESULTS: The study analyses included 3984 participants (1885 girls [47.3%] and 2099 boys [52.7%]; mean [SE] age, 10.0 [0.01] years). The results demonstrated measurement invariance across race/ethnicity and sex. A family history of psychotic disorder was associated with higher mean (SE) PQ-BC Total (3.883 [0.352]; β = 0.061; 95% CI, 0.027-0.094) and Distress (10.210 [1.043]; β = 0.051; 95% CI, 0.018-0.084) scores, whereas a family history of depression or mania was not. Higher PQ-BC scores were associated with higher rates of child-rated internalizing symptoms (Total score: β range, 0.218 [95% CI, 0.189-0.246] to 0.273 [95% CI, 0.245-0.301]; Distress score: β range, 0.248 [95% CI, 0.220-0.277] to 0.310 [95% CI, 0.281-0.338]), neuropsychological test performance deficits such as working memory (Total score: β = -0.042 [95% CI, -0.077 to -0.008]; Distress score: β = -0.051 [95% CI, -0.086 to -0.017]), and motor and speech developmental milestone delays (Total score: β = 0.057 [95% CI, 0.026-0.086] for motor; β = 0.042 [95% CI, 0.010-0.073] for speech; Distress score: β = 0.048 [95% CI, 0.017-0.079] for motor; β = 0.049 [95% CI, 0.018-0.081] for speech). CONCLUSIONS AND RELEVANCE: These results provide support for the construct validity and demonstrate adequate psychometric properties of a self-report instrument designed to measure childhood PLEs, providing evidence that the PQ-BC may be a useful measure of early risk for psychotic disorders. Furthermore, these data suggest that PLEs at school age are associated with many of the same familial, cognitive, and emotional factors associated with psychotic symptoms in older populations, consistent with the dimensionality of psychosis across the lifespan. 3982/3982Secondary AnalysisShared
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. 3921/3921Secondary 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 releases3728/3739Secondary AnalysisShared
Resting State Functional Connectivity and Psychotic-Like Experiences in Childhood: Results from the Adolescent Brain Cognitive Development StudyBackground: Psychotic-like experiences (PLEs) during childhood are associated with greater risk of developing a psychotic disorder, highlighting the importance of identifying neural correlates of childhood PLEs. Three major cortical networks- the cingulo-opercular network (CON), default mode network (DMN), and fronto-parietal network (FPN)- are consistently implicated in psychosis as well as PLEs in adults. However, it is unclear whether variation in functional connectivity is associated with PLEs in school-aged children. Methods: Using hierarchical linear models, we examined the relationships between childhood PLEs and resting-state functional connectivity of the CON, DMN, and FPN, as well as the other networks using an a priori network parcellation, using data from 3,434 9-10-year-olds in the Adolescent Brain Cognitive Development (ABCD) study. We examined within-network, between-network, and subcortical connectivity. Results: Decreased CON and DMN connectivity, as well as cingulo-parietal (CPAR) network connectivity, were associated with greater PLEs, even after accounting for family history of psychotic disorders, internalizing symptoms, and cognitive performance. Decreased DMN network connectivity was more strongly associated with increased delusional ideation, whereas decreased CON connectivity was more strongly associated with increased perceptual distortions. Increased CON-cerebellar and decreased CPAR-cerebellar connectivity were also associated with increased PLEs, and CPAR-cerebellar connectivity was more strongly associated with increased perceptual distortions. Conclusion: Consistent with hypotheses about the dimensionality of psychosis, our results provide evidence that neural correlates of PLEs, such as reduced functional connectivity of higher-order cognitive networks, are present even in school-aged children. Therefore, the results provide further validation for continuity of PLEs across the lifespan. 3434/3434Secondary AnalysisShared
Distinguishing Remitted Bipolar Disorder from Remitted Unipolar Depression in Pre-Adolescent Children: A Neural Reward Processing PerspectiveBipolar disorder (BD) is often misdiagnosed as unipolar depression (UD), highlighting the need to identify clinically useful markers to differentiate them. To provide insights into this endeavor, the current study will employ functional magnetic resonance imaging and conduct region-of-interest (ROI; ventral striatum and orbitofrontal cortex), whole-brain, and connectivity analyses to examine the similarities and differences between children with BD, children with UD, and healthy controls (HCs) in brain activation patterns and functional coupling between brain regions within the context of reward processing, as evoked by the Monetary Incentive Delay task. The current study represents the first examination of neural reward processing in preadolescent children with remitted BD or UD. We aim to (a) test whether preadolescent children with remitted BD or UD display abnormal patterns of neural activation and connectivity in response to reward, relative to HCs and (b) compare remitted UD and BD directly with each other to evaluate whether they can be distinguished by neural activation and connectivity during reward processing826/826Secondary AnalysisShared
Reward processing in preadolescents with bipolar disorder: An fMRI studyIntroduction: Reward processing dysfunction has long been implicated in adults with bipolar disorder. Nevertheless, little research has been conducted to examine whether such dysregulation also occurs in preadolescents with bipolar disorder. Methods: The current study will employ functional magnetic resonance imaging and conduct region-of-interest (ventral striatum and orbitofrontal cortex), whole-brain, and connectivity analyses to examine the similarities and differences in reward-related brain activation patterns, evoked by the Monetary Incentive Delay task, between 169 preadolescents with remitted bipolar disorder and 245 preadolescent healthy controls without personal and family history of Axis I disorders. Results: We hypothesize that activity in the prefrontal cortex (PFC) and the striatum would be elevated in preadolescents with remitted bipolar disorder during reward processing, relative to healthy controls. We also predict aberrant connectivity between the PFC and the striatum in response to reward in preadolescents with remitted bipolar disorder, relative to healthy controls. Conclusions: Early-onset bipolar disorder is often associated with remarkably long treatment delays and a persistently pernicious course of illness, underscoring the significance of studying mood disorders during this developmental period. Owing to the paucity of research and data on preadolescent bipolar disorder, accurate diagnosis in this population is challenging. Identifying objective markers of preadolescent bipolar disorder has the potential to enhance our ability to diagnose pediatric bipolar disorder accurately. Besides, early accurate diagnosis may improve our ability to intervene with appropriate treatments that may lead to a more benign course of bipolar disorder in adolescence and adulthood. 604/604Secondary AnalysisShared
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 releases414/415Secondary AnalysisShared
Sexual dimorphism and laterality in neurostructural development from late childhood to early adulthood: A cross-sectional voxel based morphometry studyIntro: Adolescence is a sensitive period for social, emotional, risk, and reward behavior and is an onset period for serious psychiatric disorders. Changes in behavior in adolescence may be mediated by the rapid changes in brain structure observed during this time period. While adolescent structural development has been extensively analyzed, less is known about the developmental effects of sex or lateralized differences. Understanding how the brain typically develops during these critical periods may give insight into when structural deviations occur that result in psychopathology. Methods: Structural Magnetic Resonance Imaging (MRI) scans were obtained from samples of children (age 9-11, n=344), adolescents (age 13-14, n=271), and adults (age 22-25, n=56) using the same scanner and acquisition sequences. Gray and white matter densities and volumes were assessed using voxel-based morphometry, as were ventricular volumes, total tissue volumes, and average tissue densities. Asymmetry indices were created by comparing the left and right hemispheres of each individual’s brain maps. Age, sex, an age and sex interaction, age2, and an age2 and sex interaction were examined in a simultaneous multiple regression for each brain metric. Results: Gray matter density and volume declined with age, while white matter increased. Males had greater total volumes while females had greater white matter densities. After correcting for total tissue volume, local sex differences were largely greater in females. Laterality analyses suggested that medial brain structures developed earlier in the right hemisphere. Conclusions: Our findings suggest structural changes occur throughout adolescence and likely continue past early adulthood in frontal structures, primarily in white matter. Changes in density appear to precede volumetric changes. Males had larger global, not local, brain volumes. Total gray matter volume and average white matter densities changed faster in females. Lateralized differences in developmental timing offer a new line of investigation in adolescent approach and avoidance behavior.344/344Primary AnalysisPrivate
Growth Effects on Velopharyngeal Anatomy From Childhood to AdulthoodPurpose: The observed sexual dimorphism of velopharyngeal structures among adult populations has not been observed in the young child (4- to 9-year-old) population. The purpose of this study was to examine the age at which sexual dimorphism of velopharyngeal structures become apparent and to examine how growth trends vary between boys and girls. Method: Static 3-dimensional magnetic resonance imaging velopharyngeal data were collected among 202 participants ranging from 4 to 21 years of age. Participants were divided into 3 groups based on age, including Group 1: 4–10 years of age, Group 2: 11–17 years of age, and Group 3: 18–21 years of age. Nine velopharyngeal measures were obtained and compared between groups. Results: Significant sex effects were evident for levator length (p = .011), origin to origin (p = .018), and velopharyngeal ratio (p = .036) for those in Group 2 (11–17 years of age). Sex effects became increasingly apparent with age, with 7 of 9 variables becoming significantly different between male and female participants in Group 3. Boys, in general, displayed a delayed growth peak in velopharyngeal growth compared to girls. Conclusion: Results from this study demonstrate the growth of velopharyngeal anatomy with sexual dimorphism becoming apparent predominantly after 18 years of age. However, velopharyngeal variables displayed variable growth trends with some variables presenting sexual dimorphism at an earlier age compared to other velopharyngeal variables.19/42Secondary AnalysisPrivate
* Data not on individual level
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