NDA Help Center

Collection - General Tab

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

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

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

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

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

Blinded Clinical Trial Status:

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

Funding Source

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

Supporting Documentation

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

Grant Information 

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

Clinical Trials

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

Frequently Asked Questions

  • When a Collection is created by NDA staff and marked as Shared, an email notification will automatically be sent to the PI(s) of the grant(s) associated with the Collection to notify them.

  • During Collection creation, NDA staff determine the appropriate Permission Group based on the type of data to be submitted, the type of access that will be available to data access users, and the information provided by the Program Officer during grant award.

  • The NDA system does not allow for a single grant to be associated with more than one Collection; therefore, a single grant will not be listed in the Grant Information section of a Collection for more than one Collection.

  • In general, each Collection is associated with only one grant; however, multiple grants may be associated if the grant has multiple competing segments for the same grant number or if multiple different grants are all working on the same project and it makes sense to hold the data in one Collection (e.g., Cooperative Agreements).

Glossary

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

  • A privilege provided to a user associated with an NDA Collection or NDA Study whereby that user can perform a full range of actions including providing privileges to other users. 

  • Generally, the Collection Owner is the contact PI listed on a grant. Only one NDA user is listed as the Collection owner. Most automated emails are primarily sent to the Collection Owner.

  • The Collection Phase provides information on data submission as opposed to grant/project completion so while the Collection phase and grant/project phase may be closely related they are often different.  Collection users with Administrative Privileges are encouraged to edit the Collection Phase.  The Program Officer as listed in eRA (for NIH funded grants) may also edit this field. Changes must be saved by clicking the Save button at the bottom of the page.  This field is sortable alphabetically in ascending or descending order. Collection Phase options include: 

    • Pre-Enrollment:  A grant/project has started, but has not yet enrolled subjects.
    • Enrolling:  A grant/project has begun enrolling subjects.  Data submission is likely ongoing at this point.
    • Data Analysis:  A grant/project has completed enrolling subjects and has completed all 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.

  • Number of human subject participants to be enrolled in an NIH-funded clinical research study. The data is provided in competing applications and annual progress reports.

  • Various documents and materials to enable efficient use of the data by investigators unfamiliar with the project and may include the research protocol, questionnaires, and study manuals.  

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

  • 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

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

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

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

If a different filter type is added to your Workspace, or a filter has already been submitted to the Filter Cart, the operation then performs a logical ‘AND’ operation. This means that given the subjects returned from the first filter, only those subjects that matched the first filter are returned by the second filter (i.e., subjects that satisfied both filters). Note that only the subjects specific to your filter will be added to your Filter Cart and only on data shared with the research community. Other data for those same subjects may exist (i.e., within another NDA Collection, associated with a data structure that was not requested in the query, etc.). So, users should select ‘Find all Subjects Data’ to identify all data for those specific subjects. 

Additional Tips:

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

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

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

Frequently Asked Questions

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

  • Viewable at the top right of NDA pages, the Filter Cart is a temporary holder of data identified by the user, through querying or browsing, as being of some potential interest. The Filter Cart is where you send the data from your Workspace after it has been filtered.

  • After filters are added to the Filter Cart, users have options to ‘Create a Package’ for download, ‘Associate to Study Cohort’, or ‘Find All Subject Data’. Selecting ‘Find All Subject Data’ identifies and pulls all data for the subjects into the Filter Cart. Choosing ‘Create a Package’ allows users to package and name their query information for download. Choosing ‘Associate to Study Cohort’ gives users the opportunity to choose the Study Cohort they wish to associate this data.

Glossary

  • Once your filter cart contains the subjects of interest, select Create Data Package/Assign to Data Study which will provide options for accessing item level data and/or assigning to a study.  

  • Once queries have been added to your workspace, the next step is to Submit the Filters in the workspace to the Filter Cart.  This process runs the queries selected, saving the results within a filter cart attached to your account.  

  • The Workspace within the General Query Tool is a holding area where you can review your pending filters prior to adding them to Filter Cart. Therefore, the first step in accessing data is to select one or more items and move it into the Workspace. 

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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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1 Numbers reported are subjects by age
New Trial
New Project

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

Please select an experiment type below

Collection - Use Existing Experiment

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

The table search feature is case insensitive and targets the experiment id, experiment name and experiment type columns. The experiment id is searched only when the search term entered is a number, and filtered using a startsWith comparison. When the search term is not numeric the experiment name is used to filter the results.

SelectExperiment IdExperiment NameExperiment Type
  • Select One
  • EEG
  • EGG
  • Eye Tracking
  • Omics
  • fMRI
Created On
1604Bulk ATAC-seq (single reads)Omics09/18/2020
1603Bulk ATAC-seq (paired reads)Omics09/18/2020
1602CNPGeneticDataOmics09/14/2020
1601Perception-Attention-Memory TaskEEG09/04/2020
1600Saliva sample genotypingOmics09/04/2020
1599Infant Face Matching task -Cry VersionEEG09/02/2020
1598Infant Face Matching Task -No Cry VersionEEG08/31/2020
1597Exome sequencingOmics08/21/2020
1596Whole genome sequence on Utah suicide deathsOmics08/21/2020
1595WGS with BGI (bulk urine)Omics08/20/2020
1594Capture-seq resequencingOmics08/20/2020
1593WGS with BGI PCR-free (for iPSC lines, bulk blood and bulk saliva)Omics08/20/2020
1592Whole Exome Sequence on Utah suicide deathsOmics08/19/2020
1591Phenotypic Characterization of Psychosis Dimensions in 22q11.2 Deletion SyndromeOmics08/12/2020
1590Utah suicide deaths: Illumina PsychArray SNP genotypingOmics08/05/2020
1589Initial Emotion Regulation ProtocolEye Tracking07/22/2020
1588Emotional Working Memory (eWM)/Placebo TaskfMRI07/22/2020
1586Emotion Regulation Transfer Task (ERTT)fMRI07/22/2020
1585UCSD_Gleeson_U01MH108898_snMPAS_AmpliSeq_NovaSeqOmics07/20/2020
1584UCSD_Gleeson_U01MH108898_MPAS_AmpliSeq_NovaSeqOmics07/20/2020
1583Genomic basis of pediatric bipolar disorderOmics07/13/2020
1582Stop-signal TaskfMRI07/08/2020
1581n-Back taskfMRI07/08/2020
1580MoviesfMRI07/08/2020
1579gradCPTfMRI07/08/2020
1578Reading the Mind in the Eyes taskfMRI07/08/2020
1577Card Guessing TaskfMRI07/08/2020
1576resting-state fMRIfMRI07/07/2020
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
Collection - Add Experiment
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Please enter the name of the data structure to search or if your definition does not exist, please upload that definition so that it can be appropriately defined for submission. Multiple data structures may be associated with a single Data Expected entry. Please add only one data structure per assessment.

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Collection 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
$189,919,308.00
47589
119736
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 82176 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 3070 2420 UNIVERSITY OF MARYLAND BALTIMORE $8,232,052.00
U01DA041022-01 ABCD-USA Consortium: Research Project 09/30/2015 03/31/2027 350 356 SRI INTERNATIONAL $5,211,982.00
U01DA041148-01 ABCD-USA Consortium: Research Project 09/30/2015 03/31/2027 4724 2320 OREGON HEALTH & SCIENCE UNIVERSITY $12,436,525.00
U01DA041106-01 ABCD-USA Consortium: Research Project 09/30/2015 03/31/2027 4300 7056 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 $10,478,099.00
U24DA041147-01 ABCD-USA Consortium: Coordinating Center 09/30/2015 03/31/2027 0 0 UNIVERSITY OF CALIFORNIA, SAN DIEGO $21,115,651.00
U01DA041156-01 FIU-ABCD: Pathways and Mechanisms to Addiction in the Latino Youth of South Florida 09/30/2015 03/31/2027 1200 1262 FLORIDA INTERNATIONAL UNIVERSITY $10,822,181.00
U01DA041134-01 Prospective Research Studies of Maturation (PRISM)- Research Project 09/30/2015 03/31/2027 1900 4000 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 680 766 MEDICAL UNIVERSITY OF SOUTH CAROLINA $3,833,398.00
U01DA041025-01 ABCD-USA Consortium: UWM SIte 07/15/2017 03/31/2027 508 387 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

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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
32803367Create StudyThe effects of nicotine and cannabis co-use during adolescence and young adulthood on white matter cerebral blood flow estimates.PsychopharmacologyCourtney KE, Baca R, Doran N, Jacobson A, Liu TT, Jacobus JAugust 2020Not Determined
32800084Create StudyCognitive Functioning Related to Binge Alcohol and Cannabis Co-Use in Abstinent Adolescents and Young Adults.Journal of studies on alcohol and drugsWade NE, Bagot KS, Tapert SF, Gruber SA, Filbey FM, Lisdahl KMJuly 2020Not Determined
32731811Create StudyReward Processing in Children With Disruptive Behavior Disorders and Callous-Unemotional Traits in the ABCD Study.The American journal of psychiatryHawes SW, Waller R, Byrd AL, Bjork JM, Dick AS, Sutherland MT, Riedel MC, Tobia MJ, Thomson N, Laird AR, Gonzalez RJuly 2020Not Determined
32731083Create StudyAssociation of prenatal alcohol exposure with preadolescent alcohol sipping in the ABCD study®.Drug and alcohol dependenceLees B, Mewton L, Stapinski LA, Teesson M, Squeglia LMSeptember 2020Not Determined
32718077Create StudySubjective Family Socioeconomic Status and Adolescents' Attention: Blacks' Diminished Returns.Children (Basel, Switzerland)Assari S, Boyce S, Bazargan MJuly 2020Not Determined
32715317Create StudyBOLD responses to inhibition in cannabis-using adolescents and emerging adults after 2 weeks of monitored cannabis abstinence.PsychopharmacologyWallace AL, Maple KE, Barr AT, Lisdahl KMJuly 2020Not Determined
32685340Create StudySleep and Alcohol Use in Women.Alcohol research : current reviewsInkelis SM, Hasler BP, Baker FCJanuary 2020Not Determined
32665545Create StudyUnderstanding the genetic determinants of the brain with MOSTest.Nature communicationsVan Der Meer D, Frei O, Kaufmann T, Shadrin AA, Devor A, Smeland OB, Thompson WK, Fan CC, Holland D, Westlye LT, Andreassen OA, Dale AMJuly 2020Not Determined
32660094Create StudyAfrican American Children's Diminished Returns of Subjective Family Socioeconomic Status on Fun Seeking.Children (Basel, Switzerland)Assari S, Akhlaghipour G, Boyce S, Bazargan M, Caldwell CHJuly 2020Not Determined
32659528Create StudyFine particulate matter exposure during childhood relates to hemispheric-specific differences in brain structure.Environment internationalCserbik D, Chen JC, Mcconnell R, Berhane K, Sowell ER, Schwartz J, Hackman DA, Kan E, Fan CC, Herting MMJuly 2020Not Determined
32656052Create StudyAfrican Americans' Diminished Returns of Parental Education on Adolescents' Depression and Suicide in the Adolescent Brain Cognitive Development (ABCD) Study.European journal of investigation in health, psychology and educationAssari S, Boyce S, Bazargan M, Caldwell CHJune 2020Not Determined
32607126Create StudyThe importance of social factors in the association between physical activity and depression in children.Child and adolescent psychiatry and mental healthConley MI, Hindley I, Baskin-Sommers A, Gee DG, Casey BJ, Rosenberg MDJanuary 2020Not Determined
32603213Create StudyNeighborhood Deprivation Shapes Motivational-Neurocircuit Recruitment in Children.Psychological scienceMullins TS, Campbell EM, Hogeveen JJuly 2020Not Determined
32601990Create StudyCaffeine intake and cognitive functions in children.PsychopharmacologyZhang H, Lee ZX, Qiu AJune 2020Not Determined
32593800Create StudyNeighborhood deprivation, prefrontal morphology and neurocognition in late childhood to early adolescence.NeuroImageVargas T, Damme KSF, Mittal VAJune 2020Not Determined
32575523Create StudyReward Responsiveness in the Adolescent Brain Cognitive Development (ABCD) Study: African Americans' Diminished Returns of Parental Education.Brain sciencesAssari S, Boyce S, Akhlaghipour G, Bazargan M, Caldwell CHJune 2020Not Determined
32541809Create StudyThe ABCD study: understanding the development of risk for mental and physical health outcomes.Neuropsychopharmacology : official publication of the American College of NeuropsychopharmacologyKarcher NR, Barch DMJune 2020Not Determined
32522466Create StudyAdverse childhood experiences and psychotic-like experiences are associated above and beyond shared correlates: Findings from the adolescent brain cognitive development study.Schizophrenia researchKarcher NR, Niendam TA, Barch DMJune 2020Not Determined
32522303Create StudyIncipient alcohol use in childhood: Early alcohol sipping and its relations with psychopathology and personality.Development and psychopathologyWatts AL, Wood PK, Jackson KM, Lisdahl KM, Heitzeg MM, Gonzalez R, Tapert SF, Barch DM, Sher KJJune 2020Not Determined
32511674Create StudyAssociation of Prenatal Opioid Exposure With Precentral Gyrus Volume in Children.JAMA pediatricsHartwell ML, Croff JM, Morris AS, Breslin FJ, Dunn KJune 2020Not Determined
32503310Create StudyFamily Socioeconomic Status and Exposure to Childhood Trauma: Racial Differences.Children (Basel, Switzerland)Assari SJune 2020Not Determined
32463952Create StudyEarly adolescent gender diversity and mental health in the Adolescent Brain Cognitive Development study.Journal of child psychology and psychiatry, and allied disciplinesPotter A, Dube S, Allgaier N, Loso H, Ivanova M, Barrios LC, Bookheimer S, Chaarani B, Dumas J, Feldstein-Ewing S, Freedman EG, Garavan H, Hoffman E, Mcglade E, Robin L, Johns MMMay 2020Not Determined
32455841Create StudyParental Education on Youth Inhibitory Control in the Adolescent Brain Cognitive Development (ABCD) Study: Blacks' Diminished Returns.Brain sciencesAssari SMay 2020Not Determined
32451322Create StudyBehavioral and Neural Signatures of Working Memory in Childhood.The Journal of neuroscience : the official journal of the Society for NeuroscienceRosenberg MD, Martinez SA, Rapuano KM, Conley MI, Cohen AO, Cornejo MD, Hagler DJ, Meredith WJ, Anderson KM, Wager TD, Feczko E, Earl E, Fair DA, Barch DM, Watts R, Casey BJJune 2020Not Determined
32443584Create StudyMinorities' Diminished Returns of Parental Educational Attainment on Adolescents' Social, Emotional, and Behavioral Problems.Children (Basel, Switzerland)Assari S, Boyce S, Caldwell CH, Bazargan MMay 2020Not Determined
32439147Create StudyParent versus child report of children's sexual orientation: associations with psychiatric morbidity in the Adolescent Brain Cognitive Development study.Annals of epidemiologyClark KA, Mennies RJ, Olino TM, Dougherty LR, Pachankis JEMay 2020Not Determined
32414481Create StudyWhat Is the Link Between Attention-Deficit/Hyperactivity Disorder and Sleep Disturbance? A Multimodal Examination of Longitudinal Relationships and Brain Structure Using Large-Scale Population-Based Cohorts.Biological psychiatryShen C, Luo Q, Chamberlain SR, Morgan S, Romero-Garcia R, Du J, Zhao X, Touchette É, Montplaisir J, Vitaro F, Boivin M, Tremblay RE, Zhao XM, Robaey P, Feng J, Sahakian BJSeptember 2020Not Determined
32399985Create StudyUnique longitudinal relationships between symptoms of psychopathology in youth: A cross-lagged panel network analysis in the ABCD study.Journal of child psychology and psychiatry, and allied disciplinesFunkhouser CJ, Chacko AA, Correa KA, Kaiser AJE, Shankman SAMay 2020Not Determined
32354687Create StudyExamining Specificity of Neural Correlates of Childhood Psychotic-like Experiences During an Emotional n-Back Task.Biological psychiatry. Cognitive neuroscience and neuroimagingO'Brien KJ, Barch DM, Kandala S, Karcher NRJune 2020Not Determined
32333792Create StudyParental Family History of Alcohol Use Disorder and Neural Correlates of Response Inhibition in Children From the Adolescent Brain Cognitive Development (ABCD) Study.Alcoholism, clinical and experimental researchLees B, Aguinaldo L, Squeglia LM, Infante MA, Wade NE, Hernandez Mejia M, Jacobus JJune 2020Not Determined
32325210Create StudyRemoval of high frequency contamination from motion estimates in single-band fMRI saves data without biasing functional connectivity.NeuroImageGratton C, Dworetsky A, Coalson RS, Adeyemo B, Laumann TO, Wig GS, Kong TS, Gratton G, Fabiani M, Barch DM, Tranel D, Miranda-Dominguez O, Fair DA, Dosenbach NUF, Snyder AZ, Perlmutter JS, Petersen SE, Campbell MCAugust 2020Not Determined
32322677Create StudyBinge and Cannabis Co-Use Episodes in Relation to White Matter Integrity in Emerging Adults.Cannabis and cannabinoid researchWade NE, Thomas AM, Gruber SA, Tapert SF, Filbey FM, Lisdahl KMMarch 2020Not Determined
32307027Create StudyImpact of 2 Weeks of Monitored Abstinence on Cognition in Adolescent and Young Adult Cannabis Users.Journal of the International Neuropsychological Society : JINSWallace AL, Wade NE, Lisdahl KMSeptember 2020Not Determined
32260480Create StudyDo Adolescents Use Substances to Relieve Uncomfortable Sensations? A Preliminary Examination of Negative Reinforcement among Adolescent Cannabis and Alcohol Users.Brain sciencesMay AC, Jacobus J, Stewart JL, Simmons AN, Paulus MP, Tapert SFApril 2020Not Determined
32257767Create StudyBehavioral Treatments for Adolescent Cannabis Use Disorder: a Rationale for Cognitive Retraining.Current addiction reportsAguinaldo LD, Squeglia LM, Gray KM, Coronado C, Lees B, Tomko RL, Jacobus JDecember 2019Not Determined
32201043Create StudyGenome-wide Association Analysis of Parkinson's Disease and Schizophrenia Reveals Shared Genetic Architecture and Identifies Novel Risk Loci.Biological psychiatrySmeland OB, Shadrin A, Bahrami S, Broce I, Tesli M, Frei O, Wirgenes KV, O'Connell KS, Krull F, Bettella F, Steen NE, Sugrue L, Wang Y, Svenningsson P, Sharma M, Pihlstrøm L, Toft M, O'Donovan M, Djurovic S, Desikan R, Dale AM, Andreassen OAFebruary 2020Not Determined
32179028Create StudyEffect of alcohol use on the adolescent brain and behavior.Pharmacology, biochemistry, and behaviorLees B, Meredith LR, Kirkland AE, Bryant BE, Squeglia LMMay 2020Not Determined
32171431Create StudyRisk and protective factors for childhood suicidality: a US population-based study.The lancet. PsychiatryJaniri D, Doucet GE, Pompili M, Sani G, Luna B, Brent DA, Frangou SApril 2020Not Determined
32153355Create StudyImage-Derived Phenotyping Informed by Independent Component Analysis-An Atlas-Based Approach.Frontiers in neuroscienceMoradi M, Ekhtiari H, Victor TA, Paulus M, Kuplicki RJanuary 2020Not Determined
32144045Create StudyDisruptive Behavior Problems, Callous-Unemotional Traits, and Regional Gray Matter Volume in the Adolescent Brain and Cognitive Development Study.Biological psychiatry. Cognitive neuroscience and neuroimagingWaller R, Hawes SW, Byrd AL, Dick AS, Sutherland MT, Riedel MC, Tobia MJ, Bottenhorn KL, Laird AR, Gonzalez RMay 2020Not Determined
32119636Create StudySleep and Women's Health: Sex- and Age-Specific Contributors to Alcohol Use Disorders.Journal of women's health (2002)Baker FC, Carskadon MA, Hasler BPMarch 2020Not Determined
32107167Create StudyComputational Evidence for Underweighting of Current Error and Overestimation of Future Error in Anxious Individuals.Biological psychiatry. Cognitive neuroscience and neuroimagingHowlett JR, Thompson WK, Paulus MPApril 2020Not Determined
32105123Create StudyAn item response theory analysis of the Prodromal Questionnaire-Brief Child Version: Developing a screening form that informs understanding of self-reported psychotic-like experiences in childhood.Journal of abnormal psychologyKarcher NR, Perino MT, Barch DMApril 2020Not Determined
32102994Create StudyParental and social factors in relation to child psychopathology, behavior, and cognitive function.Translational psychiatryZhang H, Lee ZX, White T, Qiu AFebruary 2020Not Determined
32098300Create StudyAssessing the Role of Cannabis Use on Cortical Surface Structure in Adolescents and Young Adults: Exploring Gender and Aerobic Fitness as Potential Moderators.Brain sciencesSullivan RM, Wallace AL, Wade NE, Swartz AM, Lisdahl KMFebruary 2020Not Determined
32079563Create StudyA Neurobiological Model of Alcohol Marketing Effects on Underage Drinking.Journal of studies on alcohol and drugs. SupplementCourtney AL, Casey BJ, Rapuano KMMarch 2020Not Determined
32078973Create StudyCommon and distinct brain activity associated with risky and ambiguous decision-making.Drug and alcohol dependencePoudel R, Riedel MC, Salo T, Flannery JS, Hill-Bowen LD, Eickhoff SB, Laird AR, Sutherland MTApril 2020Not Determined
32046896Create StudySleep Disturbance Predicts Depression Symptoms in Early Adolescence: Initial Findings From the Adolescent Brain Cognitive Development Study.The Journal of adolescent health : official publication of the Society for Adolescent MedicineGoldstone A, Javitz HS, Claudatos SA, Buysse DJ, Hasler BP, De Zambotti M, Clark DB, Franzen PL, Prouty DE, Colrain IM, Baker FCMay 2020Not Determined
32031652Create StudyPrevalence and Family-Related Factors Associated With Suicidal Ideation, Suicide Attempts, and Self-injury in Children Aged 9 to 10 Years.JAMA network openDeville DC, Whalen D, Breslin FJ, Morris AS, Khalsa SS, Paulus MP, Barch DMFebruary 2020Not Determined
32015467Create StudySleep duration, brain structure, and psychiatric and cognitive problems in children.Molecular psychiatryCheng W, Rolls E, Gong W, Du J, Zhang J, Zhang XY, Li F, Feng JFebruary 2020Not Determined
32005346Create StudySensors Capabilities, Performance, and Use of Consumer Sleep Technology.Sleep medicine clinicsDe Zambotti M, Cellini N, Menghini L, Sarlo M, Baker FCMarch 2020Not Determined
31983035Create StudyWhite Matter Tract Integrity, Involvement in Sports, and Depressive Symptoms in Children.Child psychiatry and human developmentGorham LS, Barch DMJune 2020Not Determined
31932788Create StudyAssociation of lead-exposure risk and family income with childhood brain outcomes.Nature medicineMarshall AT, Betts S, Kan EC, Mcconnell R, Lanphear BP, Sowell ERJanuary 2020Not Determined
31872334Create StudyMeta-analytic clustering dissociates brain activity and behavior profiles across reward processing paradigms.Cognitive, affective & behavioral neuroscienceFlannery JS, Riedel MC, Bottenhorn KL, Poudel R, Salo T, Hill-Bowen LD, Laird AR, Sutherland MTApril 2020Not Determined
31841018Create StudyFactor structure, measurement and structural invariance, and external validity of an abbreviated youth version of the UPPS-P Impulsive Behavior Scale.Psychological assessmentWatts AL, Smith GT, Barch DM, Sher KJApril 2020Not Determined
31822320Create StudyNeuropsychological Trajectories Associated with Adolescent Alcohol and Cannabis Use: A Prospective 14-Year Study.Journal of the International Neuropsychological Society : JINSInfante MA, Nguyen-Louie TT, Worley M, Courtney KE, Coronado C, Jacobus JMay 2020Not Determined
31818798Create StudyPrevalence and correlates of maladaptive guilt in middle childhood.Journal of affective disordersDonohue MR, Tillman R, Perino MT, Whalen DJ, Luby J, Barch DMFebruary 2020Not Determined
31816020Create StudyAssociations Among Body Mass Index, Cortical Thickness, and Executive Function in Children.JAMA pediatricsLaurent JS, Watts R, Adise S, Allgaier N, Chaarani B, Garavan H, Potter A, Mackey SFebruary 2020Not Determined
31796137Create StudyMinnesota Center for Twin and Family Research.Twin research and human genetics : the official journal of the International Society for Twin StudiesWilson S, Haroian K, Iacono WG, Krueger RF, Lee JJ, Luciana M, Malone SM, Mcgue M, Roisman GI, Vrieze SDecember 2019Not Determined
31778819Create StudyCorrection of respiratory artifacts in MRI head motion estimates.NeuroImageFair DA, Miranda-Dominguez O, Snyder AZ, Perrone A, Earl EA, Van AN, Koller JM, Feczko E, Tisdall MD, Van Der Kouwe A, Klein RL, Mirro AE, Hampton JM, Adeyemo B, Laumann TO, Gratton C, Greene DJ, Schlaggar BL, Hagler DJ, Watts R, Garavan H, Barch DM, Nigg JT, Petersen SE, Dale AM, et al.March 2020Not Determined
31727084Create StudyScreen time and problem behaviors in children: exploring the mediating role of sleep duration.The international journal of behavioral nutrition and physical activityGuerrero MD, Barnes JD, Chaput JP, Tremblay MSNovember 2019Not Determined
31700677Create StudySex differences in brain correlates of STEM anxiety.NPJ science of learningGonzalez AA, Bottenhorn KL, Bartley JE, Hayes T, Riedel MC, Salo T, Bravo EI, Odean R, Nazareth A, Laird RW, Sutherland MT, Brewe E, Pruden SM, Laird ARJanuary 2019Not Determined
31699293Create StudyConvergent Evidence for Predispositional Effects of Brain Gray Matter Volume on Alcohol Consumption.Biological psychiatryBaranger DAA, Demers CH, Elsayed NM, Knodt AR, Radtke SR, Desmarais A, Few LR, Agrawal A, Heath AC, Barch DM, Squeglia LM, Williamson DE, Hariri AR, Bogdan RApril 2020Not Determined
31653478Create StudyDriven by Pain, Not Gain: Computational Approaches to Aversion-Related Decision Making in Psychiatry.Biological psychiatryPaulus MPFebruary 2020Not Determined
31646343Create StudyChildhood Obesity, Cortical Structure, and Executive Function in Healthy Children.Cerebral cortex (New York, N.Y. : 1991)Ronan L, Alexander-Bloch A, Fletcher PCApril 2020Not Determined
31634568Create StudyBrain Volume Abnormalities in Youth at High Risk for Depression: Adolescent Brain and Cognitive Development Study.Journal of the American Academy of Child and Adolescent PsychiatryPagliaccio D, Alqueza KL, Marsh R, Auerbach RPOctober 2019Not Determined
31624235Create StudyDelineating and validating higher-order dimensions of psychopathology in the Adolescent Brain Cognitive Development (ABCD) study.Translational psychiatryMichelini G, Barch DM, Tian Y, Watson D, Klein DN, Kotov ROctober 2019Not Determined
31614255Create StudyIdentifying reproducible individual differences in childhood functional brain networks: An ABCD study.Developmental cognitive neuroscienceMarek S, Tervo-Clemmens B, Nielsen AN, Wheelock MD, Miller RL, Laumann TO, Earl E, Foran WW, Cordova M, Doyle O, Perrone A, Miranda-Dominguez O, Feczko E, Sturgeon D, Graham A, Hermosillo R, Snider K, Galassi A, Nagel BJ, Ewing SWF, Eggebrecht AT, Garavan H, Dale AM, Greene DJ, Barch DM, et al.December 2019Not Determined
31522280Create StudyAdolescent Substance Use Disorder Treatment: an Update on Evidence-Based Strategies.Current psychiatry reportsFadus MC, Squeglia LM, Valadez EA, Tomko RL, Bryant BE, Gray KMSeptember 2019Not Determined
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
31512192Create StudyNeurobiological and Cognitive Profile of Young Binge Drinkers: a Systematic Review and Meta-Analysis.Neuropsychology reviewLees B, Mewton L, Stapinski LA, Squeglia LM, Rae CD, Teesson MSeptember 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
31427753Create StudyPrediction of neurocognition in youth from resting state fMRI.Molecular psychiatrySripada C, Rutherford S, Angstadt M, Thompson WK, Luciana M, Weigard A, Hyde LH, Heitzeg MAugust 2019Not Determined
31415884Create StudyImage processing and analysis methods for the Adolescent Brain Cognitive Development Study.NeuroImageHagler DJ, Hatton S, Cornejo MD, Makowski C, Fair DA, Dick AS, Sutherland MT, Casey BJ, Barch DM, Harms MP, Watts R, Bjork JM, Garavan HP, Hilmer L, Pung CJ, Sicat CS, Kuperman J, Bartsch H, Xue F, Heitzeg MM, Laird AR, Trinh TT, Gonzalez R, Tapert SF, Riedel MC, et al.November 2019Not Determined
31413180Create Study24-Hour Movement Behaviors and Impulsivity.PediatricsGuerrero MD, Barnes JD, Walsh JJ, Chaput JP, Tremblay MS, Goldfield GSSeptember 2019Not Determined
31385460Create StudyCannabis and the developing brain: What does the evidence say?Birth defects researchJacobus J, Courtney KE, Hodgdon EA, Baca ROctober 2019Not Determined
31376925Create StudyMachine Learning and Brain Imaging: Opportunities and Challenges.Trends in neurosciencesPaulus MP, Kuplicki R, Yeh HWOctober 2019Not Determined
31350420Create StudyGenetic variation across RNA metabolism and cell death gene networks is implicated in the semantic variant of primary progressive aphasia.Scientific reportsBonham LW, Steele NZR, Karch CM, Broce I, Geier EG, Wen NL, Momeni P, Hardy J, Miller ZA, Gorno-Tempini ML, Hess CP, Lewis P, Miller BL, Seeley WW, Manzoni C, Desikan RS, Baranzini SE, Ferrari R, Yokoyama JS, July 2019Not Determined
31305867Create StudyEnsuring the Best Use of Data: The Adolescent Brain Cognitive Development Study.JAMA pediatricsCompton WM, Dowling GJ, Garavan HJuly 2019Not Determined
31288867Create StudyDemographic, psychological, behavioral, and cognitive correlates of BMI in youth: Findings from the Adolescent Brain Cognitive Development (ABCD) study.Psychological medicineGray JC, Schvey NA, Tanofsky-Kraff MJuly 2020Not Determined
31258099Create StudyPubertal development mediates the association between family environment and brain structure and function in childhood.Development and psychopathologyThijssen S, Collins PF, Luciana MMay 2020Not Determined
31200279Create StudyThe rise of e-cigarettes, pod mod devices, and JUUL among youth: Factors influencing use, health implications, and downstream effects.Drug and alcohol dependenceFadus MC, Smith TT, Squeglia LMAugust 2019Not Determined
31156374Create StudyAutomated, Efficient, and Accelerated Knowledge Modeling of the Cognitive Neuroimaging Literature Using the ATHENA Toolkit.Frontiers in neuroscienceRiedel MC, Salo T, Hays J, Turner MD, Sutherland MT, Turner JA, Laird ARJanuary 2019Not Determined
31134293Create StudyComputational approaches and machine learning for individual-level treatment predictions.PsychopharmacologyPaulus MP, Thompson WKMay 2019Not Determined
31110341Create StudyNo evidence for a bilingual executive function advantage in the nationally representative ABCD study.Nature human behaviourDick AS, Garcia NL, Pruden SM, Thompson WK, Hawes SW, Sutherland MT, Riedel MC, Laird AR, Gonzalez RJuly 2019Not Determined
31106219Create StudyToward a Neurobiological Basis for Understanding Learning in University Modeling Instruction Physics Courses.Frontiers in ICT (Lausanne, Switzerland)Brewe E, Bartley JE, Riedel MC, Sawtelle V, Salo T, Boeving ER, Bravo EI, Odean R, Nazareth A, Bottenhorn KL, Laird RW, Sutherland MT, Pruden SM, Laird ARMay 2018Not Determined
31079000Create StudyAnterior cingulate volume reductions in abstinent adolescent and young adult cannabis users: Association with affective processing deficits.Psychiatry research. NeuroimagingMaple KE, Thomas AM, Kangiser MM, Lisdahl KMJune 2019Not Determined
31062126Create StudyDifferential Relationships of Child Anxiety and Depression to Child Report and Parent Report of Electronic Media Use.Child psychiatry and human developmentFors PQ, Barch DMDecember 2019Not Determined
31043184Create StudyDecision-Making as a Latent Construct and its Measurement Invariance in a Large Sample of Adolescent Cannabis Users.Journal of the International Neuropsychological Society : JINSPacheco-Colón I, Hawes SW, Duperrouzel JC, Lopez-Quintero C, Gonzalez RAugust 2019Not Determined
31034667Create StudyCerebral circulation time derived from fMRI signals in large blood vessels.Journal of magnetic resonance imaging : JMRIYao JF, Wang JH, Yang HS, Liang Z, Cohen-Gadol AA, Rayz VL, Tong YNovember 2019Not Determined
31009035Create StudyNicotine Effects on White Matter Microstructure in Young Adults.Archives of clinical neuropsychology : the official journal of the National Academy of NeuropsychologistsKangiser MM, Thomas AM, Kaiver CM, Lisdahl KMJanuary 2019Not Determined
30949565Create StudyStress exposures, neurodevelopment and health measures in the ABCD study.Neurobiology of stressHoffman EA, Clark DB, Orendain N, Hudziak J, Squeglia LM, Dowling GJFebruary 2019Not Determined
30926513Create StudyMeta-analytic Evidence for Neural Dysactivity Underlying Sexual Dysfunction.The journal of sexual medicinePoeppl TB, Langguth B, Laird AR, Eickhoff SBMay 2019Not Determined
30916716Create StudyAssociation of Prenatal Cannabis Exposure With Psychosis Proneness Among Children in the Adolescent Brain Cognitive Development (ABCD) Study.JAMA psychiatryFine JD, Moreau AL, Karcher NR, Agrawal A, Rogers CE, Barch DM, Bogdan RJuly 2019Not Determined
30905689Create StudyInvolvement in Sports, Hippocampal Volume, and Depressive Symptoms in Children.Biological psychiatry. Cognitive neuroscience and neuroimagingGorham LS, Jernigan T, Hudziak J, Barch DMMay 2019Not Determined
30875890Create StudyDo Stand-Biased Desks in the Classroom Change School-Time Activity and Sedentary Behavior?International journal of environmental research and public healthSwartz AM, Tokarek NR, Lisdahl K, Maeda H, Strath SJ, Cho CCMarch 2019Not Determined
30865236Create StudyAssociation Between Childhood Anhedonia and Alterations in Large-scale Resting-State Networks and Task-Evoked Activation.JAMA psychiatryPornpattananangkul N, Leibenluft E, Pine DS, Stringaris AJune 2019Not Determined
30850130Create StudyResting-State Functional Connectivity and Psychotic-like Experiences in Childhood: Results From the Adolescent Brain Cognitive Development Study.Biological psychiatryKarcher NR, O'Brien KJ, Kandala S, Barch DMJuly 2019Not Determined
30846008Create StudyAssessing callous-unemotional traits: development of a brief, reliable measure in a large and diverse sample of preadolescent youth.Psychological medicineHawes SW, Waller R, Thompson WK, Hyde LW, Byrd AL, Burt SA, Klump KL, Gonzalez RFebruary 2020Not 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
30595399Create StudyThe structure of cognition in 9 and 10 year-old children and associations with problem behaviors: Findings from the ABCD study's baseline neurocognitive battery.Developmental cognitive neuroscienceThompson WK, Barch DM, Bjork JM, Gonzalez R, Nagel BJ, Nixon SJ, Luciana MApril 2019Not Determined
30580899Create StudyFiber pathways supporting early literacy development in 5-8-year-old children.Brain and cognitionBroce IJ, Bernal B, Altman N, Bradley C, Baez N, Cabrera L, Hernandez G, De Feria A, Dick ASAugust 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
30476983Create StudyPrevalence of Eating Disorders Among US Children Aged 9 to 10 Years: Data From the Adolescent Brain Cognitive Development (ABCD) Study.JAMA pediatricsRozzell K, Moon DY, Klimek P, Brown T, Blashill AJJanuary 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
30465762Create StudyThe Big Reveal: Precision Mapping Shines a Gigantic Floodlight on the Cerebellum.NeuronFair DANovember 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
30380617Create StudyParental Educational Attainment and Mental Well-Being of College Students; Diminished Returns of Blacks.Brain sciencesAssari SOctober 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
30268791Create StudyConvergent influences of lifestyle behaviour on neurocognitive development in children.The Lancet. Child & adolescent healthBustamante EENovember 2018Not Determined
30208469Create StudyChild Sexual Orientation and Gender Identity in the Adolescent Brain Cognitive Development Cohort Study.JAMA pediatricsCalzo JP, Blashill AJNovember 2018Not Determined
30195242Create StudyThe importance of considering polysubstance use: lessons from cocaine research.Drug and alcohol dependenceLiu Y, Williamson V, Setlow B, Cottler LB, Knackstedt LANovember 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
30006199Create StudyVolume of the Human Hippocampus and Clinical Response Following Electroconvulsive Therapy.Biological psychiatryOltedal L, Narr KL, Abbott C, Anand A, Argyelan M, Bartsch H, Dannlowski U, Dols A, Van Eijndhoven P, Emsell L, Erchinger VJ, Espinoza R, Hahn T, Hanson LG, Hellemann G, Jorgensen MB, Kessler U, Oudega ML, Paulson OB, Redlich R, Sienaert P, Stek ML, Tendolkar I, Vandenbulcke M, Oedegaard KJ, et al.October 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
29884279Create StudyTaking Aim at Interoception's Role in Mental Health.Biological psychiatry. Cognitive neuroscience and neuroimagingKhalsa SS, Feinstein JS, Simmons WK, Paulus MPJune 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
29703560Create StudyRecruiting the ABCD sample: Design considerations and procedures.Developmental cognitive neuroscienceGaravan H, Bartsch H, Conway K, Decastro A, Goldstein RZ, Heeringa S, Jernigan T, Potter A, Thompson W, Zahs DAugust 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
29496476Create StudyIntroduction.Developmental cognitive neuroscienceJernigan TL, Brown SA, August 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
29460352Create StudyThe Adolescent Brain Cognitive Development Study.Journal of research on adolescence : the official journal of the Society for Research on AdolescenceJernigan TL, Brown SA, Dowling GJMarch 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 NUFMay 2018Not Determined
29311006Create StudyThe adolescent brain cognitive development study external advisory board.Developmental cognitive neuroscienceCharness MEAugust 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
29051027Create StudyThe conception of the ABCD study: From substance use to a broad NIH collaboration.Developmental cognitive neuroscienceVolkow ND, Koob GF, Croyle RT, Bianchi DW, Gordon JA, Koroshetz WJ, Pérez-Stable EJ, Riley WT, Bloch MH, Conway K, Deeds BG, Dowling GJ, Grant S, Howlett KD, Matochik JA, Morgan GD, Murray MM, Noronha A, Spong CY, Wargo EM, Warren KR, Weiss SRBAugust 2018Not 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
28935096Create StudyThe Effect of Acute Stress on the Calculus of Reward and Punishment.Biological psychiatryPaulus MPOctober 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 DAOctober 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
Adolescent Brain Cognitive Development Study (ABCD) - Annual Release 3.0Adolescent Brain Cognitive Development Study (ABCD) - Annual Release 3.011878/11878Primary AnalysisPrivate
ABCD LPARegardless of the precise mechanism, the underlying assumption of all neurodevelopmental models of risk is that, at the population level, there exist subgroups of individuals that share similar patterns of neural function and development, and that these subgroups somehow relate to psychiatric risk. The existence of multiple neurodevelopmental subgroups at the population level has not been assessed previously. In the current study, cross-validated latent profile analysis was used to test for the presence of empirical neurodevelopmental subgroups using fMRI data from 6,758 individuals (49.4% female) in the ABCD Wave 1 release. Data were randomly split into training and testing samples and the optimal solution from the training data was validated in the testing data. Analyses in the training sample (n=3,379) identified a 7-profile solution (entropy=.880), that replicated in the held-out testing data (n=3,379, entropy=.890). Identified subgroups included a ‘majority’ group (66.8%), high reward (4.3%) and low reward (4.0%) groups, high inhibition (9.8%) and low inhibition (6.7%) groups, and high emotion regulation (4.0%) and low emotion regulation (4.3%) groups. Relative to the majority group, smaller subgroups were characterized by more males (X2=25.28, p<.0001), higher proportions of individuals from lower-income households (X2=122.17, p<.0001), poorer cognitive performance (F=14.78, p<.0001), more screen time (F=10.27, p<.0001), and heightened impulsivity (p’s<.00625). These data for the first time demonstrate the existence of multiple, distinct neurodevelopmental subgroups at the population-level. They indicate that these empirically derived, brain-based developmental profiles relate to differences in clinical features, even at a young age, and prior to the onset of significant psychopathology.11875/11875Secondary AnalysisShared
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
Adverse Childhood Experiences and Psychotic-like Experiences Are Associated Above and Beyond Shared Correlates: Findings from the Adolescent Brain Cognitive Development StudyAdverse childhood experiences (ACEs) are associated with increased risk for psychotic-like experiences (PLEs). However, ACEs and PLEs are also both associated with several shared factors (e.g., internalizing symptoms, suicidality). Few studies have explicitly examined whether the association between ACEs and PLEs remains over and above shared correlates. To address this question, using 10,800 9-11-year-olds, we examined whether ACEs and school-aged PLEs were associated when accounting for shared correlates, and whether there was evidence of mediation in associations between PLEs, ACEs, and these shared factors. Greater number of ACEs were associated with greater PLEs, including several specific ACEs (e.g., bullying). Importantly, ACEs and PLEs were related even when accounting for shared correlates. Further, PLEs partially mediated the relationships between ACEs and both internalizing symptoms and suicidality, including suicidal behavior. The current study helps clarify the nature of the associations between PLEs and ACE and has important clinical implications for addressing PLEs. 11875/11875Secondary AnalysisShared
African Americans’ Diminished Returns of Parental Education on Adolescents’ Depression and Suicide in the Adolescent Brain Cognitive Development (ABCD) StudyTo investigate racial and ethnic differences in the protective effects of parental education and marital status against adolescents’ depressed mood and suicidal attempts in the U.S. As proposed by the Marginalization-related Diminished Returns (MDRs), parental education generates fewer tangible outcomes for non-White compared to White families. Our existing knowledge is very limited regarding diminished returns of parental education and marital status on adolescents’ depressed mood and suicidal attempts. To compare racial groups for the effects of parental education and marital status on adolescents’ depressed mood and suicidal attempt. This cross-sectional study included 7076 non-Hispanic White or African American 8-11 years old adolescents from the Adolescent Brain Cognitive Development (ABCD) study. The independent variables were parental education and marital status. The main outcomes were depressed mood and suicidal attempts based on parents’ reports using the Kiddie Schedule for Affective Disorders and Schizophrenia (K-SADS). Age and gender were the covariates. Race was the moderator. Logistic regression was used to analyze the ABCD data. Overall, parental education was associated with lower odds of depressed mood (OR = 0.81; 95% CI = 0.67-0.99; p = 0.037) and having married parents was associated with lower odds of suicidal attempts (OR = 0.50; 95% CI = 0.28-0.91; p = 0.022). In the pooled sample, we found interaction terms between race with parental education and marital status on the outcomes, suggesting that the protective effect of having married parents against depressed mood (OR = 1.54; 95% CI = 1.00-2.37; p = 0.048) and the protective effect of having married parents against suicidal attempts (OR = 6.62; 95% CI = 2.21-19.86; p =0.001) are weaker for African Americans when compared to Whites. The protective effects of parent education and marital status against depressed mood and suicidal attempts are diminished for African American adolescents compared to White adolescents. There is a need for programs and interventions that equalize not only socioeconomic status (SES) but also the marginal returns of SES for racial minority groups. Such efforts require addressing structural and societal barriers that hinder African American families from translating their SES resources and human capital into tangible outcomes. There is a need for studies that can minimize MDRs for African American families, so that every individual and every family can benefit from their resources regardless of their skin color. To achieve such a goal, we need to help middle-class African American families secure tangible outcomes in the presence of SES resources.11875/11875Secondary AnalysisPrivate
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
An integrative approach to modelling suicidal thoughts and behaviour in youthsSuicide is a leading cause of death in young people. Research has identified biological, cognitive and social factors that reflect vulnerability to youth suicide. However, these predictors are correlated and hard to disentangle. To aid early detection of suicide, a better understanding of the mechanisms underlying both suicidal ideation and attempt is a critical first step. We will utilize multivariate statistical techniques to investigate an integrative model to differentiate between suicidal ideation and attempt for optimized prediction of future treatment by combining childhood adversity, cognitive ability and structural and functional brain data. We hypothesize that these factors connected to affective functioning will be associated with suicidal ideation. In contrast, relationships will be observed between these measures linked to altered executive cognitive function for suicidal attempt. Using longitudinal data, we will predict the progress from suicidal ideation to attempt. 11875/11875Secondary AnalysisPrivate
Association of Prenatal Opioid Exposure With Precentral Gyrus Volume in ChildrenThis cross-sectional study identifies structural differences of the precentral gyrus among children with reported prenatal opioid exposure compared with children with no reported exposure, controlling for present social factors.11875/11875Secondary AnalysisShared
Involvement in Sports, Hippocampal Volume, and Depressive Symptoms in ChildrenBackground: Recent studies have found that higher levels of exercise are associated with fewer symptoms of depression among young people. In addition, research suggests that exercise may modify hippocampal volume, a brain region that has been found to show reduced volume in depression. However, it is not clear whether this relationship emerges as early as preadolescence. Methods: We examined data from a nation-wide sample of 4191 children ages 9-11 years from the Adolescent Brain and Cognitive Development Study. The parents of the children completed the Child Behavior Checklist, providing data about the child’s depressive symptoms, and the Sports and Activities Questionnaire, which provided data about the child’s participation in 23 sports. Children also took part in a structural MRI scan, providing us with measures of bilateral hippocampal volume. Results: Sports involvement interacted with sex to predict depressive symptoms, with a negative relationship in boys only (t= -5.257, B= -0.115, p< 0.001). Sports involvement was positively correlated with hippocampal volume in both boys and girls (t= 2.810, B= 0.035, p= 0.007). Hippocampal volume also interacted with sex to predict depressive symptoms, with a negative relationship in boys (t= -2.562, B= -0.070, p= 0.010), and served as a partial mediator for the relationship between involvement in sports and depressive symptoms in boys. Conclusions: These findings help illuminate a potential neural mechanism for the impact of exercise on the developing brain and the differential effects in boys versus girls mirror findings in the animal literature. More research is needed to understand the causal relationships between these constructs. 11875/11875Primary 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
Minorities' Diminished Returns of Parental Educational Attainment on Adolescents' Social, Emotional, and Behavioral ProblemsAim: To compare racial groups for the effect of parental educational attainment on adolescents' social, emotional, and behavioral problems. Methods: In this cross-sectional study, 10,762 youth from the Adolescent Brain Cognitive Development (ABCD) study were included. The independent variable was parental educational attainment. The main outcomes were 1) anxious and depressed mood, 2) withdrawn and depressed affect, 3) somatic complaints, 4) social and interpersonal problems, 5) thought problems, 6) rule-breaking behaviors, 7) attention problems, and 8) violent and aggressive behaviors. These scores were generated based on parent-reported behavioral problems measured using the Child Behavior Checklist (CBCL). Race and ethnicity were the moderators. Linear regression was used to analyze the ABCD data. Results: Overall, high parental educational attainment was associated with lower scores across all domains. Race and ethnicity showed statistically significant interactions with parental educational attainment on adolescents' fewer social, emotional, and behavioral problems (all domains), net of all confounders, indicating smaller tangible gains from their parental educational attainment for Black and Hispanic compared to non-Hispanic White adolescents. Conclusion: The protective effects of parental education against social, emotional, and behavioral problems are systematically diminished for Hispanic and Black than non-Hispanic White adolescents.11875/11875Secondary AnalysisPrivate
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
Prenatal cannabis exposure and childhood outcomes: Results from the ABCD Study®Importance: In light of increasing cannabis use among pregnant women, the Surgeon General of the United States recently issued an advisory against the use of marijuana during pregnancy. Objective: To determine whether cannabis use during pregnancy is associated with adverse outcomes among offspring. Design: Cross-sectional analysis of the baseline session of the ongoing longitudinal Adolescent Brain and Cognitive Development (ABCD) Study℠. Setting: Data were collected from 22 sites across the United States between 2016 and 2018. Participants: Children ages 9-11 (n=11,489) and their parent/caregiver. Exposure: Prenatal cannabis exposure prior to and following maternal knowledge of pregnancy. Main Outcomes and Measures: Child psychopathology symptomatology (i.e., psychotic-like experiences (PLEs) and internalizing, externalizing, attention, thought, and social problems), cognition, sleep, birth weight, gestational age at birth, body mass index (BMI), and brain structure (i.e., total intracranial volume, white matter volume, gray matter volume). Covariates included familial (e.g., income, familial psychopathology), pregnancy (e.g., prenatal exposure to alcohol and tobacco), and child (e.g., substance use) variables. Results: Among 11,489 children (age 9.9±0.6 years; 47.78% female), 655 (5.70%) were prenatally exposed to cannabis. Relative to no exposure, cannabis exposure only prior to (n=413; 3.59%) and following (n=242; 2.11%) maternal knowledge of pregnancy were associated with greater offspring psychopathology characteristics (i.e., PLEs and internalizing, externalizing, attention, thought, social, and sleep problems) and BMI as well as lower cognition and gray matter volume (all |ßs|>0.02, psfdr<0.03). Only exposure after knowledge of pregnancy was associated with lower birth weight and total intracranial and white matter volumes relative to no exposure and exposure only before knowledge (|ßs|>0.02, ps<0.002). When including potentially confounding covariates, exposure after maternal knowledge of pregnancy remained associated with greater PLEs and externalizing, attention, thought, and social problems (all |ßs|>0.02, psfdr<0.02). Exposure only prior to maternal knowledge of pregnancy did not differ from no exposure on any outcomes when considering potentially confounding variables (all |ßs|<0.02, psfdr>0.70). Conclusions and Relevance: Prenatal cannabis exposure and its correlated factors are associated with greater risk for psychopathology during middle childhood. Cannabis use during pregnancy should be discouraged. 11875/11875Secondary AnalysisShared
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
Suicide ideation and neurocognition among 9- and 10-year old children in the Adolescent Brain Cognitive Development (ABCD) StudyObjective: During the past decade, the pediatric suicide rate has nearly tripled. Yet, little is known about suicide behavior (SB) in children. Identification of risk factors associated with SB during childhood may be critical to preventing future attempts. The purpose of this study was to examine the relationship between neurocognitive performance and suicide ideation (SI) in children. Method: The present study utilized baseline data from 11,875 participants in the Adolescent Brain Cognitive Development (ABCD) study, a longitudinal study that follows nine- and ten-year-old children through late adolescence to examine factors that influence developmental trajectories. Suicidality was assessed by the Kiddie Schedule for Affective Disorder and Schizophrenia (KSADS) suicide module completed by the parent. Neurocognitive ability was assessed using the NIH Toolbox Cognition measures administered to the youth. Results: Children with a history of SI reported by their parent or concordant parent and youth report of SI demonstrated lower performance on the NIH Toolbox Picture Sequence Memory Test compared to children without SI. The difference in performance on the memory task remained significant when including demographic characteristics, family history of suicide, and internalizing symptoms in the model as covariates. Conclusions: To our knowledge, this is the first study to identify decreased episodic memory in children with SI. These findings are similar to results from adult and adolescent studies which have reported decreased memory performance among suicide attempters. Deficits in episodic memory may impact a child’s ability to problem-solve and generate potential future outcomes, which may increase the risk for SB. Early identification of memory deficits in children may inform suicide prevention and intervention efforts. 11875/11875Secondary AnalysisPrivate
White Matter Tract Integrity, Involvement in Sports, and Depressive Symptoms in ChildrenWhite matter tract integrity, measured via fractional anisotropy (FA), may serve as a mediating variable between exercise and depression. To study this, we examined data from 3973 children participating in the ABCD study. Parents of children completed the Sports and Activities questionnaire and the Child Behavior Checklist, and children completed a diffusion MRI scan, providing information about the FA of the parahippocampal cingulum and fornix. Results showed that involvement in sports was associated with reduced depression in boys. The number of activities and sports that a child was involved in was negatively related to FA of the left fornix but was unrelated to FA of other tracts. FA of these white matter tracts was also unrelated to depressive symptoms. This suggests that while white matter tract integrity is associated with exercise, it may not be part of a pathway linking exercise to depression levels in preadolescent boys.11875/11875Primary AnalysisShared
Genetic and Environmental Influences on Executive Functions and Intelligence in the ABCD StudyExecutive functions (EFs) and intelligence (IQ) are phenotypically correlated and heritable; however, they show variable genetic correlations in twin studies spanning childhood to middle age. We analyzed data from over 11,000 children (9-10-year-olds, including 749 twin pairs) in the Adolescent Brain Cognitive Development (ABCD) Study to examine the phenotypic and genetic relations between EFs and IQ in childhood. We identified two EF factors – Common EF and Updating-Specific, which were both related to IQ (rs = .64-.81). Common EF and IQ were heritable (53-67%), and their genetic correlation (rG = .86) was not significantly different than 1. These results suggest that EFs and IQ are phenotypically but not genetically separable in middle childhood.11874/11874Secondary AnalysisPrivate
African American Children’s Diminished Returns of Subjective Family Socioeconomic Status on Fun SeekingBackground: Reward sensitivity (fun-seeking) is a risk factor for a wide range of high-risk behaviors. While high socioeconomic status (SES) is known to reduce reward sensitivity and associated high-risk behaviors, less is known about the differential effects of SES on reward sensitivity. It is plausible to expect weaker protective effects of family SES on reward sensitivity in racial minorities, a pattern called Minorities’ Diminished Returns (MDRs). Aim: We compared Caucasian and African American (AA) children for the effects of subjective family SES on children’s fun-seeking. Methods: This was a cross-sectional analysis of 7061 children from the Adolescent Brain Cognitive Development (ABCD) study. The independent variable was subjective family SES. The main outcome was children’s fun-seeking measured by the behavioral approach system (BAS) and behavioral avoidance system (BIS). Age, gender, marital status, and household size were the covariates. Results: In the overall sample, high subjective family SES was associated with lower levels of fun-seeking. We also found a statistically significant interaction between race and subjective family SES on children’s fun-seeking in the overall sample, suggesting that high subjective family SES is associated with a weaker effect on reducing fun-seeking among AA than Caucasian children. In race-stratified models, high subjective family SES was protective against fun-seeking of Caucasian but not AA children. Conclusion: Subjective family SES reduces the fun-seeking for Caucasian but not AA children.11867/11867Secondary AnalysisPrivate
Family Income Mediates the Effect of Parental Education on Adolescents’ Hippocampus Activation During an N-Back Memory TaskAbstract: Introduction: Hippocampus, a medial temporal lobe structure, has significant implications in memory formation and learning. Although hippocampus activity is believed to be affected by socioeconomic status (SES), limited knowledge exists on which SES indicators influence hippocampus function. Purpose: This study explored the separate and combined effects of three SES indicators, namely parental education, family income, and neighborhood income, on adolescents’ hippocampus activation during an N-Back memory task. As some of the effects of parental education may be through income, we also tested if the effect of parental education on hippocampus activation during our N-Back memory task is mediated by family or neighborhood income. Methods: The Adolescent Brain Cognitive Development (ABCD) study is a national multi-center investigation of American adolescents’ brain development. Functional magnetic resonance imaging (fMRI) data of a total sample of 3067 9–10-year-old adolescents were used. The primary outcome was left- hippocampus activation during the N-Back memory task (mean beta weight for N-Back run 1 2 back versus 0 back contrast in left hippocampus). The independent variable was parental education. Family income and neighborhood income were two possible mediators. Age, sex, and marital status were the covariates. To test mediation, we used hierarchical linear regression models first without and then with our mediators. Full mediation was defined according to Kenny. The Sobel test was used to confirm statistical mediation. Results: In the absence of family and neighborhood income in the model, higher parental educational attainment was associated with lower level of left hippocampus activation during the N-Back memory task. This effect was significant while age, sex, and marital status were controlled. The association between parental educational attainment and hippocampus activation during the N-Back memory task was no more significant when we controlled for family and neighborhood income. Instead, family income was associated with hippocampus activation during the N-Back memory task. These findings suggested that family income fully mediates the effect of parental educational attainment on left hippocampus activation during the N-Back memory task. Conclusions: The effect of parental educational attainment on adolescents’ hippocampus activation during an N-Back memory task is fully explained by family income. That means low family income is why adolescents with low-educated parents show highlighted hippocampus activation during an N-Back memory task. Given the central role of the hippocampus in learning and memory and as income is a modifiable factor by tax and economic policies, income-redistribution policies, fair taxation, and higher minimum wage may have implications for promotion of adolescent equality and social justice. There is a need to focus on family-level economic needs across all levels of neighborhood income. 11867/11867Secondary AnalysisPrivate
Race, Socioeconomic Status, and Sex Hormones among Male and Female American AdolescentsAlthough early sexual initiation and childbearing are major barriers against the upward social mobility of American adolescents, particularly those who belong to a low socioeconomic status (SES) and racial minorities such as Blacks, less is known on how SES and race correlate with adolescents' sex hormones. An understanding of the associations between race and SES with adolescents' sex hormones may help better understand why racial, and SES gaps exist in sexual risk behaviors and teen pregnancies. To extend the existing knowledge on social patterning of adolescents' sex hormones, in the current study, we studied social patterning of sex hormones in a national sample of male and female American adolescents, with a particular interest in the role of race and SES. For this cross-sectional study, data came from the baseline data (wave 1) of the Adolescent Brain Cognitive Development (ABCD) study, a national longitudinal prospective study of American adolescents. This analysis included 717 male and 576 female non-Hispanic White or Black adolescents ages 9-10. The dependent variables were sex hormones (testosterone for males and estradiol for females). Independent variables were age, race, family marital status, parental education, and financial difficulties. For data analysis, linear regression models were used. Age, race, parental education, and financial difficulties were associated with estradiol in female and testosterone levels in male adolescents. Associations were not identical for males and females, but the patterns were mainly similar. Low SES explained why race is associated with higher estradiol in female adolescents. Marital status of the family did not correlate with any of the sex hormones. Being Black and low SES were associated with a higher level of sex hormones in male and female adolescents. This information may help us understand the social patterning of sexual initiation and childbearing. Addressing racial and economic inequalities in early puberty, sexual initiation, and childbearing is an essential part of closing the racial and economic gaps in the US.11867/11867Secondary AnalysisPrivate
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
Subjective neurodevelopmental risk is more robustly associated with cortical structure than objective measures of executive function in the ABCD Study sampleNeurodevelopmental disorders (NDDs) possess some shared symptoms (e.g., executive function deficits) and structural brain presentations, so it may be useful to study neural processes in NDDs transdiagnostically. We aimed to assess subjective and objective measures of neurodevelopmental risk (ND risk) in relation to structural magnetic resonance imaging (sMRI) metrics in the baseline sample of the Adolescent Brain Cognitive Development Study (Release 2.0.1). We hypothesized that greater ND risk would cross-sectionally relate to decreased cortical volume, surface area, and thickness.11534/11534Primary AnalysisPrivate
The Role of Social and Neural Connectedness in Predicting Neurodevelopmental Functioning in AdolescenceBecause neurodevelopmental disorders (NDDs) are associated with significant impairment and public health costs and few psychological interventions are known to be effective for reducing neurodevelopmental symptomatology, identification of novel treatment targets for individuals with NDDs is needed. The present longitudinal study will address this need by utilizing a large, nationally-representative sample of youth (i.e., the ABCD Study sample; N = 11,500+; age 9-10) to examine the roles of social connectedness (i.e., extracurricular involvement, family dynamics, and relationships with peers and parents) and related neural connectedness (i.e., functional connectivity within the salience network) in predicting future neurodevelopmental functioning (indexed by both parent-reported symptoms and objective executive function tasks). Results from this study could therefore delineate modifiable social factors and underlying neural mechanisms that are protective against neurodevelopmental symptomatology in early adolescence and inform future clinical research. 11273/11273Primary AnalysisPrivate
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
Fine particulate matter exposure during childhood relates to hemispheric-specific differences in brain structure Background Emerging findings have increased concern that exposure to fine particulate matter air pollution (aerodynamic diameter ≤2.5 μm; PM2.5) may be neurotoxic, even at lower levels of exposure. Yet, additional studies are needed to determine if exposure to current PM2.5 levels may be linked to hemispheric and regional patterns of brain development in children across the United States. Objectives We examined the cross-sectional associations between geocoded measures of concurrent annual average outdoor PM2.5 exposure, regional- and hemisphere-specific differences in brain morphometry and cognition in 10,343 9- and 10- year-old children. Methods High-resolution structural T1-weighted brain magnetic resonance imaging (MRI) and NIH Toolbox measures of cognition were collected from children at ages 9-10 years. FreeSurfer was used to quantify cortical surface area, cortical thickness, as well as subcortical and cerebellum volumes in each hemisphere. PM2.5 concentrations were estimated using an ensemble-based model approach and assigned to each child’s primary residential address collected at the study visit. We used mixed-effects models to examine regional- and hemispheric- effects of PM2.5 exposure on brain estimates and cognition after considering nesting of participants by familial relationships and study site, adjustment for socio-demographic factors and multiple comparisons. Results Annual residential PM2.5 exposure (7.63 ± 1.57 µg/m3) was associated with hemispheric specific differences in gray matter across cortical regions of the frontal, parietal, temporal and occipital lobes as well as subcortical and cerebellum brain regions. There were hemispheric-specific associations between PM2.5 exposures and cortical surface area in 9/31 regions; cortical thickness in 22/27 regions; and volumes of the thalamus, pallidum, and nucleus accumbens. We found neither significant associations between PM2.5 and task performance on individual measures of neurocognition nor evidence that sex moderated the observed associations. Discussion Even at relatively low-levels, current PM2.5 exposure across the U.S. may be an important environmental factor influencing patterns of structural brain development in childhood. Prospective follow-up of this cohort will help determine how current levels of PM2.5 exposure may affect brain development and subsequent risk for cognitive and emotional problems across adolescence. 10343/10343Secondary AnalysisShared
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
Association of lead-exposure risk and family income with childhood brain outcomesSocioeconomic factors influence brain development and structure, but most studies have overlooked neurotoxic insults that impair development, such as lead exposure. Childhood lead exposure affects cognitive development at the lowest measurable concentrations, but little is known about its impact on brain development during childhood. We examined cross-sectional associations among brain structure, cognition, geocoded measures of the risk of lead exposure and sociodemographic characteristics in 9,712 9- and 10-year-old children. Here we show stronger negative associations of living in high-lead-risk census tracts in children from lower- versus higher-income families. With increasing risk of exposure, children from lower-income families exhibited lower cognitive test scores, smaller cortical volume and smaller cortical surface area. Reducing environmental insults associated with lead-exposure risk might confer greater benefit to children experiencing more environmental adversity, and further understanding of the factors associated with high lead-exposure risk will be critical for improving such outcomes in children.9712/9712Primary AnalysisShared
Direct and Indirect Associations of Widespread Individual Differences in Brain White Matter Microstructure with Executive Functioning and General and Specific Dimensions of Psychopathology in ChildrenBackground: Executive functions (EF) are centrally important because they are broadly associated with risk for psychopathology and substance abuse. Because EF has been linked to white matter microstructure, we tested the prediction that fractional anisotropy (FA) and mean diffusivity (MD) in white matter tracts are associated with EF and both general and specific dimensions of psychopathology in children younger than the age of widespread psychoactive substance use. Method: Parent symptom ratings, EF test scores, and diffusion tensor parameters were obtained from 9,500 9-10 year olds in the Adolescent Brain Cognitive Development (ABCD) Study. Results: A latent factor derived from EF test scores was significantly associated with all general and specific factors of psychopathology defined in a bifactor model. Furthermore, latent EF was associated with MD in 16 of 17 bilateral white matter tracts (range: β = -0.05; SE = 0.02; - β = -0.23; SE = 0.05) and FA in eight tracts. There were no direct associations of psychopathology with FA or MD in any tract, but there were significant indirect associations via EF of FA in multiple association and projection fibers and MD in all tracts except the forceps minor with both specific conduct problems and attention-deficit hyperactivity problems (ADHD) (range: β = 0.01; SE = 0.01; through β = 0.08; SE = 0.02). Conclusions: EF in children is inversely associated with indices of white matter microstructural integrity throughout the brain and the variance in white matter microstructure shared with EF is significantly associated with ADHD and conduct problems. 8587/8587Secondary AnalysisPrivate
Positive economic, psychosocial, and physiological ecologies predict brain structure and cognitive performance in 9- 10-year-old childrenWhile low socioeconomic status (SES) introduces risk for developmental outcomes among children, there are an array of proximal processes that determine the ecologies and thus the lived experiences of children. This study examined interrelations between 22 proximal measures in the economic, psychosocial, physiological, and perinatal ecologies of children, in association with brain structure and cognitive performance in a diverse sample of 8,158 9-10-year-old children from the Adolescent Brain Cognitive Development (ABCD) study. SES was measured by the income-to-needs ratio (INR), a measure used by federal poverty guidelines. Within the ABCD study, in what is one of the largest and most diverse cohort of children studied in the United States, we replicate associations of low SES with lower total cortical surface area and worse cognitive performance. Associations between low SES (<200% INR) and measures of development showed the steepest increases with INR, with apparent increases still visible beyond the level of economic disadvantage in the range of 200% - 400% INR. Notably, we found three latent factors encompassing positive ecologies for children across the areas of economic, psychosocial, physiological and perinatal well-being in association with better cognitive performance and higher total cortical surface area beyond the effects of SES. Specifically, latent factors encompassing youth perceived social support and perinatal well-being were positive predictors of developmental measures for all children, regardless of SES. Further, we found a general latent factor explained relationships between 20 of the proximal measures and encompassed a joint ecology of higher social and economic resources relative to low adversity across psychosocial, physiological, and perinatal domains. The association between the resource-to-adversity latent factor and cognitive performance was moderated by SES, such that for children in higher SES households, cognitive performance progressively increased with these latent factor scores, while for lower SES, cognitive performance increased only among children with the highest latent factor scores. Our findings suggest that both positive ecologies of increased access to resources and lower adversity are mutually critical for promoting better cognitive development in children from low SES households. Our findings inform future studies aiming to examine positive factors that influence healthier development in children.8158/8158Secondary AnalysisPrivate
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
Nucleus Accumbens Cytoarchitecture Predicts Weight Gain in ChildrenThe prevalence of obesity in children and adolescents worldwide has quadrupled since 1975 and is a key predictor of obesity later in life. Previous work has consistently observed relationships between macroscale measures of reward-related brain regions (e.g., the nucleus accumbens [NAcc]) and unhealthy eating behaviors and outcomes; however, the mechanisms underlying these associations remain unclear. Recent work has highlighted a potential role of neuroinflammation in the NAcc in animal models of diet-induced obesity. Here we leverage a novel diffusion MRI technique, restriction spectrum imaging, to probe the microstructure (cellular density) of subcortical brain regions. More specifically, we test the hypothesis that the cell density of reward-related regions is associated with obesity-related metrics and early weight gain. In a large cohort of nine- and ten-year-olds enrolled in the Adolescent Brain Cognitive Development (ABCD) study, we demonstrate that cellular density in the NAcc is related to individual differences in waist circumference at baseline and is predictive of increases in waist circumference after one year. These findings suggest a neurobiological mechanism for pediatric obesity consistent with rodent work showing that high saturated fat diets increase gliosis and neuroinflammation in reward-related brain regions, which in turn lead to further unhealthy eating and obesity. 5334/5334Primary AnalysisPrivate
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
Effective Velopharyngeal Ratio: A More Clinically Relevant Measure of Velopharyngeal Function Purpose: Velopharyngeal (VP) ratios are commonly used to study normal VP anatomy and normal VP function. An effective VP (EVP) ratio may be a more appropriate indicator of normal parameters for speech. The aims of this study are to examine if the VP ratio is preserved across the age span or if it varies with changes in the VP portal and to analyze if the EVP ratio is more stable across the age span. Methods: Magnetic resonance imaging (MRI) was used to analyze VP variables of 270 participants. For statistical analysis, the participants were divided into the following groups based on age: infants, children, adolescents and adults. ANOVAs and a Games Howell Post Hoc Test were used to compare variables between groups. Results: There was a statistically significant difference (p < .05) in all measurements between the age groups. Pairwise comparisons reported statistically significant adjacent group differences (p < .05) for velar length, VP ratio, effective velar length, adenoid depth, and pharyngeal depth. No statistically significant differences between adjacent age groups was reported for the EVP ratio. Conclusions: Results from this study report the EVP ratio was not statistically significant between adjacent age groups, while the VP ratio was statistically significant between adjacent age groups. This study suggests that the EVP ratio is more correlated to VP function than the VP ratio and provides a more stable and consistent ratio of VP function across the age span. 19/42Secondary 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 AnalysisShared
* Data not on individual level
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