NDA Help Center

Collection - General Tab

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

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

Funding Source

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

Supporting Documentation

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

Grant Information 

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

Clinical Trials

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

Frequently Asked Questions

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

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

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

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

Glossary

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

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

  • A Collection is a virtual container into which data will be submitted and shared.  It also provides important information about the project, funding amounts, reported enrollment amounts, data sharing schedule, and results that will help program staff better evaluate the grant.  While the general rule is that one Collection is created for each grant, it may be appropriate to associated multiple grants with a single Collection in the case of collaborative projects or projects submitting data through a single site such as a Data Coordinating Center. 

  • 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.

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

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

NDA Help Center

Collection - Shared Data Tab

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

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

 

Frequently Asked Questions

  • To see what data your project have submitted are being used by a study, simply go the Associated Studies tab of your collection.  Alternatively, you may review an NDA Study Attribution Report available on the General tab.  

  • Often it becomes more difficult to organize and format data electronically after the project has been completed and the information needed to create a GUID may not be available; however, you may still contact a program staff member at the appropriate funding institution for more information.

  • Unlike completed projects where researchers may not have the information needed to create a GUID and/or where the effort needed to organize and format data becomes prohibitive, ongoing projects have more of an opportunity to overcome these challenges.  Please contact a program staff member at the appropriate funding institution for more information.

Glossary

  • A defined organization and group of Data Elements to represent an electronic definition of a measure, assessment, questionnaire, or collection of data points.  Data structures that have been defined in the NDA Data Dictionary are available at https://ndar.nih.gov/data_dictionary.html. 

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

  • The term 'Shared' generally means available to others; however, there are some slightly different meanings based on what is Shared.  A Shared NDA Collection or NDA Study is viewable and searchable publicly regardless of the user's role or whether the user has an NDA account.  A Shared Collection or NDA Study does not necessarily mean that data submitted to the Collection or used in the NDA Study have been shared as this is independently determined.  Data are shared according the schedule defined in a Collection's Data Expected Tab and/or in accordance with data sharing expectations in the NDA Data Sharing Terms and Conditions.  Additionally, Supporting Documentation uploaded to a Collection may be shared independent of whether data are shared, but will only be viewable and accessible if the Collection is Shared.

NDA Help Center

fMRi

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

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

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

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

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

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

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

Frequently Asked Questions

Glossary

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

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

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

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

NDA Help Center

Collection - Submissions Tab

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

Here, you can:

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

Frequently Asked Questions

Glossary

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

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

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

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

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

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

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

NDA Help Center

Collection - Publications Tab

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

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

 

Frequently Asked Questions

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

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

Glossary

  • A link to the Create an NDA Study page that can be clicked to start creating an NDA Study with information such as the title, journal and authors automatically populated.

  • Indicates that the publication has not yet been reviewed and/or marked as Relevant or Not Relevant so it has not been determined whether an NDA Study is expected.

  • A publication that is not based on data related to the aims of the grant/project associated with the Collection or not based on any data such as a review article and, therefore, an NDA Study is not expected to be created.

  • PubMed provides citation information for biomedical and life sciences publications and is managed by the U.S. National Institutes of Health's National Library of Medicine.

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

  • A publication that is based on data related to the aims of the grant/project associated with the Collection and, therefore, an NDA Study is expected to be created.

NDA Help Center

EEG

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

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

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

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

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

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

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

Frequently Asked Questions

Glossary

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

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

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

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

NDA Help Center

Collection - Data Expected

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

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

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

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

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

 

Frequently Asked Questions

  • An NDA Data Structure is comprised of multiple Data Elements to make up an electronic definition of an assessment, measure, questionnaire, etc will have a corresponding Data Structure.

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

Glossary

  • Imaging+ is an NDA term which encompasses all imaging related data including, but not limited to, images (DTI, MRI, PET, Structural, Spectroscopy, etc.) as well as neurosignal data (EEG, fMRI, MEG, EGG, eye tracking, etc.) and Evaluated Data.

  • Data specific to the primary aims of the research being conducted (e.g. outcome measures, other dependent variables, observations, laboratory results, analyzed images, volumetric data, etc.) including processed images.

  • Items listed on the Data Expected list in the Collection which may be an individual and discrete Data Structure, Data Structure Category, or Data Structure Group.

  • A defined organization and group of Data Elements to represent an electronic definition of a measure, assessment, questionnaire, or collection of data points.  Data structures that have been defined in the NDA Data Dictionary are available at https://ndar.nih.gov/data_dictionary.html. 

  • An NDA term describing the affiliation of a Data Structure to a Category, which may be disease/disorder or diagnosis related (Depression, ADHD, Psychosis), specific to data type (MRI, eye tracking, omics), or type of data (physical exam, IQ).

  • A Data Item listed on the Data Expected tab of a Collection that indicates a group of Data Structures (e.g., ADOS or SCID) for which data may be submitted instead of a specific Data Structure identified by version, module, edition, etc. For example, the ADOS Data Structure Category includes every ADOS Data Structure such as ADOS Module 1, ADOS Module 2, ADOS Module 1 - 2nd Edition, etc. The SCID Data Structure Group includes every SCID Data Structure such as SCID Mania, SCID V Mania, SCID PTSD, SCID-V Diagnosis, and more. 

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

  • A new Data Structure category, Evaluated Data is analyzed data resulting from the use of computational pipelines in the Cloud and can be uploaded directly back to a miNDAR database.  Evaluated Data is expected to be listed as a Data Item in the Collection's Data Expected Tab.

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

  • The earliest date on which the data related to the Data Item may expect to start being submitted based on whether the data are considered Descriptive/Raw or Analyzed and based on the project's data collection timeline.  Descriptive/raw data are expected to be submitted every 6 months (January 15 and July 15) while Analyzed data are expected to be submitted no later than the time a manuscript is accepted.  Data for all subjects is not expected on the Initial Submission Date and modifications may be made as necessary based on the project's conduct.

  • An NDA created Data Structure used to convey basic information about the subject such as demographics, pedigree (links family GUIDs), diagnosis/phenotype, and sample location that are critical to allow for easier querying of shared data.

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

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

NDA Help Center

Collection - Permissions Tab

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

The available permission groups include:

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

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

Frequently Asked Questions

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

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

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

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

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

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

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

Glossary

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

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

NDA Help Center

Eye Tracking

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

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

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

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

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

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

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

Frequently Asked Questions

Glossary

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

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

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

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

NDA Help Center

Collection - Experiments Tab

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

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

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

Frequently Asked Questions

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

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

     

Glossary

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

  • The ID number automatically generated by NDA which must be included in the appropriate file when uploading data to link the Experiment Definition to the subject record.

NDA Help Center

Omics

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

Experiment Parameters

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

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

Platform

Continue to select the Platform (box 3).

Extraction

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

Processing

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

Additional Information

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

 

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

Frequently Asked Questions

Glossary

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

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

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

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

NDA Help Center

Filter Cart

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

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

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

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

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

Frequently Asked Questions

Glossary

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Frequently Asked Questions

Glossary

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Collection - Associated Studies

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

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

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

Frequently Asked Questions

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

Glossary

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

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  • EEG
  • EGG
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Created On
1376UH2EEG08/16/2019
1375pioneerEEG08/16/2019
1374Experiment 1fMRI08/16/2019
1373Experiment 2fMRI08/16/2019
1372spotlightEEGEEG08/16/2019
1371spotlightEYEEye Tracking08/16/2019
1370Targeted sequencing of ASD high risk candidate genesOmics08/12/2019
1369Control taskfMRI08/07/2019
1368encodingfMRI08/04/2019
1367Resting-state fMRIfMRI07/29/2019
1366Using fMRI to Measure the Neural-level Signals Underlying Population-level Responses - EyetrackingEye Tracking07/29/2019
1365Using fMRI to Measure the Neural-level Signals Underlying Population-level ResponsesfMRI07/29/2019
1364Music event segmentationfMRI07/25/2019
1363Gamma-aminobutyric acid ReceptorOmics07/24/2019
1362UCSD_Gleeson_U01MH108898_WGS_TruSeq_NovaSeq6000Omics07/19/2019
1361RNA-Seq_DLPFCOmics07/19/2019
1360ChIP-seq_HiSeq_2500Omics07/18/2019
1359WGS_HiSeq XOmics07/18/2019
1358Movie TwistfMRI07/18/2019
1357RNA-Seq_sgACCOmics07/18/2019
1356Genotyping_HumanOmni5-QuadOmics07/18/2019
1355Genotyping_Human1MDuoOmics07/18/2019
1354Genotyping_HumanHap650YOmics07/18/2019
1353MicroarrayOmics07/18/2019
1352Emotion Processing TaskfMRI07/18/2019
1351New Scanner: fMRIfMRI07/18/2019
1350Resting State (4min)fMRI07/18/2019
1349Resting State (6min)fMRI07/18/2019
1348RNA-seqOmics07/17/2019
1347Localizers retinotopy final short version TR1 fMRI07/16/2019
1346Localizers dynamic final version TR1fMRI07/16/2019
1345Localizers dynamic pilot version TR2fMRI07/15/2019
1344Foraging StudyfMRI07/15/2019
1343Frustration StudyfMRI07/15/2019
1342Real-Time NeurofeedbackfMRI07/15/2019
1341Resting StatefMRI07/15/2019
1340ACE fmri_runDShiftedfMRI07/15/2019
1339Cued Go-Nogo (shapes)EEG07/15/2019
1338ACE fmri_RunCShiftedfMRI07/15/2019
1337ACE fmri_runBShiftedfMRI07/15/2019
1336ACE fmri_runAShiftedfMRI07/15/2019
1335ACE fmri_runDfMRI07/15/2019
1334ACE fmri_runCfMRI07/15/2019
1333ACE fmri_runBfMRI07/15/2019
1332ACE fmri_runAfMRI07/15/2019
1331Child ANT Flanker - EEGEEG07/15/2019
1330Island GetawayEEG07/15/2019
1329PRV-011-EEGEEG07/15/2019
1328PRV-013-EEGEEG07/15/2019
1327Interactive Emotional FacesEEG07/15/2019
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Collection Title Collection Investigators Collection Description
Pediatric MRI
PEDIATRIC STUDY CENTERS: Michael J. Rivkin, M.D., Boston Children's Hospital; William S. Ball, M.D., Children's Hospital Medical Center of Cincinnati; Dah-Jyuu Wang, Ph.D., Children's Hospital of Philadelphia; James T. McCracken, M.D., University of California at Los Angeles; Michael Brandt, Ph.D. and Jack Fletcher, Ph.D., University of Texas Health Science Center; Robert McKinstry, M.D., Washington University. DATA COORDINATING CENTER: Alan Evans, Ph.D., Montreal Neurological Institute Center. CLINICAL COORDINATING CENTER: Kelly Botteron, M.D., Washington University. DIFFUSION TENSOR PROCESSING CENTER: Carlo Pierpaoli, M.D., National Institute of Child Health and Human Development. SPECTROSCOPY PROCESSING CENTER: Joseph O'Neill, Ph.D., University of California at Los Angeles. 
Pediatric MRI Release 5.0. Visit our website at http://pediatricmri.nih.gov for more information about this project.
NIMH Data Archive
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nihpd_sym_all_nifti.zip Other NIHPD Objective 1 L-R symmetric atlases General Public
nihpd_obj2_asym_nifti.zip Other NIHPD Objective 2 atlases General Public
nihpd_asym_all_nifti.zip Other NIHPD Objective 1 asymmetric atlases General Public
http://www.plosone.org/article/info%3Adoi%2F10.1371%2Fjournal.pone.0018712 Publication Hanson JL, Chandra A, Wolfe BL, Pollak SD (2011) Association between Income and the Hippocampus. PLoS ONE 6(5): e18712. doi:10.1371/journal.pone.0018712 Qualified Researchers
Objective1_procedure_manual.pdf Objectives Procedure manual for Objective 1 subjects (ages 4.5+ years). Version public release 1.0, January 2007. Qualified Researchers
Objective2_procedure_manual.pdf Objectives Procedure manual for Objective 2 subjects (ages 0-4.5 years). Final version. Qualified Researchers
MRI_protocol_manual.pdf Analysis Protocol MRI procedure manual distributed to each participating site in the study. Covers acquisition and transfer of MRI data only. November 2006 version. Qualified Researchers
Release3_notes.pdf Other Release 3 notes. October 2009. Qualified Researchers
Release4_notes.pdf Publication Release 4 notes. June 2010. Qualified Researchers
Neuroimaging_white_paper.pdf Methods Neuroimaging white paper. Qualified Researchers
Clinical_behavioral_white_paper.pdf Methods Clinical/behavior white paper. Qualified Researchers
Spectroscopy_white_paper.pdf Methods Spectroscopy white paper. Qualified Researchers
Release3_notes.pdf Other Release 3 notes. October 2009. Qualified Researchers
Release4_notes.pdf Other Release 4 notes. June 2010. Qualified Researchers
www.tortoisedti.org Software New free DTI software. Qualified Researchers
Objective1_Procedure_Manual.pdf Objectives Procedure manual for Objective 1 subjects (ages 4.5+ years). Qualified Researchers
Objective2_Procedure_Manual.pdf Objectives Procedure manual for Objective 2 subjects (ages 0-4.5 years). Qualified Researchers
Study_Protocol.pdf Methods Study protocol. Qualified Researchers
MRI_Procedure_Manual.pdf Methods MRI procedure manual. Qualified Researchers
Clinical_Behavioral_White_Paper.pdf Methods Clinical/behavioral white paper. Qualified Researchers
Database_White_Paper.pdf Methods Database white paper. Qualified Researchers
Neuroimaging_White_Paper.pdf Methods Structural MRI white paper. Qualified Researchers
Spectroscopy_White_Paper.pdf Methods Spectroscopy white paper. Qualified Researchers
Release3_Notes.pdf Other Release 3 notes. October 2009. Qualified Researchers
Release4_Notes.pdf Other Release 4 notes. June 2010. Qualified Researchers
www.tortoisedti.org Software DTI analysis software developed by the project. Qualified Researchers
Growth_maps.zip Other Group-averaged surface growth maps for Objective 2 data that passed QC for surface extraction. Qualified Researchers
eDTI_White_Paper_R5.1.pdf Methods Expanded diffusion tensor imaging (eDTI) white paper. Qualified Researchers
DTI_White_Paper_R5.1.pdf Methods Diffusion tensor imaging (DTI) white paper. Qualified Researchers
Release5.1_Notes.pdf Other Release 5.1 notes (July 9, 2012). Qualified Researchers
Study_Protocol.pdf Methods Study protocol Qualified Researchers
Release5_Notes.pdf Other Release 5 notes (April 5, 2012). Qualified Researchers
Anatomic_MRI_White_Paper_R5.pdf Methods Anatomic MRI white paper and project overview. Qualified Researchers
Clinical_Behavioral_White_Paper_R5.pdf Methods Clinical/behavioral white paper. Qualified Researchers
DTI_White_Paper_R5.pdf Methods Diffusion tensor imaging (DTI) white paper. Qualified Researchers
Spectroscopy_White_Paper_R5.pdf Methods Spectroscopy white paper. Qualified Researchers
Pediatric_MRI_Data_Dictionary_R5.xls Other Release 5 data dictionary. Qualified Researchers
Release5.1_Notes_revised_061213.pdf Other Release 5.1 Notes Qualified Researchers


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Shared Data

Data structures with the number of subjects submitted and shared are provided.

Image Imaging 2
Peds - BRIEF Adult Version (Informant Report) Clinical Assessments 554
Peds - BRIEF Adult Version (Self Report) Clinical Assessments 554
Peds - BRIEF Parent Form Clinical Assessments 554
Peds - BSID II Behavior Rating Scale Clinical Assessments 554
Peds - BSID II Mental Scale Clinical Assessments 554
Peds - BSID II Motor Scale Clinical Assessments 554
Peds - Brief Telephone Screening Interview Clinical Assessments 554
Peds - CANTAB Clinical Assessments 554
Peds - CDISC4 Parent Version Clinical Assessments 554
Peds - California Verbal Learning Test for Children Clinical Assessments 554
Peds - California Verbal Learning Test, 2nd ed. Clinical Assessments 554
Peds - Carey Temperament Scales, BSQ (3 to 7 years) Clinical Assessments 554
Peds - Carey Temperament Scales, EITQ (1 to 4 months) Clinical Assessments 554
Peds - Carey Temperament Scales, RITQ (4 to 11 months) Clinical Assessments 554
Peds - Carey Temperament Scales, TTS (1 to 2 years) Clinical Assessments 554
Peds - Child Behavior Checklist (CBCL) Clinical Assessments 554
Peds - Demographics Clinical Assessments 554
Peds - Differential Ability Scales (DAS) Clinical Assessments 554
Peds - Diffusion Tensor Imaging Imaging 274
Peds - Disc Predictive Scales Clinical Assessments 554
Peds - Expanded Diffusion Tensor Imaging (eDTI) Imaging 152
Peds - FIGS Family History Int. Genetic Studies Yr1 Clinical Assessments 554
Peds - FIGS Family History Int. Genetic Studies Yr3 Clinical Assessments 554
Peds - Family Biographical History Form (0:0 - 4:5 y:m) Clinical Assessments 554
Peds - Full Telephone Screening Interview - Version 1 Clinical Assessments 554
Peds - Full Telephone Screening Interview - Version 2 Clinical Assessments 554
Peds - Handedness (1:0 to 2:11 y:m) Clinical Assessments 554
Peds - Handedness (3:0 to 5:11 y:m) - Part 1 Clinical Assessments 554
Peds - Handedness (3:0 to 5:11 y:m) - Part 2 Clinical Assessments 554
Peds - Handedness (6:0+ y:m) Clinical Assessments 554
Peds - JTCI Parent Report Clinical Assessments 554
Peds - JTCI Self Report Clinical Assessments 554
Peds - Longitudinally Registered aMRI Variables Imaging 553
Peds - MRI Child History Form (4:6+ y:m) Clinical Assessments 554
Peds - NEPSY Verbal Fluency (Semantic and Phonemic) Clinical Assessments 554
Peds - NEPSY Verbal Fluency (Semantic) Clinical Assessments 554
Peds - Neuropsychological Clinical Assessments 554
Peds - Non-longitudinally Registered aMRI Variables Imaging 553
Peds - Parental Stress Index Clinical Assessments 554
Peds - Physical Clinical Assessments 554
Peds - Physical and Neurological Exam (0:0 to 0:1 y:m) Clinical Assessments 554
Peds - Physical and Neurological Exam (0:2 to 0:11 y:m) Clinical Assessments 554
Peds - Physical and Neurological Exam (1:0 to 2:11 y:m) Clinical Assessments 554
Peds - Physical and Neurological Exam (3:0 to 4:5 y:m) Clinical Assessments 554
Peds - Physical/Neurological Examination Clinical Assessments 554
Peds - Preschool Language Scale-3 Clinical Assessments 554
Peds - Psychiatric & Personality Clinical Assessments 554
Peds - Pubertal Status Questionnaire Clinical Assessments 554
Peds - Purdue Pegboard - Full Board Clinical Assessments 554
Peds - Purdue Pegboard - Half Board Clinical Assessments 554
Peds - Screening and Exclusion Form (0:0 to 4:5 y:m) Clinical Assessments 554
Peds - Spectroscopy Imaging 553
Peds - TCI Parent Report Clinical Assessments 554
Peds - TCI Self Report Clinical Assessments 554
Peds - Timepoint Clinical Assessments 554
Peds - Urine and Saliva Clinical Assessments 554
Peds - WAIS-R Digit Span & Digit Symbol Clinical Assessments 554
Peds - WISC III Digital Span & Coding Clinical Assessments 554
Peds - Wechsler Abbreviated Scale of Intelligence Clinical Assessments 554
Peds - Woodcock-Johnson III Clinical Assessments 554

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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
Derivation of Brain Structure Volumes from MRI Neuroimages hosted by NDAR using LONI WorkflowsLONI utilized de-identified data from NDAR's cloud and a LONI Pipeline (pipeline.loni.usc.edu) processing workflow to perform a secondary structural MRI examination. The workflow used in this study pulls data from and provided by NDAR to an instance on the LONI compute cluster, aligns data to a standard orientation using FSLreorient2stsd, and undergoes further image processing to eventually identify, extract, and analyze cortical and sub-cortical structures in different MRI brain volumes. Two methods were used for this image processing: the first uses Freesurfer Recon_All to extract brain cortical parcellation and surfaces, align the data to an atlas, and identify, and analyze regions of interest; the second uses FSL to extract the brain (BET), align the data to an atlas, and extract ROIs including sub-cortical regions using FSL FIRST. The second method also uses Freesurfer (mri_segstats) to perform statistical analysis of these ROIs. Lastly, the LONI Pipeline workflow updates and returns the data processed and extracted by Freesurfer and FSL as a miNDAR back to NDAR's cloud storage instance. These results can be used to assess quality control or be used to perform post-hoc comparisons of cortical and sub-cortical brain architecture between subject types. See also, Torgerson et al. (2015) Brain Imaging and Behavior, for additional details on using LONI Pipeline to access and process NDAR data.319/740Secondary AnalysisShared
Examining the relationship between minority stress, cortisol, and executive functioning in healthy childrenRecent evidence suggests that chronic stress in children and adults is associated with hypothalamic-pituitary-adrenal (HPA) axis dysregulation, as evidenced by hypocortisolism and flatter diurnal cortisol slope. Although adverse experiences in childhood have previously been linked with flatter diurnal cortisol slope, relatively few studies have examined the effects of stress related to membership in a socioeconomic or ethnic minority group. Further, the relationship between chronic stress and cognitive functioning has been understudied in children and adolescents. The purpose of the current study was to assess whether minority stress is associated with poorer executive functioning, a cognitive domain particularly sensitive to chronic stress. Analysis of a sample of 137 typically-developing children from the NIH MRI Study of Normal Brain Development found that cortisol slope was neither associated with minority status nor with reported executive functioning. Further, ethnic minority status predicted slightly lower performance within specific executive functioning domains, while socioeconomic status did not. Limitations, future directions, and clinical implications are discussed. 554/651Secondary AnalysisPrivate
Test ProductionTest Production556/569Secondary AnalysisPrivate
Hippocampal Growth after Febrile Status: FEBSTAT Five Year Follow-Up The FEBSTAT study (Consequences of Prolonged Febrile Seizures in Childhood) is prospectively examining long term outcomes of febrile status epilepticus ( FSE). We have previously reported the results of the acute2 and 1 year follow-up3 MRIs in the FEBSTAT cohort. This report describes the results of the 5 year follow-up MRIs on the FEBSTAT cohort. In this analysis we have integrated brain growth data from a large group of older normal control subjects obtained from the NIH MRI Study of Normal Brain Development. One hundred and three FSE subjects with 5 year follow-ups (Mean FU 5.45; SD 1.06; Median 5.30 yrs) are included in this report. The major findings of this study are, i) atrophy and T2 signal hyperintensity seen acutely and at one year follow-up in affected hippocampi continues into the 5 year follow up at 5 to 10 years of age and, ii) hippocampi of male FSE subjects with no hippocampal T2 signal increase and normal morphology on MRI are definitely smaller on volumetric measurement than control male hippocampi for at least the first 5 to 7 years after FSE, but female FSE hippocampi are much less affected, iii) in the absence of recurrent episodes of FSE, the finding of normal hippocampal signal on the acute post-FSE MRI reliably predicts normal hippocampal signal in later follow up MRIs and iv) hippocampal malrotation (HIMAL), a developmental abnormality associated with FSE4, slows the growth of the hippocampus.553/553Secondary AnalysisPrivate
A new template to study callosal growth shows specific growth in anterior and posterior regions of the corpus callosum in early childhoodMost of the studies conducted on the development of the corpus callosum (CC) have been limited to a relatively simple assessment of callosal area, providing an estimation of the size of the CC in two dimensions rather than its actual measurement. The goal of this study was to revisit callosal development in childhood and adolescence by using a three-dimensional (3D) magnetic resonance imaging template of the CC that considers the horizontal width of the CC and compares this with the two-dimensional (2D) callosal area. We mapped callosal growth in a large sample of youths followed longitudinally (N = 370 at T1; N = 304 at T2; and N = 246 at T3). Both techniques were based on a five-section subdivision of the CC. The results obtained with the 3D method revealed that the rate of CC growth over a 4-year period in the rostrum, the genu, the anterior body and the splenium was significantly higher in the youngest age group (< 7 years) than in older groups, indicating an intense period of development in early childhood for the anterior and posterior parts of the CC. Similar results were obtained when 2D callosal area was used for the anterior and posterior parts of the CC. However, divergent results were found in the mid-body and the caudal body of the CC. As shown by differences between 2D estimations and actual 3D measurements of callosal growth, our study highlights the importance of considering the horizontal width in measuring developmental changes in the CC.427/427Secondary AnalysisShared
T1 white/gray contrast as a predictor of chronological age, and an index of cognitive performanceKnowing the maturational schedule of typical brain development is critical to our ability to identify deviations from it; such deviations have been related to cognitive performance and even developmental disorders. Chronological age can be predicted from brain images with considerable accuracy, but with limited spatial specificity, particularly in the case of the cerebral cortex. Methods using multi-modal data have shown the greatest accuracy, but have made limited use of cortical measures. Methods using complex measures derived from voxels throughout the brain have also shown great accuracy, but are difficult to interpret in terms of cortical development. Measures based on cortical surfaces have yielded less accurate predictions, suggesting that perhaps cortical maturation is less strongly related to chronological age than is maturation of deep white matter or subcortical structures. We question this suggestion. We show that a simple metric based on the white/gray contrast at the inner border of the cortex is a good predictor of chronological age. We demonstrate this in two large datasets: the NIH Pediatric Data, with 832 scans of typically developing children, adolescents, and young adults; and the Pediatric Imaging, Neurocognition, and Genetics data, with 760 scans of individuals in a similar age-range. Further, our usage of an elastic net penalized linear regression model reveals the brain regions which contribute most to age-prediction. Moreover, we show that the residuals of age-prediction based on this white/gray contrast metric are not merely random errors, but are strongly related to IQ, suggesting that this metric is sensitive to aspects of brain development that reflect cognitive performance.401/401Primary AnalysisShared
Trajectories of cortical thickness maturation in normal brain developmentSeveral reports have described cortical thickness (CTh) developmental trajectories, with conflicting results. Some studies have reported inverted-U shape curves with peaks of CTh in late childhood to adolescence, while others suggested predominant monotonic decline after age 6. In this study, we reviewed CTh developmental trajectories in the NIH MRI Study of Normal Brain Development, and in a second step, evaluated the impact of post-processing quality control (QC) procedures on identified trajectories. The quality-controlled sample included 384 individual subjects with repeated scanning (1-3 per subject, total scans n=753) from 4.9 to 22.3years of age. The best-fit model (cubic, quadratic, or first-order linear) was identified at each vertex using mixed-effects models. The majority of brain regions showed linear monotonic decline of CTh. There were few areas of cubic trajectories, mostly in bilateral temporo-parietal areas and the right prefrontal cortex, in which CTh peaks were at, or prior to, age 8. When controlling for total brain volume, CTh trajectories were even more uniformly linear. The only sex difference was faster thinning of occipital areas in boys compared to girls. The best-fit model for whole brain mean thickness was a monotonic decline of 0.027mm per year. QC procedures had a significant impact on identified trajectories, with a clear shift toward more complex trajectories (i.e., quadratic or cubic) when including all scans without QC (n=954). Trajectories were almost exclusively linear when using only scans that passed the most stringent QC (n=598). The impact of QC probably relates to decreasing the inclusion of scans with CTh underestimation secondary to movement artifacts, which are more common in younger subjects. In summary, our results suggest that CTh follows a simple linear decline in most cortical areas by age 5, and all areas by age 8. This study further supports the crucial importance of implementing post-processing QC in CTh studies of development, aging, and neuropsychiatric disorders.379/379Secondary AnalysisShared
Prediction of brain maturity based on cortical thickness at different spatial resolutionsSeveral studies using magnetic resonance imaging (MRI) scans have shown developmental trajectories of cortical thickness. Cognitive milestones happen concurrently with these structural changes, and a delay in such changes has been implicated in developmental disorders such as attention-deficit/hyperactivity disorder (ADHD). Accurate estimation of individuals' brain maturity, therefore, is critical in establishing a baseline for normal brain development against which neurodevelopmental disorders can be assessed. In this study, cortical thickness derived from structural magnetic resonance imaging (MRI) scans of a large longitudinal dataset of normally growing children and adolescents (n = 308), were used to build a highly accurate predictive model for estimating chronological age (cross-validated correlation up to R = 0.84). Unlike previous studies which used kernelized approach in building prediction models, we used an elastic net penalized linear regression model capable of producing a spatially sparse, yet accurate predictive model of chronological age. Upon investigating different scales of cortical parcellation from 78 to 10,240 brain parcels, we observed that the accuracy in estimated age improved with increased spatial scale of brain parcellation, with the best estimations obtained for spatial resolutions consisting of 2560 and 10,240 brain parcels. The top predictors of brain maturity were found in highly localized sensorimotor and association areas. The results of our study demonstrate that cortical thickness can be used to estimate individuals' brain maturity with high accuracy, and the estimated ages relate to functional and behavioural measures, underscoring the relevance and scope of the study in the understanding of biological maturity.341/341Secondary AnalysisShared
Development and validation of a brain maturation index using longitudinal neuroanatomical scansBackground Major psychiatric disorders are increasingly being conceptualized as ‘neurodevelopmental’, because they are associated with aberrant brain maturation. Several studies have hypothesized that a brain maturation index integrating patterns of neuroanatomical measurements may reliably identify individual subjects deviating from a normative neurodevelopmental trajectory. However, while recent studies have shown great promise in developing accurate brain maturation indices using neuroimaging data and multivariate machine learning techniques, this approach has not been validated using a large sample of longitudinal data from children and adolescents. Methods T1-weighted scans from 303 healthy subjects aged 4.88 to 18.35 years were acquired from the National Institute of Health (NIH) pediatric repository (http://www.pediatricmri.nih.gov). Out of the 303 subjects, 115 subjects were re-scanned after 2 years. The least absolute shrinkage and selection operator algorithm (LASSO) was ‘trained’ to integrate neuroanatomical changes across chronological age and predict each individual's brain maturity. The resulting brain maturation index was developed using first-visit scans only, and was validated using second-visit scans. Results We report a high correlation between the first-visit chronological age and brain maturation index (r = 0.82, mean absolute error or MAE = 1.69 years), and a high correlation between the second-visit chronological age and brain maturation index (r = 0.83, MAE = 1.71 years). The brain maturation index captured neuroanatomical volume changes between the first and second visits with an MAE of 0.27 years. Conclusions The brain maturation index developed in this study accurately predicted individual subjects' brain maturation longitudinally. Due to its strong clinical potentials in identifying individuals with an abnormal brain maturation trajectory, the brain maturation index may allow timely clinical interventions for individuals at risk for psychiatric disorders. 303/303Secondary AnalysisShared
When does the youthfulness of the female brain emerge?Goyal et al., report in the February issue of PNAS that the female adult brain has a persistently lower metabolic brain age compared with the male brain at the same chronological age (1). In interpreting this remarkable finding, the authors propose that sex-related differences in brain development may in part play a role in “setting” the female brain at a younger initial brain age at puberty, allowing them to maintain a younger brain throughout adulthood. We argue that may not be the case and provide evidence to show that, in fact, the opposite may be true during childhood and adolescence. First, according to the Figure 2A in Goyal et al, surprisingly, the predicted age between 35-50 y were under-estimated for both males and females. It is unclear if the bias in this age range could have affected the overall findings or played a role in only the result from training on males and testing on females surviving a two-sided t-test. Moreover, it is unclear which age range was determinative of the significant difference between predicted and chronological age. Second, we used cortical thickness, which i) has been validated as a reliable biomarker for brain age (2, 3) and ii) has shown strong association with sex hormones during puberty maturation (4, 5) from 265 healthy children and youth (118 boys, 147 girls) between the ages of 5 and 18 from the NIH MRI Study of Normal Brain Development (6) and estimated the difference between brain age and actual chronological age. Similar to Goyal et al., we first trained the ML algorithm (support vector regression with default parameters, implemented using LIBSVM toolbox) on the male cohort only and then tested it on the female cohort, and vice versa. We found that while cortical-thickness-based brain age correlated strongly with actual chronological age in both cohorts (training on boys and testing on girls: r = 0.75, p<0.001; training on girls and testing on boys: r = 0.71, p < 0.001; Figure 1A), the mean cortical thickness brain age was on average 0.42 y older for girls compared with boys (p = 0.02, two-sided t-test; Figure 1B) when the male data was used as the training set and 0.47 y younger for boys compared with girls (p = 0.03, two-sided t-test; Figure 1B) when the female data was used as the training set. In other words, while per Goyal and colleagues’ investigation adult females may have a younger brain than adult males during development, this pattern is not the same and in fact seems to be in the opposite direction during puberty. While cortical thinning as a biomarker for aging may reflect a different aspect of aging than what metabolic changes may reflect, given that they are both strongly predictive of chronological age, it is likely that they may also be correlated. Therefore, given our finding, we propose that the mechanisms that are involved in keeping the female brain younger in adulthood may get engaged at a later point in life and not during puberty.265/265Secondary AnalysisShared
The diffusion tensor imaging (DTI) component of the NIH MRI study of normal brain development (PedsDTI)The NIH MRI Study of normal brain development sought to characterize typical brain development in a population of infants, toddlers, children and adolescents/young adults, covering the socio-economic and ethnic diversity of the population of the United States. The study began in 1999 with data collection commencing in 2001 and concluding in 2007. The study was designed with the final goal of providing a controlled-access database; open to qualified researchers and clinicians,which could serve as a powerful tool for elucidating typical brain development and identifying deviations associated with brain-based disorders and diseases, and as a resource for developing computational methods and image processing tools. This paper focuses on the DTI component of the NIH MRI study of normal brain development. In this work, we describe the DTI data acquisition protocols, data processing steps, quality assessment procedures, and data included in the database, along with database access requirements. For more details, visit http://www. pediatricmri.nih.gov. This longitudinal DTI dataset includes raw and processed diffusion data from 498 low resolution (3 mm) DTI datasets from274 unique subjects, and 193 high resolution (2.5mm) DTI datasets from152 unique subjects. Subjects range in age from10 days (from date of birth) through 22 years. Additionally, a set of age-specific DTI templates are included. This forms one component of the larger NIHMRI study of normal brain development which also includes T1-, T2-, proton density-weighted, and proton magnetic resonance spectroscopy (MRS) imaging data, and demographic, clinical and behavioral data.230/230Secondary AnalysisShared
Brain structure, cognition and behavior in child and adolescent survivors of leukemiaPediatric acute lymphoblastic leukemia (ALL) is the most common for of childhood cancer. It is successfully treated in ~90% of cases, primarily based on combination chemotherapy. Survivors of ALL are at elevated risk of cognitive or behavioral problems, which frequently include impairments in processing speed, working memory, attention, and executive function. These changes are accompanied by alterations in brain development, evident by changes in brain structure volume. In our study, data from the Pediatric MRI Data Repository was used as a secondary control data set (supplementing a control data set collected as part of the study). The results were shown as a collective, as well as in two groups matched on an individual basis to participants in the study (based on age, sex, and full-scale IQ).175/175Secondary AnalysisShared
Analysis of the contribution of experimental bias, experimental noise, and inter-subject biological variability on the assessment of developmental trajectories in diffusion MRI studies of the brainMetrics derived from the diffusion tensor, such as fractional anisotropy (FA) and mean diffusivity (MD) have been used in many studies of postnatal brain development. A common finding of previous studies is that these tensor-derived measures vary widely even in healthy populations. This variability can be due to inherent interindividual biological differences as well as experimental noise. Moreover, when comparing different studies, additional variability can be introduced by different acquisition protocols. In this study we examined scans of 61 individuals (aged 4–22 years) from the NIH MRI study of normal brain development. Two scans were collected with different protocols (low and high resolution). Our goal was to separate the contributions of biological variability and experimental noise to the overall measured variance, as well as to assess potential systematic effects related to the use of different protocols. We analyzed FA and MD in seventeen regions of interest. We found that biological variability for both FA and MD varies widely across brain regions; biological variability is highest for FA in the lateral part of the splenium and body of the corpus callosum along with the cingulum and the superior longitudinal fasciculus, and for MD in the optic radiations and the lateral part of the splenium. These regions with high inter-individual biological variability are the most likely candidates for assessing genetic and environmental effects in the developing brain. With respect to protocol-related effects, the lower resolution acquisition resulted in higher MD and lower FA values for the majority of regions compared with the higher resolution protocol. However, the majority of the regions did not show any age–protocol interaction, indicating similar trajectories were obtained irrespective of the protocol used.128/128Secondary AnalysisShared
* Data not on individual level
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