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helpcenter.filter-cart

NDA Help Center

Filter Cart

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

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

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

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

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

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

Additional Tips:

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

Once you have selected data of interest you can:

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

Please Note:

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

Frequently Asked Questions

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

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

  • How does Filter Cart Boolean logic work?

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

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

Glossary

  • Workspace
    The Workspace within the General Query Tool is a holding area where you can review your pending filters prior to adding them to Filter Cart. Therefore, the first step in accessing data is to select one or more items and move it into the Workspace.
  • Filter Cart
    Viewable at the top right of NDA pages, the Filter Cart is a temporary holder of data identified by the user through querying or browsing as being of some potential interest. The Filter Cart adds data using an AND condition. The opportunity to further refine data to determine what will be downloaded or sent to a miNDAR is available on the Data Packaging Page, the next step after the Filter Cart. Subsequent access to data is restricted by User Permission or Privilege; however Filter Cart use is not.

1 Numbers reported are subjects by age
New Trial
New Project

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

Please select an experiment type below

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

The table search feature is case insensitive and targets the experiment id, experiment name and experiment type columns. The experiment id is searched only when the search term entered is a number, and filtered using a startsWith comparison. When the search term is not numeric the experiment name is used to filter the results.
SelectExperiment IdExperiment NameExperiment Type
Created On
24HI-NGS_R1Omics02/16/2011
475MB1-10 (CHOP)Omics06/07/2016
490Illumina Infinium PsychArray BeadChip AssayOmics07/07/2016
501PharmacoBOLD Resting StatefMRI07/27/2016
506PVPREFOmics08/05/2016
509ABC-CT Resting v2EEG08/18/2016
13Comparison of FI expression in Autistic and Neurotypical Homo SapiensOmics12/28/2010
18AGRE/Broad Affymetrix 5.0 Genotype ExperimentOmics01/06/2011
22Stitching PCR SequencingOmics02/14/2011
26ASD_MethylationOmics03/01/2011
29Microarray family 03 (father, mother, sibling)Omics03/24/2011
37Standard paired-end sequencing of BCRsOmics04/19/2011
38Illumina Mate-Pair BCR sequencingOmics04/19/2011
39Custom Jumping LibrariesOmics04/19/2011
40Custom CapBPOmics04/19/2011
41ImmunofluorescenceOmics05/11/2011
43Autism brain sample genotyping, IlluminaOmics05/16/2011
47ARRA Autism Sequencing Collaboration at Baylor. SOLiD 4 SystemOmics08/01/2011
53AGRE Omni1-quadOmics10/11/2011
59AGP genotypingOmics04/03/2012
60Ultradeep 454 sequencing of synaptic genes from postmortem cerebella of individuals with ASD and neurotypical controlsOmics06/23/2012
63Microemulsion PCR and Targeted Resequencing for Variant Detection in ASDOmics07/20/2012
76Whole Genome Sequencing in Autism FamiliesOmics01/03/2013
519RestingfMRI11/08/2016
90Genotyped IAN SamplesOmics07/09/2013
91NJLAGS Axiom Genotyping ArrayOmics07/16/2013
93AGP genotyping (CNV)Omics09/06/2013
106Longitudinal Sleep Study. H20 200. Channel set 2EEG11/07/2013
107Longitudinal Sleep Study. H20 200. Channel set 3EEG11/07/2013
108Longitudinal Sleep Study. AURA 200EEG11/07/2013
105Longitudinal Sleep Study. H20 200. Channel set 1EEG11/07/2013
109Longitudinal Sleep Study. AURA 400EEG11/07/2013
116Gene Expression Analysis WG-6Omics01/07/2014
131Jeste Lab UCLA ACEii: Charlie Brown and Sesame Street - Project 1Eye Tracking02/27/2014
132Jeste Lab UCLA ACEii: Animacy - Project 1Eye Tracking02/27/2014
133Jeste Lab UCLA ACEii: Mom Stranger - Project 2Eye Tracking02/27/2014
134Jeste Lab UCLA ACEii: Face Emotion - Project 3Eye Tracking02/27/2014
145AGRE/FMR1_Illumina.JHUOmics04/14/2014
146AGRE/MECP2_Sanger.JHUOmics04/14/2014
147AGRE/MECP2_Junior.JHUOmics04/14/2014
151Candidate Gene Identification in familial AutismOmics06/09/2014
152NJLAGS Whole Genome SequencingOmics07/01/2014
154Math Autism Study - Vinod MenonfMRI07/15/2014
155RestingfMRI07/25/2014
156SpeechfMRI07/25/2014
159EmotionfMRI07/25/2014
160syllable contrastEEG07/29/2014
167School-age naturalistic stimuliEye Tracking09/19/2014
44AGRE/Broad Affymetrix 5.0 Genotype ExperimentOmics06/27/2011
45Exome Sequencing of 20 Sporadic Cases of Autism Spectrum DisorderOmics07/15/2011
Collection - Add Experiment
Add Supporting Documentation
Select File

To add an existing Data Structure, enter its title in the search bar. If you need to request changes, select the indicator "No, it requires changes to meet research needs" after selecting the Structure, and upload the file with the request changes specific to the selected Data Structure. Your file should follow the Request Changes Procedure. If the Data Structure does not exist, select "Request New Data Structure" and upload the appropriate zip file.

Request Submission Exemption
Characters Remaining:
Not Eligible

The Data Expected list for this Collection shows some raw data as missing. Contact the NDA Help Desk with any questions.

Please confirm that you will not be enrolling any more subjects and that all raw data has been collected and submitted.

Collection Updated

Your Collection is now in Data Analysis phase and exempt from biannual submissions. Analyzed data is still expected prior to publication or no later than the project end date.

[CMS] Attention
[CMS] Please confirm that you will not be enrolling any more subjects and that all raw data has been collected and submitted.
[CMS] Error

[CMS]

Unable to change collection phase where targeted enrollment is less than 90%

Delete Submission Exemption
Are you sure you want to delete this submission exemption?
You have requested to move the sharing dates for the following assessments:
Data Expected Item Original Sharing Date New Sharing Date

Please provide a reason for this change, which will be sent to the Program Officers listed within this collection:

Explanation must be between 20 and 200 characters in length.

Please press Save or Cancel
Add New Email Address - Dialog
New Email Address
Collection Summary Collection Charts
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
06/15/2010
Funding Completed
Close Out
No
556
Loading Chart...
NIH - Contract None

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
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


helpcenter.collection.general-tab

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

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

Blinded Clinical Trial Status:

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

Funding Source

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

Supporting Documentation

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

Grant Information

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

Clinical Trials

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

Frequently Asked Questions

  • How does the NIMH Data Archive (NDA) determine which Permission Group data are submitted into?
    During Collection creation, NDA staff determine the appropriate Permission Group based on the type of data to be submitted, the type of access that will be available to data access users, and the information provided by the Program Officer during grant award.
  • How do I know when a NDA Collection has been created?
    When a Collection is created by NDA staff, an email notification will automatically be sent to the PI(s) of the grant(s) associated with the Collection to notify them.
  • Is a single grant number ever associated with more than one Collection?
    The NDA system does not allow for a single grant to be associated with more than one Collection; therefore, a single grant will not be listed in the Grant Information section of a Collection for more than one Collection.
  • Why is there sometimes more than one grant included in a Collection?
    In general, each Collection is associated with only one grant; however, multiple grants may be associated if the grant has multiple competing segments for the same grant number or if multiple different grants are all working on the same project and it makes sense to hold the data in one Collection (e.g., Cooperative Agreements).

Glossary

  • Administrator Privilege
    A privilege provided to a user associated with an NDA Collection or NDA Study whereby that user can perform a full range of actions including providing privileges to other users.
  • Collection Owner
    Generally, the Collection Owner is the contact PI listed on a grant. Only one NDA user is listed as the Collection owner. Most automated emails are primarily sent to the Collection Owner.
  • Collection Phase
    The Collection Phase provides information on data submission as opposed to grant/project completion so while the Collection phase and grant/project phase may be closely related they are often different. Collection users with Administrative Privileges are encouraged to edit the Collection Phase. The Program Officer as listed in eRA (for NIH funded grants) may also edit this field. Changes must be saved by clicking the Save button at the bottom of the page. This field is sortable alphabetically in ascending or descending order. Collection Phase options include:
    • Pre-Enrollment: A grant/project has started, but has not yet enrolled subjects.
    • Enrolling: A grant/project has begun enrolling subjects. Data submission is likely ongoing at this point.
    • Data Analysis: A grant/project has completed enrolling subjects and has completed all data submissions.
    • Funding Completed: A grant/project has reached the project end date.
  • Collection Title
    An editable field with the title of the Collection, which is often the title of the grant associated with the Collection.
  • Grant
    Provides the grant number(s) for the grant(s) associated with the Collection. The field is a hyperlink so clicking on the Grant number will direct the user to the grant information in the NIH Research Portfolio Online Reporting Tools (RePORT) page.
  • Supporting Documentation
    Various documents and materials to enable efficient use of the data by investigators unfamiliar with the project and may include the research protocol, questionnaires, and study manuals.
  • NIH Research Initiative
    NDA Collections may be organized by scientific similarity into NIH Research Initiatives, to facilitate query tool user experience. NIH Research Initiatives map to one or multiple Funding Opportunity Announcements.
  • Permission Group
    Access to shared record-level data in NDA is provisioned at the level of a Permission Group. NDA Permission Groups consist of one or multiple NDA Collections that contain data with the same subject consents.
  • Planned Enrollment
    Number of human subject participants to be enrolled in an NIH-funded clinical research study. The data is provided in competing applications and annual progress reports.
  • Actual Enrollment
    Number of human subjects enrolled in an NIH-funded clinical research study. The data is provided in annual progress reports.
  • NDA Collection
    A virtual container and organization structure for data and associated documentation from one grant or one large project/consortium. It contains tools for tracking data submission and allows investigators to define a wide array of other elements that provide context for the data, including all general information regarding the data and source project, experimental parameters used to collect any event-based data contained in the Collection, methods, and other supporting documentation. They also allow investigators to link underlying data to an NDA Study, defining populations and subpopulations specific to research aims.
  • Data Use Limitations
    Data Use Limitations (DULs) describe the appropriate secondary use of a dataset and are based on the original informed consent of a research participant. NDA only accepts consent-based data use limitations defined by the NIH Office of Science Policy.
  • Total Subjects Shared
    The total number of unique subjects for whom data have been shared and are available for users with permission to access data.
IDNameCreated DateStatusType
No records found.
helpcenter.collection.experiments-tab

NDA Help Center

Collection - Experiments

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

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

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

Frequently Asked Questions

  • Can an Experiment be associated with more than one Collection?

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

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

Glossary

  • Experiment Status
    An Experiment must be Approved before data using the associated Experiment_ID may be uploaded.
  • Experiment ID
    The ID number automatically generated by NDA which must be included in the appropriate file when uploading data to link the Experiment Definition to the subject record.

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

Shared Data

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

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
helpcenter.collection.shared-data-tab

NDA Help Center

Collection - Shared Data

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

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

Frequently Asked Questions

  • How will I know if another researcher uses data that I shared through the NIMH Data Archive (NDA)?
    To see what data your project have submitted are being used by a study, simply go the Associated Studies tab of your collection. Alternatively, you may review an NDA Study Attribution Report available on the General tab.
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    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.
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    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

  • Data Structure
    A defined organization and group of Data Elements to represent an electronic definition of a measure, assessment, questionnaire, or collection of data points. Data structures that have been defined in the NDA Data Dictionary are available at https://nda.nih.gov/general-query.html?q=query=data-structure
  • Data Type
    A grouping of data by similar characteristics such as Clinical Assessments, Omics, or Neurosignal data.
  • Shared
    The term 'Shared' generally means available to others; however, there are some slightly different meanings based on what is Shared. A Shared NDA Study is viewable and searchable publicly regardless of the user's role or whether the user has an NDA account. A Shared NDA Study does not necessarily mean that data 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.

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

Publications

Publications relevant to NDA data are listed below. Most displayed publications have been associated with the grant within Pubmed. Use the "+ New Publication" button to add new publications. Publications relevant/not relevant to data expected are categorized. Relevant publications are then linked to the underlying data by selecting the Create Study link. Study provides the ability to define cohorts, assign subjects, define outcome measures and lists the study type, data analysis and results. Analyzed data and results are expected in this way.

PubMed IDStudyTitleJournalAuthorsDateStatus
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NDA Help Center

Collection - Publications

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

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

Frequently Asked Questions

  • How can I determine if a publication is relevant?
    Publications are considered relevant to a collection when the data shared is directly related to the project or collection.
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    PubMed, an online library containing journals, articles, and medical research. Sponsored by NiH and National Library of Medicine (NLM).

Glossary

  • Create Study
    A link to the Create an NDA Study page that can be clicked to start creating an NDA Study with information such as the title, journal and authors automatically populated.
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    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.
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    A publication that is not based on data related to the aims of the grant/project associated with the Collection or not based on any data such as a review article and, therefore, an NDA Study is not expected to be created.
  • PubMed
    PubMed provides citation information for biomedical and life sciences publications and is managed by the U.S. National Institutes of Health's National Library of Medicine.
  • PubMed ID
    The PUBMed ID is the unique ID number for the publication as recorded in the PubMed database.
  • Relevant Publication
    A publication that is based on data related to the aims of the grant/project associated with the Collection and, therefore, an NDA Study is expected to be created.

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

Associated Studies

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

Study NameAbstractCollection/Study SubjectsData UsageState
Examining the validity of the use of ratio IQs in psychological assessments IQ tests are amongst the most used psychological assessments, both in research and clinical settings. For participants who cannot complete IQ tests normed for their age, ratio IQ scores (RIQ) are routinely computed and used as a proxy of IQ, especially in large research databases to avoid missing data points. However, because it has never been scientifically validated, this practice is questionable. In the era of big data, it is important to examine the validity of this widely used practice. In this paper, we use the case of autism to examine the differences between standard full-scale IQ (FSIQ) and RIQ. Data was extracted from four databases in which ages, FSIQ scores and subtests raw scores were available for autistic participants between 2 and 17 years old. The IQ tests included were the MSEL (N=12033), DAS-II early years (N=1270), DAS-II school age (N=2848), WISC-IV (N=471) and WISC-V (N=129). RIQs were computed for each participant as well as the discrepancy (DSC) between RIQ and FSIQ. We performed two linear regressions to respectively assess the effect of FSIQ and of age on the DSC for each IQ test, followed by additional analyses comparing age subgroups as well as FSIQ subgroups on DSC. Participants at the extremes of the FSIQ distribution tended to have a greater DSC than participants with average FSIQ. Furthermore, age significantly predicted the DSC, with RIQ superior to FSIQ for younger participants while the opposite was found for older participants. These results question the validity of this widely used alternative scoring method, especially for individuals at the extremes of the normal distribution, with whom RIQs are most often employed.554/17423Secondary AnalysisShared
Development of sex differences in the human brainSex differences in brain anatomy have been described from early childhood through late adulthood, but without any clear consensus among studies. Here, we applied a machine learning approach to estimate ‘brain sex’ using a continuous (rather than binary) classifier in 162 boys and 185 girls aged between 5 and 18 years. Changes in the estimated sex differences over time at different age groups were subsequently calculated using a sliding window approach. We hypothesized that males and females would differ in brain structure already during childhood, but that these differences will become even more pronounced with increasing age, particularly during puberty. Overall, the classifier achieved a good performance, with an accuracy of 80.4% and an AUC of 0.897 across all age groups. Assessing changes in the estimated sex with age revealed a growing difference between the sexes with increasing age – starting with a very large effect size of d=1.2 during childhood which increased even further from age 11 onward to an effect size of d=1.6 at age 17. Overall these findings suggest a systematic sex difference in brain structure already during childhood, and a subsequent increase of this difference during puberty, matching well current models of sexual differentiation of the brain.556/556Secondary AnalysisShared
Chronic Environmental Stress Impact on DHEA(S) Levels and Executive Function in ChildrenThough we know the function of stress on many of the body’s natural resources, we remain uncertain of the neurological function of the steroid precursor dehydroepiandrosterone (DHEA) and its’ sulfated component. Some studies have shown DHEA to have a neuroprotective effect on cognitive functions, such as executive functions, though limited work has been done exploring this relationship in children. Cortisol is a well-established neurodegenerative factor associated with stress. The relationship between chronic stress factors of socioeconomic status (SES) and marginalized racial backgrounds with biological correlates is understudied. The purpose of the current study was to explore the relationship between chronic environmental stress factors of marginalized racial status or lower SES and its association with DHEA to cortisol ratios, as well as the relationship of DHEA to cortisol ratios with indirect and direct measures of executive function in children, as executive function is a cognitive domain that remains particularly sensitive to chronic stress. Analysis of a sample of 345 children from the NIH Pediatric MRI Database found that neither marginalized racial status, nor SES was associated with lower DHEA to cortisol ratios. Analysis of children (samples varying between 212 and 345 cases) found that DHEA to cortisol ratios did not predict performance on indirect or direct measures of executive function. Limitations, future directions, and clinical implications are discussed.554/554Primary AnalysisShared
Development and Evaluation of Deep Learning Models for Automated Estimation of Myelin Maturation Using Pediatric Brain MRI ScansPurpose: To predict the corresponding age of myelin maturation from brain MRI scans in infants and young children by using a deep learning algorithm and to build upon previously published models. Materials and Methods: Brain MRI scans acquired between January 1, 2011, and March 17, 2021, in our institution in patients aged 0–3 years were retrospectively retrieved from the archive. An ensemble of two-dimensional (2D) and three-dimensional (3D) convolutional neural network models was trained and internally validated in 710 patients to predict myelin maturation age on the basis of radiologist-generated labels. The model ensemble was tested on an internal dataset of 123 patients and two external datasets of 226 (0–25 months of age) and 383 (0–2 months of age) healthy children and infants, respectively. Mean absolute error (MAE) and Pearson correlation coefficients were used to assess model performance. Results: The 2D, 3D, and 2D-plus-3D ensemble models showed an MAE value of 1.43, 2.55, and 1.77 months, respectively, on the internal test set, values of 2.26, 2.27, and 1.22 months on the first external test set, and values of 0.44, 0.27, and 0.31 months on the second external test set. The ensemble model outperformed the performance of the previous state-of-the-art model on the same external test set (MAE = 1.22 vs 2.09 months). Conclusion: The proposed deep learning model accurately predicted myelin maturation age using pediatric brain MRI scans and may help reduce the time needed to complete this task, as well as interobserver variability in radiologist predictions.553/553Secondary AnalysisShared
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
A human craniofacial life-course: cross-sectional morphological covariations during postnatal growth, adolescence, and agingCovariations between anatomical structures are fundamental to craniofacial ontogeny, maturation and aging and yet are rarely studied in such a cognate fashion. Here we offer a comprehensive investigation of the human craniofacial complex using freely available software and MRI datasets representing 575 individuals from 0 to 79 years old. We employ both standard craniometrics methods as well as Procrustes based analyses to capture and document cross-sectional trends. Findings suggest that anatomical structures behave primarily as modules, and manifest integrated patterns of shape change as they compete for space, particularly with relative expansions of the brain during early postnatal life and of the face during puberty. Sexual dimorphism was detected in infancy and intensified during adolescence with gender differences in the magnitude and pattern of morphological covariation as well as of aging. These findings partly support the spatial-packing hypothesis and reveal important insights into phenotypic adjustments to deep-rooted, and presumably genetically defined, trajectories of morphological size and shape change that characterise the normal human craniofacial life-course.78/308Secondary 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
Cross-Lagged Models of Cognitive and Reading Abilities in School-Aged Children: Unexpected DirectionalityObjective: Processing speed (PS) and working memory (WM) are domain-general cognitive skills that are associated with single-word reading abilities. The present study used a cross-lagged panel design in a school-aged sample to better characterize the developmental emergence of these cognitive-academic relationships. Participants and Methods. The sample included 117 typically developing children (8-15 years old) who completed neuropsychological testing every 2 years as part of the NIH MRI Study of Normal Brain Development (publicly available). PS was measured by Coding (WISC-III), WM was measured by Digit Span (WISC-III), and single-word reading was measured by Letter-Word Id (WJ-III). Path analysis was used to test cross-sectional correlations, auto-regressive paths, and cross-lagged paths between cognitive predictors and reading abilities in two separate models (PS and reading, WM and reading). Results: A cross-lagged pattern emerged in which earlier Letter-Word Id predicted later Coding and Digit Span scores, but not the reverse. For the PS and reading model, Letter-Word Id significantly predicted Coding from 8-9 to 10-11 years (β=.23, p=.034), and this relationship diminished over time (10-11 to 12-13: β=.14, p=.084; 12-13 to 14-15: β=.08, p=.310). Similarly, in the WM and reading model, Letter-Word Id significantly predicted Digit Span from 8-9 to 10-11 years (β=.27, p=.003), and again showed a diminishing relationship over time (10-11 to 12-13: β=.11, p=.198; 12-13 to 14-15: β=.18, p=.023). The cross-lagged paths from Coding/Digit Span to Letter-Word Id never reached significance. Conclusion: These results reveal an unexpected developmental pattern in which earlier single-word reading abilities predict later PS and WM, but not the reverse. The current results warrant replication with alternative measures to rule out competing explanations. However, if replicated, the results could expand conventional notions of neuropsychological predictors as causal in academic development by showing that domain-general cognitive skills might also be the consequence of academic development, at least in the case of PS and WM with reading. 117/117Secondary AnalysisShared
* Data not on individual level
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NDA Help Center

Collection - Associated Studies

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

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

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

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Glossary

  • Associated Studies Tab
    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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