NIMH Data Archive - Data

Genomics

Neuroimaging

Phenotype¹

New Trial
Clinical Trial

¹ Numbers reported are subjects by age

New Project
Grant/Project Number

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

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.

Select	Experiment Id	Experiment Name	Experiment Type	Created On

24	HI-NGS_R1	Omics	02/16/2011
475	MB1-10 (CHOP)	Omics	06/07/2016
490	Discovery and CRISPR validation of genetic factors associated with antipsychotic-induced weight gain and cardiometabolic risk	Omics	07/07/2016
501	PharmacoBOLD Resting State	fMRI	07/27/2016
506	PVPREF	Omics	08/05/2016
509	ABC-CT Resting v2	EEG	08/18/2016
13	Comparison of FI expression in Autistic and Neurotypical Homo Sapiens	Omics	12/28/2010
18	AGRE/Broad Affymetrix 5.0 Genotype Experiment	Omics	01/06/2011
22	Stitching PCR Sequencing	Omics	02/14/2011
26	ASD_Methylation	Omics	03/01/2011
29	Microarray family 03 (father, mother, sibling)	Omics	03/24/2011
37	Standard paired-end sequencing of BCRs	Omics	04/19/2011
38	Illumina Mate-Pair BCR sequencing	Omics	04/19/2011
39	Custom Jumping Libraries	Omics	04/19/2011
40	Custom CapBP	Omics	04/19/2011
41	Immunofluorescence	Omics	05/11/2011
43	Autism brain sample genotyping, Illumina	Omics	05/16/2011
47	ARRA Autism Sequencing Collaboration at Baylor. SOLiD 4 System	Omics	08/01/2011
53	AGRE Omni1-quad	Omics	10/11/2011
59	AGP genotyping	Omics	04/03/2012
60	Ultradeep 454 sequencing of synaptic genes from postmortem cerebella of individuals with ASD and neurotypical controls	Omics	06/23/2012
63	Microemulsion PCR and Targeted Resequencing for Variant Detection in ASD	Omics	07/20/2012
76	Whole Genome Sequencing in Autism Families	Omics	01/03/2013
519	Resting	fMRI	11/08/2016
90	Genotyped IAN Samples	Omics	07/09/2013
91	NJLAGS Axiom Genotyping Array	Omics	07/16/2013
93	AGP genotyping (CNV)	Omics	09/06/2013
106	Longitudinal Sleep Study. H20 200. Channel set 2	EEG	11/07/2013
107	Longitudinal Sleep Study. H20 200. Channel set 3	EEG	11/07/2013
108	Longitudinal Sleep Study. AURA 200	EEG	11/07/2013
105	Longitudinal Sleep Study. H20 200. Channel set 1	EEG	11/07/2013
109	Longitudinal Sleep Study. AURA 400	EEG	11/07/2013
116	Gene Expression Analysis WG-6	Omics	01/07/2014
131	Jeste Lab UCLA ACEii: Charlie Brown and Sesame Street - Project 1	Eye Tracking	02/27/2014
132	Jeste Lab UCLA ACEii: Animacy - Project 1	Eye Tracking	02/27/2014
133	Jeste Lab UCLA ACEii: Mom Stranger - Project 2	Eye Tracking	02/27/2014
134	Jeste Lab UCLA ACEii: Face Emotion - Project 3	Eye Tracking	02/27/2014
145	AGRE/FMR1_Illumina.JHU	Omics	04/14/2014
146	AGRE/MECP2_Sanger.JHU	Omics	04/14/2014
147	AGRE/MECP2_Junior.JHU	Omics	04/14/2014
151	Candidate Gene Identification in familial Autism	Omics	06/09/2014
152	NJLAGS Whole Genome Sequencing	Omics	07/01/2014
154	Math Autism Study - Vinod Menon	fMRI	07/15/2014
155	Resting	fMRI	07/25/2014
156	Speech	fMRI	07/25/2014
159	Emotion	fMRI	07/25/2014
160	syllable contrast	EEG	07/29/2014
167	School-age naturalistic stimuli	Eye Tracking	09/19/2014
44	AGRE/Broad Affymetrix 5.0 Genotype Experiment	Omics	06/27/2011
45	Exome Sequencing of 20 Sporadic Cases of Autism Spectrum Disorder	Omics	07/15/2011

Collection - Add Experiment

Add Supporting Documentation

Funding Source:
URL:

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.

Use/Modify Existing Data Structure

Request New Data Structure

Targeted Enrollment:

Initial Submission Date:

Initial Share Date:

Data Structure Search:

Data Structures:

Submit

Request Submission Exemption

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] Error

[CMS]

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

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:

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HCP-D Mapping the Human Connectome During Typical Development (HCP-D) #2846

General
Experiments (6)
Shared Data
Publications (89)
Data Expected (69)
Associated Studies (23)

Collection Title	Collection Investigators	Collection Description
Collection Title:	HCP-D Mapping the Human Connectome During Typical Development (HCP-D)
Collection Investigators:	Deanna Barch; Susan Bookheimer; Randy Buckner; Mirella Dapretto; Stephen Smith; Leah Somerville; Kathleen Thomas; David Van Essen; Essa Yacoub
Collection Description:	The major technological and analytical advances in human brain imaging achieved as part of the Human Connectome Projects (HCP) enable examination of structural and functional brain connectivity at unprecedented levels of spatial and temporal resolution. This information is proving crucial to our understanding of normative variation in adult brain connectivity. It is now timely to use the tools and analytical approaches developed by the HCP to understand how structural and functional wiring of the brain develops. Using state-of-the art HCP imaging approaches will allow investigators to push our currently limited understanding of normative brain development to new levels. This knowledge will critically inform prevention and intervention efforts targeting well known public health concerns (e.g., neurological and psychiatric disorders, poverty). The majority of developmental connectivity studies to date have used fairly coarse resolution, have not been multi-modal in nature, and few studies have used comparable methods to assess individuals across a sufficiently wide age range to truly capture developmental processes (e.g., early childhood through adolescence). Here we propose a consortium of five sites (Harvard, Oxford, UCLA, University of Minnesota, Washington University), with extensive complimentary expertise in brain imaging and neural development, including many of the investigators from the adult and pilot lifespan HCP efforts. Our synergistic integration of advances from the HARVARD-MGH and WU-MINN-OXFORD HCPs with cutting edge expertise in child and adolescent brain development will enable major advances in our understanding of the normative development of human brain connectivity. The resultant unique resource will provide rich, multimodal data on several biological and cognitive constructs that are of critical importance to health and well-being across this age range and allow a wide range of investigators in the community to gain new insights about brain development and connectivity. Aim 1 will be to optimize existing HCP Lifespan Pilot project protocols on the widely available Prisma platform to respect practical constraints in studying healthy children and adolescents over a wide age range and will also collect a matched set of data on the original Skyra and proposed Prisma HCP protocols to serve as a linchpin between the past and present efforts. Aim 2 will be to collect 1500 high quality neuroimaging and associated behavioral datasets on healthy children and adolescents in the age range of 5-21, using matched protocols across sites, enabling robust characterization of age-related changes in network properties including connectivity, network integrity, response properties during tasks, and behavior. Aim 3 will be to collect and analyze longitudinal subsamples, task, and phenotypic measures that constitute intensive sub-studies of inflection points of health-relevant behavioral changes within specific developmental phases. Aim 4 will capitalize on our success in sharing data in the HCP, and use established tools, platforms and procedures to make all data publically available through the Connectome Coordinating Facility (CCF).
Data Repository:	Connectome Coordination Facility
Permission Group:
Collection Creation Date:	01/29/2018
NIH Research Initiatives:	Human Connectome Project (HCP), NIMH Repository & Genomics Resource (NRGR)
Collection Phase:	Funding Completed
Collection Sub-Phase:	Close Out
Blinded Clinical Trial:	No
Total Funded Amount:	$3,423,207.00
Subjects Shared:	652
Collection DOI:	10.15154/21be-hb51

{"values":[]}

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Funding Sources:

Funding Source Name	Funding Source URL
NIH - Extramural	None

Supporting Documentation:

File Name	File Type	Description	Audience
https://www.humanconnectome.org/storage/app/uploads/public/5f2/079/44e/5f207944eb205925764418.pdf	Other	Files and directory structure of Lifespan 1.0 Release HCP-Aging and HCP-Development imaging and behavioral data.	General Public
https://www.humanconnectome.org/storage/app/media/documentation/LS2.0/LS_Release_2.0_Access_Instructions.pdf	Results	Detailed instructions on applying for NDA access to Lifespan 2.0 Release HCP-Aging & HCP-Development data, selecting, and downloading data with download manager and on the command line.	General Public
https://www.humanconnectome.org/storage/app/media/documentation/LS2.0/LS2.0_Crosswalk_Behavioral_Data_Dictionary.xlsx	Results	Crosswalk .xlsx spreadsheet linking behavioral NDA structures and elements to the original HCP variable descriptions. hcp description column is current best data dictionary for variables and answer codes as of the Lifespan 2.0 Release.	General Public
https://www.humanconnectome.org/storage/app/media/documentation/LS2.0/HCD_LS_2.0_subject_completeness.csv	Results	CSV containing per subject completeness of imaging modalities, QC issue codes, behavioral data availability, and unrelated status for finding subjects of interest for analyses.	General Public
https://www.humanconnectome.org/storage/app/media/documentation/LS2.0/LS_2.0_Release_Appendix_2.pdf	Results	Lifespan 2.0 Release HCP-Aging and HCP-Development: Details and References for Behavioral & Clinical Instruments	General Public
https://www.humanconnectome.org/storage/app/media/documentation/LS2.0/LS_2.0_Release_Appendix_1.pdf	Results	File names and directory structure of Lifespan 2.0 Release HCP-Aging and HCP-Development preprocessed and unprocessed imaging data. Organized by OPTION 2 filters/HCP package names on NDA HCP Aging & Development query page.	General Public

Grant Information:

Project Number	Title	Start Date	Original End Date	End Date	Planned Enrollment	Actual Enrollment	Organization	Funds Obligated
U01MH109589-01	Mapping the Human Connectome During Typical Development	05/17/2016	04/30/2020	04/30/2020	2688	2435	WASHINGTON UNIVERSITY	$3,423,207.00

Clinical Trials:

helpcenter.collection.general-tab

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.

Contact NDA Help Desk

ID	Name	Created Date	Status	Type
1210	CARIT (Reward History Go/Nogo) Task	02/12/2019	Approved	fMRI
1212	Guessing Task	02/20/2019	Approved	fMRI
1213	Emotion Task	02/20/2019	Approved	fMRI
1214	Resting State	02/20/2019	Approved	fMRI
2325	CARIT-Guessing-Emotion Tasks	07/20/2023	Approved	fMRI
2326	CONCAT (concatenated fMRI, Resting-CARIT-Guessing-Emotion)	07/20/2023	Approved	fMRI

helpcenter.collection.experiments-tab

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.

Contact NDA Help Desk

Shared Data:

Title	Type	Number of Subjects
Adult Self Report	Clinical Assessments	151
Behavioral Inhibition Scale/Behavioral Activation Scale	Clinical Assessments	651
Blood Sample Collection	Clinical Assessments	652
Child Behavior Checklist (CBCL) 1-5	Clinical Assessments	2
Child Behavior Checklist (CBCL) 6-18	Clinical Assessments	498
Children's Behavior Questionnaire Parent	Clinical Assessments	55
Clinical Medical History	Clinical Assessments	652
Cognition Composite Scores	Clinical Assessments	471
Delay Discounting Task	Clinical Assessments	641
Dimensional Change Card Sort Test (DCCS)	Clinical Assessments	472
Early Adolescent Temperament Questionnaire	Clinical Assessments	500
Edinburgh Handedness Inventory	Clinical Assessments	652
Family Environment Scale	Clinical Assessments	652
Flanker Task	Clinical Assessments	471
General Behavior Inventory	Clinical Assessments	651
Image	Imaging	652
Imaging Collection	Imaging	652
Language Experience and Proficiency Questionnaire	Clinical Assessments	652
Maternal and Birth History	Clinical Assessments	500
Medications and Treatments Form	Clinical Assessments	652
Mini Mental State Exam	Clinical Assessments	340
Munich ChronoType Questionnaire	Clinical Assessments	479
NEO-Five Factor Inventory	Clinical Assessments	229
NIH Toolbox Emotion Domain - Emotional Support Survey	Clinical Assessments	464
NIH Toolbox Emotion Domain - Empathic Behavior Survey	Clinical Assessments	159
NIH Toolbox Emotion Domain - Fear Surveys	Clinical Assessments	362
NIH Toolbox Emotion Domain - Friendship Survey	Clinical Assessments	464
NIH Toolbox Emotion Domain - Peer Rejection and Perceived Rejection Surveys	Clinical Assessments	482
NIH Toolbox Emotion Domain - Perceived Hostility Surveys	Clinical Assessments	464
NIH Toolbox Emotion Domain - Psychological Well-Being	Clinical Assessments	483
NIH Toolbox Emotion Domain - Sadness Surveys	Clinical Assessments	159
NIH Toolbox Emotion Domain - Self-Efficacy Survey	Clinical Assessments	473
NIH Toolbox Emotion Domain - Social Withdrawal and Positive Peer Interaction Surveys	Clinical Assessments	159
NIH Toolbox List Sorting Working Memory Test	Clinical Assessments	472
NIH Toolbox Motor Domain	Clinical Assessments	472
NIH Toolbox Oral Reading Recognition Test	Clinical Assessments	472
NIH Toolbox Picture Vocabulary Test	Clinical Assessments	471
NIH Toolbox Sensation Domain	Clinical Assessments	473
PROMIS Anger	Clinical Assessments	483
PROMIS Emotional Distress - Depression	Clinical Assessments	120
PROMIS Emotional Distress-Anxiety	Clinical Assessments	120
PROMIS General Life Satisfaction	Clinical Assessments	458
PROMIS Social Isolation	Clinical Assessments	464
Pattern Comparison Processing Speed	Clinical Assessments	472
Penn Emotion Recognition Task	Clinical Assessments	629
Perceived Stress Scale	Clinical Assessments	436
PhenX Substance Use	Clinical Assessments	629
Picture Sequence Memory	Clinical Assessments	473
Pittsburgh Sleep Quality Index	Clinical Assessments	151
Processed MRI Data	Imaging	652
Pubertal Development Scale	Clinical Assessments	652
Research Subject	Clinical Assessments	652
Scanner Debriefing Interview	Clinical Assessments	637
Screen Time Survey	Clinical Assessments	651
Sleep Disturbance Questionnaire	Clinical Assessments	243
Social Responsiveness Scale (SRS)	Clinical Assessments	651
Sociodemographics	Clinical Assessments	652
Sports and Activities Involvement Questionnaire	Clinical Assessments	500
Strengths and Difficulties Questionnaire	Clinical Assessments	243
UPPS Impulsive Behavior Scale	Clinical Assessments	651
Vision Tests	Clinical Assessments	652
Vital Signs	Clinical Assessments	652
Wechsler Adult Intelligence Scale Fourth Edition [part 1]	Clinical Assessments	180
Wechsler Intelligence Scale for Children V	Clinical Assessments	462
Wechsler Preschool and Primary Scale of Intelligence IV Edition	Clinical Assessments	2
Youth Self Report	Clinical Assessments	334

helpcenter.collection.shared-data-tab

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.
Can I get a supplement to share data from a completed research project?

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.
Can I get a supplement to share data from a research project that is still ongoing?

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.

Contact NDA Help Desk

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 ID	Study	Title	Journal	Authors	Date	Status
41729900	Create Study	Longitudinal changes in T1w/T2w estimates of cortical myelin with age and pubertal timing.	Cerebral cortex (New York, N.Y. : 1991)	Cheng, Theresa W; Mair, Patrick; Curtis, Mark T; Baum, Graham L; Flournoy, John C; Wang, Shuyao; Glasser, Matthew F; Barch, Deanna M; Somerville, Leah H	February 9, 2026	Not Determined
41659512	Create Study	The progression of sex differences in brain networks across the lifespan.	bioRxiv : the preprint server for biology	Huang, Ke; Jamison, Keith W; Jacobs, Emily; Miolane, Nina; Kuceyeski, Amy	January 31, 2026	Not Determined
41578121	Create Study	Two axes of white matter development.	Nature communications	Luo, Audrey C; Meisler, Steven L; Sydnor, Valerie J; Alexander-Bloch, Aaron; Bagautdinova, Joëlle; Barch, Deanna M; Bassett, Dani S; Davatzikos, Christos; Franco, Alexandre R; Goldsmith, Jeff; Gur, Raquel E; Gur, Ruben C; Hu, Fengling; Jaskir, Marc; Kiar, Gregory; Keller, Arielle S; Larsen, Bart; Mackey, Allyson P; Milham, Michael P; Roalf, David R; Shafiei, Golia; Shinohara, Russell T; Somerville, Leah H; Weinstein, Sarah M; Yeatman, Jason D; Cieslak, Matthew; Rokem, Ariel; Satterthwaite, Theodore D	January 23, 2026	Not Determined
41358298	Create Study	Youth Correlates of Genetic Liability to Substance Use Disorders.	medRxiv : the preprint server for health sciences	Paul, Sarah E; Gorelik, Aaron J; Karcher, Nicole R; Miller, Alex P; Baranger, David A A; Johnson, Emma C; LeBlanc, Kimberly H; Dowling, Gayathri; Barch, Deanna M; Hatoum, Alexander S; Agrawal, Arpana; Bogdan, Ryan	November 25, 2025	Not Determined
41333387	Create Study	Multimodal neuroimaging data boosts the prediction of multifaceted cognition.	Research square	Wu, Jianxiao; Li, Jingwei; Jung, Kyesam; Eickhoff, Simon; Yeo, B T Thomas; Genon, Sarah	November 19, 2025	Not Determined
41290675	Create Study	Topological turning points across the human lifespan.	Nature communications	Mousley, Alexa; Bethlehem, Richard A I; Yeh, Fang-Cheng; Astle, Duncan E	November 25, 2025	Not Determined
41279850	Create Study	Global organisation of structural covariance networks derived from parcellated cortical surface area in atypical populations.	bioRxiv : the preprint server for biology	Roseby, William; Racey, Chris; Ward, Jamie	October 30, 2025	Not Determined
41256711	Create Study	Lifespan Normative Modeling of Brain Microstructure.	bioRxiv : the preprint server for biology	Villalón-Reina, Julio E; Zhu, Alyssa H; Benavidez, Sebastian; Moreau, Clara A; Feng, Yixue; Chattopadhyay, Tamoghna; Nabulsi, Leila; Kushan, Leila; John, John P; Joshi, Himanshu; Thomopoulos, Sophia I; Lawrence, Katherine E; Nir, Talia M; Jahanshad, Neda; Bearden, Carrie E; Kia, Seyed Mostafa; Marquand, Andre F; Thompson, Paul M; Alzheimer’s Disease Neuroimaging Initiative	October 4, 2025	Not Determined
40883286	Create Study	Using deep learning to predict internalizing problems from brain structure in youth.	Translational psychiatry	Vandewouw, Marlee M; Syed, Bilal; Barnett, Noah; Arias, Alfredo; Kelley, Elizabeth; Jones, Jessica; Ayub, Muhammad; Iaboni, Alana; Arnold, Paul D; Crosbie, Jennifer; Schachar, Russell J; Taylor, Margot J; Lerch, Jason P; Anagnostou, Evdokia; Kushki, Azadeh	August 29, 2025	Not Determined
40667167	Create Study	Population-specific brain charts reveal Chinese-Western differences in neurodevelopmental trajectories.	bioRxiv : the preprint server for biology	Sun, Lianglong; Qin, Wen; Liang, Xinyuan; Wang, Caihong; Men, Weiwei; Duan, Yunyun; Fan, Xue-Ru; Cai, Qing; Qiu, Shijun; Wang, Meiyun; Gong, Qiyong; Tian, Yanghua; Liang, Peipeng; Liu, Zeyu; Zhang, Xiaochu; Song, Hongwen; Ye, Zhaoxiang; Zhang, Peng; Dong, Qi; Tao, Sha; Zhu, Wenzhen; Zhang, Jintao; Xie, Fang; Feng, Jianfeng; Zhang, Jing; Liu, Chao; Qian, Qiujin; Zhang, Bing; Meng, Ming; Hu, Li; Gao, Jia-Hong; Jiang, Tianzi; Zhu, Xiongzhao; Zhang, Yuhan; Liu, Liping; Liu, Hanjun; Liao, Weihua; Wang, Dawei; Wang, Huali; Guo, Tengfei; Dai, Zhengjia; Lui, Su; Xu, Kai; Li, Lingjiang; Xie, Peng; Feng, Chunliang; Cui, Guangbin; Wu, Jinsong; Yin, Xuntao; Ding, Guosheng; Xian, Junfang; Zhao, Lianping; Lu, Jie; Liu, Zhifen; Han, Ying; Yuan, Zhen; Zhang, Xilin; Si, Tianmei; Zhou, Fuqing; Bi, Yanchao; Wu, Dan; Gao, Fei; Wang, Fei; Qin, Shaozheng; Wang, Gang; Chen, Feng; Zhang, Zhiqiang; Sui, Jing; Chen, Huafu; Cai, Jinhua; Liu, Shuwei; Geng, Zuojun; Zhang, Chen; Mao, Ning; Yin, Hong; Liu, Bo; Ma, Heng; Gao, Bo; Miao, Yanwei; Kong, Xiang-Zhen; Zhou, Yuan; Liu, Li; Hu, Jianping; Wang, Liang; Zhang, Quan; Shu, Hua; Wang, Peijun; Lee, Tatia M C; Cao, Qingjiu; Yang, Li; Zhang, Xi; Luo, Wenbo; Liang, Meng; Yao, Hongxiang; Li, Meng; Huang, Hao; Peng, Yun; Han, Zaizhu; Zhou, Chao; Xu, Haibo; Feng, Ming; Shen, Wen; Hu, Yuzheng; Chen, Huajun; Wang, Ying; Gong, Gaolang; Yan, Zhihan; Xu, Xiaojun; Liu, Jun; Chen, Guangxiang; Wang, Pan; Yang, Yunjun; Yao, Dezhong; Han, Tong; He, Huiguang; Chen, Ce; Zou, Qihong; Liu, Hesheng; Zhang, Hui; Chai, Chao; Lu, Chunming; Tu, Yiheng; Liu, Yong; Lin, Danhua; Zhao, Weihua; Xu, Xiufeng; Liu, Xiaoli; Cui, Zaixu; Wang, Zheng; Huang, Ruiwang; Li, Zhanjiang; Liu, Yunzhe; Li, Xiaojun; Yang, Xiujie; Zhang, Nan; Chen, Antao; Zhang, Bin; Qin, Pengmin; Liu, Chen; Yao, Zhenwei; Wei, Yanjun; Yuan, Huishu; Wang, Feng; Zhang, Yu; Zhang, Quan; Hu, Fang; Xie, Huan; Wu, Xuehai; Wang, Jiaojian; Fan, Guoguang; Wang, Zhiqun; Zhang, Dongling; Zhong, Hui; Wang, Yonggang; Bai, Lijun; Li, Yongmei; Wei, Xinhua; Wang, Jinhui; Zhang, Yi; He, Hongjian; Li, Shuyu; Zhang, Tijiang; Jiang, Fan; Yang, Jian; Chen, Feiyan; Liu, Feng; Liu, Huaigui; Chen, Nan; Yang, Jinzhu; Hou, Bo; Huang, Chu-Chung; Zhu, Jiajia; Cai, Huanhuan; Wei, Dongtao; Chen, Qunlin; Wei, Ying; Miao, Peifang; Li, Yunxia; Liu, Yaou; Yang, Ning; Gao, Xiaoxue; Liu, Yujie; Shen, Yu; Huang, Xiaoqi; Ji, Gong-Jun; Alzheimer’s Disease Neuroimaging Initiative, CHIMGEN Consortium, DIDA-MDD Working Group, MCADI, Chinese Lifespan Brain Mapping Consortium; Zhang, Longjiang; Qiu, Jiang; Yu, Yongqiang; Lin, Ching-Po; Feng, Feng; Li, Kuncheng; Yu, Chunshui; He, Yong	June 18, 2025	Not Determined
40659690	Create Study	The Chongqing Adolescent Twin Study: An Integrative Multimodal Brain Imaging and Non-imaging Dataset.	Scientific data	Zhu, Yanting; Fu, Yixiao; Han, Jiajia; Wang, Ruoming; Ma, Xingshun; Hu, Xiaomei; Li, Tao; Ma, Zhiwei	July 14, 2025	Not Determined
40654936	Create Study	Using deep learning to predict internalizing problems from brain structure in youth.	bioRxiv : the preprint server for biology	Vandewouw, Marlee M; Syed, Bilal; Barnett, Noah; Arias, Alfredo; Kelley, Elizabeth; Jones, Jessica; Ayub, Muhammad; Iaboni, Alana; Arnold, Paul D; Crosbie, Jennifer; Schachar, Russell J; Taylor, Margot J; Lerch, Jason P; Anagnostou, Evdokia; Kushki, Azadeh	May 9, 2025	Not Determined
40614528	Create Study	A Bayesian Regularized and Annotation-Informed Integrative Analysis of Cognition (BRAINIAC).	Developmental cognitive neuroscience	Zablocki, Rong W; Xu, Bohan; Fan, Chun-Chieh; Thompson, Wesley K	August 1, 2025	Not Determined
40603311	Create Study	Functional contrast across the gray-white matter boundary.	Nature communications	Li, Muwei; Xu, Lyuan; Choi, Soyoung; Qin, Yuanyuan; Gao, Fei; Schilling, Kurt G; Gao, Yurui; Zu, Zhongliang; Anderson, Adam W; Ding, Zhaohua; Gore, John C	July 2, 2025	Not Determined
40595480	Create Study	Genetic risk predicts adolescent mood pathology via sexual differentiation of brain function and physiological aging.	Nature communications	Petrican, Raluca; Fornito, Alex; Murgatroyd, Christopher; Boyland, Emma; Hardman, Charlotte A	July 1, 2025	Not Determined
40501680	Create Study	Gene dosage effects of 22q11.2 copy number variants on in-vivo measures of white matter axonal density and dispersion.	bioRxiv : the preprint server for biology	Boen, Rune; Villalón-Reina, Julio E; Kushan, Leila; O'Hora, Kathleen P; Fung, Hoki; Parker, Nadine; Akkouh, Ibrahim A; Alnæs, Dag; O'Hara, Ruth; Marzelli, Matthew John; Foland-Ross, Lara; Chick, Christina French; Cotto, Isabelle; Reiss, Allan L; Hallmayer, Joachim; Thompson, Paul M; Andreassen, Ole A; Sønderby, Ida E; Bearden, Carrie E	May 30, 2025	Not Determined
40473984	Create Study	Krakencoder: a unified brain connectome translation and fusion tool.	Nature methods	Jamison, Keith W; Gu, Zijin; Wang, Qinxin; Tozlu, Ceren; Sabuncu, Mert R; Kuceyeski, Amy	July 1, 2025	Not Determined
40328780	Create Study	Lifespan reference curves for harmonizing multi-site regional brain white matter metrics from diffusion MRI.	Scientific data	Zhu, Alyssa H; Nir, Talia M; Javid, Shayan; Villalón-Reina, Julio E; Rodrigue, Amanda L; Strike, Lachlan T; de Zubicaray, Greig I; McMahon, Katie L; Wright, Margaret J; Medland, Sarah E; Blangero, John; Glahn, David C; Kochunov, Peter; Williamson, Douglas E; Håberg, Asta K; Thompson, Paul M; Jahanshad, Neda	May 6, 2025	Not Determined
40166142	Create Study	Two Axes of White Matter Development.	bioRxiv : the preprint server for biology	Luo, Audrey C; Meisler, Steven L; Sydnor, Valerie J; Alexander-Bloch, Aaron; Bagautdinova, Joëlle; Barch, Deanna M; Bassett, Dani S; Davatzikos, Christos; Franco, Alexandre R; Goldsmith, Jeff; Gur, Raquel E; Gur, Ruben C; Hu, Fengling; Jaskir, Marc; Kiar, Gregory; Keller, Arielle S; Larsen, Bart; Mackey, Allyson P; Milham, Michael P; Roalf, David R; Shafiei, Golia; Shinohara, Russell T; Somerville, Leah H; Weinstein, Sarah M; Yeatman, Jason D; Cieslak, Matthew; Rokem, Ariel; Satterthwaite, Theodore D	May 20, 2025	Not Determined
40100626	Create Study	Connectional axis of individual functional variability: Patterns, structural correlates, and relevance for development and cognition.	Proceedings of the National Academy of Sciences of the United States of America	Yang, Hang; Wu, Guowei; Li, Yaoxin; Xu, Xiaoyu; Cong, Jing; Xu, Haoshu; Ma, Yiyao; Li, Yang; Chen, Runsen; Pines, Adam; Xu, Ting; Sydnor, Valerie J; Satterthwaite, Theodore D; Cui, Zaixu	March 25, 2025	Not Determined
40084116	Create Study	Arterial spin labelling perfusion MRI analysis for the Human Connectome Project Lifespan Ageing and Development studies.	Imaging neuroscience (Cambridge, Mass.)	Kirk, Thomas F; McConnell, Flora A Kennedy; Toner, Jack; Craig, Martin S; Carone, Davide; Li, Xiufeng; Suzuki, Yuriko; Coalson, Timothy S; Harms, Michael P; Glasser, Matthew F; Chappell, Michael A	January 1, 2025	Not Determined
40078534	Create Study	Analysis of functional connectivity changes from childhood to old age: A study using HCP-D, HCP-YA, and HCP-A datasets.	Imaging neuroscience (Cambridge, Mass.)	Wang, Yaotian; Li, Shuoran; He, Jie; Peng, Lingyi; Wang, Qiaochu; Zou, Xu; Tudorascu, Dana L; Schaeffer, David J; Schaeffer, Lauren; Szczupak, Diego; Park, Jung Eun; Sukoff Rizzo, Stacey J; Carter, Gregory W; Silva, Afonso C; Zhang, Tingting	March 1, 2025	Not Determined
39974093	Create Study	Spectral normative modeling of brain structure.	medRxiv : the preprint server for health sciences	Mansour L, Sina; Di Biase, Maria A; Yan, Hongwei; Xue, Aihuiping; Venketasubramanian, Narayanaswamy; Chong, Eddie; Alexander-Bloch, Aaron; Chen, Christopher; Zhou, Juan Helen; Yeo, B T Thomas; Zalesky, Andrew	January 21, 2025	Not Determined
39967806	Create Study	Stochastic variational inference improves quantification of multiple timepoint arterial spin labelling perfusion MRI.	Frontiers in neuroscience	Kirk, Thomas F; Kenyon, Georgia G; Craig, Martin S; Chappell, Michael A	January 1, 2025	Not Determined
39777625	Create Study	Multimodal Measurement of Pubertal Development: Stage, Timing, Tempo, and Hormones.	Child development	Omary, Adam; Curtis, Mark; Cheng, Theresa W; Mair, Patrick; Shirtcliff, Elizabeth A; Barch, Deanna M; Somerville, Leah H	January 1, 2025	Not Determined

helpcenter.collection.publications-tab

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.
Where does the NDA get the publications?

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.
Not Determined Publication

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.
Not Relevant Publication

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.

Contact NDA Help Desk

Data Expected List: Mandatory Data Structures

These data structures are mandatory for your NDA Collection. Please update the Targeted Enrollment number to accurately represent the number of subjects you expect to submit for the entire study.

For NIMH HIV-related research that involves human research participants: Select the dictionary or dictionaries most appropriate for your research. If your research does not require all three data dictionaries, just ignore the ones you do not need. There is no need to delete extra data dictionaries from your NDA Collection. You can adjust the Targeted Enrollment column in the Data Expected tab to “0” for those unnecessary data dictionaries. At least one of the three data dictionaries must have a non-zero value.

Data Expected	Targeted Enrollment	Initial Submission	Subjects Submitted	Initial Share	Subjects Shared	Status
Research Subject and Pedigree	655	03/31/2019	2,180	08/31/2020	652	Approved

To create your project's Data Expected list, use the "+New Data Expected" to add or request existing structures and to request new Data Structures that are not in the NDA Data Dictionary.

If the Structure you need already exists, locate it and specify your dates and enrollment when adding it to your Data Expected list. If you require changes to the Structure you need, select the indicator stating "No, it requires changes to meet research needs," and upload a file containing your requested changes.

If the structure you need is not yet defined in the Data Dictionary, you can select "Upload Definition" and attach the necessary materials to request its creation.

When selecting the expected dates for your data, make sure to follow the standard Data Sharing Regimen and choose dates within the date ranges that correspond to your project start and end dates.

Please visit the Completing Your Data Expected Tutorial for more information.

Data Expected List: Data Structures per Research Aims

These data structures are specific to your research aims and should list all data structures in which data will be collected and submitted for this NDA Collection. Please update the Targeted Enrollment number to accurately represent the number of subjects you expect to submit for the entire study.

Data Expected	Targeted Enrollment	Initial Submission	Subjects Submitted	Initial Share	Subjects Shared	Status
Perceived Stress Scale	1,000	09/16/2019	1,744	11/01/2019	436	Approved
Penn Emotion Recognition Task-40	1,000	09/16/2019	1,038	11/01/2019	629	Approved
Medical History	1,000	09/16/2019	1,306	11/01/2019	652	Approved
Social Responsiveness Scale (SRS)	1,200	02/02/2020	1,304	02/02/2021	651	Approved
NEO Personality Inventory	1,000	09/16/2019	468	11/01/2019	229	Approved
Demographics	1,000	08/19/2019	1,306	11/01/2019	652	Approved
Child Behavior Checklist (CBCL)	1,000	08/19/2019	1,071	11/01/2019	500	Approved
Vital Signs Assessment	1,000	09/16/2019	1,306	11/01/2019	652	Approved
Processed MRI Data	655	04/19/2019	1,306	08/31/2020	652	Approved
Pubertal Development Scale (PDS)	1,000	09/16/2019	1,305	11/01/2019	652	Approved
Wechsler Intelligence Scale for Children	1,000	09/16/2019	938	11/01/2019	462	Approved
Wechsler Adult Intelligence Scale	1,000	09/16/2019	342	11/01/2019	180	Approved
Substance Use Survey	1,000	09/16/2019	1,085	11/01/2019	629	Approved
Childrens Behavior Questionnaire (CBQ)	1,000	09/16/2019	311	11/01/2019	55	Approved
Kiddie-Sads (K-SADS)	1,200	02/02/2020	1,296	02/02/2021	0	Approved
Wechsler Preschool Primary Scale Intelligence (WPPSI)	1,000	09/16/2019	47	11/01/2019	2	Approved
Physical Exam	655	04/19/2019	1,305	08/31/2020	652	Approved
Pittsburgh Sleep Quality Index	1,000	09/16/2019	233	11/01/2019	151	Approved
Medications and Treatments Form	1,000	09/16/2019	1,306	11/01/2019	652	Approved
Picture Sequence Memory	1,000	11/02/2019	2,112	12/31/2019	473	Approved
Cognition Composite Scores	1,000	11/02/2019	2,093	12/31/2019	471	Approved
Pattern Comparison Processing Speed	1,000	11/02/2019	2,123	03/01/2020	472	Approved
List Sorting Working Memory Test	1,000	11/02/2019	2,121	03/01/2020	472	Approved
Youth Self Report	1,000	09/16/2019	663	11/01/2019	334	Approved
Drug Screen	1,000	11/02/2019	0	03/01/2020	0	Approved
Adult Self Report	1,000	09/16/2019	2,171	11/01/2019	151	Approved
Imaging (Structural, fMRI, DTI, PET, microscopy)	655	04/19/2019	1,306	08/31/2020	652	Approved
Psychological Well-Being Summary	1,000	08/19/2019	1,929	11/01/2019	483	Approved
Dimensional Change Card Sort Test	1,000	09/16/2019	2,123	11/01/2019	472	Approved
Broad Psychopathology Form	1,000	11/02/2019	675	03/01/2020	243	Approved
UPPS Impulsive Behavior Scale	1,000	11/02/2019	1,303	03/01/2020	651	Approved
Emotional Distress-Anxiety	1,000	11/01/2019	1,006	03/01/2020	120	Approved
Emotional Distress - Depression	1,000	11/01/2019	1,006	03/01/2020	120	Approved
Social Isolation	1,000	11/01/2019	1,837	03/01/2020	464	Approved
Behavioral Inhibition Scale/Behavioral Activation Scale	1,000	09/16/2019	1,303	11/01/2019	651	Approved
Early Adolescent Temperament Questionnaire	1,000	09/16/2019	1,072	11/01/2019	500	Approved
Delay Discounting Task	1,000	09/16/2019	1,232	11/01/2019	641	Approved
Blood Sample Collection	1,000	09/16/2019	1,306	11/01/2019	652	Approved
Flanker Task	1,000	11/02/2019	2,122	03/01/2020	471	Approved
Anger	1,000	11/01/2019	1,929	03/01/2020	483	Approved
Munich ChronoType Questionnaire	1,000	11/01/2019	854	03/01/2020	479	Approved
Mini Mental State Exam	1,000	11/01/2019	609	03/01/2020	340	Approved
Go-No Go Variant Task	1,000	11/01/2019	0	03/01/2020	0	Approved
NIH Toolbox Oral Reading Recognition Test	1,000	11/01/2019	2,122	03/01/2020	472	Approved
Picture Vocabulary Test	1,000	11/01/2019	2,122	03/01/2020	471	Approved
Self Efficacy Survey	1,000	11/01/2019	1,854	03/01/2020	473	Approved
NIH Toolbox Fear Anxiety Surveys	1,000	11/01/2019	923	03/01/2020	362	Approved
NIH Toolbox Motor Domain	1,000	11/01/2019	2,124	03/01/2020	472	Approved
Sadness Survey	1,000	11/01/2019	923	03/01/2020	159	Approved
NIH Toolbox Sensation Domain	1,000	11/01/2019	2,124	03/01/2020	473	Approved
NIH Toolbox Social Withdrawal	1,000	11/01/2019	502	03/01/2020	159	Approved
NIH Toolbox Emphatic Behavior Survey	1,000	11/01/2019	503	03/01/2020	159	Approved
NIH Toolbox Rejection Surveys	1,000	11/01/2019	1,928	03/01/2020	482	Approved
NIH Toolbox Emotional Support Survey	1,000	11/01/2019	1,837	03/01/2020	464	Approved
NIH Toolbox Friendship Survey	1,000	11/01/2019	1,837	03/01/2020	464	Approved
NIH Toolbox Perceived Hostility	1,000	11/01/2019	1,837	03/01/2020	464	Approved
Sports and Activities Involvement Questionnaire	1,000	11/01/2019	1,070	03/01/2020	500	Approved
Diagnostic Assessment Based on K-SADS	1,000	11/01/2019	0	03/01/2020	0	Approved
PROMIS General Life Satisfaction	1,000	11/01/2019	1,801	03/01/2020	458	Approved
Scanner Debriefing Interview	1,000	11/01/2019	1,269	03/01/2020	637	Approved
Guessing Task	1,000	11/01/2019	0	03/01/2020	0	Approved
General Behavior Inventory	1,000	11/01/2019	1,302	03/01/2020	651	Approved
Family Environment Scale	1,000	09/16/2019	1,306	11/01/2019	652	Approved
External Measures	1,000	11/01/2019	1,306	03/01/2020	652	Approved
Screen Time	1,000	11/01/2019	1,303	03/01/2020	651	Approved
Sleep Disturbance Questionnaire	1,000	11/01/2019	675	03/01/2020	243	Approved
Emotion Task	1,000	11/01/2019	0	03/01/2020	0	Approved
Language Experience Proficiency Questionnaire	1,000	11/01/2019	1,306	03/01/2020	652	Approved

Structure not yet defined

No Status history for this Data Expected has been recorded yet

helpcenter.collection.data-expected-tab

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.

Researchers who are starting their project need to update their Data Expected list to include all the Data Structures they are collecting under their grant and set their initial submission and sharing schedule according to the NDA Data Sharing Regimen.

To add existing Data Structures from the Data Dictionary, to request new Data Structure that are not in the Dictionary, or to request changes to existing Data Structures, click "+New Data Expected".

For step-by-step instructions on how to add existing Data Structures, request changes to an existing Structure, or request a new Data Structure, please visit the Completing Your Data Expected Tutorial.

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

What is an NDA Data Structure?

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.
What is the NDA Data Dictionary?

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

Glossary

Analyzed 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.
Data Item

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.
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 Structure Category

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).
Data Structure Group

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.
Evaluated Data

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

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

Initial Submission and Initial Share dates should be populated according to the NDA Data Sharing Terms and Conditions. Any modifications to these will go through the approval processes outlined above. Data will be shared with authorized users upon publication (via an NDA Study) or 1-2 years after the grant end date specified on the first Notice of Award, as defined in the applicable Data Sharing Terms and Conditions.
Initial Submission Date

Initial Submission and Initial Share dates should be populated according to these NDA Data Sharing Terms and Conditions. Any modifications to these will go through the approval processes outlined above. Data for all subjects is not expected on the Initial Submission Date and modifications may be made as necessary based on the project's conduct.
Research Subject and Pedigree

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.
Submission Cycle

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

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

Contact NDA Help Desk

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 NameFilter by Study Name	DOIFilter by DOI	AbstractFilter by Abstract	Collection/Study SubjectsFilter by Collection/Study Subjects	Data UsageFilter by Data Usage	StateFilter by State
Perception of social experiences and cortical thickness change together throughout early adolescence: findings from the ABCD cohort	10.15154/1524763	Early adolescence is a dynamic period of social and cortical development amidst rapid hormonal and puberty changes. We examined how differences and changes in positive social experiences and cortical thickness co-develop from age 9-11 and 11-13 years in the ABCD cohort (N~12,000). We used Bivariate Latent Change Score Models to capture cortical development (modelling mean whole-brain cortical thickness) and positive social experiences (modelling caregiver monitoring, family cohesion, prosocial behaviour, number of friends, school engagement, school involvement, and neighborhood safety). We found that positive social experiences decreased between age 9-11 years (baseline) and 11-13 years (2-year-follow-up), indicating that social experiences were perceived as less positive over time. We found evidence for correlated change, such that a greater reduction in positive social experiences was associated with a greater reduction in cortical thickness (est=5.23, SE=1.31, p<.001, standardized coefficient=.08), which did not differ between males and females in early and late puberty stages. We found mixed evidence for sex-specific relationships between puberty stage and social experiences. The evidence supports a transactional model of development, in that positive social experiences and cortical thickness change together throughout early adolescence. The findings also highlight the importance of supporting youth in early adolescence through school transitions.	652/13803	Secondary Analysis	Shared
Youth Screen Media Activity Patterns and Associations With Behavioral Developmental Measures and Resting-state Brain Functional Connectivity	10.15154/1526465	Objective Screen media activity (SMA) consumes considerable time in youth’s lives, raising concerns about the effects it may have on youth development. Disentangling mixed associations between youth’s SMA and developmental measures should move beyond overall screen time and consider types and patterns of SMA. We aimed to identify reliable and generalizable SMA patterns among youth and examine their associations with behavioral developmental measures and developing brain functional connectivity. Method Three waves of the Adolescent Brain and Cognitive Development (ABCD) data were examined. The Lifespan Human Connectome Project in Development (HCP-D) was interrogated as an independent sample. ABCD participants included 11,878 children at baseline. HCP-D participants included 652 children and adolescents. Youth-reported SMA and behavioral developmental measures (neurocognitive performance, behavioral problems, psychotic-like experiences, impulsivity, and sensitivities to punishment/reward) were assessed with validated instruments. We identified SMA patterns in the ABCD baseline data using K-means clustering and sensitivity analyses. The generalizability and stability of the identified SMA patterns were examined in HCP-D data and ABCD follow-up waves, respectively. Relationships were examined between SMA patterns and behavioral and brain (resting-state brain functional connectivity [RSFC]) measures using linear-mixed-effect modelling with false-discovery-rate (FDR) correction. Results SMA data from 11,815 children (Meanage = 119.0 months, SDage = 7.5; 6,159 (52.1%) boys) were examined, and 3,151 (26.7%) demonstrated a video-centric higher-frequency SMA pattern and 8,666 (73.3%) demonstrated a lower-frequency pattern. The SMA patterns were validated in similarly-aged HCP-D youth. Compared to the lower-frequency-SMA-pattern group, the video-centric-higher-frequency-SMA-pattern group showed poorer neurocognitive performance (Beta=-0.12, 95%CI, [-0.08, -0.16], FDR-corrected p<.001), more total behavioral problems (Beta=0.13, 95%CI, [0.09, 0.18], FDR-corrected p<.001), and more psychotic-like experiences (Beta=0.31, 95%CI, [0.27, 0.36], FDR-corrected p<.001). The video-centric-higher-frequency-SMA-pattern group demonstrated higher impulsivity, more sensitivity to punishment/reward and altered RSFC among brain areas implicated previously in cognitive processes. Most of the associations persisted with age in the ABCD data, with more individuals (n=3,378, 30.4%) in the video-centric higher-frequency SMA group at one-year follow-up. A social-communication-centric SMA pattern was observed in HCP-D adolescents. Conclusion Video-centric SMA patterns are reliable and generalizable during late childhood. A higher-frequency-video-entertainment-SMA-pattern group showed altered RSFC and poorer developmental measures that persisted longitudinally. The findings suggest public health strategies aiming to decrease excessive time spent by children on video-entertainment-related SMA are needed. Further studies are needed to examine potential video-centric/social-centric SMA bifurcation to understand dynamic changes and trajectories of SMA patterns and related outcomes developmentally.	652/12528	Secondary Analysis	Shared
Environmental Risk Factors and Psychotic-like Symptoms in Children Aged 9-11	10.15154/1519221	Objective: Research implicates environmental risk factors, including correlates of urbanicity, deprivation, and environmental toxins, in psychotic-like experiences (PLEs). The current study examined associations between several types of environmental risk factors and PLEs in school-age children, whether these associations were specific to PLEs or generalized to other psychopathology, and examined possible neural mechanisms for significant associations. Method: The current study used data from 10,328 9-11-year-olds from the Adolescent Brain Cognitive Development (ABCD) study. Hierarchical linear models examined associations between PLEs and geocoded environmental risk factors, and whether associations generalized to internalizing/externalizing symptoms. Mediation models examined whether structural MRI abnormalities (e.g., intracranial volume) mediated associations between PLEs and environmental risk factors. Results: The results found specific types of environmental risk factors, namely measures of urbanicity (i.e., drug offense exposure, less perception of neighborhood safety), deprivation (including overall deprivation, rate of poverty, fewer years at residence), and lead exposure risk, were associated with PLEs. These associations showed evidence of stronger associations with PLEs than internalizing/externalizing symptoms (especially overall deprivation, poverty, drug offense exposure, and lead exposure risk). There was evidence that brain volume mediated between 11-25% of the associations between poverty, perception of neighborhood safety, and lead exposure risk with PLEs. Conclusions: These results are the first to find support for neural measures partially mediating the association between PLEs and environmental exposures. Furthermore, the current study replicated and extended recent findings of the association between PLEs and environmental exposures, finding evidence for specific associations with correlates of urbanicity, deprivation, and lead exposure risk.	11/11898	Secondary Analysis	Shared
Associations Among Environmental Unpredictability, Changes in Resting-State Functional Connectivity, and Adolescent Psychopathology in the ABCD study	10.15154/jeex-3768	Background: Unpredictability is a core but understudied dimension of adversity and has been receiving increasing attention recently. The effects of unpredictability on psychopathology and the underlying neural mechanisms, however, remain unclear. It is also unknown how unpredictability interacts with other dimensions of adversity in predicting brain development and psychopathology of youth. Methods: We applied cluster robust standard errors accounting for clustering structure to examine how unpredictability alters the developmental changes in resting-state functional connectivity (rsFC) of large-scale brain networks implicated in psychopathology, as well as the moderating role of deprivation, using data from the Adolescent Brain Cognitive Development (ABCD) study, which included four measurements from baseline (mean ± SD age, 119.13 ± 7.51 months; 2815 females) to 3-year follow-up (N = 5885). Results: After controlling threat, unpredictability was associated with decreased rsFC within default mode network (DMN) and increased rsFC between cingulo-opercular network (CON) and DMN. Neighborhood educational deprivation moderated the associations between unpredictability and changes in rsFC within DMN and fronto-parietal network (FPN), as well as between CON and DMN. Changes in rsFC between CON and DMN mediated the association between unpredictability and externalizing problems. Neighborhood educational deprivation moderated the indirect pathway from unpredictability to externalizing problems via changes in rsFC between CON and DMN. Conclusions: Our findings shed light on the neural mechanisms underlying the association between unpredictability and adolescents’ psychopathology and the moderating role of deprivation, highlighting the significance of providing stable environment and abundant educational opportunities to facilitate optimal development.	1/11868	Secondary Analysis	Shared
A multi-cohort study of resting-state connectivity alterations in attention-deficit/hyperactivity disorder	10.15154/1527788	Most studies examining connectomic abnormalities associated with ADHD have used small, underpowered samples and thus produced inconsistent findings. Here we combined data from the Adolescent Brain Cognitive Development (ABCD) and Lifespan Human Connectome Project Development (HCP-D) cohorts (NDAR collections #2573, #2846 and #3165), as well as datasets from non-NDAR sources including the ADHD-200, Healthy Brain Network (HBN), National Consortium on Alcohol and Neurodevelopment in Adolescence (NCANDA) and Neurobehavioral Clinical Research (NCR) cohorts. We aimed to identify network-level resting-state features associated ADHD diagnosis and traits in this large multi-cohort sample. We applied the same 36-parameter+despiking pipeline to subjects from all datasets, and combined data using mega-analytic mixed models, which included nested random intercepts for study, site and family ID. In the group comparison, we compared 1301 subjects with diagnosed ADHD against 1301 unaffected controls (total N=2,602; 1710 males (65.72%); mean age=10.86 years, sd=2.05). Patients and controls were 1:1 nearest neighbor matched on in-scanner motion and key demographic variables. Associations between ADHD-traits and resting-state connectivity were assessed in a large multi-cohort sample (N=10,113). ADHD diagnosis was associated with less anticorrelation between the default mode and salience/ventral attention (B=0.009, t=3.45, p-FDR=0.004, d=0.14, 95% CI=0.004, 0.014), somatomotor (B=0.008, t=3.49, p-FDR=0.004, d=0.14, 95% CI=0.004, 0.013), and dorsal attention networks (B=0.01, t=4.28, p-FDR<0.001, d=0.17, 95% CI=0.006, 0.015). These results were robust to sensitivity analyses considering comorbid internalizing problems, externalizing problems and psychostimulant medication. Similar findings were observed when examining ADHD traits. Finding associations between ADHD and connectivity of the default mode to task-positive networks is consistent with default mode network models of disorder, although all effect sizes were small.	494/8817	Secondary Analysis	Shared
Lifespan development of brain asymmetry	10.15154/3er3-dc69	Lateralization is a fundamental principle of structural brain organization. In vivo imaging of brain asymmetry is essential for deciphering lateralized brain functions and their disruption in neurodevelopmental and neurodegenerative disorders. Here, we present a normative framework for benchmarking brain asymmetry across the lifespan, developed from an aggregated sample of 128 primary neuroimaging studies, including 177,701 scans from 138,231 individuals, jointly spanning the age range from 20 post menstrual weeks to 102 years. This resource includes comprehensive, hemisphere-specific brain growth charts for multiple neuroimaging phenotypes: regional cortical grey matter volume, thickness, surface area, and subcortical volumes. Our findings reveal distinct spatial patterns of asymmetry, with early leftward asymmetry observed in association cortices and late rightward asymmetry in sensory regions. These trajectories support theories of the neuroplasticity of asymmetry and the role of both genetic and environmental factors in shaping brain lateralization. Additionally, we provide tools to generate asymmetry centile scores, which allow the quantification of individual deviations from typical asymmetry throughout the lifespan and can be applied to unseen data or clinical populations. We demonstrate the utility of these models by highlighting group-level differences in asymmetry in autism spectrum disorder, schizophrenia, and Alzheimer’s disease, and exploring genetic correlations with hemispheric specialization. To facilitate further research, we have made this normative framework freely available as an interactive open-access resource (upon publication), offering an essential tool to advance both basic and clinical neuroscience.	652/8552	Secondary Analysis	Shared
ComBatLS: A location- and scale-preserving method for multi-site image harmonization	10.15154/sr0j-g796	Recent work has leveraged massive datasets and advanced harmonization methods to construct normative models of neuroanatomical features and benchmark individuals’ morphology. However, current harmonization tools do not preserve the effects of biological covariates including sex and age on features’ variances; this failure may induce error in normative scores, particularly when such factors are distributed unequally across sites. Here, we introduce a new extension of the popular ComBat harmonization method, ComBatLS, that preserves biological variance in features’ locations and scales. We use UK Biobank data to show that ComBatLS robustly replicates individuals’ normative scores better than other ComBat methods when subjects are assigned to sex-imbalanced synthetic “sites”. Additionally, we demonstrate that ComBatLS significantly reduces sex biases in normative scores compared to traditional methods. Finally, we show that ComBatLS successfully harmonizes consortium data collected across over 50 studies. R implementation of ComBatLS is available at https://github.com/andy1764/ComBatFamily.	652/6514	Secondary Analysis	Shared
Evidence for embracing normative modeling	10.15154/9c5r-0h50	In this work, we expand the normative model repository introduced in Rutherford et al., 2022a to include normative models charting lifespan trajectories of structural surface area and brain functional connectivity, measured using two unique resting-state network atlases (Yeo-17 and Smith-10), and an updated online platform for transferring these models to new data sources. We showcase the value of these models with a head-to-head comparison between the features output by normative modeling and raw data features in several benchmarking tasks: mass univariate group difference testing (schizophrenia versus control), classification (schizophrenia versus control), and regression (predicting general cognitive ability). Across all benchmarks, we show the advantage of using normative modeling features, with the strongest statistically significant results demonstrated in the group difference testing and classification tasks. We intend for these accessible resources to facilitate the wider adoption of normative modeling across the neuroimaging community.	652/2599	Secondary Analysis	Shared
Temporal and Spatial Scales of Human Resting-state Cortical Activity Across the Lifespan	10.15154/5f0d-z111	Sensorimotor and cognitive abilities undergo substantial changes throughout the human lifespan, but the corresponding changes in the functional properties of cortical network remain poorly understood. This can be studied using temporal and spatial scales of functional magnetic resonance imaging (fMRI) signals, which provide a robust description of the topological structure and temporal dynamics of neural activity. For example, timescales of resting-state fMRI signals can parsimoniously predict a significant amount of the individual variability in functional connectivity networks identified in adult human brains. In the present study, we quantified and compared temporal and spatial scales in resting-state fMRI data collected from 2,352 subjects between the ages of 5 and 100 in Developmental, Young Adult, and Aging datasets from Human Connectome Project. For most cortical regions, we found that both temporal and spatial scales largely decreased with age across most cortical areas throughout the lifespan, with the visual cortex and the limbic network consistently showing the largest and smallest scales, respectively. For some prefrontal regions, however, these two scales displayed non-monotonic trajectories during adolescence and peaked around the same time during adolescence and decreasing throughout the rest of the lifespan. We also found that cortical myelination increased monotonically throughout the lifespan, and its rate of change was significantly correlated with the changes in both temporal and spatial scales across different cortical regions in adulthood. These findings suggest that temporal and spatial scales in fMRI signals, as well as cortical myelination, are closely coordinated during both development and aging.	652/1377	Secondary Analysis	Shared
The structure of neuroanatomical variation within bilinguals	10.15154/1528104	We have developed this CVAE method that was useful for Autism (Aglinskas et al 2022). We think it might be useful for bilingualism research because bilingualism is similarly variable. We need access to this database to test our ideas.	652/1375	Secondary Analysis	Shared
Human Connectome Project-Development (HCP-D) Release 1.0	10.15154/1503530	Initial release of data from the Human Connectome Project in Development (ages 5-21). Release includes basic demographic data (sex, age, race/ethnicity, handedness) and unprocessed imaging data for all modalities (structural, resting state fMRI, task fMRI, diffusion, and ASL) for 655 subjects and preprocessed structural imaging data for 84 subjects. Full release documentation available at: https://www.humanconnectome.org/study/hcp-lifespan-development/documentation.	655/655	Primary Analysis	Shared
Creating a population-averaged structural connectomic brain atlas dataset from HCP-aging subjects	10.15154/wjhf-4v02	Population-averaged brain atlases, that are represented in a standard space with anatomical labels, are instrumental tools in neurosurgical planning and the study of neurodegenerative conditions. Traditional brain atlases are primarily derived from anatomical scans and contain limited information regarding the axonal organization of the white matter. With the advance of diffusion MRI that allows the modeling of fiber orientation distribution (FOD) in the brain tissue, there is an increasing interest for a population-averaged FOD template, especially based on a large healthy aging cohort, to offer structural connectivity information for connectomic surgery and analysis of neurodegeneration. We will create a set of multi-contrast structural connectomic MRI atlases, including T1w, T2w, and FOD templates, along with the associated whole brain tractograms. The templates will be made using multi-contrast group-wise registration based on 3T MRIs of a large cohort of subjects from the Human Connectome Project in Aging (HCP-A). To enhance the usability, probabilistic tissue maps and segmentation of 22 subcortical structures will be provided. Finally, the subthalamic nucleus shown in the atlas will be parcellated into sensorimotor, limbic, and associative sub-regions based on their structural connectivity to facilitate the analysis and planning of deep brain stimulation procedures.	652/652	Primary Analysis	Shared
Decoding Age-specific Changes in Brain Functional Connectivity Using a Sliding-window Based Clustering Method	10.15154/1528403	Functional magnetic resonance imaging (fMRI) permits detailed study of human brain function. Understanding the age-specific development of neural circuits in the typically developing brain may help us generate new hypotheses for developmental psychopathologies. Functional connectivity (FC), defined as the statistical associations between two brain regions, has been widely used in estimating functional networks from fMRI data. Previous research has shown that the evolution of FC does not follow a linear trend, particularly from childhood to young adulthood. Thus, this work aims to detect the nuanced FC changes with age from the non-linear curves and identify age-period-specific FC development patterns. We proposed a sliding-window based clustering approach to identify refined age interval of FC development. We used resting-state fMRI (rs-fMRI) data from the human connectome project-development (HCP-D), which recruited children, adolescents, and young adults aged from 5 to 21 years. Our analyses revealed different developmental patterns of resting-state FC by sex. In general, females matured earlier than males, but males had a faster development rate during age 100 -120 months. We identified four developmental phases: network construction in late childhood, segregation and integration construction in adolescence, network pruning in young adulthood, and a unique phase in males -- U-shape development. In addition, we investigated the sex effect on the slopes of FC-age correlation. Males had higher slopes during late childhood and young adulthood. These results inform trajectories of normal FC development, information that can in the future be used to pinpoint when development might go awry in neurodevelopmental disorders.	652/652	Secondary Analysis	Shared
Human Connectome Project-Development (HCP-D) Release 2.0	10.15154/1520708	The 2.0 release of data from the Human Connectome Project in Development (healthy participants, ages 5-21) includes visit 1 (V1) preprocessed structural and functional imaging data, unprocessed V1 imaging data for all modalities (structural, resting state fMRI, task fMRI, diffusion, and ASL), and non-imaging demographic and behavioral assessment data for 652 participants. For details of all the measures included in this release and access instructions see the Lifespan HCP-Development Release 2.0 documentation link below.	652/652	Primary Analysis	Shared
Integrated brain connectivity analysis with fMRI, DTI, and sMRI powered by interpretable graph neural networks	10.15154/qcw2-dq85	Multimodal neuroimaging data modeling has become a widely used approach but confronts considerable challenges due to their heterogeneity, which encompasses variability in data types, scales, and formats across modalities. This variability necessitates the deployment of advanced computational methods to integrate and interpret diverse datasets within a cohesive analytical framework. In our research, we combine functional magnetic resonance imaging (fMRI), diffusion tensor imaging (DTI), and structural MRI (sMRI) for joint analysis. This integration capitalizes on the unique strengths of each modality and their inherent interconnections, aiming for a comprehensive understanding of the brain’s connectivity and anatomical characteristics. Utilizing the Glasser atlas for parcellation, we integrate imaging-derived features from multiple modalities—functional connectivity from fMRI, structural connectivity from DTI, and anatomical features from sMRI—within consistent regions. Our approach incorporates a masking strategy to differentially weight neural connections, thereby facilitating an amalgamation of multimodal imaging data. This technique enhances interpretability at the connectivity level, transcending traditional analyses centered on singular regional attributes. The model is applied to the Human Connectome Project’s Development study to elucidate the associations between multimodal imaging and cognitive functions throughout youth. The analysis demonstrates improved prediction accuracy and uncovers crucial anatomical features and neural connections, deepening our understanding of brain structure and function. This study not only advances multimodal neuroimaging analytics by offering a novel method for integrative analysis of diverse imaging modalities but also improves the understanding of intricate relationships between brain’s structural and functional networks and cognitive development.	652/652	Secondary Analysis	Shared
Local-global functional gradients of the thalamus capturing different aspects of thalamic structure and function	10.15154/4vvp-qn12	The thalamus, a core hub positioned deep in the center of the brain, has a highly complex inter-regional communication pattern. The thalamic functional connectome has received increasing attention, showcasing its pivotal role in various high-order cognitive processes and development. However, thalamic connectome profiles across different spatial scales have not been systematically investigated. More specifically, it is unknown how each thalamic voxel is functionally connected to one another on a local level, and how thalamic voxels are functionally embedded within large-scale cortical systems on a global level. Leveraging the recent connectopic gradient mapping techniques and incorporating our methodological updates, we shed light on the thalamic local-global connectome profiles by characterizing their respective functional gradients. We show that the local gradients of the thalamus primarily reflect its internal anatomical configuration, while its global counterparts predominantly reflect the thalamus’s inherent role in supporting various cognitive functions.	652/652	Secondary Analysis	Shared
Spatiotemporal patterns of cortical microstructural maturation in children and adolescents with diffusion MRI	10.15154/nzry-he54	Neocortical maturation is a dynamic process that proceeds in a hierarchical manner; however, the spatiotemporal organization of cortical microstructure with diffusion MRI has yet to be fully defined. This study characterized cortical microstructural maturation using diffusion MRI (fwe-DTI and NODDI multi-compartment modeling) in a cohort of 637 children and adolescents between 8 and 21 years of age. We found spatially heterogeneous developmental patterns broadly demarcated into functional domains where NODDI metrics increased, and fwe-DTI metrics decreased with age. By applying nonlinear growth models in a vertex-wise analysis, we observed a general posterior-to-anterior pattern of maturation, where the fwe-DTI measures mean diffusivity (MD) and radial diffusivity (RD) reached peak maturation earlier than the NODDI metrics neurite density index. Using non-negative matrix factorization, we found occipito-parietal cortical regions that correspond to lower-order sensory domains mature earlier than fronto-temporal higher-order association domains. Our findings corroborate previous histological and neuroimaging studies that show spatially-varying patterns of cortical maturation that may reflect unique developmental processes of cytoarchitectonically-determined regional patterns of change.	652/652	Primary Analysis	Shared
Revealing the spatial pattern of brain hemodynamic sensitivity to healthy aging through sparse DCM	10.15154/hk8k-pm84	Age-related changes in the BOLD response could reflect neuro-vascular coupling modifications rather than simply impairments in neural functioning. In this study, we propose the use of a generative dynamic causal model (DCM) to decouple neuronal and vascular factors in the BOLD signal, with the aim of characterizing the whole-brain spatial pattern of hemodynamic sensitivity to healthy aging, as well as to test the role of hemodynamic features as independent predictors in an age-classification model. In this view, DCM was applied to the resting-state fMRI data of a cohort of 126 healthy individuals in a wide age range, providing reliable estimates of the hemodynamic response function (HRF) for each subject and each region of interest. Then, some features characterizing each HRF curve were extracted and used to fit a multivariate logistic regression model to predict the age class of each individual. Ultimately, we tested the final predictive model on an independent dataset of 338 healthy subjects selected from the Human Connectome Project Aging (HCP-A) and Development (HCP-D) cohorts. Our results entail the spatial heterogeneity of the age effects on the hemodynamic component, since its impact resulted to be strongly region- and population-specific, discouraging any space-invariant corrective procedures that attempt to correct for vascular factors when carrying out functional studies involving groups with different ages. Moreover, we demonstrated that a strong interaction exists between some specific hemodynamic features and age, further supporting the essential role of the hemodynamic factor as independent predictor of biological aging, rather than a simple confounding variable.	152/641	Secondary Analysis	Shared
Brain Topology Underlying Executive Functions Across the Lifespan: Focus on the Default Mode Network	10.15154/pvjz-h270	While traditional neuroimaging approaches to the study of executive functions (EFs) have typically employed task-evoked paradigms, resting state studies are gaining popularity as a tool for investigating inter-individual variability in the functional connectome and its relationship to cognitive performance outside of the scanner. Using resting state functional magnetic resonance imaging data from the Human Connectome Project Lifespan database, the present study capitalised on graph theory to chart cross-sectional variations in the intrinsic functional organisation of the frontoparietal (FPN) and the default mode (DMN) networks in 500 healthy individuals (from 10 to 100 years of age), to investigate the neural underpinnings of EFs across the lifespan. Topological properties of both the FPN and DMN were predictive of EF performance, but not of a control task of picture naming, providing specificity in support for a tight link between neuro-functional and cognitive-behavioural efficiency within the EF domain. The topological organisation of the DMN, however, appeared more sensitive to age-related changes relative to that of the FPN. Because the DMN matures earlier in life than the FPN, it is more susceptible to neurodegenerative changes. Moreover, because its activity is stronger in conditions of resting state, the DMN might be easier to measure in noncompliant populations and in those at the extremes of the life-span curve, namely very young or elder participants. Here, we argue that the study of its functional architecture in relation to higher order cognition across the lifespan might, thus, be of greater interest compared with what has been traditionally thought.	93/500	Secondary Analysis	Shared
Psychological Resilience and Neurodegenerative Risk: A Connectomics-Transcriptomics Investigation in Healthy Adolescent and Middle-Aged Females	10.15154/1526352	Adverse life events can inflict substantial long-term damage, which, paradoxically, has been posited to stem from initially adaptative responses to the challenges encountered in one’s environment. Thus, identification of the mechanisms linking resilience against recent stressors to longer-term psychological vulnerability is key to understanding optimal functioning across multiple timescales. To address this issue, our study tested the relevance of neuro-reproductive maturation and senescence, respectively, to both resilience and longer-term risk for pathologies characterised by accelerated brain aging, specifically, Alzheimer’s Disease (AD). Graph theoretical and partial least squares analyses were conducted on multimodal imaging, reported biological aging and recent adverse experience data from the Lifespan Human Connectome Project (HCP). Availability of reproductive maturation/senescence measures restricted our investigation to adolescent (N =178) and middle-aged (N=146) females. Psychological resilience was linked to age-specific brain senescence patterns suggestive of precocious functional development of somatomotor and control-relevant networks (adolescence) and earlier aging of default mode and salience/ventral attention systems (middle adulthood). Biological aging showed complementary associations with the neural patterns relevant to resilience in adolescence (positive relationship) versus middle-age (negative relationship). Transcriptomic and expression quantitative trait locus data analyses linked the neural aging patterns correlated with psychological resilience in middle adulthood to gene expression patterns suggestive of increased AD risk. Our results imply a partially antagonistic relationship between resilience against proximal stressors and longer-term psychological adjustment in later life. They thus underscore the importance of fine-tuning extant views on successful coping by considering the multiple timescales across which age-specific processes may unfold.	178/324	Secondary Analysis	Shared
Genetic Risk Predicts Adolescent Mood Pathology via Sexual Differentiation of Brain Function and Physiological Aging	10.15154/te4p-qr97	Studies focused on dichotomous sex differences report that internalizing disorders are more prevalent among females. Recent evidence challenges this traditional approach to sex differences, indicating instead that the human brain is best described along a continuum of sexual differentiation. Thus, assessments of the degree to which a brain is sexually differentiated could provide more nuanced insights into the mechanisms underpinning mental ill-health. To examine the under-explored possibility that psychiatric risk varies with sexual differentiation in brain function, we use longitudinal (N = 199) and cross-sectional (N =277) data from male and female youths. 336 healthy young adults constitute the reference group for estimating neural functional coupling patterns that vary between males and females. In the longitudinal sample, sexual differentiation along a canonical sensory-association functional hierarchy at ages 9-10 correlates, in a sex-dependent manner, with pubertal development and immune/metabolic dysregulation at ages 11-12. The sexual differentiation and physiological profiles sequentially mediate the relationship between genetic risk and rising internalizing/externalizing symptoms. The links between sexual differentiation, physiology and psychopathology are replicated in the cross-sectional sample and shown to hold across sexes. Thus, our study emphasizes the importance of integrating continuous assessments of sexual differentiation and physiology in personalizing psychiatric intervention in adolescence.	277/277	Secondary Analysis	Shared
Modelling the Developing Connectome - Graph Representation Learning with Variational Autoencoders	10.15154/1528540	The functional development of the human brain exhibits high inter-subject variability, which is influenced by factors such as genetic predisposition, environmental influences and individual learning experiences. A broadly accessible imaging modality for analysis of the brain’s functional development is found in resting state functional Magnetic Resonance Imaging (rs-fMRI), a non-invasive imaging technique covering the whole brain. However, analysis of rs-fMRI data in the context of developmental studies is challenging. Compared to adults, paediatric cohorts show higher in-scanner movement, which leads to erroneous intensity changes in the signal. Such artefacts, if not accounted for, lead to systematic errors in subsequent rs-fMRI analysis. Furthermore, approaches for developmental rs- fMRI analysis must be able to capture general trends rather than individual differences due to inter-subject variability. In this work, a framework for analysis of the topological properties of the developing brain is presented. First, a paediatric preprocessing pipeline is developed and evaluated for its performance in removing in-scanner-movement-related artefacts. Second, an adaption of the graph-based Variational Autoencoder (VAE) is developed for representation learning on brain graphs derived from developmental rs- fMRI data in the age range of 7.5 to 18.5 years. Additionally, a graph-based conditional VAE is proposed, with the goal to enhance the VAE representation learning abilities. Furthermore, the VAEs are compared to a Bayesian regression baseline. The evaluation of the VAE-based approaches is threefold: First, the models’ brain graph reconstruction capabilities are evaluated in terms of the Mean Squared Error (MSE) and a correlation-based error measure. Second, the quality of datasets generated by the models is measured in terms of Maximum Mean Discrepancy (MMD) based on graph statistics distributions. Finally, the ability to capture developmental trajectories of a developmental dataset is investigated by statistical analysis using Network Based Statistic (NBS). Results show that the developmental processes available in the used dataset cannot be modelled exactly by the presented modelling approaches. Additionally, extending the VAE with a condition does not improve its representation learning capabilities. However, trends, such as the continuous development of the Default Mode Network (DMN), are captured successfully by the graph-based VAE.	249/249	Primary Analysis	Shared
Local and global reward learning in the lateral frontal cortex show differential development during human adolescence	10.15154/1528961	Reward-guided choice is fundamental for adaptive behaviour and depends on several component processes supported by prefrontal cortex. Here, across three studies, we show that two such component processes, linking reward to specific choices and estimating the global reward state, develop during human adolescence and are linked to the lateral portions of the prefrontal cortex. These processes reflect the assignment of rewards contingently to local choices, or noncontingently, to choices that make up the global reward history. Using matched experimental tasks and analysis platforms, we show the influence of both mechanisms increase during adolescence (study 1) and that lesions to lateral frontal cortex (that included and/or disconnected both orbitofrontal and insula cortex) in human adult patients (study 2) and macaque monkeys (study 3) impair both local and global reward learning. Developmental effects were distinguishable from the influence of a decision bias on choice behaviour, known to depend on medial prefrontal cortex. Differences in local and global assignments of reward to choices across adolescence, in the context of delayed grey matter maturation of the lateral orbitofrontal and anterior insula cortex, may underlie changes in adaptive behaviour.	60/60	Primary Analysis	Shared

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Publications

NDA Help Center

Collection - Publications

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NDA Help Center

Collection - Data Expected

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Associated Studies

NDA Help Center

Collection - Associated Studies

Frequently Asked Questions

Glossary

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