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:

Explanation must be between 20 and 200 characters in length.

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Establishing Moderators/Biosignatures of Antidepressant Response - Clinical Care (EMBARC) MDD Treatment and Controls #2199

General
Experiments (37)
Shared Data
Publications (92)
Data Expected (42)
Associated Studies (11)

Collection Title	Collection Investigators	Collection Description
Collection Title:	Establishing Moderators/Biosignatures of Antidepressant Response - Clinical Care (EMBARC) MDD Treatment and Controls
Collection Investigators:	Madhukar H. Trivedi; Myrna M. Weissman; Patrick J. McGrath; Ramin V. Parsey
Collection Description:	EMBARC is a comparative effectiveness trial of three mechanistically distinct treatments for Major Depressive Disorder (MDD) (citalopram, bupropion, and cognitive behavioral therapy) in which we will assess a comprehensive array of carefully selected clinical (i.e. anxious depression, early life trauma, ) and biological (i.e. genetic, neuroimaging, serum, epigenetic ) moderators and mediators of outcome. This Collection provides data on the treatment group and the normal controls evaluated as part of the EMBARC study. Using innovative statistical approaches the identified moderators and mediators will then be used to develop a differential depression treatment response index (DTRI). The proposed study is a randomized two-stage trial (Stage 1: 12 wks; Stage 2: 12 wks) design with 675 MDD patients (with a history of one adequate trial of an SSRI except citalopram) assigned to one of three treatment conditions (n=225 each).
Data Repository:	NIMH Data Archive
Permission Group:
Collection Creation Date:	04/30/2015
NIH Research Initiative:	NIMH Repository & Genomics Resource (NRGR)
Collection Phase:	Funding Completed
Collection Sub-Phase:	Close Out
Blinded Clinical Trial:	Yes
Total Funded Amount:	$8,939,709.00
Subjects Shared:	336
Collection DOI:	10.15154/e99n-np66

{"values":[]}

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Funding Sources:

Funding Source Name	Funding Source URL
NIH - Extramural	None

Supporting Documentation:

File Name	File Type	Description	Audience
UTSW Protocol V4 for SERT_CLEAN SENT TO SITES 4 19 12.pdf	Analysis Protocol	Protocol	Qualified Researchers
All MIND variables_2016_0705.xlsx	Other	Data Dictionary	Qualified Researchers
CTall Biosig List by Task 2016_0414 FINAL.xlsx	Other	Biosignature endpoints	Qualified Researchers
Data Processing & Distributions Summary 2017_0227.pdf	Other	Distribution sets	Qualified Researchers
Embarc Baseline QIDS scores explained.xlsx	Methods	Baseline QIDS	Qualified Researchers
Embarc datafile naming conventions 2013_0814.xlsx	Other	Naming Conventions	Qualified Researchers
Raw file descriptions 2013_1219.pdf	Other	Raw file descriptions	Qualified Researchers
StudyTrax MDD Criteria.pdf	Other	MDD criteria	Qualified Researchers
Embarc Support docs_2017_0928.zip	Other	All support files compressed	Qualified Researchers
CTall MRI EEG Missed Biosig Collections 2016_0331.xlsx	Other	Missing collections	Qualified Researchers
Biosignature Data Quality Control 2016_0608.pdf	Other	Quality Control	Qualified Researchers
Embarc All Subjects List 708.xlsx	Other	All Subjects List	Qualified Researchers
StudyTrax Data Dictionary 2015_0528a.xlsx	Other	Data Dictionary	Qualified Researchers
Guide to StudyTrax Data Sets 2017_0928.xlsx	Other	Guide to Datasets	Qualified Researchers
CT_Unblinded_Stage12.xlsx	Other	CT Randomization Stage1 and 2	Qualified Researchers
Embarc Data Files - Current latest dumps 2016_1229 Locked.xlsx	Other	Data dumps - locked	Qualified Researchers
Embarc Subject Denominators Sep 1 2017.xlsx	Other	Subject Denominators	Qualified Researchers

Grant Information:

Project Number	Title	Start Date	Original End Date	End Date	Planned Enrollment	Actual Enrollment	Organization	Funds Obligated
U01MH092250-01	Biosignatures of Treatment Remission in Major Depression	09/30/2010	06/30/2014	06/30/2014	200	43	COLUMBIA UNIVERSITY HEALTH SCIENCES	$4,192,397.00
U01MH092221-01	Establishing Moderators/Biosignatures of Antidepressant Response- Clinical Care	09/30/2010	06/30/2014	06/30/2014	300	350	UT SOUTHWESTERN MEDICAL CENTER	$4,747,312.00

Clinical Trials:

Brief Summary	Status	Clinical Trial ID	Study ID	Principal Investigator	Start Date	End Date
This study will examine multiple carefully selected clinical and biological markers, using both existing state-of-the-art technologies as well as pioneering, innovative approaches. The study is designed to identify moderators and mediators of treatment response for depression in order to specify a biosignature of treatment response for depression. Evaluation of the usefulness of these markers in a carefully conducted clinical trial comparing an antidepressant to placebo will assist in developing a Depression Treatment Response Index (DTRI) to help clinicians match treatments to patients with MDD, resulting in timely selection of treatments best suited for individual patients and thus approaching personalized treatment. The resulting index provides a truly novel means of synthesizing the contribution of key clinical and biological parameters in an easy to use tool for clinical care.	Completed	NCT01407094	STU 092010-151	Madhukar H Trivedi, M.D.	July 29, 2011	April 2016

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
250	MGH Estroop	03/11/2015	Approved	fMRI
251	MGH Resting 1	03/11/2015	Approved	fMRI
252	MGH Resting 2	03/11/2015	Approved	fMRI
253	MGH Reward	03/11/2015	Approved	fMRI
254	UM Estroop	03/11/2015	Approved	fMRI
255	UM Resting 1	03/11/2015	Approved	fMRI
256	UM Resting 2	03/11/2015	Approved	fMRI
257	UM Reward	03/11/2015	Approved	fMRI
258	CU SBU Estroop	03/12/2015	Approved	fMRI
259	CU SBU Resting 1	03/12/2015	Approved	fMRI
260	CU SBU Resting 2	03/12/2015	Approved	fMRI
261	CU SBU Reward	03/12/2015	Approved	fMRI
262	CU MR750 Estroop	03/13/2015	Approved	fMRI
263	CU MR750 Resting 1	03/13/2015	Approved	fMRI
264	CU MR750 Resting 2	03/13/2015	Approved	fMRI
265	CU MR750 Reward	03/13/2015	Approved	fMRI
266	CU HDx Estroop	03/13/2015	Approved	fMRI
267	CU HDx Resting 1	03/13/2015	Approved	fMRI
268	CU HDx Resting 2	03/13/2015	Approved	fMRI
269	CU HDx Reward	03/13/2015	Approved	fMRI
270	UTSW Estroop	03/16/2015	Approved	fMRI
271	UTSW Resting 1	03/16/2015	Approved	fMRI
272	UTSW Resting 2	03/16/2015	Approved	fMRI
273	UTSW Reward	03/16/2015	Approved	fMRI
274	CU EEG Resting	03/18/2015	Approved	EEG
275	CU LDAEP	03/19/2015	Approved	EEG
280	UTSW EEG Resting	03/31/2015	Approved	EEG
281	UTSW LDAEP	03/31/2015	Approved	EEG
282	UM EEG Resting	03/31/2015	Approved	EEG
283	UM LDAEP	03/31/2015	Approved	EEG
284	MGH EEG Resting	03/31/2015	Approved	EEG
285	MGH LDAEP	03/31/2015	Approved	EEG
300	A Not B Task	05/26/2015	Approved	EEG
301	Choice Reaction Time Task	05/26/2015	Approved	EEG
302	Flanker Task	05/26/2015	Approved	EEG
303	Probabilistic Reward Task	05/26/2015	Approved	EEG
304	Word Fluency Task	05/26/2015	Approved	EEG

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
Altman Self-Rating Mania Scale	Clinical Assessments	333
Anger Attack Questionnaire	Clinical Assessments	333
Antidepressant Treatment Response Questionnaire	Clinical Assessments	336
Behavioral Phenotyping Form	Imaging	336
Childhood Trauma Questionnaire	Clinical Assessments	334
Clinical Global Impression (CGI)	Clinical Assessments	287
Columbia Suicide Severity Rating Scale	Clinical Assessments	95
Concise Associated Symptoms Tracking Scale	Clinical Assessments	333
Concise Health Risk Tracking	Clinical Assessments	335
EEG Phenotype Event Form	Clinical Assessments	336
EEG Scalar Variables	Imaging	336
EEG Subject	Clinical Assessments	336
EEG Subject Files	Imaging	327
EEG Task Questionnaire	Clinical Assessments	336
EMBARC Demographics	Clinical Assessments	336
EMBARC Medical History	Clinical Assessments	336
Early Termination Form	Clinical Assessments	110
Edinburgh Handedness Inventory	Clinical Assessments	336
Fagerstrom Test for Nicotine Dependence	Clinical Assessments	336
Family History Screen	Clinical Assessments	336
FreeSurfer Volumetrix	Imaging	243
FreeSurfer Volumetrix Part 2	Imaging	286
FreeSurfer Volumetrix Part 3	Imaging	243
Frequency Intensity Burden Side Effects	Clinical Assessments	281
Hamilton Rating Scale for Depression	Clinical Assessments	332
Image	Imaging	330
MRI Event Form	Imaging	336
MRI Scalar	Imaging	336
Massachusetts General Hospital Sexual Functioning Inventory	Clinical Assessments	333
Medication Adherance Form	Clinical Assessments	294
Mood Disorder Questionnaire	Clinical Assessments	333
Mood and Anxiety Symptom Questionnaire	Clinical Assessments	334
NEO Five-Factor Inventory Form S Adult	Clinical Assessments	333
Pain Frequency, Intensity and Burden	Clinical Assessments	330
Participant Adherence Questionnaire	Clinical Assessments	283
Processed DTI	Imaging	261
Processed DTI Part 2	Imaging	264
Processed DTI Part 3	Imaging	264
Quick Inventory of Depressive Symptomatology	Clinical Assessments	336
Research Subject	Clinical Assessments	336
Screening Test Results	Clinical Assessments	336
Snaith-Hamilton Pleasure Scale	Clinical Assessments	335
Social Adjustment Scale	Clinical Assessments	334
Standard Assessment of Personality	Clinical Assessments	336
State-Trait Anxiety Inventory for Adults	Clinical Assessments	336
Structured Clinical Interview for DSM-IV	Clinical Assessments	336
Subject Miscellaneous Information	Clinical Assessments	336
Treatment Emergent Symptoms Scale	Clinical Assessments	292
Visual Analog Mood Scale	Clinical Assessments	336
Wechsler Abbreviated Scale of Intelligence (WASI)	Clinical Assessments	288

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
40964020	Create Study	Turbulent-like Dynamics Predict Treatment Outcome in Major Depressive Disorder.	Research square	Ruhe, Henricus; Poirot, Maarten; Perl, Yonatan Sanz; Caan, Matthan; Marquering, Henk; Deco, Gustavo; Kringelbach, Morten; Reneman, Liesbeth	September 9, 2025	Not Determined
40913094	Create Study	Towards causal inference-based antidepressant selection with brain and blood biomarkers.	Neuropsychopharmacology : official publication of the American College of Neuropsychopharmacology	Barac, Milica; Grant, Caroline W; Toll, Russell; Carmody, Thomas; Minhajuddin, Abu; Fatt, Cherise Chin; Foster, Jane A; Croarkin, Paul E; Bobo, William V; Jha, Manish K; Athreya, Arjun P; Trivedi, Madhukar H	February 1, 2026	Not Determined
40317273	Create Study	Cellular immune phenotype of major depressive disorder - findings from the EMBARC study.	The world journal of biological psychiatry : the official journal of the World Federation of Societies of Biological Psychiatry	Chin Fatt, Cherise R; Vasu, Srividya; Haque, Nabila; Ayvaci, Emine Rabia; Jha, Manish K; Foster, Jane A; Trivedi, Madhukar H	June 1, 2025	Not Determined
40111362	Create Study	Generalizability of Treatment Outcome Prediction Across Antidepressant Treatment Trials in Depression.	JAMA network open	Zhukovsky, Peter; Trivedi, Madhukar H; Weissman, Myrna; Parsey, Ramin; Kennedy, Sidney; Pizzagalli, Diego A	March 3, 2025	Not Determined
38948238	Create Study	Neuroanatomical dimensions in medication-free individuals with major depressive disorder and treatment response to SSRI antidepressant medications or placebo.	Nature. Mental health	Fu, Cynthia H Y; Antoniades, Mathilde; Erus, Guray; Garcia, Jose A; Fan, Yong; Arnone, Danilo; Arnott, Stephen R; Chen, Taolin; Choi, Ki Sueng; Fatt, Cherise Chin; Frey, Benicio N; Frokjaer, Vibe G; Ganz, Melanie; Godlewska, Beata R; Hassel, Stefanie; Ho, Keith; McIntosh, Andrew M; Qin, Kun; Rotzinger, Susan; Sacchet, Matthew D; Savitz, Jonathan; Shou, Haochang; Singh, Ashish; Stolicyn, Aleks; Strigo, Irina; Strother, Stephen C; Tosun, Duygu; Victor, Teresa A; Wei, Dongtao; Wise, Toby; Zahn, Roland; Anderson, Ian M; Craighead, W Edward; Deakin, J F William; Dunlop, Boadie W; Elliott, Rebecca; Gong, Qiyong; Gotlib, Ian H; Harmer, Catherine J; Kennedy, Sidney H; Knudsen, Gitte M; Mayberg, Helen S; Paulus, Martin P; Qiu, Jiang; Trivedi, Madhukar H; Whalley, Heather C; Yan, Chao-Gan; Young, Allan H; Davatzikos, Christos	January 1, 2024	Not Determined
38186634	Create Study	Brain ventricle and choroid plexus morphology as predictor of treatment response in major depression: Findings from the EMBARC study.	Brain, behavior, & immunity - health	Murck, Harald; Fava, Maurizio; Cusin, Cristina; Fatt, Cherise Chin; Trivedi, Madhukar	February 1, 2024	Not Determined
38130122	Create Study	Features of immunometabolic depression as predictors of antidepressant treatment outcomes: pooled analysis of four clinical trials.	The British journal of psychiatry : the journal of mental science	Vreijling, Sarah R; Chin Fatt, Cherise R; Williams, Leanne M; Schatzberg, Alan F; Usherwood, Tim; Nemeroff, Charles B; Rush, A John; Uher, Rudolf; Aitchison, Katherine J; Köhler-Forsberg, Ole; Rietschel, Marcella; Trivedi, Madhukar H; Jha, Manish K; Penninx, Brenda W J H; Beekman, Aartjan T F; Jansen, Rick; Lamers, Femke	March 1, 2024	Not Determined
37622759	Create Study	Reward Behavior Disengagement, a Neuroeconomic Model-Based Objective Measure of Reward Pathology in Depression: Findings from the EMBARC Trial.	Behavioral sciences (Basel, Switzerland)	Giles, Michael A; Cooper, Crystal M; Jha, Manish K; Chin Fatt, Cherise R; Pizzagalli, Diego A; Mayes, Taryn L; Webb, Christian A; Greer, Tracy L; Etkin, Amit; Trombello, Joseph M; Chase, Henry W; Phillips, Mary L; McInnis, Melvin G; Carmody, Thomas; Adams, Phillip; Parsey, Ramin V; McGrath, Patrick J; Weissman, Myrna; Kurian, Benji T; Fava, Maurizio; Trivedi, Madhukar H	July 25, 2023	Not Determined
36909585	Create Study	Brain Ventricle and Choroid Plexus Morphology as Predictor of Treatment Response: Findings from the EMBARC Study.	Research square	Murck, Harald; Fava, Maurizio; Cusin, Cristina; Chin Fatt, Cherise; Trivedi, Madhukar	March 1, 2023	Not Determined
36690972	Create Study	AI-based dimensional neuroimaging system for characterizing heterogeneity in brain structure and function in major depressive disorder: COORDINATE-MDD consortium design and rationale.	BMC psychiatry	Fu, Cynthia H Y; Erus, Guray; Fan, Yong; Antoniades, Mathilde; Arnone, Danilo; Arnott, Stephen R; Chen, Taolin; Choi, Ki Sueng; Fatt, Cherise Chin; Frey, Benicio N; Frokjaer, Vibe G; Ganz, Melanie; Garcia, Jose; Godlewska, Beata R; Hassel, Stefanie; Ho, Keith; McIntosh, Andrew M; Qin, Kun; Rotzinger, Susan; Sacchet, Matthew D; Savitz, Jonathan; Shou, Haochang; Singh, Ashish; Stolicyn, Aleks; Strigo, Irina; Strother, Stephen C; Tosun, Duygu; Victor, Teresa A; Wei, Dongtao; Wise, Toby; Woodham, Rachel D; Zahn, Roland; Anderson, Ian M; Deakin, J F William; Dunlop, Boadie W; Elliott, Rebecca; Gong, Qiyong; Gotlib, Ian H; Harmer, Catherine J; Kennedy, Sidney H; Knudsen, Gitte M; Mayberg, Helen S; Paulus, Martin P; Qiu, Jiang; Trivedi, Madhukar H; Whalley, Heather C; Yan, Chao-Gan; Young, Allan H; Davatzikos, Christos	January 23, 2023	Not Determined
36586213	Create Study	Data driven clusters derived from resting state functional connectivity: Findings from the EMBARC study.	Journal of psychiatric research	Chin Fatt, Cherise R; Minhajuddin, Abu; Jha, Manish K; Mayes, Taryn; Rush, A John; Trivedi, Madhukar H	February 1, 2023	Not Determined
36179972	Create Study	Accelerated Brain Aging in Adults With Major Depressive Disorder Predicts Poorer Outcome With Sertraline: Findings From the EMBARC Study.	Biological psychiatry. Cognitive neuroscience and neuroimaging	Jha, Manish K; Chin Fatt, Cherise; Minhajuddin, Abu; Mayes, Taryn L; Trivedi, Madhukar H	April 1, 2023	Not Determined
35680431	Create Study	Dynamic Resting-State Network Biomarkers of Antidepressant Treatment Response.	Biological psychiatry	Kaiser, Roselinde H; Chase, Henry W; Phillips, Mary L; Deckersbach, Thilo; Parsey, Ramin V; Fava, Maurizio; McGrath, Patrick J; Weissman, Myrna; Oquendo, Maria A; McInnis, Melvin G; Carmody, Thomas; Cooper, Crystal M; Trivedi, Madhukar H; Pizzagalli, Diego A	October 1, 2022	Not Determined
35670301	Create Study	Alterations in resting-state functional activity and connectivity for major depressive disorder appetite and weight disturbance phenotypes.	Psychological medicine	Piccolo, Mayron; Belleau, Emily L; Holsen, Laura M; Trivedi, Madhukar H; Parsey, Ramin V; McGrath, Patrick J; Weissman, Myrna M; Pizzagalli, Diego A; Javaras, Kristin N	July 1, 2023	Not Determined
35478408	Create Study	A systematic data-driven approach to analyze sensor-level EEG connectivity: Identifying robust phase-synchronized network components using surface Laplacian with spectral-spatial PCA.	Psychophysiology	Smith, Ezra E; Bel-Bahar, Tarik S; Kayser, Jürgen	October 1, 2022	Not Determined
35290819	Create Study	Neural substrates of emotional conflict with anxiety in major depressive disorder: Findings from the Establishing Moderators and biosignatures of Antidepressant Response in Clinical Care (EMBARC) randomized controlled trial.	Journal of psychiatric research	Trombello, Joseph M; Cooper, Crystal M; Fatt, Cherise Chin; Grannemann, Bruce D; Carmody, Thomas J; Jha, Manish K; Mayes, Taryn L; Greer, Tracy L; Yezhuvath, Uma; Aslan, Sina; Pizzagalli, Diego A; Weissman, Myrna M; Webb, Christian A; Dillon, Daniel G; McGrath, Patrick J; Fava, Maurizio; Parsey, Ramin V; McInnis, Melvin G; Etkin, Amit; Trivedi, Madhukar H	May 1, 2022	Not Determined
35017468	Create Study	Fibroblast growth factor 21 (FGF21) is increased in MDD and interacts with body mass index (BMI) to affect depression trajectory.	Translational psychiatry	Mason, Brittany L; Minhajuddin, Abu; Czysz, Andrew H; Jha, Manish K; Gadad, Bharathi S; Mayes, Taryn L; Trivedi, Madhukar H	January 11, 2022	Not Determined
34916068	Create Study	Patterns of Pretreatment Reward Task Brain Activation Predict Individual Antidepressant Response: Key Results From the EMBARC Randomized Clinical Trial.	Biological psychiatry	Nguyen, Kevin P; Chin Fatt, Cherise; Treacher, Alex; Mellema, Cooper; Cooper, Crystal; Jha, Manish K; Kurian, Benji; Fava, Maurizio; McGrath, Patrick J; Weissman, Myrna; Phillips, Mary L; Trivedi, Madhukar H; Montillo, Albert A	March 15, 2022	Not Determined
34000130	Create Study	Cross-Sectional Associations Among Symptoms of Pain, Irritability, and Depression and How These Symptoms Relate to Social Functioning and Quality of Life: Findings From the EMBARC and STRIDE Studies and the VitalSign6 Project.	The Journal of clinical psychiatry	Jha, Manish K; Schatzberg, Alan; Minhajuddin, Abu; Chin Fatt, Cherise; Mayes, Taryn L; Trivedi, Madhukar H	April 13, 2021	Not Determined
33767520	Create Study	Anatomically-Informed Data Augmentation for Functional MRI with Applications to Deep Learning.	Proceedings of SPIE--the International Society for Optical Engineering	Nguyen, Kevin P; Fatt, Cherise Chin; Treacher, Alex; Mellema, Cooper; Trivedi, Madhukar H; Montillo, Albert	February 1, 2020	Not Determined
33589737	Create Study	Structural brain measures linked to clinical phenotypes in major depression replicate across clinical centres.	Molecular psychiatry	Yu, Meichen; Cullen, Nicholas; Linn, Kristin A; Oathes, Desmond J; Seok, Darsol; Cook, Philip A; Duprat, Romain; Aselcioglu, Irem; Moore, Tyler M; Davatzikos, Christos; Oquendo, Maria A; Weissman, Myrna M; Shinohara, Russell T; Sheline, Yvette I	July 1, 2021	Not Determined
33566317	Create Study	Preliminary Evidence for Sociotropy and Autonomy in Relation to Antidepressant Treatment Outcome.	The Psychiatric quarterly	Cardinale, Ryan; Menkes, Margo W; Andrews, Carolyn M; Webb, Christian A; Jha, Manish K; Trombello, Joseph M; Trivedi, Madhukar H; McInnis, Melvin G; Deldin, Patricia J	September 1, 2021	Not Determined
33445082	Create Study	Differential response to SSRI versus Placebo and distinct neural signatures among data-driven subgroups of patients with major depressive disorder.	Journal of affective disorders	Chin Fatt, Cherise R; Cooper, Crystal M; Jha, Manish K; Minhajuddin, Abu; Rush, A John; Trombello, Joseph M; Fava, Maurizio; McInnis, Melvin; Weissman, Myrna; Trivedi, Madhukar H	March 1, 2021	Not Determined
33213541	Create Study	Exploration of baseline and early changes in neurocognitive characteristics as predictors of treatment response to bupropion, sertraline, and placebo in the EMBARC clinical trial.	Psychological medicine	Ang, Yuen-Siang; Bruder, Gerard E; Keilp, John G; Rutherford, Ashleigh; Alschuler, Daniel M; Pechtel, Pia; Webb, Christian A; Carmody, Thomas; Fava, Maurizio; Cusin, Cristina; McGrath, Patrick J; Weissman, Myrna; Parsey, Ramin; Oquendo, Maria A; McInnis, Melvin G; Cooper, Crystal M; Deldin, Patricia; Trivedi, Madhukar H; Pizzagalli, Diego A	October 1, 2022	Not Determined
33129637	Create Study	Examination of structural brain changes in recent suicidal behavior.	Psychiatry research. Neuroimaging	Kim, Diane J; Bartlett, Elizabeth A; DeLorenzo, Christine; Parsey, Ramin V; Kilts, Clinton; Cáceda, Ricardo	January 30, 2021	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	340	07/15/2015	336	12/15/2017	336	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
Mania Rating Scale	340	07/15/2015	333	12/15/2017	333	Approved
Antidepressant Treatment Response Questionnaire	340	07/15/2015	336	12/15/2017	336	Approved
Anger Attack Questionnaire	340	07/15/2015	333	12/15/2017	333	Approved
Childhood Trauma Questionnaire	340	07/15/2015	334	12/15/2017	334	Approved
Medical History	340	07/15/2015	336	12/15/2017	336	Approved
Concise Associated Symptoms Tracking Scale	340	07/15/2015	333	12/15/2017	333	Approved
EEG Task Questionnaire	340	07/15/2015	336	12/15/2017	336	Approved
Frequency Intensity Burden Side effects	300	07/15/2015	281	12/15/2017	281	Approved
Mood and Anxiety Symptom Questionnaire	340	07/15/2015	334	12/15/2017	334	Approved
Mood Disorder Questionnaire	340	07/15/2015	333	12/15/2017	333	Approved
Wechsler Abbreviated Scale of Intelligence (WASI)	340	07/15/2015	288	12/15/2017	288	Approved
Social Adjustment Scale	340	07/15/2015	334	12/15/2017	334	Approved
Standard Assessment of Personality	340	07/15/2015	336	12/15/2017	336	Approved
Treatment Emergent Symptoms Scale	300	07/15/2015	292	12/15/2017	292	Approved
NEO Personality Inventory	340	07/15/2015	333	12/15/2017	333	Approved
Demographics	340	07/15/2015	336	12/15/2017	336	Approved
Clinical Global Impression (CGI)	340	07/15/2015	287	12/15/2017	287	Approved
Visual Analog Mood Scale	340	07/15/2015	336	12/15/2017	336	Approved
Evaluated Data	340	07/15/2015	286	12/15/2017	286	Approved
Structured Clinical Interview for DSM (SCID)	340	07/15/2015	336	12/15/2017	336	Approved
MRI Event Form	340	07/15/2015	336	12/15/2017	336	Approved
MRI Scalar	340	07/15/2015	336	12/15/2017	336	Approved
Concise Health Risk Tracking	340	07/15/2015	335	12/15/2017	335	Approved
Medication Adherance Form	300	07/15/2015	294	12/15/2017	294	Approved
Physical Exam	340	07/15/2015	336	12/15/2017	336	Approved
Subject Miscelaneous Information	340	07/15/2015	336	12/15/2017	336	Approved
Fagerstrom Test for Nicotine Dependence	340	07/15/2015	336	12/15/2017	336	Approved
Analyzed EEG	140	07/15/2015	336	12/15/2017	336	Approved
State-Trait Anxiety Inventory for Adults	340	07/15/2015	336	12/15/2017	336	Approved
Participant Adherence Questionnaire	300	07/15/2015	283	12/15/2017	283	Approved
Hamilton Rating Scale for Depression	340	07/15/2015	332	12/15/2017	332	Approved
EEG Phenotype Event Form	340	07/15/2015	336	12/15/2017	336	Approved
Imaging (Structural, fMRI, DTI, PET, microscopy)	340	07/15/2015	330	12/15/2017	330	Approved
Quick Inventory of Depressive Symptomatology	340	07/15/2015	336	12/15/2017	336	Approved
Snaith-Hamilton Pleasure Scale	340	07/15/2015	335	12/15/2017	335	Approved
EEG	340	07/15/2015	327	12/15/2017	327	Approved
Early Termination Form	140	07/15/2015	110	12/15/2017	110	Approved
Pain Frequency, Intensity and Burden	300	07/15/2015	330	12/15/2017	330	Approved
EEG Scalar Variables	340	07/15/2015	336	12/15/2017	336	Approved
EEG Subject	340	07/15/2015	336	12/15/2017	336	Approved
Columbia Suicide Severity Rating Scale	340	07/15/2015	95	12/15/2017	95	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
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.	336/8552	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.	336/6514	Secondary Analysis	Shared
Towards Outcome-Driven Patient Subgroups: A Machine Learning Analysis Across Six Depression Treatment Studies	10.15154/1528714	Importance: Major depressive disorder (MDD) is a heterogeneous condition; multiple underlying neurobiological substrates could be associated with treatment response variability. Understanding the sources of this variability and predicting outcomes has been elusive. Machine learning (ML) has shown promise in predicting treatment response in MDD, but one limitation has been the lack of clinical interpretability of machine learning models, limiting clinician confidence in model results. Objective: To develop a machine learning model to derive treatment-relevant patient profiles using clinical and demographic information. Design: We analyzed data from six clinical trials of pharmacological treatment for depression (total n = 5438) using the Differential Prototypes Neural Network (DPNN), a neural network model that derives patient prototypes which can be used to derive treatment-relevant patient clusters while learning to generate probabilities for differential treatment response. A model classifying remission and outputting individual remission probabilities for five first-line monotherapies and three combination treatments was trained using clinical and demographic data. Setting: Previously-conducted clinical trials of antidepressant medications. Participants: Patients with MDD. Main outcomes and measures: Model validity and clinical utility were measured based on area under the curve (AUC) and expected improvement in sample remission rate with model-guided treatment, respectively. Post-hoc analyses yielded clusters (subgroups) based on patient prototypes learned during training. Prototypes were evaluated for interpretability by assessing differences in feature distributions (e.g. age, sex, symptom severity) and treatment-specific outcomes. Results: A 3-prototype model achieved an AUC of 0.66 and an expected absolute improvement in population remission rate of 6.5% (relative improvement of 15.6%). We identified three treatment-relevant patient clusters. Cluster A patients tended to be younger, to have increased levels of fatigue and more severe symptoms. Cluster B patients tended to be older, female with less severe symptoms, and the highest remission rates. Cluster C patients had more severe symptoms, lower remission rates, more psychomotor agitation, more intense suicidal ideation, more somatic genital symptoms, and showed improved remission with venlafaxine. Conclusion and Relevance: It is possible to produce novel treatment-relevant patient profiles using machine learning models; doing so may improve precision medicine for depression. Note: This model is not currently the subject of any active clinical trials and is not intended for clinical use.	336/6074	Secondary Analysis	Shared
Treatment selection using prototyping in latent-space with application to depression treatment	10.15154/1523049	Machine-assisted treatment selection commonly follows one of two paradigms: a fully personalized paradigm which ignores any possible clustering of patients; or a sub-grouping paradigm which ignores personal differences within the identified groups. While both paradigms have shown promising results, each of them suffers from important limitations. In this article, we propose a novel deep learning-based treatment selection approach that is shown to strike a balance between the two paradigms using latent-space prototyping. Our approach is specifically tailored for domains in which effective prototypes and sub-groups of patients are assumed to exist, but groupings relevant to the training objective are not observable in the non-latent space. In an extensive evaluation, using both synthetic and Major Depressive Disorder (MDD) real-world clinical data describing 4754 MDD patients from clinical trials for depression treatment, we show that our approach favorably compares with state-of-the-art approaches. Specifically, the model produced an 8% absolute and 23% relative improvement over random treatment allocation. This is potentially clinically significant, given the large number of patients with MDD. Therefore, the model can bring about a much desired leap forward in the way depression is treated today.	336/5946	Secondary Analysis	Shared
Biomarkers of Response to Antidepressants	10.15154/1517602	More than six decades have passed since the discovery of monoaminergic antidepressants. Yet, it remains a mystery why these drugs take weeks to months to achieve therapeutic effects, although their monoaminergic actions are present rapidly after treatment. This project investigates the neural correlates of behavioral severity in patients treated with the antidepressant sertraline.	336/336	Secondary Analysis	Shared
Developing an Electroencephalography-Based Model for Predicting Response to Antidepressant Medication	10.15154/1528397	Importance Untreated depression is a growing public health concern, with patients often facing a prolonged trial-and-error process in search of effective treatment. Developing a predictive model for treatment response in clinical practice remains challenging. Objective To establish a model based on electroencephalography (EEG) to predict response to 2 distinct selective serotonin reuptake inhibitor (SSRI) medications. Design, Setting, and Participants This prognostic study developed a predictive model using EEG data collected between 2011 and 2017 from 2 independent cohorts of participants with depression: 1 from the first Canadian Biomarker Integration Network in Depression (CAN-BIND) group and the other from the Establishing Moderators and Biosignatures of Antidepressant Response for Clinical Care (EMBARC) consortium. Eligible participants included those aged 18 to 65 years who had a diagnosis of major depressive disorder. Data were analyzed from January to December 2022. Exposures In an open-label trial, CAN-BIND participants received an 8-week treatment regimen of escitalopram treatment (10-20 mg), and EMBARC participants were randomized in a double-blind trial to receive an 8-week sertraline (50-200 mg) treatment or placebo treatment. Main Outcomes and Measures The model’s performance was estimated using balanced accuracy, specificity, and sensitivity metrics. The model used data from the CAN-BIND cohort for internal validation, and data from the treatment group of the EMBARC cohort for external validation. At week 8, response to treatment was defined as a 50% or greater reduction in the primary, clinician-rated scale of depression severity. Results The CAN-BIND cohort included 125 participants (mean [SD] age, 36.4 [13.0] years; 78 [62.4%] women), and the EMBARC sertraline treatment group included 105 participants (mean [SD] age, 38.4 [13.8] years; 72 [68.6%] women). The model achieved a balanced accuracy of 64.2% (95% CI, 55.8%-72.6%), sensitivity of 66.1% (95% CI, 53.7%-78.5%), and specificity of 62.3% (95% CI, 50.1%-73.8%) during internal validation with CAN-BIND. During external validation with EMBARC, the model achieved a balanced accuracy of 63.7% (95% CI, 54.5%-72.8%), sensitivity of 58.8% (95% CI, 45.3%-72.3%), and specificity of 68.5% (95% CI, 56.1%-80.9%). Additionally, the balanced accuracy for the EMBARC placebo group (118 participants) was 48.7% (95% CI, 39.3%-58.0%), the sensitivity was 50.0% (95% CI, 35.2%-64.8%), and the specificity was 47.3% (95% CI, 35.9%-58.7%), suggesting the model’s specificity in predicting SSRIs treatment response. Conclusions and Relevance In this prognostic study, an EEG-based model was developed and validated in 2 independent cohorts. The model showed promising accuracy in predicting treatment response to 2 distinct SSRIs, suggesting potential applications for personalized depression treatment.	336/336	Secondary Analysis	Shared
EEG Biomarkers to Predict Response to Sertraline and Placebo Treatment in Major Depressive Disorder	10.15154/1528058	Objective : Major depressive disorder (MDD) is a persistent psychiatric condition, and the leading cause of disability, affecting up to 5% of the population worldwide. Antidepressant medications (ADMs) are often the first-line treatment for MDD, but it may take the clinician months of “trial and error” to find an effective ADM for a particular patient. Therefore, identification of predictive biomarkers that can be used to accurately determine the effectiveness of a specific treatment for an individual patient is extremely valuable. Method : Using resting EEG data, we develop a machine learning algorithm (MLA) that searches for connectivity patterns within an individual's EEG signal that are predictive of the probability of responding to the antidepressant Sertraline or Placebo. The MLA has two steps: 1) Directed phase lag index (DPLI), a measure of phase synchronization between brain regions, that is not sensitive to volume conduction is applied to resting-state EEG data, 2) the resulting DPLI matrix is searched for a pattern set of features that can be used to successfully predict the response to Sertraline or Placebo. Results: Our MLA predicted response to Sertraline ( N = 105) or Placebo ( N = 119) with more than 80% accuracy. Conclusion: Our findings suggest that feature patterns selected from a DPLI matrix may be predictive of response to Sertraline and to Placebo. Significance : The proposed MLA may provide an inexpensive, non-invasive, and readily available tool that clinicians may use to enhance treatment effectiveness, accelerate time to recovery, reduce personal suffering, and decrease treatment costs.	336/336	Secondary Analysis	Shared
Individual deviations from normative electroencephalographic connectivity predict antidepressant response.	10.15154/ba67-cd20	BACKGROUND: Antidepressant medications yield unsatisfactory treatment outcomes in patients with major depressive disorder (MDD) with modest advantages over the placebo, partly due to the elusive mechanisms of antidepressant responses and unexplained heterogeneity in patient's response to treatment. Here we develop a novel normative modeling framework to quantify individual deviations in psychopathological dimensions that offers a promising avenue for the personalized treatment for psychiatric disorders. METHODS: We built a normative model with resting-state electroencephalography (EEG) connectivity data from healthy controls of three independent cohorts. We characterized the individual deviation of MDD patients from the healthy norms, based on which we trained sparse predictive models for treatment responses of MDD patients (102 sertraline-medicated and 119 placebo-medicated). Hamilton depression rating scale (HAMD-17) was assessed at both baseline and after the eight-week antidepressant treatment. RESULTS: We successfully predicted treatment outcomes for patients receiving sertraline (r = 0.43, p < 0.001) and placebo (r = 0.33, p < 0.001). We also showed that the normative modeling framework successfully distinguished subclinical and diagnostic variabilities among subjects. From the predictive models, we identified key connectivity signatures in resting-state EEG for antidepressant treatment, suggesting differences in neural circuit involvement between sertraline and placebo responses. CONCLUSIONS: Our findings and highly generalizable framework advance the neurobiological understanding in the potential pathways of antidepressant responses, enabling more targeted and effective personalized MDD treatment. TRIAL REGISTRATION: Establishing Moderators and Biosignatures of Antidepressant Response for Clinical Care for Depression (EMBARC), NCT#01407094.	336/336	Secondary Analysis	Shared
Using Probabilistic Reward Learning Task to Characterize Decision Making for Mental Disorders	10.15154/fpa3-pd23	Major depressive disorder (MDD), a leading cause of disability and mor- tality, is associated with reward-processing abnormalities and concentration difficulties. Motivated by the probabilistic reward task from the Establishing Moderators and Biosignatures of Antidepressant Response in Clinical Care (EMBARC) study, we propose a novel framework that integrates the reinforcement learning (RL) model and drift-diffusion model (DDM) to jointly analyze reward-based decision making with response times. To account for emerging evidence suggesting that decision making may alternate between multiple interleaved strategies, we model latent state switching using a hidden Markov model (HMM). In the engaged state, decisions follow an RL-DDM, simultaneously capturing reward processing, decision dynamics and temporal structure. In contrast, in the lapsed state, decision making is modeled using a simplified DDM, where specific parameters are fixed to approximate random guessing with equal probability. The proposed method is implemented using a computationally efficient generalized expectation-maximization (EM) algorithm with forward-backward procedures. Through extensive numerical studies, we demonstrate that our proposed method outperforms competing approaches across various reward-generating distributions, under both strategy-switching and nonswitching scenarios, as well as in the presence of input perturbations. When applied to the EMBARC study, our framework reveals that MDD patients exhibit lower overall engagement than healthy controls and experience longer responses when they do engage. Additionally, we show that neuroimaging measures of brain activities are associated with decision- making characteristics in the engaged state but not in the lapsed state, providing evidence of a brain-behavior association specific to the engaged state. We share the engagement scores in this collection.	336/336	Secondary Analysis	Shared
Relations Among Individualized Brain Systems Patch Sizes and Diagnosis, Symptoms, and Treatment Outcomes in Major Depressive Disorder: Preliminary Results	10.15154/2z0d-w675	Individualized brain mapping promises to provide a significant advance for neural models of, and treatments for, major depressive disorder (MDD). Developments in computational neuroimaging methods facilitate new precision in neural characterization at the person-specific level. Here, we describe preliminary results that link brain systems defined at the individual level – “individualized brain systems” – to MDD, including in relation to diagnosis, symptoms, and treatment outcomes.	325/325	Secondary Analysis	Shared
Electroencephalographic Network Topologies Predict Antidepressant Responses in Patients With Major Depressive Disorder.	10.15154/1528746	Medication therapy seems to be an effective treatment for major depressive disorder (MDD). However, although the efficacies of various medicines are equal or similar on average, they vary widely among individuals. Therefore, an understanding of methods for the timely evaluation of short-term therapeutic response and prediction of symptom improvement after a specific course of medication at the individual level at the initial stage of treatment is very important. In our present study, we sought to identify a neurobiological signature of the response to short-term antidepressant treatment. Related brain network analysis was applied in resting-state electroencephalogram (EEG) datasets from patients with MDD. The corresponding EEG networks were constructed accordingly and then quantitatively measured to predict the efficacy after eight weeks of medication, as well as to distinguish the therapeutic responders from non-responders. The results of our present study revealed that the corresponding resting-state EEG networks became significantly weaker after one week of treatment, and the eventual medication efficacy was reliably predicted using the changes in those network properties within the one-week medication regimen. Moreover, the corresponding resting-state networks at baseline were also proven to precisely distinguish those responders from other individuals with an accuracy of 96.67% when using the spatial network topologies as the discriminative features. These findings consistently provide a deeper neurobiological understanding of antidepressant treatment and a reliable and quantitative approach for personalized treatment of MDD.	30/30	Secondary Analysis	Shared

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