NIMH Data Archive - Data

Genomics

Neuroimaging

Phenotype¹

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Clinical Trial

¹ Numbers reported are subjects by age

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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
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
78	MET Genotypes	Omics	03/18/2013

Collection - Add Experiment

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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.

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

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Bipolar & Schizophrenia Consortium for Parsing Intermediate Phenotypes (B-SNIP 1) #2274

General
Experiments (12)
Shared Data
Publications (235)
Data Expected (21)
Associated Studies (12)

Collection Title	Collection Investigators	Collection Description
Collection Title:	Bipolar & Schizophrenia Consortium for Parsing Intermediate Phenotypes (B-SNIP 1)
Collection Investigators:	Carol Tamminga
Collection Description:	The overall goal of the proposed research is to examine a broad panel of putative endophenotypes in affected individuals with schizophrenia and bipolar and their unaffected relatives in order to: 1) characterize the degree of familial phenotypic overlap between SZ and psychotic BP; 2) identify patterns of endophenotypes unique t the two disorders, and 3) contrast the heritability of endophenotypes across the disorders. To achieve these goals, we will recruit 500 SZ and 500 BP I (with psychosis) probands, ~1700-2000 1st degree relative probands, and 500 unrelated non-psychiatric controls from five centers. We will obtain measures of neurophysiology (e.g., eye tracking, P50 gating, PPI, and P300), neurocognition (e.g., attention/vigilance, episodic and working memory), and brain structure (e.g., volumes of gray and white matter in specified brain regions). We will collect blood for future genetic studies. We will assess the degree of familial aggregation endophenotypes in SZ and BP relatives. Establishing similarities and differences in the endophenotypic signatures within SZ and BP families will provide important insights for future genetic studies, and clarify concepts about common and distinct aspects of pathophysiology, potentially meaningful heterogeneity with disorders, and the clinical boundaries of the two commonest psychotic disorders in adult psychiatry. This research will be conducted by 5 experienced research groups, with a long history of close and productive collaboration.
Data Repository:	NIMH Data Archive
Permission Group:
Collection Creation Date:	12/09/2014
Collection Phase:	Funding Completed
Collection Sub-Phase:	Close Out
Blinded Clinical Trial:	No
Total Funded Amount:	$17,870,101.00
Subjects Shared:	2,440
Collection DOI:	10.15154/tnzs-a323

{"values":[]}

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Funding Sources:

Funding Source Name	Funding Source URL
NIH - Extramural	None

Supporting Documentation:

Grant Information:

Project Number	Title	Start Date	Original End Date	End Date	Planned Enrollment	Actual Enrollment	Organization	Funds Obligated
R01MH078113-01	Bipolar & Schizophrenia Consortium for Parsing Intermediate Phenotypes	09/28/2007	05/31/2011	03/31/2020	640	215	BETH ISRAEL DEACONESS MEDICAL CENTER	$4,082,240.00
R01MH077852-01	Bipolar & Schizophrenia Consortium for Parsing Intermediate Phenotypes	09/28/2007	05/31/2011	05/31/2012	650	377	UNIVERSITY OF MARYLAND BALTIMORE	$3,495,195.00
R01MH077851-01	Bipolar & Schizophrenia Consortium for Parsing Intermediate Phenotypes	09/29/2007	05/31/2011	05/31/2013	650	263	UT SOUTHWESTERN MEDICAL CENTER	$3,647,643.00
R01MH077945-01	Bipolar & Schizophrenia Consortium for Parsing Intermediate Phenotypes	09/29/2007	05/31/2011	05/31/2013	650	365	YALE UNIVERSITY	$3,518,485.00
R01MH077862-01	Bipolar & Schizophrenia Consortium for Parsing Intermediate Phenotypes	09/29/2007	05/31/2013	05/31/2013	650	394	UT SOUTHWESTERN MEDICAL CENTER	$3,126,538.00

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
519	Resting	11/08/2016	Approved	fMRI
520	Resting-Chicago	11/10/2016	Approved	fMRI
521	Resting - Hartford	11/15/2016	Approved	fMRI
528	Rest - eyes open	11/30/2016	Approved	EEG
529	ODDBALL	12/01/2016	Approved	EEG
530	GATING	12/02/2016	Approved	EEG
556	Rest - eyes closed	12/20/2016	Approved	EEG
562	Pro-Saccade	12/28/2016	Approved	Eye Tracking
565	Antisaccade	01/03/2017	Approved	Eye Tracking
574	Ramp Mask Ramp	01/05/2017	Approved	Eye Tracking
576	Gaze Contingency Task	01/06/2017	Approved	Eye Tracking
603	Polhemus	01/18/2017	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
Akiskal Temperament Scale	Clinical Assessments	1314
Barratt Impulsivity Scale	Clinical Assessments	2160
Brief Assessment of Cognition	Clinical Assessments	1926
Clinical Trials: Inclusion/Exclusion Criteria	Clinical Assessments	2415
Demographics Data	Clinical Assessments	2415
EEG Subject Files	Imaging	1532
Eye Tracking Subject-Experiment	Imaging	1499
Family History Form	Clinical Assessments	2230
Hollingshead Socioeconomic Rating Scale	Clinical Assessments	2318
Image	Imaging	1098
Lethality Scale	Clinical Assessments	359
Medications	Clinical Assessments	1713
Montgomery-Asberg Depression Rating Scale	Clinical Assessments	1083
Psychiatric Medical History	Clinical Assessments	2415
Psychosocial Interview	Clinical Assessments	2295
Research Subject	Clinical Assessments	2415
Social Functioning Scale	Clinical Assessments	2156
Structured Clinical Interview for the Positive and Negative Syndrome Scale	Clinical Assessments	967
Structured Interview for DSM IV Personality	Clinical Assessments	1286
Wechsler Memory Scale, Third Edition (WMS-III)	Clinical Assessments	1906
Wide Range Achievement Test 4 (WRAT4)	Clinical Assessments	2167
Young Mania Rating Scale	Clinical Assessments	1070

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
41863364	Create Study	Neuroanatomical Deficits in Visual Cortex Subregions of Individuals With Psychosis Spectrum Disorders Linked to Symptoms, Cognition, and Childhood Trauma.	Schizophrenia bulletin	Türközer, Halide Bilge; Zeng, Victor; Hoang, Dung; Sritharan, Jothini; Iska, Neha; Ivleva, Elena I; Clementz, Brett A; Pearlson, Godfrey D; Keedy, Sarah; Gershon, Elliot S; Tamminga, Carol A; Keshavan, Matcheri S; Lizano, Paulo	March 7, 2026	Not Determined
41819238	Create Study	Signatures of altered free-water and cognition and associations with symptom severity in psychosis spectrum disorders.	Brain, behavior, and immunity	Stiltner, Brendan; Kelly, Sinead; Cetin-Karayumak, Suheyla; Trotti, Rebekah; Parker, David A; Zeng, Victor; Pearlson, Godfrey; Clementz, Brett A; McDowell, Jennifer E; Hill, Scot K; Tamminga, Carol A; Pasternak, Ofer; Shenton, Martha E; Keshavan, Matcheri S; Lizano, Paulo	March 10, 2026	Not Determined
41256162	Create Study	Defining and Engaging a Novel rTMS Target for Nicotine Craving in Psychotic Disorders.	medRxiv : the preprint server for health sciences	Ward, Heather Burrell; Blyth, Sophia H; Vandekar, Simon; Rogers, Baxter P; Yildiz, Gulcan; Connolly, Jillian G; Clementz, Brett; Gershon, Elliot; Keshavan, Matcheri; Meda, Shashwath; Pearlson, Godfrey; Tamminga, Carol; Halko, Mark A; Brady Jr, Roscoe O	September 30, 2025	Not Determined
40855769	Create Study	The BAsic NeuroCognitive Continuum (BANCC): Delineation of dimensional and categorical features for etiological and treatment investigations of idiopathic psychosis.	Psychiatry and clinical neurosciences	Warren, Hailey C; Parker, David A; Trotti, Rebekah L; Zeng, Victor; Meda, Shashwath; Lencer, Rebekka; Sprenger, Andreas; Hill, S Kristian; Brown, Jennifer; Doss, Isaac; Dumas, Emily; Ivleva, Elena I; Pearlson, Godfrey; Keshavan, Matcheri; Keedy, Sarah; Gershon, Elliot; Del Re, Elisabetta; Tamminga, Carol A; McDowell, Jennifer E; Gibbons, Robert; Clementz, Brett A	November 1, 2025	Not Determined
40813865	Create Study	Differentiating biomarker features and familial characteristics of B-SNIP psychosis Biotypes.	Translational psychiatry	Parker, David A; Trotti, Rebekah L; McDowell, Jennifer E; Keedy, Sarah K; Keshavan, Matcheri S; Pearlson, Godfrey D; Gershon, Elliot S; Ivleva, Elena I; Huang, Ling-Yu; Sauer, Kodiak; Hill, S Kristian; Sweeney, John A; Tamminga, Carol A; Clementz, Brett A	August 14, 2025	Not Determined
40781464	Create Study	Exposotypes in psychotic disorders.	Scientific reports	Yassin, Walid; Kromenacker, Bryan; Green, James B; Tamminga, Carol A; Del Re, Elisabetta C; Seif, Pegah; Xia, Cuihua; Alliey-Rodriguez, Ney; Gershon, Elliot S; Clementz, Brett A; Pearlson, Godfrey D; Keedy, Sarah K; Ivleva, Elena I; Hill, Scott Kristian; McDowell, Jennifer E; Keshavan, Matcheri S	August 8, 2025	Not Determined
40603295	Create Study	Neural fingerprints of data driven cognitive subtypes across the psychosis spectrum: a B-SNIP study.	Translational psychiatry	Meda, Shashwath A; Dykins, Madison M; Hill, Scot K; Clementz, Brett A; Keedy, Sarah K; McDowell, Jennifer E; Ivleva, Elena I; Gershon, Elliot S; Keshavan, Matcheri S; Tamminga, Carol; Pearlson, Godfrey D	July 2, 2025	Not Determined
40432343	Create Study	Impact of Polygenic Interactions With Anticholinergic Burden on Cognition and Brain Structure in Psychosis Spectrum Disorders.	The American journal of psychiatry	Zhang, Lusi; Ivleva, Elena I; Parker, David A; Hill, Scot K; Lizano, Paulo L; Keefe, Richard S E; Keedy, Sarah K; McDowell, Jennifer E; Pearlson, Godfrey D; Clementz, Brett A; Keshavan, Matcheri S; Gershon, Elliot S; Tamminga, Carol A; Sweeney, John A; Bishop, Jeffrey R	August 1, 2025	Not Determined
39990589	Create Study	Neuroanatomical Deficits in Visual Cortex Subregions of Individuals with Psychosis Spectrum Disorders linked to Symptoms, Cognition, and Childhood Trauma.	medRxiv : the preprint server for health sciences	Türközer, Halide Bilge; Zeng, Victor; Hoang, Dung; Sritharan, Jothini; Iska, Neha; Ivleva, Elena I; Clementz, Brett A; Pearlson, Godfrey D; Keedy, Sarah; Gershon, Elliot S; Tamminga, Carol A; Keshavan, Matcheri S; Lizano, Paulo	February 13, 2025	Not Determined
39777534	Create Study	Evaluating the Exposome Score for Schizophrenia in a Transdiagnostic Psychosis Cohort: Associations With Psychosis Risk, Symptom Severity, and Personality Traits.	Schizophrenia bulletin	Kromenacker, Bryan; Yassin, Walid; Keshavan, Matcheri; Parker, David; Thakkar, Vishal J; Pearlson, Godfrey; Keedy, Sarah; McDowell, Jennifer; Gershon, Elliot; Ivleva, Elena; Hill, S Kristian; Clementz, Brett A; Tamminga, Carol A	September 8, 2025	Not Determined
39709506	Create Study	Genetic analysis of psychosis Biotypes: shared Ancestry-adjusted polygenic risk and unique genomic associations.	Molecular psychiatry	Xia, Cuihua; Alliey-Rodriguez, Ney; Tamminga, Carol A; Keshavan, Matcheri S; Pearlson, Godfrey D; Keedy, Sarah K; Clementz, Brett; McDowell, Jennifer E; Parker, David; Lencer, Rebekka; Hill, S Kristian; Bishop, Jeffrey R; Ivleva, Elena I; Wen, Cindy; Dai, Rujia; Chen, Chao; Liu, Chunyu; Gershon, Elliot S	June 1, 2025	Not Determined
39677452	Create Study	Genetic Analysis of Psychosis Biotypes: Shared Ancestry-Adjusted Polygenic Risk and Unique Genomic Associations.	medRxiv : the preprint server for health sciences	Xia, Cuihua; Alliey-Rodriguez, Ney; Tamminga, Carol A; Keshavan, Matcheri S; Pearlson, Godfrey D; Keedy, Sarah K; Clementz, Brett; McDowell, Jennifer E; Parker, David; Lencer, Rebekka; Hill, S Kristian; Bishop, Jeffrey R; Ivleva, Elena I; Wen, Cindy; Dai, Rujia; Chen, Chao; Liu, Chunyu; Gershon, Elliot S	December 8, 2024	Not Determined
39032429	Create Study	Gyrification across psychotic disorders: A bipolar-schizophrenia network of intermediate phenotypes study.	Schizophrenia research	Rychagov, Nicole; Del Re, Elisabetta C; Zeng, Victor; Oykhman, Efim; Lizano, Paulo; McDowell, Jennifer; Yassin, Walid; Clementz, Brett A; Gershon, Elliot; Pearlson, Godfrey; Sweeney, John A; Tamminga, Carol A; Keshavan, Matcheri S	September 1, 2024	Not Determined
38357733	Create Study	Enlarged pituitary gland volume: a possible state rather than trait marker of psychotic disorders.	Psychological medicine	Guimond, Synthia; Alftieh, Ahmad; Devenyi, Gabriel A; Mike, Luke; Chakravarty, M Mallar; Shah, Jai L; Parker, David A; Sweeney, John A; Pearlson, Godfrey; Clementz, Brett A; Tamminga, Carol A; Keshavan, Matcheri	June 1, 2024	Not Determined
38311290	Create Study	Pons-to-Cerebellum Hypoconnectivity Along the Psychosis Spectrum and Associations With Sensory Prediction and Hallucinations in Schizophrenia.	Biological psychiatry. Cognitive neuroscience and neuroimaging	Abram, Samantha V; Hua, Jessica P Y; Nicholas, Spero; Roach, Brian; Keedy, Sarah; Sweeney, John A; Mathalon, Daniel H; Ford, Judith M	July 1, 2024	Not Determined
38260530	Create Study	Differentiating Biomarker Features and Familial Characteristics of B-SNIP Psychosis Biotypes.	Research square	Parker, David; Trotti, Rebekah; McDowell, Jennifer; Keedy, Sarah; Keshavan, Matcheri; Pearlson, Godfrey; Gershon, Elliot; Ivleva, Elena; Huang, Ling-Yu; Sauer, Kodiak; Hill, Scot; Sweeny, John; Tamminga, Carol; Clementz, Brett	January 5, 2024	Not Determined
38163279	Create Study	Manual Segmentation of the Human Choroid Plexus Using Brain MRI.	Journal of visualized experiments : JoVE	Bannai, Deepthi; Cao, Yuan; Keshavan, Matcheri; Reuter, Martin; Lizano, Paulo	December 15, 2023	Not Determined
37844289	Create Study	Multimodal Neuroimaging Summary Scores as Neurobiological Markers of Psychosis.	Schizophrenia bulletin	Rodrigue, Amanda L; Hayes, Rebecca A; Waite, Emma; Corcoran, Mary; Glahn, David C; Jalbrzikowski, Maria	July 27, 2024	Not Determined
37776647	Create Study	Double dissociation between P300 components and task switch error type in healthy but not psychosis participants.	Schizophrenia research	Huang, Ling-Yu; Parker, David A; Ethridge, Lauren E; Hamm, Jordan P; Keedy, Sarah S; Tamminga, Carol A; Pearlson, Godfrey D; Keshavan, Matcheri S; Hill, S Kristian; Sweeney, John A; McDowell, Jennifer E; Clementz, Brett A	November 1, 2023	Not Determined
37563219	Create Study	Supervised machine learning classification of psychosis biotypes based on brain structure: findings from the Bipolar-Schizophrenia network for intermediate phenotypes (B-SNIP).	Scientific reports	Koen, Joshua D; Lewis, Leslie; Rugg, Michael D; Clementz, Brett A; Keshavan, Matcheri S; Pearlson, Godfrey D; Sweeney, John A; Tamminga, Carol A; Ivleva, Elena I	August 10, 2023	Not Determined
37506949	Create Study	Peripheral inflammatory subgroup differences in anterior Default Mode network and multiplex functional network topology are associated with cognition in psychosis.	Brain, behavior, and immunity	Lizano, Paulo; Kiely, Chelsea; Mijalkov, Mite; Meda, Shashwath A; Keedy, Sarah K; Hoang, Dung; Zeng, Victor; Lutz, Olivia; Pereira, Joana B; Ivleva, Elena I; Volpe, Giovanni; Xu, Yanxun; Lee, Adam M; Rubin, Leah H; Kristian Hill, S; Clementz, Brett A; Tamminga, Carol A; Pearlson, Godfrey D; Sweeney, John A; Gershon, Elliot S; Keshavan, Matcheri S; Bishop, Jeffrey R	November 1, 2023	Not Determined
37121219	Create Study	Emotional scene processing in biotypes of psychosis.	Psychiatry research	Trotti, R L; Parker, D A; Sabatinelli, D; Keshavan, M S; Keedy, S K; Gershon, E S; Pearlson, G D; Hill, S K; Tamminga, C A; McDowell, J E; Clementz, B A	June 1, 2023	Not Determined
37095352	Create Study	Characterization of the extracellular free water signal in schizophrenia using multi-site diffusion MRI harmonization.	Molecular psychiatry	Cetin-Karayumak, Suheyla; Lyall, Amanda E; Di Biase, Maria A; Seitz-Holland, Johanna; Zhang, Fan; Kelly, Sinead; Elad, Doron; Pearlson, Godfrey; Tamminga, Carol A; Sweeney, John A; Clementz, Brett A; Schretlen, David; Stegmayer, Katharina; Walther, Sebastian; Lee, Jungsun; Crow, Tim; James, Anthony; Voineskos, Aristotle; Buchanan, Robert W; Szeszko, Philip R; Malhotra, Anil K; Keshavan, Matcheri; Shenton, Martha E; Rathi, Yogesh; Pasternak, Ofer; Kubicki, Marek	May 1, 2023	Not Determined
36989667	Create Study	Characterization of childhood trauma, hippocampal mediation and Cannabis use in a large dataset of psychosis and non-psychosis individuals.	Schizophrenia research	Del Re, Elisabetta C; Yassin, Walid; Zeng, Victor; Keedy, Sarah; Alliey-Rodriguez, Ney; Ivleva, Elena; Hill, Scott; Rychagov, Nicole; McDowell, Jennifer E; Bishop, Jeffrey R; Mesholam-Gately, Raquelle; Merola, Giovanni; Lizano, Paulo; Gershon, Elliot; Pearlson, Godfrey; Sweeney, John A; Clementz, Brett; Tamminga, Carol; Keshavan, Matcheri	May 1, 2023	Not Determined
36965362	Create Study	Peripheral inflammation is associated with impairments of inhibitory behavioral control and visual sensorimotor function in psychotic disorders.	Schizophrenia research	Zhang, Lusi; Lizano, Paulo; Xu, Yanxun; Rubin, Leah H; Lee, Adam M; Lencer, Rebekka; Reilly, James L; Keefe, Richard S E; Keedy, Sarah K; Pearlson, Godfrey D; Clementz, Brett A; Keshavan, Matcheri S; Gershon, Elliot S; Tamminga, Carol A; Sweeney, John A; Hill, S Kristian; Bishop, Jeffrey R	May 1, 2023	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	2,415	12/31/2016	2,415	04/30/2017	2,415	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	1,070	12/31/2016	1,070	04/30/2017	1,070	Approved
Medical History	2,415	12/02/2012	2,415	09/28/2013	2,415	Approved
Eye Tracking	1,499	01/31/2017	1,499	05/31/2017	1,499	Approved
Demographics	2,415	12/31/2016	2,415	04/30/2017	2,415	Approved
Positive and Negative Syndrome Scale (PANSS)	967	12/31/2016	967	04/30/2017	967	Approved
Social Functioning Scale (SFS)	2,156	12/31/2016	2,156	04/30/2017	2,156	Approved
Wechsler Memory Scale, Third Edition (WMS-III)	1,906	12/31/2016	1,906	04/30/2017	1,906	Approved
Medications	1,713	12/31/2016	1,713	04/30/2017	1,713	Approved
Brief Assessment of Cognition	1,926	12/31/2016	1,926	04/30/2017	1,926	Approved
Wide Range Achievement Test	2,167	12/31/2016	2,167	04/30/2017	2,167	Approved
Structured Interview for DSM IV Personality	1,286	12/31/2016	1,286	04/30/2017	1,286	Approved
Clinical Trials: Inclusion/Exclusion Criteria	2,415	12/31/2016	2,415	04/30/2017	2,415	Approved
Imaging (Structural, fMRI, DTI, PET, microscopy)	1,098	01/31/2017	1,098	05/31/2017	1,098	Approved
Family History Form	2,230	12/31/2016	2,230	04/30/2017	2,230	Approved
Barratt Impulsivity Scale	2,160	12/31/2016	2,160	04/30/2017	2,160	Approved
Psychosocial Interview	2,295	12/31/2016	2,295	04/30/2017	2,295	Approved
EEG	1,459	01/31/2017	1,532	05/31/2017	1,532	Approved
Montgomery-Asberg Depression Rating Scale	1,083	12/31/2016	1,083	04/30/2017	1,083	Approved
Lethality Scale	359	12/31/2016	359	04/30/2017	359	Approved
Akiskal Temperament Scale	1,314	12/31/2016	1,314	04/30/2017	1,314	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
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.	2410/6514	Secondary Analysis	Shared
The Frequency of Symptom-Based Phenotypes of Mental Disorders is Long-Tailed	10.15154/1522637	The heterogeneity of symptoms among individuals diagnosed with the same mental disorder has been blamed to hinder research in mental health and the development of effective treatments. Although widely acknowledged as problematic, the characteristics of this heterogeneity are largely unknown. We assessed the frequency of symptom phenotypes across a variety of clinical and non-clinical populations and found a consistent, long-tailed distribution. This distribution represents a mixture of a few very commonly expressed phenotypes and the sum of many, each only rarely displayed ones. As a consequence, the non-normality of this distribution induces a systematic bias, affecting all research and treatments relying on a symptom-based definition of mental disorders.	967/5743	Secondary Analysis	Shared
A new multimodality fusion classification approach to explore the uniqueness of schizophrenia and autism spectrum disorder	10.15154/1528639	Schizophrenia (SZ) and autism spectrum disorder (ASD) sharing overlapping symptoms have a long history of diagnostic confusion. It is unclear what their differences at a brain level are. Here, we propose a multimodality fusion classification approach to investigate their divergence in brain function and structure. Using brain functional network connectivity (FNC) calculated from resting-state fMRI data and gray matter volume (GMV) estimated from sMRI data, we classify the two disorders using the main data (335 SZ and 380 ASD patients) via an unbiased 10-fold cross-validation pipeline, and also validate the classification generalization ability on an independent cohort (120 SZ and 349 ASD patients). The classification accuracy reached up to 83.08% for the testing data and 72.10% for the independent data, significantly better than the results from using the single-modality features. The discriminative FNCs that were automatically selected primarily involved the sub-cortical, default mode, and visual domains. Interestingly, all discriminative FNCs relating to the default mode network showed an intermediate strength in healthy controls (HCs) between SZ and ASD patients. Their GMV differences were mainly driven by the frontal gyrus, temporal gyrus, and insula. Regarding these regions, the mean GMV of HC fell intermediate between that of SZ and ASD, and ASD showed the highest GMV. The middle frontal gyrus was associated with both functional and structural differences. In summary, our work reveals the unique neuroimaging characteristics of SZ and ASD that can achieve high and generalizable classification accuracy, supporting their potential as disorder-specific neural substrates of the two entwined disorders.	2440/2440	Secondary Analysis	Shared
A novel neighborhood rough set-based feature selection method and its application to biomarker identification of schizophrenia	10.15154/1528640	Feature selection can disclose biomarkers of mental disorders that have unclear biological mechanisms. Although neighborhood rough set (NRS) has been applied to discover important sparse features, it has hardly ever been utilized in neuroimaging-based biomarker identification, probably due to the inadequate feature evaluation metric and incomplete information provided under a single-granularity. Here, we propose a new NRS-based feature selection method and successfully identify brain functional connectivity biomarkers of schizophrenia (SZ) using functional magnetic resonance imaging (fMRI) data. Specifically, we develop a new weighted metric based on NRS combined with information entropy to evaluate the capacity of features in distinguishing different groups. Inspired by multi-granularity information maximization theory, we further take advantage of the complementary information from different neighborhood sizes via a multi-granularity fusion to obtain the most discriminative and stable features. For validation, we compare our method with six popular feature selection methods using three public omics datasets as well as resting-state fMRI data of 393 SZ patients and 429 healthy controls. Results show that our method obtained higher classification accuracies on both omics data (100.0%, 88.6%, and 72.2% for three omics datasets, respectively) and fMRI data (93.9% for main dataset, and 76.3% and 83.8% for two independent datasets, respectively). Moreover, our findings reveal biologically meaningful substrates of SZ, notably involving the connectivity between the thalamus and superior temporal gyrus as well as between the postcentral gyrus and calcarine gyrus. Taken together, we propose a new NRS-based feature selection method that shows the potential of exploring effective and sparse neuroimaging-based biomarkers of mental disorders.	2440/2440	Secondary Analysis	Shared
More reliable biomarkers and more accurate prediction for mental disorders using a label-noise filtering-based dimensional prediction method	10.15154/278d-j753	Neuroimaging techniques combined with advanced artificial intelligence (AI) methods provide unique opportunities to complement diagnoses of mental disorders. However, most AI-based neuroimaging findings could be controversial due to incomplete matching between diagnostic labels and neuroimaging measures. To address the issue, we propose a label-noise filtering-based dimensional prediction (LAMP) method for studying mental disorders, which can identify reliable biomarkers and aid in accurate prediction using neuroimaging data by automatically filtering out the negative effects of indistinctive subjects from a neuroimaging perspective. In our work, typical subjects whose diagnostic labels align with neuroimaging measures are first identified by a label-noise filtering model and then used to construct a dimensional prediction model to score the independent subjects. Using cross-validation on large-scale functional magnetic resonance imaging data of schizophrenia patients and healthy controls acquired from four datasets (n = 1,245), significantly improved alignment between labels and measures of typical subjects is validated. Importantly, dimensional scores for independent subjects based on our model result in more separable relabeled groups with 31.89% improvement on average in classification accuracy. Notably, we identified stable abnormal parietal lobe-occipital gyrus and paracentral lobule-cerebellum connectivity in schizophrenia. In short, the proposed method compatibly integrates clinical diagnosis and neuroimaging to explore reliable biomarkers and characterize the underlying continuous changes in the brain, potentially assisting in accurate diagnostics.	2440/2440	Secondary Analysis	Shared
Grey matter volume, psychotic disorders, and heredity of alcohol misuse: Reconceptualization through B-SNIP Biotypes.	10.15154/1519195	Background. Co-morbidity of psychotic disorders and alcohol misuse has significant consequences for diagnosis and treatment, and yet, this topic remains incompletely understood. Further examination of the intersection of predilection by way of family history and critical brain networks has the potential to increase understanding. In the current paper, neuroanatomical MRI data were used to further understand the biomarker of total and regional grey matter volume (GMV) in relation to the Bipolar and Schizophrenia Network for Intermediate Phenotypes (B-SNIP) Biotypes and their interaction with family history of alcohol misuse (FH-AUD). Methods. FH-AUD, Biotype, and demographic variables were used in hierarchical regression analyses to predict total GMV and GM values in the central executive and default mode network regions in psychosis probands (n = 347) and their first-degree relatives (n = 346) separately. Results. Effects of FH-AUD and its interaction with Biotype level were specifically observed in one brain region relevant to cognitive dysfunction. Conclusions. Results reveal the relevance of a positive family history of alcohol misuse to the clinical status of persons diagnosed with psychotic disorder, particularly as related to a cognitive brain network, and point to the need for more research on the mechanisms of interaction and implications for risk and vulnerability to symptoms of severe psychopathology.	2417/2417	Secondary Analysis	Shared
A Generative Approach to Harmonising Multi-Site Functional Connectivity Networks	10.15154/0eqf-td03	Functional brain connectivity is central to neurodevelopment and mental health. However, the magnetic resonance imaging (MRI) data from which functional connectivity is estimated often exhibit substantial differences depending on the scanning site at which data was acquired. Removing these site effects is a key challenge for the field, given the routine need to combine data from different sites to increase statistical power and improve the generalisability of results. Here we apply Generative Adversarial Networks (GANs) using Multi-Task Learning with Random Loss Weighting to harmonise multi-site functional connectivity data, whilst retaining the information required to perform a phenotypic classification task of interest, chosen here as distinguishing patients with psychotic disorders from a control group. Additionally, we assess the GAN’s ability to preserve sex differences, a biological signal it was not directly trained to preserve. After GAN harmonisation, we obtained, on average, a mean site classification accuracy of 32%, lower (better harmonisation performance) than pre-harmonisation (74%) and following harmonisation with ComBat (40%). For diagnostic group classification, we obtained, on average, a mean diagnostic group classification accuracy of 62%, compared to 59% with the raw data and 59% post-ComBat harmonisation. However, harmonisation by the GAN reduced our ability to predict sex from 60% (raw and ComBat-harmonised data) to 52%, suggesting the GAN may struggle to retain biological signals it was not directly trained to preserve. Ultimately, using deep learning to remove site effects could enable Psychiatric phenotypes to be predicted with increased accuracy. However, further work is required to develop models that generalise to multiple phenotypes.	970/970	Secondary Analysis	Shared
comparing EEG metrics during eyes closed versus eyes open rest in schizophrenia	10.15154/1528589	Understanding the complex relationship between brain dynamics and mental disorders has proved difficult. Sample sizes have often been small, and brain dynamics have often been evaluated in only one state. Here, data obtained from the NIMH data archive were used to create a sample of 800 individuals with both eyes open and eyes closed resting state EEG data. All data were submitted to a standard pipeline to extract power spectra, peak alpha frequency, the slope of the 1/f curve, multi scale sample entropy, phase amplitude coupling, and intersite phase clustering. These data along with the survey data collected at the time of data collection form a valuable resource for interogating the relationship between brain state changes and schizophrenia symptoms.	927/927	Secondary Analysis	Shared
Investigating possible biomarkers of autism in resting EEG	10.15154/1528473	There are no clinically useful biomarkers of autism spectrum disorder (ASD). Electroencephalogram (EEG) can measure ongoing brain dynamics using cheap and widely available technology and is minimally invasive. As such, any measurement drived from EEG that is capable of serving as a biomarker for ASD would be hugely beneficial. Previous research has been conflicting and a large list of EEG measures have been suggested.	9/771	Secondary Analysis	Shared
Mapping data-driven transdiagnostic symptom dimensions to neural circuits	10.15154/rjp0-b535	Variation in symptoms should be reflected as variation in the neural circuits that give rise to them. Prior work suggested that a low-dimensional quantifications of psychosis-spectrum disorder (PSD) symptom variance can produce robust brain-behavioral associations. Here we test if iterative optimization of the low dimensional PSD symptoms via non-negative matrix factorization (NMF) produces a robust, reproducible and more clinically actionable brain-behavioral solution.	393/691	Secondary Analysis	Shared
Multimodal Neuroimaging Summary Scores as Neurobiological Markers of Psychosis	10.15154/9ytt-rd87	Background and hypothesis: Structural brain alterations are well-established features of schizophrenia but they do not effectively predict disease/disease risk. Similar to polygenic risk scores in genetics, we integrated multifactorial aspects of brain structure into a summary "Neuroscore" and examined its potential as a marker of disease. Study design: We extracted measures from T1-weighted scans and diffusion tensor imaging (DTI) models from three studies with schizophrenia and healthy individuals. We calculated individual-level summary scores (Neuroscores) for T1-weighted and DTI measures and a combined score (Multimodal Neuroscore-MM). We assessed each score's ability to differentiate schizophrenia cases from controls and its relationship to clinical symptomatology, intelligence quotient (IQ), and medication dosage. We assessed Neuroscore specificity by performing all analyses in a more inclusive psychosis sample and by using scores generated from MDD effect sizes. Study results: All Neuroscores significantly differentiated schizophrenia cases from controls (T1 d = 0.56, DTI d = 0.29, MM d = 0.64) to a greater degree than individual brain regions. Higher Neuroscores (ie, increased liability) were associated with lower IQ (T1 β = -0.26, DTI β = -0.15, MM β = -0.30). Higher T1-weighted Neuroscores were associated with higher positive and negative symptom severity (Positive β = 0.21, Negative β = 0.16); Higher Multimodal Neuroscores were associated with higher positive symptom severity (β = 0.30). SZ Neuroscores outperformed MDD Neuroscores in predicting IQ (T1: z = 3.5, q = 0.0007; MM: z = 1.8, q = 0.05). Conclusions: Neuroscores are a step toward leveraging widespread structural brain alterations in psychosis to identify robust neurobiological markers of disease. Future studies will assess ways to improve neuroscore calculation, including developing the optimal methods to calculate neuroscores and considering disorder overlap.	657/657	Secondary Analysis	Shared
Development of EEG dynamics throughout the lifespan	10.15154/1528600	Combining data from across several datasets available on the NIMH data repository, multiple metrics of EEG dynamics were examined in a large cross sectional sample of healthy participants from across the lifespan. The goal was to examine changes in brain dynamics that occur across development.	171/551	Secondary Analysis	Shared

* Data not on individual level

helpcenter.collection.associated-studies-tab

Collection - Associated Studies

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

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

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

Frequently Asked Questions

How do I associate a study to my collection?

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

Glossary

Associated Studies Tab

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

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