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	Illumina Infinium PsychArray BeadChip Assay	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:

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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

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Explanation must be between 20 and 200 characters in length.

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UNC/UMN Baby Connectome Project #2848 Collection State:Shared

General
Experiments (1)
Shared Data
Publications (71)
Data Expected (18)
Associated Studies (3)

Collection Title	Collection Investigators	Collection Description
Collection Title:	UNC/UMN Baby Connectome Project
Collection Investigators:	Jed Elison, Weili Lin
Collection Description:	This application is in response to the RFA-MH-16-160, entitled Lifespan Human Connectome Project (HCP): Baby Connectome. Investigators at The University of North Carolina at Chapel Hill (UNC) and The University of Minnesota (UMN) will join forces to accomplish the goals outlined by this RFA. The team at UNC has over 10 years of experience in recruiting and imaging typically developing and at-risk children, scanning over 1000 children from birth to five years1-40. Well established infrastructure at the Biomedical Research Imaging Center (BRIC) at UNC and Center for Magnetic Resonance Research (CMRR) at UMN are in place to recruit and retain pediatric subjects and facilitate the coordination of pediatric imaging studies. Our past and ongoing studies for imaging children (birth five years of age) without sedation have achieved an overall success rate of 81% and attrition rate of 29.3%. Our track record demonstrates that we possess the critical and essential components to successfully conduct longitudinal pediatric imaging studies focusing on early brain development, a critically-important aspect of this RFA. Our ability to recruit, retain, and image non-sedated, typically developing children is further strengthened by our image analysis team, which has developed novel image analysis tools specifically for early brain development. The expertise at UNC is complementary to and strengthened by the expertise of the team at UMN. The CMRR at UMN has been one of the leading groups in the HCP project and has developed novel MR imaging approaches to dramatically shorten data acquisition time. Furthermore, the team at UMN has extensive experience in behavioral and cognitive studies of early child development. Together, our combined team is well positioned to accomplish the goals of this RFA. To this end, a total of 500 typically developing children between birth and five years of age will be recruited across two data collection sites in a sequential cohort, accelerated longitudinal study design. The participants are divided into two main groups, longitudinal (n=285) and cross-sectional (n=215) groups, respectively. This hybrid longitudinal and cross-sectional design enables detailed characterization of early brain development from both brain structural/functional using MRI and behavioral aspects using behavioral assessments. All of the acquired images and behavioral assessments will undergo extensive quality assurance and control processes to ensure that high quality data is obtained and transferred to the Central Connectome Facility at Washington University. In addition, we will integrate novel image analysis tools, developed by our team onto the existing HCP pipelines.
Data Repository:	Connectome Coordination Facility
Permission Group:
Collection Creation Date:	01/31/2018
NIH Research Initiative:	Human Connectome Project (HCP)
Collection Phase:	Funding Completed
Collection Sub-Phase:	Close Out
Collection State:	Shared
Blinded Clinical Trial:	No
Total Funded Amount:	$3,374,422.00
Subjects Shared:	399

{"values":[]}

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Funding Sources:

Funding Source Name	Funding Source URL
NIH - Extramural	None

Supporting Documentation:

Grant Information:

Project Number	Project Title	Start Date	End Date	Planned Enrollment	Actual Enrollment	Organization	Funds Obligated
U01MH110274-01	UNC/UMN Baby Connectome Project	09/01/2016	05/31/2020	534	468	UNIV OF NORTH CAROLINA CHAPEL HILL	$3,374,422.00

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 Status: The visibility status of an NDA Collection. Collection Status can be Shared or Private. Collections in Shared status are visible to all users and can be searched in the NDA Query Tool. Private Collections are not visible to NDA users. The Status of an NDA Collection only affects the visibility of information about the Collection (metadata) and does not relate to the status of the record-level research data in the NDA Collection.

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 and marked as Shared, an email notification will automatically be sent to the PI(s) of the grant(s) associated with the Collection to notify them.
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 State

The Collection State indicates whether the Collection is viewable and searchable. Collections can be either Private, Shared, or an Ongoing Study. A Collection that is shared does not necessarily have shared data as the Collection State and state of data are independent of each other. This field can be edited by Collection users with Administrative Privileges and the Program Officer as listed in eRA (for NIH funded grants). Changes must be saved by clicking the Save button at the bottom of the page.
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
1414	BCP Resting state	10/11/2019	Approved	fMRI

helpcenter.collection.experiments-tab

Collection - Experiments

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

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

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

Frequently Asked Questions

Can an Experiment be associated with more than one Collection?
Yes -see the “Copy” button in the bottom left when viewing an experiment. There are two actions that can be performed via this button:
1. Copy the experiment with intent for modifications.
2. Associate the experiment to the collection. No modifications can be made to the experiment.

Glossary

Experiment Status

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

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

Contact NDA Help Desk

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

Shared Data

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

Shared Data:

Title	Type	Number of Subjects
Child Behavior Checklist (CBCL) 1-5	Clinical Assessments	181
Children's Social Understanding Scale	Clinical Assessments	129
Dimensional Joint Attention Assessment	Clinical Assessments	166
Early Childhood Behavior Questionnaire	Clinical Assessments	97
Image	Imaging	343
Infant Behavior Questionnaire Revised	Clinical Assessments	169
Infant-Toddler Social-Emotional Assessment	Clinical Assessments	228
Life Events Survey	Clinical Assessments	275
MacArthur-Bates CDI - Words and Gestures Form	Clinical Assessments	185
MacArthur-Bates CDI - Words and Sentences Form	Clinical Assessments	88
Minnesota Executive Function Scale	Clinical Assessments	148
Mullen Scales of Early Learning	Clinical Assessments	305
Repetitive Behavior Scales - Early Childhood Supplement	Clinical Assessments	248
Research Subject	Clinical Assessments	281
SRS-2. Adult, Preschool and School Age	Clinical Assessments	93
State-Trait Anxiety Inventory for Adults	Clinical Assessments	246
Strengths and Difficulties Questionnaire	Clinical Assessments	158
Video-Referenced Ratings of Reciprocal Social Behavior	Clinical Assessments	189
Vineland-II - Parent and Caregiver Rating Form (2005)	Clinical Assessments	206

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

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
36746299	Create Study	An attention-based context-informed deep framework for infant brain subcortical segmentation.	NeuroImage	Chen, Liangjun; Wu, Zhengwang; Zhao, Fenqiang; Wang, Ya; Lin, Weili; Wang, Li; Li, Gang	April 1, 2023	Not Determined
36404295	Create Study	Responding to joint attention as a developmental catalyst: Longitudinal associations with language and social responsiveness.	Infancy : the official journal of the International Society on Infant Studies	Lasch, Carolyn; Carlson, Stephanie M; Elison, Jed T	March 1, 2023	Not Determined
36053250	Create Study	Reference-Relation Guided Autoencoder with Deep CCA Restriction for Awake-to-Sleep Brain Functional Connectome Prediction.	Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention	Hu, Dan; Yin, Weiyan; Wu, Zhengwang; Chen, Liangjun; Wang, Li; Lin, Weili; Li, Gang; UNC/UMN Baby Connectome Project Consortium	September 1, 2021	Not Determined
36053245	Create Study	Learning 4D Infant Cortical Surface Atlas with Unsupervised Spherical Networks.	Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention	Zhao, Fenqiang; Wu, Zhengwang; Wang, Li; Lin, Weili; Xia, Shunren; Li, Gang; UNC/UMN Baby Connectome Project Consortium	September 1, 2021	Not Determined
35994035	Create Study	A Deep Network for Joint Registration and Parcellation of Cortical Surfaces.	Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention	Zhao, Fenqiang; Wu, Zhengwang; Wang, Li; Lin, Weili; Xia, Shunren; Li, Gang; UNC/UMN Baby Connectome Project Consortium	September 1, 2021	Not Determined
35967125	Create Study	Harmonized neonatal brain MR image segmentation model for cross-site datasets.	Biomedical signal processing and control	Chen, Jian; Sun, Yue; Fang, Zhenghan; Lin, Weili; Li, Gang; Wang, Li; UNC UMN Baby Connectome Project Consortium	August 1, 2021	Not Determined
35869272	Create Study	Altered neural flexibility in children with attention-deficit/hyperactivity disorder.	Molecular psychiatry	Yin, Weiyan; Li, Tengfei; Mucha, Peter J; Cohen, Jessica R; Zhu, Hongtu; Zhu, Ziliang; Lin, Weili	November 1, 2022	Not Determined
35751994	Create Study	Resting-state functional connectivity identifies individuals and predicts age in 8-to-26-month-olds.	Developmental cognitive neuroscience	Kardan, Omid; Kaplan, Sydney; Wheelock, Muriah D; Feczko, Eric; Day, Trevor K M; Miranda-Domínguez, Óscar; Meyer, Dominique; Eggebrecht, Adam T; Moore, Lucille A; Sung, Sooyeon; Chamberlain, Taylor A; Earl, Eric; Snider, Kathy; Graham, Alice; Berman, Marc G; Uğurbil, Kamil; Yacoub, Essa; Elison, Jed T; Smyser, Christopher D; Fair, Damien A; Rosenberg, Monica D	August 1, 2022	Not Determined
35720673	Create Study	Local Extraction of Extra-Axial CSF from structural MRI.	Proceedings of SPIE--the International Society for Optical Engineering	Deddah, Tahya; Styner, Martin; Prieto, Juan	February 1, 2022	Not Determined
35720672	Create Study	CONTINUITY: CONnectivity Tool with INtegration of sUbcortical regions, regIstration and visualization of TractographY.	Proceedings of SPIE--the International Society for Optical Engineering	Piot, Elodie; Bagonis, Maria; Prieto, Juan C; Styner, Martin	February 1, 2022	Not Determined
35528703	Create Study	Multi-Scale Self-Supervised Learning for Multi-Site Pediatric Brain MR Image Segmentation with Motion/Gibbs Artifacts.	Machine learning in medical imaging. MLMI (Workshop)	Sun, Yue; Gao, Kun; Lin, Weili; Li, Gang; Niu, Sijie; Wang, Li	September 1, 2021	Not Determined
35337965	Create Study	Deep attentive spatio-temporal feature learning for automatic resting-state fMRI denoising.	NeuroImage	Heo, Keun-Soo; Shin, Dong-Hee; Hung, Sheng-Che; Lin, Weili; Zhang, Han; Shen, Dinggang; Kam, Tae-Eui	July 1, 2022	Not Determined
35301130	Create Study	A 4D infant brain volumetric atlas based on the UNC/UMN baby connectome project (BCP) cohort.	NeuroImage	Chen, Liangjun; Wu, Zhengwang; Hu, Dan; Wang, Ya; Zhao, Fenqiang; Zhong, Tao; Lin, Weili; Wang, Li; Li, Gang	June 1, 2022	Not Determined
35200037	Create Study	Detection of Azoxystrobin Fungicide and Metabolite Azoxystrobin-Acid in Pregnant Women and Children, Estimation of Daily Intake, and Evaluation of Placental and Lactational Transfer in Mice.	Environmental health perspectives	Hu, Wenxin; Liu, Chih-Wei; Jiménez, Jessica A; McCoy, Eric S; Hsiao, Yun-Chung; Lin, Weili; Engel, Stephanie M; Lu, Kun; Zylka, Mark J	February 1, 2022	Not Determined
35128548	Create Study	Construction of Longitudinally Consistent 4D Infant Cerebellum Atlases Based on Deep Learning.	Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention	Chen, Liangjun; Wu, Zhengwang; Hu, Dan; Pei, Yuchen; Zhao, Fenqiang; Sun, Yue; Wang, Ya; Lin, Weili; Wang, Li; Li, Gang; UNC/UMN Baby Connectome Project Consortium	September 1, 2021	Not Determined
34974250	Create Study	Dear reviewers: Responses to common reviewer critiques about infant neuroimaging studies.	Developmental cognitive neuroscience	Korom, Marta; Camacho, M Catalina; Filippi, Courtney A; Licandro, Roxane; Moore, Lucille A; Dufford, Alexander; Zöllei, Lilla; Graham, Alice M; Spann, Marisa; Howell, Brittany; FIT’NG; Shultz, Sarah; Scheinost, Dustin	February 1, 2022	Not Determined
34942363	Create Study	Filtering respiratory motion artifact from resting state fMRI data in infant and toddler populations.	NeuroImage	Kaplan, Sydney; Meyer, Dominique; Miranda-Dominguez, Oscar; Perrone, Anders; Earl, Eric; Alexopoulos, Dimitrios; Barch, Deanna M; Day, Trevor K M; Dust, Joseph; Eggebrecht, Adam T; Feczko, Eric; Kardan, Omid; Kenley, Jeanette K; Rogers, Cynthia E; Wheelock, Muriah D; Yacoub, Essa; Rosenberg, Monica; Elison, Jed T; Fair, Damien A; Smyser, Christopher D	February 15, 2022	Not Determined
34789554	Create Study	Existence of Functional Connectome Fingerprint during Infancy and Its Stability over Months.	The Journal of neuroscience : the official journal of the Society for Neuroscience	Hu, Dan; Wang, Fan; Zhang, Han; Wu, Zhengwang; Zhou, Zhen; Li, Guoshi; Wang, Li; Lin, Weili; Li, Gang; UNC/UMN Baby Connectome Project Consortium	January 19, 2022	Not Determined
34308440	Create Study	Estimating Tissue Microstructure with Undersampled Diffusion Data via Graph Convolutional Neural Networks.	Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention	Chen, Geng; Hong, Yoonmi; Zhang, Yongqin; Kim, Jaeil; Huynh, Khoi Minh; Ma, Jiquan; Lin, Weili; Shen, Dinggang; Yap, Pew-Thian; UNC/UMN Baby Connectome Project Consortium	October 1, 2020	Not Determined
34225011	Create Study	ABCnet: Adversarial bias correction network for infant brain MR images.	Medical image analysis	Chen, Liangjun; Wu, Zhengwang; Hu, Dan; Wang, Fan; Smith, J Keith; Lin, Weili; Wang, Li; Shen, Dinggang; Li, Gang; Consortium, For Unc/Umn Baby Connectome Project	August 1, 2021	Not Determined
34195699	Create Study	Tract Dictionary Learning for Fast and Robust Recognition of Fiber Bundles.	Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention	Wu, Ye; Hong, Yoonmi; Ahmad, Sahar; Lin, Weili; Shen, Dinggang; Yap, Pew-Thian; UNC/UMN Baby Connectome Project Consortium	October 1, 2020	Not Determined
34035413	Create Study	Brainwide functional networks associated with anatomically- and functionally-defined hippocampal subfields using ultrahigh-resolution fMRI.	Scientific reports	Chang, Wei-Tang; Langella, Stephanie K; Tang, Yichuan; Ahmad, Sahar; Zhang, Han; Yap, Pew-Thian; Giovanello, Kelly S; Lin, Weili	May 25, 2021	Not Determined
33784617	Create Study	S3Reg: Superfast Spherical Surface Registration Based on Deep Learning.	IEEE transactions on medical imaging	Zhao, Fenqiang; Wu, Zhengwang; Wang, Fan; Lin, Weili; Xia, Shunren; Shen, Dinggang; Wang, Li; Li, Gang	August 1, 2021	Not Determined
33569552	Create Study	Unsupervised Learning for Spherical Surface Registration.	Machine learning in medical imaging. MLMI (Workshop)	Zhao, Fenqiang; Wu, Zhengwang; Wang, Li; Lin, Weili; Xia, Shunren; Shen, Dinggang; Li, Gang; UNC/UMN Baby Connectome Project Consortium	October 1, 2020	Not Determined
33564753	Create Study	A Deep Spatial Context Guided Framework for Infant Brain Subcortical Segmentation.	Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention	Chen, Liangjun; Wu, Zhengwang; Hu, Dan; Wang, Ya; Mo, Zhanhao; Wang, Li; Lin, Weili; Shen, Dinggang; Li, Gang; UNC/UMN Baby Connectome Program Consortium	October 1, 2020	Not Determined
33057030	Create Study	Infants'' gaze exhibits a fractal structure that varies by age and stimulus salience.	Scientific reports	Stallworthy, Isabella C; Sifre, Robin; Berry, Daniel; Lasch, Carolyn; Smith, Tim J; Elison, Jed T	October 14, 2020	Not Determined
32868436	Create Study	The emergence of a functionally flexible brain during early infancy.	Proceedings of the National Academy of Sciences of the United States of America	Yin, Weiyan; Li, Tengfei; Hung, Sheng-Che; Zhang, Han; Wang, Li; Shen, Dinggang; Zhu, Hongtu; Mucha, Peter J; Cohen, Jessica R; Lin, Weili	September 22, 2020	Not Determined
32746154	Create Study	Disentangled-Multimodal Adversarial Autoencoder: Application to Infant Age Prediction With Incomplete Multimodal Neuroimages.	IEEE transactions on medical imaging	Hu, Dan; Zhang, Han; Wu, Zhengwang; Wang, Fan; Wang, Li; Smith, J Keith; Lin, Weili; Li, Gang; Shen, Dinggang	December 1, 2020	Not Determined
32746115	Create Study	Hierarchical Nonlocal Residual Networks for Image Quality Assessment of Pediatric Diffusion MRI With Limited and Noisy Annotations.	IEEE transactions on medical imaging	Liu, Siyuan; Thung, Kim-Han; Lin, Weili; Shen, Dinggang; Yap, Pew-Thian	November 1, 2020	Not Determined
32746109	Create Study	Probing Tissue Microarchitecture of the Baby Brain via Spherical Mean Spectrum Imaging.	IEEE transactions on medical imaging	Huynh, Khoi Minh; Xu, Tiantian; Wu, Ye; Wang, Xifeng; Chen, Geng; Wu, Haiyong; Thung, Kim-Han; Lin, Weili; Shen, Dinggang; Yap, Pew-Thian	November 1, 2020	Not Determined
32396089	Create Study	Real-Time Quality Assessment of Pediatric MRI via Semi-Supervised Deep Nonlocal Residual Neural Networks.	IEEE transactions on image processing : a publication of the IEEE Signal Processing Society	Liu, Siyuan; Thung, Kim-Han; Lin, Weili; Yap, Pew-Thian; Shen, Dinggang	May 8, 2020	Not Determined
32128521	Create Study	Surface-Volume Consistent Construction of Longitudinal Atlases for the Early Developing Brain.	Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention	Ahmad, Sahar; Wu, Zhengwang; Li, Gang; Wang, Li; Lin, Weili; Yap, Pew-Thian; Shen, Dinggang; UNC/UMN Baby Connectome Project Consortium	October 1, 2019	Not Determined
31930620	Create Study	Individual identification and individual variability analysis based on cortical folding features in developing infant singletons and twins.	Human brain mapping	Duan, Dingna; Xia, Shunren; Rekik, Islem; Wu, Zhengwang; Wang, Li; Lin, Weili; Gilmore, John H; Shen, Dinggang; Li, Gang	June 1, 2020	Not Determined
31279215	Create Study	XQ-SR: Joint x-q space super-resolution with application to infant diffusion MRI.	Medical image analysis	Chen, Geng; Dong, Bin; Zhang, Yong; Lin, Weili; Shen, Dinggang; Yap, Pew-Thian	October 1, 2019	Not Determined
31263805	Create Study	Registration-Free Infant Cortical Surface Parcellation using Deep Convolutional Neural Networks.	Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention	Wu, Zhengwang; Li, Gang; Li, Wang; Shi, Feng; Lin, Weili; Gilmore, John H; Shen, Dinggang	September 1, 2018	Not Determined
31115143	Create Study	Construction of 4D infant cortical surface atlases with sharp folding patterns via spherical patch-based group-wise sparse representation.	Human brain mapping	Wu, Zhengwang; Wang, Li; Lin, Weili; Gilmore, John H; Li, Gang; Shen, Dinggang	September 1, 2019	Not Determined
31071025	Create Study	Denoising of Diffusion MRI Data via Graph Framelet Matching in x-q Space.	IEEE transactions on medical imaging	Chen, Geng; Dong, Bin; Zhang, Yong; Lin, Weili; Shen, Dinggang; Yap, Pew-Thian	December 1, 2019	Not Determined
31029865	Create Study	Super-resolution reconstruction of neonatal brain magnetic resonance images via residual structured sparse representation.	Medical image analysis	Zhang, Yongqin; Yap, Pew-Thian; Chen, Geng; Lin, Weili; Wang, Li; Shen, Dinggang	July 1, 2019	Not Determined
30957107	Create Study	Dual-Domain Cascaded Regression for Synthesizing 7T from 3T MRI.	Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention	Zhang, Yongqin; Cheng, Jie-Zhi; Xiang, Lei; Yap, Pew-Thian; Shen, Dinggang	September 1, 2018	Not Determined
30716056	Create Study	Hierarchical Rough-to-Fine Model for Infant Age Prediction Based on Cortical Features.	IEEE journal of biomedical and health informatics	Hu, Dan; Wu, Zhengwang; Lin, Weili; Li, Gang; Shen, Dinggang	January 1, 2020	Not Determined
30498562	Create Study	A COMPUTATIONAL METHOD FOR LONGITUDINAL MAPPING OF ORIENTATION-SPECIFIC EXPANSION OF CORTICAL SURFACE AREA IN INFANTS.	Proceedings. IEEE International Symposium on Biomedical Imaging	Xia, Jing; Zhang, Caiming; Wang, Fan; Meng, Yu; Wu, Zhengwang; Wang, Li; Lin, Weili; Shen, Dinggang; Li, Gang	April 1, 2018	Not Determined
30472371	Create Study	MR fingerprinting enables quantitative measures of brain tissue relaxation times and myelin water fraction in the first five years of life.	NeuroImage	Chen, Yong; Chen, Meng-Hsiang; Baluyot, Kristine R; Potts, Taylor M; Jimenez, Jordan; Lin, Weili; UNC/UMN Baby Connectome Project Consortium	February 1, 2019	Not Determined
30450154	Create Study	ESTIMATION OF SHAPE AND GROWTH BRAIN NETWORK ATLASES FOR CONNECTOMIC BRAIN MAPPING IN DEVELOPING INFANTS.	Proceedings. IEEE International Symposium on Biomedical Imaging	Rekik, Islem; Li, Gang; Lin, Weili; Shen, Dinggang	April 1, 2018	Not Determined
30416672	Create Study	CONSTRUCTION OF SPATIOTEMPORAL NEONATAL CORTICAL SURFACE ATLASES USING A LARGE-SCALE DATASET.	Proceedings. IEEE International Symposium on Biomedical Imaging	Wu, Zhengwang; Li, Gang; Wang, Li; Lin, Weili; Gilmore, John H; Shen, Dinggang	April 1, 2018	Not Determined
30416670	Create Study	FETAL CORTICAL PARCELLATION BASED ON GROWTH PATTERNS.	Proceedings. IEEE International Symposium on Biomedical Imaging	Xia, Jing; Zhang, Caiming; Wang, Fan; Benkarim, Oualid M; Sanroma, Gerard; Piella, Gemma; González Balleste, Miguel A; Hahner, Nadine; Eixarch, Elisenda; Shen, Dinggang; Li, Gang	April 1, 2018	Not Determined
30316743	Create Study	Development of Amygdala Functional Connectivity During Infancy and Its Relationship With 4-Year Behavioral Outcomes.	Biological psychiatry. Cognitive neuroscience and neuroimaging	Salzwedel, Andrew P; Stephens, Rebecca L; Goldman, Barbara D; Lin, Weili; Gilmore, John H; Gao, Wei	January 1, 2019	Not Determined
30092545	Create Study	A computational method for longitudinal mapping of orientation-specific expansion of cortical surface in infants.	Medical image analysis	Xia, Jing; Wang, Fan; Meng, Yu; Wu, Zhengwang; Wang, Li; Lin, Weili; Zhang, Caiming; Shen, Dinggang; Li, Gang	October 1, 2018	Not Determined
29994137	Create Study	Sparse Multiview Task-Centralized Ensemble Learning for ASD Diagnosis Based on Age- and Sex-Related Functional Connectivity Patterns.	IEEE transactions on cybernetics	Wang, Jun; Wang, Qian; Zhang, Han; Chen, Jiawei; Wang, Shitong; Shen, Dinggang	August 1, 2019	Not Determined
29990581	Create Study	Resting-state functional MRI studies on infant brains: A decade of gap-filling efforts.	NeuroImage	Zhang, Han; Shen, Dinggang; Lin, Weili	January 15, 2019	Not Determined
29700891	Create Study	Discovering cortical sulcal folding patterns in neonates using large-scale dataset.	Human brain mapping	Meng, Yu; Li, Gang; Wang, Li; Lin, Weili; Gilmore, John H; Shen, Dinggang	September 1, 2018	Not Determined
29673965	Create Study	A review on neuroimaging studies of genetic and environmental influences on early brain development.	NeuroImage	Gao, Wei; Grewen, Karen; Knickmeyer, Rebecca C; Qiu, Anqi; Salzwedel, Andrew; Lin, Weili; Gilmore, John H	January 15, 2019	Not Determined
29578031	Create Study	The UNC/UMN Baby Connectome Project (BCP): An overview of the study design and protocol development.	NeuroImage	Howell BR, Styner MA, Gao W, Yap PT, Wang L, Baluyot K, Yacoub E, Chen G, Potts T, Salzwedel A, Li G, Gilmore JH, Piven J, Smith JK, Shen D, Ugurbil K, Zhu H, Lin W, Elison JT	March 2018	Not Determined
29574734	Create Study	Commentary: The neonatal brain and the challenge of imaging biomarkers, reflections on Batalle et al. (2018).	Journal of child psychology and psychiatry, and allied disciplines	Gilmore, John H	April 1, 2018	Not Determined
29574033	Create Study	Computational neuroanatomy of baby brains: A review.	NeuroImage	Li G, Wang L, Yap PT, Wang F, Wu Z, Meng Y, Dong P, Kim J, Shi F, Rekik I, Lin W, Shen D	March 2018	Not Determined
29568823	Create Study	Graph-Constrained Sparse Construction of Longitudinal Diffusion-Weighted Infant Atlases.	Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention	Kim J, Chen G, Lin W, Yap PT, Shen D	September 2017	Not Determined
29317597	Create Study	Preliminary evidence for genetic overlap between body mass index and striatal reward response.	Translational psychiatry	Lancaster TM, Ihssen I, Brindley LM, Linden DE	January 2018	Not Determined
29124254	Create Study	Developmental Patterns Based Individualized Parcellation of Infant Cortical Surface.	Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention	Li G, Wang L, Lin W, Shen D	September 2017	Not Determined
29124253	Create Study	Exploring Gyral Patterns of Infant Cortical Folding based on Multi-view Curvature Information.	Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention	Duan D, Xia S, Meng Y, Wang L, Lin W, Gilmore JH, Shen D, Li G	September 2017	Not Determined
28902466	Create Study	Learning-based deformable registration for infant MRI by integrating random forest with auto-context model.	Medical physics	Wei, Lifang; Cao, Xiaohuan; Wang, Zhensong; Gao, Yaozong; Hu, Shunbo; Wang, Li; Wu, Guorong; Shen, Dinggang	December 2017	Not Determined
28824362	Create Study	Test-Retest Reliability of "High-Order" Functional Connectivity in Young Healthy Adults.	Frontiers in neuroscience	Zhang, Han; Chen, Xiaobo; Zhang, Yu; Shen, Dinggang	January 1, 2017	Not Determined
28819140	Create Study	Enhancement of Perivascular Spaces in 7 T MR Image using Haar Transform of Non-local Cubes and Block-matching Filtering.	Scientific reports	Hou Y, Park SH, Wang Q, Zhang J, Zong X, Lin W, Shen D	August 2017	Not Determined
28665045	Create Study	Extraction of dynamic functional connectivity from brain grey matter and white matter for MCI classification.	Human brain mapping	Chen X, Zhang H, Zhang L, Shen C, Lee SW, Shen D	October 2017	Not Determined
28624881	Create Study	Discriminative self-representation sparse regression for neuroimaging-based alzheimer's disease diagnosis.	Brain imaging and behavior	Zhu X, Suk HI, Lee SW, Shen D	June 2017	Not Determined
28603790	Create Study	Dual-Layer Groupwise Registration for Consistent Labeling of Longitudinal Brain Images.	Machine learning in medical imaging. MLMI (Workshop)	Kim, Minjeong; Wu, Guorong; Rekik, Isrem; Shen, Dinggang	October 2016	Not Determined
28358032	Create Study	A Hierarchical Feature and Sample Selection Framework and Its Application for Alzheimer's Disease Diagnosis.	Scientific reports	An L, Adeli E, Liu M, Zhang J, Lee SW, Shen D	March 2017	Not Determined
28295833	Create Study	Can we predict subject-specific dynamic cortical thickness maps during infancy from birth?	Human brain mapping	Meng Y, Li G, Rekik I, Zhang H, Gao Y, Lin W, Shen D	June 2017	Not Determined
28284800	Create Study	Joint prediction of longitudinal development of cortical surfaces and white matter fibers from neonatal MRI.	NeuroImage	Rekik, Islem; Li, Gang; Yap, Pew-Thian; Chen, Geng; Lin, Weili; Shen, Dinggang	May 2017	Not Determined
28102945	Create Study	Learning-based deformable image registration for infant MR images in the first year of life.	Medical physics	Hu, Shunbo; Wei, Lifang; Gao, Yaozong; Guo, Yanrong; Wu, Guorong; Shen, Dinggang	January 2017	Not Determined
27798142	Create Study	Longitudinal Study of the Emerging Functional Connectivity Asymmetry of Primary Language Regions during Infancy.	The Journal of neuroscience : the official journal of the Society for Neuroscience	Emerson RW, Gao W, Lin W	October 2016	Not Determined
27380969	Create Study	Learning-based subject-specific estimation of dynamic maps of cortical morphology at missing time points in longitudinal infant studies.	Human brain mapping	Meng Y, Li G, Gao Y, Lin W, Shen D	November 2016	Not Determined
26874184	Create Study	Structural and Maturational Covariance in Early Childhood Brain Development.	Cerebral cortex (New York, N.Y. : 1991)	Geng X, Li G, Lu Z, Gao W, Wang L, Shen D, Zhu H, Gilmore JH	March 2017	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.

Data Expected	Targeted Enrollment	Initial Submission	Subjects Submitted	Initial Share	Subjects Shared	Status
Research Subject and Pedigree	500	07/15/2019	352	11/15/2019	281	Approved

You can use "Add New Data Expected" to add exsiting structures and create your project's list. However, this is also the method you can use to request new structures be created for your project. When adding the Data Expected item, if the structure already exists you can locate it and specify your dates and enrollment. To add a new structure and request it be defined in the Data Dictionary, select Upload Definition and attach the definition or material needed to create it, including manual, codebooks, forms, etc. If you have multiple files, please upload a zipped archive containing them all.

Expected dates should be selected based on the standard Data Sharing Regimen and are restricted to within date ranges based on the project start and end dates.

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
Mullen Scales of Early Learning	500	07/15/2019	348	11/15/2019	305	Approved
Life Events Checklist	500	07/15/2019	329	11/15/2019	275	Approved
Social Responsiveness Scale (SRS)	355	07/15/2019	157	11/15/2019	93	Approved
Infant Behavior Questionnaire (IBQ)	195	07/15/2019	191	11/15/2019	169	Approved
Child Behavior Checklist (CBCL)	460	07/15/2019	256	11/15/2019	181	Approved
MacArthur Bates Communicative Development Inventory	295	07/15/2019	238	11/15/2019	211	Approved
Repetitive Behavior Scale - Revised (RBS-R)	490	07/15/2019	317	11/15/2019	248	Approved
Early Childhood Behavioral Questionnaire (ECBQ)	285	07/15/2019	129	11/15/2019	97	Approved
State-Trait Anxiety Inventory for Adults	500	07/15/2019	326	11/15/2019	246	Approved
Vineland (Parent and Caregiver)	500	07/15/2019	272	11/15/2019	206	Approved
Imaging (Structural, fMRI, DTI, PET, microscopy)	460	07/15/2019	409	11/15/2019	343	Approved
Video-Referenced Ratings of Reciprocal Social Behavior	285	07/15/2019	219	11/15/2019	189	Approved
Broad Psychopathology Form	460	07/15/2019	229	11/15/2019	158	Approved
Infant-Toddler Social-Emotional Assessment	295	07/15/2019	250	11/15/2019	228	Approved
Minnesota Executive Function Scale (MEFS)	460	07/15/2019	183	11/15/2019	148	Approved
Dimensional Joint Attention Assessment (DJAA)	205	07/15/2019	189	11/15/2019	166	Approved
Children's Social Understanding Scale (CSUS)	460	07/15/2019	202	11/15/2019	129	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.

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

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

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

Frequently Asked Questions

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	AbstractFilter by Abstract	Collection/Study SubjectsFilter by Collection/Study Subjects	Data UsageFilter by Data Usage	StateFilter by State
Diffusion Basis Spectrum Imaging Neuroinflammation Metrics in Adolescents	Human obesity is related to alterations in brain structure and function. Rodent models of obesity show that diet-induced obesity causes neuroinflammation and impairment in learning and memory. In humans, non-invasive measurement of putative neuroinflammation is accomplished with diffusion-based magnetic resonance (MR) imaging. We have shown that, relative to normal-weight, obese adults have greater putative neuroinflammation, including indicators of cellularity and vasogenic edema, using diffusion basis spectrum imaging (DBSI) to model anisotropic and isotropic water diffusion in white matter tracts and regions of interest including hippocampus. Characterization of relationships between adiposity and brain structure and function in adolescents may provide insight into mechanisms underlying development of chronic overfeeding and obesity. Recently, Rapuano et al. (2020) used diffusion-based restricted spectrum imaging (RSI) to quantify putative neuroinflammation in reward-related brain regions in a large sample of 9 and 10 year old children enrolled in the Adolescent Brain Cognitive Development (ABCD) multi-site study and found that greater cellular density in the nucleus accumbens related to higher body mass index (BMI) and waist circumference at baseline and 1 year after MR imaging. We compared DBSI-measured putative neuroinflammation to those of RSI by selecting a sample of 9 and 10 year old children enrolled in the ABCD study representing a wide range of BMI categories (n=200 normal-weight; 50 overweight; 50 obese) and based on effect sizes and inclusion and exclusion criteria in the Rapuano et al. (2020) study. We hypothesize that greater neuroinflammation, as measured by DBSI, in white matter tracts and reward-related brain regions will relate to higher BMI and waist circumference at baseline and 1 year after MR imaging. Findings from the current study will provide insight into whether two different diffusion-based models yield convergent evidence regarding neuroinflammation and its relationship to adiposity in adolescents.	343/13751	Secondary Analysis	Private
Lateralization of brain activity in infants and toddlers	The cerebral lateralization of language, fine motor coordination and visuospatial abilities in humans has been studied extensively in adults, and direct relationships have been observed between the degree of skill in these domains and the degree of brain lateralization (e.g. Gotts et al., 2013). However, less is known about the developmental trajectory of brain lateralization, especially at the earliest ages. In the current study, we use fMRI data from the Baby Connectome project (Howell et al., 2019) to examine the overall organization and lateralization of resting-state brain activity in sleeping infants and toddlers from birth (0 months) through 5 years of age (N = 260 individuals). Each participant had anatomical scans (MPRAGE, T2) and 10 minutes of resting-state fMRI data, each initially aligned to an age-specific MNI template and subsequently to a common template (14-17 months of age, median age = 15 months). Organizing age into non-overlapping bins of approximately 50 participants each (0-6 mo., 7-11 mo., 12-16 mo., 17-24 mo, 25-70 mo.), the resting-state data in each bin were parcellated into networks across a range of thresholds using the InfoMap algorithm (as in Persichetti et al., 2021). Using a common threshold (top 10% of connections) that maximized split-half agreement and number of parcels within each age bin, parcellations were found to be highly similar across bins, with 4 large cortical parcels (Occipital, Lateral Temporal, Somatomotor, and Frontal) and 2 subcortical parcels (Basal Ganglia and Thalamus/Cerebellum). The only prominent changes observed across the age bins involved the differential grouping of the posterior cingulate cortex (PCC) and frontal cortex with the lateral temporal cortical parcels. At the youngest ages, these elements remain in separate parcels (0-6 mo.). From 7-11 months and older, the PCC is grouped with the lateral temporal cortex. Only at the oldest ages (25-70 months) was the frontal cortex grouped with the PCC and the lateral temporal parcels as is typical in adulthood, suggestive of the emergence of a language/speech system. Examining continuous effects of age within and across parcels (partialing head motion), prominent and widespread increases in long-range functional connectivity between parcels were observed with age, whereas increased local functional connectivity (within parcel) was only observed in occipital cortex (P<.028, q<.05 for all). Finally, the lateralization of the cortical parcels was examined using the Segregation (within-across hemisphere correlation) and Integration (within+across hemisphere correlation) metrics developed by Gotts et al. (2013). In contrast to the adult, the strongest lateralization effects were detected with the Integration metric, with left lateralization observed across ages for the PCC, Occipital, and Somatomotor parcels and right lateralization observed for the Frontal parcel. Left lateralization for the PCC parcel (which is part of the larger language system in the adult) was highly significant even for the earliest ages (0-6 mo., P=.0018, q<.05). Only the Occipital parcels exhibited lateralization with the Segregation metric (rightward) (P<.0039, q<.05 for all), and neither metric detected strong changes in lateralization over this age range. Taken together, the results suggest that much of the early changes in brain organization involve the establishment of long-range functional connections between large scale brain networks, and cerebral lateralization – while detectable from 0-6 months, differs markedly from the adult in both quantity and quality.	260/260	Secondary Analysis	Private
Leveraging artificial intelligence to develop novel tools for studying infant brain development	Little is known about the growth trajectories of brain development across the first 24-months of life because of insufficient approaches to analyze infant MRI scans especially brain segmentation. This proposal leverages artificial intelligence to address unmet technical challenges in infant brain segmentation, to delineate the growth trajectories of brain structure/function and measure their relationship with neuropsychological functions, and to predict the developmental outcomes. Results from this study will yield a crucial new tool for studying developmental neuroscience and improve our capacity to efficiently measure and identify relevant infant brain structures, function, and their role in long-term development.	50/50	Secondary Analysis	Shared

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Collection - Associated Studies

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

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

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