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1 Numbers reported are subjects by age
New Trial
New Project

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

Please select an experiment type below

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

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

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

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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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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.

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Collection Summary Collection Charts
Collection Title Collection Investigators Collection Description
HCP-D Mapping the Human Connectome During Typical Development (HCP-D)
Deanna Barch, Susan Bookheimer, Randy Buckner, Mirella Dapretto, Stephen Smith, Leah Somerville, Kathleen Thomas, David Van Essen, Essa Yacoub 
The major technological and analytical advances in human brain imaging achieved as part of the Human Connectome Projects (HCP) enable examination of structural and functional brain connectivity at unprecedented levels of spatial and temporal resolution. This information is proving crucial to our understanding of normative variation in adult brain connectivity. It is now timely to use the tools and analytical approaches developed by the HCP to understand how structural and functional wiring of the brain develops. Using state-of-the art HCP imaging approaches will allow investigators to push our currently limited understanding of normative brain development to new levels. This knowledge will critically inform prevention and intervention efforts targeting well known public health concerns (e.g., neurological and psychiatric disorders, poverty). The majority of developmental connectivity studies to date have used fairly coarse resolution, have not been multi-modal in nature, and few studies have used comparable methods to assess individuals across a sufficiently wide age range to truly capture developmental processes (e.g., early childhood through adolescence). Here we propose a consortium of five sites (Harvard, Oxford, UCLA, University of Minnesota, Washington University), with extensive complimentary expertise in brain imaging and neural development, including many of the investigators from the adult and pilot lifespan HCP efforts. Our synergistic integration of advances from the HARVARD-MGH and WU-MINN-OXFORD HCPs with cutting edge expertise in child and adolescent brain development will enable major advances in our understanding of the normative development of human brain connectivity. The resultant unique resource will provide rich, multimodal data on several biological and cognitive constructs that are of critical importance to health and well-being across this age range and allow a wide range of investigators in the community to gain new insights about brain development and connectivity. Aim 1 will be to optimize existing HCP Lifespan Pilot project protocols on the widely available Prisma platform to respect practical constraints in studying healthy children and adolescents over a wide age range and will also collect a matched set of data on the original Skyra and proposed Prisma HCP protocols to serve as a linchpin between the past and present efforts. Aim 2 will be to collect 1500 high quality neuroimaging and associated behavioral datasets on healthy children and adolescents in the age range of 5-21, using matched protocols across sites, enabling robust characterization of age-related changes in network properties including connectivity, network integrity, response properties during tasks, and behavior. Aim 3 will be to collect and analyze longitudinal subsamples, task, and phenotypic measures that constitute intensive sub-studies of inflection points of health-relevant behavioral changes within specific developmental phases. Aim 4 will capitalize on our success in sharing data in the HCP, and use established tools, platforms and procedures to make all data publically available through the Connectome Coordinating Facility (CCF).
Connectome Coordination Facility
Human Connectome Project (HCP), NIMH Repository & Genomics Resource (NRGR)
Funding Completed
Close Out
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NIH - Extramural None

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

U01MH109589-01 Mapping the Human Connectome During Typical Development 05/17/2016 04/30/2020 2688 2435 WASHINGTON UNIVERSITY $3,423,207.00


NDA Help Center

Collection - General Tab

Fields available for edit on the top portion of the page include:

  • Collection Title
  • Investigators
  • Collection Description
  • Collection Phase
  • Funding Source
  • Clinical Trials

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

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

Blinded Clinical Trial Status:

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

Funding Source

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

Supporting Documentation

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

Grant Information

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

Clinical Trials

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

Frequently Asked Questions

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


  • Administrator Privilege
    A privilege provided to a user associated with an NDA Collection or NDA Study whereby that user can perform a full range of actions including providing privileges to other users.
  • Collection Owner
    Generally, the Collection Owner is the contact PI listed on a grant. Only one NDA user is listed as the Collection owner. Most automated emails are primarily sent to the Collection Owner.
  • Collection Phase
    The Collection Phase provides information on data submission as opposed to grant/project completion so while the Collection phase and grant/project phase may be closely related they are often different. Collection users with Administrative Privileges are encouraged to edit the Collection Phase. The Program Officer as listed in eRA (for NIH funded grants) may also edit this field. Changes must be saved by clicking the Save button at the bottom of the page. This field is sortable alphabetically in ascending or descending order. Collection Phase options include:
    • Pre-Enrollment: A grant/project has started, but has not yet enrolled subjects.
    • Enrolling: A grant/project has begun enrolling subjects. Data submission is likely ongoing at this point.
    • Data Analysis: A grant/project has completed enrolling subjects and has completed all data submissions.
    • Funding Completed: A grant/project has reached the project end date.
  • Collection Title
    An editable field with the title of the Collection, which is often the title of the grant associated with the Collection.
  • Grant
    Provides the grant number(s) for the grant(s) associated with the Collection. The field is a hyperlink so clicking on the Grant number will direct the user to the grant information in the NIH Research Portfolio Online Reporting Tools (RePORT) page.
  • Supporting Documentation
    Various documents and materials to enable efficient use of the data by investigators unfamiliar with the project and may include the research protocol, questionnaires, and study manuals.
  • NIH Research Initiative
    NDA Collections may be organized by scientific similarity into NIH Research Initiatives, to facilitate query tool user experience. NIH Research Initiatives map to one or multiple Funding Opportunity Announcements.
  • Permission Group
    Access to shared record-level data in NDA is provisioned at the level of a Permission Group. NDA Permission Groups consist of one or multiple NDA Collections that contain data with the same subject consents.
  • Planned Enrollment
    Number of human subject participants to be enrolled in an NIH-funded clinical research study. The data is provided in competing applications and annual progress reports.
  • Actual Enrollment
    Number of human subjects enrolled in an NIH-funded clinical research study. The data is provided in annual progress reports.
  • NDA Collection
    A virtual container and organization structure for data and associated documentation from one grant or one large project/consortium. It contains tools for tracking data submission and allows investigators to define a wide array of other elements that provide context for the data, including all general information regarding the data and source project, experimental parameters used to collect any event-based data contained in the Collection, methods, and other supporting documentation. They also allow investigators to link underlying data to an NDA Study, defining populations and subpopulations specific to research aims.
  • Data Use Limitations
    Data Use Limitations (DULs) describe the appropriate secondary use of a dataset and are based on the original informed consent of a research participant. NDA only accepts consent-based data use limitations defined by the NIH Office of Science Policy.
  • Total Subjects Shared
    The total number of unique subjects for whom data have been shared and are available for users with permission to access data.
IDNameCreated DateStatusType
1210CARIT (Reward History Go/Nogo) Task02/12/2019ApprovedfMRI
1212Guessing Task02/20/2019ApprovedfMRI
1213Emotion Task02/20/2019ApprovedfMRI
1214Resting State02/20/2019ApprovedfMRI
2325CARIT-Guessing-Emotion Tasks07/20/2023ApprovedfMRI
2326CONCAT (concatenated fMRI, Resting-CARIT-Guessing-Emotion)07/20/2023ApprovedfMRI

NDA Help Center

Collection - Experiments

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

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

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

Frequently Asked Questions

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

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

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


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

NDA Help Center

Collection - Shared Data

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

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

Frequently Asked Questions

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


  • Data Structure
    A defined organization and group of Data Elements to represent an electronic definition of a measure, assessment, questionnaire, or collection of data points. Data structures that have been defined in the NDA Data Dictionary are available at https://nda.nih.gov/general-query.html?q=query=data-structure
  • Data Type
    A grouping of data by similar characteristics such as Clinical Assessments, Omics, or Neurosignal data.
  • Shared
    The term 'Shared' generally means available to others; however, there are some slightly different meanings based on what is Shared. A Shared NDA Study is viewable and searchable publicly regardless of the user's role or whether the user has an NDA account. A Shared NDA Study does not necessarily mean that data used in the NDA Study have been shared as this is independently determined. Data are shared according the schedule defined in a Collection's Data Expected Tab and/or in accordance with data sharing expectations in the NDA Data Sharing Terms and Conditions. Additionally, Supporting Documentation uploaded to a Collection may be shared independent of whether data are shared.

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


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

PubMed IDStudyTitleJournalAuthorsDateStatus
39006919Create StudyFamily income is not significantly associated with T1w/T2w ratio in the Human Connectome Project in Development.Imaging neuroscience (Cambridge, Mass.)Weissman, David G; Baum, Graham L; Sanders, Ashley; Rosen, Maya L; Barch, Deanna M; McLaughlin, Katie A; Somerville, Leah HJanuary 1, 2023Not Determined
38948845Create StudyStructural connectivity matures along a sensorimotor-association connectional axis in youth.bioRxiv : the preprint server for biologyXu, Xiaoyu; Yang, Hang; Cong, Jing; Sydnor, Valerie; Cui, ZaixuJune 17, 2024Not Determined
38900742Create StudyMorphometric brain organization across the human lifespan reveals increased dispersion linked to cognitive performance.PLoS biologyLi, Jiao; Zhang, Chao; Meng, Yao; Yang, Siqi; Xia, Jie; Chen, Huafu; Liao, WeiJune 1, 2024Not Determined
38900563Create StudySpatial and temporal pattern of structure-function coupling of human brain connectome with development.eLifeFeng, Guozheng; Wang, Yiwen; Huang, Weijie; Chen, Haojie; Cheng, Jian; Shu, NiJune 20, 2024Not Determined
38712073Create StudyCerebral perivascular spaces as predictors of dementia risk and accelerated brain atrophy.medRxiv : the preprint server for health sciencesBarisano, Giuseppe; Iv, Michael; Alzheimer’s Disease Neuroimaging Initiative; Choupan, Jeiran; Hayden-Gephart, MelanieApril 26, 2024Not Determined
38659856Create StudyRelease the Krakencoder: A unified brain connectome translation and fusion tool.bioRxiv : the preprint server for biologyJamison, Keith W; Gu, Zijin; Wang, Qinxin; Sabuncu, Mert R; Kuceyeski, AmyApril 15, 2024Not Determined
38617291Create StudyUnveiling the Core Functional Networks of Cognition: An Ontology-Guided Machine Learning Approach.bioRxiv : the preprint server for biologyWu, Guowei; Cui, Zaixu; Wang, Xiuyi; Du, YiApril 4, 2024Not Determined
38559278Create StudySpatial and temporal pattern of structure-function coupling of human brain connectome with development.bioRxiv : the preprint server for biologyFeng, Guozheng; Wang, Yiwen; Huang, Weijie; Chen, Haojie; Cheng, Jian; Shu, NiMarch 12, 2024Not Determined
38496426Create StudyNormative modeling of thalamic nuclear volumes.medRxiv : the preprint server for health sciencesYoung, Taylor; Kumar, Vinod Jangid; Saranathan, ManojkumarMarch 8, 2024Not Determined
38463962Create StudyLifespan reference curves for harmonizing multi-site regional brain white matter metrics from diffusion MRI.bioRxiv : the preprint server for biologyZhu, Alyssa H; Nir, Talia M; Javid, Shayan; Villalon-Reina, Julio E; Rodrigue, Amanda L; Strike, Lachlan T; de Zubicaray, Greig I; McMahon, Katie L; Wright, Margaret J; Medland, Sarah E; Blangero, John; Glahn, David C; Kochunov, Peter; Håberg, Asta K; Thompson, Paul M; Jahanshad, Neda; Alzheimer’s Disease Neuroimaging InitiativeMarch 1, 2024Not Determined
38418819Create StudyData leakage inflates prediction performance in connectome-based machine learning models.Nature communicationsRosenblatt, Matthew; Tejavibulya, Link; Jiang, Rongtao; Noble, Stephanie; Scheinost, DustinFebruary 28, 2024Not Determined
38405819Create StudyParsing Brain Network Specialization: A Replication and Expansion of Wang et al. (2014).bioRxiv : the preprint server for biologyPeterson, Madeline; Floris, Dorothea L; Nielsen, Jared AFebruary 14, 2024Not Determined
38278807Create StudyStructural connectome architecture shapes the maturation of cortical morphology from childhood to adolescence.Nature communicationsLiang, Xinyuan; Sun, Lianglong; Liao, Xuhong; Lei, Tianyuan; Xia, Mingrui; Duan, Dingna; Zeng, Zilong; Li, Qiongling; Xu, Zhilei; Men, Weiwei; Wang, Yanpei; Tan, Shuping; Gao, Jia-Hong; Qin, Shaozheng; Tao, Sha; Dong, Qi; Zhao, Tengda; He, YongJanuary 26, 2024Not Determined
38234740Create StudyThe effects of data leakage on connectome-based machine learning models.bioRxiv : the preprint server for biologyRosenblatt, Matthew; Tejavibulya, Link; Jiang, Rongtao; Noble, Stephanie; Scheinost, DustinDecember 28, 2023Not Determined
38106130Create StudyEvidence for a Compensatory Relationship between Left- and Right-Lateralized Brain Networks.bioRxiv : the preprint server for biologyPeterson, Madeline; Braga, Rodrigo M; Floris, Dorothea L; Nielsen, Jared ADecember 9, 2023Not Determined
38087047Create StudyLongitudinal development of the human white matter structural connectome and its association with brain transcriptomic and cellular architecture.Communications biologyFeng, Guozheng; Chen, Rui; Zhao, Rui; Li, Yuanyuan; Ma, Leilei; Wang, Yanpei; Men, Weiwei; Gao, Jiahong; Tan, Shuping; Cheng, Jian; He, Yong; Qin, Shaozheng; Dong, Qi; Tao, Sha; Shu, NiDecember 12, 2023Not Determined
38045396Create StudyGroup-common and individual-specific effects of structure-function coupling in human brain networks with graph neural networks.bioRxiv : the preprint server for biologyChen, Peiyu; Yang, Hang; Zheng, Xin; Jia, Hai; Hao, Jiachang; Xu, Xiaoyu; Li, Chao; He, Xiaosong; Chen, Runsen; Okubo, Tatsuo S; Cui, ZaixuMarch 15, 2024Not Determined
37961654Create StudyPower and reproducibility in the external validation of brain-phenotype predictions.bioRxiv : the preprint server for biologyRosenblatt, Matthew; Tejavibulya, Link; Camp, Chris C; Jiang, Rongtao; Westwater, Margaret L; Noble, Stephanie; Scheinost, DustinOctober 30, 2023Not Determined
37850792Create StudyChanges of gray matter volumes of subcortical regions across the lifespan: a Human Connectome Project study.Journal of neurophysiologyChristova, Peka; Georgopoulos, Apostolos PNovember 1, 2023Not Determined
37745373Create StudyFunctional connectome through the human life span.bioRxiv : the preprint server for biologySun, Lianglong; Zhao, Tengda; Liang, Xinyuan; Xia, Mingrui; Li, Qiongling; Liao, Xuhong; Gong, Gaolang; Wang, Qian; Pang, Chenxuan; Yu, Qian; Bi, Yanchao; Chen, Pindong; Chen, Rui; Chen, Yuan; Chen, Taolin; Cheng, Jingliang; Cheng, Yuqi; Cui, Zaixu; Dai, Zhengjia; Deng, Yao; Ding, Yuyin; Dong, Qi; Duan, Dingna; Gao, Jia-Hong; Gong, Qiyong; Han, Ying; Han, Zaizhu; Huang, Chu-Chung; Huang, Ruiwang; Huo, Ran; Li, Lingjiang; Lin, Ching-Po; Lin, Qixiang; Liu, Bangshan; Liu, Chao; Liu, Ningyu; Liu, Ying; Liu, Yong; Lu, Jing; Ma, Leilei; Men, Weiwei; Qin, Shaozheng; Qiu, Jiang; Qiu, Shijun; Si, Tianmei; Tan, Shuping; Tang, Yanqing; Tao, Sha; Wang, Dawei; Wang, Fei; Wang, Jiali; Wang, Pan; Wang, Xiaoqin; Wang, Yanpei; Wei, Dongtao; Wu, Yankun; Xie, Peng; Xu, Xiufeng; Xu, Yuehua; Xu, Zhilei; Yang, Liyuan; Yuan, Huishu; Zeng, Zilong; Zhang, Haibo; Zhang, Xi; Zhao, Gai; Zheng, Yanting; Zhong, Suyu; Alzheimer’s Disease Neuroimaging Initiative; Cam-CAN; Developing Human Connectome Project; DIDA-MDD Working Group; MCADI; NSPN; He, YongJune 10, 2024Not Determined
37314080Create StudyChanges of cortical gray matter volume during development: a Human Connectome Project study.Journal of neurophysiologyChristova, Peka; Georgopoulos, Apostolos PJuly 1, 2023Not Determined
37126677Create StudyWhite matter plasticity following cataract surgery in congenitally blind patients.Proceedings of the National Academy of Sciences of the United States of AmericaPedersini, Caterina A; Miller, Nathaniel P; Gandhi, Tapan K; Gilad-Gutnick, Sharon; Mahajan, Vidur; Sinha, Pawan; Rokers, BasMay 9, 2023Not Determined
36963562Create StudyYouth Screen Media Activity Patterns and Associations With Behavioral Developmental Measures and Resting-state Brain Functional Connectivity.Journal of the American Academy of Child and Adolescent PsychiatrySong, Kunru; Zhang, Jia-Lin; Zhou, Nan; Fu, Yu; Zou, Bowen; Xu, Lin-Xuan; Wang, Ziliang; Li, Xin; Zhao, Yihong; Potenza, Marc; Fang, Xiaoyi; Zhang, Jin-TaoSeptember 1, 2023Not Determined
36824847Create StudyPhenome-wide Investigation of Behavioral, Environmental, and Neural Associations with Cross-Disorder Genetic Liability in Youth of European Ancestry.medRxiv : the preprint server for health sciencesPaul, Sarah E; Colbert, Sarah M C; Gorelik, Aaron J; Hansen, Isabella S; Nagella, I; Blaydon, L; Hornstein, A; Johnson, Emma C; Hatoum, Alexander S; Baranger, David A A; Elsayed, Nourhan M; Barch, Deanna M; Bogdan, Ryan; Karcher, Nicole RFebruary 14, 2023Not Determined
36803653Create StudyDevelopment of top-down cortical propagations in youth.NeuronPines, Adam; Keller, Arielle S; Larsen, Bart; Bertolero, Maxwell; Ashourvan, Arian; Bassett, Dani S; Cieslak, Matthew; Covitz, Sydney; Fan, Yong; Feczko, Eric; Houghton, Audrey; Rueter, Amanda R; Saggar, Manish; Shafiei, Golia; Tapera, Tinashe M; Vogel, Jacob; Weinstein, Sarah M; Shinohara, Russell T; Williams, Leanne M; Fair, Damien A; Satterthwaite, Theodore DApril 19, 2023Not Determined
36724055Create StudyAge-related differences in resting-state functional connectivity from childhood to adolescence.Cerebral cortex (New York, N.Y. : 1991)Sanders, Ashley F P; Harms, Michael P; Kandala, Sridhar; Marek, Scott; Somerville, Leah H; Bookheimer, Susan Y; Dapretto, Mirella; Thomas, Kathleen M; Van Essen, David C; Yacoub, Essa; Barch, Deanna MMay 24, 2023Not Determined
36609028Create StudyA Mega-analytic Study of White Matter Microstructural Differences Across 5 Cohorts of Youths With Attention-Deficit/Hyperactivity Disorder.Biological psychiatrySudre, Gustavo; Norman, Luke; Bouyssi-Kobar, Marine; Price, Jolie; Shastri, Gauri Ganesh; Shaw, PhilipJuly 1, 2023Not Determined
36346213Create StudyBenchmarking the generalizability of brain age models: Challenges posed by scanner variance and prediction bias.Human brain mappingJirsaraie, Robert J; Kaufmann, Tobias; Bashyam, Vishnu; Erus, Guray; Luby, Joan L; Westlye, Lars T; Davatzikos, Christos; Barch, Deanna M; Sotiras, AristeidisFebruary 15, 2023Not Determined
36100657Create StudyEvidence from "big data" for the default-mode hypothesis of ADHD: a mega-analysis of multiple large samples.Neuropsychopharmacology : official publication of the American College of NeuropsychopharmacologyNorman, Luke J; Sudre, Gustavo; Price, Jolie; Shastri, Gauri G; Shaw, PhilipJanuary 1, 2023Not Determined
35944340Create StudyDevelopmental trajectories of cortical thickness by functional brain network: The roles of pubertal timing and socioeconomic status.Developmental cognitive neuroscienceSanders, Ashley F P; Baum, Graham L; Harms, Michael P; Kandala, Sridhar; Bookheimer, Susan Y; Dapretto, Mirella; Somerville, Leah H; Thomas, Kathleen M; Van Essen, David C; Yacoub, Essa; Barch, Deanna MOctober 1, 2022Not Determined
35705486Create StudyGraded Variation in T1w/T2w Ratio during Adolescence: Measurement, Caveats, and Implications for Development of Cortical Myelin.The Journal of neuroscience : the official journal of the Society for NeuroscienceBaum, Graham L; Flournoy, John C; Glasser, Matthew F; Harms, Michael P; Mair, Patrick; Sanders, Ashley F P; Barch, Deanna M; Buckner, Randy L; Bookheimer, Susan; Dapretto, Mirella; Smith, Stephen; Thomas, Kathleen M; Yacoub, Essa; Van Essen, David C; Somerville, Leah HJuly 20, 2022Not Determined
35697132Create StudyEmpirical transmit field bias correction of T1w/T2w myelin maps.NeuroImageGlasser, Matthew F; Coalson, Timothy S; Harms, Michael P; Xu, Junqian; Baum, Graham L; Autio, Joonas A; Auerbach, Edward J; Greve, Douglas N; Yacoub, Essa; Van Essen, David C; Bock, Nicholas A; Hayashi, TakuyaSeptember 1, 2022Not Determined
35429627Create StudyPsychological resilience and neurodegenerative risk: A connectomics-transcriptomics investigation in healthy adolescent and middle-aged females.NeuroImagePetrican, Raluca; Fornito, Alex; Jones, NatalieJuly 15, 2022Not Determined
35201792Create StudyShifting qualities of negative affective experience through adolescence: Age-related change and associations with functional outcomes.Emotion (Washington, D.C.)Grisanzio, Katherine A; Flournoy, John C; Mair, Patrick; Somerville, Leah HFebruary 1, 2023Not Determined
35067249Create StudyNeural signatures of data-driven psychopathology dimensions at the transition to adolescence.European psychiatry : the journal of the Association of European PsychiatristsModabbernia, Amirhossein; Michelini, Giorgia; Reichenberg, Abraham; Kotov, Roman; Barch, Deanna; Frangou, SophiaJanuary 24, 2022Not Determined
35033950Create StudyAssociations between cognition and polygenic liability to substance involvement in middle childhood: Results from the ABCD study.Drug and alcohol dependencePaul, Sarah E; Hatoum, Alexander S; Barch, Deanna M; Thompson, Wesley K; Agrawal, Arpana; Bogdan, Ryan; Johnson, Emma CMarch 1, 2022Not Determined
34508893Create StudyThe Human Connectome Project: A retrospective.NeuroImageElam, Jennifer Stine; Glasser, Matthew F; Harms, Michael P; Sotiropoulos, Stamatios N; Andersson, Jesper L R; Burgess, Gregory C; Curtiss, Sandra W; Oostenveld, Robert; Larson-Prior, Linda J; Schoffelen, Jan-Mathijs; Hodge, Michael R; Cler, Eileen A; Marcus, Daniel M; Barch, Deanna M; Yacoub, Essa; Smith, Stephen M; Ugurbil, Kamil; Van Essen, David CDecember 1, 2021Not Determined
34450262Create StudyHierarchical modelling of functional brain networks in population and individuals from big fMRI data.NeuroImageFarahibozorg, Seyedeh-Rezvan; Bijsterbosch, Janine D; Gong, Weikang; Jbabdi, Saad; Smith, Stephen M; Harrison, Samuel J; Woolrich, Mark WNovember 1, 2021Not Determined
33683660Create StudyAmygdala Activation in Cognitive Task fMRI Varies with Individual Differences in Cognitive Traits.Cognitive, affective & behavioral neuroscienceWest, Haley V; Burgess, Gregory C; Dust, Joseph; Kandala, Sridhar; Barch, Deanna MFebruary 1, 2021Not Determined
33524575Create StudyCharacterizing cerebral hemodynamics across the adult lifespan with arterial spin labeling MRI data from the Human Connectome Project-Aging.NeuroImageJuttukonda, Meher R; Li, Binyin; Almaktoum, Randa; Stephens, Kimberly A; Yochim, Kathryn M; Yacoub, Essa; Buckner, Randy L; Salat, David HApril 15, 2021Not Determined
32965490Create StudyAssociations Between Prenatal Cannabis Exposure and Childhood Outcomes: Results From the ABCD Study.JAMA psychiatryPaul, Sarah E; Hatoum, Alexander S; Fine, Jeremy D; Johnson, Emma C; Hansen, Isabella; Karcher, Nicole R; Moreau, Allison L; Bondy, Erin; Qu, Yueyue; Carter, Ebony B; Rogers, Cynthia E; Agrawal, Arpana; Barch, Deanna M; Bogdan, RyanJanuary 1, 2021Not Determined
32866666Create StudyThe developing Human Connectome Project (dHCP) automated resting-state functional processing framework for newborn infants.NeuroImageFitzgibbon, Sean P; Harrison, Samuel J; Jenkinson, Mark; Baxter, Luke; Robinson, Emma C; Bastiani, Matteo; Bozek, Jelena; Karolis, Vyacheslav; Cordero Grande, Lucilio; Price, Anthony N; Hughes, Emer; Makropoulos, Antonios; Passerat-Palmbach, Jonathan; Schuh, Andreas; Gao, Jianliang; Farahibozorg, Seyedeh-Rezvan; O'Muircheartaigh, Jonathan; Ciarrusta, Judit; O'Keeffe, Camilla; Brandon, Jakki; Arichi, Tomoki; Rueckert, Daniel; Hajnal, Joseph V; Edwards, A David; Smith, Stephen M; Duff, Eugene; Andersson, JesperDecember 1, 2020Not Determined
32702486Create StudyCortical surface registration using unsupervised learning.NeuroImageCheng, Jieyu; Dalca, Adrian V; Fischl, Bruce; Zöllei, Lilla; Alzheimer’s Disease Neuroimaging InitiativeNovember 1, 2020Not Determined
32497785Create StudyA Symmetric Prior for the Regularisation of Elastic Deformations: Improved anatomical plausibility in nonlinear image registration.NeuroImageLange, Frederik J; Ashburner, John; Smith, Stephen M; Andersson, Jesper L ROctober 1, 2020Not Determined
31215864Create StudyA connectional hub in the rostral anterior cingulate cortex links areas of emotion and cognitive control.eLifeTang, Wei; Jbabdi, Saad; Zhu, Ziyi; Cottaar, Michiel; Grisot, Giorgia; Lehman, Julia F; Yendiki, Anastasia; Haber, Suzanne NJune 19, 2019Not Determined
30916716Create StudyAssociation of Prenatal Cannabis Exposure With Psychosis Proneness Among Children in the Adolescent Brain Cognitive Development (ABCD) Study.JAMA psychiatryFine, Jeremy D; Moreau, Allison L; Karcher, Nicole R; Agrawal, Arpana; Rogers, Cynthia E; Barch, Deanna M; Bogdan, RyanJuly 1, 2019Not Determined
30261308Create StudyExtending the Human Connectome Project across ages: Imaging protocols for the Lifespan Development and Aging projects.NeuroImageHarms, Michael P; Somerville, Leah H; Ances, Beau M; Andersson, Jesper; Barch, Deanna M; Bastiani, Matteo; Bookheimer, Susan Y; Brown, Timothy B; Buckner, Randy L; Burgess, Gregory C; Coalson, Timothy S; Chappell, Michael A; Dapretto, Mirella; Douaud, Gwenaëlle; Fischl, Bruce; Glasser, Matthew F; Greve, Douglas N; Hodge, Cynthia; Jamison, Keith W; Jbabdi, Saad; Kandala, Sridhar; Li, Xiufeng; Mair, Ross W; Mangia, Silvia; Marcus, Daniel; Mascali, Daniele; Moeller, Steen; Nichols, Thomas E; Robinson, Emma C; Salat, David H; Smith, Stephen M; Sotiropoulos, Stamatios N; Terpstra, Melissa; Thomas, Kathleen M; Tisdall, M Dylan; Ugurbil, Kamil; van der Kouwe, Andre; Woods, Roger P; Zöllei, Lilla; Van Essen, David C; Yacoub, EssaDecember 2018Relevant
30142446Create StudyThe Lifespan Human Connectome Project in Development: A large-scale study of brain connectivity development in 5-21 year olds.NeuroImageSomerville, Leah H; Bookheimer, Susan Y; Buckner, Randy L; Burgess, Gregory C; Curtiss, Sandra W; Dapretto, Mirella; Elam, Jennifer Stine; Gaffrey, Michael S; Harms, Michael P; Hodge, Cynthia; Kandala, Sridhar; Kastman, Erik K; Nichols, Thomas E; Schlaggar, Bradley L; Smith, Stephen M; Thomas, Kathleen M; Yacoub, Essa; Van Essen, David C; Barch, Deanna MDecember 2018Relevant
29852283Create StudyAutomated processing pipeline for neonatal diffusion MRI in the developing Human Connectome Project.NeuroImageBastiani, Matteo; Andersson, Jesper L R; Cordero-Grande, Lucilio; Murgasova, Maria; Hutter, Jana; Price, Anthony N; Makropoulos, Antonios; Fitzgibbon, Sean P; Hughes, Emer; Rueckert, Daniel; Victor, Suresh; Rutherford, Mary; Edwards, A David; Smith, Stephen M; Tournier, Jacques-Donald; Hajnal, Joseph V; Jbabdi, Saad; Sotiropoulos, Stamatios NJanuary 15, 2019Not Determined
29277648Create StudySusceptibility-induced distortion that varies due to motion: Correction in diffusion MR without acquiring additional data.NeuroImageAndersson, Jesper L R; Graham, Mark S; Drobnjak, Ivana; Zhang, Hui; Campbell, JonMay 1, 2018Not Determined
28816791Create StudyResting-State Functional Connectivity in the Human Connectome Project: Current Status and Relevance to Understanding Psychopathology.Harvard review of psychiatryBarch, Deanna MSeptember 1, 2017Not Determined

NDA Help Center

Collection - Publications

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

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

Frequently Asked Questions

  • How can I determine if a publication is relevant?
    Publications are considered relevant to a collection when the data shared is directly related to the project or collection.
  • 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).


  • 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.
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 ExpectedTargeted EnrollmentInitial SubmissionSubjects SharedStatus
Research Subject and Pedigree info icon
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 ExpectedTargeted EnrollmentInitial SubmissionSubjects SharedStatus
Perceived Stress Scale info icon
Penn Emotion Recognition Task-40 info icon
Medical History info icon
Social Responsiveness Scale (SRS) info icon
NEO Personality Inventory info icon
Demographics info icon
Child Behavior Checklist (CBCL) info icon
Vital Signs Assessment info icon
Processed MRI Data info icon
Pubertal Development Scale (PDS) info icon
Wechsler Intelligence Scale for Children info icon
Wechsler Adult Intelligence Scale info icon
Substance Use Survey info icon
Childrens Behavior Questionnaire (CBQ) info icon
Kiddie-Sads (K-SADS) info icon
Wechsler Preschool Primary Scale Intelligence (WPPSI) info icon
Physical Exam info icon
Pittsburgh Sleep Quality Index info icon
Medications and Treatments Form info icon
Picture Sequence Memory info icon
Cognition Composite Scores info icon
Pattern Comparison Processing Speed info icon
List Sorting Working Memory Test info icon
Youth Self Report info icon
Drug Screen info icon
Adult Self Report info icon
Imaging (Structural, fMRI, DTI, PET, microscopy) info icon
Psychological Well-Being Summary info icon
Dimensional Change Card Sort Test info icon
Broad Psychopathology Form info icon
UPPS Impulsive Behavior Scale info icon
Emotional Distress-Anxiety info icon
Emotional Distress - Depression info icon
Social Isolation info icon
Behavioral Inhibition Scale/Behavioral Activation Scale info icon
Early Adolescent Temperament Questionnaire info icon
Delay Discounting Task info icon
Blood Sample Collection info icon
Flanker Task info icon
Anger info icon
Munich ChronoType Questionnaire info icon
Mini Mental State Exam info icon
Go-No Go Variant Task info icon
NIH Toolbox Oral Reading Recognition Test info icon
Picture Vocabulary Test info icon
Self Efficacy Survey info icon
NIH Toolbox Fear Anxiety Surveys info icon
NIH Toolbox Motor Domain info icon
Sadness Survey info icon
NIH Toolbox Sensation Domain info icon
NIH Toolbox Social Withdrawal info icon
NIH Toolbox Emphatic Behavior Survey info icon
NIH Toolbox Rejection Surveys info icon
NIH Toolbox Emotional Support Survey info icon
NIH Toolbox Friendship Survey info icon
NIH Toolbox Perceived Hostility info icon
Sports and Activities Involvement Questionnaire info icon
Diagnostic Assessment Based on K-SADS info icon
PROMIS General Life Satisfaction info icon
Scanner Debriefing Interview info icon
Guessing Task info icon
General Behavior Inventory info icon
Family Environment Scale info icon
External Measures info icon
Screen Time info icon
Sleep Disturbance Questionnaire info icon
Emotion Task info icon
Language Experience Proficiency Questionnaire info icon
Structure not yet defined
No Status history for this Data Expected has been recorded yet

NDA Help Center

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.


  • 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.

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

Associated Studies

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

Study NameAbstractCollection/Study SubjectsData UsageState
Youth Screen Media Activity Patterns and Associations With Behavioral Developmental Measures and Resting-state Brain Functional ConnectivityObjective Screen media activity (SMA) consumes considerable time in youth’s lives, raising concerns about the effects it may have on youth development. Disentangling mixed associations between youth’s SMA and developmental measures should move beyond overall screen time and consider types and patterns of SMA. We aimed to identify reliable and generalizable SMA patterns among youth and examine their associations with behavioral developmental measures and developing brain functional connectivity. Method Three waves of the Adolescent Brain and Cognitive Development (ABCD) data were examined. The Lifespan Human Connectome Project in Development (HCP-D) was interrogated as an independent sample. ABCD participants included 11,878 children at baseline. HCP-D participants included 652 children and adolescents. Youth-reported SMA and behavioral developmental measures (neurocognitive performance, behavioral problems, psychotic-like experiences, impulsivity, and sensitivities to punishment/reward) were assessed with validated instruments. We identified SMA patterns in the ABCD baseline data using K-means clustering and sensitivity analyses. The generalizability and stability of the identified SMA patterns were examined in HCP-D data and ABCD follow-up waves, respectively. Relationships were examined between SMA patterns and behavioral and brain (resting-state brain functional connectivity [RSFC]) measures using linear-mixed-effect modelling with false-discovery-rate (FDR) correction. Results SMA data from 11,815 children (Meanage = 119.0 months, SDage = 7.5; 6,159 (52.1%) boys) were examined, and 3,151 (26.7%) demonstrated a video-centric higher-frequency SMA pattern and 8,666 (73.3%) demonstrated a lower-frequency pattern. The SMA patterns were validated in similarly-aged HCP-D youth. Compared to the lower-frequency-SMA-pattern group, the video-centric-higher-frequency-SMA-pattern group showed poorer neurocognitive performance (Beta=-0.12, 95%CI, [-0.08, -0.16], FDR-corrected p<.001), more total behavioral problems (Beta=0.13, 95%CI, [0.09, 0.18], FDR-corrected p<.001), and more psychotic-like experiences (Beta=0.31, 95%CI, [0.27, 0.36], FDR-corrected p<.001). The video-centric-higher-frequency-SMA-pattern group demonstrated higher impulsivity, more sensitivity to punishment/reward and altered RSFC among brain areas implicated previously in cognitive processes. Most of the associations persisted with age in the ABCD data, with more individuals (n=3,378, 30.4%) in the video-centric higher-frequency SMA group at one-year follow-up. A social-communication-centric SMA pattern was observed in HCP-D adolescents. Conclusion Video-centric SMA patterns are reliable and generalizable during late childhood. A higher-frequency-video-entertainment-SMA-pattern group showed altered RSFC and poorer developmental measures that persisted longitudinally. The findings suggest public health strategies aiming to decrease excessive time spent by children on video-entertainment-related SMA are needed. Further studies are needed to examine potential video-centric/social-centric SMA bifurcation to understand dynamic changes and trajectories of SMA patterns and related outcomes developmentally.652/12528Secondary AnalysisShared
Environmental Risk Factors and Psychotic-like Symptoms in Children Aged 9-11Objective: Research implicates environmental risk factors, including correlates of urbanicity, deprivation, and environmental toxins, in psychotic-like experiences (PLEs). The current study examined associations between several types of environmental risk factors and PLEs in school-age children, whether these associations were specific to PLEs or generalized to other psychopathology, and examined possible neural mechanisms for significant associations. Method: The current study used data from 10,328 9-11-year-olds from the Adolescent Brain Cognitive Development (ABCD) study. Hierarchical linear models examined associations between PLEs and geocoded environmental risk factors, and whether associations generalized to internalizing/externalizing symptoms. Mediation models examined whether structural MRI abnormalities (e.g., intracranial volume) mediated associations between PLEs and environmental risk factors. Results: The results found specific types of environmental risk factors, namely measures of urbanicity (i.e., drug offense exposure, less perception of neighborhood safety), deprivation (including overall deprivation, rate of poverty, fewer years at residence), and lead exposure risk, were associated with PLEs. These associations showed evidence of stronger associations with PLEs than internalizing/externalizing symptoms (especially overall deprivation, poverty, drug offense exposure, and lead exposure risk). There was evidence that brain volume mediated between 11-25% of the associations between poverty, perception of neighborhood safety, and lead exposure risk with PLEs. Conclusions: These results are the first to find support for neural measures partially mediating the association between PLEs and environmental exposures. Furthermore, the current study replicated and extended recent findings of the association between PLEs and environmental exposures, finding evidence for specific associations with correlates of urbanicity, deprivation, and lead exposure risk. 11/11898Secondary AnalysisShared
A multi-cohort study of resting-state connectivity alterations in attention-deficit/hyperactivity disorderMost studies examining connectomic abnormalities associated with ADHD have used small, underpowered samples and thus produced inconsistent findings. Here we combined data from the Adolescent Brain Cognitive Development (ABCD) and Lifespan Human Connectome Project Development (HCP-D) cohorts (NDAR collections #2573, #2846 and #3165), as well as datasets from non-NDAR sources including the ADHD-200, Healthy Brain Network (HBN), National Consortium on Alcohol and Neurodevelopment in Adolescence (NCANDA) and Neurobehavioral Clinical Research (NCR) cohorts. We aimed to identify network-level resting-state features associated ADHD diagnosis and traits in this large multi-cohort sample. We applied the same 36-parameter+despiking pipeline to subjects from all datasets, and combined data using mega-analytic mixed models, which included nested random intercepts for study, site and family ID. In the group comparison, we compared 1301 subjects with diagnosed ADHD against 1301 unaffected controls (total N=2,602; 1710 males (65.72%); mean age=10.86 years, sd=2.05). Patients and controls were 1:1 nearest neighbor matched on in-scanner motion and key demographic variables. Associations between ADHD-traits and resting-state connectivity were assessed in a large multi-cohort sample (N=10,113). ADHD diagnosis was associated with less anticorrelation between the default mode and salience/ventral attention (B=0.009, t=3.45, p-FDR=0.004, d=0.14, 95% CI=0.004, 0.014), somatomotor (B=0.008, t=3.49, p-FDR=0.004, d=0.14, 95% CI=0.004, 0.013), and dorsal attention networks (B=0.01, t=4.28, p-FDR<0.001, d=0.17, 95% CI=0.006, 0.015). These results were robust to sensitivity analyses considering comorbid internalizing problems, externalizing problems and psychostimulant medication. Similar findings were observed when examining ADHD traits. Finding associations between ADHD and connectivity of the default mode to task-positive networks is consistent with default mode network models of disorder, although all effect sizes were small.494/8817Secondary AnalysisShared
Evidence for embracing normative modelingIn this work, we expand the normative model repository introduced in Rutherford et al., 2022a to include normative models charting lifespan trajectories of structural surface area and brain functional connectivity, measured using two unique resting-state network atlases (Yeo-17 and Smith-10), and an updated online platform for transferring these models to new data sources. We showcase the value of these models with a head-to-head comparison between the features output by normative modeling and raw data features in several benchmarking tasks: mass univariate group difference testing (schizophrenia versus control), classification (schizophrenia versus control), and regression (predicting general cognitive ability). Across all benchmarks, we show the advantage of using normative modeling features, with the strongest statistically significant results demonstrated in the group difference testing and classification tasks. We intend for these accessible resources to facilitate the wider adoption of normative modeling across the neuroimaging community.652/2599Secondary AnalysisShared
The structure of neuroanatomical variation within bilingualsWe have developed this CVAE method that was useful for Autism (Aglinskas et al 2022). We think it might be useful for bilingualism research because bilingualism is similarly variable. We need access to this database to test our ideas. 652/1375Secondary AnalysisShared
Human Connectome Project-Development (HCP-D) Release 1.0Initial release of data from the Human Connectome Project in Development (ages 5-21). Release includes basic demographic data (sex, age, race/ethnicity, handedness) and unprocessed imaging data for all modalities (structural, resting state fMRI, task fMRI, diffusion, and ASL) for 655 subjects and preprocessed structural imaging data for 84 subjects. Full release documentation available at: https://www.humanconnectome.org/study/hcp-lifespan-development/documentation. 655/655Primary AnalysisShared
Creating a population-averaged structural connectomic brain atlas dataset from HCP-aging subjectsPopulation-averaged brain atlases, that are represented in a standard space with anatomical labels, are instrumental tools in neurosurgical planning and the study of neurodegenerative conditions. Traditional brain atlases are primarily derived from anatomical scans and contain limited information regarding the axonal organization of the white matter. With the advance of diffusion MRI that allows the modeling of fiber orientation distribution (FOD) in the brain tissue, there is an increasing interest for a population-averaged FOD template, especially based on a large healthy aging cohort, to offer structural connectivity information for connectomic surgery and analysis of neurodegeneration. We will create a set of multi-contrast structural connectomic MRI atlases, including T1w, T2w, and FOD templates, along with the associated whole brain tractograms. The templates will be made using multi-contrast group-wise registration based on 3T MRIs of a large cohort of subjects from the Human Connectome Project in Aging (HCP-A). To enhance the usability, probabilistic tissue maps and segmentation of 22 subcortical structures will be provided. Finally, the subthalamic nucleus shown in the atlas will be parcellated into sensorimotor, limbic, and associative sub-regions based on their structural connectivity to facilitate the analysis and planning of deep brain stimulation procedures. 652/652Primary AnalysisShared
Decoding Age-specific Changes in Brain Functional Connectivity Using a Sliding-window Based Clustering MethodFunctional magnetic resonance imaging (fMRI) permits detailed study of human brain function. Understanding the age-specific development of neural circuits in the typically developing brain may help us generate new hypotheses for developmental psychopathologies. Functional connectivity (FC), defined as the statistical associations between two brain regions, has been widely used in estimating functional networks from fMRI data. Previous research has shown that the evolution of FC does not follow a linear trend, particularly from childhood to young adulthood. Thus, this work aims to detect the nuanced FC changes with age from the non-linear curves and identify age-period-specific FC development patterns. We proposed a sliding-window based clustering approach to identify refined age interval of FC development. We used resting-state fMRI (rs-fMRI) data from the human connectome project-development (HCP-D), which recruited children, adolescents, and young adults aged from 5 to 21 years. Our analyses revealed different developmental patterns of resting-state FC by sex. In general, females matured earlier than males, but males had a faster development rate during age 100 -120 months. We identified four developmental phases: network construction in late childhood, segregation and integration construction in adolescence, network pruning in young adulthood, and a unique phase in males -- U-shape development. In addition, we investigated the sex effect on the slopes of FC-age correlation. Males had higher slopes during late childhood and young adulthood. These results inform trajectories of normal FC development, information that can in the future be used to pinpoint when development might go awry in neurodevelopmental disorders.652/652Secondary AnalysisShared
Human Connectome Project-Development (HCP-D) Release 2.0The 2.0 release of data from the Human Connectome Project in Development (healthy participants, ages 5-21) includes visit 1 (V1) preprocessed structural and functional imaging data, unprocessed V1 imaging data for all modalities (structural, resting state fMRI, task fMRI, diffusion, and ASL), and non-imaging demographic and behavioral assessment data for 652 participants. For details of all the measures included in this release and access instructions see the Lifespan HCP-Development Release 2.0 documentation link below.652/652Primary AnalysisShared
Spatiotemporal patterns of cortical microstructural maturation in children and adolescents with diffusion MRINeocortical maturation is a dynamic process that proceeds in a hierarchical manner; however, the spatiotemporal organization of cortical microstructure with diffusion MRI has yet to be fully defined. This study characterized cortical microstructural maturation using diffusion MRI (fwe-DTI and NODDI multi-compartment modeling) in a cohort of 637 children and adolescents between 8 and 21 years of age. We found spatially heterogeneous developmental patterns broadly demarcated into functional domains where NODDI metrics increased, and fwe-DTI metrics decreased with age. By applying nonlinear growth models in a vertex-wise analysis, we observed a general posterior-to-anterior pattern of maturation, where the fwe-DTI measures mean diffusivity (MD) and radial diffusivity (RD) reached peak maturation earlier than the NODDI metrics neurite density index. Using non-negative matrix factorization, we found occipito-parietal cortical regions that correspond to lower-order sensory domains mature earlier than fronto-temporal higher-order association domains. Our findings corroborate previous histological and neuroimaging studies that show spatially-varying patterns of cortical maturation that may reflect unique developmental processes of cytoarchitectonically-determined regional patterns of change. 652/652Primary AnalysisShared
Revealing the spatial pattern of brain hemodynamic sensitivity to healthy aging through sparse DCMAge-related changes in the BOLD response could reflect neuro-vascular coupling modifications rather than simply impairments in neural functioning. In this study, we propose the use of a generative dynamic causal model (DCM) to decouple neuronal and vascular factors in the BOLD signal, with the aim of characterizing the whole-brain spatial pattern of hemodynamic sensitivity to healthy aging, as well as to test the role of hemodynamic features as independent predictors in an age-classification model. In this view, DCM was applied to the resting-state fMRI data of a cohort of 126 healthy individuals in a wide age range, providing reliable estimates of the hemodynamic response function (HRF) for each subject and each region of interest. Then, some features characterizing each HRF curve were extracted and used to fit a multivariate logistic regression model to predict the age class of each individual. Ultimately, we tested the final predictive model on an independent dataset of 338 healthy subjects selected from the Human Connectome Project Aging (HCP-A) and Development (HCP-D) cohorts. Our results entail the spatial heterogeneity of the age effects on the hemodynamic component, since its impact resulted to be strongly region- and population-specific, discouraging any space-invariant corrective procedures that attempt to correct for vascular factors when carrying out functional studies involving groups with different ages. Moreover, we demonstrated that a strong interaction exists between some specific hemodynamic features and age, further supporting the essential role of the hemodynamic factor as independent predictor of biological aging, rather than a simple confounding variable. 152/641Secondary AnalysisShared
Psychological Resilience and Neurodegenerative Risk: A Connectomics-Transcriptomics Investigation in Healthy Adolescent and Middle-Aged FemalesAdverse life events can inflict substantial long-term damage, which, paradoxically, has been posited to stem from initially adaptative responses to the challenges encountered in one’s environment. Thus, identification of the mechanisms linking resilience against recent stressors to longer-term psychological vulnerability is key to understanding optimal functioning across multiple timescales. To address this issue, our study tested the relevance of neuro-reproductive maturation and senescence, respectively, to both resilience and longer-term risk for pathologies characterised by accelerated brain aging, specifically, Alzheimer’s Disease (AD). Graph theoretical and partial least squares analyses were conducted on multimodal imaging, reported biological aging and recent adverse experience data from the Lifespan Human Connectome Project (HCP). Availability of reproductive maturation/senescence measures restricted our investigation to adolescent (N =178) and middle-aged (N=146) females. Psychological resilience was linked to age-specific brain senescence patterns suggestive of precocious functional development of somatomotor and control-relevant networks (adolescence) and earlier aging of default mode and salience/ventral attention systems (middle adulthood). Biological aging showed complementary associations with the neural patterns relevant to resilience in adolescence (positive relationship) versus middle-age (negative relationship). Transcriptomic and expression quantitative trait locus data analyses linked the neural aging patterns correlated with psychological resilience in middle adulthood to gene expression patterns suggestive of increased AD risk. Our results imply a partially antagonistic relationship between resilience against proximal stressors and longer-term psychological adjustment in later life. They thus underscore the importance of fine-tuning extant views on successful coping by considering the multiple timescales across which age-specific processes may unfold. 178/324Secondary AnalysisShared
Modelling the Developing Connectome - Graph Representation Learning with Variational AutoencodersThe functional development of the human brain exhibits high inter-subject variability, which is influenced by factors such as genetic predisposition, environmental influences and individual learning experiences. A broadly accessible imaging modality for analysis of the brain’s functional development is found in resting state functional Magnetic Resonance Imaging (rs-fMRI), a non-invasive imaging technique covering the whole brain. However, analysis of rs-fMRI data in the context of developmental studies is challenging. Compared to adults, paediatric cohorts show higher in-scanner movement, which leads to erroneous intensity changes in the signal. Such artefacts, if not accounted for, lead to systematic errors in subsequent rs-fMRI analysis. Furthermore, approaches for developmental rs- fMRI analysis must be able to capture general trends rather than individual differences due to inter-subject variability. In this work, a framework for analysis of the topological properties of the developing brain is presented. First, a paediatric preprocessing pipeline is developed and evaluated for its performance in removing in-scanner-movement-related artefacts. Second, an adaption of the graph-based Variational Autoencoder (VAE) is developed for representation learning on brain graphs derived from developmental rs- fMRI data in the age range of 7.5 to 18.5 years. Additionally, a graph-based conditional VAE is proposed, with the goal to enhance the VAE representation learning abilities. Furthermore, the VAEs are compared to a Bayesian regression baseline. The evaluation of the VAE-based approaches is threefold: First, the models’ brain graph reconstruction capabilities are evaluated in terms of the Mean Squared Error (MSE) and a correlation-based error measure. Second, the quality of datasets generated by the models is measured in terms of Maximum Mean Discrepancy (MMD) based on graph statistics distributions. Finally, the ability to capture developmental trajectories of a developmental dataset is investigated by statistical analysis using Network Based Statistic (NBS). Results show that the developmental processes available in the used dataset cannot be modelled exactly by the presented modelling approaches. Additionally, extending the VAE with a condition does not improve its representation learning capabilities. However, trends, such as the continuous development of the Default Mode Network (DMN), are captured successfully by the graph-based VAE. 249/249Primary AnalysisShared
Local and global reward learning in the lateral frontal cortex show differential development during human adolescenceReward-guided choice is fundamental for adaptive behaviour and depends on several component processes supported by prefrontal cortex. Here, across three studies, we show that two such component processes, linking reward to specific choices and estimating the global reward state, develop during human adolescence and are linked to the lateral portions of the prefrontal cortex. These processes reflect the assignment of rewards contingently to local choices, or noncontingently, to choices that make up the global reward history. Using matched experimental tasks and analysis platforms, we show the influence of both mechanisms increase during adolescence (study 1) and that lesions to lateral frontal cortex (that included and/or disconnected both orbitofrontal and insula cortex) in human adult patients (study 2) and macaque monkeys (study 3) impair both local and global reward learning. Developmental effects were distinguishable from the influence of a decision bias on choice behaviour, known to depend on medial prefrontal cortex. Differences in local and global assignments of reward to choices across adolescence, in the context of delayed grey matter maturation of the lateral orbitofrontal and anterior insula cortex, may underlie changes in adaptive behaviour.60/60Primary AnalysisShared
* Data not on individual level

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Collection - Associated Studies

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