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


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

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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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Collection Summary Collection Charts
Collection Title Collection Investigators Collection Description
ABCD Neurocognitive Prediction Challenge 2019
Kilian Pohl and Wes Thompson; Ehsan Adeli, Stanford University; Bennett A. Landman, Vanderbilt University; Marius G. Linguraru, Children’s National Health System; Susan F. Tapert, University of California – San Diego 
Phenotype data were derived from the Adolescent Brain Cognitive Development 1.1 Release (http://dx.doi.org/10.15154/1460410, accessed on or before November 15, 2018). Raw imaging data were retrieved from the Fast Track DICOM share in the Adolescent Brain Cognitive Development Study Collection 2573 https://ndar.nih.gov/edit_collection.html?id=2573, accessed on or before November 15, 2018). The individual-level imaging phenotype data in this Collection was computed by a custom processing pipeline developed by the organizers of the ABCD Prediction Challenge. The imaging phenotype data may therefore differ from the values shared by the ABCD Study investigators in Release 1.1 or future releases. The website provides a detailed description of the processing. When using the data in publications, the Data Supplement of "Pfefferbaum et al., Altered Brain Developmental Trajectories in Adolescents After Initiating Drinking. Am J Psychiatry, 175(4), pp. 370-380, 2018" should be cited as a description of the processing pipeline.
Adolescent Brain Cognitive Development
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NIH - Extramural None

https://sibis.sri.com/abcd-np-challenge Methods ABCD NP Challenge site General Public


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
No records found.

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.
Brain Tissue Segmentaion Volumetrix Imaging 8670
Processed MRI Data Imaging 8670

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
31520123Create StudyDiscovery of shared genomic loci using the conditional false discovery rate approach.Human geneticsSmeland OB, Frei O, Shadrin A, O'Connell K, Fan CC, Bahrami S, Holland D, Djurovic S, Thompson WK, Dale AM, Andreassen OASeptember 2019Not Determined
31464996Create StudyThe emerging pattern of shared polygenic architecture of psychiatric disorders, conceptual and methodological challenges.Psychiatric geneticsSmeland, Olav B; Frei, Oleksandr; Fan, Chun-Chieh; Shadrin, Alexey; Dale, Anders M; Andreassen, Ole AOctober 2019Not Determined
30610197Create StudyGenome-wide analysis reveals extensive genetic overlap between schizophrenia, bipolar disorder, and intelligence.Molecular psychiatrySmeland, Olav B; Bahrami, Shahram; Frei, Oleksandr; Shadrin, Alexey; O'Connell, Kevin; Savage, Jeanne; Watanabe, Kyoko; Krull, Florian; Bettella, Francesco; Steen, Nils Eiel; Ueland, Torill; Posthuma, Danielle; Djurovic, Srdjan; Dale, Anders M; Andreassen, Ole AApril 2020Not Determined
30578952Create StudySexual minority children: Mood disorders and suicidality disparities.Journal of affective disordersBlashill, Aaron J; Calzo, Jerel PMarch 2019Not Determined
30339913Create StudyScreen media activity and brain structure in youth: Evidence for diverse structural correlation networks from the ABCD study.NeuroImagePaulus, Martin P; Squeglia, Lindsay M; Bagot, Kara; Jacobus, Joanna; Kuplicki, Rayus; Breslin, Florence J; Bodurka, Jerzy; Morris, Amanda Sheffield; Thompson, Wesley K; Bartsch, Hauke; Tapert, Susan FJanuary 2019Not Determined
30268792Create StudyAssociations between 24 hour movement behaviours and global cognition in US children: a cross-sectional observational study.The Lancet. Child & adolescent healthWalsh, Jeremy J; Barnes, Joel D; Cameron, Jameason D; Goldfield, Gary S; Chaput, Jean-Philippe; Gunnell, Katie E; Ledoux, Andrée-Anne; Zemek, Roger L; Tremblay, Mark SNovember 2018Not Determined
29874361Create StudyAssessment of the Prodromal Questionnaire-Brief Child Version for Measurement of Self-reported Psychoticlike Experiences in Childhood.JAMA psychiatryKarcher, Nicole R; Barch, Deanna M; Avenevoli, Shelli; Savill, Mark; Huber, Rebekah S; Simon, Tony J; Leckliter, Ingrid N; Sher, Kenneth J; Loewy, Rachel LAugust 2018Not Determined
29627333Create StudyAssessment of culture and environment in the Adolescent Brain and Cognitive Development Study: Rationale, description of measures, and early data.Developmental cognitive neuroscienceZucker RA, Gonzalez R, Feldstein Ewing SW, Paulus MP, Arroyo J, Fuligni A, Morris AS, Sanchez M, Wills TAugust 2018Not Determined
29567376Create StudyThe Adolescent Brain Cognitive Development (ABCD) study: Imaging acquisition across 21 sites.Developmental cognitive neuroscienceCasey BJ, Cannonier T, Conley MI, Cohen AO, Barch DM, Heitzeg MM, Soules ME, Teslovich T, Dellarco DV, Garavan H, Orr CA, Wager TD, Banich MT, Speer NK, Sutherland MT, Riedel MC, Dick AS, Bjork JM, Thomas KM, Chaarani B, Mejia MH, Hagler DJ, Daniela Cornejo M, Sicat CS, Harms MP, et al.August 2018Not Determined
29556250Create StudyDetermining Genetic Causal Variants Through Multivariate Regression Using Mixture Model Penalty.Frontiers in geneticsSundar, V S; Fan, Chun-Chieh; Holland, Dominic; Dale, Anders MJanuary 2018Not Determined
29437252Create StudyHigh temporal resolution motion estimation using a self-navigated simultaneous multi-slice echo planar imaging acquisition.Journal of magnetic resonance imaging : JMRITeruel, Jose R; Kuperman, Joshua M; Dale, Anders M; White, Nathan SFebruary 2018Not Determined
28828560Create StudyLinking tuberous sclerosis complex, excessive mTOR signaling, and age-related neurodegeneration: a new association between TSC1 mutation and frontotemporal dementia.Acta neuropathologicaOlney, Nicholas T; Alquezar, Carolina; Ramos, Eliana Marisa; Nana, Alissa L; Fong, Jamie C; Karydas, Anna M; Taylor, Joanne B; Stephens, Melanie L; Argouarch, Andrea R; Van Berlo, Victoria A; Dokuru, Deepika R; Sherr, Elliott H; Jicha, Gregory A; Dillon, William P; Desikan, Rahul S; De May, Mary; Seeley, William W; Coppola, Giovanni; Miller, Bruce L; Kao, Aimee WNovember 2017Not Determined
28803940Create StudyReal-time motion analytics during brain MRI improve data quality and reduce costs.NeuroImageDosenbach NUF, Koller JM, Earl EA, Miranda-Dominguez O, Klein RL, Van AN, Snyder AZ, Nagel BJ, Nigg JT, Nguyen AL, Wesevich V, Greene DJ, Fair DANovember 2017Not Determined
28716389Create StudyBiomedical ethics and clinical oversight in multisite observational neuroimaging studies with children and adolescents: The ABCD experience.Developmental cognitive neuroscienceClark, Duncan B; Fisher, Celia B; Bookheimer, Susan; Brown, Sandra A; Evans, John H; Hopfer, Christian; Hudziak, James; Montoya, Ivan; Murray, Margaret; Pfefferbaum, Adolf; Yurgelun-Todd, DeborahAugust 2018Not Determined
28279988Create StudyEntorhinal Cortex: Antemortem Cortical Thickness and Postmortem Neurofibrillary Tangles and Amyloid Pathology.AJNR. American journal of neuroradiologyThaker, A A; Weinberg, B D; Dillon, W P; Hess, C P; Cabral, H J; Fleischman, D A; Leurgans, S E; Bennett, D A; Hyman, B T; Albert, M S; Killiany, R J; Fischl, B; Dale, A M; Desikan, R SMay 2017Not Determined
28271184Create StudyShared genetic risk between corticobasal degeneration, progressive supranuclear palsy, and frontotemporal dementia.Acta neuropathologicaYokoyama JS, Karch CM, Fan CC, Bonham LW, Kouri N, Ross OA, Rademakers R, Kim J, Wang Y, Höglinger GU, Müller U, Ferrari R, Hardy J, Momeni P, Sugrue LP, Hess CP, James Barkovich A, Boxer AL, Seeley WW, Rabinovici GD, Rosen HJ, Miller BL, Schmansky NJ, Fischl B, et al.May 2017Not Determined
27899424Create StudyGenetic architecture of sporadic frontotemporal dementia and overlap with Alzheimer''s and Parkinson''s diseases.Journal of neurology, neurosurgery, and psychiatryFerrari, Raffaele; Wang, Yunpeng; Vandrovcova, Jana; Guelfi, Sebastian; Witeolar, Aree; Karch, Celeste M; Schork, Andrew J; Fan, Chun C; Brewer, James B; International FTD-Genomics Consortium (IFGC),; International Parkinson's Disease Genomics Consortium (IPDGC),; International Genomics of Alzheimer's Project (IGAP),; Momeni, Parastoo; Schellenberg, Gerard D; Dillon, William P; Sugrue, Leo P; Hess, Christopher P; Yokoyama, Jennifer S; Bonham, Luke W; Rabinovici, Gil D; Miller, Bruce L; Andreassen, Ole A; Dale, Anders M; Hardy, John; Desikan, Rahul SFebruary 2017Not Determined
27862206Create StudyMalformations of cortical development.Annals of neurologyDesikan RS, Barkovich AJDecember 2016Not 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
No Mandatory Data Expected
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
Processed MRI Data info icon
Evaluated Data 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".

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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
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. 8669/11898Secondary AnalysisShared
Longitudinally stable, brain-based predictive models mediate the relationships between childhood cognition and socio-demographic, psychological and genetic factors. Cognitive abilities are one of the major transdiagnostic domains in the National Institute of Mental Health's Research Domain Criteria (RDoC). Following RDoC's integrative approach, we aimed to develop brain-based predictive models for cognitive abilities that (a) are developmentally stable over years during adolescence and (b) account for the relationships between cognitive abilities and socio-demographic, psychological and genetic factors. For this, we leveraged the unique power of the large-scale, longitudinal data from the Adolescent Brain Cognitive Development (ABCD) study (n ~ 11 k) and combined MRI data across modalities (task-fMRI from three tasks: resting-state fMRI, structural MRI and DTI) using machine-learning. Our brain-based, predictive models for cognitive abilities were stable across 2 years during young adolescence and generalisable to different sites, partially predicting childhood cognition at around 20% of the variance. Moreover, our use of ‘opportunistic stacking’ allowed the model to handle missing values, reducing the exclusion from around 80% to around 5% of the data. We found fronto-parietal networks during a working-memory task to drive childhood-cognition prediction. The brain-based, predictive models significantly, albeit partially, accounted for variance in childhood cognition due to (1) key socio-demographic and psychological factors (proportion mediated = 18.65% [17.29%–20.12%]) and (2) genetic variation, as reflected by the polygenic score of cognition (proportion mediated = 15.6% [11%–20.7%]). Thus, our brain-based predictive models for cognitive abilities facilitate the development of a robust, transdiagnostic research tool for cognition at the neural level in keeping with the RDoC's integrative framework.8662/11878Secondary AnalysisShared
Prenatal substance exposure and child health: Understanding the role of environmental factors, genetics, and brain development This current study examined the interactions of 4 most common prenatal substance exposures (PSE), which are coffee, alcohol, tobacco and cannabis with poly-environmental and genetic factors and is the first to establish the comprehensive pathway map of the PSE in the context of both poly-environmental and genetic factors and adolescent brain structure. Using the large cohort from the ABCD study, this study not only demonstrates that PSE is widely associated with offspring health, but also elucidates the existence of possible modifiable factors for those who have already had the PSE. Associations of prenatal alcohol exposure with more physical and psychological impairment, impulsivity, and better cognitive performance, and associations of prenatal coffee exposure with more externalizing and total problems remained significant after adjustment for poly-environmental and -genetic risks while associations of prenatal marijuana/tobacco exposure diminished. While prenatal alcohol exposure was associated with a larger total cortical volume and regional volumes, prenatal tobacco exposure was associated with a smaller total cortical volume and surface area, smaller regional volumes and surface areas. Prenatal coffee exposure was associated with larger postcentral gyrus volume, smaller regional volume and surface area in the pericalcarine cortex, thinner superior frontal gyrus and rostral middle frontal gyrus, thicker right lingual gyrus and isthmus-cingulate cortex.Of the four PSE, environmental factors contributed to more health associations of prenatal tobacco exposure via moderation and mediation, while genetic factors confounded more health associations of prenatal marijuana exposure. The brain mediation analysis found that at baseline, cortical volume in the right middle temporal gyrus mediated the association of prenatal alcohol exposure with externalizing problems, whereas cortical volume in the right postcentral gyrus mediated the association of prenatal coffee exposure with externalizing problems. 8662/11878Secondary AnalysisShared
Prevalence of anhedonia and related risk and protective factors in the general population: results from UK Biobank and ABCD studyBackground Anhedonia (defined as the capacity to experience pleasure) is present in healthy people and in mental disorders but its prevalence and predictors are largely unknown in the general population. We aimed to estimate the prevalence of anhedonia and identify risk and protective factors within two large population-based samples, UK Biobank (UKB) and the US Adolescent Brain and Cognitive Development (ABCD) study. Methods A total of 487,631 adults from UKB (mean age = 56.56 years, 54.39% female) and 9,829 early adolescents from ABCD (mean age = 9.9 years, 47.64% female) were included. The prevalence of anhedonia and common mental disorders were estimated at baseline in both cohorts separately. Multiple factors were assessed, including demographics, family history, early life factors, lifestyle, physical factors, mental health conditions, and family, school and social environments. We conducted bivariate analyses, multinomial logistic regression, Poisson regression and logistic regression in subsamples with complete data to identify factors associated with current or future anhedonia. Results In UKB, 21.47% [95% CI, 21.36-21.59%] had state anhedonia during the preceding two weeks and 36.92% [95% CI, 36.68-37.15%] endorsed lifetime severe anhedonia. Risk factors associated with state anhedonia and lifetime severe anhedonia included parental depression history, sleeplessness, poor overall health, any lifetime mental disorders, childhood trauma, and adulthood traumatic life events, whereas social support was a protective factor. In ABCD, youth report and parent report revealed a prevalence of 10.11% (95% CI, 9.44-10.82%) for state anhedonia and 8.70% (95% CI, 8.07-9.37%) for lifetime severe anhedonia. Contributing risk factors included Black and Hispanic race/ethnicity, high family conflict, any lifetime mental disorders, high school disengagement and high impact of adverse life experiences. High parent acceptance and higher parental educational degree were protective. Conclusions Anhedonia is common in the general population and multiple risk factors are implicated during early adolescence and rom middle to later adulthood. These risk factors are largely consistent with previous findings for diagnoses such as depression and bipolar disorder and may represent promising prevention targets for addressing anhedonia in the general population. 8655/11876Primary AnalysisShared
Auditory Cortex Asymmetry Associations with Individual Differences in Language and CognitionA longstanding cerebral lateralization hypothesis predicts that disrupted development of typical leftward structural asymmetry of auditory cortex explains why children have problems learning to read. Small sample sizes and small effects, potential sex-specific effects, and associations that are limited to specific dimensions of language are thought to have contributed inconsistent results. The large ABCD study dataset (baseline visit: N = 11,859) was used to test the hypothesis of significant associations between surface area asymmetry of auditory cortex and receptive vocabulary performance across boys and girls, as well as an oral word reading effect that was specific to boys. The results provide modest support (Cohen’s d effect sizes ≤ 0.10) for the cerebral lateralization hypothesis.8655/11859Secondary AnalysisShared
Integrating large-scale genomic data CNV Calling with MRI data in the ABCD CohortChildhood represents a crucial developmental phase for mental health and cognitive function, both of which are implicated in psychiatric disorders. This neurodevelopmental trajectory is shaped by a complex interplay of genetic and environmental factors. While common genetic variants account for a large proportion of inherited genetic risk, rare genetic variations, particularly copy number variants (CNVs), play a significant role in the genetic architecture of neurodevelopmental disorders. Despite their importance, the relevance of CNVs to child psychopathology and cognitive function in the general population remains underexplored. In this study, we utilized PennCNV and QuantiSNP algorithms to identify duplications and deletions larger than 50Kb across a cohort of 11,088 individuals from the Adolescent Brain Cognitive Development (ABCD) study. CNVs meeting quality control standards were subjected to a genome-wide association scan to identify regions associated with psychopathological and cognitive outcomes. Additionally, a CNV risk score, reflecting the aggregated burden of genetic intolerance to inactivation and dosage sensitivity, was calculated to assess its impact on variability in overall and dimensional child psychiatric and cognitive phenotypes. In a final sample of 8,564 individuals passing quality control, we identified 4,111 individuals carrying 5,760 autosomal CNVs. Our results revealed significant associations between specific CNVs and psychopathology and cognitive function. For instance, a duplication at 10q26.3 was associated with total psychopathology, and somatic complaints in particular. Additionally, deletions at 1q12.1, along with duplications at 14q11.2 and 10q26.3, were linked to total cognitive function, with particular contributions from fluid intelligence, working memory, and reading ability. Moreover, individuals carrying CNVs previously associated with neurodevelopmental disorders exhibited greater impairment in social functioning and cognitive performance across multiple domains, in particular working memory. Notably, a higher deletion CNV risk score was significantly correlated with increased total psychopathology (especially in dimensions of social functioning, thought disorder, and attention) as well as cognitive impairment across various domains. In summary, our findings shed light on the contribution of CNV to interindividual variability in complex traits related to neurocognitive development and child psychopathology.8565/11665Primary AnalysisShared
Neuroanatomical correlates of impulsive traits in children aged 9 to 10Impulsivity refers to a set of traits that are generally negatively related to critical domains of adaptive functioning and are core features of numerous psychiatric disorders. The current study examined the gray and white matter correlates of five impulsive traits measured using an abbreviated version of the UPPS-P (Urgency, (lack of) Premeditation, (lack of) Perseverance, Sensation-Seeking, Positive Urgency) impulsivity scale in children aged 9 to 10 (N = 11,052) from the Adolescent Brain and Cognitive Development (ABCD) study. Linear mixed effect models and elastic net regression were used to examine features of regional gray matter and white matter tractography most associated with each UPPS-P scale; intraclass correlations were computed to examine the similarity of the neuroanatomical correlates among the scales. Positive Urgency showed the most robust association with neuroanatomy, with similar but less robust associations found for Negative Urgency. Perseverance showed little association with neuroanatomy. Premeditation and Sensation Seeking showed intermediate associations with neuroanatomy. Critical regions across measures include the dorsolateral prefrontal cortex, lateral temporal cortex, and orbitofrontal cortex; critical tracts included the superior longitudinal fasciculus and inferior fronto-occipital fasciculus. Negative Urgency and Positive Urgency showed the greatest neuroanatomical similarity. Some UPPS-P traits share neuroanatomical correlates, while others have distinct correlates or essentially no relation to neuroanatomy. Neuroanatomy tended to account for relatively little variance in UPPS-P traits (i.e., Model R2 < 1%) and effects were spread throughout the brain, highlighting the importance of well powered samples.8253/11051Secondary AnalysisShared
Association of Mental Health Burden With Prenatal Cannabis Exposure From Childhood to Early Adolescence: Longitudinal Findings From the Adolescent Brain Cognitive Development (ABCD) Study Dramatic increases in cannabis use during pregnancy are alarming because of evidence that prenatal exposure may be associated with a host of adverse outcomes.1 We previously found that prenatal cannabis exposure (PCE) following maternal knowledge of pregnancy is associated with increased psychopathology during middle childhood using baseline data from the Adolescent Brain Cognitive Development (ABCD) study.2 Here, leveraging longitudinal ABCD study data (data release 4.0), we examined whether associations with psychopathology persist into early adolescence.7787/10640Secondary AnalysisShared
Effect of exposure to maternal diabetes during pregnancy on offspring’s brain cortical thickness and neurocognitive functioningOBJECTIVE: Maternal diabetes may affect the developing brain of the fetus, which may adversely affect the neurocognitive functioning (NCF) of diabetes-exposed children. We examined the effect of prenatal exposure to maternal diabetes (DP) on brain structure and neurocognition in preadolescent children, ages 9-10. RESEARCH DESIGN AND METHODS: This secondary data analysis study used cross-sectional structural neuroimaging and NCF data from the Adolescent Brain and Cognitive Development (ABCD) study (N=9,963). Differences in brain cortical thickness (CTh) and five cognitive abilities (executive function, working and episodic memory, processing speed, and language abilities) between diabetes-exposed and unexposed children were examined. Generalized linear models were used to adjust for the effect of confounding variables. Indirect effect of CTh into the relationship between maternal DP and NCF were also examined. RESULTS: The average age of the children was 9.9 years (SD 7.5); half of them were male and non-Hispanic White. Diabetes-exposed children (n=714) had lower CTh of the whole-brain (2.744mm VS 2.756mm; p 0.008) and lower scores in processing speed task (85.97 VS 87.28; p=0.021 compared to unexposed children (n=9249) after adjusting for demographic and other confounding variables. Diabetes-exposed children also had lower score in fluid intelligence [β (95%CI): -0.837 (-1.604, -0.171)]) and total cognition [β (95%CI): -0.728 (-1.338,-0.119)]. CTh partially mediated [Direct effect=β (95%CI): -3.239 (-5.834, -0.644); indirect effect=β (95%CI): -3.239 (-5.834, -0.644)] the effect of maternal DP on offspring’s processing speed. CONCLUSION: Diabetes-exposed children have reduced CTh and NCF during preadolescence age, which may have implications for psychomotor development during later life. Prospective studies are needed to confirm our findings7435/10218Secondary AnalysisShared
Effect of maternal hypertensive disorder on their children’s neurocognitive functioningObjective: The aim of the study was to examine the effect of prenatal exposure to maternal HDP on brain structure and NCF in singleton children aged between 9-10 years the baseline wave of the Adolescent Brain and Cognitive Development (ABCD) Study. Methods: The ABCD Study® interviewed each child (and their parents), measured NCF, and performed neuroimaging. Exposure to maternal high blood pressure (HBP) and preeclampsia or eclampsia (PE/EL) were extracted from the developmental history questionnaire. Differences in cortical thickness (CTh) and five cognitive abilities (executive function, working and episodic memory, processing speed, and language abilities) between exposed and unexposed children were examined using generalized linear models. The mediating effects of CTh, birthweight, and BMI on the relationship between maternal HDP on NCF were also examined. Result: A total of 584-children exposed to HBP, 387-children exposed to PE/EL, and 5,877 unexposed children were included in the analysis. Neither CTh nor NCF differed between the exposed and unexposed children with or without adjusting for the confounders including the child’s age, sex, race, education, and birth histories. The whole-brain CTh did not mediate any of the relationships between HDP and NCF. However, the relationship between HDP and most of the NCF was mediated by birthweight and BMI. Conclusions: Our results do not support maternal HDP, in comparison to other perinatal risk factors, as a direct risk factor for later-life cognitive functions. Prospective longitudinal studies, following up from infancy, are needed to further delineate the effect of HDP on children’s cognitive abilities.7408/10183Secondary AnalysisShared
Prenatal cannabis exposure, the brain, and psychopathology during early adolescencePrenatal cannabis exposure (PCE) is associated with mental health problems in early adolescence, but the possible neurobiological mechanisms remain unknown. In a large longitudinal sample of adolescents (ages 9-12, n=9,322-10,186), we find that PCE is associated with localized differences in gray and white matter of the frontal and parietal cortices, their associated white matter tracts, and with striatal resting state connectivity, even after accounting for potential pregnancy, familial, and child confounds. Variability in forceps minor and pars triangularis diffusion metrics partially longitudinally mediate associations of PCE with attention problems and attention-deficit/hyperactivity disorder (ADHD) symptoms. PCE-related differences in brain development may confer vulnerability to worse mental health in early adolescence. Analyses used the 5.0 ABCD release: https://dx.doi.org/10.15154/8873-zj657534/10159Secondary AnalysisShared
Hierarchical Individual Variation and Socioeconomic Impact on Personalized Functional Network Topography in ABCD ChildrenThe spatial topography of large-scale functional networks on the cerebral cortex varies substantially across individuals, particularly in the higher-order association cortices. Childhood functional organization has been linked to academical performance, quality of life and mental health outcomes throughout adolescence and adulthood. However, the individual variability of personalized functional network topography and its relationship with socioeconomic status (SES) during childhood remain unclear. Here, we delineated 17 personalized functional networks for children aged 9–10 years using 20 minutes of high-quality functional MRI data from the Adolescent Brain Cognitive Development study. We found that individual variations in personalized functional network topography increase along a hierarchical sensorimotor-association axis across the cortex. Furthermore, we observed that functional network topography significantly predicts unseen individuals’ SES, which was defined as the family income-to-needs ratio. Moreover, the association between topography and SES is hierarchically organized along the sensorimotor-association cortical axis, with stronger positive associations at the higher-order association cortex. Finally, we have publicly released children’s functional networks at https://nda.nih.gov/study.html?id=2484. These findings highlight a hierarchically organized cortical plasticity of childhood functional neuroanatomy in humans. 76/10073Secondary AnalysisShared
Polygenic risk scores for alcohol involvement relate to brain structure in substance‐naïve children: results from the ABCD StudyBrain imaging-derived structural correlates of alcohol involvement have largely been speculated to arise as a consequence of alcohol exposure. However, they may also reflect predispositional risk. In substance naïve children of European ancestry who completed the baseline session of the Adolescent Brain Cognitive Development (ABCD) Study (n = 3013), mixed-effects models estimated whether polygenic risk scores (PRS) for problematic alcohol use (PAU-PRS) and drinks per week (DPW-PRS) are associated with magnetic resonance imaging-derived brain structure phenotypes (i.e., total and regional: cortical thickness, surface area and volume; subcortical volume; white matter volume, fractional anisotropy, mean diffusivity). Follow-up analyses evaluated whether any identified regions were also associated with polygenic risk among substance naïve children of African ancestry (n = 898). After adjustment for multiple testing correction, polygenic risk for PAU was associated with lower volume of the left frontal pole and greater cortical thickness of the right supramarginal gyrus (|βs| > 0.009; ps < 0.001; psfdr < 0.046; r2s < 0.004). PAU PRS and DPW PRS showed nominally significant associations with a host of other regional brain structure phenotypes (e.g., insula surface area and volume). None of these regions showed any, even nominal association among children of African ancestry. Genomic liability to alcohol involvement may manifest as variability in brain structure during middle childhood prior to alcohol use initiation. Broadly, alcohol-related variability in brain morphometry may partially reflect predisposing genomic influence. Larger discovery genome-wide association studies and target samples of diverse ancestries are needed to determine whether observed associations may generalize across ancestral origins.1415/10054Secondary AnalysisShared
Explainable machine learning approach to predict and explain the relationship between task-based fMRI and individual differences in cognitionDespite decades of costly research, we still cannot accurately predict individual differences in cognition from task-based functional magnetic resonance imaging (fMRI). Moreover, aiming for methods with higher prediction is not sufficient. To understand brain-cognition relationships, we need to explain how these methods draw brain information to make the prediction. Here we applied an explainable machine-learning (ML) framework to predict cognition from task-based fMRI during the n-back working-memory task, using data from the Adolescent Brain Cognitive Development (n = 3,989). We compared 9 predictive algorithms in their ability to predict 12 cognitive abilities. We found better out-of-sample prediction from ML algorithms over the mass-univariate and ordinary least squares (OLS) multiple regression. Among ML algorithms, Elastic Net, a linear and additive algorithm, performed either similar to or better than nonlinear and interactive algorithms. We explained how these algorithms drew information, using SHapley Additive explanation, eNetXplorer, Accumulated Local Effects, and Friedman’s H-statistic. These explainers demonstrated benefits of ML over the OLS multiple regression. For example, ML provided some consistency in variable importance with a previous study and consistency with the mass-univariate approach in the directionality of brain-cognition relationships at different regions. Accordingly, our explainable-ML framework predicted cognition from task-based fMRI with boosted prediction and explainability over standard methodologies.6989/9468Secondary AnalysisShared
Neural correlates of obesity across the lifespanAssociations between brain and obesity are bidirectional: changes in brain structure and function can underpin over-eating, while chronic adiposity might lead to brain atrophy. Consequently, investigating brain-BMI associations across the lifespan can help to understand causality of those relationships. This study, conducted using multiple large-scale datasets, delves into the dynamic interplay between obesity and cortical morphometry across distinct age groups, encompassing children, young adults, adults, and older adults. Additionally, we investigate the genetic, neurochemical, and cognitive correlates of these alterations. Our findings reveal a consistent pattern of lower cortical thickness in fronto-temporal brain regions associated with obesity across all age cohorts. Moreover, in adults and older adults, obesity correlates with neurochemical changes and differential gene expression related to inflammation and mitochondrial function. In addition, in children and older adults, elevated body mass index corresponds to modifications in brain regions involved in emotional and attentional processes implicated in feeding regulation. In summary, obesity might originate from cognitive changes during early adolescence, subsequently leading to neurodegeneration in later life through mitochondrial and inflammatory mechanisms. 2507/9186Secondary AnalysisShared
Differentiating distinct and converging neural correlates of types of systemic environmental exposuresBackground: Systemic environmental disadvantage relates to a host of health and functional outcomes. Specific structural factors have seldom been linked to neural structure, however, clouding understanding of putative mechanisms. Examining relations during childhood/preadolescence, a dynamic period of neurodevelopment, could aid bridge this gap. Methods: A total of 10,213 youth were recruited from the Adolescent Brain and Cognitive Development study. Self-report and objective measures (Census and Federal bureau of investigation metrics extracted using geocoding) of environmental exposures were used, including stimulation indexing lack of safety and high attentional demands, discrepancy indexing social exclusion/lack of belonging, and deprivation indexing lack of environmental enrichment. Environmental measures were related to cortical thickness, surface area and subcortical volume regions, controlling for other environmental exposures and accounting for other brain regions. Results: Self-report (|β|=0.04-0.09) and objective (|β|=0.02-0.06) environmental domains related to area/thickness in overlapping (e.g. insula, caudal anterior cingulate), and unique regions (e.g. for discrepancy, rostral anterior and isthmus cingulate, implicated in socioemotional functions; for stimulation, precuneus, critical for cue reactivity and integration of environmental cues, and for deprivation, superior frontal, integral to executive functioning). For stimulation and discrepancy exposures, self-report and objective measures showed similarities in correlate regions, while deprivation exposures evidenced distinct correlates for self-report and objective measures. Conclusions: Results represent a necessary step toward broader work aimed at establishing mechanisms and correlates of structural disadvantage, highlighting the relevance of going beyond aggregate models by considering types of environmental factors, and the need to incorporate both subjective and objective measurements in these efforts. 6961/9043Primary AnalysisShared
Predicting Fluid Intelligence of Children using T1-weighted MR Images and a StackNetIn this work, we utilize T1-weighted MR images and StackNet to predict fluid intelligence in adolescents. Our framework includes feature extraction, feature normalization, feature denoising, feature selection, training a StackNet, and predicting fluid intelligence. The extracted feature is the distribution of different brain tissues in different brain parcellation regions. The proposed StackNet consists of three layers and 11 models. Each layer uses the predictions from all previous layers including the input layer. The proposed StackNet is tested on a public benchmark Adolescent Brain Cognitive Development Neurocognitive Prediction Challenge 2019 and achieves a mean squared error of 82.42 on the combined training and validation set with 10-fold cross-validation. In addition, the proposed StackNet also achieves a mean squared error of 94.25 on the testing data. The source code is available on GitHub.8670/8670Secondary AnalysisShared
Multimethod investigation of the neurobiological basis of ADHD symptomatology in children aged 9-10: baseline data from the ABCD studyAttention deficit/hyperactivity disorder is associated with numerous neurocognitive deficits, including poor working memory and difficulty inhibiting undesirable behaviors that cause academic and behavioral problems in children. Prior work has attempted to determine how these differences are instantiated in the structure and function of the brain, but much of that work has been done in small samples, focused on older adolescents or adults, and used statistical approaches that were not robust to model overfitting. The current study used cross-validated elastic net regression to predict a continuous measure of ADHD symptomatology using brain morphometry and activation during tasks of working memory, inhibitory control, and reward processing, with separate models for each MRI measure. The best model using activation during the working memory task to predict ADHD symptomatology had an out-of-sample R2 = 2% and was robust to residualizing the effects of age, sex, race, parental income and education, handedness, pubertal status, and internalizing symptoms from ADHD symptomatology. This model used reduced activation in task positive regions and reduced deactivation in task negative regions to predict ADHD symptomatology. The best model with morphometry alone predicted ADHD symptomatology with an R2 = 1% but this effect dissipated when including covariates. The inhibitory control and reward tasks did not yield generalizable models. In summary, these analyses show, with a large and well-characterized sample, that the brain correlates of ADHD symptomatology are modest in effect size and captured best by brain morphometry and activation during a working memory task.5837/7999Secondary AnalysisShared
Human cortex development is shaped by molecular and cellular brain systemsHuman brain morphology undergoes complex developmental changes with diverse regional trajectories. Various biological factors influence cortical thickness development, but human data are scarce. Building on methodological advances in neuroimaging of large cohorts, we show that population-based developmental trajectories of cortical thickness unfold along patterns of molecular and cellular brain organization. During childhood and adolescence, distributions of dopaminergic receptors, inhibitory neurons, glial cell populations as well as features of brain metabolism explain up to 50% of variance associated with regional cortical thickness trajectories. Cortical maturation patterns in later life are best explained by distributions of cholinergic and glutamatergic systems. These observations are validated in longitudinal data from over 8,000 adolescents, explaining up to 59% of developmental change at population- and 18% at single-subject level. Integrating multilevel brain atlases with normative modeling and population neuroimaging provides a biologically and clinically meaningful path to understand typical and atypical brain development in living humans.5593/7209Secondary AnalysisShared
Stability of polygenic scores across discovery genome-wide association studiesPolygenic scores (PGS) are commonly evaluated in terms of their predictive accuracy at the population level by the proportion of phenotypic variance they explain. To be useful for precision medicine applications, they also need to be evaluated at the individual level when phenotypes are not necessarily already known. We investigated the stability of PGS in European American (EUR) and African American (AFR)-ancestry individuals from the Philadelphia Neurodevelopmental Cohort and the Adolescent Brain Cognitive Development study using different discovery genome-wide association study (GWAS) results for post-traumatic stress disorder (PTSD), type 2 diabetes (T2D), and height. We found that pairs of EUR-ancestry GWAS for the same trait had genetic correlations >0.92. However, PGS calculated from pairs of same-ancestry and different-ancestry GWAS had correlations that ranged from <0.01 to 0.74. PGS stability was greater for height than for PTSD or T2D. A series of height GWAS in the UK Biobank suggested that correlation between PGS is strongly dependent on the extent of sample overlap between the discovery GWAS. Focusing on the upper end of the PGS distribution, different discovery GWAS do not consistently identify the same individuals in the upper quantiles, with the best case being 60% of individuals above the 80th percentile of PGS overlapping from one height GWAS to another. The degree of overlap decreases sharply as higher quantiles, less heritable traits, and different-ancestry GWAS are considered. PGS computed from different discovery GWAS have only modest correlation at the individual level, underscoring the need to proceed cautiously with integrating PGS into precision medicine applications.4415/5962Secondary AnalysisShared
A Phenome-Wide Association Study (PheWAS) of Late Onset Alzheimer Disease Genetic Risk in Children of European Ancestry at Middle Childhood: Results from the ABCD StudyGenetic risk for Late Onset Alzheimer Disease (AD) has been associated with lower cognition and smaller hippocampal volume in healthy young adults. However, whether these and other associations are present during childhood remains unclear. Using data from 5556 genomically-confirmed European ancestry youth who completed the baseline session of the ongoing the Adolescent Brain Cognitive DevelopmentSM Study (ABCD Study®), our phenome-wide association study estimating associations between four indices of genetic risk for late-onset AD (i.e., AD polygenic risk scores (PRS), APOE rs429358 genotype, AD PRS with the APOE region removed (ADPRS-APOE), and an interaction between ADPRS-APOE and APOE genotype) and 1687 psychosocial, behavioral, and neural phenotypes revealed no significant associations after correction for multiple testing (all ps > 0.0002; all pfdr > 0.07). These data suggest that AD genetic risk may not phenotypically manifest during middle-childhood or that effects are smaller than this sample is powered to detect.4403/5556Secondary AnalysisShared
Changes in patterns of age-related network connectivity are associated with risk for schizophreniaAlterations in fMRI-based brain functional network connectivity (FNC) are associated with schizophrenia (SCZ) and the genetic risk or subthreshold clinical symptoms preceding the onset of SCZ, which often occurs in early adulthood. Thus, age-sensitive FNC changes may be relevant to SCZ risk-related FNC. We used independent component analysis to estimate FNC from childhood to adulthood in 9,236 individuals. To capture individual brain features more accurately than single-session fMRI, we studied an average of three fMRI scans per individual. To identify potential familial risk-related FNC changes, we compared age-related FNC in unaffected siblings (SIB) and first-degree relatives (FHR) of SCZ patients with neurotypical controls (NC) at the same age-stage. Then, we examined how polygenic risk scores for SCZ influenced risk-related FNC patterns. Finally, we investigated the same risk-related FNC patterns in adult SCZ patients (oSCZ) and young individuals with subclinical psychotic symptoms (PSY). Age-sensitive risk-related FNC patterns emerge during adolescence and early adulthood, but not before. Young SIB always followed older NC patterns, with decreased FNC in a cerebellar-occipitoparietal circuit and increased FNC in two prefrontal-sensorimotor circuits when compared to young NC. Some of these FNC alterations were also found in oSCZ, with one exhibiting reversed pattern. All were linked to polygenic risk for SCZ in unrelated individuals (R2 varied from 0.02 to 0.09). Young PSY showed FNC alterations in the same direction as SIB when compared to NC. These results suggest that age-related neurotypical FNC correlate with genetic risk for SCZ and are detectable with MRI in young participants. 3615/4936Secondary AnalysisShared
ABCD Neurocognitive Prediction Challenge 2019: Test SetThe test data set for the ABCD Neurocognitive Prediction Challenge 2019 contains skull stripped and segmented T1-weighted MRIs, and volumetric brain measures of 3648 participants of the ABCD study. https://sibis.sri.com/abcd-np-challenge provides a detailed description about the processing. When using the data in publications, the Data Supplement of "Pfefferbaum et al., Altered Brain Developmental Trajectories in Adolescents After Initiating Drinking. Am J Psychiatry, 175(4), pp. 370-380, 2018" for should be cited as description of the processing pipeline. The data in this Study were derived from the Adolescent Brain Cognitive Development 1.1 Release (http://dx.doi.org/10.15154/1460410, accessed on or before November 15, 2018) and the Fast Track DICOM share in the Adolescent Brain Cognitive Development Study Collection 2573 (https://ndar.nih.gov/edit_collection.html?id=2573, accessed on or before November 15, 2018). The individual-level imaging phenotype data in this Collection was computed by a custom processing pipeline developed by the organizers of the ABCD Prediction Challenge. The imaging phenotype data may therefore differ from the values shared by the ABCD Study investigators in Release 1.1 or future releases4515/4516Secondary AnalysisShared
Overlapping brain correlates of superior cognition among children at genetic risk for Alzheimer’s disease and/ or major depressive disorderEarly life adversity (ELA) tends to accelerate neurobiological ageing, which, in turn, is thought to heighten vulnerability to both Major Depressive Disorder (MDD) and Alzheimer’s Disease (AD). The two conditions are putatively related, with MDD representing either a risk factor or early symptom of AD. Given the substantial environmental susceptibility of both disorders, timely identification of their neurocognitive markers could facilitate interventions to prevent clinical onset. To this end, we analysed multimodal data from the Adolescent Brain and Cognitive Development study (ages 9-10 years). To disentangle genetic from correlated genetic-environmental influences, while also probing gene-adversity interactions, we compared adoptees, a group generally exposed to substantial ELA, with children raised by their biological families via genetic risk scores (GRS) from genome-wide association studies. AD and MDD GRSs predicted overlapping and widespread neurodevelopmental alterations associated with superior fluid cognition. Specifically, among adoptees only, greater AD GRS were related to accelerated structural maturation (i.e., cortical thinning) and higher MDD GRS were linked to delayed functional neurodevelopment, as reflected in compensatory brain activation on an inhibitory control task. Our study identifies compensatory mechanisms linked to MDD risk and highlights the potential cognitive benefits of accelerated maturation linked to AD vulnerability in late childhood.3535/4499Secondary AnalysisShared
Shared heritability of human face and brain shapeEvidence from model organisms and clinical genetics suggests coordination between the developing brain and face, but the role of this link in common genetic variation remains unknown. We performed a multivariate genome-wide association study of cortical surface morphology in 19,644 individuals of European ancestry, identifying 472 genomic loci influencing brain shape, of which 76 are also linked to face shape. Shared loci include transcription factors involved in craniofacial development, as well as members of signaling pathways implicated in brain-face cross-talk. Brain shape heritability is equivalently enriched near regulatory regions active in either forebrain organoids or facial progenitors. However, we do not detect significant overlap between shared brain-face genome-wide association study signals and variants affecting behavioral-cognitive traits. These results suggest that early in embryogenesis, the face and brain mutually shape each other through both structural effects and paracrine signaling, but this interplay may not impact later brain development associated with cognitive function. 3640/4470Secondary AnalysisShared
Associations among household and neighborhood socioeconomic disadvantages, resting-state frontoamygdala connectivity, and internalizing symptoms in youthExposure to socioeconomic disadvantages (SED) can have negative impacts on mental health, yet SED is a multifaceted construct and the precise processes by which SED confer deleterious effects are less clear. Using a large and diverse sample of preadolescents (ages 9-10 at baseline; N = 4,038; 49% female) from the Adolescent Brain Cognitive Development Study, we examined associations among SED at both household (i.e., income-to-needs and material hardship) and neighborhood (i.e., area deprivation and neighborhood unsafety) levels, frontoamygdala resting-state functional connectivity, and internalizing symptoms at baseline and 1-year follow-up. SED were positively associated with internalizing symptoms at baseline, and indirectly predicted symptoms one year later through elevated symptoms at baseline. At the household level, youth in households characterized by higher disadvantage (i.e., lower income-to-needs ratio) exhibited more strongly negative frontoamygdala coupling, particularly between the bilateral amygdala and medial orbitofrontal (mOFC) regions within the Frontoparietal Network. While more strongly positive amygdala-mOFC coupling was associated with higher levels of internalizing symptoms at baseline and 1-year follow-up, it did not mediate the association between income-to-needs ratio and internalizing symptoms. However, at the neighborhood level, amygdala-mOFC functional coupling moderated the effect of neighborhood deprivation on internalizing symptoms. Specifically, higher neighborhood deprivation was associated with higher internalizing symptoms for youth with more strongly positive connectivity, but not for youth with more strongly negative connectivity, suggesting a potential buffering effect. Findings highlight the importance of capturing multileveled socioecological contexts in which youth develop to identify youth who are most likely to benefit from early interventions. Exposure to socioeconomic disadvantages (SED) can have negative impacts on mental health, yet SED is a multifaceted construct and the precise processes by which SED confer deleterious effects are less clear. Using a large and diverse sample of preadolescents (ages 9-10 at baseline; N = 4,038; 49% female) from the Adolescent Brain Cognitive Development Study, we examined associations among SED at both household (i.e., income-to-needs and material hardship) and neighborhood (i.e., area deprivation and neighborhood unsafety) levels, frontoamygdala resting-state functional connectivity, and internalizing symptoms at baseline and 1-year follow-up. SED were positively associated with internalizing symptoms at baseline, and indirectly predicted symptoms one year later through elevated symptoms at baseline. At the household level, youth in households characterized by higher disadvantage (i.e., lower income-to-needs ratio) exhibited more strongly negative frontoamygdala coupling, particularly between the bilateral amygdala and medial orbitofrontal (mOFC) regions within the Frontoparietal Network. While more strongly positive amygdala-mOFC coupling was associated with higher levels of internalizing symptoms at baseline and 1-year follow-up, it did not mediate the association between income-to-needs ratio and internalizing symptoms. However, at the neighborhood level, amygdala-mOFC functional coupling moderated the effect of neighborhood deprivation on internalizing symptoms. Specifically, higher neighborhood deprivation was associated with higher internalizing symptoms for youth with more strongly positive connectivity, but not for youth with more strongly negative connectivity, suggesting a potential buffering effect. Findings highlight the importance of capturing multileveled socioecological contexts in which youth develop to identify youth who are most likely to benefit from early interventions. 4014/4163Secondary AnalysisShared
Neuroanatomical correlates of genetic risk for obesity in children Obesity has a strong genetic component, with up to 20% of variance in body mass index (BMI) being accounted for by common polygenic variation. Most genetic polymorphisms associated with BMI are related to genes expressed in the central nervous system. At the same time, higher BMI is associated with neurocognitive changes. However, the direct link between genetics of obesity and neurobehavioral mechanisms related to weight gain is missing. Here, we use a large sample of participants (n > 4000) from the Adolescent Brain Cognitive Development cohort to investigate how genetic risk for obesity, expressed as polygenic risk score for BMI (BMI-PRS), is related to brain and behavioral measures in adolescents. In a series of analyses, we show that BMI-PRS is related to lower cortical volume and thickness in the frontal and temporal areas, relative to age-expected values. Relatedly, using structural equation modeling, we find that lower overall cortical volume is associated with higher impulsivity, which in turn is related to an increase in BMI 1 year later. In sum, our study shows that obesity might partially stem from genetic risk as expressed in brain changes in the frontal and temporal brain areas, and changes in impulsivity. 3417/4157Secondary AnalysisShared
Childhood obesity, cortical structure and executive function in healthy childrenThe development of executive function is linked to maturation of prefrontal cortex in childhood. Childhood obesity has been associated with changes in brain structure, particularly in prefrontal cortex, as well as deficits in executive functions. We aimed to determine whether differences in cortical structure mediate the relationship between executive function and childhood obesity. We analysed MR-derived measures of cortical thickness for 2,700 children between the ages of 9-11 years, recruited as part of the NIH ABCD study. We related our findings to measures of executive function and body mass index (BMI). In our analysis, increased BMI was associated with significantly reduced mean cortical thickness, as well as specific bilateral reduced cortical thickness in prefrontal cortical regions. This relationship remained after accounting for age, sex, race, parental education, household income, birth-weight and in-scanner motion. Increased BMI was also associated with lower executive function. Reduced cortical thickness was found to mediate the relationship between BMI and executive function such that reduced thickness in the rostral medial and superior frontal cortex, the inferior frontal gyrus and the lateral orbitofrontal cortex accounted for partial reductions in executive function. These results suggest that childhood obesity is associated with compromised executive function. This relationship may be partly explained by BMI-associated reduced cortical thickness in the prefrontal cortex. 3766/3921Secondary AnalysisShared
ABCD Neurocognitive Prediction Challenge 2019: Training SetTraining data set for the ABCD Neurocognitive Prediction Challenge 2019 containing skull stripped and segmented T1-weighted MRIs, volumetric brain measures, and residual fluid intelligence scores of 3739 participants of the ABCD study. https://sibis.sri.com/abcd-np-challenge provides a detailed description about the processing. When using the data in publications, the Data Supplement of "Pfefferbaum et al., Altered Brain Developmental Trajectories in Adolescents After Initiating Drinking. Am J Psychiatry, 175(4), pp. 370-380, 2018" for should be cited as description of the processing pipeline. The data in this Study were derived from the Adolescent Brain Cognitive Development 1.1 Release (http://dx.doi.org/10.15154/1460410, accessed on or before November 15, 2018) and the Fast Track DICOM share in the Adolescent Brain Cognitive Development Study Collection 2573 (https://ndar.nih.gov/edit_collection.html?id=2573, accessed on or before November 15, 2018). The individual-level imaging phenotype data in this Collection was computed by a custom processing pipeline developed by the organizers of the ABCD Prediction Challenge. The imaging phenotype data may therefore differ from the values shared by the ABCD Study investigators in Release 1.1 or future releases3739/3739Secondary AnalysisShared
Relating neighborhood deprivation to childhood obesity in the ABCD Study®: evidence for theories of neuroinflammation and neuronal stressObjective: We evaluated whether relationships between area deprivation (ADI), body mass index (BMI) and brain structure (e.g., cortical thickness, subcortical volume) during pre-adolescence supported the immunologic model of self-regulation failure (NI) and/or neuronal stress (NS) theories of overeating. The NI theory proposes that ADI causes structural alteration in the brain due to the neuroinflammatory effects of overeating unhealthy foods. The NS theory proposes that ADI-related stress negatively impacts brain structure, which causes stress-related overeating and subsequent obesity. Methods: Data were gathered from the Adolescent Brain Cognitive DevelopmentSM Study® (9-12-years-old; n=3,087, 51% male). Linear mixed-effects models identified brain regions that were associated with both ADI and BMI; longitudinal associations were evaluated with mediation models. The NI model included ADI and BMI at 9/10-years-old and brain data at 11/12-years-old. The NS model included ADI and brain data at 9/10-years-old and BMI at 11/12-years-old. Results: BMI at 9/10-years-old partially mediated the relationship between ADI and Ventral DC volume at 11/12-years-old. Additionally, the Ventral DC at 9/10-years-old partially mediated the relationship between ADI and BMI at 11/12-years-old, even in youth who at baseline, were of a healthy weight. Results were unchanged when controlling for differences in brain structure and weight across the two-years. Conclusion: Greater area deprivation may indicate fewer access to resources that support healthy development, like nutritious food and nonstressful environments. Our findings provide evidence in support of the NI and NS theories of overeating, specifically, with greater ADI influencing health outcomes of obesity via brain structure alterations. 2769/3087Secondary AnalysisShared
What Is the Link Between Attention-Deficit/Hyperactivity Disorder and Sleep Disturbance? A Multimodal Examination of Longitudinal Relationships and Brain Structure Using Large-Scale Population-Based CohortsBackground: Attention-deficit/hyperactivity disorder (ADHD) comorbid with sleep disturbances can produce profound disruption in daily life and negatively impact quality of life of both the child and the family. However, the temporal relationship between ADHD and sleep impairment is unclear, as are underlying common brain mechanisms. Methods: This study used data from the Quebec Longitudinal Study of Child Development (n = 1601, 52% female) and the Adolescent Brain Cognitive Development Study (n = 3515, 48% female). Longitudinal relationships between symptoms were examined using cross-lagged panel models. Gray matter volume neural correlates were identified using linear regression. The transcriptomic signature of the identified brain-ADHD-sleep relationship was characterized by gene enrichment analysis. Confounding factors, such as stimulant drugs for ADHD and socioeconomic status, were controlled for. Results: ADHD symptoms contributed to sleep disturbances at one or more subsequent time points in both cohorts. Lower gray matter volumes in the middle frontal gyrus and inferior frontal gyrus, amygdala, striatum, and insula were associated with both ADHD symptoms and sleep disturbances. ADHD symptoms significantly mediated the link between these structural brain abnormalities and sleep dysregulation, and genes were differentially expressed in the implicated brain regions, including those involved in neurotransmission and circadian entrainment. Conclusions: This study indicates that ADHD symptoms and sleep disturbances have common neural correlates, including structural changes of the ventral attention system and frontostriatal circuitry. Leveraging data from large datasets, these results offer new mechanistic insights into this clinically important relationship between ADHD and sleep impairment, with potential implications for neurobiological models and future therapeutic directions.2974/3075Secondary AnalysisShared
Investigation of Psychiatric and Neuropsychological Correlates of Default Mode Network and Dorsal Attention Network Anticorrelation in Children.The default mode network (DMN) and dorsal attention network (DAN) demonstrate an intrinsic "anticorrelation" in healthy adults, which is thought to represent the functional segregation between internally and externally directed thought. Reduced segregation of these networks has been proposed as a mechanism for cognitive deficits that occurs in many psychiatric disorders, but this association has rarely been tested in pre-adolescent children. The current analysis used data from the Adolescent Brain Cognitive Development study to examine the relationship between the strength of DMN/DAN anticorrelation and psychiatric symptoms in the largest sample to date of 9- to 10-year-old children (N = 6543). The relationship of DMN/DAN anticorrelation to a battery of neuropsychological tests was also assessed. DMN/DAN anticorrelation was robustly linked to attention problems, as well as age, sex, and socioeconomic factors. Other psychiatric correlates identified in prior reports were not robustly linked to DMN/DAN anticorrelation after controlling for demographic covariates. Among neuropsychological measures, the clearest correlates of DMN/DAN anticorrelation were the Card Sort task of executive function and cognitive flexibility and the NIH Toolbox Total Cognitive Score, although these did not survive correction for socioeconomic factors. These findings indicate a complicated relationship between DMN/DAN anticorrelation and demographics, neuropsychological function, and psychiatric problems.2201/3004Secondary AnalysisShared
Different patterns of intrinsic functional connectivity at the default mode and attentional networks predict crystallized and fluid abilities in childhoodCrystallized abilities are skills used to solve problems based on experience, while fluid abilities are linked to reasoning without prior knowledge. To what extent crystallized and fluid abilities involve dissociated or overlapping neural systems is debatable. Due to often deployed small sample sizes or different study settings in prior work, the neural basis of crystallized and fluid abilities in childhood remains largely unknown. Here we analyzed within and between network connectivity patterns from resting-state functional MRI of 2707 children from the ABCD study. We hypothesized that differences in functional connectivity at the default mode network (DMN) and ventral and dorsal attentional networks (VAN, DAN) explain differences in fluid and crystallized abilities. We found that stronger between-network connectivity of the DMN and VAN, DMN and DAN, and VAN and DAN predicted crystallized abilities. Within-network connectivity of the DAN predicted both crystallized and fluid abilities. Our findings reveal that crystallized abilities rely on the functional coupling between attentional networks and the DMN, whereas fluid abilities are associated with a focal connectivity configuration at the DAN. Our study provides new evidence into the neural basis of child intelligence and calls for future comparative research in adulthood during neuropsychiatric diseases.2156/2707Secondary AnalysisShared
A pattern of cognitive resource disruptions in childhood psychopathologyThe Hurst exponent ( H ) isolated in fractal analyses of neuroimaging time-series is implicated broadly in cognition. Within this literature, H is associated with multiple mental disorders, suggesting that H is transdimensionally associated with psychopathology. Here, we unify these results and demonstrate a pattern of decreased H with increased general psychopathology and attention-deficit/hyperactivity factor scores during a working memory task in 1,839 children. This pattern predicts current and future cognitive performance in children and some psychopathology in 703 adults. This pattern also defines psychological and functional axes associating psychopathology with an imbalance in resource allocation between fronto-parietal and sensory-motor regions, driven by reduced resource allocation to fronto-parietal regions. This suggests the hypothesis that impaired working memory function in psychopathology follows from a reduced cognitive resource pool and a reduction in resources allocated to the task at hand.1445/1839Secondary AnalysisShared
Comparison Between Gradients and Parcellations for Functional Connectivity Prediction of BehaviorResting-state functional connectivity (RSFC) is widely used to predict behavioral measures. To predict behavioral measures, representing RSFC with parcellations and gradients are the two most popular approaches. Here, we compare parcellation and gradient approaches for RSFC-based prediction of a broad range of behavioral measures in the Human Connectome Project (HCP) and Adolescent Brain Cognitive Development (ABCD) datasets. Among the parcellation approaches, we consider group-average “hard” parcellations (Schaefer et al., 2018), individual-specific “hard” parcellations (Kong et al., 2021a), and an individual-specific “soft” parcellation (spatial independent component analysis with dual regression; Beckmann et al., 2009). For gradient approaches, we consider the well-known principal gradients (Margulies et al., 2016) and the local gradient approach that detects local RSFC changes (Laumann et al., 2015). Across two regression algorithms, individual-specific hard-parcellation performs the best in the HCP dataset, while the principal gradients, spatial independent component analysis and group-average “hard” parcellations exhibit similar performance. On the other hand, principal gradients and all parcellation approaches perform similarly in the ABCD dataset. Across both datasets, local gradients perform the worst. Finally, we find that the principal gradient approach requires at least 40 to 60 gradients to perform as well as parcellation approaches. While most principal gradient studies utilize a single gradient, our results suggest that incorporating higher order gradients can provide significant behaviorally relevant information. Future work will consider the inclusion of additional parcellation and gradient approaches for comparison.1143/1476Secondary AnalysisShared
A morphometrics approach for inclusion of localised characteristics from medical imaging studies into genome-wide association studiesMedical images, such as magnetic resonance or computed tomography, are increasingly being used to investigate the genetic architecture of neurological diseases like Alzheimer's disease, or psychiatric disorders like attention-deficit hyperactivity disorder. The quantified global or regional brain imaging measures are commonly known as imaging-specific or -derived phenotypes (IDPs) when conducting genotype-phenotype association studies. Inclusion of whole medical images rather than derived tabular data as IDPs has been done by either a voxel-wise approach or a global approach of whole medical images via principal component analysis. Limitations with multiple testing and inability to isolate high variation regions within the principal components arise with either of these approaches. This work proposes a principal component analysis-like localised approach of dimensionality reduction using diffeomorphic morphometry allowing for the selection of distances to model more regional effects.The main benefit of the proposed method is that it can can reduce the dimensionality of the problem considerably in comparison to the medical image's variability it is describing while grouping spatial information potentially lost in dimensionality reduction techniques like principal component analyses. Moreover, the approach not only allows to include locality in the analysis but can also be used as a generative model to explore the morphometric changes across an axis of particular components of interest. To demonstrate the feasibility of this pipeline for inclusion in a multivariate genome-wide association study, it was applied to 1,359 subjects from the Adolescent Brain Cognitive Development Study for traits related to attention-deficit disorder. The results show that the proposed method can identify more specific morphometric features associated with genome regions.1107/1359Secondary AnalysisShared
Predicting multilingual effects on executive function and individual connectomes in children: An ABCD studyWhile there is a substantial amount of work studying multilingualism’s effect on cognitive functions, little is known about how the multilingual experience modulates the brain as a whole. In this study, we analyzed data of over 1,000 children from the Adolescent Brain Cognitive Development (ABCD) Study to examine whether monolinguals and multilinguals differ in executive function, functional brain connectivity, and brain–behavior associations. We observed significantly better performance from multilingual children than monolinguals in working-memory tasks. In one finding, we were able to classify multilinguals from monolinguals using only their whole-brain functional connectome at rest and during an emotional n-back task. Compared to monolinguals, the multilingual group had different functional connectivity mainly in the occipital lobe and subcortical areas during the emotional n-back task and in the occipital lobe and prefrontal cortex at rest. In contrast, we did not find any differences in behavioral performance and functional connectivity when performing a stop-signal task. As a second finding, we investigated the degree to which behavior is reflected in the brain by implementing a connectome-based behavior prediction approach. The multilingual group showed a significant correlation between observed and connectome-predicted individual working-memory performance scores, while the monolingual group did not show any correlations. Overall, our observations suggest that multilingualism enhances executive function and reliably modulates the corresponding brain functional connectome, distinguishing multilinguals from monolinguals even at the developmental stage.1030/1075Secondary AnalysisShared
Lifestyle Factors Counteract the Neurodevelopmental Impact of Genetic Risk for Accelerated Brain Aging in AdolescenceBackground The transition from childhood to adolescence is characterised by enhanced neural plasticity and a consequent susceptibility to both beneficial and adverse aspects of one’s milieu. Methods To understand the implications of the interplay between protective and risk-enhancing factors, we analysed longitudinal data from the Adolescent Brain and Cognitive Development study (N = 834; 394 female). We probed the maturational correlates of positive lifestyle variables (friendships, parental warmth, school engagement, physical exercise, healthy nutrition) and of genetic vulnerability to neuropsychiatric disorders (Major Depressive Disorder, Alzheimer’s Disease, Anxiety Disorders, Bipolar Disorder, Schizophrenia), and further sought to elucidate their implications for psychological well-being. Results Genetic risk factors and lifestyle buffers showed divergent relationships with later attentional and interpersonal problems. These effects were mediated by distinguishable functional neurodevelopmental deviations spanning the limbic, default mode, visual and control systems. Specifically, greater genetic vulnerability was associated with alterations in the normative maturation of areas rich in dopamine (D2), glutamate and serotonin receptors, and of areas with stronger expression of astrocytic and microglial genes, a molecular signature implicated in the brain disorders discussed here. Greater availability of lifestyle buffers predicted deviations in the normative functional development of higher density GABA-ergic receptor regions. The two profiles of neurodevelopmental alterations showed complementary roles in protection against psychopathology, which varied with environmental stress levels. Conclusions Our results underscore the importance of educational involvement and healthy nutrition in attenuating the neurodevelopmental sequelae of genetic risk factors. They also underscore the importance of characterising early life biomarkers associated with adult-onset pathologies.797/843Secondary AnalysisShared
Longitudinal assessment of brain structure and behavior in youth with rapid weight gain: Potential contributing causes and consequencesObjective: Independent of weight status, rapid weight gain has been associated with underlying brain structure variation in regions associated with food intake and impulsivity among pre-adolescents. Yet, we lack clarity on how developmental maturation coincides with rapid weight gain and weight stability. Methods: We identified brain predictors of two-year rapid weight gain and its longitudinal effects on brain structure and impulsivity in the Adolescent Brain Cognitive DevelopmentSM Study®. Youth were categorized as Healthy Weight/Weight Stable (WSHW, n=527) or Weight Gainers (WG, n=221, >38lbs); 63% of the WG group were healthy weight at 9-to-10-years-old. Results: A five-fold cross-validated logistic elastic-net regression revealed that rapid weight gain was associated with structural variation amongst 39 brain features at 9-to-10-years-old in regions involved with executive functioning, appetitive control, and reward sensitivity. Two years later, WG youth showed differences in change over time in several of these regions and performed worse on measures of impulsivity. Conclusions: These findings suggest that brain structure in pre-adolescence may predispose some to rapid weight gain and that weight gain itself may alter maturational brain change in regions important for food intake and impulsivity. Behavioral interventions that target inhibitory control may improve trajectories of brain maturation and facilitate healthier behaviors. 693/748Secondary AnalysisShared
Latent Profiles of Sleep Patterns in Adolescence: Associations with Behavioral Health RiskPurpose: The present study characterized sleep profiles in a national longitudinal sample of early adolescents and examined whether profiles predicted later behavioral problems. Methods: Three waves of data (2016-2021) were obtained from the Adolescent Behavior and Cognitive Development study, including 3,326 participants with both weekday and weekend sleep data measured by Fitbit wearables (age range 10.58 to 13.67; 49.3% female). Latent profile analysis was utilized to identify sleep profiles using multiple sleep indicators (duration, latency, efficiency, wake minutes, wake counts, midpoint). We then explored whether demographic predictors predicted profile membership and tested the latent sleep profiles’ predictive utility of internalizing and externalizing symptoms. Results: Four profiles were identified: average sleep (40.39%), high duration & high wakefulness (28.58%), high efficiency, low duration & low wakefulness (16.86%), and low duration & low efficiency (14.17%). Participants with older age, males, higher BMI, and advanced pubertal status were more likely to be classified in the low duration & low efficiency profile than average group. Participants with lower income, minority identification, older age, and higher BMI were more likely to be classified in the high efficiency, low duration & low wakefulness than the average group. Participants with lower parental education and males were more likely to be in the high sleep duration & high wakefulness than the average group. The low duration & low efficiency group had the highest attention problems, social problems, and rule-breaking behaviors. Conclusion: Our findings highlight unique sleep patterns in early adolescence and their prospective links with internalizing and externalizing problems. Keywords: sleep, risk, internalizing problems, externalizing problems, latent profile 459/500Secondary AnalysisShared
ABCD Neurocognitive Prediction Challenge 2019: Validation setValidation data set for the ABCD Neurocognitive Prediction Challenge 2019 containing skull stripped and segmented T1-weighted MRIs, volumetric brain measures, and residual fluid intelligence scores of 415 participants of the ABCD study. https://sibis.sri.com/abcd-np-challenge provides a detailed description about the processing. When using the data in publications, the Data Supplement of "Pfefferbaum et al., Altered Brain Developmental Trajectories in Adolescents After Initiating Drinking. Am J Psychiatry, 175(4), pp. 370-380, 2018" for should be cited as description of the processing pipeline. The data in this Study were derived from the Adolescent Brain Cognitive Development 1.1 Release (http://dx.doi.org/10.15154/1460410, accessed on or before November 15, 2018) and the Fast Track DICOM share in the Adolescent Brain Cognitive Development Study Collection 2573 (https://ndar.nih.gov/edit_collection.html?id=2573, accessed on or before November 15, 2018). The individual-level imaging phenotype data in this Collection was computed by a custom processing pipeline developed by the organizers of the ABCD Prediction Challenge. The imaging phenotype data may therefore differ from the values shared by the ABCD Study investigators in Release 1.1 or future releases415/415Secondary AnalysisShared
* Data not on individual level

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