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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.

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Your Collection is now in Data Analysis phase and exempt from biannual submissions. Analyzed data is still expected prior to publication or no later than the project end date.

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[CMS] Please confirm that you will not be enrolling any more subjects and that all raw data has been collected and submitted.
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Unable to change collection phase where targeted enrollment is less than 90%

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Collection Summary Collection Charts
Collection Title Collection Investigators Collection Description
HCP-A Mapping the Human Connectome During Typical Aging
Beau Ances, Susan Bookheimer, Randy Buckner, David Salat, Stephen Smith, Melissa Terpstra, Kamil Ugurbil, David Van Essen, Roger Woods 
The major technological and analytical advances in human brain imaging achieved as part of the Human Connectome Projects (HCP) enable examination of structural and functional brain connectivity at unprecedented levels of spatial and temporal resolution. This information is proving invaluable for enhancing our understanding of normative variation in young adult brain connectivity. It is now timely to use the tools and analytical approaches developed by the HCP to understand how structural and functional wiring of the brain changes during the aging process. Using state-of-the art HCP imaging approaches will allow investigators to push our currently limited understanding of normative brain aging to new levels. We propose an effort involving a consortium of five sites (Massachusetts General Hospital, University of California at Los Angeles, University of Minnesota, Washington University in St. Louis, and Oxford University), with extensive complementary expertise in human brain imaging and aging and including many investigators associated with the original adult and pilot lifespan HCP efforts. This synergistic integration of advances from the MGH and WU-MINN-OXFORD HCPs with cutting-edge expertise in aging provides an unprecedented opportunity to advance our understanding of the normative changes in human brain connectivity with aging. Aim 1 will be to optimize existing HCP Lifespan Pilot project protocols to respect practical constraints in studying adults over a wide age range, including the very old (80+ years). Aim 2 will be to collect high quality neuroimaging, behavioral, and other datasets on 1200 individuals in the age range of 36 - 100+ years, using matched protocols across sites. This will enable robust cross-sectional analyses of age-related changes in network properties including metrics of connectivity, network integrity, response properties during tasks, and behavior. Aim 3 will be to collect and analyze longitudinal data on a subset of 300 individuals in three understudied and scientifically interesting groups: ages 36-44 (when late maturational and early aging processes may co-occur); ages 45-59 (perimenopausal, when rapid hormonal changes can affect cognition and the brain); and ages 80 - 100+ (the `very old', whose brains may reflect a `healthy survivor' state). The information gained relating to these important periods will enhance our understanding of how important phenomena such as hormonal changes affect the brain and will provide insights into factors that enable cognitively intact function into advanced aging. Aim 4 will capitalize on our success in sharing data in the Human Connectome Project (HCP), and will use these established tools, platforms, and procedures to make this data publicly available through the Connectome Coordination Facility.
Connectome Coordination Facility
Human Connectome Project (HCP), NIMH Repository & Genomics Resource (NRGR)
Funding Completed
Close Out
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NIH - Extramural None

1-Supp_Bookheimer_LS_HCA.docx Background tesat General Public
https://www.humanconnectome.org/storage/app/media/documentation/LS2.0/LS_Release_2.0_Access_Instructions.pdf Results Detailed instructions on applying for NDA access to Lifespan 2.0 Release HCP-Aging & HCP-Development data, selecting, and downloading data with download manager and on the command line. General Public
https://www.humanconnectome.org/storage/app/media/documentation/LS2.0/LS2.0_Crosswalk_Behavioral_Data_Dictionary.xlsx Results Crosswalk .xlsx spreadsheet linking behavioral NDA structures and elements to the original HCP variable descriptions. hcp description column is current best data dictionary for variables and answer codes as of the Lifespan 2.0 Release. General Public
https://www.humanconnectome.org/storage/app/media/documentation/LS2.0/HCA_LS_2.0_subject_completeness.csv Results CSV containing per subject completeness of imaging modalities, QC issue codes, behavioral data availability, and unrelated status for finding subjects of interest for analyses. General Public
https://www.humanconnectome.org/storage/app/media/documentation/LS2.0/LS_2.0_Release_Appendix_2.pdf Results Lifespan 2.0 Release HCP-Aging and HCP-Development: Details and References for Behavioral & Clinical Instruments General Public
https://www.humanconnectome.org/storage/app/media/documentation/LS2.0/LS_2.0_Release_Appendix_1.pdf Results File names and directory structure of Lifespan 2.0 Release HCP-Aging and HCP-Development preprocessed and unprocessed imaging data. Organized by OPTION 2 filters/HCP package names on NDA HCP Aging & Development query page. General Public

U01AG052564-01 Mapping the Human Connectome During Typical Aging 08/19/2016 05/31/2020 3600 5850 WASHINGTON UNIVERSITY $3,760,565.00


NDA Help Center

Collection - General Tab

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

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

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

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

Blinded Clinical Trial Status:

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

Funding Source

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

Supporting Documentation

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

Grant Information

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

Clinical Trials

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

Frequently Asked Questions

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


  • Administrator Privilege
    A privilege provided to a user associated with an NDA Collection or NDA Study whereby that user can perform a full range of actions including providing privileges to other users.
  • Collection Owner
    Generally, the Collection Owner is the contact PI listed on a grant. Only one NDA user is listed as the Collection owner. Most automated emails are primarily sent to the Collection Owner.
  • Collection Phase
    The Collection Phase provides information on data submission as opposed to grant/project completion so while the Collection phase and grant/project phase may be closely related they are often different. Collection users with Administrative Privileges are encouraged to edit the Collection Phase. The Program Officer as listed in eRA (for NIH funded grants) may also edit this field. Changes must be saved by clicking the Save button at the bottom of the page. This field is sortable alphabetically in ascending or descending order. Collection Phase options include:
    • Pre-Enrollment: A grant/project has started, but has not yet enrolled subjects.
    • Enrolling: A grant/project has begun enrolling subjects. Data submission is likely ongoing at this point.
    • Data Analysis: A grant/project has completed enrolling subjects and has completed all data submissions.
    • Funding Completed: A grant/project has reached the project end date.
  • Collection Title
    An editable field with the title of the Collection, which is often the title of the grant associated with the Collection.
  • Grant
    Provides the grant number(s) for the grant(s) associated with the Collection. The field is a hyperlink so clicking on the Grant number will direct the user to the grant information in the NIH Research Portfolio Online Reporting Tools (RePORT) page.
  • Supporting Documentation
    Various documents and materials to enable efficient use of the data by investigators unfamiliar with the project and may include the research protocol, questionnaires, and study manuals.
  • NIH Research Initiative
    NDA Collections may be organized by scientific similarity into NIH Research Initiatives, to facilitate query tool user experience. NIH Research Initiatives map to one or multiple Funding Opportunity Announcements.
  • Permission Group
    Access to shared record-level data in NDA is provisioned at the level of a Permission Group. NDA Permission Groups consist of one or multiple NDA Collections that contain data with the same subject consents.
  • Planned Enrollment
    Number of human subject participants to be enrolled in an NIH-funded clinical research study. The data is provided in competing applications and annual progress reports.
  • Actual Enrollment
    Number of human subjects enrolled in an NIH-funded clinical research study. The data is provided in annual progress reports.
  • NDA Collection
    A virtual container and organization structure for data and associated documentation from one grant or one large project/consortium. It contains tools for tracking data submission and allows investigators to define a wide array of other elements that provide context for the data, including all general information regarding the data and source project, experimental parameters used to collect any event-based data contained in the Collection, methods, and other supporting documentation. They also allow investigators to link underlying data to an NDA Study, defining populations and subpopulations specific to research aims.
  • Data Use Limitations
    Data Use Limitations (DULs) describe the appropriate secondary use of a dataset and are based on the original informed consent of a research participant. NDA only accepts consent-based data use limitations defined by the NIH Office of Science Policy.
  • Total Subjects Shared
    The total number of unique subjects for whom data have been shared and are available for users with permission to access data.
IDNameCreated DateStatusType
1215CARIT (Go/Nogo) Task02/20/2019ApprovedfMRI
1216Vismotor Task02/20/2019ApprovedfMRI
1217FaceName Task02/20/2019ApprovedfMRI
1218Resting State02/20/2019ApprovedfMRI
2327CARIT-Vismotor-FaceName Tasks07/20/2023ApprovedfMRI
2328CONCAT (concatenated fMRI, Resting-CARIT-Vismotor-FaceName)07/20/2023ApprovedfMRI

NDA Help Center

Collection - Experiments

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

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

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

Frequently Asked Questions

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

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

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


  • Experiment Status
    An Experiment must be Approved before data using the associated Experiment_ID may be uploaded.
  • Experiment ID
    The ID number automatically generated by NDA which must be included in the appropriate file when uploading data to link the Experiment Definition to the subject record.
Adult Self Report Clinical Assessments 720
Blood Sample Collection Clinical Assessments 725
Boston Assessment of Traumatic Brain Injury Clinical Assessments 725
Clinical Medical History Clinical Assessments 715
Cognition Composite Scores Clinical Assessments 632
Current Menstrual State and Menstrual History Clinical Assessments 406
Delay Discounting Task Clinical Assessments 719
Dimensional Change Card Sort Test (DCCS) Clinical Assessments 633
Edinburgh Handedness Inventory Clinical Assessments 725
FaceName Task Clinical Assessments 686
Flanker Task Clinical Assessments 633
Geriatric Adverse Life Events Scale Clinical Assessments 723
Image Imaging 725
Imaging Collection Imaging 725
International Physical Activity Questionnaire Clinical Assessments 725
Language Experience and Proficiency Questionnaire Clinical Assessments 725
Lawton and Brody Activities of Daily Living Clinical Assessments 339
Medications and Treatments Form Clinical Assessments 725
Montreal Cognitive Assessment Clinical Assessments 725
NEO-Five Factor Inventory Clinical Assessments 725
NIH Toolbox Emotion Domain - Emotional Support Survey Clinical Assessments 632
NIH Toolbox Emotion Domain - Friendship Survey Clinical Assessments 632
NIH Toolbox Emotion Domain - Peer Rejection and Perceived Rejection Surveys Clinical Assessments 632
NIH Toolbox Emotion Domain - Perceived Hostility Surveys Clinical Assessments 632
NIH Toolbox Emotion Domain - Psychological Well-Being Clinical Assessments 632
NIH Toolbox Emotion Domain - Self-Efficacy Survey Clinical Assessments 632
NIH Toolbox List Sorting Working Memory Test Clinical Assessments 634
NIH Toolbox Motor Domain Clinical Assessments 634
NIH Toolbox Oral Reading Recognition Test Clinical Assessments 634
NIH Toolbox Picture Vocabulary Test Clinical Assessments 634
NIH Toolbox Sensation Domain Clinical Assessments 633
PROMIS Anger Clinical Assessments 632
PROMIS Emotional Distress - Depression Clinical Assessments 632
PROMIS Emotional Distress-Anxiety Clinical Assessments 632
PROMIS General Life Satisfaction Clinical Assessments 602
PROMIS Social Isolation Clinical Assessments 632
Pattern Comparison Processing Speed Clinical Assessments 634
Penn Emotion Recognition Task Clinical Assessments 723
Perceived Stress Scale Clinical Assessments 632
Picture Sequence Memory Clinical Assessments 634
Pittsburgh Sleep Quality Index Clinical Assessments 725
Processed MRI Data Imaging 725
Research Subject Clinical Assessments 725
Rey Auditory Verbal Learning Test Clinical Assessments 720
SSAGA Cover Page and Demographics Clinical Assessments 719
Scanner Debriefing Interview Clinical Assessments 715
Trail Making Test, Child and Adult Clinical Assessments 724
Vital Signs Clinical Assessments 725

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
38900742Create StudyMorphometric brain organization across the human lifespan reveals increased dispersion linked to cognitive performance.PLoS biologyLi, Jiao; Zhang, Chao; Meng, Yao; Yang, Siqi; Xia, Jie; Chen, Huafu; Liao, WeiJune 1, 2024Not Determined
38895316Create StudyLower motor performance is linked with poor sleep quality, depressive symptoms, and grey matter volume alterations.bioRxiv : the preprint server for biologyKüppers, Vincent; Bi, Hanwen; Nicolaisen-Sobesky, Eliana; Hoffstaedter, Felix; Yeo, B T Thomas; Drzezga, Alexander; Eickhoff, Simon B; Tahmasian, MasoudJune 8, 2024Not Determined
38869938Create StudyBrain age has limited utility as a biomarker for capturing fluid cognition in older individuals.eLifeTetereva, Alina; Pat, NarunJune 13, 2024Not Determined
38831181Create StudyAge-related decline in thickness and surface area in the cortical surface and hippocampus: lifespan trajectories and decade-by-decade analyses.GeroScienceYu, JunhongJune 3, 2024Not Determined
38751218Create StudyDivergent association between pain intensity and resting-state fMRI-based brain entropy in different age groups.Journal of neuroscience researchDel Mauro, Gianpaolo; Sevel, Landrew Samuel; Boissoneault, Jeff; Wang, ZeMay 1, 2024Not Determined
38712073Create StudyCerebral perivascular spaces as predictors of dementia risk and accelerated brain atrophy.medRxiv : the preprint server for health sciencesBarisano, Giuseppe; Iv, Michael; Alzheimer’s Disease Neuroimaging Initiative; Choupan, Jeiran; Hayden-Gephart, MelanieApril 26, 2024Not Determined
38659856Create StudyRelease the Krakencoder: A unified brain connectome translation and fusion tool.bioRxiv : the preprint server for biologyJamison, Keith W; Gu, Zijin; Wang, Qinxin; Sabuncu, Mert R; Kuceyeski, AmyApril 15, 2024Not Determined
38496426Create StudyNormative modeling of thalamic nuclear volumes.medRxiv : the preprint server for health sciencesYoung, Taylor; Kumar, Vinod Jangid; Saranathan, ManojkumarMarch 8, 2024Not Determined
38385891Create StudyMeasures of resting-state brain network segregation and integration vary in relation to data quantity: implications for within and between subject comparisons of functional brain network organization.Cerebral cortex (New York, N.Y. : 1991)Han, Liang; Chan, Micaela Y; Agres, Phillip F; Winter-Nelson, Ezra; Zhang, Ziwei; Wig, Gagan SJanuary 31, 2024Not Determined
38350930Create StudyDeep learning segmentation of peri-sinus structures from structural magnetic resonance imaging: validation and normative ranges across the adult lifespan.Fluids and barriers of the CNSHett, Kilian; McKnight, Colin D; Leguizamon, Melanie; Lindsey, Jennifer S; Eisma, Jarrod J; Elenberger, Jason; Stark, Adam J; Song, Alexander K; Aumann, Megan; Considine, Ciaran M; Claassen, Daniel O; Donahue, Manus JFebruary 13, 2024Not Determined
38262221Create StudyImpact of white matter hyperintensities on structural connectivity and cognition in cognitively intact ADNI participants.Neurobiology of agingTaghvaei, Mohammad; Mechanic-Hamilton, Dawn J; Sadaghiani, Shokufeh; Shakibajahromi, Banafsheh; Dolui, Sudipto; Das, Sandhitsu; Brown, Christopher; Tackett, William; Khandelwal, Pulkit; Cook, Philip; Shinohara, Russell T; Yushkevich, Paul; Bassett, Danielle S; Wolk, David A; Detre, John A; Alzheimer’s Disease Neuroimaging InitiativeMarch 1, 2024Not Determined
38260665Create StudyTranslating phenotypic prediction models from big to small anatomical MRI data using meta-matching.bioRxiv : the preprint server for biologyWulan, Naren; An, Lijun; Zhang, Chen; Kong, Ru; Chen, Pansheng; Bzdok, Danilo; Eickhoff, Simon B; Holmes, Avram J; Yeo, B T ThomasJanuary 2, 2024Not Determined
38260265Create StudyAssessing White Matter Engagement in Brain Networks through Functional and Structural Connectivity Mapping.bioRxiv : the preprint server for biologyLi, Muwei; Schilling, Kurt G; Ding, Zhaohua; Gore, John CJanuary 5, 2024Not Determined
38213549Create StudyLEARNING MRI CONTRAST-AGNOSTIC REGISTRATION.Proceedings. IEEE International Symposium on Biomedical ImagingHoffmann, Malte; Billot, Benjamin; Iglesias, Juan E; Fischl, Bruce; Dalca, Adrian VApril 1, 2021Not Determined
38179863Create StudyMRI Assessment of Cerebral White Matter Microvascular Hemodynamics Across the Adult Lifespan.Journal of magnetic resonance imaging : JMRIDamestani, Nikou L; Jacoby, John; Michel, Christa B; Rashid, Barnaly; Salat, David H; Juttukonda, Meher RJanuary 5, 2024Not Determined
38106085Create StudyMultilayer meta-matching: translating phenotypic prediction models from multiple datasets to small data.bioRxiv : the preprint server for biologyChen, Pansheng; An, Lijun; Wulan, Naren; Zhang, Chen; Zhang, Shaoshi; Ooi, Leon Qi Rong; Kong, Ru; Chen, Jianzhong; Wu, Jianxiao; Chopra, Sidhant; Bzdok, Danilo; Eickhoff, Simon B; Holmes, Avram J; Yeo, B T ThomasDecember 7, 2023Not Determined
38014139Create StudyBrain signaling becomes less integrated and more segregated with age.bioRxiv : the preprint server for biologyRazban, Rostam M; Antal, Botond B; Dill, Ken A; Mujica-Parodi, Lilianne RMay 2, 2024Not Determined
38006514Create StudyAge-related decrease in inter-subject similarity of cortical morphology and task and resting-state functional connectivity.GeroScienceYu, JunhongFebruary 1, 2024Not Determined
37850792Create StudyChanges of gray matter volumes of subcortical regions across the lifespan: a Human Connectome Project study.Journal of neurophysiologyChristova, Peka; Georgopoulos, Apostolos PNovember 1, 2023Not Determined
37782994Create StudyEffects of sleep on brain perivascular space in a cognitively healthy population.Sleep medicineShih, Nien-Chu; Barisano, Giuseppe; Lincoln, Karen D; Mack, Wendy J; Sepehrband, Farshid; Choupan, JeiranNovember 1, 2023Not Determined
37609310Create StudyPredicting phenotypes of elderly from resting state fMRI.Research squareVerovnik, Barbara; Hajduk, Stefan; Hulle, Marc VanAugust 7, 2023Not Determined
37576741Create StudyReal-time brain masking algorithm improves motion tracking accuracy in scans with volumetric navigators (vNavs).Proceedings of the International Society for Magnetic Resonance in Medicine ... Scientific Meeting and Exhibition. International Society for Magnetic Resonance in Medicine. Scientific Meeting and ExhibitionHoffmann, Malte; Frost, Robert; Salat, David; Tisdall, M Dylan; Polimeni, Jonathan; van der Kouwe, AndréAugust 1, 2020Not Determined
37247763Create StudyCardiovascular and metabolic health is associated with functional brain connectivity in middle-aged and older adults: Results from the Human Connectome Project-Aging study.NeuroImageRashid, Barnaly; Glasser, Matthew F; Nichols, Thomas; Van Essen, David; Juttukonda, Meher R; Schwab, Nadine A; Greve, Douglas N; Yacoub, Essa; Lovely, Allison; Terpstra, Melissa; Harms, Michael P; Bookheimer, Susan Y; Ances, Beau M; Salat, David H; Arnold, Steven EAugust 1, 2023Not Determined
37187365Create StudyAssociations between age, sex, APOE genotype, and regional vascular physiology in typically aging adults.NeuroImageDamestani, Nikou L; Jacoby, John; Yadav, Shrikanth M; Lovely, Allison E; Michael, Aurea; Terpstra, Melissa; Eshghi, Marziye; Rashid, Barnaly; Cruchaga, Carlos; Salat, David H; Juttukonda, Meher RJuly 15, 2023Not Determined
37148501Create StudyYoung versus older subject diffusion magnetic resonance imaging data for virtual white matter lesion tractography.Human brain mappingTaghvaei, Mohammad; Cook, Philip; Sadaghiani, Shokufeh; Shakibajahromi, Banafsheh; Tackett, William; Dolui, Sudipto; De, Debarun; Brown, Christopher; Khandelwal, Pulkit; Yushkevich, Paul; Das, Sandhitsu; Wolk, David A; Detre, John AJuly 1, 2023Not Determined
37040498Create StudyMean Arterial Pressure and Cerebral Hemodynamics Across The Lifespan: A Cross-Sectional Study From Human Connectome Project-Aging.Journal of magnetic resonance imaging : JMRIYetim, Ezgi; Jacoby, John; Damestani, Nikou L; Lovely, Allison E; Salat, David H; Juttukonda, Meher RDecember 1, 2023Not Determined
36922162Create StudyDifferential reduction of gray matter volume with age in 35 cortical areas in men (more) and women (less).Journal of neurophysiologyChristova, Peka; Georgopoulos, Apostolos PApril 1, 2023Not Determined
36871816Create StudyPhysical fitness, cognition, and structural network efficiency of brain connections across the lifespan.NeuropsychologiaCallow, Daniel D; Smith, J CarsonApril 15, 2023Not Determined
36799333Create StudyCortical thickness of the left parahippocampal cortex links central hearing and cognitive performance in aging.Annals of the New York Academy of SciencesLi, Rui; Miao, Xiaoyan; Han, Buxin; Li, JuanApril 1, 2023Not Determined
36595679Create StudyAnatomically interpretable deep learning of brain age captures domain-specific cognitive impairment.Proceedings of the National Academy of Sciences of the United States of AmericaYin, Chenzhong; Imms, Phoebe; Cheng, Mingxi; Amgalan, Anar; Chowdhury, Nahian F; Massett, Roy J; Chaudhari, Nikhil N; Chen, Xinghe; Thompson, Paul M; Bogdan, Paul; Irimia, Andrei; Alzheimer’s Disease Neuroimaging InitiativeJanuary 10, 2023Not Determined
36563018Create StudySex differences in default mode network connectivity in healthy aging adults.Cerebral cortex (New York, N.Y. : 1991)Ficek-Tani, Bronte; Horien, Corey; Ju, Suyeon; Xu, Wanwan; Li, Nancy; Lacadie, Cheryl; Shen, Xilin; Scheinost, Dustin; Constable, Todd; Fredericks, CarolynMay 9, 2023Not Determined
36550942Create StudyWave-Encoded Model-Based Deep Learning for Highly Accelerated Imaging with Joint Reconstruction.Bioengineering (Basel, Switzerland)Cho, Jaejin; Gagoski, Borjan; Kim, Tae Hyung; Tian, Qiyuan; Frost, Robert; Chatnuntawech, Itthi; Bilgic, BerkinNovember 29, 2022Not Determined
36458845Create StudyCorrection to: Endogenous oscillations time-constrain linguistic segmentation: cycling the garden path.Cerebral cortex (New York, N.Y. : 1991)January 5, 2023Not Determined
36435870Create StudyA cross-cohort replicable and heritable latent dimension linking behaviour to multi-featured brain structure.Communications biologyNicolaisen-Sobesky, Eliana; Mihalik, Agoston; Kharabian-Masouleh, Shahrzad; Ferreira, Fabio S; Hoffstaedter, Felix; Schwender, Holger; Maleki Balajoo, Somayeh; Valk, Sofie L; Eickhoff, Simon B; Yeo, B T Thomas; Mourao-Miranda, Janaina; Genon, SarahNovember 26, 2022Not Determined
36368498Create StudyCortical myelin profile variations in healthy aging brain: A T1w/T2w ratio study.NeuroImageSui, Yu Veronica; Masurkar, Arjun V; Rusinek, Henry; Reisberg, Barry; Lazar, MarianaDecember 1, 2022Not Determined
36302824Create StudyEducational quality may be a closer correlate of cardiometabolic health than educational attainment.Scientific reportsCundiff, Jenny M; Lin, Shayne S-H; Faulk, Robert D; McDonough, Ian MOctober 27, 2022Not Determined
36183259Create StudyRegional Neuroanatomic Effects on Brain Age Inferred Using Magnetic Resonance Imaging and Ridge Regression.The journals of gerontology. Series A, Biological sciences and medical sciencesMassett, Roy J; Maher, Alexander S; Imms, Phoebe E; Amgalan, Anar; Chaudhari, Nikhil N; Chowdhury, Nahian F; Irimia, Andrei; Alzheimer’s Disease Neuroimaging InitiativeJune 1, 2023Not Determined
35985618Create StudyCross-cohort replicability and generalizability of connectivity-based psychometric prediction patterns.NeuroImageWu, Jianxiao; Li, Jingwei; Eickhoff, Simon B; Hoffstaedter, Felix; Hanke, Michael; Yeo, B T Thomas; Genon, SarahNovember 15, 2022Not Determined
35697132Create StudyEmpirical transmit field bias correction of T1w/T2w myelin maps.NeuroImageGlasser, Matthew F; Coalson, Timothy S; Harms, Michael P; Xu, Junqian; Baum, Graham L; Autio, Joonas A; Auerbach, Edward J; Greve, Douglas N; Yacoub, Essa; Van Essen, David C; Bock, Nicholas A; Hayashi, TakuyaSeptember 1, 2022Not Determined
35475571Create StudyMethodological evaluation of individual cognitive prediction based on the brain white matter structural connectome.Human brain mappingFeng, Guozheng; Wang, Yiwen; Huang, Weijie; Chen, Haojie; Dai, Zhengjia; Ma, Guolin; Li, Xin; Zhang, Zhanjun; Shu, NiAugust 15, 2022Not Determined
35433118Create StudyThe Impact of Personality Pathology Across Three Generations: Evidence from the St. Louis Personality and Intergenerational Network Study.Clinical psychological science : a journal of the Association for Psychological ScienceShields, Allison N; Oltmanns, Thomas F; Boudreaux, Michael J; Paul, Sarah E; Bogdan, Ryan; Tackett, Jennifer LSeptember 1, 2021Not Determined
35429627Create StudyPsychological resilience and neurodegenerative risk: A connectomics-transcriptomics investigation in healthy adolescent and middle-aged females.NeuroImagePetrican, Raluca; Fornito, Alex; Jones, NatalieJuly 15, 2022Not Determined
35278806Create StudyCan bilingualism increase neuroplasticity of language networks in epilepsy?Epilepsy researchStasenko, Alena; Schadler, Adam; Kaestner, Erik; Reyes, Anny; Díaz-Santos, Mirella; Połczyńska, Monika; McDonald, Carrie RMay 1, 2022Not Determined
35240299Create StudySDnDTI: Self-supervised deep learning-based denoising for diffusion tensor MRI.NeuroImageTian, Qiyuan; Li, Ziyu; Fan, Qiuyun; Polimeni, Jonathan R; Bilgic, Berkin; Salat, David H; Huang, Susie YJune 1, 2022Not Determined
35033950Create StudyAssociations between cognition and polygenic liability to substance involvement in middle childhood: Results from the ABCD study.Drug and alcohol dependencePaul, Sarah E; Hatoum, Alexander S; Barch, Deanna M; Thompson, Wesley K; Agrawal, Arpana; Bogdan, Ryan; Johnson, Emma CMarch 1, 2022Not Determined
34642974Create StudySingle-shell NODDI using dictionary-learner-estimated isotropic volume fraction.NMR in biomedicineFaiyaz, Abrar; Doyley, Marvin; Schifitto, Giovanni; Zhong, Jianhui; Uddin, Md NasirFebruary 1, 2022Not Determined
34587005Create StudySynthMorph: Learning Contrast-Invariant Registration Without Acquired Images.IEEE transactions on medical imagingHoffmann, Malte; Billot, Benjamin; Greve, Douglas N; Iglesias, Juan Eugenio; Fischl, Bruce; Dalca, Adrian VMarch 1, 2022Not Determined
34582057Create StudyIdentifying individuals with Alzheimer''s disease-like brains based on structural imaging in the Human Connectome Project Aging cohort.Human brain mappingLi, Binyin; Jang, Ikbeom; Riphagen, Joost; Almaktoum, Randa; Yochim, Kathryn Morrison; Ances, Beau M; Bookheimer, Susan Y; Salat, David H; Alzheimer's Disease Neuroimaging InitiativeDecember 1, 2021Not Determined
34508893Create StudyThe Human Connectome Project: A retrospective.NeuroImageElam, Jennifer Stine; Glasser, Matthew F; Harms, Michael P; Sotiropoulos, Stamatios N; Andersson, Jesper L R; Burgess, Gregory C; Curtiss, Sandra W; Oostenveld, Robert; Larson-Prior, Linda J; Schoffelen, Jan-Mathijs; Hodge, Michael R; Cler, Eileen A; Marcus, Daniel M; Barch, Deanna M; Yacoub, Essa; Smith, Stephen M; Ugurbil, Kamil; Van Essen, David CDecember 1, 2021Not Determined
34450262Create StudyHierarchical modelling of functional brain networks in population and individuals from big fMRI data.NeuroImageFarahibozorg, Seyedeh-Rezvan; Bijsterbosch, Janine D; Gong, Weikang; Jbabdi, Saad; Smith, Stephen M; Harrison, Samuel J; Woolrich, Mark WNovember 1, 2021Not Determined
34421216Create StudyRapid head-pose detection for automated slice prescription of fetal-brain MRI.International journal of imaging systems and technologyHoffmann, Malte; Turk, Esra Abaci; Gagoski, Borjan; Morgan, Leah; Wighton, Paul; Tisdall, M Dylan; Reuter, Martin; Adalsteinsson, Elfar; Grant, P Ellen; Wald, Lawrence L; van der Kouwe, André J WSeptember 1, 2021Not Determined
34287779Create StudyBrain structure and problematic alcohol use: a test of plausible causation using latent causal variable analysis.Brain imaging and behaviorHatoum, Alexander S; Johnson, Emma C; Agrawal, Arpana; Bogdan, RyanDecember 1, 2021Not Determined
34271214Create StudyPsychotic-like Experiences and Polygenic Liability in the Adolescent Brain Cognitive Development Study.Biological psychiatry. Cognitive neuroscience and neuroimagingKarcher, Nicole R; Paul, Sarah E; Johnson, Emma C; Hatoum, Alexander S; Baranger, David A A; Agrawal, Arpana; Thompson, Wesley K; Barch, Deanna M; Bogdan, RyanJanuary 1, 2022Not Determined
34235496Create StudyGenetic Liability to Cannabis Use Disorder and COVID-19 Hospitalization.Biological psychiatry global open scienceHatoum, Alexander S; Morrison, Claire L; Colbert, Sarah M C; Winiger, Evan A; Johnson, Emma C; Agrawal, Arpana; Bogdan, RyanDecember 1, 2021Not Determined
34092032Create StudyPolygenic risk scores for alcohol involvement relate to brain structure in substance-naïve children: Results from the ABCD study.Genes, brain, and behaviorHatoum, Alexander S; Johnson, Emma C; Baranger, David A A; Paul, Sarah E; Agrawal, Arpana; Bogdan, RyanJune 6, 2021Not Determined
33539118Create StudyNeuroticism and reward-related ventral striatum activity: Probing vulnerability to stress-related depression.Journal of abnormal psychologyBondy, Erin; Baranger, David A A; Balbona, Jared; Sputo, Kendall; Paul, Sarah E; Oltmanns, Thomas F; Bogdan, RyanApril 1, 2021Not Determined
33524575Create StudyCharacterizing cerebral hemodynamics across the adult lifespan with arterial spin labeling MRI data from the Human Connectome Project-Aging.NeuroImageJuttukonda, Meher R; Li, Binyin; Almaktoum, Randa; Stephens, Kimberly A; Yochim, Kathryn M; Yacoub, Essa; Buckner, Randy L; Salat, David HApril 15, 2021Not Determined
33406867Create StudyHeterogeneity of Cerebral White Matter Lesions and Clinical Correlates in Older Adults.StrokeJung, Keun-Hwa; Stephens, Kimberly A; Yochim, Kathryn M; Riphagen, Joost M; Kim, Chan Mi; Buckner, Randy L; Salat, David HJanuary 1, 2021Not Determined
33380275Create StudyUpdated demographically adjusted norms for the Brief Visuospatial Memory Test-revised and Hopkins Verbal Learning Test-revised in Spanish-speakers from the U.S.-Mexico border region: The NP-NUMBRS project.The Clinical neuropsychologistDíaz-Santos, Mirella; Suárez, Paola A; Marquine, María J; Umlauf, Anya; Rivera Mindt, Monica; Artiola I Fortuny, Lidia; Heaton, Robert K; Cherner, MarianaFebruary 1, 2021Not Determined
33356892Create StudyNative Spanish-speaker''s test performance and the effects of Spanish-English bilingualism: results from the neuropsychological norms for the U.S.-Mexico Border Region in Spanish (NP-NUMBRS) project.The Clinical neuropsychologistSuárez, Paola A; Marquine, María J; Díaz-Santos, Mirella; Gollan, Tamar; Artiola I Fortuny, Lidia; Rivera Mindt, Monica; Heaton, Robert; Cherner, MarianaFebruary 1, 2021Not Determined
33236033Create StudyGenetic Liability to Cannabis Use Disorder and COVID-19 Hospitalization.medRxiv : the preprint server for health sciencesHatoum, Alexander S; Morrison, Claire L; Winiger, Evan A; Johnson, Emma C; Agrawal, Arpana; Bogdan, RyanNovember 18, 2020Not Determined
32985352Create StudyDemographically adjusted norms for the Trail Making Test in native Spanish speakers: Results from the neuropsychological norms for the US-Mexico border region in Spanish (NP-NUMBRS) project.The Clinical neuropsychologistSuarez, Paola A; Díaz-Santos, Mirella; Marquine, Maria J; Kamalyan, Lily; Mindt, Monica Rivera; Umlauf, Anya; Heaton, Robert K; Grant, Igor; Cherner, MarianaFebruary 1, 2021Not Determined
32871388Create StudyPrediction of clinical and biomarker conformed Alzheimer''s disease and mild cognitive impairment from multi-feature brain structural MRI using age-correction from a large independent lifespan sample.NeuroImage. ClinicalLi, Binyin; Zhang, Miao; Riphagen, Joost; Morrison Yochim, Kathryn; Li, Biao; Liu, Jun; Salat, David H; Alzheimer's Disease Neuroimaging InitiativeJanuary 1, 2020Not Determined
32866666Create StudyThe developing Human Connectome Project (dHCP) automated resting-state functional processing framework for newborn infants.NeuroImageFitzgibbon, Sean P; Harrison, Samuel J; Jenkinson, Mark; Baxter, Luke; Robinson, Emma C; Bastiani, Matteo; Bozek, Jelena; Karolis, Vyacheslav; Cordero Grande, Lucilio; Price, Anthony N; Hughes, Emer; Makropoulos, Antonios; Passerat-Palmbach, Jonathan; Schuh, Andreas; Gao, Jianliang; Farahibozorg, Seyedeh-Rezvan; O'Muircheartaigh, Jonathan; Ciarrusta, Judit; O'Keeffe, Camilla; Brandon, Jakki; Arichi, Tomoki; Rueckert, Daniel; Hajnal, Joseph V; Edwards, A David; Smith, Stephen M; Duff, Eugene; Andersson, JesperDecember 1, 2020Not Determined
32497785Create StudyA Symmetric Prior for the Regularisation of Elastic Deformations: Improved anatomical plausibility in nonlinear image registration.NeuroImageLange, Frederik J; Ashburner, John; Smith, Stephen M; Andersson, Jesper L ROctober 1, 2020Not Determined
32474754Create StudyHemodynamic latency is associated with reduced intelligence across the lifespan: an fMRI DCM study of aging, cerebrovascular integrity, and cognitive ability.Brain structure & functionAnderson, Ariana E; Diaz-Santos, Mirella; Frei, Spencer; Dang, Bianca H; Kaur, Pashmeen; Lyden, Patrick; Buxton, Richard; Douglas, Pamela K; Bilder, Robert M; Esfandiari, Mahtash; Friston, Karl J; Nookala, Usha; Bookheimer, Susan YJuly 1, 2020Not Determined
32312691Create StudyBorderline Personality Traits Are Not Correlated With Brain Structure in Two Large Samples.Biological psychiatry. Cognitive neuroscience and neuroimagingBaranger, David A A; Few, Lauren R; Sheinbein, Daniel H; Agrawal, Arpana; Oltmanns, Thomas F; Knodt, Annchen R; Barch, Deanna M; Hariri, Ahmad R; Bogdan, RyanJuly 1, 2020Not Determined
32039857Create StudySerial Reaction Time Task Performance in Older Adults with Neuropsychologically Defined Mild Cognitive Impairment.Journal of Alzheimer''s disease : JADHong, Yue; Alvarado, Rachel L; Jog, Amod; Greve, Douglas N; Salat, David HJanuary 1, 2020Not Determined
31699293Create StudyConvergent Evidence for Predispositional Effects of Brain Gray Matter Volume on Alcohol Consumption.Biological psychiatryBaranger, David A A; Demers, Catherine H; Elsayed, Nourhan M; Knodt, Annchen R; Radtke, Spenser R; Desmarais, Aline; Few, Lauren R; Agrawal, Arpana; Heath, Andrew C; Barch, Deanna M; Squeglia, Lindsay M; Williamson, Douglas E; Hariri, Ahmad R; Bogdan, RyanApril 1, 2020Not Determined
31343240Create StudyDepressive symptoms precede cognitive impairment in de novo Parkinson''s disease patients: Analysis of the PPMI cohort.NeuropsychologyJones, Jacob D; Kurniadi, Natalie E; Kuhn, Taylor P; Szymkowicz, Sarah M; Bunch, Joseph; Rahmani, ElizabethNovember 1, 2019Not Determined
31099175Create StudyGenome-wide association study identifies loci associated with liability to alcohol and drug dependence that is associated with variability in reward-related ventral striatum activity in African- and European-Americans.Genes, brain, and behaviorWetherill, Leah; Lai, Dongbing; Johnson, Emma C; Anokhin, Andrey; Bauer, Lance; Bucholz, Kathleen K; Dick, Danielle M; Hariri, Ahmad R; Hesselbrock, Victor; Kamarajan, Chella; Kramer, John; Kuperman, Samuel; Meyers, Jacquelyn L; Nurnberger Jr, John I; Schuckit, Marc; Scott, Denise M; Taylor, Robert E; Tischfield, Jay; Porjesz, Bernice; Goate, Alison M; Edenberg, Howard J; Foroud, Tatiana; Bogdan, Ryan; Agrawal, ArpanaJuly 1, 2019Not Determined
31090166Create StudyGenome-wide association studies of alcohol dependence, DSM-IV criterion count and individual criteria.Genes, brain, and behaviorLai, Dongbing; Wetherill, Leah; Bertelsen, Sarah; Carey, Caitlin E; Kamarajan, Chella; Kapoor, Manav; Meyers, Jacquelyn L; Anokhin, Andrey P; Bennett, David A; Bucholz, Kathleen K; Chang, Katharine K; De Jager, Philip L; Dick, Danielle M; Hesselbrock, Victor; Kramer, John; Kuperman, Samuel; Nurnberger Jr, John I; Raj, Towfique; Schuckit, Marc; Scott, Denise M; Taylor, Robert E; Tischfield, Jay; Hariri, Ahmad R; Edenberg, Howard J; Agrawal, Arpana; Bogdan, Ryan; Porjesz, Bernice; Goate, Alison M; Foroud, TatianaJuly 1, 2019Not Determined
30332613Create StudyThe Lifespan Human Connectome Project in Aging: An overview.NeuroImageBookheimer, Susan Y; Salat, David H; Terpstra, Melissa; Ances, Beau M; Barch, Deanna M; Buckner, Randy L; Burgess, Gregory C; Curtiss, Sandra W; Diaz-Santos, Mirella; Elam, Jennifer Stine; Fischl, Bruce; Greve, Douglas N; Hagy, Hannah A; Harms, Michael P; Hatch, Olivia M; Hedden, Trey; Hodge, Cynthia; Japardi, Kevin C; Kuhn, Taylor P; Ly, Timothy K; Smith, Stephen M; Somerville, Leah H; Uğurbil, Kâmil; van der Kouwe, Andre; Van Essen, David; Woods, Roger P; Yacoub, EssaJanuary 15, 2019Not Determined
30261308Create StudyExtending the Human Connectome Project across ages: Imaging protocols for the Lifespan Development and Aging projects.NeuroImageHarms, Michael P; Somerville, Leah H; Ances, Beau M; Andersson, Jesper; Barch, Deanna M; Bastiani, Matteo; Bookheimer, Susan Y; Brown, Timothy B; Buckner, Randy L; Burgess, Gregory C; Coalson, Timothy S; Chappell, Michael A; Dapretto, Mirella; Douaud, Gwenaëlle; Fischl, Bruce; Glasser, Matthew F; Greve, Douglas N; Hodge, Cynthia; Jamison, Keith W; Jbabdi, Saad; Kandala, Sridhar; Li, Xiufeng; Mair, Ross W; Mangia, Silvia; Marcus, Daniel; Mascali, Daniele; Moeller, Steen; Nichols, Thomas E; Robinson, Emma C; Salat, David H; Smith, Stephen M; Sotiropoulos, Stamatios N; Terpstra, Melissa; Thomas, Kathleen M; Tisdall, M Dylan; Ugurbil, Kamil; van der Kouwe, Andre; Woods, Roger P; Zöllei, Lilla; Van Essen, David C; Yacoub, EssaDecember 2018Not Determined
29852283Create StudyAutomated processing pipeline for neonatal diffusion MRI in the developing Human Connectome Project.NeuroImageBastiani, Matteo; Andersson, Jesper L R; Cordero-Grande, Lucilio; Murgasova, Maria; Hutter, Jana; Price, Anthony N; Makropoulos, Antonios; Fitzgibbon, Sean P; Hughes, Emer; Rueckert, Daniel; Victor, Suresh; Rutherford, Mary; Edwards, A David; Smith, Stephen M; Tournier, Jacques-Donald; Hajnal, Joseph V; Jbabdi, Saad; Sotiropoulos, Stamatios NJanuary 15, 2019Not Determined
29579395Create StudyPolygenic Risk Scores in Clinical Psychology: Bridging Genomic Risk to Individual Differences.Annual review of clinical psychologyBogdan, Ryan; Baranger, David A A; Agrawal, ArpanaMay 7, 2018Not Determined
29277648Create StudySusceptibility-induced distortion that varies due to motion: Correction in diffusion MR without acquiring additional data.NeuroImageAndersson, Jesper L R; Graham, Mark S; Drobnjak, Ivana; Zhang, Hui; Campbell, JonMay 1, 2018Not Determined
29112194Create StudyGenome-wide association study identifies a novel locus for cannabis dependence.Molecular psychiatryAgrawal, A; Chou, Y-L; Carey, C E; Baranger, D A A; Zhang, B; Sherva, R; Wetherill, L; Kapoor, M; Wang, J-C; Bertelsen, S; Anokhin, A P; Hesselbrock, V; Kramer, J; Lynskey, M T; Meyers, J L; Nurnberger, J I; Rice, J P; Tischfield, J; Bierut, L J; Degenhardt, L; Farrer, L A; Gelernter, J; Hariri, A R; Heath, A C; Kranzler, H R; Madden, P A F; Martin, N G; Montgomery, G W; Porjesz, B; Wang, T; Whitfield, J B; Edenberg, H J; Foroud, T; Goate, A M; Bogdan, R; Nelson, E CMay 1, 2018Not Determined
28749606Create StudyPerceived stress is associated with increased rostral middle frontal gyrus cortical thickness: a family-based and discordant-sibling investigation.Genes, brain, and behaviorMichalski, L J; Demers, C H; Baranger, D A A; Barch, D M; Harms, M P; Burgess, G C; Bogdan, RNovember 1, 2017Not Determined
28739213Create StudyAssociation Between Substance Use Disorder and Polygenic Liability to Schizophrenia.Biological psychiatryHartz, Sarah M; Horton, Amy C; Oehlert, Mary; Carey, Caitlin E; Agrawal, Arpana; Bogdan, Ryan; Chen, Li-Shiun; Hancock, Dana B; Johnson, Eric O; Pato, Carlos N; Pato, Michele T; Rice, John P; Bierut, Laura JNovember 15, 2017Not Determined
28645098Create StudyThe behavioral economics of social anxiety disorder reveal a robust effect for interpersonal traits.Behaviour research and therapyRodebaugh, Thomas L; Tonge, Natasha A; Weisman, Jaclyn S; Lim, Michelle H; Fernandez, Katya C; Bogdan, RyanAugust 1, 2017Not Determined
28379578Create StudyAn earlier time of scan is associated with greater threat-related amygdala reactivity.Social cognitive and affective neuroscienceBaranger, David A A; Margolis, Seth; Hariri, Ahmad R; Bogdan, RyanAugust 1, 2017Not Determined
28283186Create StudyImaging Genetics and Genomics in Psychiatry: A Critical Review of Progress and Potential.Biological psychiatryBogdan, Ryan; Salmeron, Betty Jo; Carey, Caitlin E; Agrawal, Arpana; Calhoun, Vince D; Garavan, Hugh; Hariri, Ahmad R; Heinz, Andreas; Hill, Matthew N; Holmes, Andrew; Kalin, Ned H; Goldman, DavidAugust 1, 2017Not Determined

NDA Help Center

Collection - Publications

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

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

Frequently Asked Questions

  • How can I determine if a publication is relevant?
    Publications are considered relevant to a collection when the data shared is directly related to the project or collection.
  • Where does the NDA get the publications?
    PubMed, an online library containing journals, articles, and medical research. Sponsored by NiH and National Library of Medicine (NLM).


  • Create Study
    A link to the Create an NDA Study page that can be clicked to start creating an NDA Study with information such as the title, journal and authors automatically populated.
  • Not Determined Publication
    Indicates that the publication has not yet been reviewed and/or marked as Relevant or Not Relevant so it has not been determined whether an NDA Study is expected.
  • Not Relevant Publication
    A publication that is not based on data related to the aims of the grant/project associated with the Collection or not based on any data such as a review article and, therefore, an NDA Study is not expected to be created.
  • PubMed
    PubMed provides citation information for biomedical and life sciences publications and is managed by the U.S. National Institutes of Health's National Library of Medicine.
  • PubMed ID
    The PUBMed ID is the unique ID number for the publication as recorded in the PubMed database.
  • Relevant Publication
    A publication that is based on data related to the aims of the grant/project associated with the Collection and, therefore, an NDA Study is expected to be created.
Data Expected List: Mandatory Data Structures

These data structures are mandatory for your NDA Collection. Please update the Targeted Enrollment number to accurately represent the number of subjects you expect to submit for the entire study.

For NIMH HIV-related research that involves human research participants: Select the dictionary or dictionaries most appropriate for your research. If your research does not require all three data dictionaries, just ignore the ones you do not need. There is no need to delete extra data dictionaries from your NDA Collection. You can adjust the Targeted Enrollment column in the Data Expected tab to “0” for those unnecessary data dictionaries. At least one of the three data dictionaries must have a non-zero value.

Data ExpectedTargeted EnrollmentInitial SubmissionSubjects SharedStatus
Research Subject and Pedigree info icon
To create your project's Data Expected list, use the "+New Data Expected" to add or request existing structures and to request new Data Structures that are not in the NDA Data Dictionary.

If the Structure you need already exists, locate it and specify your dates and enrollment when adding it to your Data Expected list. If you require changes to the Structure you need, select the indicator stating "No, it requires changes to meet research needs," and upload a file containing your requested changes.

If the structure you need is not yet defined in the Data Dictionary, you can select "Upload Definition" and attach the necessary materials to request its creation.

When selecting the expected dates for your data, make sure to follow the standard Data Sharing Regimen and choose dates within the date ranges that correspond to your project start and end dates.

Please visit the Completing Your Data Expected Tutorial for more information.
Data Expected List: Data Structures per Research Aims

These data structures are specific to your research aims and should list all data structures in which data will be collected and submitted for this NDA Collection. Please update the Targeted Enrollment number to accurately represent the number of subjects you expect to submit for the entire study.

Data ExpectedTargeted EnrollmentInitial SubmissionSubjects SharedStatus
Perceived Stress Scale info icon
Penn Emotion Recognition Task-40 info icon
Medical History info icon
NEO Personality Inventory info icon
Vital Signs Assessment info icon
Processed MRI Data info icon
Physical Exam info icon
Pittsburgh Sleep Quality Index info icon
Medications and Treatments Form info icon
Trail Making Test (Child and Adult) info icon
Picture Sequence Memory info icon
Cognition Composite Scores info icon
Pattern Comparison Processing Speed info icon
List Sorting Working Memory Test info icon
Auditory Verbal Learning Task info icon
International Physical Activity Questionnaire info icon
Adult Self Report info icon
Imaging (Structural, fMRI, DTI, PET, microscopy) info icon
Psychological Well-Being Summary info icon
Dimensional Change Card Sort Test info icon
Emotional Distress-Anxiety info icon
Emotional Distress - Depression info icon
Social Isolation info icon
Delay Discounting Task info icon
Blood Sample Collection info icon
Flanker Task info icon
Anger info icon
NIH Toolbox Oral Reading Recognition Test info icon
Picture Vocabulary Test info icon
Self Efficacy Survey info icon
NIH Toolbox Motor Domain info icon
NIH Toolbox Sensation Domain info icon
NIH Toolbox Rejection Surveys info icon
NIH Toolbox Emotional Support Survey info icon
NIH Toolbox Friendship Survey info icon
NIH Toolbox Perceived Hostility info icon
Montreal Cognitive Assessment info icon
FaceName Task info icon
PROMIS General Life Satisfaction info icon
Boston Assessment of Traumatic Brain Injury info icon
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NDA Help Center

Collection - Data Expected

The Data Expected tab displays the list of all data that NDA expects to receive in association with the Collection as defined by the contributing researcher, as well as the dates for the expected initial upload of the data, and when it is first expected to be shared, or with the research community. Above the primary table of Data Expected, any publications determined to be relevant to the data within the Collection are also displayed - members of the contributing research group can use these to define NDA Studies, connecting those papers to underlying data in NDA.

The tab is used both as a reference for those accessing shared data, providing information on what is expected and when it will be shared, and as the primary tracking mechanism for contributing projects. It is used by both contributing primary researchers, secondary researchers, and NIH Program and Grants Management staff.

Researchers who are starting their project need to update their Data Expected list to include all the Data Structures they are collecting under their grant and set their initial submission and sharing schedule according to the NDA Data Sharing Regimen.

To add existing Data Structures from the Data Dictionary, to request new Data Structure that are not in the Dictionary, or to request changes to existing Data Structures, click "+New Data Expected".

For step-by-step instructions on how to add existing Data Structures, request changes to an existing Structure, or request a new Data Structure, please visit the Completing Your Data Expected Tutorial.

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

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  • Adding a new structure to this list is the only way to request the creation of a new Data Dictionary definition.

Frequently Asked Questions

  • What is an NDA Data Structure?
    An NDA Data Structure is comprised of multiple Data Elements to make up an electronic definition of an assessment, measure, questionnaire, etc will have a corresponding Data Structure.
  • What is the NDA Data Dictionary?
    The NDA Data Dictionary is comprised of electronic definitions known as Data Structures.


  • Analyzed Data
    Data specific to the primary aims of the research being conducted (e.g. outcome measures, other dependent variables, observations, laboratory results, analyzed images, volumetric data, etc.) including processed images.
  • Data Item
    Items listed on the Data Expected list in the Collection which may be an individual and discrete Data Structure, Data Structure Category, or Data Structure Group.
  • Data Structure
    A defined organization and group of Data Elements to represent an electronic definition of a measure, assessment, questionnaire, or collection of data points. Data structures that have been defined in the NDA Data Dictionary are available at https://nda.nih.gov/general-query.html?q=query=data-structure
  • Data Structure Category
    An NDA term describing the affiliation of a Data Structure to a Category, which may be disease/disorder or diagnosis related (Depression, ADHD, Psychosis), specific to data type (MRI, eye tracking, omics), or type of data (physical exam, IQ).
  • Data Structure Group
    A Data Item listed on the Data Expected tab of a Collection that indicates a group of Data Structures (e.g., ADOS or SCID) for which data may be submitted instead of a specific Data Structure identified by version, module, edition, etc. For example, the ADOS Data Structure Category includes every ADOS Data Structure such as ADOS Module 1, ADOS Module 2, ADOS Module 1 - 2nd Edition, etc. The SCID Data Structure Group includes every SCID Data Structure such as SCID Mania, SCID V Mania, SCID PTSD, SCID-V Diagnosis, and more.
  • Evaluated Data
    A new Data Structure category, Evaluated Data is analyzed data resulting from the use of computational pipelines in the Cloud and can be uploaded directly back to a miNDAR database. Evaluated Data is expected to be listed as a Data Item in the Collection's Data Expected Tab.
  • Imaging Data
    Imaging+ is an NDA term which encompasses all imaging related data including, but not limited to, images (DTI, MRI, PET, Structural, Spectroscopy, etc.) as well as neurosignal data (EEG, fMRI, MEG, EGG, eye tracking, etc.) and Evaluated Data.
  • Initial Share Date
    Initial Submission and Initial Share dates should be populated according to the NDA Data Sharing Terms and Conditions. Any modifications to these will go through the approval processes outlined above. Data will be shared with authorized users upon publication (via an NDA Study) or 1-2 years after the grant end date specified on the first Notice of Award, as defined in the applicable Data Sharing Terms and Conditions.
  • Initial Submission Date
    Initial Submission and Initial Share dates should be populated according to these NDA Data Sharing Terms and Conditions. Any modifications to these will go through the approval processes outlined above. Data for all subjects is not expected on the Initial Submission Date and modifications may be made as necessary based on the project's conduct.
  • Research Subject and Pedigree
    An NDA created Data Structure used to convey basic information about the subject such as demographics, pedigree (links family GUIDs), diagnosis/phenotype, and sample location that are critical to allow for easier querying of shared data.
  • Submission Cycle
    The NDA has two Submission Cycles per year - January 15 and July 15.
  • Submission Exemption
    An interface to notify NDA that data may not be submitted during the upcoming/current submission cycle.

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

Associated Studies

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

Study NameAbstractCollection/Study SubjectsData UsageState
Verbal Learning HarmonizationMotivation: Auditory verbal learning tasks (AVLTs) are a core component of neuropsychological assessment, but the variety of AVLTs in common use makes it difficult to compare scores across instruments. This limits integration of research findings. The objective of this study was to derive and disseminate crosswalks that directly equate raw scores across common AVLTs. Methods: A large, international repository of raw AVLT data was compiled, and a multisite mega study analysis was conducted. Empirical Bayes harmonization was used to isolate and remove site effects, followed by linear models which adjusted for covariates, including age, sex, education, and race/ethnicity. After corrections, a continuous item response theory (IRT) model was then used to estimate each individual subject’s latent verbal learning ability while accounting for different item difficulties. Results: We aggregated raw data from studies of clinical samples and healthy controls from around the world that measured at least one verbal learning task. After applying exclusion criteria, the final sample was comprised of N = 10,505 individuals with and without history of traumatic brain injury from 53 studies above the age of 16 years who were tested on the California Verbal Learning Test (CVLT), Rey Auditory Verbal Learning Test (RAVLT), or the Hopkins Verbal Learning Test-Revised (HVLT). Harmonization significantly reduced inter-site variance by 37% while preserving covariate effects for further study. The effects of age, sex, and education on scores were reported and were found to be consistent across all AVLTs. Crosswalks were created by linking scores of individuals with the same verbal learning ability across AVLTs. The derived conversions agreed with held-out data of dually-administered tests. Conclusion: This study reports the co-calibration and validation of methods to harmonize raw scores across three common verbal learning instruments. Moreover, we developed a free online tool for cross-assessment raw score conversion. These methods address longstanding data compatibility issues for AVLTs, and offer perspectives on how large-scale data harmonization initiatives can increase the robustness and reproducibility of research and findings across the behavioral sciences. 720/3745Secondary AnalysisShared
Evidence for embracing normative modelingIn this work, we expand the normative model repository introduced in Rutherford et al., 2022a to include normative models charting lifespan trajectories of structural surface area and brain functional connectivity, measured using two unique resting-state network atlases (Yeo-17 and Smith-10), and an updated online platform for transferring these models to new data sources. We showcase the value of these models with a head-to-head comparison between the features output by normative modeling and raw data features in several benchmarking tasks: mass univariate group difference testing (schizophrenia versus control), classification (schizophrenia versus control), and regression (predicting general cognitive ability). Across all benchmarks, we show the advantage of using normative modeling features, with the strongest statistically significant results demonstrated in the group difference testing and classification tasks. We intend for these accessible resources to facilitate the wider adoption of normative modeling across the neuroimaging community.725/2599Secondary AnalysisShared
The structure of neuroanatomical variation within bilingualsWe have developed this CVAE method that was useful for Autism (Aglinskas et al 2022). We think it might be useful for bilingualism research because bilingualism is similarly variable. We need access to this database to test our ideas. 723/1375Secondary AnalysisShared
A cross-cohort replicable and heritable latent dimension linking behaviour to multi-featured brain structureIdentifying the complex association between inter-individual variability in brain structure and in behaviour is challenging, requiring large cohorts, multivariate methods, out-of-sample validation and, ideally, out-of-cohort replication. Moreover, the influence of nature vs nurture on such associations could be better understood through heritability analysis. Here, we analysed associations between brain structure and behaviour using regularized canonical correlation analysis and a recently proposed machine learning framework that tests the generalisability of such associations. We linked behaviour (spanning cognition, emotion, and alertness) and multi-featured brain structure (grey matter volume, cortical thickness, and surface area). The replicability of such brain-behaviour associations was assessed in two large and independent cohorts. The load of genetic factors on these latent dimensions was analysed with heritability and genetic correlation. We found one heritable and replicable latent dimension. This latent dimension was positively associated with cognitive-control/executive-functions and positive affect, and negatively associated with impulsivity and negative affect. This behavioural profile was related to brain structural variability in areas typically associated with higher cognitive functions, as well as with areas typically associated with sensorimotor functions. These results revealed a major axis of interindividual variability in behaviour that links to a whole-brain structural pattern.725/725Primary AnalysisShared
Cortical myelin profile variations in healthy aging brain: A T1w/T2w ratio studyDemyelination is observed in both healthy aging and age-related neurodegenerative disorders. While the significance of myelin within the cortex is well acknowledged, studies focused on intracortical demyelination and depth-specific structural alterations in normal aging are lacking. Using the recently available Human Connectome Project Aging dataset, we investigated intracortical myelin in a normal aging population using the T1w/T2w ratio. To capture the fine changes across cortical depths, we employed a surface-based approach by constructing cortical profiles traveling perpendicularly through the cortical ribbon and sampling T1w/T2w values. The curvatures of T1w/T2w cortical profiles may be influenced by differences in local myeloarchitecture and other tissue properties, which are known to vary across cortical regions. To quantify the shape of these profiles, we parametrized the level of curvature using a nonlinearity index (NLI) that measures the deviation of the profile from a straight line. We showed that NLI exhibited a steep decline in aging that was independent of local cortical thinning. Further examination of the profiles revealed that lower T1w/T2w near the gray-white matter boundary and superficial cortical depths were major contributors to the apparent NLI variations with age. These findings suggest that demyelination and changes in other T1w/T2w related tissue properties in normal aging may be depth-specific and highlight the potential of NLI as a unique marker of microstructural alterations within the cerebral cortex.725/725Secondary AnalysisShared
Cross-cohort replicability and generalizability of connectivity-based psychometric prediction patternsAn increasing number of studies have investigated the relationships between inter-individual variability in brain regions’ connectivity and behavioral phenotypes, making use of large population neuroimaging datasets. However, the replicability of brain-behavior associations identified by these approaches remains an open question. In this study, we examined the cross-dataset replicability of brain-behavior association patterns for fluid cognition and openness predictions using a previously developed region-wise approach, as well as using a standard whole-brain approach. Overall, we found moderate similarity in patterns for fluid cognition predictions across cohorts, especially in the Human Connectome Project Young Adult, Human Connectome Project Aging, and Enhanced Nathan Kline Institute Rockland Sample cohorts, but low similarity in patterns for openness predictions. In addition, we assessed the generalizability of prediction models in cross-dataset predictions, by training the model in one dataset and testing in another. Making use of the region-wise prediction approach, we showed that first, a moderate extent of generalizability could be achieved with fluid cognition prediction, and that, second, a set of common brain regions related to fluid cognition across cohorts could be identified. Nevertheless, the moderate replicability and generalizability could only be achieved in specific contexts. Thus, we argue that replicability and generalizability in connectivity-based prediction remain limited and deserve greater attention in future studies.725/725Secondary AnalysisShared
Human Connectome Project-Aging (HCP-A) Release 2.0The 2.0 release of data from the Human Connectome Project in Aging (healthy participants, ages 36-100+) includes visit 1 (V1) preprocessed structural and functional imaging data, unprocessed V1 imaging data for all modalities (structural, high-res hippocampal T2, resting state fMRI, task fMRI, diffusion, and ASL), and non-imaging demographic and behavioral assessment data for 725 participants. For details of all the measures included in this release and access instructions see the Lifespan HCP-Aging Release 2.0 documentation link below. 725/725Primary AnalysisShared
Physical Fitness, Cognition, and Structural Network Efficiency of Brain Connections Across the LifespanInadequate levels of exercise is one of the most potent modifiable risk factors for preventing cognitive decline and dementia as we age. Meanwhile, network science-based measures of structural brain network global and local efficiency show promise as robust biomarkers of aging, cognitive decline, and pathological disease progression. Despite this, little to no work has established how maintaining physical activity (PA) and physical fitness might relate to cognition and network efficiency measures across the lifespan. Therefore the purpose of this study was to determine the relationship between (1) PA and fitness and cognition, (2) fitness and network efficiency, and (3) how network efficiency measures relate to cognition. To accomplish this, we analyzed a large cross-sectional data set (n=720; 36-100 years) from the aging human connectome project, which included the Trail Making Task (TMT) A and B, a measure of fitness (2-minute walk test), physical activity (International Physical Activity Questionnaire), and high-resolution diffusion imaging data. Our analysis consisted of employing multiple linear regression while controlling for age, sex, and education. Age was associated with lower global and local brain network efficiency and poorer Trail A & B performance. Meanwhile, fitness, but not physical activity, was related to better Trail A and B performance and fitness, and was positively associated with local and global brain efficiency. Finally, local efficiency was related to better TMT B performance and partially mediated the relationship between fitness and TMT B performance. These results indicate aging may be associated with a shift towards less efficient local and global neural networks and that maintaining physical fitness might protect against age-related cognitive performance deterioration by bolstering structural network efficiency.725/725Secondary AnalysisShared
Regional neuroanatomic effects on brain age inferred using magnetic resonance imaging and ridge regressionThe biological age of the brain differs from its chronological age (CA) and can be used as biomarker of neural/cognitive disease processes and as predictor of mortality. Brain age (BA) is often estimated from magnetic resonance images (MRIs) using machine learning (ML) that rarely indicates how regional brain features contribute to BA. Leveraging an aggregate training sample of 3 418 healthy controls (HCs), we describe a ridge regression model that quantifies each region’s contribution to BA. After model testing on an independent sample of 651 HCs, we compute the coefficient of partial determination R¯2p for each regional brain volume to quantify its contribution to BA. Model performance is also evaluated using the correlation r between chronological and biological ages, the mean absolute error (⁠MAE) and mean squared error (⁠MSE) of BA estimates. On training data, r=0.92, MSE=70.94 years, MAE=6.57 years, and R¯2=0.81; on test data, r=0.90⁠, MSE=81.96 years, MAE=7.00 years, and R¯2=0.79⁠. The regions whose volumes contribute most to BA are the nucleus accumbens (⁠R¯2p=7.27%⁠), inferior temporal gyrus (⁠R¯2p=4.03%⁠), thalamus (⁠R¯2p=3.61%⁠), brainstem (⁠R¯2p=3.29%⁠), posterior lateral sulcus (⁠R¯2p=3.22%⁠), caudate nucleus (⁠R¯2p=3.05%⁠), orbital gyrus (⁠R¯2p=2.96%⁠), and precentral gyrus (⁠R¯2p=2.80%⁠). Our ridge regression, although outperformed by the most sophisticated ML approaches, identifies the importance and relative contribution of each brain structure to overall BA. Aside from its interpretability and quasi-mechanistic insights, our model can be used to validate future ML approaches for BA estimation.725/725Primary AnalysisShared
Sleep Quality Moderates Associations between Fitness and Hippocampal and Entorhinal Structure in Middle Aged and Older AdultsAs individuals age, the entorhinal cortex (ERC) and hippocampus, crucial structures for memory tend to experience atrophy, and relate to cognitive decline. Simultaneously, lifestyle factors that can be modified, such as exercise and sleep, have been separately shown to slow atrophy and functional decline. Yet, the synergistic impact of fitness and sleep quality on susceptible brain structures in aging adults remains uncertain. This study aimed to examine both independent and interactive relationships between fitness and subjective sleep quality and their influence on ERC thickness and hippocampal volume. We conducted our analysis on data obtained from a sample of 598 middle-aged and older adults from the Human Connectome Lifespan Aging Project. Cardiorespiratory fitness was assessed using the 2-minute walk test (2MWT), while subjective sleep quality was measured with the Pittsburgh Sleep Quality Index (PSQI). High-resolution structural magnetic resonance imaging were used to examine mean ERC thickness and bilateral hippocampal volume. Through multiple linear regression analyses, we explored the moderating effects of subjective sleep quality on the relationship between fitness and brain structure, accounting for variables such as age, sex, education, body mass index, gait speed, and subjective physical activity. Our findings revealed that greater cardiorespiratory fitness, but not subjective sleep quality, was positively associated with bilateral hippocampal volume and ERC thickness. Notably, the positive correlation between fitness and both hippocampal volume and ERC thickness was significantly diminished among individuals reporting the poorest subjective sleep quality. In conclusion, this study underscores the importance of both cardiorespiratory fitness and subjective sleep quality in preserving critical, age-vulnerable brain structures. It suggests that interventions targeting sleep health and exercise should take into consideration the combined effects of sleep and fitness on brain health.725/725Secondary AnalysisShared
Structural Brain Changes in Emotion Recognition Across the Adult LifespanEmotion recognition (ER) declines with increasing age, yet little is known whether this observation bases on structural brain changes conveyed by differential atrophy. To investigate whether age-related ER decline correlates with reduced grey matter (GM) volume in emotion-related brain regions, we conducted a voxel-based morphometry analysis using data of the Human Connectome Project-Aging (N = 238, aged 36 - 87) in which facial ER was tested. We expected to find brain regions that show an additive or super-additive age-related change in GM volume indicating atrophic processes that reduce ER in older adults. The data did not support our hypotheses after correction for multiple comparisons. Exploratory analyses with a threshold of p < .001 (uncorrected), however, suggested that relationships between GM volume and age-related general ER may be widely distributed across the cortex. Yet, small effect sizes imply that only a small fraction of the decline of ER in older adults can be attributed to local GM volume changes in single voxels or their multivariate patterns. 725/725Primary AnalysisShared
Transfer learning for cognitive reserve quantificationCognitive reserve (CR) has been introduced to explain individual differences in susceptibility to cognitive or functional impairment in the presence of age or pathology. We developed a deep learning model to quantify the CR as residual variance in memory performance using the Structural Magnetic Resonance Imaging (sMRI) data from a lifespan healthy cohort. The generalizability of the sMRI-based deep learning model was tested in two independent healthy and Alzheimer's cohorts using transfer learning framework. Structural MRIs were collected from three cohorts: 495 healthy adults (age: 20-80) from RANN, 620 healthy adults (age: 36-100) from lifespan Human Connectome Project Aging (HCPA), and 941 adults (age: 55-92) from Alzheimer's Disease Neuroimaging Initiative (ADNI). Region of interest (ROI)-specific cortical thickness and volume measures were extracted using the Desikan-Killiany Atlas. CR was quantified by residuals which subtract the predicted memory from the true memory. Cascade neural network (CNN) models were used to train RANN dataset for memory prediction. Transfer learning was applied to transfer the T1 imaging-based model from source domain (RANN) to the target domains (HCPA or ADNI). The CNN model trained on the RANN dataset exhibited strong linear correlation between true and predicted memory based on the T1 cortical thickness and volume predictors. In addition, the model generated from healthy lifespan data (RANN) was able to generalize to an independent healthy lifespan data (HCPA) and older demented participants (ADNI) across different scanner types. The estimated CR was correlated with CR proxies such education and IQ across all three datasets. The current findings suggest that the transfer learning approach is an effective way to generalize the residual-based CR estimation. It is applicable to various diseases and may flexibly incorporate different imaging modalities such as fMRI and PET, making it a promising tool for scientific and clinical purposes.725/725Secondary AnalysisShared
anatomically interpretable deep learning of brain age captures domain-specific cognitive impairmentThe gap between chronological age (CA) and biological brain age, as estimated from magnetic resonance images (MRIs), reflects how individual patterns of neuroanatomic aging deviate from their typical trajectories. MRI-derived brain age (BA) estimates are often obtained using deep learning models that may perform relatively poorly on new data or that lack neuroanatomic interpretability. This study introduces a convolutional neural network (CNN) to estimate BA after training on the MRIs of 4,681 cognitively normal (CN) participants and testing on 1,170 CN participants from an independent sample. BA estimation errors are notably lower than those of previous studies. At both individual and cohort levels, the CNN provides detailed anatomic maps of brain aging patterns that reveal sex dimorphisms and neurocognitive trajectories in adults with mild cognitive impairment (MCI, N = 351) and Alzheimer’s disease (AD, N = 359). In individuals with MCI (54% of whom were diagnosed with dementia within 10.9 y from MRI acquisition), BA is significantly better than CA in capturing dementia symptom severity, functional disability, and executive function. Profiles of sex dimorphism and lateralization in brain aging also map onto patterns of neuroanatomic change that reflect cognitive decline. Significant associations between BA and neurocognitive measures suggest that the proposed framework can map, systematically, the relationship between aging-related neuroanatomy changes in CN individuals and in participants with MCI or AD. Early identification of such neuroanatomy changes can help to screen individuals according to their AD risk. 725/725Primary AnalysisShared
Metabolism modulates network synchrony in the aging brainBrain aging is associated with hypometabolism and global changes in functional connectivity. Using functional MRI (fMRI), we show that network synchrony, a collective property of brain activity, decreases with age. Applying quantitative methods from statistical physics, we provide a generative (Ising) model for these changes as a function of the average communication strength between brain regions. We find that older brains are closer to a critical point of this communication strength, in which even small changes in metabolism lead to abrupt changes in network synchrony. Finally, by experimentally modulating metabolic activity in younger adults, we show how metabolism alone—independent of other changes associated with aging—can provide a plausible candidate mechanism for marked reorganization of brain network topology.719/719Secondary AnalysisShared
The interaction between age and sleep quality impacts cognitionBackground: The association between sleep quality and cognition is widely established but the role of the aging process on this relationship is largely unknown. Purpose: To examine how age impacts the sleep-cognition relationship and determine critical age ranges when sleep is most strongly associated with cognition. This investigation could help identify individuals at risk for sleep-related cognitive impairment. Methods: Sample included 711 individuals (59.66 ± 14.91, 55.7 % female) who were assessed for sleep quality using the Pittsburgh Sleep Quality Index (PSQI) and cognition as part of the Human Connectome Project Aging (HCP-A). Results: There was a significant interaction term between the PSQI and non-linear age term (age2) on Trail Making Test B (TMT-B) (p = 0.02) and NIH Toolbox (TB) crystallized cognition (p = 0.02), with critical age ranges at ages 50-75 for TMT-B and ages 66-70 for crystallized cognition. Conclusions: The relationship between sleep quality and cognitive performance may be modified by age. Furthermore, middle-age to early older adulthood may be the most vulnerable to sleep-related cognitive impairment. 712/712Secondary AnalysisShared
Human Connectome Project-Aging (HCP-A) Release 1.0Initial release of data from the Human Connectome Project in Aging (ages 35-100+). Release includes basic demographic data (sex, age, race/ethnicity, handedness) and unprocessed imaging data for all modalities (structural, high-res hippocampal T2, resting state fMRI, task fMRI, diffusion, and ASL) for 689 subjects and preprocessed structural imaging data for 128 subjects. Full release documentation available at: https://www.humanconnectome.org/study/hcp-lifespan-aging/documentation 689/689Primary AnalysisShared
Revealing the spatial pattern of brain hemodynamic sensitivity to healthy aging through sparse DCMAge-related changes in the BOLD response could reflect neuro-vascular coupling modifications rather than simply impairments in neural functioning. In this study, we propose the use of a generative dynamic causal model (DCM) to decouple neuronal and vascular factors in the BOLD signal, with the aim of characterizing the whole-brain spatial pattern of hemodynamic sensitivity to healthy aging, as well as to test the role of hemodynamic features as independent predictors in an age-classification model. In this view, DCM was applied to the resting-state fMRI data of a cohort of 126 healthy individuals in a wide age range, providing reliable estimates of the hemodynamic response function (HRF) for each subject and each region of interest. Then, some features characterizing each HRF curve were extracted and used to fit a multivariate logistic regression model to predict the age class of each individual. Ultimately, we tested the final predictive model on an independent dataset of 338 healthy subjects selected from the Human Connectome Project Aging (HCP-A) and Development (HCP-D) cohorts. Our results entail the spatial heterogeneity of the age effects on the hemodynamic component, since its impact resulted to be strongly region- and population-specific, discouraging any space-invariant corrective procedures that attempt to correct for vascular factors when carrying out functional studies involving groups with different ages. Moreover, we demonstrated that a strong interaction exists between some specific hemodynamic features and age, further supporting the essential role of the hemodynamic factor as independent predictor of biological aging, rather than a simple confounding variable. 489/641Secondary AnalysisShared
Educational quality may be a closer correlate of cardiometabolic health than educational attainmentEducational quality may be a closer correlate of physical health than more commonly used measures of educational attainment (e.g., years in school). We examined whether a widely-used performance-based measure of educational quality is more closely associated with cardiometabolic health than educational attainment (highest level of education completed), and whether perceived control (smaller sample only), executive functioning (both samples), and health literacy (smaller sample only) link educational quality to cardiometabolic health. In two samples (N=98 and N=586) collected from different regions of the US, educational quality was associated with cardiometabolic health above and beyond educational attainment, other demographic factors (age, ethnoracial category, sex), and fluid intelligence. Counter to expectations, neither perceived control, executive function, nor health literacy significantly mediated the association between educational quality and cardiometabolic health. Findings add to the growing literature suggesting that current operationalizations of the construct of education likely underestimate the association between education and multiple forms of health. To the extent that educational programs may have been overlooked based on the apparent size of associations with outcomes, such actions may have been premature.586/586Secondary AnalysisShared
Intermediately Synchronised Brain States optimise trade-off between Subject Identifiability and Predictive CapacityFunctional connectivity (FC) refers to the statistical dependencies between activity of distinct brain areas. To study temporal fluctuations in FC within the duration of a functional magnetic resonance imaging (fMRI) scanning session, researchers have proposed the computation of an edge time series (ETS) and their derivatives. Evidence suggests that FC is driven by a few time points of high-amplitude co- fluctuation (HACF) in the ETS, which may also contribute disproportionately to interindividual differences. However, it remains unclear to what degree different time points actually contribute to brain-behaviour associations. Here, we systematically evaluate this question by assessing the predictive utility of FC estimates at different levels of co-fluctuation using machine learning (ML) approaches. We demonstrate that time points of lower and intermediate co-fluctuation levels provide overall highest subject specificity as well as highest predictive capacity of individual-level phenotypes. 558/558Secondary AnalysisShared
Identifying and characterizing cognitive profiles in midlife females: A latent profile analysisFemales are at greater risk of developing Alzheimer’s disease (AD) than men. The menopause transition, which involves a neuroendocrine shift, is a potential contributor to this sex difference. Multiple estrogen-regulated systems (i.e., circadian rhythms) are disrupted during this transition which may affect cognitive functioning (Barha & Liu-Ambrose, 2020), most notably verbal learning and memory. Midlife females are chronically understudied, and little is known about how individual factors (i.e., sleep, physical activity (PA), stress, depressive symptoms) may relate to cognitive functioning across midlife for females, a period marked by the menopausal transition. Utilizing data from the Human Connectome Aging project (HCP-A), the current study will examine whether distinct cognitive profiles determined by performance-based tasks relate to emotional and physical functioning among a sample of middle-aged females. Late reproductive, perimenopause, and postmenopausal females (ages 40 to 60) from the HCP-A were included (n =202, M age = 50.5, SD = 6.2). Demographic information, sleep problems (Pittsburgh Sleep Quality Index), PA (International Physical Activity Questionnaire), stress (Distress subscale of the Perceived Stress Scale), depressive symptoms (NIH toolbox Emotion Module) were assessed with surveys, and participants completed several performance-based tasks including: global cognitive function (MoCA), dimensional change card sort (DCCS), flanker, pattern recognition, working memory (WM), picture sequencing, receptive language task, Trail Making Test B (TMT-B), and Rey Auditory Verbal Learning (RAVLT) tasks. Using latent profile analysis (LPA), cognitive profiles were identified via performance-based cognitive tasks. Emergent profiles were characterized in terms of demographic information, psychological, and behavioral factors. Fit indices indicated that a three-class solution fit the sample best: below average to low average performance across domains (Class 1, n=22), average performance across domains (Class 2, n= 72) and low average to average performance across domains (Class 3, n= 76). There was no significant multivariate effect of cognitive profile on psychological and behavioral factors, (p= .14), after controlling for age and education. Univariate analyses revealed significant differences between classes based on depressive symptoms, F(2,52.7) = 3.69, p = .027, η^2= .043) such that females in Class 1 reported higher levels of symptoms than both Class 2 and 3. There was no difference between Class 2 and 3 regarding depressive symptoms, and contrary to hypotheses, no difference in PA, stress, or sleep problems were observed between any classes. Results suggest three distinct cognitive profiles exist in this analytic sample. After controlling for age and education, only depressive symptoms significantly differed between cognitive profiles. The class characterized by low to below-average cognitive performance demonstrated higher levels of reported depressive symptoms as compared to other classes. These findings provide preliminary evidence that middle-aged females who perform worse on cognitive tasks may be experiencing heightened depressive symptoms, which are known to worsen in the perimenopause. Future research should explore more psychological and behavioral factors and whether emerging associations are moderated by menopausal stage. 332/332Secondary AnalysisShared
Neurocognitive Differences Between Lifestyle Profiles of Women Across the Menopausal TransitionWomen are at greater risk of developing Alzheimer’s disease (AD) than men. The menopausal transition, which involves a neuroendocrine shift for women, is a potential contributor to this sex difference. Multiple estrogen-regulated systems (i.e., circadian rhythms) are disrupted during this transition which affects cognitive functioning (Barha & Liu-Ambrose, 2020), most notably verbal learning and memory. Little is known about how lifestyle factors (i.e., sleep, physical activity (PA), stress) may promote neurocognitive functioning across this transition (Maki & Weber, 2021). Utilizing data from the Human Connectome Aging project (HCP-A), the current study will examine whether distinct lifestyle profiles including sleep, PA, and stress relate to multiple domains of cognitive performance among a sample of perimenopausal/menopausal women. Perimenopausal/menopausal women (ages 45 to 65) from the HCP-A were included (n =150, M age = 54.6, SD = 5.5). Demographic information, menopausal status, sleep problems (Pittsburgh Sleep Quality Index), PA (International Physical Activity Questionnaire), stress (Distress subscale of the Perceived Stress Scale) were assessed with surveys, and participants completed several lab-based tasks including: dimensional change card sort (DCCS), flanker, pattern recognition, working memory (WM), picture sequencing, oral reading, Trails Making Test A and B (TMT), and Rey Auditory Verbal Learning (RAVLT) tasks. Using latent profile analysis (LPA), lifestyle profiles were identified via sleep problems, PA, and stress levels. A MANOVA compared cognitive performance between these lifestyle profiles, above and beyond age and education status. Fit indices indicated that a three-class solution fit the sample best: high PA, low stress and sleep problems (Class 1, n=38), high PA, stress, and sleep problems (Class 2, n= 17) and low PA, high stress and sleep problems (Class 3, n= 95) which were not significantly different based on age or menopausal status (p>0.05). A significant multivariate effect of age and education on cognitive performance (p<.001) emerged. There was a significant multivariate effect of lifestyle profile on cognitive performance, F (18, 260) = 1.73, p=.034, η^2= .11, after controlling for age and education. Univariate analyses determined that certain lifestyle profiles were associated with better performance on all cognitive tasks except verbal memory. Contrary to expectation, Class 3 performed better on TMT A & B, DCCS, flanker, WM, and pattern recognition tasks as compared to Class 1. Class 3 performed better on reading and picture sequencing tasks than Class 2. There was no difference in performance between Class 1 and 2. Results suggest three distinct lifestyle profiles exist in this analytic sample. After controlling for age and education, cognitive performance on all tasks except for verbal memory significantly differed between lifestyle profiles. The profile characterized by low PA and high stress and sleep problems demonstrated superior performance as compared to other classes. These findings provide preliminary evidence that women who have high levels of stress and sleep problems with low PA are performing better on cognitive tasks, but replication of these findings utilizing longitudinal designs are needed. 332/332Secondary AnalysisShared
Psychological Resilience and Neurodegenerative Risk: A Connectomics-Transcriptomics Investigation in Healthy Adolescent and Middle-Aged FemalesAdverse life events can inflict substantial long-term damage, which, paradoxically, has been posited to stem from initially adaptative responses to the challenges encountered in one’s environment. Thus, identification of the mechanisms linking resilience against recent stressors to longer-term psychological vulnerability is key to understanding optimal functioning across multiple timescales. To address this issue, our study tested the relevance of neuro-reproductive maturation and senescence, respectively, to both resilience and longer-term risk for pathologies characterised by accelerated brain aging, specifically, Alzheimer’s Disease (AD). Graph theoretical and partial least squares analyses were conducted on multimodal imaging, reported biological aging and recent adverse experience data from the Lifespan Human Connectome Project (HCP). Availability of reproductive maturation/senescence measures restricted our investigation to adolescent (N =178) and middle-aged (N=146) females. Psychological resilience was linked to age-specific brain senescence patterns suggestive of precocious functional development of somatomotor and control-relevant networks (adolescence) and earlier aging of default mode and salience/ventral attention systems (middle adulthood). Biological aging showed complementary associations with the neural patterns relevant to resilience in adolescence (positive relationship) versus middle-age (negative relationship). Transcriptomic and expression quantitative trait locus data analyses linked the neural aging patterns correlated with psychological resilience in middle adulthood to gene expression patterns suggestive of increased AD risk. Our results imply a partially antagonistic relationship between resilience against proximal stressors and longer-term psychological adjustment in later life. They thus underscore the importance of fine-tuning extant views on successful coping by considering the multiple timescales across which age-specific processes may unfold. 146/324Secondary AnalysisShared
* Data not on individual level

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Collection - Associated Studies

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