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NDAR provides a single access to de-identified autism research data. For permission to download data, you will need an NDAR account with approved access to NDAR or a connected repository (AGRE, IAN, or the ATP). For NDAR access, you need to be a research investigator sponsored by an NIH recognized institution with federal wide assurance. See Request Access for more information.

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The filters you have selected from various query interfaces will be stored here, in the 'Filter Cart'. The database will be queried using filters added to your 'Filter Cart', when multiple filters are defined, each will be executed using 'AND' logic, so with each filter that is applied the result set gets smaller.

From the 'Filter Cart' you can inspect each of the filters that have been defined, and you also have the option to remove filters. The 'Filter Cart' itself will display the number of filters applied along with the number of subjects that are identified by the combination of those filters. For example a GUID filter with two subjects, followed by a GUID filter for just one of those subjects would return only data for the subject that is in both GUID filters.

If you have a question about the filter cart, or underlying filters please contact the help desk at The NDA Help Desk

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1 Numbers reported are subjects by age
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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
  • Select One
  • EEG
  • EGG
  • Eye Tracking
  • Omics
  • fMRI
Created On
894Dot ProbeEye Tracking03/07/2018
893Startle Habituation and Shock Sensitivity EvaluationEEG03/03/2018
892NPU EEG Task EEG03/03/2018
891Duke ACE ETEye Tracking03/02/2018
887Memory Guided Saccade Encode and Maintenance (EyeTracking)Eye Tracking02/26/2018
884Plasma metabolic profileOmics02/05/2018
878Social Challenge AssessmentEye Tracking01/26/2018
876Mixed Anti and Pro (vgs) saccade mixed blocked (EyeTracking)Eye Tracking01/22/2018
875Attention modulation taskEye Tracking01/17/2018
874ruthldopa resting 17 and 18fMRI01/16/2018
873ruthldopa resting 15 and 16fMRI01/16/2018
872ruthldopa resting 13 and 14fMRI01/16/2018
871ruthldopa resting 11 and 12fMRI01/16/2018
870Resting State fMRIfMRI01/12/2018
867Velten Mood Induction State-ItemfMRI01/12/2018
866Emotional Hemifield Task (EHT)EEG01/12/2018
865Genome EditingOmics01/12/2018
855Regulating Emotional Responses to Visual Images Across the Affective Instability SpectrumfMRI01/12/2018
853R61 Ezogabine Resting State FMRIfMRI01/11/2018
852Paired AssociatesfMRI01/11/2018
850Reward ProcessingfMRI01/11/2018
849Resting EEG (1024 samples/s)EEG01/11/2018
845Fast face -1 runfMRI01/10/2018
843RTT AHA-1Omics01/09/2018
84230 words (simultaneous fMRI and EEG acquisition)EEG01/08/2018
841Neural Correlates of Episodic Retrieval fMRI01/08/2018
84030 words (simultaneous fMRI and EEG acquisition)fMRI01/08/2018
838remove mefMRI01/05/2018
837remove mefMRI01/05/2018
836Food ExposurefMRI01/03/2018
Collection - Add Experiment
Add Supporting Documentation
Select File

Please enter the name of the data structure to search or if your definition does not exist, please upload that definition so that it can be appropriately defined for submission. Multiple data structures may be associated with a single Data Expected entry. Please add only one data structure per assessment.

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Not Eligible

The Data Expected list for this Collection shows some raw data as missing. Contact the NDA Help Desk with any questions.

Collection Updated

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

You have requested to move the sharing dates for the following assessments:
Data Expected Item Original Sharing Date New Sharing Date

Please provide a reason for this change, which will be sent to the Program Officers listed within this collection:

Explanation must be between 20 and 200 bytes in length.

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


Title, investigators, and Collection Description may be edited along with the Collection Phase. For Collection Phase, the options Pre-enrollment, Enrollment, and Completed can be chosen allowing the Collection Owner to indicate the stage of data collection.

Funding Source

The ability to associate the funding source for the project is provided. For NIH funded grants, linkage to Project Reporter information (e.g. R01MH123456) is supported. Projects funded by others, including the URL of the project, are listed. Non NIH funded projects will become available here to link that data with the appropriate funding agency.

Supporting Documentation

Any documents related to the project may be uploaded clarifying the data or acquisition methods used may be uploaded and made available here. The default is to share these documents to the general public. An option to share only to qualified Researchers is also an option.

Clinical Trials

For clinical trials, the option to link to the clinical trial in clinicaltrials.gov is optionally provided.

Collection Summary Collection Charts
Collection Title Collection Investigators Collection Description
UNC/UMN Baby Connectome Project
Jed Elison, Weili Lin 
This application is in response to the RFA-MH-16-160, entitled Lifespan Human Connectome Project (HCP): Baby Connectome. Investigators at The University of North Carolina at Chapel Hill (UNC) and The University of Minnesota (UMN) will join forces to accomplish the goals outlined by this RFA. The team at UNC has over 10 years of experience in recruiting and imaging typically developing and at-risk children, scanning over 1000 children from birth to five years1-40. Well established infrastructure at the Biomedical Research Imaging Center (BRIC) at UNC and Center for Magnetic Resonance Research (CMRR) at UMN are in place to recruit and retain pediatric subjects and facilitate the coordination of pediatric imaging studies. Our past and ongoing studies for imaging children (birth five years of age) without sedation have achieved an overall success rate of 81% and attrition rate of 29.3%. Our track record demonstrates that we possess the critical and essential components to successfully conduct longitudinal pediatric imaging studies focusing on early brain development, a critically-important aspect of this RFA. Our ability to recruit, retain, and image non-sedated, typically developing children is further strengthened by our image analysis team, which has developed novel image analysis tools specifically for early brain development. The expertise at UNC is complementary to and strengthened by the expertise of the team at UMN. The CMRR at UMN has been one of the leading groups in the HCP project and has developed novel MR imaging approaches to dramatically shorten data acquisition time. Furthermore, the team at UMN has extensive experience in behavioral and cognitive studies of early child development. Together, our combined team is well positioned to accomplish the goals of this RFA. To this end, a total of 500 typically developing children between birth and five years of age will be recruited across two data collection sites in a sequential cohort, accelerated longitudinal study design. The participants are divided into two main groups, longitudinal (n=285) and cross-sectional (n=215) groups, respectively. This hybrid longitudinal and cross-sectional design enables detailed characterization of early brain development from both brain structural/functional using MRI and behavioral aspects using behavioral assessments. All of the acquired images and behavioral assessments will undergo extensive quality assurance and control processes to ensure that high quality data is obtained and transferred to the Central Connectome Facility at Washington University. In addition, we will integrate novel image analysis tools, developed by our team onto the existing HCP pipelines.
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NIH - Extramural None

U01MH110274-01 UNC/UMN Baby Connectome 09/01/2016 05/31/2020 Not Reported 83 UNIV OF NORTH CAROLINA CHAPEL HILL $2,071,009.00

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


To create a new Omics, eye tracking, fMRI, or EEG experiment, press the "+ New Experiment" button. Once an experiment is created, then raw files for these types of experiments should be provided, associating the experiment – through Experiment_ID – with the metadata defined in the experiments interface.

IDNameCreated DateStatusType
No records found.

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

Shared Data

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

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 NDAR 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
29317597Create StudyPreliminary evidence for genetic overlap between body mass index and striatal reward response.Translational psychiatryLancaster TM, Ihssen I, Brindley LM, Linden DEJanuary 2018Not Determined
29124254Create StudyDevelopmental Patterns Based Individualized Parcellation of Infant Cortical Surface.Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted InterventionLi G, Wang L, Lin W, Shen DSeptember 2017Not Determined
29124253Create StudyExploring Gyral Patterns of Infant Cortical Folding based on Multi-view Curvature Information.Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted InterventionDuan D, Xia S, Meng Y, Wang L, Lin W, Gilmore JH, Shen D, Li GSeptember 2017Not Determined
28902466Create StudyLearning-based deformable registration for infant MRI by integrating random forest with auto-context model.Medical physicsWei L, Cao X, Wang Z, Gao Y, Hu S, Wang L, Wu G, Shen DDecember 2017Not Determined
28819140Create StudyEnhancement of Perivascular Spaces in 7 T MR Image using Haar Transform of Non-local Cubes and Block-matching Filtering.Scientific reportsHou Y, Park SH, Wang Q, Zhang J, Zong X, Lin W, Shen DAugust 2017Not Determined
28665045Create StudyExtraction of dynamic functional connectivity from brain grey matter and white matter for MCI classification.Human brain mappingChen X, Zhang H, Zhang L, Shen C, Lee SW, Shen DOctober 2017Not Determined
28624881Create StudyDiscriminative self-representation sparse regression for neuroimaging-based alzheimer's disease diagnosis.Brain imaging and behaviorZhu X, Suk HI, Lee SW, Shen DJune 2017Not Determined
28603790Create StudyDual-Layer Groupwise Registration for Consistent Labeling of Longitudinal Brain Images.Machine learning in medical imaging. MLMI (Workshop)Kim M, Wu G, Rekik I, Shen DOctober 2016Not Determined
28358032Create StudyA Hierarchical Feature and Sample Selection Framework and Its Application for Alzheimer's Disease Diagnosis.Scientific reportsAn L, Adeli E, Liu M, Zhang J, Lee SW, Shen DMarch 2017Not Determined
28295833Create StudyCan we predict subject-specific dynamic cortical thickness maps during infancy from birth?Human brain mappingMeng Y, Li G, Rekik I, Zhang H, Gao Y, Lin W, Shen DJune 2017Not Determined
28284800Create StudyJoint prediction of longitudinal development of cortical surfaces and white matter fibers from neonatal MRI.NeuroImageRekik I, Li G, Yap PT, Chen G, Lin W, Shen DMay 2017Not Determined
28102945Create StudyLearning-based deformable image registration for infant MR images in the first year of life.Medical physicsHu S, Wei L, Gao Y, Guo Y, Wu G, Shen DJanuary 2017Not Determined
27798142Create StudyLongitudinal Study of the Emerging Functional Connectivity Asymmetry of Primary Language Regions during Infancy.The Journal of neuroscience : the official journal of the Society for NeuroscienceEmerson RW, Gao W, Lin WOctober 2016Not Determined
27668065Create StudyFULLY CONVOLUTIONAL NETWORKS FOR MULTI-MODALITY ISOINTENSE INFANT BRAIN IMAGE SEGMENTATION.Proceedings. IEEE International Symposium on Biomedical ImagingNie D, Wang L, Gao Y, Shen D2016Not Determined
27380969Create StudyLearning-based subject-specific estimation of dynamic maps of cortical morphology at missing time points in longitudinal infant studies.Human brain mappingMeng Y, Li G, Gao Y, Lin W, Shen DNovember 2016Not Determined
26874184Create StudyStructural and Maturational Covariance in Early Childhood Brain Development.Cerebral cortex (New York, N.Y. : 1991)Geng X, Li G, Lu Z, Gao W, Wang L, Shen D, Zhu H, Gilmore JHMarch 2017Not Determined

The Data Expected tab is a tracking and scheduling tool. It displays the data that is expected from the project, along with final expected enrollment counts and initial dates for the submission and sharing of the data. There are two types of Data Expected displayed:

  1. Data Expected from Relevant Publications: Publications reported in association with the Collection’s grant and determined as relevant to data expected for sharing are listed first. Any data specific to these publications are expected to be shared using the NDA Study feature. If a publication on this list is marked relevant in error, the PI of the project can correct the status in the Publications tab. Once created Studies are visible in the Associated Studies tab. When sharing a Study, only the outcome measures for the subjects/time-points specific to the publication are shared. Other data with share dates that have not yet been met as defined below remain embargoed. Investigators can initiate Study creation from this page when logged in.
  2. Data Expected by Data Structure: The table displayed second is defined by the investigator and lists all of the measures expected for sharing as defined in the Data Dictionary. Targeted Enrollment indicates the expected final unique subject count for that structure. Initial submission dates indicate when NDA should expect the first upload of those data, and initial sharing dates indicate when the first round of those data is expected to be shared. Click the i next to the Data Expected title to view the structures that are counted within that item.

Those with Administrator control over the Collection may also use the Data Expected tab to request an exemption period from an expected biannual submission by providing a timeframe and reason, and then saving the Collection. Please note the program officer of the associated grant may review this request.

Relevant Publications
PubMed IDStudyTitleJournalAuthorsDate
No records found.

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

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

Data Expected
Data ExpectedTargeted EnrollmentInitial SubmissionSubjects SharedStatus
No records found.
Structure not yet defined

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.