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

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

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

Blinded Clinical Trial Status:

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

  • When a Collection is created by NDA staff and marked as Shared, an email notification will automatically be sent to the PI(s) of the grant(s) associated with the Collection to notify them.

  • 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.

  • 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.

  • In general, each Collection is associated with only one grant; however, multiple grants may be associated if the grant has multiple competing segments for the same grant number or if multiple different grants are all working on the same project and it makes sense to hold the data in one Collection (e.g., Cooperative Agreements).

Glossary

  • Number of human subjects enrolled in an NIH-funded clinical research study. The data is provided in annual progress reports.

  • 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. 

  • 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.

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

  • An editable field with the title of the Collection, which is often the title of the grant associated with the Collection.

  • 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.

  • 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.

  • 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. 

  • 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. 

  • 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.

  • 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.

  • 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.  

  • The total number of unique subjects for whom data have been shared and are available for users with permission to access data.

NDA Help Center

Collection - Shared Data Tab

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

  • 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.  

  • 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.

  • Unlike completed projects where researchers may not have the information needed to create a GUID and/or where the effort needed to organize and format data becomes prohibitive, ongoing projects have more of an opportunity to overcome these challenges.  Please contact a program staff member at the appropriate funding institution for more information.

Glossary

  • 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

  • A grouping of data by similar characteristics such as Clinical Assessments, Omics, or Neurosignal data.

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

NDA Help Center

fMRi

fMRI stands for functional magnetic resonance imaging. fMRI tests measure blood flow, providing detailed functional images of the brain or body. 

Acquisition
The Acquisition parameters needed for an experiment include the following:

The name of the experiment is required. Please be concise and specific as possible.
Following experiment name, selection boxes are provided for the Equipment, Software, or other items specific to the experiment type. At least one selection is required for each. If NDAR does not have the appropriate listing, select Add New to add the information provided. Following the selection boxes, provide additional information may be required depending on the experiment type. Any required items are denoted by an asterisk (*).

Block/Event Design
At least one block/event is required. Note that any fields denoted with an asterisk (*) are required. All data must be devoid of personally identifiable data, including the contents of any files attached to the experiment.

Note: To simplify the definition of multiple events, we provide an Import from XML function. This function supports importing data from all three experiment sections (Acquisition, Block/Event Design, and Post Processing), at this time files cannot be uploaded from XML A test format is provided here and our XML Schema Definition (xsd) can be found here.

Post Processing
If you have completed any post-processing on your data, please choose 'Yes' for Has Postprocessing? If not, select 'No'. Depending on this selection the remaining post-processing fields will be enabled (some of which will be required). If you are initially providing data you can select 'No', then return to the experiment to add post-processing steps at a later date when the data are being provided.

Please provide information about post-processing manipulations, i.e. artifact detection algorithms, segmentation used for post data collection, items denoted with an asterisk (*) are required.

Frequently Asked Questions

Glossary

  • This button will add all selections to the Filter Cart. 

  • This button will allow you to copy all of the Experiment details as a template for a new experiment. 

  • Adds all data from the current selections in a Collection or NDA Study to the Filter Cart.

  • This button will allow you to return to the Experiments tab. 

NDA Help Center

Collection - Submissions Tab

Users with permission to access Shared data in the Collection’s assigned Permission Group may use this tab. 

Here, you can:

  • Review your uploads to your Collection, monitor their status, and download them individually to verify their contents.
  • Download individual datasets as a secondary user of the data approved for access.
  • Identify and download datasets containing errors identified by NDA's QA/QC process for review and resolution.
  • Report suspected or discovered Personally Identifiable Information in a submission via the Actions column.

Frequently Asked Questions

Glossary

  • The default view of Datasets within a Collection's Submission tab.

  • A Submission Loading Status on a Collection's Submission Tab that indicates that an issue has prevented the successful loading of the submission.  Users should contact the NDA Help Desk for assistance at NDAHelp@mail.nih.gov.

  • The NDA has two Submission Cycles per year - January 15 and July 15.

  • An interface to notify NDA that data may not be submitted during the upcoming/current submission cycle.  

  • The unique and sequentially assigned ID for a submission (e.g. a discrete upload via the Validation and Upload Tool), which may contain any number of datafiles, Data Structures and/or Data Types, regardless of the Submission Loading Status. A single submission may be divided into multiple Datasets, which are based on Data Type.

  • The total number of unique subjects for whom data have been shared and are available for users with permission to access data.

  • The total number of unique subjects for whom data have been submitted, which includes data in both a Private State and a Shared State.

NDA Help Center

Collection - Publications Tab

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

  • Publications are considered relevant to a collection when the data shared is directly related to the project or collection.

  • PubMed, an online library containing journals, articles, and medical research. Sponsored by NiH and National Library of Medicine (NLM). 

Glossary

  • 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.

  • 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.

  • 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 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.

  • The PUBMed ID is the unique ID number for the publication as recorded in the PubMed database.  

  • 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.

NDA Help Center

EEG

EEG stands for electroencencephalogram and is a test used to measure electrical activity in the brain.

Acquisition
The Acquisition parameters needed for an experiment include the following:

Name of the experiment is required. Please be concise and specific as possible.
Following experiment name, selection boxes are provided for the Equipment, Software, or other items specific to experiment type. At least one selection is required for each. If NDAR does not have the appropriate listing, select Add New to add the information provided. Following the selection boxes, provide additional information may be required depending on experiment type. Any required items are denoted by an asterisk (*).

Block/Event Design
At least one block/event is required. Note that any fields denoted with an asterisk (*) are required. All data must be devoid of personally identifiable data, including the contents of any files attached to the experiment.

Note: To simplify definition of multiple events, we provide an Import from XML function. This function supports importing data from all three experiment sections (Acquisition, Block/Event Design, and Post Processing), at this time files cannot be uploaded from XML A test format is provided here and our XML Schema Definition (xsd) can be found here.

Post Processing
If you have completed any post processing on your data, please choose 'Yes' for Has Postprocessing? If not, select 'No'. Depending on this selection the remaining post processing fields will be enabled (some of which will be required). If you are initially providing data you can select 'No', then return to the experiment to add post processing steps at a later date when the data are being provided.

Please provide information about post-processing manipulations, i.e. artifact detection algorithms, segmentation used for post data collection, items denoted with an asterisk (*) are required.

Frequently Asked Questions

Glossary

  • This button will add all selections to the Filter Cart. 

  • This button will allow you to copy all of the Experiment details as a template for a new experiment. 

  • Adds all data from the current selections in a Collection or NDA Study to the Filter Cart.

  • This button will allow you to return to the Experiments tab. 

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.

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

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

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

 

Frequently Asked Questions

  • 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.

  • The NDA Data Dictionary is comprised of electronic definitions known as Data Structures.

Glossary

  • 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.

  • 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.

  • 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

  • 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).

  • 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. 

  • 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+ 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 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 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.

  • 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.

  • The NDA has two Submission Cycles per year - January 15 and July 15.

  • An interface to notify NDA that data may not be submitted during the upcoming/current submission cycle.  

NDA Help Center

Collection - Permissions Tab

Collection Owners, Program Officers, and users with Administrator privileges may view this tab.

The available permission groups include:

  • Query: This read-only access is generally for NIH Program Officers
  • Submission: This will grant read access and allow the user to upload data and create experiment definitions. This is for the typical contributing personnel member.
  • Administrator: In addition to the access provided to Query and Submission users, Admins can also edit the Collection itself, create or edit the Data Expected list, and edit user permissions. This access is for the PI, data managers, and anyone they wish to delegate this to.

The PI has a special designation as the Collection Owner in addition to administrator access.

Frequently Asked Questions

  • Collection Owners and Admins may assign Collection Privileges to anyone.

  • Yes, you can assign various Privileges to other users with an NDA account.

  • If you are the Collection Owner or have Admin privileges, you can view and make changes to the list of individuals who have access to the Collection on the Collection's Permissions tab.  Information on users who have access to data Shared in your Collection because they were granted access to a Permission Group is not available.

  • Staff/collaborators who are working submitting data to the Collection, checking the quality of the data, and/or analyzing data should have access for the duration of the project until all data have been submitted, NDA Studies have been created for data used in publications, and/or a collaborative relationship with the user exists.  

  • The individual listed as an Investigator on the General tab of the NDA Collection will generally be able to provide a user access to the NDA Collection.  Additional users may also have this ability if granted Administrator access to an NDA Collection; however, these users are not viewable unless your account has access to the NDA Collection.  Given this, it is best to contact the Investigator to request access to the Collection.

  • Privileges that can be assigned to a user include:
    Submission allows a user to submit data to Collection
    Query allows the user to download data from Collection even when in a Private state
    Admin is both the Submission and Query Privilege + the ability to give privileges to other users.

  • You may have staff who are working on the submission of data or other activities associated with data sharing such as the definition of the Data Expected list or NDA Experiment creation.  Also, many projects have multiple performance sites and wish to share data among the site PIs.  Submitting to the NDA facilitates access by all investigators working on a project even before data have been shared with other users.  You can control who gets access to data while in a Private state.

Glossary

  • 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. 

  • 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.

NDA Help Center

Eye Tracking

EyeTracking tests follow the movement of the eye. The visual trajectory or focus can help determine predictions and assist in diagnoses. 

Acquisition
The Acquisition parameters needed for an experiment include the following:

The name of the experiment is required. Please be concise and specific as possible.
Following experiment name, selection boxes are provided for the Equipment, Software, or other items specific to the experiment type. At least one selection is required for each. If NDAR does not have the appropriate listing, select Add New to add the information provided. Following the selection boxes, provide additional information may be required depending on the experiment type. Any required items are denoted by an asterisk (*).

Block/Event Design
At least one block/event is required. Note that any fields denoted with an asterisk (*) are required. All data must be devoid of personally identifiable data, including the contents of any files attached to the experiment.

Note: To simplify the definition of multiple events, we provide an Import from XML function. This function supports importing data from all three experiment sections (Acquisition, Block/Event Design, and Post Processing), at this time files cannot be uploaded from XML A test format is provided here and our XML Schema Definition (xsd) can be found here.

Post Processing
If you have completed any post-processing on your data, please choose 'Yes' for Has Postprocessing? If not, select 'No'. Depending on this selection the remaining post-processing fields will be enabled (some of which will be required). If you are initially providing data you can select 'No', then return to the experiment to add post-processing steps at a later date when the data are being provided.

Please provide information about post-processing manipulations, i.e. artifact detection algorithms, segmentation used for post data collection, items denoted with an asterisk (*) are required.

Frequently Asked Questions

Glossary

  • This button will add all selections to the Filter Cart. 

  • This button will allow you to copy all of the Experiment details as a template for a new experiment. 

  • Adds all data from the current selections in a Collection or NDA Study to the Filter Cart.

  • This button will allow you to return to the Experiments tab. 

NDA Help Center

Collection - Experiments Tab

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

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

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

     

Glossary

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

  • 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.

NDA Help Center

Omics

Omics is a collective group of technologies, related to a field of study in Biology such as Genomics or proteomics. 

Experiment Parameters

To define an Omics experiment, provide a meaningful name and select a single molecule. The standard molecules are listed. However, if you are doing proteomic or environmental experiments, simply “Add New” and the new selection will be created. Only one value for molecule is permitted.

Next the technology (box 2) associated with the molecule will be presented along with its application. Again, only one selection is possible. If you wish to see all of NDAR’s options for any one box, Select “Show All”.

Platform

Continue to select the Platform (box 3).

Extraction

Next, the Extraction Protocol (box 4) and Kits (box 5) are presented based upon the Molecule selected and the Processing Protocol (box 6) and Kits (box 7) are presented based upon the Molecule and Technology Application (Box 1 and 2)

Processing

Note that for each of these (boxes 4, 5, 6, and 7) multiple selections are possible.

Additional Information

Lastly, the Software (box 8) and Equipment (box 9) is expected.

 

Once saved, the experiment will be associated with the Collection and by using the returned Experiment_ID, the NDA makes it possible to associate the experiment meta data directly with the data from the experiment.

Frequently Asked Questions

Glossary

  • This button will add all selections to the Filter Cart. 

  • This button will allow you to copy all of the Experiment details as a template for a new experiment. 

  • Adds all data from the current selections in a Collection or NDA Study to the Filter Cart.

  • This button will allow you to return to the Experiments tab. 

NDA Help Center

Collection - Associated Studies

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

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

Private v. Shared State: Studies that remain private indicate the associated study is only available to users who are able to access the collection. A shared study is accessible to the general public. 

Frequently Asked Questions

  • Studies are associated to the Collection automatically when the data is defined in the Study. 

Glossary

  • A tab in a Collection that lists the NDA Studies that have been created using data from that Collection including both Primary and Secondary Analysis NDA Studies.

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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
24HI-NGS_R1Omics02/16/2011
475MB1-10 (CHOP)Omics06/07/2016
490Illumina Infinium PsychArray BeadChip AssayOmics07/07/2016
501PharmacoBOLD Resting StatefMRI07/27/2016
506PVPREFOmics08/05/2016
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
26ASD_MethylationOmics03/01/2011
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
41ImmunofluorescenceOmics05/11/2011
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
519RestingfMRI11/08/2016
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
145AGRE/FMR1_Illumina.JHUOmics04/14/2014
146AGRE/MECP2_Sanger.JHUOmics04/14/2014
147AGRE/MECP2_Junior.JHUOmics04/14/2014
151Candidate Gene Identification in familial AutismOmics06/09/2014
152NJLAGS Whole Genome SequencingOmics07/01/2014
154Math Autism Study - Vinod MenonfMRI07/15/2014
155RestingfMRI07/25/2014
156SpeechfMRI07/25/2014
159EmotionfMRI07/25/2014
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
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Collection Summary Collection Charts
Collection Title Collection Investigators Collection Description
Longitudinal MRI Study of Infants at Risk for Autism
Joe Piven 
Research data for the IBIS Autism project. Autism Centers of Excellence (ACE) Network is a collaborative effort by investigators at four clinical sites: University of North Carolina (UNC), University of Washington (UW), Washington University (WU), and Yale University; and one data coordinating center (DCC) at the Montreal Neurological Institute (MNI) to conduct a longitudinal MRI/DTI and behavioral study of infants at high risk for autism (i.e., siblings of autistic individuals) at 6, 12 and 24 months (m) of age.
NIMH Data Archive
04/01/2008
Autism Centers of Excellence (ACE)
Funding Completed
Close Out
Shared
No
$6,179,607.00
1,039
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NIH - Extramural None


R01HD055741-01 A Longitudinal MRI Study of Infants at Risk for Autism 07/01/2007 06/30/2013 664 526 UNIV OF NORTH CAROLINA CHAPEL HILL $6,179,607.00

helpcenter.collection.general-tab

NDA Help Center

Collection - General Tab

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

  • Collection Title
  • Investigators
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  • Funding Source
  • Clinical Trials

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

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  • Pre-Enrollment: The default entry made when the NDA Collection is created.
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  • 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.
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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 withAdministrator privileges, may upload and attach supporting documentation. By default, supporting documentation is shared to the general public, however, the optionis also available tolimit this information to qualified researchers only.

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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 capturedby 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 do I know when a NDA Collection has been created???
    When a Collection is created by NDA staff and marked as Shared, an email notification will automatically be sent to the PI(s) of the grant(s) associated with the Collection to notify them.
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    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.
  • Is a single grant number ever associated with more than one Collection?
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Glossary

  • Actual Enrollment
    Number of human subjects enrolled in an NIH-funded clinical research study. The data is provided in annual progress reports.
IDNameCreated DateStatusType
370Longitudinal MRI Study of Infants at Risk for Autism08/11/2015ApprovedfMRI

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.

Autism Diagnostic Interview, Revised (ADI-R) Clinical Assessments 350
Autism Diagnostic Observation Schedule (ADOS)- Module 1 Clinical Assessments 312
Autism Diagnostic Observation Schedule (ADOS)- Module 2 Clinical Assessments 34
Autism Diagnostic Observation Schedule, 2nd Edition (ADOS-2) - Module 1 Clinical Assessments 63
Autism Diagnostic Observation Schedule, 2nd Edition (ADOS-2) - Module 2 Clinical Assessments 2
Autism Observation Scale for Infants Clinical Assessments 481
CHARGE Medical History Clinical Assessments 487
CHARGE Physical Exam Clinical Assessments 370
CSBS DP Behavior Sample Clinical Assessments 420
Early Development Interview Clinical Assessments 479
First Year Inventory Clinical Assessments 375
Height and Weight Clinical Assessments 485
Image Imaging 456
Infant Behavior Questionnaire Revised Clinical Assessments 449
M-CHAT Clinical Assessments 17
MacArthur-Bates CDI - Words and Gestures Form Clinical Assessments 420
Modified Checklist for Autism in Toddlers (M-CHAT) Clinical Assessments 2
Mullen Scales of Early Learning Clinical Assessments 519
Prefrontal task Clinical Assessments 301
Repetitive Behavior Scale - Revised (RBS-R) Clinical Assessments 422
Research Subject Clinical Assessments 934
Sensory Experiences Questionnaire Clinical Assessments 417
Social Communication Questionnaire (SCQ) - Current Form Clinical Assessments 134
Social Communication Questionnaire (SCQ) - Lifetime Clinical Assessments 282
Vineland-II - Survey Form (2005) Clinical Assessments 832

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

Publications

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

PubMed IDStudyTitleJournalAuthorsDateStatus
35485579Create StudyExamining the factor structure and discriminative utility of the Infant Behavior Questionnaire-Revised in infant siblings of autistic children.Child developmentSung, Sooyeon; Fenoglio, Angela; Wolff, Jason J; Schultz, Robert T; Botteron, Kelly N; Dager, Stephen R; Estes, Annette M; Hazlett, Heather C; Zwaigenbaum, Lonnie; Piven, Joseph; Elison, Jed T; IBIS NetworkApril 29, 2022Not Determined
34708871Create StudyInfant vocalizing and phenotypic outcomes in autism: Evidence from the first 2 years.Child developmentPlate, Samantha; Yankowitz, Lisa; Resorla, Leslie; Swanson, Meghan R; Meera, Shoba Sreenath; Estes, Annette; Marrus, Natasha; Cola, Meredith; Petrulla, Victoria; Faggen, Aubrey; Pandey, Juhi; Paterson, Sarah; Pruett Jr, John R; Hazlett, Heather; Dager, Stephen; St John, Tanya; Botteron, Kelly; Zwaigenbaum, Lonnie; Piven, Joseph; Schultz, Robert T; Parish-Morris, Julia; IBIS NetworkMarch 1, 2022Not Determined
34368815Create StudyA framework to construct a longitudinal DW-MRI infant atlas based on mixed effects modeling of dODF coefficients.Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted InterventionKim, Heejong; Styner, Martin; Piven, Joseph; Gerig, GuidoJanuary 1, 2020Not Determined
34327517Create StudyHierarchical geodesic modeling on the diffusion orientation distribution function for longitudinal DW-MRI analysis.Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted InterventionKim, Heejong; Hong, Sungmin; Styner, Martin; Piven, Joseph; Botteron, Kelly; Gerig, GuidoOctober 1, 2020Not Determined
34145789Create StudySocial and non-social sensory responsivity in toddlers at high-risk for autism spectrum disorder.Autism research : official journal of the International Society for Autism ResearchGunderson, Jaclyn; Worthley, Emma; Grzadzinski, Rebecca; Burrows, Catherine; Estes, Annette; Zwaigenbaum, Lonnie; Botteron, Kelly; Dager, Stephen; Hazlett, Heather; Schultz, Robert; Piven, Joseph; Wolff, Jason; IBIS NetworkOctober 1, 2021Not Determined
34021722Create StudyCataloguing and characterizing interests in typically developing toddlers and toddlers who develop ASD.Autism research : official journal of the International Society for Autism ResearchBurrows, Catherine A; Bodfish, James W; Wolff, Jason J; Vollman, Elayne P; Altschuler, Melody R; Botteron, Kelly N; Dager, Stephen R; Estes, Annette M; Hazlett, Heather C; Pruett Jr, John R; Schultz, Robert T; Zwaigenbaum, Lonnie; Piven, Joseph; Elison, Jed T; IBIS NetworkAugust 1, 2021Not Determined
34020453Create StudyHuman milk 3''-Sialyllactose is positively associated with language development during infancy.The American journal of clinical nutritionCho, Seoyoon; Zhu, Ziliang; Li, Tengfei; Baluyot, Kristine; Howell, Brittany R; Hazlett, Heather C; Elison, Jed T; Hauser, Jonas; Sprenger, Norbert; Wu, Di; Lin, WeiliAugust 2, 2021Not Determined
33965519Create StudyVariability in Responding to Joint Attention Cues in the First Year is Associated With Autism Outcome.Journal of the American Academy of Child and Adolescent PsychiatryStallworthy, Isabella C; Lasch, Carolyn; Berry, Daniel; Wolff, Jason J; Pruett Jr, John R; Marrus, Natasha; Swanson, Meghan R; Botteron, Kelly N; Dager, Stephen R; Estes, Annette M; Hazlett, Heather C; Schultz, Robert T; Zwaigenbaum, Lonnie; Piven, Joseph; Elison, Jed T; IBIS NetworkMarch 1, 2022Not Determined
33956255Create StudyRelations of Restricted and Repetitive Behaviors to Social Skills in Toddlers with Autism.Journal of autism and developmental disordersChaxiong, Pang; Burrows, Catherine; Botteron, Kelly N; Dager, Stephen R; Estes, Annette M; Hazlett, Heather C; Schultz, Robert T; Zwaigenbaum, Lonnie; Piven, Joseph; Wolff, Jason; IBIS NetworkApril 1, 2022Not Determined
33826159Create StudyDiagnostic shifts in autism spectrum disorder can be linked to the fuzzy nature of the diagnostic boundary: a data-driven approach.Journal of child psychology and psychiatry, and allied disciplinesTunç, Birkan; Pandey, Juhi; St John, Tanya; Meera, Shoba S; Maldarelli, Jennifer E; Zwaigenbaum, Lonnie; Hazlett, Heather C; Dager, Stephen R; Botteron, Kelly N; Girault, Jessica B; McKinstry, Robert C; Verma, Ragini; Elison, Jed T; Pruett Jr, John R; Piven, Joseph; Estes, Annette M; Schultz, Robert T; IBIS NetworkOctober 1, 2021Not Determined
33591913Create StudySegmentation-Renormalized Deep Feature Modulation for Unpaired Image Harmonization.IEEE transactions on medical imagingRen, Mengwei; Dey, Neel; Fishbaugh, James; Gerig, GuidoJune 1, 2021Not Determined
33421871Create StudyA voxel-wise assessment of growth differences in infants developing autism spectrum disorder.NeuroImage. ClinicalCárdenas-de-la-Parra, A; Lewis, J D; Fonov, V S; Botteron, K N; McKinstry, R C; Gerig, G; Pruett Jr, J R; Dager, S R; Elison, J T; Styner, M A; Evans, A C; Piven, J; Collins, D L; IBIS NetworkJanuary 1, 2021Not Determined
33161063Create StudyTowards a Data-Driven Approach to Screen for Autism Risk at 12 Months of Age.Journal of the American Academy of Child and Adolescent PsychiatryMeera, Shoba S; Donovan, Kevin; Wolff, Jason J; Zwaigenbaum, Lonnie; Elison, Jed T; Kinh, Truong; Shen, Mark D; Estes, Annette M; Hazlett, Heather C; Watson, Linda R; Baranek, Grace T; Swanson, Meghan R; St John, Tanya; Burrows, Catherine A; Schultz, Robert T; Dager, Stephen R; Botteron, Kelly N; Pandey, Juhi; Piven, Joseph; IBIS NetworkAugust 1, 2021Not Determined
33161062Create StudyPredicting Autism in Infancy.Journal of the American Academy of Child and Adolescent PsychiatryWolff, Jason J; Piven, JosephAugust 1, 2021Not Determined
33132824Create StudyA Novel Method for High-Dimensional Anatomical Mapping of Extra-Axial Cerebrospinal Fluid: Application to the Infant Brain.Frontiers in neuroscienceMostapha, Mahmoud; Kim, Sun Hyung; Evans, Alan C; Dager, Stephen R; Estes, Annette M; McKinstry, Robert C; Botteron, Kelly N; Gerig, Guido; Pizer, Stephen M; Schultz, Robert T; Hazlett, Heather C; Piven, Joseph; Girault, Jessica B; Shen, Mark D; Styner, Martin AJanuary 1, 2020Not Determined
32944847Create StudyDistributional Properties and Criterion Validity of a Shortened Version of the Social Responsiveness Scale: Results from the ECHO Program and Implications for Social Communication Research.Journal of autism and developmental disordersLyall, Kristen; Hosseini, Mina; Ladd-Acosta, Christine; Ning, Xuejuan; Catellier, Diane; Constantino, John N; Croen, Lisa A; Kaat, Aaron J; Botteron, Kelly; Bush, Nicole R; Dager, Stephen R; Duarte, Cristiane S; Fallin, M Daniele; Hazlett, Heather; Hertz-Picciotto, Irva; Joseph, Robert M; Karagas, Margaret R; Korrick, Susan; Landa, Rebecca; Messinger, Daniel; Oken, Emily; Ozonoff, Sally; Piven, Joseph; Pandey, Juhi; Sathyanarayana, Sheela; Schultz, Robert T; St John, Tanya; Schmidt, Rebecca; Volk, Heather; Newschaffer, Craig J; program collaborators for Environmental influences on Child Health OutcomesJuly 1, 2021Not Determined
32728309Create StudyAutomatic Measurement of Extra-Axial CSF from Infant MRI Data.Proceedings of SPIE--the International Society for Optical EngineeringLeMaout, Arthur; Yoon, Han Bit; Kim, Sun Hyung; Mostapha, Mahmoud; Shen, Mark D; Prieto, Juan; Styner, MartinFebruary 2020Not Determined
32375538Create StudySleep Onset Problems and Subcortical Development in Infants Later Diagnosed With Autism Spectrum Disorder.The American journal of psychiatryMacDuffie, Katherine E; Shen, Mark D; Dager, Stephen R; Styner, Martin A; Kim, Sun Hyung; Paterson, Sarah; Pandey, Juhi; St John, Tanya; Elison, Jed T; Wolff, Jason J; Swanson, Meghan R; Botteron, Kelly N; Zwaigenbaum, Lonnie; Piven, Joseph; Estes, Annette MJune 2020Not Determined
32314879Create StudyThe Association Between Parental Age and Autism-Related Outcomes in Children at High Familial Risk for Autism.Autism research : official journal of the International Society for Autism ResearchLyall, Kristen; Song, Lanxin; Botteron, Kelly; Croen, Lisa A; Dager, Stephen R; Fallin, M Daniele; Hazlett, Heather C; Kauffman, Elizabeth; Landa, Rebecca; Ladd-Acosta, Christine; Messinger, Daniel S; Ozonoff, Sally; Pandey, Juhi; Piven, Joseph; Schmidt, Rebecca J; Schultz, Robert T; Stone, Wendy L; Newschaffer, Craig J; Volk, Heather EJune 2020Not Determined
32276067Create StudySex differences associated with corpus callosum development in human infants: A longitudinal multimodal imaging study.NeuroImageSchmied, Astrid; Soda, Takahiro; Gerig, Guido; Styner, Martin; Swanson, Meghan R; Elison, Jed T; Shen, Mark D; McKinstry, Robert C; Pruett Jr, John R; Botteron, Kelly N; Estes, Annette M; Dager, Stephen R; Hazlett, Heather C; Schultz, Robert T; Piven, Joseph; Wolff, Jason J; IBIS NetworkJuly 2020Not Determined
32024459Create StudyQuantitative trait variation in ASD probands and toddler sibling outcomes at 24 months.Journal of neurodevelopmental disordersGirault, Jessica B; Swanson, Meghan R; Meera, Shoba S; Grzadzinski, Rebecca L; Shen, Mark D; Burrows, Catherine A; Wolff, Jason J; Pandey, Juhi; John, Tanya St; Estes, Annette; Zwaigenbaum, Lonnie; Botteron, Kelly N; Hazlett, Heather C; Dager, Stephen R; Schultz, Robert T; Constantino, John N; Piven, Joseph; IBIS NetworkFebruary 2020Not Determined
31839003Create StudyWhite matter as a monitoring biomarker for neurodevelopmental disorder intervention studies.Journal of neurodevelopmental disordersSwanson MR, Hazlett HCDecember 2019Not Determined
31764985Create Study"If He Has it, We Know What to Do": Parent Perspectives on Familial Risk for Autism Spectrum Disorder.Journal of pediatric psychologyMacDuffie, Katherine E; Turner-Brown, Lauren; Estes, Annette M; Wilfond, Benjamin S; Dager, Stephen R; Pandey, Juhi; Zwaigenbaum, Lonnie; Botteron, Kelly N; Pruett, John R; Piven, Joseph; Peay, Holly L; IBIS NetworkMarch 2020Not Determined
31759576Create StudyThe Neurodevelopment of Autism from Infancy Through Toddlerhood.Neuroimaging clinics of North AmericaGirault, Jessica B; Piven, JosephFebruary 2020Not Determined
31254329Create StudyEarly language exposure supports later language skills in infants with and without autism.Autism research : official journal of the International Society for Autism ResearchSwanson, Meghan R; Donovan, Kevin; Paterson, Sarah; Wolff, Jason J; Parish-Morris, Julia; Meera, Shoba S; Watson, Linda R; Estes, Annette M; Marrus, Natasha; Elison, Jed T; Shen, Mark D; McNeilly, Heidi B; MacIntyre, Leigh; Zwaigenbaum, Lonnie; St John, Tanya; Botteron, Kelly; Dager, Stephen; Piven, Joseph; IBIS NetworkDecember 2019Not Determined
31229667Create StudyRole of deep learning in infant brain MRI analysis.Magnetic resonance imagingMostapha, Mahmoud; Styner, MartinDecember 2019Not Determined
31172134Create StudyExploratory Population Analysis with Unbalanced Optimal Transport.Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted InterventionGerber S, Niethammer M, Styner M, Aylward SSeptember 2018Not Determined
30993502Create StudyThe Importance of Temperament for Understanding Early Manifestations of Autism Spectrum Disorder in High-Risk Infants.Journal of autism and developmental disordersPaterson, Sarah J; Wolff, Jason J; Elison, Jed T; Winder-Patel, Breanna; Zwaigenbaum, Lonnie; Estes, Annette; Pandey, Juhi; Schultz, Robert T; Botteron, Kelly; Dager, Stephen R; Hazlett, Heather C; Piven, Joseph; IBIS NetworkJuly 1, 2019Not Determined
30628809Create StudyEarly motor abilities in infants at heightened versus low risk for ASD: A Baby Siblings Research Consortium (BSRC) study.Journal of abnormal psychologyIverson, Jana M; Shic, Frederick; Wall, Carla A; Chawarska, Katarzyna; Curtin, Suzanne; Estes, Annette; Gardner, Judith M; Hutman, Ted; Landa, Rebecca J; Levin, April R; Libertus, Klaus; Messinger, Daniel S; Nelson, Charles A; Ozonoff, Sally; Sacrey, Lori-Ann R; Sheperd, Kelly; Stone, Wendy L; Tager-Flusberg, Helen B; Wolff, Jason J; Yirmiya, Nurit; Young, Gregory SJanuary 2019Not Determined
30541429Create StudyCerebrospinal fluid and the early brain development of autism.Journal of neurodevelopmental disordersShen, Mark DDecember 2018Not Relevant
30446435Create StudyRestricted and Repetitive Behavior and Brain Functional Connectivity in Infants at Risk for Developing Autism Spectrum Disorder.Biological psychiatry. Cognitive neuroscience and neuroimagingMckinnon CJ, Eggebrecht AT, Todorov A, Wolff JJ, Elison JT, Adams CM, Snyder AZ, Estes AM, Zwaigenbaum L, Botteron KN, Mckinstry RC, Marrus N, Evans A, Hazlett HC, Dager SR, Paterson SJ, Pandey J, Schultz RT, Styner MA, Gerig G, Schlaggar BL, Petersen SE, Piven J, Pruett JR, January 2019Not Determined
30364770Create StudyNON-EUCLIDEAN, CONVOLUTIONAL LEARNING ON CORTICAL BRAIN SURFACES.Proceedings. IEEE International Symposium on Biomedical ImagingMostapha, Mahmoud; Kim, SunHyung; Wu, Guorong; Zsembik, Leo; Pizer, Stephen; Styner, MartinApril 2018Not Relevant
30364673Create StudyA Novel Framework for the Local Extraction of Extra-Axial Cerebrospinal Fluid from MR Brain Images.Proceedings of SPIE--the International Society for Optical EngineeringMostapha, Mahmoud; Shen, Mark D; Kim, SunHyung; Swanson, Meghan; Collins, D Louis; Fonov, Vladimir; Gerig, Guido; Piven, Joseph; Styner, Martin A; IBIS NetworkMarch 2018Not Relevant
30350375Create StudyA longitudinal study of parent-reported sensory responsiveness in toddlers at-risk for autism.Journal of child psychology and psychiatry, and allied disciplinesWolff, Jason J; Dimian, Adele F; Botteron, Kelly N; Dager, Stephen R; Elison, Jed T; Estes, Annette M; Hazlett, Heather C; Schultz, Robert T; Zwaigenbaum, Lonnie; Piven, Joseph; IBIS NetworkMarch 2019Not Relevant
30348077Create StudyLanguage delay aggregates in toddler siblings of children with autism spectrum disorder.Journal of neurodevelopmental disordersMarrus, N; Hall, L P; Paterson, S J; Elison, J T; Wolff, J J; Swanson, M R; Parish-Morris, J; Eggebrecht, A T; Pruett Jr, J R; Hazlett, H C; Zwaigenbaum, L; Dager, S; Estes, A M; Schultz, R T; Botteron, K N; Piven, J; Constantino, J N; IBIS NetworkOctober 2018Not Relevant
30153278Create StudyRapid face orienting in infants and school-age children with and without autism: Exploring measurement invariance in eye-tracking.PloS oneDalrymple, Kirsten A; Wall, Natalie; Spezio, Michael; Hazlett, Heather C; Piven, Joseph; Elison, Jed TJanuary 2018Not Determined
29780197Create StudyTRAFIC: Fiber Tract Classification Using Deep Learning.Proceedings of SPIE--the International Society for Optical EngineeringNgattai Lam, Prince D; Belhomme, Gaetan; Ferrall, Jessica; Patterson, Billie; Styner, Martin; Prieto, Juan CFebruary 2018Not Relevant
29617515Create StudyDevelopment of White Matter Circuitry in Infants With Fragile X Syndrome.JAMA psychiatrySwanson, Meghan R; Wolff, Jason J; Shen, Mark D; Styner, Martin; Estes, Annette; Gerig, Guido; McKinstry, Robert C; Botteron, Kelly N; Piven, Joseph; Hazlett, Heather C; Infant Brain Imaging Study (IBIS) NetworkMay 2018Not Relevant
29560900Create StudySubcortical Brain and Behavior Phenotypes Differentiate Infants With Autism Versus Language Delay.Biological psychiatry. Cognitive neuroscience and neuroimagingSwanson, Meghan R; Shen, Mark D; Wolff, Jason J; Elison, Jed T; Emerson, Robert W; Styner, Martin A; Hazlett, Heather C; Truong, Kinh; Watson, Linda R; Paterson, Sarah; Marrus, Natasha; Botteron, Kelly N; Pandey, Juhi; Schultz, Robert T; Dager, Stephen R; Zwaigenbaum, Lonnie; Estes, Annette M; Piven, Joseph; IBIS NetworkNovember 2017Not Relevant
29492184Create StudyCompressive Sensing Based Q-Space Resampling for Handling Fast Bulk Motion in Hardi Acquisitions.Proceedings. IEEE International Symposium on Biomedical ImagingElhabian, Shireen; Vachet, Clement; Piven, Joseph; for IBIS; Styner, Martin; Gerig, GuidoJanuary 2016Not Relevant
29398928Create StudyBrain and behavior development in autism from birth through infancy.Dialogues in clinical neuroscienceShen, Mark D; Piven, JosephDecember 2017Not Relevant
29353953Create StudyA Segmentation Editing Framework Based on Shape Change Statistics.Proceedings of SPIE--the International Society for Optical EngineeringMostapha, Mahmoud; Vicory, Jared; Styner, Martin; Pizer, StephenJanuary 2017Not Relevant
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22362397Create StudyDifferences in white matter fiber tract development present from 6 to 24 months in infants with autism.The American journal of psychiatryWolff JJ, Gu H, Gerig G, Elison JT, Styner M, Gouttard S, Botteron KN, Dager SR, Dawson G, Estes AM, Evans AC, Hazlett HC, Kostopoulos P, Mckinstry RC, Paterson SJ, Schultz RT, Zwaigenbaum L, Piven J, June 2012Not Relevant
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18606031Create StudyEarly behavioral intervention, brain plasticity, and the prevention of autism spectrum disorder.Development and psychopathologyDawson, Geraldine2008Not Relevant
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Relevant Publications
PubMed IDStudyTitleJournalAuthorsDate
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Data Expected
Data ExpectedTargeted EnrollmentInitial SubmissionSubjects SharedStatus
Mullen Scales of Early Learning info icon
52507/31/2014
519
Approved
ADOS info icon
41108/31/2012
410
Approved
Early Development Inventory (EDI) info icon
48107/31/2014
479
Approved
ADI-R info icon
35007/31/2014
350
Approved
Medical History info icon
49001/15/2009
487
Approved
Sensory Experiences Questionnaire (SEQ) info icon
41807/31/2014
417
Approved
Infant Behavior Questionnaire (IBQ) info icon
45107/31/2014
449
Approved
Social Communication Questionnaire (SCQ) info icon
37807/31/2014
371
Approved
M-CHAT info icon
1907/31/2014
17
Approved
Autism Observation Scale for Infants (AOSI) info icon
35007/15/2015
481
Approved
Communication and Symbolic Behavior Scales (CSBS) info icon
42101/15/2014
420
Approved
Prefrontal Task info icon
30001/15/2011
301
Approved
MacArthur Bates Communicative Development Inventory info icon
42107/31/2014
420
Approved
Repetitive Behavior Scale - Revised (RBS-R) info icon
42307/31/2014
422
Approved
First Year Inventory (FYI) info icon
37507/31/2014
375
Approved
Physical Exam info icon
49107/31/2014
490
Approved
Research Subject and Pedigree info icon
52607/31/2014
934
Approved
Vineland (Parent and Caregiver) info icon
52601/15/2009
832
Approved
Imaging (Structural, fMRI, DTI, PET, microscopy) info icon
50008/31/2012
456
Approved
Structure not yet defined
No Status history for this Data Expected has been recorded yet

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Associated Studies

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

Study NameAbstractCollection/Study SubjectsData UsageState
Examining the validity of the use of ratio IQs in psychological assessments IQ tests are amongst the most used psychological assessments, both in research and clinical settings. For participants who cannot complete IQ tests normed for their age, ratio IQ scores (RIQ) are routinely computed and used as a proxy of IQ, especially in large research databases to avoid missing data points. However, because it has never been scientifically validated, this practice is questionable. In the era of big data, it is important to examine the validity of this widely used practice. In this paper, we use the case of autism to examine the differences between standard full-scale IQ (FSIQ) and RIQ. Data was extracted from four databases in which ages, FSIQ scores and subtests raw scores were available for autistic participants between 2 and 17 years old. The IQ tests included were the MSEL (N=12033), DAS-II early years (N=1270), DAS-II school age (N=2848), WISC-IV (N=471) and WISC-V (N=129). RIQs were computed for each participant as well as the discrepancy (DSC) between RIQ and FSIQ. We performed two linear regressions to respectively assess the effect of FSIQ and of age on the DSC for each IQ test, followed by additional analyses comparing age subgroups as well as FSIQ subgroups on DSC. Participants at the extremes of the FSIQ distribution tended to have a greater DSC than participants with average FSIQ. Furthermore, age significantly predicted the DSC, with RIQ superior to FSIQ for younger participants while the opposite was found for older participants. These results question the validity of this widely used alternative scoring method, especially for individuals at the extremes of the normal distribution, with whom RIQs are most often employed.519/17423Secondary AnalysisShared
Controls for SCCRIPTo establish a well characterized cohort for pediatric patients living with sickle cell disease7/11185Secondary AnalysisPrivate
Characterizing Auditory Hyperreactivity in AutismObjective: To answer the following research questions: 1) What is the prevalence of auditory hyper-reactivity in ASD? 2) Does auditory hyper-reactivity severity change with age? and 3) What are the most common auditory stimuli reported to be bothersome? Research Design: Primarily descriptive secondary data analysis. Methods: Type of data: Questionnaire items regarding auditory hyper-reactivity will be filtered from: Autism Diagnostic Interview-Revised, Sensory Profile (all forms), Sensory Over-Responsivity Scale, and Sensory Experiences Questionnaire in addition to demographics (i.e., age, race, ethnicity, diagnoses). Analysis Plan: Descriptive statistics, tables and figures will be used to summarize the prevalence and severity of auditory hyper-reactivity by age. Linear regression modeling will be used to evaluate changes in auditory hyper-reactivity by age. If data is available for control subjects, statistical analyses will be conducted for means comparison (ASD vs. non-ASD). 350/7001Secondary AnalysisPrivate
The effect of compensatory mechanisms during and after pregnancy on a child's developmentEarly childhood involves rapid processes of human growth leading to different trajectories in physical, cognitive, social, and emotional development (Graignic-Philippe et al., 2014). These processes are influenced by a wide variety of factors such as maternal health, environmental stressors, and early childhood experiences. Current literature has shown how exposure to both acute and chronic stress during pregnancy have a pathogenetic effect throughout childhood (Kim & Leventhal, 2015; Rice, et al, 2010), leading to neurotypical or atypical development. Studies have shown how these stressors are linked neurodevelopmental disorders such Autism Spectrum Disorders (Zerbo et al., 2015; Atladóttir et al., 2012) or Attention Deficit Hyperactivity Disorder (Rosenqvist et al., 2019). In recent years, there has been a shift from traditional diagnostic research models to synthesis of different scientific fields to map lifecourse development in order for rapid translation into healthcare practices (Halfon et al., 2014). Whilst there are studies showing links between stress and atypical developmental outcomes, there is still very limited literature on compensatory mechanisms found pre- and post-pregnancy, which illustrate development of protective factors (such as presence of self-regulation, high verbal intelligence, sociability, adept social communication) against atypical developmental outcomes. This study aims to identify and measure the presence of these protective factors that appear to guard against or mitigate the emergence of neurodevelopmental disorders. Therefore, nationwide and longitudinal data are needed in order to accurately create risk models in order to map developmental trajectories. 752/5717Secondary AnalysisPrivate
Investigating autism etiology and heterogeneity by decision tree algorithmAutism spectrum disorder (ASD) is a neurodevelopmental disorder that causes deficits in cognition, communication and social skills. ASD, however, is a highly heterogeneous disorder. This heterogeneity has made identifying the etiology of ASD a particularly difficult challenge, as patients exhibit a wide spectrum of symptoms without any unifying genetic or environmental factors to account for the disorder. For better understanding of ASD, it is paramount to identify potential genetic and environmental risk factors that are comorbid with it. Identifying such factors is of great importance to determine potential causes for the disorder, and understand its heterogeneity. Existing large-scale datasets offer an opportunity for computer scientists to undertake this task by utilizing machine learning to reliably and efficiently obtain insight about potential ASD risk factors, which would in turn assist in guiding research in the field. In this study, decision tree algorithms were utilized to analyze related factors in datasets obtained from the National Database for Autism Research (NDAR) consisting of nearly 3000 individuals. We were able to identify 15 medical conditions that were highly associated with ASD diagnoses in patients; furthermore, we extended our analysis to the family medical history of patients and we report six potentially hereditary medical conditions associated with ASD. Associations reported had a 90% accuracy. Meanwhile, gender comparisons highlighted conditions that were unique to each gender and others that overlapped. Those findings were validated by the academic literature, thus opening the way for new directions for the use of decision tree algorithms to further understand the etiology of autism. 2/3382Secondary AnalysisShared
Autism Sensory Research Consortium Cross-lab Integrative Data Analysis Since 2013, when sensory features were officially added to the diagnostic criteria for autism, research into the sensory manifestations of the condition has increased dramatically. However, the majority of this research has primarily been conducted using small laboratory-based samples of children on the autism spectrum, substantially limiting the hypotheses that can be tested in any one dataset and the generalizability of results to the wider autistic population. The Autism Sensory Research Consortium (ASRC), funded by the Nancy Lurie Marks Family Foundation, represents the first major international collaboration of over a dozen research groups that study sensory functioning in autism. As a major thrust of this collaboration, the ASRC has begun a data sharing initiative, in which all participating labs can contribute existing data from their past and present research studies to a centralized database. These “Big Data” can then be systematically examined using powerful large-sample statistical techniques such as structural equation modeling and item response theory, which will allow researchers to test more complex hypotheses regarding the nature of sensory differences in autism and their relationships with sociodemographic and non-sensory clinical features. Once data from all sites has been pooled, it will be analyzed using a method called integrative data analysis, which is specially designed to derive insights from large and heterogeneous samples. One major advantage of this methodology is the ability to construct and test measurement models of sensory symptoms, determining the most appropriate set of questions for assessing each construct and making sure that the scales do not produce biased comparisons when they are examined across diagnostic groups or subsets of the autistic population. Furthermore, measurement models can be constructed to bridge multiple questionnaires, allowing for the calculation of robust composite scores that can be compared between studies that only administered items from one of the contributing questionnaires. These models can further facilitate pooling of data across studies, allowing us to amass even larger datasets to answer questions about sensory function in the autistic population. Furthermore, moving forward, the composite sensory measures from the integrative data analysis can be employed in other studies, providing investigators in sensory autism research with a suite of reliable and valid behavioral measures that can be used as outcomes in trials of interventions targeting these symptoms. In the long term, this project has the potential to help us better understand the nature of sensory function in persons on the spectrum, as well as how sensory alterations relate to broader features of the condition—specifically, for whom and/or at what point in development sensory features are most predictive of core autism behaviors or other meaningful clinical outcomes such as language acquisition and adaptive behavior. Incorporation of neuroscientific data collected within the ASRC can also possibly shed some light on the neural basis of sensory disruptions in the autistic population. All of this will help to lay a foundation for future work testing the efficacy of candidate interventions aimed at improving sensory function and more distal skills in autistic individuals.416/2110Secondary AnalysisPrivate
Psychometric Analysis of the Social Communication Questionnaire Using an Item-Response Theory Framework: Implications for the Use of the Lifetime and Current FormsThe Social Communication Questionnaire (SCQ) was developed as a screener of Autism Spectrum Disorder (ASD). To date, the majority of the SCQ utility studies focused on its external validity (e.g., ROC curve analyses), but very few have addressed the internal validity issues. With samples consisting of 2,134 individuals available from the National Database for Autism Research (NDAR), the current study examined the factor structure, item-level characteristics, and measurement equivalence of the SCQ forms (i.e., Lifetime form and Current form) using both the classical true score theory and the item response theory (IRT). While our findings indicate sufficient psychometric properties of the SCQ Lifetime form, measurement issues emerged with respect to the SCQ Current form. These issues include lower internal consistencies, a weaker factor structure, lower item discriminations, significant pseudo-guessing effects, and subscale-level measurement bias. Thus, we caution researchers and clinicians about the use of the SCQ Current form. In particular, it seems inappropriate to use the Current form as an alternative to the Lifetime form among children younger than 5 years old or under other special situations (e.g., teacher-report data), although such practices were advised by the publisher of the SCQ. Instead, we recommend modifying the wording of the Lifetime form items rather than switching to the Current form where a 3-month timeframe is specified for responding to SCQ items. Future studies may consider investigating the association between the temporality of certain behaviors and the individual’s potential for being diagnosed with ASD, as well as the age neutrality of the SCQ.240/2054Secondary AnalysisShared
Unravelling the Collective Diagnostic Power Behind the Features in the Autism Diagnostic Observation ScheduleBackground: Autism is a group of heterogeneous disorders defined by deficits in social interaction and communication. Typically, diagnosis depends on the results of a behavioural examination called the Autism Diagnostic Observation Schedule (ADOS). Unfortunately, administration of the ADOS exam is time-consuming and requires a significant amount of expert intervention, leading to delays in diagnosis and access to early intervention programs. The diagnostic power of each feature in the ADOS exam is currently unknown. Our hypothesis is that certain features could be removed from the exam without a significant reduction in diagnostic accuracy, sensitivity or specificity. Objective: Determine the smallest subset of predictive features in ADOS module-1 (an exam variant for patients with minimal verbal skills). Methodology: ADOS module-1 datasets were acquired from the Autism Genetic Resource Exchange and the National Database for Autism Research. The datasets contained 2572 samples with the following labels: autism (1763), autism spectrum (513), and non-autism (296). The datasets were used as input to 4 different cost-sensitive classifiers in Weka (functional trees, LADTree, logistic model trees, and PART). For each classifier, a 10-fold cross validation was preformed and the number of predictive features, accuracy, sensitivity, and specificity was recorded. Results & Conclusion: Each classifier resulted in a reduction of the number of ADOS features required for autism diagnosis. The LADtree classifier was able to obtain the largest reduction, utilizing only 10 of 29 ADOS module-1 features (96.8% accuracy, 96.9% sensitivity, and 95.9% specificity). Overall, these results are a step towards a more efficient behavioural exam for autism diagnosis. 64/1832Secondary AnalysisShared
Computer-Based Testing to Shorten the Social Communication Questionnaire (SCQ): A Proof-of-Principle Study of the Lifetime and Current FormsThe Social Communication Questionnaire (SCQ) is a 40-item instrument designed to screen children at risk for Autism Spectrum Disorder (ASD). Both Lifetime and Current forms of the scale are available. Although these forms are manageable for many respondents, their use may result in substantial respondent and administrative burden, particularly among individuals who have difficulty reading, have physical illness, and/or are asked to take multiple questionnaires. The objective of this research was to examine the potential of two stopping rules for computer-based testing (namely, curtailment and stochastic curtailment) to shorten the SCQ without compromising its screening properties. A retrospective analysis was conducted using data from the National Database for Autism Research (NDAR); responses regarding 1236 at-risk individuals from the SCQ Lifetime and 709 at-risk individuals from the SCQ Current were analyzed. In post-hoc simulation, curtailment reduced mean test lengths by 29% to 44% compared to the full-length Lifetime form, and by 25% to 39% compared to the full-length Current form, while providing the same screening result as the corresponding full-length form in 100% of cases. Stochastic curtailment made further reductions in test length, but was not always concordant with the full-length form’s screening result. These findings suggest that curtailment has potential to improve the efficiency of the SCQ in computer-based administrations and should be tested prospectively.203/1820Secondary AnalysisPrivate
Imbalanced social-communicative and restricted repetitive behavior subtypes in autism spectrum disorder exhibit different neural circuitrySocial-communication (SC) and restricted repetitive behaviors (RRB) are autism diagnostic symptom domains. SC and RRB severity can markedly differ within and between individuals and may be underpinned by different neural circuitry and genetic mechanisms. Modeling SC-RRB balance could help identify how neural circuitry and genetic mechanisms map onto such phenotypic heterogeneity. Here, we developed a phenotypic stratification model that makes highly accurate (97–99%) out-of-sample SC = RRB, SC > RRB, and RRB > SC subtype predictions. Applying this model to resting state fMRI data from the EU-AIMS LEAP dataset (n = 509), we find that while the phenotypic subtypes share many commonalities in terms of intrinsic functional connectivity, they also show replicable differences within some networks compared to a typically-developing group (TD). Specifically, the somatomotor network is hypoconnected with perisylvian circuitry in SC > RRB and visual association circuitry in SC = RRB. The SC = RRB subtype show hyperconnectivity between medial motor and anterior salience circuitry. Genes that are highly expressed within these networks show a differential enrichment pattern with known autism-associated genes, indicating that such circuits are affected by differing autism-associated genomic mechanisms. These results suggest that SC-RRB imbalance subtypes share many commonalities, but also express subtle differences in functional neural circuitry and the genomic underpinnings behind such circuitry.7/1708Secondary AnalysisShared
Development of a Short Form of the SRS: An Application of IRTBackground: Research and practice in autism spectrum disorder (ASD) rely on quantitative measures, such as the Social Responsiveness Scale (SRS), for characterization and diagnosis. Like many ASD diagnostic measures, SRS scores are influenced by factors unrelated to ASD core features. This study further interrogates the psychometric properties of the SRS using item response theory (IRT), and demonstrates a strategy to enhance measure specificity by applying IRT results. Methods: SRS analyses were conducted on a large sample (N=21,426) of youth from four ASD databases. Items were subjected to item factor analyses and evaluation of item bias by gender, age, and expressive language level. Results: Item selection based on dimensionality and DIF analyses produced a reduced item SRS subscale that was unidimensional in structure, highly reliable (α=.96), and free of gender, age, expressive language, and non-verbal IQ influence. The subscale also showed strong relationships with established measures of autism symptom severity (ADOS, ADI-R, Vineland). Degree of association between all measures varied as a function of expressive language. Conclusions: Results identified specific SRS items that are more vulnerable to non-ASD-related traits. The resultant 16-item SRS subscale may possess superior psychometric properties compared to the original scale and emerge as a more precise measure of ASD core symptom severity, facilitating research and practice. Future research using IRT is needed to further refine existing measures of autism symptomatology. 1/1478Secondary AnalysisPrivate
Revising the Social Communication Questionnaire scoring procedures for Autism Spectrum Disorder and potential Social Communication DisorderIn analyzing data from the National Database for Autism Research, we examine revising the Social Communication Questionnaire (SCQ), a commonly used screening instrument for Autism Spectrum Disorder. A combination of Item Response Theory and Mokken scaling techniques were utilized to achieve this and abbreviated scoring of the SCQ is suggested. The psychometric sensitivity of this abbreviated SCQ was examined via bootstrapped Receiver Operator Characteristic (ROC) curve analyses. Additionally, we examined the sensitivity of the abbreviated and total scaled SCQ as it relates to a potential diagnosis of Social (Pragmatic) Communication Disorder (SCD). As SCD is a new disorder introduced with the fifth edition of the Diagnostic and Statistical Manual (DSM-5), we identified individuals with potential diagnosis of SCD among individuals with ASD via mixture modeling techniques using the same NDAR data. These analyses revealed two classes or clusters of individuals when considering the two core areas of impairment among individuals with ASD: social communication and restricted, repetitive patterns of behavior. 4/889Secondary AnalysisShared
Identification of Infants at High-Risk for Autism Spectrum Disorder Using Multiparameter Multiscale White Matter Connectivity NetworksAutism spectrum disorder (ASD) is a wide range of disabilities that cause life-long cognitive impairment and social, communication, and behavioral challenges. Early diagnosis and medical intervention are important for improving the life quality of autistic patients. However, in the current practice, diagnosis often has to be delayed until the behavioral symptoms become evident during childhood. In this study, we demonstrate the feasibility of using machine learning techniques for identifying high-risk ASD infants at as early as six months after birth. This is based on the observation that ASD-induced abnormalities in white matter (WM) tracts and whole-brain connectivity have already started to appear within 24 months after birth. In particular, we propose a novel multikernel support vector machine classification framework by using the connectivity features gathered from WM connectivity networks, which are generated via multiscale regions of interest (ROIs) and multiple diffusion statistics such as fractional anisotropy, mean diffusivity, and average fiber length. Our proposed framework achieves an accuracy of 76% and an area of 0.80 under the receiver operating characteristic curve (AUC), in comparison to the accuracy of 70% and the AUC of 70% provided by the best singleparameter single-scale network. The improvement in accuracy is mainly due to the complementary information provided by multiparameter multiscale networks. In addition, our framework also provides the potential imaging connectomic markers and an objective means for early ASD diagnosis.489/489Secondary AnalysisShared
The Sensitivity and Specificity of the Social Communication Questionnaire for Autism Spectrum Disorder with Respect to AgeScientific Abstract The Social Communication Questionnaire (SCQ) assesses communication skills and social functioning in screening for symptoms of autism-spectrum disorder (ASD). The SCQ is recommended for individuals between 4 to 40 years with a cutoff score of 15 for referral. Mixed findings have been reported regarding the recommended cutoff score’s ability to accurately classify an individual as at-risk for ASD (sensitivity) versus an individual as not at-risk for ASD (specificity). Based on a sample from the National Database for Autism Research (n=344; age: 1.58 to 25.92 years old), the present study examined the SCQ’s sensitivity versus specificity across a range of ages. We recommend that the cutoff scores for the SCQ be re-evaluated with age as a consideration. Lay Abstract The age neutrality of the Social Communication Questionnaire (SCQ) was examined as a common screener for ASD. Mixed findings have been reported regarding the recommended cutoff score’s ability to accurately classify an individual as at-risk for ASD (sensitivity) versus accurately classifying an individual as not at-risk for ASD (specificity). With a sample from the National Database for Autism Research, the present study examined the SCQ’s sensitivity versus specificity. Analyses indicated that the actual sensitivity and specificity scores were lower than initially reported by the creators of the SCQ.5/339Secondary AnalysisShared
Word Learning and Word FeaturesVocabulary composition and word-learning biases are closely interrelated in typical development. Learning new words involves attending to certain properties to facilitate word learning. Such word-learning biases are influenced by perceptually and conceptually salient word features, including high imageability, concreteness, and iconicity. This study examined the association of vocabulary knowledge and word features in young children with ASD (n = 280) and typically developing (TD) toddlers (n = 1,054). Secondary analyses were conducted using data from the National Database for Autism Research and the Wordbank database. Expressive vocabulary was measured using the MacArthur-Bates Communicative Development Inventory. Although the trajectories for concreteness, iconicity, and imageability are similar between children with ASD and TD toddlers, divergences were observed. Differences in imageability are seen early but resolve to a common trajectory; differences in iconicity are small but consistent; and differences in concreteness only emerge after both groups reach a simultaneous peak, before converging again. This study reports unique information about the nonlinear growth patterns associated with each word feature for and distinctions in these growth patterns between the groups.30/280Primary AnalysisPrivate
Deviant vocabulary development in children with Autism Spectrum DisorderChildren diagnosed with autism spectrum disorder (ASD) have core impairments in social communication and have restricted interests and repetitive behaviors. Additionally, the majority of young children with ASD have early language delays. Although these early delays are well-documented, it is remains unclear whether language skills are simply delayed or if they are deviant. The current study aimed to expand on previous studies (e.g., Charman et al., 2003; Luyster, Lopez, & Lord, 2007; Rescorla & Safyer, 2012) to provide a large-scale comparison of early language profiles between typically developing (TD) toddlers and young children with ASD. Specifically, we sought to examine the composition of word classes (i.e., nouns, predicates, and close classed words) and semantic categories (i.e., games and routines, sound effects and animal noises) in the early TD and ASD language profiles. A series of linear regression analyses revealed that children with ASD produced a smaller percentage of nouns, and that the percentage of nouns in a vocabulary decreased as children learned more words, but that this reduction was less steep in the ASD group. When examining predicates, we found that children with ASD produced a significantly higher percentage of predicates. Also, as vocabulary size increased, the percentage of predicates increased; however, the slope was less steep for children with ASD. Lastly, children with SD produced a significantly higher percentage of closed class words and the trajectory of growth of the percentage of closed class words differed between groups. The current findings suggest that children with ASD may employ different word-learning strategies during early lexical development.30/247Secondary AnalysisShared
EVIDENCE FOR THE DIMENSIONAL AND CATEGORIAL ACCOUNTS OF LANGUAGE DEVELOPMENTThis study compared the lexical composition of 216 children with ASD aged 11 to 173 months with that of 7,287 typically developing toddlers with and without language delay aged 8 to 30 months. The children with ASD and late talkers produced a lower proportion of nouns and a higher proportion of predicates than typical talkers. The ASD group produced a higher proportion of action words and place words as well as a lower proportion of sound words than the neurotypical groups. We found that children with ASD produced fewer high-social verbs as rated by adults. We discuss how these differences might be associated with features of ASD in a way that supports the categorical view of language development.28/216Secondary AnalysisShared
A growth curve of the human eye from 0-20 yearsThis study involves the semi automatic segmentation of the eyes of pediatric subjects for volume measurements1/173Secondary AnalysisPrivate
Critical test items to differentiate individuals with SPCD from those with ASD and typical controlsSocial (pragmatic) communication disorder (SPCD) is a new category in the DSM-5. This study used IRT modelling to analyze archive data of item responses to the Social Communication Question-Lifetime (SCQ) from the National Database of Autism Research (NDAR), to select critical test items that could efficiently differentiate SPCD from ASD and TD. Methods: The SCQ records were downloaded from the NDAR. The item difficulty values and participants ability in the social communication and repetitive behavior and restricted interests were estimated through Winsteps. The items with difficulty values mostly matching the participants ability at the cut-off zones among three groups were selected. Result: The eight test items were identified for screening SPCD with 75% sensitivity. The specificity for differentiating SPCD from TD and ASD is 86.27% and 68.9% respectively. Conclusion: This study provides a short list of critical items that could be used to screen SPCD from TD and ASD. 5/151Secondary AnalysisPrivate
Early brain development in infants at high risk for autism spectrum disorderBrain enlargement has been observed in children with autism spectrum disorder (ASD), but the timing of this phenomenon, and the relationship between ASD and the appearance of behavioural symptoms, are unknown. Retrospective head circumference and longitudinal brain volume studies of two-year olds followed up at four years of age have provided evidence that increased brain volume may emerge early in development1, 2. Studies of infants at high familial risk of autism can provide insight into the early development of autism and have shown that characteristic social deficits in ASD emerge during the latter part of the first and in the second year of life3, 4. These observations suggest that prospective brain-imaging studies of infants at high familial risk of ASD might identify early postnatal changes in brain volume that occur before an ASD diagnosis. In this prospective neuroimaging study of 106 infants at high familial risk of ASD and 42 low-risk infants, we show that hyperexpansion of the cortical surface area between 6 and 12 months of age precedes brain volume overgrowth observed between 12 and 24 months in 15 high-risk infants who were diagnosed with autism at 24 months. Brain volume overgrowth was linked to the emergence and severity of autistic social deficits. A deep-learning algorithm that primarily uses surface area information from magnetic resonance imaging of the brain of 6–12-month-old individuals predicted the diagnosis of autism in individual high-risk children at 24 months (with a positive predictive value of 81% and a sensitivity of 88%). These findings demonstrate that early brain changes occur during the period in which autistic behaviours are first emerging.136/138Primary AnalysisShared
Identifying Areas of Overlap and Distinction in Early Lexical Profiles of Children with Autism Spectrum Disorder, Late Talkers, and Typical TalkersThis study compares the lexical composition of 11827 children with autism spectrum disorder (ASD) aged 121 to 84173 months with 4,626 vocabulary-matched typically developing toddlers with and without language delay, aged 8 to 30 months. Children with ASD produced a higher proportion of verbs than typical and late talkers, but a similar number of nouns. Additionally, differences were identified in five four semantic categories, four three of them related to play. Most differences appear to reflect the extent of the language delay between the groups. However, children with ASD produced fewer high-social verbs than neurotypical children. We discuss how these lexical differences might be associated with ASD features and language delay, providing partial support for a categorical view of language delay.4/118Secondary AnalysisShared
Sex differences associated with corpus callosum development in human infants: A longitudinal multimodal imaging studyThe corpus callosum (CC) is the largest connective pathway in the human brain, linking cerebral hemispheres. There is longstanding debate in the scientific literature whether sex differences are evident in this structure, with many studies indicating the structure is relatively larger in females. However, there are few data pertaining to this issue in infancy, during which time the most rapid developmental changes to the CC occur. In this study, we examined longitudinal brain imaging data collected from 104 infants at ages 6, 12, and 24 months. We identified sex differences in brain-size adjusted CC area and thickness characterized by a steeper rate of growth in males versus females from ages 6 to 24 months. In contrast to studies of older children and adults, relative CC size was larger for male compared to female infants. Based on diffusion tensor imaging data, we found that CC thickness is significantly associated with underlying microstructural organization. However, we observed no sex differences in the association between microstructure and thickness, suggesting that the role of factors such as axon density and/or myelination in determining CC size is generally equivalent between sexes. Finally, we found that CC length was negatively associated with nonverbal ability among females.93/93Primary AnalysisPrivate
Do children with Autism Spectrum Disorder learn words differently? Children with ASD often are late to start to produce words. However, despite the importance of language abilities for child outcomes in children with ASD, we still have only scratched the surface of understanding these children's early lexicons. Therefore, in the current study we examined the semantic networks of the words that children with ASD have been reported to produce and compared them to typically developing children. 4/82Secondary AnalysisShared
Individual variability in the nonlinear development of the corpus callosum during infancy and toddlerhoodThe human brain spends several years bootstrapping itself through intrinsic and extrinsic modulation, thus gradually developing both spatial organization and functions. Based on previous studies on developmental patterns and inter-individual variability of the corpus callosum (CC), we hypothesized that inherent variations of CC shape among infants emerge, depending on the position within the CC, along the developmental timeline. Here we used longitudinal magnetic resonance imaging data from infancy to toddlerhood and investigated the area, thickness, and shape of the midsagittal plane of the CC by applying multilevel modeling. The shape characteristics were extracted using the Procrustes method. We found nonlinearity, region- dependency, and inter-individual variability, as well as intra-individual consistencies, in CC development. Overall, the growth rate is faster in the first year than in the second year, and the trajectory differs between infants; the direction of CC formation in individual infants was determined within six months and maintained to two years. The anterior and posterior subregions increase in area and thickness faster than other subregions. Moreover, we clarified that the growth rate of the middle part of the CC is faster in the second year than in the first year in some individuals. Since the division of regions exhibiting different tendencies coincides with previously reported divisions based on the diameter of axons that make up the region, our results suggest that subregion-dependent individual variability occurs due to the increase in the diameter of the axon caliber, myelination partly due to experience and axon elimination during the early developmental period.34/36Secondary AnalysisPrivate
Resting-state fMRI in sleeping infants more closely resembles adult sleep than adult wakefulnessResting state functional magnetic resonance imaging (rs-fMRI) in infants enables important studies of functional brain organization early in human development. However, rs-fMRI in infants has universally been obtained during sleep to reduce participant motion artifact, raising the question of whether differences in functional organization between awake adults and sleeping infants that are commonly attributed to development may instead derive, at least in part, from sleep. This question is especially important as rs-fMRI differences in adult wake vs. sleep are well documented. To investigate this question, we compared functional connectivity and BOLD signal propagation patterns in 6, 12, and 24 month old sleeping infants with patterns in adult wakefulness and non-REM sleep. We find that important functional connectivity features seen during infant sleep closely resemble those seen during adult sleep, including reduced default mode network functional connectivity. However, we also find differences between infant and adult sleep, especially in thalamic BOLD signal propagation patterns. These findings highlight the importance of considering sleep state when drawing developmental inferences in infant rs-fMRI.4/4Primary AnalysisPrivate
* Data not on individual level
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