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1 Numbers reported are subjects by age
New Trial
New Project

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

Please select an experiment type below

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

The table search feature is case insensitive and targets the experiment id, experiment name and experiment type columns. The experiment id is searched only when the search term entered is a number, and filtered using a startsWith comparison. When the search term is not numeric the experiment name is used to filter the results.
SelectExperiment IdExperiment NameExperiment Type
Created On
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
Collection - Add Experiment
Add Supporting Documentation
Select File

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

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

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

Collection Updated

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

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

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

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

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Explanation must be between 20 and 200 characters in length.

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Collection Summary Collection Charts
Collection Title Collection Investigators Collection Description
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), NIMH Repository & Genomics Resource (NRGR)
Funding Completed
Close Out
No
$6,179,607.00
1,039
Loading Chart...
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
  • Collection Description
  • Collection Phase
  • Funding Source
  • Clinical Trials

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

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

Blinded Clinical Trial Status:

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

Funding Source

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

Supporting Documentation

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

Grant Information

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

Clinical Trials

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

Frequently Asked Questions

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

Glossary

  • Administrator Privilege
    A privilege provided to a user associated with an NDA Collection or NDA Study whereby that user can perform a full range of actions including providing privileges to other users.
  • Collection Owner
    Generally, the Collection Owner is the contact PI listed on a grant. Only one NDA user is listed as the Collection owner. Most automated emails are primarily sent to the Collection Owner.
  • Collection Phase
    The Collection Phase provides information on data submission as opposed to grant/project completion so while the Collection phase and grant/project phase may be closely related they are often different. Collection users with Administrative Privileges are encouraged to edit the Collection Phase. The Program Officer as listed in eRA (for NIH funded grants) may also edit this field. Changes must be saved by clicking the Save button at the bottom of the page. This field is sortable alphabetically in ascending or descending order. Collection Phase options include:
    • Pre-Enrollment: A grant/project has started, but has not yet enrolled subjects.
    • Enrolling: A grant/project has begun enrolling subjects. Data submission is likely ongoing at this point.
    • Data Analysis: A grant/project has completed enrolling subjects and has completed all data submissions.
    • Funding Completed: A grant/project has reached the project end date.
  • Collection Title
    An editable field with the title of the Collection, which is often the title of the grant associated with the Collection.
  • Grant
    Provides the grant number(s) for the grant(s) associated with the Collection. The field is a hyperlink so clicking on the Grant number will direct the user to the grant information in the NIH Research Portfolio Online Reporting Tools (RePORT) page.
  • Supporting Documentation
    Various documents and materials to enable efficient use of the data by investigators unfamiliar with the project and may include the research protocol, questionnaires, and study manuals.
  • NIH Research Initiative
    NDA Collections may be organized by scientific similarity into NIH Research Initiatives, to facilitate query tool user experience. NIH Research Initiatives map to one or multiple Funding Opportunity Announcements.
  • Permission Group
    Access to shared record-level data in NDA is provisioned at the level of a Permission Group. NDA Permission Groups consist of one or multiple NDA Collections that contain data with the same subject consents.
  • Planned Enrollment
    Number of human subject participants to be enrolled in an NIH-funded clinical research study. The data is provided in competing applications and annual progress reports.
  • Actual Enrollment
    Number of human subjects enrolled in an NIH-funded clinical research study. The data is provided in annual progress reports.
  • NDA Collection
    A virtual container and organization structure for data and associated documentation from one grant or one large project/consortium. It contains tools for tracking data submission and allows investigators to define a wide array of other elements that provide context for the data, including all general information regarding the data and source project, experimental parameters used to collect any event-based data contained in the Collection, methods, and other supporting documentation. They also allow investigators to link underlying data to an NDA Study, defining populations and subpopulations specific to research aims.
  • Data Use Limitations
    Data Use Limitations (DULs) describe the appropriate secondary use of a dataset and are based on the original informed consent of a research participant. NDA only accepts consent-based data use limitations defined by the NIH Office of Science Policy.
  • Total Subjects Shared
    The total number of unique subjects for whom data have been shared and are available for users with permission to access data.
IDNameCreated DateStatusType
370Longitudinal MRI Study of Infants at Risk for Autism08/11/2015ApprovedfMRI
helpcenter.collection.experiments-tab

NDA Help Center

Collection - Experiments

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

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

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

Frequently Asked Questions

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

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

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

Glossary

  • Experiment Status
    An Experiment must be Approved before data using the associated Experiment_ID may be uploaded.
  • Experiment ID
    The ID number automatically generated by NDA which must be included in the appropriate file when uploading data to link the Experiment Definition to the subject record.
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
helpcenter.collection.shared-data-tab

NDA Help Center

Collection - Shared Data

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

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

Frequently Asked Questions

  • How will I know if another researcher uses data that I shared through the NIMH Data Archive (NDA)?
    To see what data your project have submitted are being used by a study, simply go the Associated Studies tab of your collection. Alternatively, you may review an NDA Study Attribution Report available on the General tab.
  • Can I get a supplement to share data from a completed research project?
    Often it becomes more difficult to organize and format data electronically after the project has been completed and the information needed to create a GUID may not be available; however, you may still contact a program staff member at the appropriate funding institution for more information.
  • Can I get a supplement to share data from a research project that is still ongoing?
    Unlike completed projects where researchers may not have the information needed to create a GUID and/or where the effort needed to organize and format data becomes prohibitive, ongoing projects have more of an opportunity to overcome these challenges. Please contact a program staff member at the appropriate funding institution for more information.

Glossary

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

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

Publications

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
38419749Create StudyE(3) × SO(3)-Equivariant Networks for Spherical Deconvolution in Diffusion MRI.Proceedings of machine learning researchElaldi, Axel; Gerig, Guido; Dey, NeelJanuary 1, 2024Not Determined
38204321Create StudyInfants who develop autism show smaller inventories of deictic and symbolic gestures at 12 months of age.Autism research : official journal of the International Society for Autism ResearchWu, Dennis; Wolff, Jason J; Ravi, Shruthi; Elison, Jed T; Estes, Annette; Paterson, Sarah; St John, Tanya; Abdi, Hervé; Moraglia, Luke E; Piven, Joseph; Swanson, Meghan R; IBIS NetworkJanuary 10, 2024Not Determined
37996530Create StudyA global multicohort study to map subcortical brain development and cognition in infancy and early childhood.Nature neuroscienceAlex, Ann M; Aguate, Fernando; Botteron, Kelly; Buss, Claudia; Chong, Yap-Seng; Dager, Stephen R; Donald, Kirsten A; Entringer, Sonja; Fair, Damien A; Fortier, Marielle V; Gaab, Nadine; Gilmore, John H; Girault, Jessica B; Graham, Alice M; Groenewold, Nynke A; Hazlett, Heather; Lin, Weili; Meaney, Michael J; Piven, Joseph; Qiu, Anqi; Rasmussen, Jerod M; Roos, Annerine; Schultz, Robert T; Skeide, Michael A; Stein, Dan J; Styner, Martin; Thompson, Paul M; Turesky, Ted K; Wadhwa, Pathik D; Zar, Heather J; Zöllei, Lilla; de Los Campos, Gustavo; Knickmeyer, Rebecca C; ENIGMA ORIGINs groupJanuary 1, 2024Not Determined
37898019Create StudyStructural and functional connectome relationships in early childhood.Developmental cognitive neuroscienceHong, Yoonmi; Cornea, Emil; Girault, Jessica B; Bagonis, Maria; Foster, Mark; Kim, Sun Hyung; Prieto, Juan Carlos; Chen, Haitao; Gao, Wei; Styner, Martin A; Gilmore, John HDecember 1, 2023Not Determined
37614415Create StudyLocal Spatiotemporal Representation Learning for Longitudinally-consistent Neuroimage Analysis.Advances in neural information processing systemsRen, Mengwei; Dey, Neel; Styner, Martin A; Botteron, Kelly N; Gerig, GuidoDecember 1, 2022Not Determined
37576905Create StudyEquivariant Spherical Deconvolution: Learning Sparse Orientation Distribution Functions from Spherical Data.Information processing in medical imaging : proceedings of the ... conferenceElaldi, Axel; Dey, Neel; Kim, Heejong; Gerig, GuidoJune 1, 2021Not Determined
37168581Create StudyAre early social communication skills a harbinger for language development in infants later diagnosed autistic?-A longitudinal study using a standardized social communication assessment.Frontiers in communicationRavi, Shruthi; Bradshaw, Allison; Abdi, Hervé; Meera, Shoba Sreenath; Parish-Morris, Julia; Yankowitz, Lisa; Paterson, Sarah; Dager, Stephen R; Burrows, Catherine A; Chappell, Chad; St John, Tanya; Estes, Annette M; Piven, Joseph; Swanson, Meghan R; IBIS NetworkJanuary 1, 2022Not Determined
37140923Create StudyAssociation of Sex With Neurobehavioral Markers of Executive Function in 2-Year-Olds at High and Low Likelihood of Autism.JAMA network openSt John, Tanya; Estes, Annette M; Hazlett, Heather C; Marrus, Natasha; Burrows, Catherine A; Donovan, Kevin; Torres Gomez, Santiago; Grzadzinski, Rebecca L; Parish-Morris, Julia; Smith, Rachel; Styner, Martin; Garic, Dea; Pandey, Juhi; Lee, Chimei M; Schultz, Robert T; Botteron, Kelly N; Zwaigenbaum, Lonnie; Piven, Joseph; Dager, Stephen R; IBIS NetworkMay 1, 2023Not Determined
37089869Create StudyDmriprep: open-source diffusion MRI quality control framework with graphical user interface.Proceedings of SPIE--the International Society for Optical EngineeringDubos, Johanna; Park, Sang Kyoon; Vlasova, Roza; Prieto, Juan Carlos; Styner, MartinFebruary 1, 2023Not Determined
37076628Create StudyA somato-cognitive action network alternates with effector regions in motor cortex.NatureGordon, Evan M; Chauvin, Roselyne J; Van, Andrew N; Rajesh, Aishwarya; Nielsen, Ashley; Newbold, Dillan J; Lynch, Charles J; Seider, Nicole A; Krimmel, Samuel R; Scheidter, Kristen M; Monk, Julia; Miller, Ryland L; Metoki, Athanasia; Montez, David F; Zheng, Annie; Elbau, Immanuel; Madison, Thomas; Nishino, Tomoyuki; Myers, Michael J; Kaplan, Sydney; Badke D'Andrea, Carolina; Demeter, Damion V; Feigelis, Matthew; Ramirez, Julian S B; Xu, Ting; Barch, Deanna M; Smyser, Christopher D; Rogers, Cynthia E; Zimmermann, Jan; Botteron, Kelly N; Pruett, John R; Willie, Jon T; Brunner, Peter; Shimony, Joshua S; Kay, Benjamin P; Marek, Scott; Norris, Scott A; Gratton, Caterina; Sylvester, Chad M; Power, Jonathan D; Liston, Conor; Greene, Deanna J; Roland, Jarod L; Petersen, Steven E; Raichle, Marcus E; Laumann, Timothy O; Fair, Damien A; Dosenbach, Nico U FMay 1, 2023Not Determined
37060675Create StudyLanguage exposure during infancy is negatively associated with white matter microstructure in the arcuate fasciculus.Developmental cognitive neuroscienceEstrada, Katiana A; Govindaraj, Sharnya; Abdi, Hervé; Moraglia, Luke E; Wolff, Jason J; Meera, Shoba Sreenath; Dager, Stephen R; McKinstry, Robert C; Styner, Martin A; Zwaigenbaum, Lonnie; Piven, Joseph; Swanson, Meghan R; IBIS NetworkJune 1, 2023Not Determined
37017863Create StudySensory Profiles in Relation to Later Adaptive Functioning Among Toddlers at High-Familial Likelihood for Autism.Journal of autism and developmental disordersWorthley, Emma; Grzadzinski, Rebecca; Zwaigenbaum, Lonnie; Dager, Stephen R; Estes, Annette M; Hazlett, Heather C; Schultz, Robert T; Piven, Joseph; Wolff, Jason J; IBIS NetworkApril 5, 2023Not Determined
36968615Create StudyLearning Strategies for Contrast-agnostic Segmentation via SynthSeg for Infant MRI data.Proceedings of machine learning researchShang, Ziyao; Turja, Md Asadullah; Feczko, Eric; Houghton, Audrey; Rueter, Amanda; Moore, Lucille A; Snider, Kathy; Hendrickson, Timothy; Reiners, Paul; Stoyell, Sally; Kardan, Omid; Rosenberg, Monica; Elison, Jed T; Fair, Damien A; Styner, Martin AJuly 1, 2022Not Determined
36932005Create StudyGenetic Influences on the Developing Young Brain and Risk for Neuropsychiatric Disorders.Biological psychiatryAlex, Ann M; Buss, Claudia; Davis, Elysia Poggi; Campos, Gustavo de Los; Donald, Kirsten A; Fair, Damien A; Gaab, Nadine; Gao, Wei; Gilmore, John H; Girault, Jessica B; Grewen, Karen; Groenewold, Nynke A; Hankin, Benjamin L; Ipser, Jonathan; Kapoor, Shreya; Kim, Pilyoung; Lin, Weili; Luo, Shan; Norton, Elizabeth S; O'Connor, Thomas G; Piven, Joseph; Qiu, Anqi; Rasmussen, Jerod M; Skeide, Michael A; Stein, Dan J; Styner, Martin A; Thompson, Paul M; Wakschlag, Laurie; Knickmeyer, Rebecca; ENIGMA ORIGINs groupMay 15, 2023Not Determined
36573033Create StudyConfirmatory factor analysis of the BRIEF2 in a sample of youth with Down syndrome.Journal of intellectual disability research : JIDRSoltani, A; Schworer, E K; Jacobson, L A; Channell, M M; Lee, N R; Faught, G G; Grzadzinski, R; Fidler, D; Esbensen, A JFebruary 1, 2023Not Determined
36517753Create StudySocial attention during object engagement: toward a cross-species measure of preferential social orienting.Journal of neurodevelopmental disordersWeichselbaum, Claire; Hendrix, Nicole; Albright, Jordan; Dougherty, Joseph D; Botteron, Kelly N; Constantino, John N; Marrus, NatashaDecember 14, 2022Not Determined
36495600Create StudyOptimal transport features for morphometric population analysis.Medical image analysisGerber, Samuel; Niethammer, Marc; Ebrahim, Ebrahim; Piven, Joseph; Dager, Stephen R; Styner, Martin; Aylward, Stephen; Enquobahrie, AndinetFebruary 1, 2023Not Determined
36383495Create StudyQ-space Conditioned Translation Networks for Directional Synthesis of Diffusion Weighted Images from Multi-modal Structural MRI.Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted InterventionRen, Mengwei; Kim, Heejong; Dey, Neel; Gerig, GuidoJanuary 1, 2021Not Determined
36222317Create StudyQuantifying latent social motivation and its associations with joint attention and language in infants at high and low likelihood for autism spectrum disorder.Developmental scienceStallworthy, Isabella C; Berry, Daniel; Davis, Savannah; Wolff, Jason J; Burrows, Catherine A; Swanson, Meghan R; Grzadzinski, Rebecca L; Botteron, Kelly; Dager, Stephen R; Estes, Annette M; Schultz, Robert T; Piven, Joseph; Elison, Jed T; Pruett Jr, John R; Marrus, Natasha; IBIS NetworkMay 1, 2023Not Determined
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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 NetworkSeptember 1, 2022Not Determined
35331012Create StudySubcortical Brain Development in Autism and Fragile X Syndrome: Evidence for Dynamic, Age- and Disorder-Specific Trajectories in Infancy.The American journal of psychiatryShen, Mark D; Swanson, Meghan R; Wolff, Jason J; Elison, Jed T; Girault, Jessica B; Kim, Sun Hyung; Smith, Rachel G; Graves, Michael M; Weisenfeld, Leigh Anne H; Flake, Lisa; MacIntyre, Leigh; Gross, Julia L; Burrows, Catherine A; Fonov, Vladimir S; Collins, D Louis; Evans, Alan C; Gerig, Guido; McKinstry, Robert C; Pandey, Juhi; St John, Tanya; Zwaigenbaum, Lonnie; Estes, Annette M; Dager, Stephen R; Schultz, Robert T; Styner, Martin A; Botteron, Kelly N; Hazlett, Heather C; Piven, Joseph; IBIS NetworkAugust 1, 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
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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
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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
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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
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32140983Create StudySleep Problems and Trajectories of Restricted and Repetitive Behaviors in Children with Neurodevelopmental Disabilities.Journal of autism and developmental disordersMacDuffie, Katherine E; Munson, Jeffrey; Greenson, Jessica; Ward, Teresa M; Rogers, Sally J; Dawson, Geraldine; Estes, AnnetteNovember 1, 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, Meghan R; Hazlett, Heather CDecember 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
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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
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29149191Create StudyResting-state fMRI in sleeping infants more closely resembles adult sleep than adult wakefulness.PloS oneMitra, Anish; Snyder, Abraham Z; Tagliazucchi, Enzo; Laufs, Helmut; Elison, Jed; Emerson, Robert W; Shen, Mark D; Wolff, Jason J; Botteron, Kelly N; Dager, Stephen; Estes, Annette M; Evans, Alan; Gerig, Guido; Hazlett, Heather C; Paterson, Sarah J; Schultz, Robert T; Styner, Martin A; Zwaigenbaum, Lonnie; IBIS Network; Schlaggar, Bradley L; Piven, Joseph; Pruett Jr, John R; Raichle, MarcusJanuary 2017Not Relevant
29090080Create StudyCritical region within 22q11.2 linked to higher rate of autism spectrum disorder.Molecular autismClements, Caitlin C; Wenger, Tara L; Zoltowski, Alisa R; Bertollo, Jennifer R; Miller, Judith S; de Marchena, Ashley B; Mitteer, Lauren M; Carey, John C; Yerys, Benjamin E; Zackai, Elaine H; Emanuel, Beverly S; McDonald-McGinn, Donna M; Schultz, Robert TJanuary 2017Not Relevant
28937691Create StudyToward a conceptual framework for early brain and behavior development in autism.Molecular psychiatryPiven, J; Elison, J T; Zylka, M JOctober 2017Not Relevant
28931307Create StudyAutism spectrum disorder screening with the CBCL/1½-5: Findings for young children at high risk for autism spectrum disorder.Autism : the international journal of research and practiceRescorla, Leslie A; Winder-Patel, Breanna M; Paterson, Sarah J; Pandey, Juhi; Wolff, Jason J; Schultz, Robert T; Piven, JosephJanuary 2019Not Relevant
28889315Create StudyBrief Report: Executive Function as a Predictor of Academic Achievement in School-Aged Children with ASD.Journal of autism and developmental disordersSt John, Tanya; Dawson, Geraldine; Estes, AnnetteJanuary 2018Not Relevant
28838582Create StudyCost Offset Associated With Early Start Denver Model for Children With Autism.Journal of the American Academy of Child and Adolescent PsychiatryCidav Z, Munson J, Estes A, Dawson G, Rogers S, Mandell DSeptember 2017Not Determined
28705497Create StudyQuicksilver: Fast predictive image registration - A deep learning approach.NeuroImageYang, Xiao; Kwitt, Roland; Styner, Martin; Niethammer, MarcSeptember 2017Not Relevant
28634707Create StudyParent Support of Preschool Peer Relationships in Younger Siblings of Children with Autism Spectrum Disorder.Journal of autism and developmental disordersEstes, Annette; Munson, Jeffrey; John, Tanya St; Dager, Stephen R; Rodda, Amy; Botteron, Kelly; Hazlett, Heather; Schultz, Robert T; Zwaigenbaum, Lonnie; Piven, Joseph; Guralnick, Michael J; IBIS networkApril 2018Not Relevant
28592562Create StudyFunctional neuroimaging of high-risk 6-month-old infants predicts a diagnosis of autism at 24 months of age.Science translational medicineEmerson, Robert W; Adams, Chloe; Nishino, Tomoyuki; Hazlett, Heather Cody; Wolff, Jason J; Zwaigenbaum, Lonnie; Constantino, John N; Shen, Mark D; Swanson, Meghan R; Elison, Jed T; Kandala, Sridhar; Estes, Annette M; Botteron, Kelly N; Collins, Louis; Dager, Stephen R; Evans, Alan C; Gerig, Guido; Gu, Hongbin; McKinstry, Robert C; Paterson, Sarah; Schultz, Robert T; Styner, Martin; IBIS Network; Schlaggar, Bradley L; Pruett Jr, John R; Piven, JosephJune 2017Not Relevant
28460842Create StudyThe Emergence of Network Inefficiencies in Infants With Autism Spectrum Disorder.Biological psychiatryLewis, John D; Evans, Alan C; Pruett Jr, John R; Botteron, Kelly N; McKinstry, Robert C; Zwaigenbaum, Lonnie; Estes, Annette M; Collins, D Louis; Kostopoulos, Penelope; Gerig, Guido; Dager, Stephen R; Paterson, Sarah; Schultz, Robert T; Styner, Martin A; Hazlett, Heather C; Piven, Joseph; Infant Brain Imaging Study NetworkAugust 1, 2017Not Relevant
28399476Create StudyGeodesic shape regression with multiple geometries and sparse parameters.Medical image analysisFishbaugh J, Durrleman S, Prastawa M, Gerig GJuly 2017Not Relevant
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24353377Create StudyEvaluation of DTI Property Maps as Basis of DTI Atlas Building.Proceedings of SPIE--the International Society for Optical EngineeringLiu, Zhexing; Goodlett, Casey; Gerig, Guido; Styner, MartinMarch 12, 2010Not Relevant
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23595508Create StudyENTROPY BASED DTI QUALITY CONTROL VIA REGIONAL ORIENTATION DISTRIBUTION.Proceedings. IEEE International Symposium on Biomedical ImagingFarzinfar, M; Dietrich, C; Smith, Rg; Li, Y; Gupta, A; Liu, Z; Styner, Ma2012Not Relevant
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23235270Create StudyRegional characterization of longitudinal DT-MRI to study white matter maturation of the early developing brain.NeuroImageSadeghi, Neda; Prastawa, Marcel; Fletcher, P Thomas; Wolff, Jason; Gilmore, John H; Gerig, GuidoMarch 2013Not Relevant
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23032117Create StudyAdaptive prior probability and spatial temporal intensity change estimation for segmentation of the one-year-old human brain.Journal of neuroscience methodsKim SH, Fonov VS, Dietrich C, Vachet C, Hazlett HC, Smith RG, Graves MM, Piven J, Gilmore JH, Dager SR, Mckinstry RC, Paterson S, Evans AC, Collins DL, Gerig G, Styner MA, January 2013Not Relevant
22684595Create StudyBrain volume findings in 6-month-old infants at high familial risk for autism.The American journal of psychiatryHazlett HC, Gu H, McKinstry RC, Shaw DW, Botteron KN, Dager SR, Styner M, Vachet C, Gerig G, Paterson SJ, Schultz RT, Estes AM, Evans AC, Piven J, IBIS NetworkJune 2012Not Relevant
22370873Create StudyConsensus paper: pathological role of the cerebellum in autism.Cerebellum (London, England)Fatemi, S Hossein; Aldinger, Kimberly A; Ashwood, Paul; Bauman, Margaret L; Blaha, Charles D; Blatt, Gene J; Chauhan, Abha; Chauhan, Ved; Dager, Stephen R; Dickson, Price E; Estes, Annette M; Goldowitz, Dan; Heck, Detlef H; Kemper, Thomas L; King, Bryan H; Martin, Loren A; Millen, Kathleen J; Mittleman, Guy; Mosconi, Matthew W; Persico, Antonio M; Sweeney, John A; Webb, Sara J; Welsh, John PSeptember 2012Not Relevant
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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21761651Create StudyOptimal data-driven sparse parameterization of diffeomorphisms for population analysis.Information processing in medical imaging : proceedings of the ... conferenceDurrleman, Sandy; Prastawa, Marcel; Gerig, Guido; Joshi, SarangJanuary 2011Not Relevant
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21244423Create StudyPoor fine-motor and visuospatial skills predict persistence of pediatric-onset obsessive-compulsive disorder into adulthood.Journal of child psychology and psychiatry, and allied disciplinesBloch MH, Sukhodolsky DG, Dombrowski PA, Panza KE, Craiglow BG, Landeros-Weisenberger A, Leckman JF, Peterson BS, Schultz RTSeptember 2011Not Relevant
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  • Create Study
    A link to the Create an NDA Study page that can be clicked to start creating an NDA Study with information such as the title, journal and authors automatically populated.
  • Not Determined Publication
    Indicates that the publication has not yet been reviewed and/or marked as Relevant or Not Relevant so it has not been determined whether an NDA Study is expected.
  • Not Relevant Publication
    A publication that is not based on data related to the aims of the grant/project associated with the Collection or not based on any data such as a review article and, therefore, an NDA Study is not expected to be created.
  • PubMed
    PubMed provides citation information for biomedical and life sciences publications and is managed by the U.S. National Institutes of Health's National Library of Medicine.
  • PubMed ID
    The PUBMed ID is the unique ID number for the publication as recorded in the PubMed database.
  • Relevant Publication
    A publication that is based on data related to the aims of the grant/project associated with the Collection and, therefore, an NDA Study is expected to be created.
Data Expected List: Mandatory Data Structures

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

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

Data ExpectedTargeted EnrollmentInitial SubmissionSubjects SharedStatus
Research Subject and Pedigree info icon
52607/31/2014
934
Approved
To create your project's Data Expected list, use the "+New Data Expected" to add or request existing structures and to request new Data Structures that are not in the NDA Data Dictionary.

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

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

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

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

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

Data ExpectedTargeted EnrollmentInitial SubmissionSubjects SharedStatus
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
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
helpcenter.collection.data-expected-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.

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

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

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

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

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

Frequently Asked Questions

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

Glossary

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

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

Associated Studies

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

Study NameAbstractCollection/Study SubjectsData UsageState
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
The importance of low IQ to early diagnosis of autismSome individuals can flexibly adapt to life’s changing demands while others, in particular those with Autism Spectrum Disorder (ASD), find it challenging. The origin of early individual differences in cognitive abilities, the putative tools with which to navigate novel information in life, including in infants later diagnosed with ASD remains unexplored. Moreover, the role of intelligence quotient (IQ) vis-à-vis core features of autism remains debated. We systematically investigate the contribution of early IQ in future autism outcomes in an extremely large, population-based study of 8,000 newborns, infants, and toddlers from the US between 2 and 68 months with over 15,000 cross-sectional and longitudinal assessments, and for whom autism outcomes are ascertained or ruled out by about 2-4 years. This population is representative of subjects involved in the National Institutes of Health (NIH)-funded research, mainly on atypical development, in the US. Analyses using predetermined age bins showed that IQ scores are consistently lower in ASD relative to TD at all ages (p<0.001), and IQ significantly correlates with calibrated severity scores (total CSS, as well as non-verbal and verbal CSS) on the ADOS. Note, VIQ is no better than the full-scale IQ to predict ASD cases. These findings raise new, compelling questions about potential atypical brain circuitry affecting performance in both verbal and nonverbal abilities and that precede an ASD diagnosis. This study is the first to establish prospectively that low early IQ is a major feature of ASD in early childhood. 491/6323Secondary AnalysisShared
Prognostic early snapshot stratification of autism based on adaptive functioningA major goal of precision medicine is to predict prognosis based on individualized information at the earliest possible points in development. Using early snapshots of adaptive functioning and unsupervised data- driven discovery methods, we uncover highly stable early autism subtypes that yield information relevant to later prognosis. Data from the National Institute of Mental Health Data Archive (NDA) (n = 1,098) was used to uncover three early subtypes (<72 months) that generalize with 96% accuracy. Outcome data from NDA (n = 2,561; mean age, 13 years) also reproducibly clusters into three subtypes with 99% generalization accuracy. Early snapshot subtypes predict developmental trajectories in non-verbal cognitive, language and motor domains and are predictive of membership in different adaptive functioning outcome subtypes. Robust and prognosis- relevant subtyping of autism based on early snapshots of adaptive functioning may aid future research work via prediction of these subtypes with our reproducible stratification model.1/3517Secondary AnalysisShared
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
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
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
Joint attention in infants at high familial risk for autism spectrum disorder and the association with thalamic and hippocampal macrostructure Autism spectrum disorder (ASD) is a heritable neurodevelopmental disorder. Infants diagnosed with ASD can show impairments in spontaneous gaze-following and will seldom engage in joint attention (JA). The ability to initiate JA (IJA) can be more significantly impaired than the ability to respond to JA (RJA). In a longitudinal study, 101 infants who had a familial risk for ASD were enrolled (62% males). Participants completed magnetic resonance imaging scans at 4 or 6 months of age. Subcortical volumes (thalamus, hippocampus, amygdala, basal ganglia, ventral diencephalon, and cerebellum) were automatically extracted. Early gaze and JA behaviors were assessed with standardized measures. The majority of infants were IJA nonresponders (n = 93, 92%), and over half were RJA nonresponders (n = 50, 52%). In the nonresponder groups, models testing the association of subcortical volumes with later ASD diagnosis accounted for age, sex, and cerebral volumes. In the nonresponder IJA group, using regression method, the left hippocampus (B = -0.009, aOR = 0.991, P = 0.025), the right thalamus (B = -0.016, aOR = 0.984, P = 0.026), as well as the left thalamus (B = 0.015, aOR = 1.015, P = 0.019), predicted later ASD diagnosis. Alterations in thalamic and hippocampal macrostructure in at-risk infants who do not engage in IJA may reflect an enhanced vulnerability and may be the key predictors of later ASD development. 833/950Secondary AnalysisShared
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
Investigating possible biomarkers of autism in resting EEGThere are no clinically useful biomarkers of autism spectrum disorder (ASD). Electroencephalogram (EEG) can measure ongoing brain dynamics using cheap and widely available technology and is minimally invasive. As such, any measurement drived from EEG that is capable of serving as a biomarker for ASD would be hugely beneficial. Previous research has been conflicting and a large list of EEG measures have been suggested. 1/771Secondary 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 AnalysisShared
Examining the Shape Bias in Young Autistic Children: A Vocabulary Composition AnalysisShape is a salient object property and one of the first that children use to categorize objects under one label. Colunga and Sims (2017) suggest that noun vocabulary composition and word learning biases are closely interrelated in typical development. The current study examined the association between noun vocabulary knowledge and perceptual word features, specifically shape and material features. Participants included 249 autistic children and 1,245 non-autistic toddlers who were matched on expressive noun vocabulary size and gender. Nouns were categorized using the Samuelson and Smith (1999) noun feature database. A simple group comparison revealed no group differences in shape bias; both groups evidenced developing noun vocabularies that favored shape+solid and nonsolid+material nouns. However, the trajectory of evidence of shape bias as a function of vocabulary size differed between the groups, with autistic children demonstrating a reduced shape-bias initially. Future work should examine how children’s learning biases shift over development and whether the shape bias promotes lexical development to the same degree across groups.30/249Secondary AnalysisShared
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
Semantic modeling 2023Although it is well documented that children with ASD are slower to develop their lexicons, we still have a limited understanding of the structure of early lexical knowledge in children with ASD. The current study uses network analysis and differential item functioning anlaysis to examine the structure of semantic knowledge, which may provide insight into the learning processes that influence early word learning.30/208Secondary AnalysisShared
Semantic Network ModelingAlthough it is well documented that children with ASD are slower to develop their lexicons, we still have a limited understanding of the structure of early lexical knowledge in children with ASD. The current study uses network analysis to examine the structure of semantic knowledge, which may provide insight into the learning processes that influence early word learning.30/200Secondary AnalysisShared
Semantic Network Modeling in Young Autistic ChildrenBackground: Most young autistic children have delayed vocabulary growth relative to their non-autistic peers. Additionally, previous studies have revealed that autistic children are less likely to encode associated features of novel objects, suggesting inefficient encoding or different processes for acquiring semantic information about words. Recent network analyses of vocabulary growth revealed important relationships between early vocabulary acquisition and the structure of the sematic environment. Methods: We studied the expressive vocabularies of 970 non-autistic toddlers (Mage = 20.82 months) and 194 autistic children (Mage = 54.58 months) in two studies. The groups were vocabulary-matched (words produced: MAutistic = 213.60, MNon-autistic = 213.72). In study 1, we estimated their trajectories of semantic development using network analyses. Network structure was based on child-oriented adult-generated word associations. We compared child semantic networks according to indegree, average shortest path length, and clustering coefficient (features that holistically contribute to well-connected network structure). Then, in study 2, we attempted to relate vocabulary-level effects to word-level learning biases. Results: Study 1 revealed that autistic and non-autistic children are sensitive to the structure of their semantic environment. Both groups demonstrated nonlinear vocabulary trajectories that differed from random acquisition networks. Despite similarities, group differences were observed for each network metric. Differences were most pronounced for clustering coefficient (how closely connected groups of words are), with earlier peaks for autistic children. Study 2 demonstrated that many words differ in their expected vocabulary size of acquisition. Conclusions: Group differences at the vocabulary- and word-levels indicate that, although autistic children are learning from their semantic environment, they may be processing their semantic environment differently. These deviations indicate that autistic children have distinctive learning biases that may align with core autism features. 16/194Secondary AnalysisShared
Does Anxiety Mediate the Relationship between Sensory Hyperresponsiveness and Restricted, Repetitive Behaviors during Early Development?Sensory hyperresponsiveness, anxiety, and restricted, repetitive behaviors are known to be associated with one another, especially in autistic youth, and may be important to the development and presentation of autism over time. Few studies, however, have studied the nature of this three-way relationship prospectively, or in young children at elevated likelihood for autism. The goals of the current study were to gain greater insight into the development of autism from a symptom level before a diagnosis can be made, and specifically, to examine the relationship between sensory hyperresponsiveness, anxiety, and restricted, repetitive behaviors across time during early development in children at elevated likelihood for autism. Extant longitudinal data for a group of children at elevated likelihood for autism (N = 147) were used to conduct path analyses for two mediation model configurations, which included measures of sensory hyperresponsiveness at baseline, and anxiety and restricted and repetitive behaviors at follow-up. Results did not indicate mediating effects for either model; however, higher levels of sensory hyperresponsiveness at baseline were significantly associated with higher levels of anxiety symptoms at follow-up (b = 0.09, SE = 0.04, β = 0.24, p = 0.005, 95% CIs [0.07, 0.40]). Findings suggest that sensory hyperresponsiveness during early development later predicts anxiety symptoms in children at elevated likelihood for autism, which is consistent with prior findings in both autistic (Green et al., 2012) and non-autistic children (Carpenter et al., 2019). Although we are unable to determine whether this is a unidirectional or bidirectional relationship in the current study given the lack of concurrent data on anxiety symptoms at baseline, this result adds to emerging research suggesting that sensory hyperresponsiveness may be a risk factor for later developing anxiety. 2/159Secondary AnalysisShared
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
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 AnalysisShared
* Data not on individual level
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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.

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