NIMH Data Archive - Data

Genomics

Neuroimaging

Phenotype¹

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Clinical Trial

¹ Numbers reported are subjects by age

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

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.

Select	Experiment Id	Experiment Name	Experiment Type	Created On

24	HI-NGS_R1	Omics	02/16/2011
475	MB1-10 (CHOP)	Omics	06/07/2016
490	Discovery and CRISPR validation of genetic factors associated with antipsychotic-induced weight gain and cardiometabolic risk	Omics	07/07/2016
501	PharmacoBOLD Resting State	fMRI	07/27/2016
506	PVPREF	Omics	08/05/2016
13	Comparison of FI expression in Autistic and Neurotypical Homo Sapiens	Omics	12/28/2010
18	AGRE/Broad Affymetrix 5.0 Genotype Experiment	Omics	01/06/2011
22	Stitching PCR Sequencing	Omics	02/14/2011
26	ASD_Methylation	Omics	03/01/2011
29	Microarray family 03 (father, mother, sibling)	Omics	03/24/2011
37	Standard paired-end sequencing of BCRs	Omics	04/19/2011
38	Illumina Mate-Pair BCR sequencing	Omics	04/19/2011
39	Custom Jumping Libraries	Omics	04/19/2011
40	Custom CapBP	Omics	04/19/2011
41	Immunofluorescence	Omics	05/11/2011
43	Autism brain sample genotyping, Illumina	Omics	05/16/2011
47	ARRA Autism Sequencing Collaboration at Baylor. SOLiD 4 System	Omics	08/01/2011
53	AGRE Omni1-quad	Omics	10/11/2011
59	AGP genotyping	Omics	04/03/2012
60	Ultradeep 454 sequencing of synaptic genes from postmortem cerebella of individuals with ASD and neurotypical controls	Omics	06/23/2012
63	Microemulsion PCR and Targeted Resequencing for Variant Detection in ASD	Omics	07/20/2012
76	Whole Genome Sequencing in Autism Families	Omics	01/03/2013
519	Resting	fMRI	11/08/2016
90	Genotyped IAN Samples	Omics	07/09/2013
91	NJLAGS Axiom Genotyping Array	Omics	07/16/2013
93	AGP genotyping (CNV)	Omics	09/06/2013
106	Longitudinal Sleep Study. H20 200. Channel set 2	EEG	11/07/2013
107	Longitudinal Sleep Study. H20 200. Channel set 3	EEG	11/07/2013
108	Longitudinal Sleep Study. AURA 200	EEG	11/07/2013
105	Longitudinal Sleep Study. H20 200. Channel set 1	EEG	11/07/2013
109	Longitudinal Sleep Study. AURA 400	EEG	11/07/2013
116	Gene Expression Analysis WG-6	Omics	01/07/2014
131	Jeste Lab UCLA ACEii: Charlie Brown and Sesame Street - Project 1	Eye Tracking	02/27/2014
132	Jeste Lab UCLA ACEii: Animacy - Project 1	Eye Tracking	02/27/2014
133	Jeste Lab UCLA ACEii: Mom Stranger - Project 2	Eye Tracking	02/27/2014
134	Jeste Lab UCLA ACEii: Face Emotion - Project 3	Eye Tracking	02/27/2014
145	AGRE/FMR1_Illumina.JHU	Omics	04/14/2014
146	AGRE/MECP2_Sanger.JHU	Omics	04/14/2014
147	AGRE/MECP2_Junior.JHU	Omics	04/14/2014
151	Candidate Gene Identification in familial Autism	Omics	06/09/2014
152	NJLAGS Whole Genome Sequencing	Omics	07/01/2014
154	Math Autism Study - Vinod Menon	fMRI	07/15/2014
155	Resting	fMRI	07/25/2014
156	Speech	fMRI	07/25/2014
159	Emotion	fMRI	07/25/2014
160	syllable contrast	EEG	07/29/2014
167	School-age naturalistic stimuli	Eye Tracking	09/19/2014
44	AGRE/Broad Affymetrix 5.0 Genotype Experiment	Omics	06/27/2011
45	Exome Sequencing of 20 Sporadic Cases of Autism Spectrum Disorder	Omics	07/15/2011
78	MET Genotypes	Omics	03/18/2013

Collection - Add Experiment

Add Supporting Documentation

Funding Source:
URL:

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 Autism Biomarkers Consortium for Clinical Trials #2288

General
Experiments (15)
Shared Data
Publications (68)
Data Expected (33)
Associated Studies (21)

Collection Title	Collection Investigators	Collection Description
Collection Title:	The Autism Biomarkers Consortium for Clinical Trials
Collection Investigators:	James McPartland
Collection Description:	The goal of this consortium is to establish tools that can be used as biomarkers and/or sensitive and reliable objective assays of social impairment in autism spectrum disorder (ASD) clinical trials. Specifically, we aim to accelerate the development of effective treatments for social impairment in ASD by validating (a) outcome measures that will be sensitive and reliable assessments of response to treatment and EEG and (b) eye-tracking (ET) biomarkers that can be used to reduce heterogeneity of samples via stratification, indicate early efficacy, and/or demonstrate target engagement. The consortium will conduct a naturalistic, longitudinal study of preschool (3-5 years) and school aged (6-11 years) children with ASD and typical development (TD) with IQ ranging from 50-115. Children will be assessed across three time points (T1: Baseline, T2: 6 weeks, T3: 24 weeks) using clinician, caregiver and lab-based (LB) measures of social impairment, along with a battery of conceptually related EEG and ET tasks and independent ratings of clinical status. This battery measures key facets of social-communication in ASD using well-validated paradigms appropriate for this developmental and cognitive range. Five Collaborating Implementation Sites ("Sites"), all highly experienced in multi-site collaborative clinical research using the methodologies proposed here in both typical and atypical development, will contribute equally to recruitment, screening, diagnosis, testing, and longitudinal assessment. The Data Coordinating Core (DCC) will provide a secure informatics infrastructure to streamline communication and data flow throughout the consortium to ensure organized, secure data management, quality control, and reliable upload to the National Database for Autism Research and NIH/NIMH Data Repositories. The Data Acquisition and Analysis Core (DAAC) will oversee consistent application of scientific standards and methodological rigor for standardized data collection, processing, and analytics. The Administrative Core will oversee the operations of the Sites, the DCC, and the DAAC to coordinate with federal and private partners in this cooperative agreement to: 1) Compare whether LB measures versus clinician and caregiver assessments of social impairment are more sensitive indicators of clinical status; 2) Evaluate whether this set of ET and EEG measures, individually or in combination, has potential utility as stratification biomarkers and/or sensitive and reliable measures of change in clinical trials, assessing viability in terms of: construct validity; test-retest reliability, consistency, and stability; discriminant validity ; convergent validity; and sensitivity to change; 3) Collect blood (DNA) samples from subjects and parents of ASD subjects for future genomic analyses and share raw, processed, and analyzed data to create a community resource accessible for use by all qualified investigators.
Data Repository:	NIMH Data Archive
Permission Group:
Collection Creation Date:	07/26/2015
NIH Research Initiative:	NIMH Repository & Genomics Resource (NRGR)
Collection Phase:	Funding Completed
Collection Sub-Phase:	Close Out
Blinded Clinical Trial:	No
Total Funded Amount:	$29,161,446.00
Subjects Shared:	510
Collection DOI:	10.15154/3y3w-8z64

{"values":[]}

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Funding Sources:

Funding Source Name	Funding Source URL
NIH - Extramural	None

Supporting Documentation:

File Name	File Type	Description	Audience
QA-notification.txt	Other	Quality Assurance Notification	Qualified Researchers
Protocol M6.0 2018.06.07 & Memo.pdf	Methods	ABC-CT Feasibility and Main Study Protocol	Qualified Researchers
ABC-CT Main Study Counterbalance Protocol_2016-10-05.pdf	Methods	ABC-CT Main Study Counterbalance Protocol_2016-10-05	Qualified Researchers
ABC-CT CLINICAL MOP_M1.9_version 14July2017.pdf	Methods	ABC-CT Feasibility and Main Study Clinical Manual of Procedure	Qualified Researchers
ABC-CTDataManagementMOPv1.202012017.pdf	Methods	ABC-CT Feasibility and Main Study Data Management Manual of Procedure	Qualified Researchers
ABC-CT_EEG_MOP_M2.0.pdf	Methods	ABC-CT Feasibility and Main Study EEG Manual of Procedure	Qualified Researchers
ABC-CTETMOP4.0.5.pdf	Methods	ABC-CT Feasibility and Main Study Eye Tracking Manual of Procedure	Qualified Researchers
CGI MOP_ M1_version 06SEP2016.pdf	Methods	ABC-CT Feasibility and Main Study CGI-SI Manual of Procedure	Qualified Researchers
VT MOP v1.4; 9.8.2017.pdf	Methods	ABC-CT Feasibility and Main Study VT Manual of Procedure	Qualified Researchers
Behavior Management_M1; 12SEP2016.pdf	Methods	ABC-CT Feasibility and Main Study Behavior Management MOP	Qualified Researchers

Grant Information:

Project Number	Title	Start Date	Original End Date	End Date	Planned Enrollment	Actual Enrollment	Organization	Funds Obligated
U19MH108206-01	The Autism Biomarkers Consortium for Clinical Trials	07/01/2015	06/30/2019	06/30/2020	1300	1366	YALE UNIVERSITY	$29,161,446.00

Clinical Trials:

helpcenter.collection.general-tab

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.

Contact NDA Help Desk

ID	Name	Created Date	Status	Type
470	ABC-CT ET	05/25/2016	Approved	Eye Tracking
472	ABC-CT Resting	06/03/2016	Approved	EEG
479	ABC-CT Social/Nonsocial	06/20/2016	Approved	EEG
480	ABC-CT Biomotion	06/20/2016	Approved	EEG
481	ABC-CT Emotion	06/20/2016	Approved	EEG
482	ABC-CT EU AIMS Faces	06/20/2016	Approved	EEG
483	ABC-CT VEP	06/20/2016	Approved	EEG
509	ABC-CT Resting v2	08/18/2016	Approved	EEG
513	ABC-CT ET: Main Study	09/09/2016	Approved	Eye Tracking
544	ABC-CT Faces v2	12/13/2016	Approved	EEG
545	ABC-CT VEP v2	12/13/2016	Approved	EEG
546	ABC-CT Biomotion v2	12/13/2016	Approved	EEG
1229	ABC-CT Faces v3	03/25/2019	Approved	EEG
1230	ABC-CT VEP v3	03/25/2019	Approved	EEG
1231	ABC-CT Biomotion v3	03/25/2019	Approved	EEG

helpcenter.collection.experiments-tab

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.

Contact NDA Help Desk

Shared Data:

Title	Type	Number of Subjects
A Developmental NEuroPSYchological Assessment (NEPSY-II)	Clinical Assessments	434
ABC-CT Eye Tracking Derived Results	Imaging	400
ACE Family Medical History	Clinical Assessments	305
ACE Subject Medical History	Clinical Assessments	305
Aberrant Behavior Checklist (ABC) - Community	Clinical Assessments	438
Adverse Events	Clinical Assessments	4
Autism Diagnostic Interview, Revised (ADI-R)	Clinical Assessments	298
Autism Diagnostic Observation Schedule, 2nd Edition (ADOS-2) - Module 1	Clinical Assessments	11
Autism Diagnostic Observation Schedule, 2nd Edition (ADOS-2) - Module 2	Clinical Assessments	46
Autism Diagnostic Observation Schedule, 2nd Edition (ADOS-2) - Module 3	Clinical Assessments	386
Autism Impact Measure	Clinical Assessments	437
BASC 3	Clinical Assessments	49
Child/Adolescent Symptom Inventory	Clinical Assessments	430
Clinical Global Impression (CGI)	Clinical Assessments	298
DAS-II: Differential Ability Scales 2nd Ed. School Age	Clinical Assessments	361
DAS-II:Differential Ability Scales 2nd Ed. Early Years	Clinical Assessments	139
Demographics	Clinical Assessments	438
EEG Resting Eyes Calm Viewing	Imaging	399
EEG Subject Files	Imaging	436
Early Childhood Inventory	Clinical Assessments	9
Event Related Potential - Visual Evoked Potential	Imaging	399
Event Related Potential to Faces - Calm Viewing	Imaging	399
Event Related Potential- Biomotion	Imaging	399
Eye Tracking Subject-Experiment	Imaging	440
Intervention History	Clinical Assessments	436
Kaufman Assessment Battery for Children, Second Edition	Clinical Assessments	9
LENA Data Model Form	Clinical Assessments	47
Noldus Data Model Form	Clinical Assessments	351
PDD Behavior Inventory (Parent)	Clinical Assessments	438
Pediatric Quality of Life Inventory	Clinical Assessments	50
Research Subject	Clinical Assessments	438
Social Opportunities Questionnaire	Clinical Assessments	50
Social Responsiveness Scale (SRS)	Clinical Assessments	438
Social Skills Improvement System Parent Scale	Clinical Assessments	50
Study Completion Questionnaire	Clinical Assessments	472
Video Tracking Derived Results	Imaging	315
Vineland 3	Clinical Assessments	398
Vineland-II - Survey Form (2005)	Clinical Assessments	50

helpcenter.collection.shared-data-tab

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.

Contact NDA Help Desk

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 ID	Study	Title	Journal	Authors	Date	Status
41830983	Create Study	Conceptualizing Autism as a Behavioral Network: Transdiagnostic Associations with Co-occurring Psychiatric Conditions.	Research on child and adolescent psychopathology	Han, Gloria T; Kala, Shashwat; Naples, Adam; Dawson, Geraldine; Faja, Susan; Shic, Frederick; Webb, Sara Jane; Sugar, Catherine A; Jeste, Shafali; Kleinhans, Natalia; Chawarska, Katarzyna; Bernier, Raphael; Dziura, James; McPartland, James C; Autism Biomarkers Consortium for Clinical Trials	March 14, 2026	Not Determined
41602441	Create Study	Multilevel Multivariate Functional Principal Component Analysis of Evoked and Induced Event-Related Spectral Perturbations.	Statistics in biosciences	Dong, Mingfei; Telesca, Donatello; Dickinson, Abigail; Sugar, Catherine; Webb, Sara J; Jeste, Shafali; Levin, April R; Shic, Frederick; Naples, Adam; Faja, Susan; Dawson, Geraldine; McPartland, James C; Şentürk, Damla	December 29, 2025	Not Determined
41100279	Create Study	Reduced social prioritization: An underlying mechanism driving slower latency to look at faces in autism.	Journal of psychopathology and clinical science	Griffin, Jason W; Willis, Hope C; Dommer, Kelsey; Naples, Adam; Dawson, Geraldine; Jeste, Shafali; Faja, Susan; Bernier, Raphael; Kleinhans, Natalia; Levin, April; Hellemann, Gerhard; Senturk, Damla; Dziura, James; Sugar, Catherine; Webb, Sara Jane; McPartland, James C; Shic, Frederick	February 1, 2026	Not Determined
40885894	Create Study	Face perception, attention, and memory as predictors of social change in autistic children.	Journal of neurodevelopmental disorders	Webb, Sara Jane; Kwan, Brian; Bernier, Raphael; Charwarska, Katarzyna; Dawson, Geraldine; Dziura, James; Faja, Susan; Hellmann, Gerhard; Jeste, Shafali; Kleinhans, Natalia; Levin, April; Naples, Adam; Sabatos-DeVito, Maura; Şentürk, Damla; Shic, Frederick; Sugar, Catherine; McPartland, James C; Autism Biomarkers Consortium for Clinical Trials	August 30, 2025	Not Determined
40170613	Create Study	Longitudinal relationships between social anhedonia and internalizing symptoms in autistic children: results from the Autism Biomarkers Consortium for Clinical Trials.	Psychological medicine	Gerber, Alan H; Naples, Adam; Chawarska, Katarzyna; Dawson, Geraldine; Kleinhans, Natalia; Jeste, Shafali; Faja, Susan; Dziura, James; Webb, Sara; Sugar, Catherine; Shic, Frederick; Levin, April R; McPartland, James C; Autism Biomarkers Consortium for Clinical Trials	April 2, 2025	Not Determined
40091308	Create Study	Parsing the heterogeneity of social motivation in autism.	Journal of child psychology and psychiatry, and allied disciplines	Chetcuti, Lacey; Hardan, Antonio Y; Spackman, Emily; Loth, Eva; McPartland, James C; Frazier, Thomas W; Youngstrom, Eric A; Uljarevic, Mirko	September 1, 2025	Not Determined
39703730	Create Study	Constrained Multivariate Functional Principal Components Analysis for Novel Outcomes in Eye-Tracking Experiments.	Statistics in biosciences	Kwan, Brian; Sugar, Catherine A; Qian, Qi; Shic, Frederick; Naples, Adam; Johnson, Scott P; Webb, Sara J; Jeste, Shafali; Faja, Susan; Levin, April R; Dawson, Geraldine; McPartland, James C; Şentürk, Damla	December 1, 2024	Not Determined
39562457	Create Study	EEG Biomarkers for Autism: Rational, Support, and the Qualification Process.	Advances in neurobiology	Hudac, Caitlin M; Webb, Sara Jane	January 1, 2024	Not Determined
39556296	Create Study	Social Anxiety Reduces Visual Attention to the Eyes of Emotional Faces in Autistic Youth.	Journal of autism and developmental disorders	Franke, Cassandra J; Griffin, Jason W; Naples, Adam J; Wolf, Julie M; McPartland, James C	April 1, 2026	Not Determined
39344965	Create Study	Clinician-caregiver informant discrepancy is associated with sex, diagnosis age, and intervention use among autistic children.	Autism : the international journal of research and practice	Azu, Margaret A; Han, Gloria T; Wolf, Julie M; Naples, Adam J; Chawarska, Katarzyna; Dawson, Geraldine; Bernier, Raphael A; Jeste, Shafali S; Dziura, James D; Webb, Sara J; Sugar, Catherine A; Shic, Frederick; McPartland, James C	March 1, 2025	Not Determined
39237004	Create Study	Spatiotemporal Eye Movement Dynamics Reveal Altered Face Prioritization in Early Visual Processing Among Autistic Children.	Biological psychiatry. Cognitive neuroscience and neuroimaging	Griffin, Jason W; Naples, Adam; Bernier, Raphael; Chawarska, Katarzyna; Dawson, Geraldine; Dziura, James; Faja, Susan; Jeste, Shafali; Kleinhans, Natalia; Sugar, Catherine; Webb, Sara Jane; Shic, Frederick; McPartland, James C; Autism Biomarkers Consortium for Clinical Trials	January 1, 2025	Not Determined
38959536	Create Study	Parsing evoked and induced gamma response differences in Autism: A visual evoked potential study.	Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology	Dickinson, Abigail; Ryan, Declan; McNaughton, Gabrielle; Levin, April; Naples, Adam; Borland, Heather; Bernier, Raphael; Chawarska, Katarzyna; Dawson, Geraldine; Dziura, James; Faja, Susan; Kleinhans, Natalia; Sugar, Catherine; Senturk, Damla; Shic, Frederick; Webb, Sara Jane; McPartland, James C; Jeste, Shafali; Autism Biomarkers Consortium for Clinical Trials	September 1, 2024	Not Determined
38822707	Create Study	Modeling intra-individual inter-trial EEG response variability in autism.	Statistics in medicine	Dong, Mingfei; Telesca, Donatello; Guindani, Michele; Sugar, Catherine; Webb, Sara J; Jeste, Shafali; Dickinson, Abigail; Levin, April R; Shic, Frederick; Naples, Adam; Faja, Susan; Dawson, Geraldine; McPartland, James C; Şentürk, Damla	July 30, 2024	Not Determined
38430386	Create Study	Autistic Individuals Do Not Alter Visual Processing Strategy During Encoding Versus Recognition of Faces: A Hidden Markov Modeling Approach.	Journal of autism and developmental disorders	Griffin, Jason W; Webb, Sara Jane; Keehn, Brandon; Dawson, Geraldine; McPartland, James C	January 1, 2026	Not Determined
37749934	Create Study	The Selective Social Attention task in children with autism spectrum disorder: Results from the Autism Biomarkers Consortium for Clinical Trials (ABC-CT) feasibility study.	Autism research : official journal of the International Society for Autism Research	Shic, Frederick; Barney, Erin C; Naples, Adam J; Dommer, Kelsey J; Chang, Shou An; Li, Beibin; McAllister, Takumi; Atyabi, Adham; Wang, Quan; Bernier, Raphael; Dawson, Geraldine; Dziura, James; Faja, Susan; Jeste, Shafali Spurling; Murias, Michael; Johnson, Scott P; Sabatos-DeVito, Maura; Helleman, Gerhard; Senturk, Damla; Sugar, Catherine A; Webb, Sara Jane; McPartland, James C; Chawarska, Katarzyna; Autism Biomarkers Consortium for Clinical Trials	November 1, 2023	Not Determined
37740093	Create Study	Systematic review: emotion dysregulation and challenging behavior interventions for children and adolescents on the autism spectrum with graded key evidence-based strategy recommendations.	European child & adolescent psychiatry	Nuske, Heather J; Young, Amanda V; Khan, Farzana Y; Palermo, Emma H; Ajanaku, Bukola; Pellecchia, Melanie; Vivanti, Giacomo; Mazefsky, Carla A; Brookman-Frazee, Lauren; McPartland, James C; Goodwin, Matthew S; Mandell, David S	June 1, 2024	Not Determined
37638733	Create Study	Development of peak alpha frequency reflects a distinct trajectory of neural maturation in autistic children.	Autism research : official journal of the International Society for Autism Research	Finn, Caroline E; Han, Gloria T; Naples, Adam J; Wolf, Julie M; McPartland, James C	November 1, 2023	Not Determined
37405787	Create Study	Investigating the Face Inversion Effect in Autism Across Behavioral and Neural Measures of Face Processing: A Systematic Review and Bayesian Meta-Analysis.	JAMA psychiatry	Griffin, Jason W; Azu, Margaret A; Cramer-Benjamin, Sophie; Franke, Cassandra J; Herman, Nicole; Iqbal, Reeda; Keifer, Cara M; Rosenthal, Lindsey H; McPartland, James C	October 1, 2023	Not Determined
37077750	Create Study	A functional model for studying common trends across trial time in eye tracking experiments.	Statistics in biosciences	Dong, Mingfei; Telesca, Donatello; Sugar, Catherine; Shic, Frederick; Naples, Adam; Johnson, Scott P; Li, Beibin; Atyabi, Adham; Xie, Minhang; Webb, Sara J; Jeste, Shafali; Faja, Susan; Levin, April R; Dawson, Geraldine; McPartland, James C; Şentürk, Damla; Autism Biomarkers Consortium for Clinical Trials	April 1, 2023	Not Determined
36943905	Create Study	Neural mechanisms of language development in infancy.	Infancy : the official journal of the International Society on Infant Studies	Huberty, Scott; O'Reilly, Christian; Carter Leno, Virginia; Steiman, Mandy; Webb, Sara; Elsabbagh, Mayada; BASIS Team	January 1, 2023	Not Determined
36929131	Create Study	Evaluation of clinical assessments of social abilities for use in autism clinical trials by the autism biomarkers consortium for clinical trials.	Autism research : official journal of the International Society for Autism Research	Faja, Susan; Sabatos-DeVito, Maura; Sridhar, Aksheya; Kuhn, Jocelyn L; Nikolaeva, Julia I; Sugar, Catherine A; Webb, Sara Jane; Bernier, Raphael A; Sikich, Linmarie; Hellemann, Gerhard; Senturk, Damla; Naples, Adam J; Shic, Frederick; Levin, April R; Seow, Helen A; Dziura, James D; Jeste, Shafali S; Chawarska, Katarzyna; Nelson 3rd, Charles A; Dawson, Geraldine; McPartland, James C; Autism Biomarkers Consortium for Clinical Trials	May 1, 2023	Not Determined
36568688	Create Study	Electrophysiological Studies of Reception of Facial Communication in Autism Spectrum Disorder and Schizophrenia.	Review journal of autism and developmental disorders	Levy, Emily J; Foss-Feig, Jennifer; Isenstein, Emily L; Srihari, Vinod; Anticevic, Alan; Naples, Adam J; McPartland, James C	December 1, 2022	Not Determined
36350848	Create Study	Neural correlates of eye contact and social function in autism spectrum disorder.	PloS one	Hirsch, Joy; Zhang, Xian; Noah, J Adam; Dravida, Swethasri; Naples, Adam; Tiede, Mark; Wolf, Julie M; McPartland, James C	January 1, 2022	Not Determined
36309762	Create Study	Predictability modulates neural response to eye contact in ASD.	Molecular autism	Naples, Adam J; Foss-Feig, Jennifer H; Wolf, Julie M; Srihari, Vinod H; McPartland, James C	October 29, 2022	Not Determined
36086805	Create Study	Concomitant medication use in children with autism spectrum disorder: Data from the Autism Biomarkers Consortium for Clinical Trials.	Autism : the international journal of research and practice	Shurtz, Logan; Schwartz, Chloe; DiStefano, Charlotte; McPartland, James C; Levin, April R; Dawson, Geraldine; Kleinhans, Natalia M; Faja, Susan; Webb, Sara J; Shic, Frederick; Naples, Adam J; Seow, Helen; Bernier, Raphael A; Chawarska, Katarzyna; Sugar, Catherine A; Dziura, James; Senturk, Damla; Santhosh, Megha; Jeste, Shafali S	May 1, 2023	Not Determined

helpcenter.collection.publications-tab

Collection - Publications

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

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

Frequently Asked Questions

How can I determine if a publication is relevant?

Publications are considered relevant to a collection when the data shared is directly related to the project or collection.
Where does the NDA get the publications?

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

Glossary

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.

Contact NDA Help Desk

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 Expected	Targeted Enrollment	Initial Submission	Subjects Submitted	Initial Share	Subjects Shared	Status
Research Subject and Pedigree	300	10/15/2016	438	11/15/2016	438	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 Expected	Targeted Enrollment	Initial Submission	Subjects Submitted	Initial Share	Subjects Shared	Status
ABC Community	300	07/15/2016	438	11/15/2016	438	Approved
ADOS	300	07/15/2016	438	11/15/2016	438	Approved
ADI-R	300	04/15/2016	298	11/15/2016	298	Approved
PDD Behavior Inventory (PDDBI)	300	04/15/2016	438	11/15/2016	438	Approved
Medical History	24	05/09/2016	305	11/15/2016	305	Approved
Eye Tracking	300	07/15/2016	440	11/15/2016	440	Approved
Social Responsiveness Scale (SRS)	300	04/15/2016	438	11/15/2016	438	Approved
Child and Adolescent Symptom Inventory (CASI)	300	04/15/2016	430	11/15/2016	430	Approved
Demographics	300	07/15/2016	438	11/15/2016	438	Approved
Clinical Global Impression (CGI)	24	05/09/2016	298	11/15/2016	298	Approved
Behavior Assessment System for Children (BASC)	49	04/15/2016	49	11/15/2016	49	Approved
Social Skills Improvement System (SSIS)	49	07/15/2016	50	11/15/2016	50	Approved
Quality of Life Assessment	24	05/09/2016	50	11/15/2016	50	Approved
DAS-II: Differential Ability Scales	300	04/15/2016	438	11/15/2016	438	Approved
Intervention History	300	10/15/2016	436	11/15/2016	436	Approved
NEPSY	300	07/15/2016	434	11/15/2016	434	Approved
Early Childhood Inventory	9	07/15/2016	9	11/15/2016	9	Approved
Adverse Event (AE) and Serious Adverse Event (SAE)	4	07/15/2016	4	11/15/2016	4	Approved
Noldus Tools	300	10/15/2016	351	11/15/2016	351	Approved
Vineland (Parent and Caregiver)	300	04/15/2016	436	11/15/2016	436	Approved
Autism Impact Measure (AIM)	300	10/15/2016	437	11/15/2016	437	Approved
EEG	300	07/15/2016	436	11/15/2016	436	Approved
Social Opportunities Questionnaire	49	04/15/2016	50	11/15/2016	50	Approved
Kaufman Assessment Battery for Children, Second Edition	9	05/09/2016	9	11/15/2016	9	Approved
LENA Tools	47	04/15/2016	47	11/15/2016	47	Approved
ABC-CT Derived Results - Eye Tracking	300	06/01/2020	400	06/01/2021	400	Approved
ABC-CT Derived Results - ERP Visual Evoked Potential	300	06/01/2020	399	06/01/2021	399	Approved
ABC-CT Derived Results - ERP Biomotion	300	06/01/2020	399	06/01/2021	399	Approved
ABC-CT Derived Results - EEG	300	06/01/2020	399	06/01/2021	399	Approved
Study Completion	300	06/01/2020	472	06/01/2021	472	Approved
ABC-CT Derived Results - Video Tracking	300	06/01/2020	315	06/01/2021	315	Approved
ABC-CT Derived Results - ERP Faces	300	06/01/2020	399	06/01/2021	399	Approved

Structure not yet defined

No Status history for this Data Expected has been recorded yet

helpcenter.collection.data-expected-tab

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.

Contact NDA Help Desk

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 NameFilter by Study Name	DOIFilter by DOI	AbstractFilter by Abstract	Collection/Study SubjectsFilter by Collection/Study Subjects	Data UsageFilter by Data Usage	StateFilter by State
Examining the validity of the use of ratio IQs in psychological assessments	10.15154/1522624	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.	438/17423	Secondary Analysis	Shared
Examining Diagnostic Trends and Gender Differences in the ADOS-II	10.15154/jxd2-vw05	Approximately 3–4 boys for every girl meet the clinical criteria for autism in studies of community diagnostic patterns and studies of autism using samples of convenience. However, girls with autism have been hypothesized to be underdiagnosed, possibly because they may present with differing symptom profiles as compared to boys. This secondary data analysis used the National Database of Autism Research (NDAR) to examine how gender and symptom profiles are associated with one another in a gold standard assessment of autism symptoms, the Autism Diagnostic Observation Schedule II (ADOS-II; Lord, C., Luyster, R., Guthrie, W., & Pickles A. (2012a). Patterns of developmental trajectories in toddlers with autism spectrum disorder. Journal of Consulting and Clinical Psychology, 80(3):477–489. https://doi.org/10.1037/a0027214. Epub 2012 Apr 16. PMID: 22506796, PMCID: PMC3365612). ADOS-II scores from 6183 children ages 6–14 years from 78 different studies in the NDAR indicated that gender was a significant predictor of total algorithm, restrictive and repetitive behavioral, and social communicative difficulties composite severity scores. These findings suggest that gender differences in ADOS scores are common in many samples and may reflect on current diagnostic practices.	424/5615	Secondary Analysis	Shared
Gender Differences: Confirmatory Factor Analysis of the ADOS-II	10.15154/4sxe-qh09	Purpose Recent research has suggested that autism may present differently in girls compared to boys, encouraging the exploration of a sex-differential diagnostic criteria. Gender differences in diagnostic assessments have been shown on the ADOS-II, such that, on average, females score significantly lower than males on all scales and are less likely to show atypicality on most items related to social communicative difficulties. Yet, gender differences in the latent structure of instruments like the ADOS-II have not been examined systematically. Methods As such, this secondary data analysis examined 4,100 youth diagnosed with autism (Mage = 9.9, 813 female & 3287 male) examined item response trends by gender on the ADOS-II Module 3. Results Multi-Group Confirmatory Factor Analysis results show that the factor loadings of four ADOS-II items differ across the genders. One SCD item and one RRB item are strongly related to the latent factor in the female group, while two RRB items have larger factor loadings in the male group. Conclusion The assumption of an identical latent factor structure for the ADOS-II Module 3 for males and females might not be justifiable. Possible diagnostic implications are discussed.	424/5615	Secondary Analysis	Shared
Prognostic early snapshot stratification of autism based on adaptive functioning	10.15154/0z1c-1d37	A 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.	265/3517	Secondary Analysis	Shared
Investigating autism etiology and heterogeneity by decision tree algorithm	10.15154/1518655	Autism 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.	66/3382	Secondary Analysis	Shared
The striatal matrix compartment is expanded in autism spectrum disorder.	10.15154/khn8-jf08	Background: Autism spectrum disorder (ASD) is the second-most common neurodevelopmental disorder in childhood. This complex developmental disorder that manifests with restricted interests, repetitive behaviors, and difficulties in communication and social awareness. The inherited and acquired causes of ASD impact many and diverse brain regions, challenging efforts to identify a shared neuroanatomical substrate for this range of symptoms. The striatum and its connections are among the most implicated sites of abnormal structure and/or function in ASD. Striatal projection neurons develop in segregated tissue compartments, the matrix and striosome, that are histochemically, pharmacologically, and functionally distinct. Immunohistochemical assessment of ASD and animal models of autism described abnormal matrix:striosome volume ratios, with an possible shift from striosome to matrix volume. Shifting the matrix:striosome ratio could result from expansion in matrix, reduction in striosome, spatial redistribution of the compartments, or a combination of these changes. Each type of ratio-shifting abnormality may predispose to ASD but yield different combinations of ASD features. Methods: We developed a cohort of 426 children and adults (213 matched ASD-control pairs) and performed connectivity-based parcellation (diffusion tractography) of the striatum. This identified voxels with matrix-like and striosome-like patterns of structural connectivity. Results: Matrix-like volume was increased in ASD, with no evident change in the volume or organization of the striosome-like compartment. The inter-compartment volume difference (matrix minus striosome) within each individual was 31% larger in ASD. Matrix-like volume was increased in both caudate and putamen, and in somatotopic zones throughout the rostral-caudal extent of the striatum. Subjects with moderate elevations in ADOS (Autism Diagnostic Observation Schedule) scores had increased matrix-like volume, but those with highly elevated ADOS scores had 3.7-fold larger increases in matrix-like volume. Conclusions: Matrix and striosome are embedded in distinct structural and functional networks, suggesting that compartment-selective injury or maldevelopment may mediate specific and distinct clinical features. Previously, assessing the striatal compartments in humans required post mortem tissue. Striatal parcellation provides a means to assess neuropsychiatric diseases for compartment-specific abnormalities in vivo. While this ASD cohort had increased matrix-like volume, other mechanisms that shift the matrix:striosome ratio may also increase the chance of developing the diverse social, sensory, and motor phenotypes of ASD.	3/2166	Secondary Analysis	Shared
Imbalanced social-communicative and restricted repetitive behavior subtypes in autism spectrum disorder exhibit different neural circuitry	10.15154/1524418	Social-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.	235/1708	Secondary Analysis	Shared
Sensorimotor variability distinguishes early features of cognition in toddlers with autism	10.15154/qfej-cf04	The potential role of early sensorimotor features to atypical human cognition in autistic children has received surprisingly little attention given that appropriate movements are a crucial element that connects us to other people. Crucially it is sensorimotor function prior to the development of autism which would be of most interest in terms of establishing causal links. We examined movements acquired during natural sleep in over 200 toddlers recruited for neuroimaging studies, months before they were diagnosed with Autism Spectrum Disorder (ASD) or were ascertained as typically developing. We show there are significant correlations between objective, quantitative features of the distribution of head speed in the scanner (coefficient of variance and skewness) and subsequent cognitive abilities (intelligence quotient, IQ) in these children. Relative to higher-IQ ASD toddlers, those with lower-IQ had significantly altered sensorimotor features. Remarkably, we found that higher cognitive abilities in autistic toddlers confer resilience to atypical movement, as sensorimotor features in higher-IQ ASD toddlers were indistinguishable from those of typically developing healthy control toddlers. In a second set of analyses, we examined general motor skills assessed using observational instruments during wakefulness in an independent sample of over 1,000 infants and toddlers who received ASD diagnoses by 3-4 years. We detected significantly lower motor scores for lower-IQ vs. higher IQ autistic children, at 6, 12, 18, 24, and 30 months. We suggest that the altered movement patterns may affect key autistic behaviors in those with lower intelligence by affecting sensorimotor learning mechanisms. Atypical sensorimotor functioning is a novel, key feature in lower-IQ early childhood autism.	7/1078	Secondary Analysis	Shared
Investigating possible biomarkers of autism in resting EEG	10.15154/1528473	There 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.	297/771	Secondary Analysis	Shared
Combining Gaze and Demographic Feature Descriptors for Autism Classification	10.15154/1376893	People with autism suffer from social challenges and communication difficulties, which may prevent them from leading a fruitful and enjoyable life. It is imperative to diagnose and start treatments for autism as early as possible and, in order to do so, accurate methods of identifying the disorder are vital. We propose a novel method for classifying autism through the use of eye gaze and demographic feature descriptors that include a subject’s age and gender. We construct feature descriptors that incorporate the subject’s age and gender, as well as features based on eye gaze data. Using eye gaze information from the National Database for Autism Research, we tested our constructed feature descriptors on three different classifiers; random regression forests, C4.5 decision tree, and PART. Our proposed method for classifying autism resulted in a top classification rate of 96.2%.	51/756	Secondary Analysis	Shared
Development of EEG dynamics throughout the lifespan	10.15154/1528600	Combining data from across several datasets available on the NIMH data repository, multiple metrics of EEG dynamics were examined in a large cross sectional sample of healthy participants from across the lifespan. The goal was to examine changes in brain dynamics that occur across development.	108/551	Secondary Analysis	Shared
Symptom dimensions of resting-state electroencephalographic functional connectivity in autism	10.15154/qp5k-v842	Autism spectrum disorder (ASD) is a common neurodevelopmental disorder characterized by social and communication deficits (SCDs), restricted and repetitive behaviors (RRBs) and fixated interests. Despite its prevalence, development of effective therapy for ASD is hindered by its symptomatic and neurophysiological heterogeneities. To comprehensively explore these heterogeneities, we developed a new analytical framework combining contrastive learning and sparse canonical correlation analysis that identifies symptom-linked resting-state electroencephalographic connectivity dimensions within 392 ASD samples. We present two dimensions with multivariate connectivity basis exhibiting significant correlations with SCD and RRB, confirm their robustness through cross-validation and demonstrate their conceptual generalizability using an independent dataset (n = 222). Specifically, the right inferior parietal lobe is the core region for RRB, while connectivity between the left angular gyrus and the right middle temporal gyrus show key contribution to SCD. These findings provide a promising avenue to parse ASD heterogeneity with high clinical translatability, paving the way for ASD treatment development and precision medicine.	510/510	Secondary Analysis	Shared
Face-processing performance is an independent predictor of social affect as measured by the Autism Diagnostic Observation Schedule across large-scale datasets	10.15154/1520631	Face-processing deficits, while not required for the diagnosis of Autism Spectrum Disorder (ASD), have been associated with impaired social skills—a core feature of ASD; however, the strength and prevalence of this relationship remains unclear. Across 445 participants from the NIMH Data Archive, we examined the relationship between Benton Face Recognition Test (BFRT) performance and Autism Diagnostic Observation Schedule-Social Affect (ADOS-SA) scores. Lower BFRT scores (worse face-processing performance) were associated with higher ADOS-SA scores (higher ASD severity)–a relationship that held after controlling for other factors associated with face processing, i.e., age, sex, and IQ. These findings underscore the utility of face discrimination, not just recognition of facial emotion, as a key covariate for the severity of symptoms that characterize ASD.	2/445	Secondary Analysis	Shared
comparing EEG metrics during eyes closed versus eyes open rest in autism	10.15154/1528590	Understanding the complex relationship between brain dynamics and mental disorders has proved difficult. Sample sizes have often been small, and brain dynamics have often been evaluated in only one state. Here, data obtained from the NIMH data archive were used to create a sample of 395 individuals with both eyes open and eyes closed resting state EEG data. All data were submitted to a standard pipeline to extract power spectra, peak alpha frequency, the slope of the 1/f curve, multi scale sample entropy, phase amplitude coupling, and intersite phase clustering. These data along with the survey data collected at the time of data collection form a valuable resource for interogating the relationship between brain state changes and autism diagnosis.	3/336	Secondary Analysis	Shared
Word Learning and Word Features	10.15154/1526346	Vocabulary 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.	1/280	Primary Analysis	Shared
Examining the Shape Bias in Young Autistic Children: A Vocabulary Composition Analysis	10.15154/wjfb-vy71	Shape 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.	1/249	Secondary Analysis	Shared
Modeling Vocabulary Growth in Autistic and Non-Autistic Children	10.15154/q64a-9k34	We assessed the goodness of fit of three models of vocabulary growth, with varying sensitivity to the structure of the environment and the learner’s internal state, to estimated vocabulary growth trajectories in autistic and non-autistic children. We first computed word-level acquisition norms that indicate the vocabulary size at which individual words tend to be learned by each group. We then evaluated how well network growth models based on natural language co-occurrence structure and word associations account for variance in the autistic and non-autistic acquisition norms. In addition to replicating key observations from prior work and observing that the growth models explained similar amounts of variance in each group, we found that autistic vocabulary growth also exhibits growth consistent with “the lure of the associates” model. Thus, both groups leverage semantic structure in the learning environment for vocabulary development, but autistic vocabulary growth is also strongly influenced by existing vocabulary knowledge.	1/247	Secondary Analysis	Shared
Semantic modeling 2023	10.15154/1528994	Although 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.	1/208	Secondary Analysis	Shared
Semantic Network Modeling	10.15154/1522607	Although 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.	1/200	Secondary Analysis	Shared
Semantic Network Modeling in Young Autistic Children	10.15154/z865-cy61	Background: 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.	1/194	Secondary Analysis	Shared
Cortico-Basal Ganglia Brain Structure and Links to Restricted, Repetitive Behavior in Autism Spectrum Disorder	10.15154/1528130	Restricted repetitive behavior (RRB) is one of two criteria domains required for the diagnosis of autism spectrum disorder (ASD). Neuroimaging is widely used to study brain alterations associated with ASD and the domain of social and communication deficits, but there has been less work regarding alterations associated with RRB. In this study we utilized neuroimaging data available from the National Database for Autism Research to assess volume in the basal ganglia and cerebellum, as well as microstructure in basal ganglia and cerebellar white matter tracts in ASD. We also investigated whether these measures differed between males and females with ASD, and how these factors correlated with clinical measures of RRB from the same individuals. We found that individuals with ASD had significant differences in free-water corrected fractional anisotropy (FAT) and free-water in cortico-basal ganglia white matter tracts, but that these measures did not differ between males versus females with ASD. Moreover, both FAT and free-water in these tracts were significantly correlated with measures of RRB. Despite no differences in volumetric measures in basal ganglia and cerebellum, these findings suggest the links between RRB and brain structure are within specific cortico-basal ganglia white matter tracts.	2/192	Secondary Analysis	Shared

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