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1 Numbers reported are subjects by age
New Trial
New Project

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

Please select an experiment type below

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

The table search feature is case insensitive and targets the experiment id, experiment name and experiment type columns. The experiment id is searched only when the search term entered is a number, and filtered using a startsWith comparison. When the search term is not numeric the experiment name is used to filter the results.
SelectExperiment IdExperiment NameExperiment Type
Created On
475MB1-10 (CHOP)Omics06/07/2016
490Illumina Infinium PsychArray BeadChip AssayOmics07/07/2016
501PharmacoBOLD Resting StatefMRI07/27/2016
509ABC-CT Resting v2EEG08/18/2016
13Comparison of FI expression in Autistic and Neurotypical Homo SapiensOmics12/28/2010
18AGRE/Broad Affymetrix 5.0 Genotype ExperimentOmics01/06/2011
22Stitching PCR SequencingOmics02/14/2011
29Microarray family 03 (father, mother, sibling)Omics03/24/2011
37Standard paired-end sequencing of BCRsOmics04/19/2011
38Illumina Mate-Pair BCR sequencingOmics04/19/2011
39Custom Jumping LibrariesOmics04/19/2011
40Custom CapBPOmics04/19/2011
43Autism brain sample genotyping, IlluminaOmics05/16/2011
47ARRA Autism Sequencing Collaboration at Baylor. SOLiD 4 SystemOmics08/01/2011
53AGRE Omni1-quadOmics10/11/2011
59AGP genotypingOmics04/03/2012
60Ultradeep 454 sequencing of synaptic genes from postmortem cerebella of individuals with ASD and neurotypical controlsOmics06/23/2012
63Microemulsion PCR and Targeted Resequencing for Variant Detection in ASDOmics07/20/2012
76Whole Genome Sequencing in Autism FamiliesOmics01/03/2013
90Genotyped IAN SamplesOmics07/09/2013
91NJLAGS Axiom Genotyping ArrayOmics07/16/2013
93AGP genotyping (CNV)Omics09/06/2013
106Longitudinal Sleep Study. H20 200. Channel set 2EEG11/07/2013
107Longitudinal Sleep Study. H20 200. Channel set 3EEG11/07/2013
108Longitudinal Sleep Study. AURA 200EEG11/07/2013
105Longitudinal Sleep Study. H20 200. Channel set 1EEG11/07/2013
109Longitudinal Sleep Study. AURA 400EEG11/07/2013
116Gene Expression Analysis WG-6Omics01/07/2014
151Candidate Gene Identification in familial AutismOmics06/09/2014
152NJLAGS Whole Genome SequencingOmics07/01/2014
154Math Autism Study - Vinod MenonfMRI07/15/2014
160syllable contrastEEG07/29/2014
167School-age naturalistic stimuliEye Tracking09/19/2014
44AGRE/Broad Affymetrix 5.0 Genotype ExperimentOmics06/27/2011
45Exome Sequencing of 20 Sporadic Cases of Autism Spectrum DisorderOmics07/15/2011
78MET GenotypesOmics03/18/2013
61Ultradeep Illumina sequencing of A-to-I edited sites in synaptic genes from postmortem cerebella of individuals with ASD and neurotypical controlsOmics06/23/2012
65SNP genotypes Illumina 370kOmics07/30/2012
66SSC samples exome sequencingOmics08/03/2012
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.

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

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Collection Summary Collection Charts
Collection Title Collection Investigators Collection Description
Biomarkers of Developmental Trajectories and Treatment in ASD
Susan Bookheimer 
This is a renewal for the UCLA Autism Center of Excellence. The primary focus of the UCLA ACE renewal is to understand the relationship between aberrant brain development and core deficits in autism by identifying mechanisms relating genes to brain structure/function and brain to behavior, and to develop effective interventions based on basic experimental and clinical research findings that will change outcomes in autism. In five interdependent projects and cores, our center builds on our expertise in autism genetics, multimodal brain imaging, early detection and analysis of core autism features, and experience in implementing randomized control trials of novel interventions that target these core symptoms. In this renewal application, projects focus on defining longitudinal trajectories of brain and behavioral development in infants with multiple risks for autism (Proj I); infants and toddlers with early signs of autism (Proj II), nonverbal school aged children with autism (Proj III), and higher functioning children/ adolescents (Proj IV),using a shared set of imaging, neurophysiological and neurobehavioral biomarkers, as well as genetic risk and expression analysis (Proj V) in longitudinal studies. This center is focused on understanding both early and later trajectories of emerging and developing functional connectivity and behavioral change in relation to variation in autism phenotypes, defining how genetic risks mediate both imaging and behavioral phenotypes, and altering trajectories through two separate treatments focused on core deficits in ASD, one targeted developing joint attention and social orientation in infants, and one focused on improving language nonverbal children with augmentative pharmacological intervention. Four cores support the scientific goals: an Administrative Core, facilitating scientific progress and providing data management/statistics support; a Diagnositc and Phenotyping Core; a Neuroimaging/Neurophysiology core, and a Research Education and Outreach core. The UCLA ACE benefits from our years of working together as a team. We present a highly integrated center with multiple collaborations across levels of analysis to further a strongly translational research strategy aimed at changing outcomes in children with autism spectrum disorders.
NIMH Data Archive
Autism Centers of Excellence (ACE), NIMH Repository & Genomics Resource (NRGR)
Funding Completed
Close Out
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NIH - Extramural None

P50HD055784-06 Biomarkers of Developmental Trajectories and Treatment in ASD 09/04/2012 07/31/2022 748 Not Reported UNIVERSITY OF CALIFORNIA LOS ANGELES $12,075,743.00


NDA Help Center

Collection - General Tab

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

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

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

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

Blinded Clinical Trial Status:

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

Funding Source

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

Supporting Documentation

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

Grant Information

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

Clinical Trials

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

Frequently Asked Questions

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


  • Administrator Privilege
    A privilege provided to a user associated with an NDA Collection or NDA Study whereby that user can perform a full range of actions including providing privileges to other users.
  • Collection Owner
    Generally, the Collection Owner is the contact PI listed on a grant. Only one NDA user is listed as the Collection owner. Most automated emails are primarily sent to the Collection Owner.
  • Collection Phase
    The Collection Phase provides information on data submission as opposed to grant/project completion so while the Collection phase and grant/project phase may be closely related they are often different. Collection users with Administrative Privileges are encouraged to edit the Collection Phase. The Program Officer as listed in eRA (for NIH funded grants) may also edit this field. Changes must be saved by clicking the Save button at the bottom of the page. This field is sortable alphabetically in ascending or descending order. Collection Phase options include:
    • Pre-Enrollment: A grant/project has started, but has not yet enrolled subjects.
    • Enrolling: A grant/project has begun enrolling subjects. Data submission is likely ongoing at this point.
    • Data Analysis: A grant/project has completed enrolling subjects and has completed all data submissions.
    • Funding Completed: A grant/project has reached the project end date.
  • Collection Title
    An editable field with the title of the Collection, which is often the title of the grant associated with the Collection.
  • Grant
    Provides the grant number(s) for the grant(s) associated with the Collection. The field is a hyperlink so clicking on the Grant number will direct the user to the grant information in the NIH Research Portfolio Online Reporting Tools (RePORT) page.
  • Supporting Documentation
    Various documents and materials to enable efficient use of the data by investigators unfamiliar with the project and may include the research protocol, questionnaires, and study manuals.
  • NIH Research Initiative
    NDA Collections may be organized by scientific similarity into NIH Research Initiatives, to facilitate query tool user experience. NIH Research Initiatives map to one or multiple Funding Opportunity Announcements.
  • Permission Group
    Access to shared record-level data in NDA is provisioned at the level of a Permission Group. NDA Permission Groups consist of one or multiple NDA Collections that contain data with the same subject consents.
  • Planned Enrollment
    Number of human subject participants to be enrolled in an NIH-funded clinical research study. The data is provided in competing applications and annual progress reports.
  • Actual Enrollment
    Number of human subjects enrolled in an NIH-funded clinical research study. The data is provided in annual progress reports.
  • NDA Collection
    A virtual container and organization structure for data and associated documentation from one grant or one large project/consortium. It contains tools for tracking data submission and allows investigators to define a wide array of other elements that provide context for the data, including all general information regarding the data and source project, experimental parameters used to collect any event-based data contained in the Collection, methods, and other supporting documentation. They also allow investigators to link underlying data to an NDA Study, defining populations and subpopulations specific to research aims.
  • Data Use Limitations
    Data Use Limitations (DULs) describe the appropriate secondary use of a dataset and are based on the original informed consent of a research participant. NDA only accepts consent-based data use limitations defined by the NIH Office of Science Policy.
  • Total Subjects Shared
    The total number of unique subjects for whom data have been shared and are available for users with permission to access data.
IDNameCreated DateStatusType
130Jeste Lab UCLA ACEii EEG02/24/2014ApprovedEEG
131Jeste Lab UCLA ACEii: Charlie Brown and Sesame Street - Project 102/27/2014ApprovedEye Tracking
132Jeste Lab UCLA ACEii: Animacy - Project 102/27/2014ApprovedEye Tracking
133Jeste Lab UCLA ACEii: Mom Stranger - Project 202/27/2014ApprovedEye Tracking
134Jeste Lab UCLA ACEii: Face Emotion - Project 302/27/2014ApprovedEye Tracking
320Geschwind 2015-9017 Omni2.5-806/10/2015ApprovedOmics

NDA Help Center

Collection - Experiments

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

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

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

Frequently Asked Questions

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

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

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


  • Experiment Status
    An Experiment must be Approved before data using the associated Experiment_ID may be uploaded.
  • Experiment ID
    The ID number automatically generated by NDA which must be included in the appropriate file when uploading data to link the Experiment Definition to the subject record.
ACE Family Medical History Clinical Assessments 208
ACE Subject Medical History Clinical Assessments 214
ACE Subject Physical Exam Clinical Assessments 74
Aberrant Behavior Checklist (ABC) - Community Clinical Assessments 40
Abnormal Involuntary Movement Scale Clinical Assessments 41
Autism Diagnostic Interview, Revised (ADI-R) Clinical Assessments 89
Autism Diagnostic Observation Schedule (ADOS)- Module 3 Clinical Assessments 22
Autism Diagnostic Observation Schedule, 2nd Edition (ADOS-2) - Module 1 Clinical Assessments 76
Autism Diagnostic Observation Schedule, 2nd Edition (ADOS-2) - Module 2 Clinical Assessments 74
Autism Diagnostic Observation Schedule, 2nd Edition (ADOS-2) - Module 3 Clinical Assessments 10
Autism Diagnostic Observation Schedule, 2nd Edition (ADOS-2) - Toddler Module Clinical Assessments 162
Autism Observation Scale for Infants Clinical Assessments 86
BRIEF-Parent Clinical Assessments 77
Child Behavior Checklist (CBCL) 6-18 Clinical Assessments 76
Child Symptom Inventory Parent/Teacher Checklist Clinical Assessments 77
Children's Yale-Brown Obsessive Compulsive Scale Clinical Assessments 21
Clinical Global Impression (CGI) Clinical Assessments 41
Clinical Lab Tests Clinical Assessments 30
Concomitant Medications Clinical Assessments 20
Demographics form UCLA Clinical Assessments 33
EEG Subject Files Imaging 195
Early Social Communication Scale (ESCS) Clinical Assessments 145
Eye Tracking Subject-Experiment Imaging 200
Genomics Genetic Test Genomics 104
Genomics Sample Genomics 169
Genomics Subject Genomics 169
Image Imaging 166
Infant Medical History Questionnaire Clinical Assessments 87
Interpersonal Competence Scale Clinical Assessments 78
Interpersonal Reactivity Index (IRI) Clinical Assessments 74
Leiter International Performance Scale-Revised (Leiter-R), Visualization and Reasoning Battery Clinical Assessments 41
M-CHAT Clinical Assessments 59
MacArthur-Bates CDI - Words and Gestures Form Clinical Assessments 88
MacArthur-Bates CDI - Words and Sentences Form Clinical Assessments 74
Mullen Scales of Early Learning Clinical Assessments 177
Parent Concerns Questionaire Clinical Assessments 94
Peabody Picture Vocabulary Test, Fourth Edition-Form A Clinical Assessments 72
Preschool Language Scales Fifth edition (PLS-5) Clinical Assessments 41
Repetitive Behavior Scale - Revised (RBS-R) Clinical Assessments 120
Research Subject Clinical Assessments 608
SRS-2. Adult, Preschool and School Age Clinical Assessments 340
Sensory Profile Short Clinical Assessments 76
Side Effects Clinical Assessments 41
Simpson-Angus Extrapyramidal Side Effects Scale Clinical Assessments 41
Social Communication Questionnaire (SCQ) - Current Form Clinical Assessments 117
Social Communication Questionnaire (SCQ) - Lifetime Clinical Assessments 45
Social Responsiveness Scale (SRS) Clinical Assessments 78
Vineland-II - Parent and Caregiver Rating Form (2005) Clinical Assessments 86
Vineland-II - Survey Form (2005) Clinical Assessments 73
Vital Signs Clinical Assessments 41
WASI-2 Clinical Assessments 27
Wechsler Abbreviated Scale of Intelligence (WASI) Clinical Assessments 31
Wechsler Intelligence Scale for Children - IV [part 1] Clinical Assessments 16
Wechsler Intelligence Scale for Children - IV [part 2] Clinical Assessments 16

NDA Help Center

Collection - Shared Data

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

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

Frequently Asked Questions

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


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

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


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

PubMed IDStudyTitleJournalAuthorsDateStatus
38649483Create StudySalience network connectivity is altered in 6-week-old infants at heightened likelihood for developing autism.Communications biologyTsang, Tawny; Green, Shulamite A; Liu, Janelle; Lawrence, Katherine; Jeste, Shafali; Bookheimer, Susan Y; Dapretto, MirellaApril 22, 2024Not Determined
38009228Create StudySlower pace in early walking onset is related to communication, motor skills, and adaptive function in autistic toddlers.Autism research : official journal of the International Society for Autism ResearchWilson, Rujuta B; Burdekin, Emma D; Jackson, Nicholas J; Hughart, Lauren; Anderson, Jeff; Dusing, Stacey C; Gulsrud, Amanda; Kasari, ConnieJanuary 1, 2024Not Determined
37840458Create StudyShared and distinct biological mechanisms for anxiety and sensory over-responsivity in youth with autism versus anxiety disorders.Journal of neuroscience researchCummings, Kaitlin K; Jung, Jiwon; Zbozinek, Tomislav D; Wilhelm, Frank H; Dapretto, Mirella; Craske, Michelle G; Bookheimer, Susan Y; Green, Shulamite AJanuary 1, 2024Not Determined
37817282Create StudyAge-related changes in neural responses to sensory stimulation in autism: a cross-sectional study.Molecular autismCakar, Melis E; Cummings, Kaitlin K; Bookheimer, Susan Y; Dapretto, Mirella; Green, Shulamite AOctober 11, 2023Not Determined
37506195Create StudyThe contributions of rare inherited and polygenic risk to ASD in multiplex families.Proceedings of the National Academy of Sciences of the United States of AmericaCirnigliaro, Matilde; Chang, Timothy S; Arteaga, Stephanie A; Pérez-Cano, Laura; Ruzzo, Elizabeth K; Gordon, Aaron; Bicks, Lucy K; Jung, Jae-Yoon; Lowe, Jennifer K; Wall, Dennis P; Geschwind, Daniel HAugust 1, 2023Not Determined
37451263Create StudyImprovement of sensory deficits in fragile X mice by increasing cortical interneuron activity after the critical period.NeuronKourdougli, Nazim; Suresh, Anand; Liu, Benjamin; Juarez, Pablo; Lin, Ashley; Chung, David T; Graven Sams, Anette; Gandal, Michael J; Martínez-Cerdeño, Verónica; Buonomano, Dean V; Hall, Benjamin J; Mombereau, Cédric; Portera-Cailliau, CarlosSeptember 20, 2023Not Determined
37408377Create StudyHeterogeneity of autism symptoms in community-referred infants and toddlers at elevated or low familial likelihood of autism.Autism research : official journal of the International Society for Autism ResearchCohenour, Torrey L; Gulsrud, Amanda; Kasari, ConnieSeptember 1, 2023Not Determined
37005061Create StudyAssociations between thalamocortical functional connectivity and sensory over-responsivity in infants at high likelihood for ASD.Cerebral cortex (New York, N.Y. : 1991)Wagner, Lauren; Banchik, Megan; Okada, Nana J; McDonald, Nicole; Jeste, Shafali S; Bookheimer, Susan Y; Green, Shulamite A; Dapretto, MirellaJune 8, 2023Not Determined
36829214Create StudySex differences in friendships and loneliness in autistic and non-autistic children across development.Molecular autismLibster, Natalie; Knox, Azia; Engin, Selin; Geschwind, Daniel; Parish-Morris, Julia; Kasari, ConnieFebruary 24, 2023Not Determined
36566252Create StudyPersonal victimization experiences of autistic and non-autistic children.Molecular autismLibster, Natalie; Knox, Azia; Engin, Selin; Geschwind, Daniel; Parish-Morris, Julia; Kasari, ConnieDecember 24, 2022Not Determined
36183905Create StudyChallenges and opportunities for precision medicine in neurodevelopmental disorders.Advanced drug delivery reviewsChen, George T; Geschwind, Daniel HDecember 1, 2022Not Determined
35678946Create StudyPredictors of Attrition in a Randomized Trial of a Social Communication Intervention for Infant-Toddlers at Risk for Autism.Journal of autism and developmental disordersSterrett, Kyle; Magaña, Maira Tafolla; Gulsrud, Amanda; Paparella, Tanya; Kasari, ConnieAugust 1, 2023Not Determined
35437928Create StudySuper responders: Predicting language gains from JASPER among limited language children with autism spectrum disorder.Autism research : official journal of the International Society for Autism ResearchPanganiban, Jonathan; Kasari, ConnieAugust 1, 2022Not Determined
35176551Create StudyElectrophysiological signatures of brain aging in autism spectrum disorder.Cortex; a journal devoted to the study of the nervous system and behaviorDickinson, Abigail; Jeste, Shafali; Milne, ElizabethMarch 1, 2022Not Determined
34882790Create StudyAtypical cerebellar functional connectivity at 9 months of age predicts delayed socio-communicative profiles in infants at high and low risk for autism.Journal of child psychology and psychiatry, and allied disciplinesOkada, Nana J; Liu, Janelle; Tsang, Tawny; Nosco, Erin; McDonald, Nicole M; Cummings, Kaitlin K; Jung, Jiwon; Patterson, Genevieve; Bookheimer, Susan Y; Green, Shulamite A; Jeste, Shafali S; Dapretto, MirellaSeptember 1, 2022Not Determined
34807457Create StudyStudying the early emergence of autism: what is the goal of baby siblings research?Developmental medicine and child neurologyMcDonald, Nicole MMay 1, 2022Not Determined
34797038Create StudyParenting stress in caregiver-mediated interventions for toddlers with autism: An application of quantile regression mixed models.Autism research : official journal of the International Society for Autism ResearchSchlink, Andrew; Williams, Justin; Pizzano, Maria; Gulsrud, Amanda; Kasari, ConnieFebruary 1, 2022Not Determined
34158222Create StudyDrug development for Autism Spectrum Disorder (ASD): Progress, challenges, and future directions.European neuropsychopharmacology : the journal of the European College of NeuropsychopharmacologyMcCracken, James T; Anagnostou, Evdokia; Arango, Celso; Dawson, Geraldine; Farchione, Tiffany; Mantua, Valentina; McPartland, James; Murphy, Declan; Pandina, Gahan; Veenstra-VanderWeele, Jeremy; ISCTM/ECNP ASD Working GroupJuly 1, 2021Not Determined
34109620Create StudyEarly concerns in parents of infants at risk for autism.Developmental medicine and child neurologyTran, Amanda T; Del Rosario, Mithi; Nosco, Erin; Li, Yihao; Senturk, Damla; Mcdonald, Nicole M; Wilson, Rujuta B; Dapretto, Mirella; Jeste, Shafali SDecember 1, 2021Not Determined
34098753Create StudyWhat are the odds? Predicting the likelihood of a negative episode in a sample of toddlers with autism spectrum disorder.Autism : the international journal of research and practiceDimachkie Nunnally, Amanda; Sterrett, Kyle; Gulsrud, Amanda; Kasari, ConnieNovember 1, 2021Not Determined
34050248Create StudyTranscriptome analysis of MBD5-associated neurodevelopmental disorder (MAND) neural progenitor cells reveals dysregulation of autism-associated genes.Scientific reportsMullegama, Sureni V; Klein, Steven D; Williams, Stephen R; Innis, Jeffrey W; Probst, Frank J; Haldeman-Englert, Chad; Martinez-Agosto, Julian A; Yang, Ying; Tian, Yuchen; Elsea, Sarah H; Ezashi, ToshihikoMay 28, 2021Not Determined
33866373Create StudyAltered Thalamocortical Connectivity in 6-Week-Old Infants at High Familial Risk for Autism Spectrum Disorder.Cerebral cortex (New York, N.Y. : 1991)Nair, Aarti; Jalal, Rhideeta; Liu, Janelle; Tsang, Tawny; McDonald, Nicole M; Jackson, Lisa; Ponting, Carolyn; Jeste, Shafali S; Bookheimer, Susan Y; Dapretto, MirellaJuly 29, 2021Not Determined
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25973164Create StudyRecurrence rates provide evidence for sex-differential, familial genetic liability for autism spectrum disorders in multiplex families and twins.Molecular autismWerling DM, Geschwind DH2015Not Determined
25948600Create StudyCharacterizing caregiver responses to restricted and repetitive behaviors in toddlers with autism spectrum disorder.Autism : the international journal of research and practiceHarrop C, Gulsrud A, Shih W, Hovsepyan L, Kasari CApril 2016Not Determined
25891009Create StudyGene hunting in autism spectrum disorder: on the path to precision medicine.The Lancet. NeurologyGeschwind, Daniel H; State, Matthew WNovember 2015Not Determined
25891007Create StudyNeuroimaging in autism spectrum disorder: brain structure and function across the lifespan.The Lancet. NeurologyEcker, Christine; Bookheimer, Susan Y; Murphy, Declan G MNovember 2015Not Determined
25822242Create StudyRandomized comparative efficacy study of parent-mediated interventions for toddlers with autism.Journal of consulting and clinical psychologyKasari, Connie; Gulsrud, Amanda; Paparella, Tanya; Hellemann, Gerhard; Berry, KathleenJune 2015Not Determined
25727539Create StudySocial responsiveness, an autism endophenotype: genomewide significant linkage to two regions on chromosome 8.The American journal of psychiatryLowe JK, Werling DM, Constantino JN, Cantor RM, Geschwind DHMarch 1, 2015Not Determined
25715178Create StudyCognitive decline preceding the onset of psychosis in patients with 22q11.2 deletion syndrome.JAMA psychiatryVorstman, Jacob A S; Breetvelt, Elemi J; Duijff, Sasja N; Eliez, Stephan; Schneider, Maude; Jalbrzikowski, Maria; Armando, Marco; Vicari, Stefano; Shashi, Vandana; Hooper, Stephen R; Chow, Eva W C; Fung, Wai Lun Alan; Butcher, Nancy J; Young, Donald A; McDonald-McGinn, Donna M; Vogels, Annick; van Amelsvoort, Therese; Gothelf, Doron; Weinberger, Ronnie; Weizman, Abraham; Klaassen, Petra W J; Koops, Sanne; Kates, Wendy R; Antshel, Kevin M; Simon, Tony J; Ousley, Opal Y; Swillen, Ann; Gur, Raquel E; Bearden, Carrie E; Kahn, René S; Bassett, Anne S; International Consortium on Brain and Behavior in 22q11.2 Deletion SyndromeApril 2015Not Determined
25695138Create StudyDevelopmental disorders.Current opinion in neurologyJeste, Shafali S; Geschwind, Daniel HApril 2015Not Relevant
25609168Create StudyExogenous and evoked oxytocin restores social behavior in the Cntnap2 mouse model of autism.Science translational medicinePeñagarikano, Olga; Lázaro, María T; Lu, Xiao-Hong; Gordon, Aaron; Dong, Hongmei; Lam, Hoa A; Peles, Elior; Maidment, Nigel T; Murphy, Niall P; Yang, X William; Golshani, Peyman; Geschwind, Daniel HJanuary 21, 2015Not Determined
25463468Create StudyFunctional connectivity in BOLD and CBF data: similarity and reliability of resting brain networks.NeuroImageJann K, Gee DG, Kilroy E, Schwab S, Smith RX, Cannon TD, Wang DJFebruary 1, 2015Not Determined
25457930Create Study18-month predictors of later outcomes in younger siblings of children with autism spectrum disorder: a baby siblings research consortium study.Journal of the American Academy of Child and Adolescent PsychiatryChawarska, Katarzyna; Shic, Frederick; Macari, Suzanne; Campbell, Daniel J; Brian, Jessica; Landa, Rebecca; Hutman, Ted; Nelson, Charles A; Ozonoff, Sally; Tager-Flusberg, Helen; Young, Gregory S; Zwaigenbaum, Lonnie; Cohen, Ira L; Charman, Tony; Messinger, Daniel S; Klin, Ami; Johnson, Scott; Bryson, SusanDecember 2014Not Determined
25418720Create StudyMaternal inflammation contributes to brain overgrowth and autism-associated behaviors through altered redox signaling in stem and progenitor cells.Stem cell reportsLe Belle, Janel E; Sperry, Jantzen; Ngo, Amy; Ghochani, Yasmin; Laks, Dan R; López-Aranda, Manuel; Silva, Alcino J; Kornblum, Harley INovember 11, 2014Not Determined
25409314Create StudyProtein interaction networks reveal novel autism risk genes within GWAS statistical noise.PloS oneCorreia, Catarina; Oliveira, Guiomar; Vicente, Astrid M2014Not Determined
25315782Create StudyEarly pragmatic language difficulties in siblings of children with autism: implications for DSM-5 social communication disorder?Journal of child psychology and psychiatry, and allied disciplinesMiller, Meghan; Young, Gregory S; Hutman, Ted; Johnson, Scott; Schwichtenberg, A J; Ozonoff, SallyJuly 2015Not Determined
25245349Create StudyEarly head growth in infants at risk of autism: a baby siblings research consortium study.Journal of the American Academy of Child and Adolescent PsychiatryZwaigenbaum, Lonnie; Young, Gregory S; Stone, Wendy L; Dobkins, Karen; Ozonoff, Sally; Brian, Jessica; Bryson, Susan E; Carver, Leslie J; Hutman, Ted; Iverson, Jana M; Landa, Rebecca J; Messinger, DanielOctober 2014Not Determined
25078724Create StudyTime reproduction performance is associated with age and working memory in high-functioning youth with autism spectrum disorder.Autism research : official journal of the International Society for Autism ResearchBrenner, Laurie A; Shih, Vivian H; Colich, Natalie L; Sugar, Catherine A; Bearden, Carrie E; Dapretto, MirellaFebruary 2015Not Determined
25038753Create StudyMost genetic risk for autism resides with common variation.Nature geneticsGaugler, Trent; Klei, Lambertus; Sanders, Stephan J; Bodea, Corneliu A; Goldberg, Arthur P; Lee, Ann B; Mahajan, Milind; Manaa, Dina; Pawitan, Yudi; Reichert, Jennifer; Ripke, Stephan; Sandin, Sven; Sklar, Pamela; Svantesson, Oscar; Reichenberg, Abraham; Hultman, Christina M; Devlin, Bernie; Roeder, Kathryn; Buxbaum, Joseph DAugust 2014Not Determined
25011468Create StudyNeural signatures of autism spectrum disorders: insights into brain network dynamics.Neuropsychopharmacology : official publication of the American College of NeuropsychopharmacologyHernandez, Leanna M; Rudie, Jeffrey D; Green, Shulamite A; Bookheimer, Susan; Dapretto, MirellaJanuary 2015Not Determined
24577245Create StudyPsychiatric disorders from childhood to adulthood in 22q11.2 deletion syndrome: results from the International Consortium on Brain and Behavior in 22q11.2 Deletion Syndrome.The American journal of psychiatrySchneider, Maude; Debbané, Martin; Bassett, Anne S; Chow, Eva W C; Fung, Wai Lun Alan; van den Bree, Marianne; Owen, Michael; Murphy, Kieran C; Niarchou, Maria; Kates, Wendy R; Antshel, Kevin M; Fremont, Wanda; McDonald-McGinn, Donna M; Gur, Raquel E; Zackai, Elaine H; Vorstman, Jacob; Duijff, Sasja N; Klaassen, Petra W J; Swillen, Ann; Gothelf, Doron; Green, Tamar; Weizman, Abraham; Van Amelsvoort, Therese; Evers, Laurens; Boot, Erik; Shashi, Vandana; Hooper, Stephen R; Bearden, Carrie E; Jalbrzikowski, Maria; Armando, Marco; Vicari, Stefano; Murphy, Declan G; Ousley, Opal; Campbell, Linda E; Simon, Tony J; Eliez, Stephan; International Consortium on Brain and Behavior in 22q11.2 Deletion SyndromeJune 2014Not Determined
24353275Create StudyIs early joint attention associated with school-age pragmatic language?Autism : the international journal of research and practiceGillespie-Lynch, Kristen; Khalulyan, Allie; Del Rosario, Mithi; McCarthy, Brigid; Gomez, Lovella; Sigman, Marian; Hutman, TedFebruary 2015Not Determined
24183016Create StudyCortical evolution: judge the brain by its cover.NeuronGeschwind, Daniel H; Rakic, PaskoOctober 30, 2013Not Determined
24177988Create StudyNeural substrates of inhibitory control deficits in 22q11.2 deletion syndrome.Cerebral cortex (New York, N.Y. : 1991)Montojo, C A; Jalbrzikowski, M; Congdon, E; Domicoli, S; Chow, C; Dawson, C; Karlsgodt, K H; Bilder, R M; Bearden, C EApril 2015Not Relevant
24157390Create StudyOverreactive brain responses to sensory stimuli in youth with autism spectrum disorders.Journal of the American Academy of Child and Adolescent PsychiatryGreen SA, Rudie JD, Colich NL, Wood JJ, Shirinyan D, Hernandez L, Tottenham N, Dapretto M, Bookheimer SYNovember 2013Not Determined
24104518Create StudyThe association between parental interaction style and children's joint engagement in families with toddlers with autism.Autism : the international journal of research and practicePatterson SY, Elder L, Gulsrud A, Kasari CJuly 2014Not Determined
24099842Create StudyDynamic and static contributions of the cerebrovasculature to the resting-state BOLD signal.NeuroImageTak, Sungho; Wang, Danny J J; Polimeni, Jonathan R; Yan, Lirong; Chen, J JeanJanuary 1, 2014Not Determined
24078018Create StudyUnderconnectivity of the superior temporal sulcus predicts emotion recognition deficits in autism.Social cognitive and affective neuroscienceAlaerts K, Woolley DG, Steyaert J, Di Martino A, Swinnen SP, Wenderoth NOctober 2014Not Determined
23992925Create StudyThe 22q11.2 deletion syndrome as a window into complex neuropsychiatric disorders over the lifespan.Biological psychiatryJonas, Rachel K; Montojo, Caroline A; Bearden, Carrie EMarch 1, 2014Not Determined
23912681Create StudyDefault mode network connectivity and reciprocal social behavior in 22q11.2 deletion syndrome.Social cognitive and affective neuroscienceSchreiner MJ, Karlsgodt KH, Uddin LQ, Chow C, Congdon E, Jalbrzikowski M, Bearden CESeptember 2014Not Determined
23820765Create StudyParent-reported temperament trajectories among infant siblings of children with autism.Journal of autism and developmental disordersDel Rosario, Mithi; Gillespie-Lynch, Kristen; Johnson, Scott; Sigman, Marian; Hutman, TedFebruary 2014Not Determined
23774715Create StudyThe autism brain imaging data exchange: towards a large-scale evaluation of the intrinsic brain architecture in autism.Molecular psychiatryDi Martino, A; Yan, C-G; Li, Q; Denio, E; Castellanos, F X; Alaerts, K; Anderson, J S; Assaf, M; Bookheimer, S Y; Dapretto, M; Deen, B; Delmonte, S; Dinstein, I; Ertl-Wagner, B; Fair, D A; Gallagher, L; Kennedy, D P; Keown, C L; Keysers, C; Lainhart, J E; Lord, C; Luna, B; Menon, V; Minshew, N J; Monk, C S; Mueller, S; Müller, R-A; Nebel, M B; Nigg, J T; O'Hearn, K; Pelphrey, K A; Peltier, S J; Rudie, J D; Sunaert, S; Thioux, M; Tyszka, J M; Uddin, L Q; Verhoeven, J S; Wenderoth, N; Wiggins, J L; Mostofsky, S H; Milham, M PJune 2014Not Determined
23619947Create StudyAtypical gaze following in autism: a comparison of three potential mechanisms.Journal of autism and developmental disordersGillespie-Lynch, K; Elias, R; Escudero, P; Hutman, T; Johnson, S PDecember 2013Not Determined
23452686Create StudyBeyond autism: a baby siblings research consortium study of high-risk children at three years of age.Journal of the American Academy of Child and Adolescent PsychiatryMessinger, Daniel; Young, Gregory S; Ozonoff, Sally; Dobkins, Karen; Carter, Alice; Zwaigenbaum, Lonnie; Landa, Rebecca J; Charman, Tony; Stone, Wendy L; Constantino, John N; Hutman, Ted; Carver, Leslie J; Bryson, Susan; Iverson, Jana M; Strauss, Mark S; Rogers, Sally J; Sigman, MarianMarch 2013Not Determined
23352155Create StudyRare inherited variation in autism: beginning to see the forest and a few trees.NeuronStein JL, Parikshak NN, Geschwind DHJanuary 23, 2013Not Determined
23298182Create StudyDecreased reelin expression and organophosphate pesticide exposure alters mouse behaviour and brain morphology.ASN neuroMullen, Brian R; Khialeeva, Elvira; Hoffman, Daniel B; Ghiani, Cristina A; Carpenter, Ellen M2013Not Determined
23098994Create StudyConverging levels of analysis on a genomic hotspot for psychosis: insights from 22q11.2 deletion syndrome.NeuropharmacologySchreiner, Matthew J; Lazaro, Maria T; Jalbrzikowski, Maria; Bearden, Carrie EMay 1, 2013Not Determined
22965298Create StudyPreschool based JASPER intervention in minimally verbal children with autism: pilot RCT.Journal of autism and developmental disordersGoods KS, Ishijima E, Chang YC, Kasari CMay 2013Not Determined
22962003Create StudyDeficits in mental state attributions in individuals with 22q11.2 deletion syndrome (velo-cardio-facial syndrome).Autism research : official journal of the International Society for Autism ResearchHo JS, Radoeva PD, Jalbrzikowski M, Chow C, Hopkins J, Tran WC, Mehta A, Enrique N, Gilbert C, Antshel KM, Fremont W, Kates WR, Bearden CEDecember 2012Not Determined
22958829Create StudyAutism-associated promoter variant in MET impacts functional and structural brain networks.NeuronRudie, Jeffrey D; Hernandez, Leanna M; Brown, Jesse A; Beck-Pancer, Devora; Colich, Natalie L; Gorrindo, Philip; Thompson, Paul M; Geschwind, Daniel H; Bookheimer, Susan Y; Levitt, Pat; Dapretto, MirellaSeptember 6, 2012Not Determined
22843504Create StudyIndividual common variants exert weak effects on the risk for autism spectrum disorders.Human molecular geneticsAnney, Richard; Klei, Lambertus; Pinto, Dalila; Almeida, Joana; Bacchelli, Elena; Baird, Gillian; Bolshakova, Nadia; Bölte, Sven; Bolton, Patrick F; Bourgeron, Thomas; Brennan, Sean; Brian, Jessica; Casey, Jillian; Conroy, Judith; Correia, Catarina; Corsello, Christina; Crawford, Emily L; de Jonge, Maretha; Delorme, Richard; Duketis, Eftichia; Duque, Frederico; Estes, Annette; Farrar, Penny; Fernandez, Bridget A; Folstein, Susan E; Fombonne, Eric; Gilbert, John; Gillberg, Christopher; Glessner, Joseph T; Green, Andrew; Green, Jonathan; Guter, Stephen J; Heron, Elizabeth A; Holt, Richard; Howe, Jennifer L; Hughes, Gillian; Hus, Vanessa; Igliozzi, Roberta; Jacob, Suma; Kenny, Graham P; Kim, Cecilia; Kolevzon, Alexander; Kustanovich, Vlad; Lajonchere, Clara M; Lamb, Janine A; Law-Smith, Miriam; Leboyer, Marion; Le Couteur, Ann; Leventhal, Bennett L; Liu, Xiao-Qing; Lombard, Frances; Lord, Catherine; Lotspeich, Linda; Lund, Sabata C; Magalhaes, Tiago R; Mantoulan, Carine; McDougle, Christopher J; Melhem, Nadine M; Merikangas, Alison; Minshew, Nancy J; Mirza, Ghazala K; Munson, Jeff; Noakes, Carolyn; Nygren, Gudrun; Papanikolaou, Katerina; Pagnamenta, Alistair T; Parrini, Barbara; Paton, Tara; Pickles, Andrew; Posey, David J; Poustka, Fritz; Ragoussis, Jiannis; Regan, Regina; Roberts, Wendy; Roeder, Kathryn; Roge, Bernadette; Rutter, Michael L; Schlitt, Sabine; Shah, Naisha; Sheffield, Val C; Soorya, Latha; Sousa, Inês; Stoppioni, Vera; Sykes, Nuala; Tancredi, Raffaella; Thompson, Ann P; Thomson, Susanne; Tryfon, Ana; Tsiantis, John; Van Engeland, Herman; Vincent, John B; Volkmar, Fred; Vorstman, J A S; Wallace, Simon; Wing, Kirsty; Wittemeyer, Kerstin; Wood, Shawn; Zurawiecki, Danielle; Zwaigenbaum, Lonnie; Bailey, Anthony J; Battaglia, Agatino; Cantor, Rita M; Coon, Hilary; Cuccaro, Michael L; Dawson, Geraldine; Ennis, Sean; Freitag, Christine M; Geschwind, Daniel H; Haines, Jonathan L; Klauck, Sabine M; McMahon, William M; Maestrini, Elena; Miller, Judith; Monaco, Anthony P; Nelson, Stanley F; Nurnberger Jr, John I; Oliveira, Guiomar; Parr, Jeremy R; Pericak-Vance, Margaret A; Piven, Joseph; Schellenberg, Gerard D; Scherer, Stephen W; Vicente, Astrid M; Wassink, Thomas H; Wijsman, Ellen M; Betancur, Catalina; Buxbaum, Joseph D; Cook, Edwin H; Gallagher, Louise; Gill, Michael; Hallmayer, Joachim; Paterson, Andrew D; Sutcliffe, James S; Szatmari, Peter; Vieland, Veronica J; Hakonarson, Hakon; Devlin, BernieNovember 1, 2012Not Determined
22760337Create StudyNeural and behavioral responses during self-evaluative processes differ in youth with and without autism.Journal of autism and developmental disordersPfeifer, Jennifer H; Merchant, Junaid S; Colich, Natalie L; Hernandez, Leanna M; Rudie, Jeff D; Dapretto, MirellaFebruary 2013Not Determined

NDA Help Center

Collection - Publications

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

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

Frequently Asked Questions

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


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

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

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

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

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

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

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

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

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

Data ExpectedTargeted EnrollmentInitial SubmissionSubjects SharedStatus
Mullen Scales of Early Learning info icon
Genomics/omics info icon
ABC Community info icon
Leiter International Performance Scale-Revised (Leiter-R) info icon
ADOS info icon
Simpson-Angus Rating Scale (SAS) info icon
ADI-R info icon
Childrens Yale-Brown OC Scale (CY-BOCS) info icon
Medical History info icon
Eye Tracking info icon
Social Responsiveness Scale (SRS) info icon
Wechsler Abbreviated Scale of Intelligence (WASI) info icon
Genetic Test info icon
Interpersonal Reactivity Index info icon
Social Communication Questionnaire (SCQ) info icon
Demographics info icon
Child Behavior Checklist (CBCL) info icon
M-CHAT info icon
Abnormal Involuntary Movement Scale (AIMS) info icon
Clinical Global Impression (CGI) info icon
Autism Observation Scale for Infants (AOSI) info icon
Vital Signs Assessment info icon
Early Social Communication Scales (ESCS) info icon
Child Symptom Inventory Parent Checklist info icon
Interpersonal Competence Scale (ICS) info icon
Parent Concerns Questionaire info icon
Wechsler Intelligence Scale for Children info icon
MacArthur Bates Communicative Development Inventory info icon
Peabody Picture Vocabulary Test, Fourth Edition info icon
Repetitive Behavior Scale - Revised (RBS-R) info icon
Preschool Language Scale (PLS) info icon
Physical Exam info icon
Sensory Profile info icon
Behavior Rating Inventory of Executive Function (BRIEF) info icon
Clinical Lab Tests info icon
Side Effects info icon
Vineland (Parent and Caregiver) info icon
Concomitant Medication info icon
Imaging (Structural, fMRI, DTI, PET, microscopy) info icon
EEG info icon
Structure not yet defined
No Status history for this Data Expected has been recorded yet

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.


  • 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.193/17423Secondary 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. 176/6323Secondary 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.70/3517Secondary 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. 38/3382Secondary 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.60/1708Secondary 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. 129/771Secondary AnalysisShared
Brain-based sex differences in autism spectrum disorder across the lifespan: A systematic review of structural MRI, fMRI, and DTI findingsFemales with autism spectrum disorder (ASD) have been long overlooked in neuroscience research, but emerging evidence suggests they show distinct phenotypic trajectories and age-related brain differences. Sex-related biological factors (e.g., hormones, genes) may play a role in ASD etiology and have been shown to influence neurodevelopmental trajectories. Thus, a lifespan approach is warranted to understand brain-based sex differences in ASD. This systematic review on MRI-based sex differences in ASD was conducted to elucidate variations across the lifespan and inform biomarker discovery of ASD in females. We identified articles through two database searches. Fifty studies met criteria and underwent integrative review. We found that regions expressing replicable sex-by-diagnosis differences across studies overlapped with regions showing sex differences in neurotypical (NT) cohorts, in particular regions showing NT male>female volumes. Furthermore, studies investigating age-related brain differences across a broad age-span suggest distinct neurodevelopmental patterns in females with ASD. Qualitative comparison across youth and adult studies also supported this hypothesis. However, many studies collapsed across age, which may mask differences. Furthermore, accumulating evidence supports the female protective effect in ASD, although only one study examined brain circuits implicated in “protection.” When synthesized with the broader literature, brain-based sex differences in ASD may come from various sources, including genetic and endocrine processes involved in brain “masculinization” and “feminization” across early development, puberty, and other lifespan windows of hormonal transition. Furthermore, sex-related biology may interact with peripheral processes, in particular the stress axis and brain arousal system, to produce distinct neurodevelopmental patterns in males and females with ASD. Future research on neuroimaging-based sex differences in ASD would benefit from a lifespan approach in well-controlled and multivariate studies. Possible relationships between behavior, sex hormones, and brain development in ASD remain largely unexamined.162/759Secondary AnalysisShared
Combining Gaze and Demographic Feature Descriptors for Autism ClassificationPeople 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%. 162/756Secondary AnalysisShared
Development of EEG dynamics throughout the lifespanCombining 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. 54/551Secondary AnalysisShared
Derivation of Quality Measures for Time-Series Images by Neuroimaging PipelinesUsing the National Database for Autism Research cloud platform, MRI data were analyzed using neuroimaging pipelines that included packages available as part of the Neuroimaging Informatics Tools and Resources Clearinghouse (NITRC) Computational Environment to derive standardized measures of MR image quality. Time series QA was performed according to Friedman, et al. (http://www.ncbi.nlm.nih.gov/pubmed/16952468) providing values for Signal to Noise Ratio that can be compared to other subjects.41/356Secondary AnalysisShared
comparing EEG metrics during eyes closed versus eyes open rest in autismUnderstanding 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.1/336Secondary 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.24/280Primary 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.23/249Secondary 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.20/208Secondary 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.20/200Secondary 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. 20/194Secondary AnalysisShared
Cortico-Basal Ganglia Brain Structure and Links to Restricted, Repetitive Behavior in Autism Spectrum DisorderRestricted 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.4/192Secondary AnalysisShared
Early Detection of Autism Spectrum Disorder Using Non-Invasive EEGThe Autistic Spectrum Disorder (ASD) is a neurodevelopmental disorder that has been increasingly diagnosed in children. Symptoms are commonly noticed in childhood and include impairments in communication and social interaction. Anticipating the diagnosis to before the onset of symptoms would allow different therapies to be started without compromising the child’s development. Hence, several studies have searched for ASD biomarkers using non-invasive electroencephalography (EEG), a low-cost technique, using different strategies. In this scenario, this work compares different machine learning techniques to automatically identify the ASD from the EEG records. In addition, an analysis of possible biomarkers will be performed to assist in early diagnosis.181/181Secondary AnalysisShared
Inflexible neurobiological signatures precede atypical development in infants at high risk for autismVariability in neurobiological signatures is ubiquitous in early life but the link to adverse developmental milestones in humans is unknown. We examined how levels of signal and noise in movement signatures during the 1st year of life constrain early development in 71 healthy typically developing infants, either at High or Low familial Risk (HR or LR, respectively) for developing Autism Spectrum Disorders (ASD). Delays in early learning developmental trajectories in HR infants (validated in an analysis of 1,445 infants from representative infant-sibling studies) were predicted by worse stochastic patterns in their spontaneous head movements as early as 1–2 months after birth, relative to HR infants who showed more rapid developmental progress, as well as relative to all LR infants. While LR 1–2 mo-old infants’ movements were significantly different during a language listening task compared to during sleep, HR infants’ movements were more similar during both conditions, a striking lack of diversity that reveals context-inflexible experience of ambient information. Contrary to expectation, it is not the level of variability per se that is particularly detrimental in early life. Rather, inflexible sensorimotor systems and/or atypical transition between behavioral states may interfere with the establishment of capacity to extract structure and important cues from sensory input at birth, preceding and contributing to an atypical brain developmental trajectory in toddlerhood.70/70Secondary AnalysisShared
Genetic vulnerability of exposures to antenatal maternal treatments in 1-2 mo-old infantsThe growth and maturation of the nervous system is vulnerable during pregnancy. The impact of antenatal exposures to maternal treatments in the context of genetic vulnerability of the fetus, on sensorimotor functioning in early infancy, remains unexplored. Statistical features of head movements obtained from resting-state sleep fMRI scans are examined in 1-2 month-old infants, both at high risk (HR) for Autism Spectrum Disorder (ASD) due to a biological sibling with ASD and at low risk (LR) (N=56). In utero exposures include maternal prescription medications (psychotropic Rx: N=3HR;N=5LR vs. non-psychotropic Rx: N=11HR;N=9LR vs. none: N=11HR;N=16LR), psychiatric diagnoses (two or more Dx2: N=5HR;N=1LR;one Dx1: N=4HR; N=5LR;no Dx: N=12HR; N=20LR), infections requiring antibiotics (infection: N=5HR; N=8LR; no infection: N=20HR; N=22LR), or high fever (fever:N=2HR;N=2LR; no fever:N=23HR;N=27LR). Movements with significantly higher variability are detected in infants exposed to psychotropics (e.g.opioid analgesics) and those whose mothers had fever, and this effect is significantly worse for infants at HR for ASD. Movements are significantly less variable in HR infants with non-psychotropic exposures (e.g.antibiotics). Heightened number of psychiatric or mental health conditions is associated with noisier movements in both risk groups. Genetic vulnerability due to in utero exposure to maternal treatments is an important future approach to be advanced in the field of early mind and brain development.56/56Secondary AnalysisShared
Altered Salience Network Connectivity in 6-Week-Old Infants at Risk for AutismConverging evidence implicates disrupted brain connectivity in autism spectrum disorder (ASD); however, the mechanisms linking altered connectivity early in development to the emergence of ASD symptomatology remain poorly understood. Here we examined whether atypicalities in the Salience Network (SN) – an early-emerging neural network involved in orienting attention to the most salient aspects of one’s internal and external environment – may predict the development of ASD markers such as reduced social attention and atypical sensory processing. Six-week-old infants at high-risk for ASD exhibited stronger SN connectivity with sensorimotor regions; low-risk infants demonstrated stronger SN connectivity with prefrontal regions involved in social attention. Infants with higher connectivity with sensorimotor regions had lower connectivity with prefrontal regions, suggesting a direct tradeoff between attention to basic sensory versus socially-relevant information. Early alterations in SN connectivity predicted subsequent ASD symptomatology, providing a plausible mechanistic account for the unfolding of atypical developmental trajectories associated with ASD risk.53/53Primary AnalysisShared
Failure to attune to language predicts autism in high risk infantsYoung humans are typically sensitive to evolutionarily important aspects of information in the surrounding environment in a way that makes us thrive. Seeking to probe the putative disruptions of this process in infancy, I examined the statistical character of head movements in 52 9–10 mo-old infants, half at high familial risk (HR) for Autism Spectrum Disorders (ASD), who underwent an fMRI scan while listening to words spoken with alternating stress patterns on syllables. Relative to low risk (LR) infants, HR infants, in particular those showing the least rapid receptive language progress, had significantly lower noise-to-signal levels and increased symmetry. A comparison of patterns during a native language and a sleep scan revealed the most atypical ordering of signatures on the 3 tasks in a subset of HR infants, suggesting that the biological mechanism of language development is least acquisitive in those HR infants who go on to develop ASD in toddlerhood.52/52Secondary AnalysisShared
* Data not on individual level

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