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

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

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

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Shared

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General

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

Funding Source

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

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

Clinical Trials

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

Collection Summary Collection Charts
Collection Title Collection Investigators Collection Description
Sensor-based technology in the study of motor skills in infants at risk for ASD (Infant motor planning)
Jana M. Iverson 
Longitudinal and cross-sectional data on motor planning in infants at HR for ASD and comparison infants. This research is designed to examine fine motor and object manipulation skills in HR and comparison infants with no such risk (Low Risk infants; LR) between the ages of 12 and 36 months. The approach taken is innovative because it involves the application of newly-developed, technologically sophisticated sensor technology designed for use in unstructured settings in combination with longitudinal behavioral observation to collect finely detailed information about the organization and quality of childrens movements as they perform three motor tasks at varying levels of difficulty. Supplemented by standardized motor assessments, these data will support the analysis of fine motor and object manipulation abilities in a much more subtle and sophisticated fashion than has heretofore been possible. Twenty HR infants already being followed from 5 to 36 months as part of a larger study and 20 LR infants will be observed at home at ages 12, 14, 18, 24, and 36 months as they perform motor tasks that involve reaching for, transporting, and placing an object at a target location. An additional cross-sectional group of 50 children, 10 at each age listed above, will also participate. Kinematic data will be collected via lightweight sensorized wristbands and sensors embedded in the objects. Analyses will focus on overall risk group differences, on change in the kinematic and behavioral variables as a function of task difficulty and age, and on group by difficulty interactions at varying ages. These will be used to evaluate developmental trajectories and possible developmental delays and impairments in the motor skill of HR children, to examine the relation between motor skills and delays exhibited by HR children in other developmental domains, and to provide preliminary information about kinematic and behavioral signatures that may be related to an eventual diagnosis of ASD.
NDAR
Closed
Shared
$433,676.00
63
40
20
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NIH - Extramural None


R21HD068584-01 Sensor-based technology in the study of motor skills in infants at risk for ASD 04/15/2011 03/31/2013 40 20 UNIVERSITY OF PITTSBURGH AT PITTSBURGH $433,676.00

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

Experiments

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

IDNameCreated DateStatusType
No records found.

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

Shared Data

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

Demographics Clinical Assessments 63
Motor Task Clinical Assessments 62

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

PubMed IDStudyTitleJournalAuthorsDateStatus
27242630Create StudyPerformance of Motor Sequences in Children at Heightened vs. Low Risk for ASD: A Longitudinal Study from 18 to 36 Months of Age.Frontiers in psychologyFocaroli V, Taffoni F, Parsons SM, Keller F, Iverson JMJanuary 2016Relevant
25570779Create StudyA technological approach to studying motor planning ability in children at high risk for ASD.Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual ConferenceTaffoni F, Focaroli V, Keller F, Iverson JM2014Relevant
25055839Create StudySensor-based technology in the study of motor skills in infants at risk for ASD.Proceedings of the ... IEEE/RAS-EMBS International Conference on Biomedical Robotics and Biomechatronics. IEEE/RAS-EMBS International Conference on Biomedical Robotics and BiomechatronicsTaffoni F, Focaroli V, Formica D, Gugliemelli E, Keller F, Iverson JMJune 2012Relevant
22744786Create StudyEmbedding inertial-magnetic sensors in everyday objects: assessing spatial cognition in children.Journal of integrative neuroscienceCampolo D, Taffoni F, Formica D, Iverson J, Sparaci L, Keller F, Guglielmelli EMarch 2012Not Relevant
help.tab.dataexpected

Relevant Publications
PubMed IDStudyTitleJournalAuthorsDate
27242630Create StudyPerformance of Motor Sequences in Children at Heightened vs. Low Risk for ASD: A Longitudinal Study from 18 to 36 Months of Age.Frontiers in psychologyFocaroli V, Taffoni F, Parsons SM, Keller F, Iverson JMJanuary 2016
25570779Create StudyA technological approach to studying motor planning ability in children at high risk for ASD.Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual ConferenceTaffoni F, Focaroli V, Keller F, Iverson JM2014
25055839Create StudySensor-based technology in the study of motor skills in infants at risk for ASD.Proceedings of the ... IEEE/RAS-EMBS International Conference on Biomedical Robotics and Biomechatronics. IEEE/RAS-EMBS International Conference on Biomedical Robotics and BiomechatronicsTaffoni F, Focaroli V, Formica D, Gugliemelli E, Keller F, Iverson JMJune 2012
help.tab.dataexpected.addnew
Data Expected
Data ExpectedTargeted EnrollmentInitial SubmissionSubjects SharedStatus
Demographics info iconApproved
Research Subject and Pedigree info iconApproved
Motor Task info iconApproved
Peabody Developmental Motor Scales - Second Edition (PDMS-2) info iconApproved
Structure not yet defined

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

Associated Studies

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

Study Name Description Number of Subjects
Collection / Total
Data Use State
Upper limb motor coordination based early diagnosis in high risk subjects for Autism Autism is a lifelong condition present from early childhood. Medical specialists' diagnosis autism based on observation is of great difficulty in communicating, difficulties for forming relationships with other people, and delayed speech. The scientists tried to discover other early signs to reach the early detection of Autism Spectrum Disorders (ASD). Early diagnosing is very important to initiate and improve treatment results. One of these signs is based on examination of upper limb motor movements. This study aims to determine whether a simple upper limb motor movement could be useful to classify High Risk (HR) infants for autism and comparison infants with Low Risk (LR) for autism. Also, this paper presents a computational intelligence method that uses HR and LR subjects between the ages of 12 and 36 months to make an early autism diagnosing. The paper examined one task which asks to insert an object into a box. It analyzed the data by using Support Vector Machine (SVM) and Extreme Learning Machine (ELM). The results show engorging results in comparison to other state or art methods. 57 / 57 Secondary Analysis Shared
* Data not on individual level
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