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Warning Notice This is a U.S. Government computer system, which may be accessed and used only for authorized Government business by authorized personnel. Unauthorized access or use of this computer system may subject violators to criminal, civil, and/or administrative action. All information on this computer system may be intercepted, recorded, read, copied, and disclosed by and to authorized personnel for official purposes, including criminal investigations. Such information includes sensitive data encrypted to comply with confidentiality and privacy requirements. Access or use of this computer system by any person, whether authorized or unauthorized, constitutes consent to these terms. There is no right of privacy in this system.
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This is a U.S. Government computer system, which may be accessed and used only for authorized Government business by authorized personnel. Unauthorized access or use of this computer system may subject violators to criminal, civil, and/or administrative action. All information on this computer system may be intercepted, recorded, read, copied, and disclosed by and to authorized personnel for official purposes, including criminal investigations. Such information includes sensitive data encrypted to comply with confidentiality and privacy requirements. Access or use of this computer system by any person, whether authorized or unauthorized, constitutes consent to these terms. There is no right of privacy in this system.

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Packaging, downloading data, and MINDAR databases are currently unavailable.
View Cohort
        helpcenter.ndastudy.view-cohort

        NDA Help Center

        NDA Study - View Cohort

        The View Cohort shows the Study cohort characteristics which will includes the name, type, sex and age in months range criteria. Note that age and sex restrict the data that can be added to the cohort. Medical criteria and phenotype are also included. Of particular importance on this page are any comments related to included subjects, which may be different than published results, often attributed to data sharing consent.

        Frequently Asked Questions

        • How do I know the characteristics of the cohort?
          The appropriate Medical Criteria, Phenotype, and Sub Type defined by the Study Owner are highlighted. The option trees are automatically expanded so the highlighted options are immediately viewable.

        Glossary

        • Age Range
          Age is expressed in months from 0 (birth) to 1200 (100 years old).
        • Medical Criteria
          NDA defines Medical Criteria as a diagnostically based determination of an individual's disease or condition.
        • Phenotype
          NDA defines Phenotype as any physical, morphologic, or biochemical characteristics that are observable in an individual.
        • Sub Types
          NDA defines Sub Type as a subordinate condition or disease that further characterizes an individual.
        • Cohort Name
          A user defined name for the cohort with a 200 character limit.
        • Cohort Type
          Cohort Type options include Baseline, Control, and Test.
        • Cohort Comments
          Comments further characterize the cohort beyond the options provided in the interface to define the cohort.
        • Cohort Subjects
          The Cohort Subjects provides the number of unique subjects for whom data have been associated with the cohort.
        • Cohort
          Summarizes the characteristics of a group of research subjects. The subject count is noted in parentheses.
        Contact NDA Help Desk
        Add URL
        Add File
        Select File
        Study DOI
        Study DOI Status Info
        • DOI: 10.15154/1524272
        • DataCite Status: Findable
        • NDAR Status: Registered
        • DOI Registered with DataCite
        • Public URL Resolves at DOI
        Edit Subjects Dialog
        Select Subjects
        Selected
        No records found.
        Philip Spechler pspechler@laureateinstitute.org Analysis Shared
        Click for more info Spechler, P.; Thompson, W., Paulus, M.P.P.
        Researchers studying human behavior are often interested in answering causal research questions like, what effect does a drug exposure or environmental feature have on an outcome measure related to the brain or mental health. Prior studies attempting to answer these questions have often used small samples and cross-sectional designs, which make it challenging, if not impossible, to understand the causal nature of these relationships. Furthermore, the use of experimental designs like randomized controlled trials are practically impossible to implement with illicit drugs (e.g., cannabis) and vulnerable populations (e.g., children or pregnant individuals). One way to navigate these challenges is to use statistical causal inference methods applied to large observational studies, like the Adolescent Brain and Cognitive Development study ("ABCD"; N=11,873). Broadly, statistical causal inference methods seek to leverage a dataset to approximate an experimental design. Techniques like a causal random forest (CRF) model infers an "average treatment effect" of an “exposure” or “treatment” on an outcome measure by using decision trees to compare similar subgroups of participants matched on variables that would have otherwise confounded a causal relationship. CRFs also identify "heterogeneous treatment effects" by estimating moderators between the exposure and outcome measure. Quasi-experimental methods like regression discontinuity designs (RDD) infers causality by estimating a discontinuity in an outcome measure between participants who are close to a "threshold" for their inclusion vs. exclusion from an “exposure” or “treatment”. Using a causal inference framework, this project seeks to identify modifiable factors (e.g., sleep, extracurriculars, screen media hours) that are causally related to brain and behavioral health outcome measures (e.g., internalizing disorders, brain activation measures). For example, a CRF model will test the hypothesis that there is an average treatment effect of sleep hours on internalizing symptoms, and this causal pathway might be moderated by prenatal or adolescent cannabis exposure. Moreover, an RDD model will test the hypothesis that living within 250mi. of a time-zone boundary (i.e., the discontinuity threshold) will have an effect on cognitive or other psychosocial measures. This is hypothesized due to the discrepancy between sunlight exposure and clock-time that is exaggerated near time-zone boundaries (early sunsets just west vs. late sunsets just east of a boundary), yet social responsibilities (school start times) are constant across boundaries ("social jetlag"). Similar methods will also be pursued with other exposure/treatment measures, and outcome measures, pertaining adolescent brain and behavioral health. By illuminating causal factors like these, one can begin to develop explanatory models and novel interventions aimed at promoting adolescent mental health and wellbeing. The use of a causal framework is key, as the modification of causal processes are most likely to have an effect on critical outcome measures
        10.15154/1524272
        Click for more info Secondary Analysis
        Click for more info
        IDCollectionPermission GroupSubjects
        2573 Adolescent Brain Cognitive Development Study (ABCD) Adolescent Brain Cognitive Development (ABCD) / Connectome Coordination Facility (CCF)11,876
        Analysis
        Age: 108 to 1,260 months
        Sex: Both
        helpcenter.ndastudy.summary-tab

        NDA Help Center

        NDA Study - Summary Tab

        An NDA Study describes an analysis, pipeline result, or curated data release - including the cohorts, measures, and methods - referencing the underlying source data in the NDA. Once the data is referenced and the study is shared, the underlying data will not change. It will be immutable allowing others to access those data without change into near perpetuity. Only the assessments (i.e. typically rows in a database) and outcome measures (i.e. columns in a database) included in the study are shared. By sharing data this way (e.g. specific outcome measures by subject/timepoint) an ongoing project's other unpublished data - typically other research aims - will remain embargoed according to a project's established data sharing schedule (see collection).

        When a study is created, a Digital Object Identifier (DOI) is reserved by the NDA, but will not be active. Once the study is shared, the DOI will then be issued and if associated with a publication, the NDA DOI will be linked to the publication's DOI.

        Frequently Asked Questions

        • How can I get access to an NDA Study if I am involved in creating/modifying the NDA Study?
          The individual listed as an Investigator on the General tab of the NDA Collection and any individuals defined as administrators can be provided this privilege. When the study is shared, all of those that have approved access to the data are given read-only access.

        Glossary

        • Abstract
          The Abstract of the publication or other public disclosure of analysis results. The publication abstract is automatically imported when creating a new NDA Study from a Create Study link associated with a Publication listed in a Collection. The field is editable.
        • Documents
          Provides links to additional documentation to help explain the NDA Study.
        • Cohort
          Summarizes the characteristics of a group of research subjects. The subject count is noted in parentheses.
        • Data Analysis
          Summarizes the Data Analysis methods used.
        • Data Usage
          Identifies the NDA Study as a Primary Analysis or Secondary Analysis.
        • Digital Object Identifier (DOI)
          A persistent and unique identifier that is issued for each NDA Study that may increase the visibility of a researcher's work. Authors are expected to reference the location of data in the publication using the DOI.
        • Download Button
          Adds all data from the current Collection or NDA Study to the Filter Cart.
        • Investigators
          The authors of the publication or other public disclosure of analysis results. The publication's authors are automatically imported when creating a new NDA Study from a Create Study link associated with a Publication listed in a Collection. The field is editable and uses a last name, first name format with multiple names separated by semicolons.
        • Primary Analysis
          Primary Analysis is one of the Data Use types NDA recognizes for NDA Studies. It indicates that the NDA Study's creator was involved in the collection of the data (in whole or in part) used in the NDA Study. In other words, at least some and potentially all of the data used was originally collected by the creator of the NDA Study. A Primary Analysis Study.
        • NDA Study Owner
          The NDA user, who does not necessarily have to be one of the Investigators listed in the NDA Study, primarily involved in creating the NDA Study who is serving as the point of contact. The NDA user who creates the NDA Study is automatically assigned as the Study Owner.
        • Private
          The term 'Private' generally means not available to others; however, there are some slightly different meanings based on what is Private. The default state for an NDA Collection, NDA Study, Supporting Documentation, and data is Private and, therefore, accessible only to those users with appropriate Privileges so it will not be viewable and searchable publicly. A Collection may be Shared while the data and Supporting Documentation remain Private.
        • Results
          Provides a link to the publication or other public disclosure of analysis results defined in the NDA Study.
        • Secondary Analysis
          Secondary Analysis is one of the Data Use types NDA recognizes for NDA Studies. It indicates that the NDA Study's creator was not involved in the collection of the data (in whole or in part) used in the NDA Study, but was involved in the analysis of those data. In other words, none of the data used was originally collected by the creator of the NDA Study. A Secondary Analysis Study will provide an Attribution Report listing the Collections and the researchers responsible for collecting and depositing the original data.
        • Study Type
          The NDA Study Type can be either Analysis (i.e. often associated with a computational pipeline), Clinical Trial, Finding (i.e. a result associated with a publication, or Release (i.e. a dataset release, often annually) to indicate the type of work defined in the NDA Study. Studies, once shared do not change.
        • Analysis Study Type
          The Analysis Study Type is primarily used to share a pipeline; however, it could be used to share a statistical analysis or method.
        • Release Study Type
          A static, curated data set.
        • Finding Study Type
          The Finding Study Type associates data to a publication or some type of result.
        • Clinical Trial Study Type
          A Clinical Trial Study is a finding specific to a clinical trial.
        • Study ID Number
          The Study ID number is a unique three digit number routinely expressed as S### that is assigned by the NDA.
        • Study State
          The Study State indicates whether the Study is viewable, searchable, and if the associated data can be accessed by users with appropriate data access Permissions. The default Study State is Private, which means that only the Collection Owner and users with Read Only Privileges. Only the Study Owner can Share the Study.
        • Study Title
          The Study Title is generally the title of the publication or other public disclosure of analysis results. The publication title is automatically imported when creating a new NDA Study from a Create Study link associated with a Publication listed in a Collection. The field is editable.
        • Edit Button
          The Edit Button allows a user to modify or add information once authenticated and if the user has appropriate permissions.
        • Return Button (Study)
          The Return Button brings the user back to the Data from Papers page.
        • Sex
          Sex is defined at birth and can only be Male or Female. In some places in the NDA, we incorrectly used the data_element name of Gender. Users are encouraged to review definitions and not make assumptions based upon a data element name.
        • 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.
        • 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.
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