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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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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

New Documentation

Please enter the name of the data structure to search or if your definition does not exist, please upload that definition so that it can be appropriately defined for submission.

Shared

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

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.

Supporting Documentation

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
Pediatric MRI
PEDIATRIC STUDY CENTERS: Michael J. Rivkin, M.D., Boston Children's Hospital; William S. Ball, M.D., Children's Hospital Medical Center of Cincinnati; Dah-Jyuu Wang, Ph.D., Children's Hospital of Philadelphia; James T. McCracken, M.D., University of California at Los Angeles; Michael Brandt, Ph.D. and Jack Fletcher, Ph.D., University of Texas Health Science Center; Robert McKinstry, M.D., Washington University. DATA COORDINATING CENTER: Alan Evans, Ph.D., Montreal Neurological Institute Center. CLINICAL COORDINATING CENTER: Kelly Botteron, M.D., Washington University. DIFFUSION TENSOR PROCESSING CENTER: Carlo Pierpaoli, M.D., National Institute of Child Health and Human Development. SPECTROSCOPY PROCESSING CENTER: Joseph O'Neill, Ph.D., University of California at Los Angeles. 
Pediatric MRI Release 5.0. Visit our website at http://pediatricmri.nih.gov for more information about this project.
Pediatric MRI
Closed
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NIH - Contract None
NIH - Contract None

http://www.plosone.org/article/info%3Adoi%2F10.1371%2Fjournal.pone.0018712 Publication Hanson JL, Chandra A, Wolfe BL, Pollak SD (2011) Association between Income and the Hippocampus. PLoS ONE 6(5): e18712. doi:10.1371/journal.pone.0018712 Qualified Researchers
Objective1_procedure_manual.pdf Objectives Procedure manual for Objective 1 subjects (ages 4.5+ years). Version public release 1.0, January 2007. Qualified Researchers
Objective2_procedure_manual.pdf Objectives Procedure manual for Objective 2 subjects (ages 0-4.5 years). Final version. Qualified Researchers
MRI_protocol_manual.pdf Analysis Protocol MRI procedure manual distributed to each participating site in the study. Covers acquisition and transfer of MRI data only. November 2006 version. Qualified Researchers
Release3_notes.pdf Other Release 3 notes. October 2009. Qualified Researchers
Release4_notes.pdf Publication Release 4 notes. June 2010. Qualified Researchers
Neuroimaging_white_paper.pdf Methods Neuroimaging white paper. Qualified Researchers
Clinical_behavioral_white_paper.pdf Methods Clinical/behavior white paper. Qualified Researchers
Spectroscopy_white_paper.pdf Methods Spectroscopy white paper. Qualified Researchers
Release3_notes.pdf Other Release 3 notes. October 2009. Qualified Researchers
Release4_notes.pdf Other Release 4 notes. June 2010. Qualified Researchers
www.tortoisedti.org Software New free DTI software. Qualified Researchers
Objective1_Procedure_Manual.pdf Objectives Procedure manual for Objective 1 subjects (ages 4.5+ years). Qualified Researchers
Objective2_Procedure_Manual.pdf Objectives Procedure manual for Objective 2 subjects (ages 0-4.5 years). Qualified Researchers
Study_Protocol.pdf Methods Study protocol. Qualified Researchers
MRI_Procedure_Manual.pdf Methods MRI procedure manual. Qualified Researchers
Clinical_Behavioral_White_Paper.pdf Methods Clinical/behavioral white paper. Qualified Researchers
Database_White_Paper.pdf Methods Database white paper. Qualified Researchers
Neuroimaging_White_Paper.pdf Methods Structural MRI white paper. Qualified Researchers
Spectroscopy_White_Paper.pdf Methods Spectroscopy white paper. Qualified Researchers
Release3_Notes.pdf Other Release 3 notes. October 2009. Qualified Researchers
Release4_Notes.pdf Other Release 4 notes. June 2010. Qualified Researchers
www.tortoisedti.org Software DTI analysis software developed by the project. Qualified Researchers
Growth maps.zip Other Group-averaged surface growth maps for Objective 2 data that passed QC for surface extraction. R1 Qualified Researchers
Growth_maps.zip Other Group-averaged surface growth maps for Objective 2 data that passed QC for surface extraction. Qualified Researchers
eDTI_White_Paper_R5.1.pdf Methods Expanded diffusion tensor imaging (eDTI) white paper. Qualified Researchers
DTI_White_Paper_R5.1.pdf Methods Diffusion tensor imaging (DTI) white paper. Qualified Researchers
Release5.1_Notes.pdf Other Release 5.1 notes (July 9, 2012). Qualified Researchers
Study_Protocol.pdf Methods Study protocol Qualified Researchers
Release5_Notes.pdf Other Release 5 notes (April 5, 2012). Qualified Researchers
Anatomic_MRI_White_Paper_R5.pdf Methods Anatomic MRI white paper and project overview. Qualified Researchers
Clinical_Behavioral_White_Paper_R5.pdf Methods Clinical/behavioral white paper. Qualified Researchers
DTI_White_Paper_R5.pdf Methods Diffusion tensor imaging (DTI) white paper. Qualified Researchers
Spectroscopy_White_Paper_R5.pdf Methods Spectroscopy white paper. Qualified Researchers
Pediatric_MRI_Data_Dictionary_R5.xls Other Release 5 data dictionary. Qualified Researchers
Release5.1_Notes_revised_061213.pdf Other Release 5.1 Notes Qualified Researchers
Results published in European Journal of Neuroscience


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.

Image Imaging 2
Peds - BRIEF Adult Version (Informant Report) Clinical Assessments 554
Peds - BRIEF Adult Version (Self Report) Clinical Assessments 554
Peds - BRIEF Parent Form Clinical Assessments 554
Peds - BSID II Behavior Rating Scale Clinical Assessments 554
Peds - BSID II Mental Scale Clinical Assessments 554
Peds - BSID II Motor Scale Clinical Assessments 554
Peds - Brief Telephone Screening Interview Clinical Assessments 554
Peds - CANTAB Clinical Assessments 554
Peds - CDISC4 Parent Version Clinical Assessments 554
Peds - California Verbal Learning Test for Children Clinical Assessments 554
Peds - California Verbal Learning Test, 2nd ed. Clinical Assessments 554
Peds - Carey Temperament Scales, BSQ (3 to 7 years) Clinical Assessments 554
Peds - Carey Temperament Scales, EITQ (1 to 4 months) Clinical Assessments 554
Peds - Carey Temperament Scales, RITQ (4 to 11 months) Clinical Assessments 554
Peds - Carey Temperament Scales, TTS (1 to 2 years) Clinical Assessments 554
Peds - Child Behavior Checklist (CBCL) Clinical Assessments 554
Peds - Demographics Clinical Assessments 554
Peds - Differential Ability Scales (DAS) Clinical Assessments 554
Peds - Diffusion Tensor Imaging Imaging 274
Peds - Disc Predictive Scales Clinical Assessments 554
Peds - Expanded Diffusion Tensor Imaging (eDTI) Imaging 152
Peds - FIGS Family History Int. Genetic Studies Yr1 Clinical Assessments 554
Peds - FIGS Family History Int. Genetic Studies Yr3 Clinical Assessments 554
Peds - Family Biographical History Form (0:0 - 4:5 y:m) Clinical Assessments 554
Peds - Full Telephone Screening Interview - Version 1 Clinical Assessments 554
Peds - Full Telephone Screening Interview - Version 2 Clinical Assessments 554
Peds - Handedness (1:0 to 2:11 y:m) Clinical Assessments 554
Peds - Handedness (3:0 to 5:11 y:m) - Part 1 Clinical Assessments 554
Peds - Handedness (3:0 to 5:11 y:m) - Part 2 Clinical Assessments 554
Peds - Handedness (6:0+ y:m) Clinical Assessments 554
Peds - JTCI Parent Report Clinical Assessments 554
Peds - JTCI Self Report Clinical Assessments 554
Peds - Longitudinally Registered aMRI Variables Imaging 553
Peds - MRI Child History Form (4:6+ y:m) Clinical Assessments 554
Peds - NEPSY Verbal Fluency (Semantic and Phonemic) Clinical Assessments 554
Peds - NEPSY Verbal Fluency (Semantic) Clinical Assessments 554
Peds - Neuropsychological Clinical Assessments 554
Peds - Non-longitudinally Registered aMRI Variables Imaging 553
Peds - Parental Stress Index Clinical Assessments 554
Peds - Physical Clinical Assessments 554
Peds - Physical and Neurological Exam (0:0 to 0:1 y:m) Clinical Assessments 554
Peds - Physical and Neurological Exam (0:2 to 0:11 y:m) Clinical Assessments 554
Peds - Physical and Neurological Exam (1:0 to 2:11 y:m) Clinical Assessments 554
Peds - Physical and Neurological Exam (3:0 to 4:5 y:m) Clinical Assessments 554
Peds - Physical/Neurological Examination Clinical Assessments 554
Peds - Preschool Language Scale-3 Clinical Assessments 554
Peds - Psychiatric & Personality Clinical Assessments 554
Peds - Pubertal Status Questionnaire Clinical Assessments 554
Peds - Purdue Pegboard - Full Board Clinical Assessments 554
Peds - Purdue Pegboard - Half Board Clinical Assessments 554
Peds - Screening and Exclusion Form (0:0 to 4:5 y:m) Clinical Assessments 554
Peds - Spectroscopy Imaging 553
Peds - TCI Parent Report Clinical Assessments 554
Peds - TCI Self Report Clinical Assessments 554
Peds - Timepoint Clinical Assessments 554
Peds - Urine and Saliva Clinical Assessments 554
Peds - WAIS-R Digit Span & Digit Symbol Clinical Assessments 554
Peds - WISC III Digital Span & Coding Clinical Assessments 554
Peds - Wechsler Abbreviated Scale of Intelligence Clinical Assessments 554
Peds - Woodcock-Johnson III Clinical Assessments 554

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
No records found.

This tab provides a general status on the data expected to be shared. There are two types of data expected.

  1. By Relevant publications — Those publications that reported for the collection's grant and have a status of "relevant" for sharing are listed first. The grantee is expected to share the data specific to those publications using the NDA Study feature. If a publication is erroneously marked relevant, the PI should simply change the status. When sharing a study, only the outcome measures for the subjects/time-points are shared. Other data that have not met the share date, defined below, will remain embargoed. To initiate study creation, simply login, mark your publication as relevant and click on the link listed to begin.

  2. By Data Structure — The number of subjects expected, received and shared is provided. Investigators are expected to update the data that they are collecting, the initial submission date and initial share dates. The NIMH Data Archive shares data when those dates are met.

Relevant Publications
PubMed IDStudyTitleJournalAuthorsDate
No records found.
Data Expected is not applicable to this collection
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
A new template to study callosal growth shows specific growth in anterior and posterior regions of the corpus callosum in early childhood Most of the studies conducted on the development of the corpus callosum (CC) have been limited to a relatively simple assessment of callosal area, providing an estimation of the size of the CC in two dimensions rather than its actual measurement. The goal of this study was to revisit callosal development in childhood and adolescence by using a three-dimensional (3D) magnetic resonance imaging template of the CC that considers the horizontal width of the CC and compares this with the two-dimensional (2D) callosal area. We mapped callosal growth in a large sample of youths followed longitudinally (N = 370 at T1; N = 304 at T2; and N = 246 at T3). Both techniques were based on a five-section subdivision of the CC. The results obtained with the 3D method revealed that the rate of CC growth over a 4-year period in the rostrum, the genu, the anterior body and the splenium was significantly higher in the youngest age group (< 7 years) than in older groups, indicating an intense period of development in early childhood for the anterior and posterior parts of the CC. Similar results were obtained when 2D callosal area was used for the anterior and posterior parts of the CC. However, divergent results were found in the mid-body and the caudal body of the CC. As shown by differences between 2D estimations and actual 3D measurements of callosal growth, our study highlights the importance of considering the horizontal width in measuring developmental changes in the CC. 433 / 433 Primary Analysis Shared
Prediction of brain maturity based on cortical thickness at different spatial resolutions Several studies using magnetic resonance imaging (MRI) scans have shown developmental trajectories of cortical thickness. Cognitive milestones happen concurrently with these structural changes, and a delay in such changes has been implicated in developmental disorders such as attention-deficit/hyperactivity disorder (ADHD). Accurate estimation of individuals' brain maturity, therefore, is critical in establishing a baseline for normal brain development against which neurodevelopmental disorders can be assessed. In this study, cortical thickness derived from structural magnetic resonance imaging (MRI) scans of a large longitudinal dataset of normally growing children and adolescents (n = 308), were used to build a highly accurate predictive model for estimating chronological age (cross-validated correlation up to R = 0.84). Unlike previous studies which used kernelized approach in building prediction models, we used an elastic net penalized linear regression model capable of producing a spatially sparse, yet accurate predictive model of chronological age. Upon investigating different scales of cortical parcellation from 78 to 10,240 brain parcels, we observed that the accuracy in estimated age improved with increased spatial scale of brain parcellation, with the best estimations obtained for spatial resolutions consisting of 2560 and 10,240 brain parcels. The top predictors of brain maturity were found in highly localized sensorimotor and association areas. The results of our study demonstrate that cortical thickness can be used to estimate individuals' brain maturity with high accuracy, and the estimated ages relate to functional and behavioural measures, underscoring the relevance and scope of the study in the understanding of biological maturity. 343 / 343 Primary Analysis Shared
Development and validation of a brain maturation index using longitudinal neuroanatomical scans Background Major psychiatric disorders are increasingly being conceptualized as ‘neurodevelopmental’, because they are associated with aberrant brain maturation. Several studies have hypothesized that a brain maturation index integrating patterns of neuroanatomical measurements may reliably identify individual subjects deviating from a normative neurodevelopmental trajectory. However, while recent studies have shown great promise in developing accurate brain maturation indices using neuroimaging data and multivariate machine learning techniques, this approach has not been validated using a large sample of longitudinal data from children and adolescents. Methods T1-weighted scans from 303 healthy subjects aged 4.88 to 18.35 years were acquired from the National Institute of Health (NIH) pediatric repository (http://www.pediatricmri.nih.gov). Out of the 303 subjects, 115 subjects were re-scanned after 2 years. The least absolute shrinkage and selection operator algorithm (LASSO) was ‘trained’ to integrate neuroanatomical changes across chronological age and predict each individual's brain maturity. The resulting brain maturation index was developed using first-visit scans only, and was validated using second-visit scans. Results We report a high correlation between the first-visit chronological age and brain maturation index (r = 0.82, mean absolute error or MAE = 1.69 years), and a high correlation between the second-visit chronological age and brain maturation index (r = 0.83, MAE = 1.71 years). The brain maturation index captured neuroanatomical volume changes between the first and second visits with an MAE of 0.27 years. Conclusions The brain maturation index developed in this study accurately predicted individual subjects' brain maturation longitudinally. Due to its strong clinical potentials in identifying individuals with an abnormal brain maturation trajectory, the brain maturation index may allow timely clinical interventions for individuals at risk for psychiatric disorders. 303 / 303 Primary Analysis Shared
The diffusion tensor imaging (DTI) component of the NIH MRI study of normal brain development (PedsDTI) The NIH MRI Study of normal brain development sought to characterize typical brain development in a population of infants, toddlers, children and adolescents/young adults, covering the socio-economic and ethnic diversity of the population of the United States. The study began in 1999 with data collection commencing in 2001 and concluding in 2007. The study was designed with the final goal of providing a controlled-access database; open to qualified researchers and clinicians,which could serve as a powerful tool for elucidating typical brain development and identifying deviations associated with brain-based disorders and diseases, and as a resource for developing computational methods and image processing tools. This paper focuses on the DTI component of the NIH MRI study of normal brain development. In this work, we describe the DTI data acquisition protocols, data processing steps, quality assessment procedures, and data included in the database, along with database access requirements. For more details, visit http://www. pediatricmri.nih.gov. This longitudinal DTI dataset includes raw and processed diffusion data from 498 low resolution (3 mm) DTI datasets from274 unique subjects, and 193 high resolution (2.5mm) DTI datasets from152 unique subjects. Subjects range in age from10 days (from date of birth) through 22 years. Additionally, a set of age-specific DTI templates are included. This forms one component of the larger NIHMRI study of normal brain development which also includes T1-, T2-, proton density-weighted, and proton magnetic resonance spectroscopy (MRS) imaging data, and demographic, clinical and behavioral data. 298 / 298 Primary Analysis Shared
Trajectories of cortical thickness maturation in normal brain development Several reports have described cortical thickness (CTh) developmental trajectories, with conflicting results. Some studies have reported inverted-U shape curves with peaks of CTh in late childhood to adolescence, while others suggested predominant monotonic decline after age 6. In this study, we reviewed CTh developmental trajectories in the NIH MRI Study of Normal Brain Development, and in a second step, evaluated the impact of post-processing quality control (QC) procedures on identified trajectories. The quality-controlled sample included 384 individual subjects with repeated scanning (1-3 per subject, total scans n=753) from 4.9 to 22.3years of age. The best-fit model (cubic, quadratic, or first-order linear) was identified at each vertex using mixed-effects models. The majority of brain regions showed linear monotonic decline of CTh. There were few areas of cubic trajectories, mostly in bilateral temporo-parietal areas and the right prefrontal cortex, in which CTh peaks were at, or prior to, age 8. When controlling for total brain volume, CTh trajectories were even more uniformly linear. The only sex difference was faster thinning of occipital areas in boys compared to girls. The best-fit model for whole brain mean thickness was a monotonic decline of 0.027mm per year. QC procedures had a significant impact on identified trajectories, with a clear shift toward more complex trajectories (i.e., quadratic or cubic) when including all scans without QC (n=954). Trajectories were almost exclusively linear when using only scans that passed the most stringent QC (n=598). The impact of QC probably relates to decreasing the inclusion of scans with CTh underestimation secondary to movement artifacts, which are more common in younger subjects. In summary, our results suggest that CTh follows a simple linear decline in most cortical areas by age 5, and all areas by age 8. This study further supports the crucial importance of implementing post-processing QC in CTh studies of development, aging, and neuropsychiatric disorders. 433 / 433 Primary Analysis Shared
Analysis of the contribution of experimental bias, experimental noise, and inter-subject biological variability on the assessment of developmental trajectories in diffusion MRI studies of the brain Metrics derived from the diffusion tensor, such as fractional anisotropy (FA) and mean diffusivity (MD) have been used in many studies of postnatal brain development. A common finding of previous studies is that these tensor-derived measures vary widely even in healthy populations. This variability can be due to inherent interindividual biological differences as well as experimental noise. Moreover, when comparing different studies, additional variability can be introduced by different acquisition protocols. In this study we examined scans of 61 individuals (aged 4–22 years) from the NIH MRI study of normal brain development. Two scans were collected with different protocols (low and high resolution). Our goal was to separate the contributions of biological variability and experimental noise to the overall measured variance, as well as to assess potential systematic effects related to the use of different protocols. We analyzed FA and MD in seventeen regions of interest. We found that biological variability for both FA and MD varies widely across brain regions; biological variability is highest for FA in the lateral part of the splenium and body of the corpus callosum along with the cingulum and the superior longitudinal fasciculus, and for MD in the optic radiations and the lateral part of the splenium. These regions with high inter-individual biological variability are the most likely candidates for assessing genetic and environmental effects in the developing brain. With respect to protocol-related effects, the lower resolution acquisition resulted in higher MD and lower FA values for the majority of regions compared with the higher resolution protocol. However, the majority of the regions did not show any age–protocol interaction, indicating similar trajectories were obtained irrespective of the protocol used. 128 / 128 Primary Analysis Shared
Derivation of Brain Structure Volumes from MRI Neuroimages hosted by NDAR using LONI Workflows LONI utilized de-identified data from NDAR's cloud and a LONI Pipeline (pipeline.loni.usc.edu) processing workflow to perform a secondary structural MRI examination. The workflow used in this study pulls data from and provided by NDAR to an instance on the LONI compute cluster, aligns data to a standard orientation using FSLreorient2stsd, and undergoes further image processing to eventually identify, extract, and analyze cortical and sub-cortical structures in different MRI brain volumes. Two methods were used for this image processing: the first uses Freesurfer Recon_All to extract brain cortical parcellation and surfaces, align the data to an atlas, and identify, and analyze regions of interest; the second uses FSL to extract the brain (BET), align the data to an atlas, and extract ROIs including sub-cortical regions using FSL FIRST. The second method also uses Freesurfer (mri_segstats) to perform statistical analysis of these ROIs. Lastly, the LONI Pipeline workflow updates and returns the data processed and extracted by Freesurfer and FSL as a miNDAR back to NDAR's cloud storage instance. These results can be used to assess quality control or be used to perform post-hoc comparisons of cortical and sub-cortical brain architecture between subject types. See also, Torgerson et al. (2015) Brain Imaging and Behavior, for additional details on using LONI Pipeline to access and process NDAR data. 398 / 780 Primary Analysis Shared
* Data not on individual level