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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.

From the 'Filter Cart' you can inspect each of the filters that have been defined, and you also have the option to remove filters. The 'Filter Cart' itself will display the number of filters applied along with the number of subjects that are identified by the combination of those filters. For example a GUID filter with two subjects, followed by a GUID filter for just one of those subjects would return only data for the subject that is in both GUID filters.

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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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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
  • Select One
  • EEG
  • EGG
  • Eye Tracking
  • Omics
  • fMRI
Created On
884Plasma metabolic profileOmics02/05/2018
878Social Challenge AssessmentEye Tracking01/26/2018
876Mixed Anti and Pro (vgs) saccade mixed blocked eye trackingEye Tracking01/22/2018
875Attention modulation taskEye Tracking01/17/2018
874ruthldopa resting 17 and 18fMRI01/16/2018
873ruthldopa resting 15 and 16fMRI01/16/2018
872ruthldopa resting 13 and 14fMRI01/16/2018
871ruthldopa resting 11 and 12fMRI01/16/2018
870Resting State fMRIfMRI01/12/2018
867Velten Mood Induction State-ItemfMRI01/12/2018
866Emotional Hemifield Task (EHT)EEG01/12/2018
865Genome EditingOmics01/12/2018
855Regulating Emotional Responses to Visual Images Across the Affective Instability SpectrumfMRI01/12/2018
853R61 Ezogabine Resting State FMRIfMRI01/11/2018
852Paired AssociatesfMRI01/11/2018
850Reward ProcessingfMRI01/11/2018
849Resting EEG (1024 samples/s)EEG01/11/2018
845Fast face -1 runfMRI01/10/2018
843RTT AHA-1Omics01/09/2018
84230 words (simultaneous fMRI and EEG acquisition)EEG01/08/2018
841Neural Correlates of Episodic Retrieval fMRI01/08/2018
84030 words (simultaneous fMRI and EEG acquisition)fMRI01/08/2018
838Memory Guided Saccade Encode and Maintenance v3fMRI01/05/2018
837Memory Guided Saccade Encode and Maintenance v2fMRI01/05/2018
836Food ExposurefMRI01/03/2018
835Model ExposurefMRI01/02/2018
834BlankingEye Tracking12/28/2017
833DoubleStepEye Tracking12/26/2017
832Memory Guided Saccade Encode and Maintenance v1 fMRI12/21/2017
831Mixed Anti and Pro (vgs) saccade mixed blocked taskfMRI12/21/2017
830T1 AlternativefMRI12/19/2017
829fMRI Movie Trailer Video 2fMRI12/18/2017
828fMRI Movie Trailer Video 1fMRI12/18/2017
Collection - Add Experiment
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Collection Owners and those with Collection Administrator permission, may edit a collection. The following is currently available for Edit on this page:


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
Funding Completed
Loading Chart...
NIH - Contract None

nihpd_asym_all_nifti.zip Other NIHPD Objective 1 asymmetric atlases General Public
nihpd_obj2_asym_nifti.zip Other NIHPD Objective 2 atlases General Public
nihpd_sym_all_nifti.zip Other NIHPD Objective 1 L-R symmetric atlases General Public
Release5.1_Notes_revised_061213.pdf Other Release 5.1 Notes Qualified Researchers
Pediatric_MRI_Data_Dictionary_R5.xls Other Release 5 data dictionary. Qualified Researchers
Spectroscopy_White_Paper_R5.pdf Methods Spectroscopy white paper. Qualified Researchers
DTI_White_Paper_R5.pdf Methods Diffusion tensor imaging (DTI) white paper. Qualified Researchers
Clinical_Behavioral_White_Paper_R5.pdf Methods Clinical/behavioral white paper. Qualified Researchers
Anatomic_MRI_White_Paper_R5.pdf Methods Anatomic MRI white paper and project overview. Qualified Researchers
Release5_Notes.pdf Other Release 5 notes (April 5, 2012). Qualified Researchers
Study_Protocol.pdf Methods Study protocol Qualified Researchers
Release5.1_Notes.pdf Other Release 5.1 notes (July 9, 2012). Qualified Researchers
DTI_White_Paper_R5.1.pdf Methods Diffusion tensor imaging (DTI) white paper. Qualified Researchers
eDTI_White_Paper_R5.1.pdf Methods Expanded diffusion tensor imaging (eDTI) white paper. Qualified Researchers
Growth_maps.zip Other Group-averaged surface growth maps for Objective 2 data that passed QC for surface extraction. Qualified Researchers
www.tortoisedti.org Software DTI analysis software developed by the project. Qualified Researchers
Release4_Notes.pdf Other Release 4 notes. June 2010. Qualified Researchers
Release3_Notes.pdf Other Release 3 notes. October 2009. Qualified Researchers
Spectroscopy_White_Paper.pdf Methods Spectroscopy white paper. Qualified Researchers
Neuroimaging_White_Paper.pdf Methods Structural MRI white paper. Qualified Researchers
Database_White_Paper.pdf Methods Database white paper. Qualified Researchers
Clinical_Behavioral_White_Paper.pdf Methods Clinical/behavioral white paper. Qualified Researchers
MRI_Procedure_Manual.pdf Methods MRI procedure manual. Qualified Researchers
Study_Protocol.pdf Methods Study protocol. Qualified Researchers
Objective2_Procedure_Manual.pdf Objectives Procedure manual for Objective 2 subjects (ages 0-4.5 years). Qualified Researchers
Objective1_Procedure_Manual.pdf Objectives Procedure manual for Objective 1 subjects (ages 4.5+ years). Qualified Researchers
www.tortoisedti.org Software New free DTI software. Qualified Researchers
Release4_notes.pdf Other Release 4 notes. June 2010. Qualified Researchers
Release3_notes.pdf Other Release 3 notes. October 2009. Qualified Researchers
Spectroscopy_white_paper.pdf Methods Spectroscopy white paper. Qualified Researchers
Clinical_behavioral_white_paper.pdf Methods Clinical/behavior white paper. Qualified Researchers
Neuroimaging_white_paper.pdf Methods Neuroimaging white paper. Qualified Researchers
Release4_notes.pdf Publication Release 4 notes. June 2010. Qualified Researchers
Release3_notes.pdf Other Release 3 notes. October 2009. 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
Objective2_procedure_manual.pdf Objectives Procedure manual for Objective 2 subjects (ages 0-4.5 years). Final version. 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
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

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


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 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.

The Data Expected tab is a tracking and scheduling tool. It displays the data that is expected from the project, along with final expected enrollment counts and initial dates for the submission and sharing of the data. There are two types of Data Expected displayed:

  1. Data Expected from Relevant Publications: Publications reported in association with the Collection’s grant and determined as relevant to data expected for sharing are listed first. Any data specific to these publications are expected to be shared using the NDA Study feature. If a publication on this list is marked relevant in error, the PI of the project can correct the status in the Publications tab. Once created Studies are visible in the Associated Studies tab. When sharing a Study, only the outcome measures for the subjects/time-points specific to the publication are shared. Other data with share dates that have not yet been met as defined below remain embargoed. Investigators can initiate Study creation from this page when logged in.
  2. Data Expected by Data Structure: The table displayed second is defined by the investigator and lists all of the measures expected for sharing as defined in the Data Dictionary. Targeted Enrollment indicates the expected final unique subject count for that structure. Initial submission dates indicate when NDA should expect the first upload of those data, and initial sharing dates indicate when the first round of those data is expected to be shared. Click the i next to the Data Expected title to view the structures that are counted within that item.

Those with Administrator control over the Collection may also use the Data Expected tab to request an exemption period from an expected biannual submission by providing a timeframe and reason, and then saving the Collection. Please note the program officer of the associated grant may review this request.

Relevant Publications
PubMed IDStudyTitleJournalAuthorsDate
No records found.
Data Expected is not applicable to this collection

You can use "Add New Data Expected" to add exsiting structures and create your project's list. However, this is also the method you can use to request new structures be created for your project. When adding the Data Expected item, if the structure already exists you can locate it and specify your dates and enrollment. To add a new structure and request it be defined in the Data Dictionary, select Upload Definition and attach the definition or material needed to create it, including manual, codebooks, forms, etc. If you have multiple files, please upload a zipped archive containing them all.

Expected dates should be selected based on the standard Data Sharing Regimen and are restricted to within date ranges based on the project start and end dates.

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
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 Secondary 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 Secondary Analysis Shared
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 Secondary 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 Secondary 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 Secondary 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. 384 / 384 Secondary 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 Secondary Analysis Shared
* Data not on individual level