Literature DB >> 16376577

The NIH MRI study of normal brain development.

Alan C Evans1.   

Abstract

MRI is increasingly used to study normal and abnormal brain development, but we lack a clear understanding of "normal". Previous studies have been limited by small samples, narrow age ranges and few behavioral measures. This multi-center project conducted epidemiologically based recruitment of a large, demographically balanced sample across a wide age range, using strict exclusion factors and comprehensive clinical/behavioral measures. A mixed cross-sectional and longitudinal design was used to create a MRI/clinical/behavioral database from approximately 500 children aged 7 days to 18 years to be shared with researchers and the clinical medicine community. Using a uniform acquisition protocol, data were collected at six Pediatric Study Centers and consolidated at a Data Coordinating Center. All data were transferred via a web-network into a MYSQL database that allowed (i) secure data transfer, (ii) automated MRI segmentation, (iii) correlation of neuroanatomical and clinical/behavioral variables as 3D statistical maps and (iv) remote interrogation and 3D viewing of database content. A population-based epidemiologic sampling strategy minimizes bias and enhances generalizability of the results. Target accrual tables reflect the demographics of the U.S. population (2000 Census data). Enrolled subjects underwent a standardized protocol to characterize neurobehavioral and pubertal status. All subjects underwent multi-spectral structural MRI. In a subset, we acquired T1/T2 relaxometry, diffusion tensor imaging, single-voxel proton spectroscopy and spectroscopic imaging. In the first of three cycles, successful structural MRI data were acquired in 392 subjects aged 4:6-18:3 years and in 72 subjects aged 7 days to 4:6 years. We describe the methodologies of MRI data acquisition and analysis, using illustrative results. This database will provide a basis for characterizing healthy brain maturation in relationship to behavior and serve as a source of control data for studies of childhood disorders. All data described here will be available to the scientific community from July, 2006.

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Year:  2006        PMID: 16376577     DOI: 10.1016/j.neuroimage.2005.09.068

Source DB:  PubMed          Journal:  Neuroimage        ISSN: 1053-8119            Impact factor:   6.556


  204 in total

1.  Network-level structural covariance in the developing brain.

Authors:  Brandon A Zielinski; Efstathios D Gennatas; Juan Zhou; William W Seeley
Journal:  Proc Natl Acad Sci U S A       Date:  2010-10-04       Impact factor: 11.205

2.  Developmental change in regional brain structure over 7 months in early adolescence: comparison of approaches for longitudinal atlas-based parcellation.

Authors:  Edith V Sullivan; Adolf Pfefferbaum; Torsten Rohlfing; Fiona C Baker; Mayra L Padilla; Ian M Colrain
Journal:  Neuroimage       Date:  2011-04-12       Impact factor: 6.556

3.  White matter integrity, language, and childhood onset schizophrenia.

Authors:  Kristi Clark; Katherine L Narr; Joseph O'Neill; Jennifer Levitt; Prabha Siddarth; Owen Phillips; Arthur Toga; Rochelle Caplan
Journal:  Schizophr Res       Date:  2012-03-10       Impact factor: 4.939

4.  NeuroLOG: sharing neuroimaging data using an ontology-based federated approach.

Authors:  Bernard Gibaud; Gilles Kassel; Michel Dojat; Bénédicte Batrancourt; Franck Michel; Alban Gaignard; Johan Montagnat
Journal:  AMIA Annu Symp Proc       Date:  2011-10-22

Review 5.  Do brain image databanks support understanding of normal ageing brain structure? A systematic review.

Authors:  David Alexander Dickie; Dominic E Job; Ian Poole; Trevor S Ahearn; Roger T Staff; Alison D Murray; Joanna M Wardlaw
Journal:  Eur Radiol       Date:  2012-02-22       Impact factor: 5.315

Review 6.  Sharing heterogeneous data: the national database for autism research.

Authors:  Dan Hall; Michael F Huerta; Matthew J McAuliffe; Gregory K Farber
Journal:  Neuroinformatics       Date:  2012-10

7.  Developmental changes in organization of structural brain networks.

Authors:  Budhachandra S Khundrakpam; Andrew Reid; Jens Brauer; Felix Carbonell; John Lewis; Stephanie Ameis; Sherif Karama; Junki Lee; Zhang Chen; Samir Das; Alan C Evans
Journal:  Cereb Cortex       Date:  2012-07-10       Impact factor: 5.357

8.  Beyond age and gender: relationships between cortical and subcortical brain volume and cognitive-motor abilities in school-age children.

Authors:  Melissa M Pangelinan; Guangyu Zhang; John W VanMeter; Jane E Clark; Bradley D Hatfield; Amy J Haufler
Journal:  Neuroimage       Date:  2010-11-13       Impact factor: 6.556

9.  The development of the corpus callosum in the healthy human brain.

Authors:  Eileen Luders; Paul M Thompson; Arthur W Toga
Journal:  J Neurosci       Date:  2010-08-18       Impact factor: 6.167

10.  Longitudinal Associations between Neurodevelopment and Psychosocial Health Status in Patients with Repaired D-Transposition of the Great Arteries.

Authors:  Victoria K Robson; Christian Stopp; David Wypij; Carolyn Dunbar-Masterson; David C Bellinger; David R DeMaso; Leonard A Rappaport; Jane W Newburger
Journal:  J Pediatr       Date:  2018-09-28       Impact factor: 4.406

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