A multisample study of longitudinal changes in brain network architecture in 4-13-year-old children
Wierenga, Lara M.; van den Heuvel, Martijn P.; Oranje, Bob; Giedd, Jay N.; Durston, Sarah; Peper, Jiska S.; Brown, Timothy T.; Crone, Eveline A
(2018) Human Brain Mapping, volume 39, issue 1, pp. 157 - 170
(Article)
Abstract
Recent advances in human neuroimaging research have revealed that white-matter connectivity can be described in terms of an integrated network, which is the basis of the human connectome. However, the developmental changes of this connectome in childhood are not well understood. This study made use of two independent longitudinal diffusion-weighted
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imaging data sets to characterize developmental changes in the connectome by estimating age-related changes in fractional anisotropy (FA) for reconstructed fibers (edges) between 68 cortical regions. The first sample included 237 diffusion-weighted scans of 146 typically developing children (4–13 years old, 74 females) derived from the Pediatric Longitudinal Imaging, Neurocognition, and Genetics (PLING) study. The second sample included 141 scans of 97 individuals (8–13 years old, 62 females) derived from the BrainTime project. In both data sets, we compared edges that had the most substantial age-related change in FA to edges that showed little change in FA. This allowed us to investigate if developmental changes in white matter reorganize network topology. We observed substantial increases in edges connecting peripheral and a set of highly connected hub regions, referred to as the rich club. Together with the observed topological differences between regions connecting to edges showing the smallest and largest changes in FA, this indicates that changes in white matter affect network organization, such that highly connected regions become even more strongly imbedded in the network. These findings suggest that an important process in brain development involves organizing patterns of inter-regional interactions. Hum Brain Mapp 39:157–170, 2018.
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Keywords: DWI, MRI, brain development, brain network, graph theory, Clinical Neurology, Neurology, Radiological and Ultrasound Technology, Radiology Nuclear Medicine and imaging, Anatomy
ISSN: 1065-9471
Publisher: Wiley-Liss Inc.
Note: Funding Information: Contract grant sponsor: National Institutes of Health; Contract grant number: RC2DA029475; R24HD075489; R01DA038958; RC2DA029475; Contract grant sponsor: European Research Council; Contract grant number: ERC-2010-StG-263234 (to E.A.C.); Contract grant sponsor: The Netherlands Organisation for Scientific Research VICI grant NWO; Contract grant number: 451-10-007 (to S.D.); Contract grant sponsor: KNAW ter Meulen (to L.M.W.); Contract grant sponsor: The VIDI; Contract grant number: 452-16-015 (to M.P.H.); Contract grant sponsor: en MQ (MQ fellowship) (to M.P.H.) Data used in preparation of this article were obtained from the Pediatric Longitudinal Imaging, Neurocognition, and Genetics Study (PLING) (http://www.chd.ucsd.edu/research/pling.html). As such, the PLING investigators contributed to the design and Funding Information: The authors thank all subjects and their parents for participating in this study. PLING data are disseminated by the Center for Human Development, University of California, San Diego. Publisher Copyright: © 2017 Wiley Periodicals, Inc.
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