Title |
Towards the “Baby Connectome”: Mapping the Structural Connectivity of the Newborn Brain
|
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Published in |
PLOS ONE, February 2012
|
DOI | 10.1371/journal.pone.0031029 |
Pubmed ID | |
Authors |
Olga Tymofiyeva, Christopher P. Hess, Etay Ziv, Nan Tian, Sonia L. Bonifacio, Patrick S. McQuillen, Donna M. Ferriero, A. James Barkovich, Duan Xu |
Abstract |
Defining the structural and functional connectivity of the human brain (the human "connectome") is a basic challenge in neuroscience. Recently, techniques for noninvasively characterizing structural connectivity networks in the adult brain have been developed using diffusion and high-resolution anatomic MRI. The purpose of this study was to establish a framework for assessing structural connectivity in the newborn brain at any stage of development and to show how network properties can be derived in a clinical cohort of six-month old infants sustaining perinatal hypoxic ischemic encephalopathy (HIE). Two different anatomically unconstrained parcellation schemes were proposed and the resulting network metrics were correlated with neurological outcome at 6 months. Elimination and correction of unreliable data, automated parcellation of the cortical surface, and assembling the large-scale baby connectome allowed an unbiased study of the network properties of the newborn brain using graph theoretic analysis. In the application to infants with HIE, a trend to declining brain network integration and segregation was observed with increasing neuromotor deficit scores. |
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Country | Count | As % |
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Canada | 2 | 33% |
Chile | 1 | 17% |
Japan | 1 | 17% |
Unknown | 2 | 33% |
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Scientists | 1 | 17% |
Practitioners (doctors, other healthcare professionals) | 1 | 17% |
Mendeley readers
Geographical breakdown
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United Kingdom | 3 | 2% |
Canada | 3 | 2% |
Australia | 1 | <1% |
Netherlands | 1 | <1% |
Belarus | 1 | <1% |
Germany | 1 | <1% |
Spain | 1 | <1% |
Luxembourg | 1 | <1% |
Other | 0 | 0% |
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Researcher | 43 | 23% |
Student > Master | 22 | 12% |
Professor > Associate Professor | 13 | 7% |
Other | 11 | 6% |
Other | 29 | 16% |
Unknown | 19 | 10% |
Readers by discipline | Count | As % |
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Engineering | 25 | 13% |
Psychology | 22 | 12% |
Agricultural and Biological Sciences | 21 | 11% |
Neuroscience | 21 | 11% |
Other | 34 | 18% |
Unknown | 29 | 16% |