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Graph theoretical modeling of baby brain networks.
Zhao, Tengda; Xu, Yuehua; He, Yong.
Afiliación
  • Zhao T; State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, 100875, China; Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, 100875, China; IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, 100875, China.
  • Xu Y; State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, 100875, China; Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, 100875, China; IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, 100875, China.
  • He Y; State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, 100875, China; Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, 100875, China; IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, 100875, China. Electronic address: yong.he@bnu.edu.cn.
Neuroimage ; 185: 711-727, 2019 01 15.
Article en En | MEDLINE | ID: mdl-29906633
ABSTRACT
The human brain undergoes explosive growth during the prenatal period and the first few postnatal years, establishing an early infrastructure for the later development of behaviors and cognitions. Revealing the developmental rules during the early phase is essential for understanding the emergence of brain functions and the origin of developmental disorders. Graph-theoretical network modeling in combination with multiple neuroimaging probes provides an important research framework to explore the early development of the topological wiring and organizational paradigms of the brain. Here, we reviewed studies that employed neuroimaging and graph-theoretical modeling to investigate brain network development from approximately 20 gestational weeks to 2 years of age. Specifically, the structural and functional brain networks have evolved to highly efficient topological architectures in the early stage; where the structural network remains ahead and paves the way for the development of the functional network. The brain network develops in a heterogeneous order, from primary to higher-order systems and from a tendency of network segregation to network integration in the prenatal and postnatal periods. The early brain network topologies show abilities in predicting certain cognitive and behavior performance in later life, and their impairments are likely to continue into childhood and even adulthood. These macroscopic topological changes may be associated with possible microstructural maturations, such as axonal growth and myelinations. Collectively, this review provides a detailed delineation of the early changes in the baby brains in a graph-theoretical modeling framework, which opens up a new avenue for understanding the developmental principles of the connectome.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Encéfalo / Modelos Teóricos / Red Nerviosa Tipo de estudio: Prognostic_studies Límite: Female / Humans / Infant / Male / Newborn Idioma: En Revista: Neuroimage Asunto de la revista: DIAGNOSTICO POR IMAGEM Año: 2019 Tipo del documento: Article País de afiliación: China

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Encéfalo / Modelos Teóricos / Red Nerviosa Tipo de estudio: Prognostic_studies Límite: Female / Humans / Infant / Male / Newborn Idioma: En Revista: Neuroimage Asunto de la revista: DIAGNOSTICO POR IMAGEM Año: 2019 Tipo del documento: Article País de afiliación: China
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