Moon, Joon-Young Kim, Junhyeok Ko, Tae-Wook Kim, Minkyung Iturria-Medina, Yasser Choi, Jee-Hyun Lee, Joseph Mashour, George A. Lee, UnCheol 2024-01-20T01:34:02Z 2024-01-20T01:34:02Z 2021-09-01 2017-04-20 2045-2322 https://pubs.kist.re.kr/handle/201004/122840 Identifying how spatially distributed information becomes integrated in the brain is essential to understanding higher cognitive functions. Previous computational and empirical studies suggest a significant influence of brain network structure on brain network function. However, there have been few analytical approaches to explain the role of network structure in shaping regional activities and directionality patterns. In this study, analytical methods are applied to a coupled oscillator model implemented in inhomogeneous networks. We first derive a mathematical principle that explains the emergence of directionality from the underlying brain network structure. We then apply the analytical methods to the anatomical brain networks of human, macaque, and mouse, successfully predicting simulation and empirical electroencephalographic data. The results demonstrate that the global directionality patterns in resting state brain networks can be predicted solely by their unique network structures. This study forms a foundation for a more comprehensive understanding of how neural information is directed and integrated in complex brain networks. English NATURE PUBLISHING GROUP PHASE-LAG INDEX FUNCTIONAL CONNECTIVITY MODELS ORGANIZATION OSCILLATIONS INFORMATION PATTERNS PREDICTS KURAMOTO AREAS Structure Shapes Dynamics and Directionality in Diverse Brain Networks: Mathematical Principles and Empirical Confirmation in Three Species Article 10.1038/srep46606 1 SCIENTIFIC REPORTS, v.7 SCIENTIFIC REPORTS 7 scie scopus 000399748500001 2-s2.0-85021643111 Multidisciplinary Sciences Science & Technology - Other Topics Article PHASE-LAG INDEX FUNCTIONAL CONNECTIVITY MODELS ORGANIZATION OSCILLATIONS INFORMATION PATTERNS PREDICTS KURAMOTO AREAS