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NeuroImage v.160, 2017년, pp.73 - 83   SCI SCIE SCOPUS
본 등재정보는 저널의 등재정보를 참고하여 보여주는 베타서비스로 정확한 논문의 등재여부는 등재기관에 확인하시기 바랍니다.

Multi-scale brain networks

Betzel, Richard F. (School of Engineering and Applied Science, Department of Bioengineering, USA ); Bassett, Danielle S. (School of Engineering and Applied Science, Department of Bioengineering, USA );
  • 초록  

    Abstract The network architecture of the human brain has become a feature of increasing interest to the neuroscientific community, largely because of its potential to illuminate human cognition, its variation over development and aging, and its alteration in disease or injury. Traditional tools and approaches to study this architecture have largely focused on single scales—of topology, time, and space. Expanding beyond this narrow view, we focus this review on pertinent questions and novel methodological advances for the multi-scale brain. We separate our exposition into content related to multi-scale topological structure, multi-scale temporal structure, and multi-scale spatial structure. In each case, we recount empirical evidence for such structures, survey network-based methodological approaches to reveal these structures, and outline current frontiers and open questions. Although predominantly peppered with examples from human neuroimaging, we hope that this account will offer an accessible guide to any neuroscientist aiming to measure, characterize, and understand the full richness of the brain's multiscale network structure—irrespective of species, imaging modality, or spatial resolution. Highlights The human brain can be represented as a multi-scale network. Characterizing the architecture of multi-scale networks requires new tools. We review promising multilayer tools from network science.


  • 주제어

    Complex networks .   Brain networks .   Multi-scale .   Network neuroscience .   Multi-resolution .   Multi-layer .   Graph theory.  

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