A tutorial in connectome analysis: Topological and spatial features of brain networks

被引:252
作者
Kaiser, Marcus [1 ,2 ,3 ]
机构
[1] Newcastle Univ, Sch Comp Sci, Newcastle Upon Tyne NE1 7RU, Tyne & Wear, England
[2] Newcastle Univ, Inst Neurosci, Newcastle Upon Tyne NE1 7RU, Tyne & Wear, England
[3] Seoul Natl Univ, Dept Brain & Cognit Sci, Seoul 151, South Korea
基金
英国工程与自然科学研究理事会;
关键词
Cortical networks; Neural networks; Neuronal networks; Brain connectivity; Connectome; Network analysis; FUNCTIONAL CONNECTIVITY; HIERARCHICAL ORGANIZATION; COMMUNITY STRUCTURE; CORTICAL NETWORKS; COMPLEX NETWORKS; TIME WINDOWS; OPTIMIZATION; DYNAMICS; AREAS; EVOLUTIONARY;
D O I
10.1016/j.neuroimage.2011.05.025
中图分类号
Q189 [神经科学];
学科分类号
071006 ;
摘要
High-throughput methods for yielding the set of connections in a neural system, the connectome, are now being developed. This tutorial describes ways to analyze the topological and spatial organizations of the connectome at the macroscopic level of connectivity between brain regions as well as the microscopic level of connectivity between neurons. We will describe topological features at three different levels: the local scale of individual nodes, the regional scale of sets of nodes, and the global scale of the complete set of nodes in a network. Such features can be used to characterize components of a network and to compare different networks, e.g. the connectome of patients and control subjects for clinical studies. At the global scale, different types of networks can be distinguished and we will describe Erdos-Renyi random, scale-free, small-world, modular, and hierarchical archetypes of networks. Finally, the connectome also has a spatial organization and we describe methods for analyzing wiring lengths of neural systems. As an introduction for new researchers in the field of connectome analysis, we discuss the benefits and limitations of each analysis approach. (c) 2011 Elsevier Inc. All rights reserved.
引用
收藏
页码:892 / 907
页数:16
相关论文
共 134 条
  • [1] A resilient, low-frequency, small-world human brain functional network with highly connected association cortical hubs
    Achard, S
    Salvador, R
    Whitcher, B
    Suckling, J
    Bullmore, ET
    [J]. JOURNAL OF NEUROSCIENCE, 2006, 26 (01) : 63 - 72
  • [2] Efficiency and cost of economical brain functional networks
    Achard, Sophie
    Bullmore, Edward T.
    [J]. PLOS COMPUTATIONAL BIOLOGY, 2007, 3 (02) : 174 - 183
  • [3] Link communities reveal multiscale complexity in networks
    Ahn, Yong-Yeol
    Bagrow, James P.
    Lehmann, Sune
    [J]. NATURE, 2010, 466 (7307) : 761 - U11
  • [4] Challenges and Opportunities in Mining Neuroscience Data
    Akil, Huda
    Martone, Maryann E.
    Van Essen, David C.
    [J]. SCIENCE, 2011, 331 (6018) : 708 - 712
  • [5] Biological networks: The tinkerer as an engineer
    Alon, U
    [J]. SCIENCE, 2003, 301 (5641) : 1866 - 1867
  • [6] [Anonymous], [No title captured]
  • [7] [Anonymous], FRONT NEUROINFORMATI
  • [8] [Anonymous], 2004, Principles of Brain Evolution
  • [9] [Anonymous], 1992, AY's Neuroanatomy of C. elegans for Computation
  • [10] [Anonymous], 1999, Small Worlds