Complex brain networks: graph theoretical analysis of structural and functional systems View Full Text


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Article Info

DATE

2009-02-04

AUTHORS

Ed Bullmore, Olaf Sporns

ABSTRACT

Key PointsUnderstanding the network organization of the brain has been a long-standing challenge for neuroscience. In the past decade, developments in graph theory have provided many new methods for topologically analysing complex networks, some of which have already been translated to the characterization of anatomical and functional brain networks.Anatomical networks at whole-brain and cellular scales in several species consistently demonstrate conservation of wiring costs and small-world topology (high clustering and short path length). Human brain anatomical networks, derived from MRI or diffusion tensor imaging data, have high-degree cortical 'hubs' and modular and hierarchical properties.Functional networks also demonstrate small-world properties at whole-brain and cellular spatial scales. Additionally, complex network properties including small-worldness and the existence of hubs are conserved over different frequency scales in functional MRI and electrophysiological data.Convergent experimental and computational data suggest that there is interdependence in the organization of structural and functional networks. The topology, synchronizability and other dynamic properties of functional networks are strongly affected by small-world and other metrics of structural connectivity. Conversely, over a slower timescale the dynamics can modulate structural network topology.Neuropsychiatric disorders can be thought of as dysconnectivity syndromes, and graph theory has already been used to quantify abnormality of structural and functional network properties in schizophrenia, Alzheimer's disease and other disorders. Graph theory can help us to understand the vulnerability of brain networks to lesions and could in future be used to provide markers of genetic risk for disorders or to measure therapeutic effects of drug treatments on functional networks.The network organization of the brain, as it is beginning to be revealed by graph theory, is compatible with the hypothesis that the brain, perhaps in common with other complex networks, has evolved both to maximize the efficiency of information transfer and to minimize connection cost, at all scales of space and time. Key issues for future work include clarifying the relationship between the brain's network properties and its emergent cognitive behaviours in health and disease. More... »

PAGES

186-198

References to SciGraph publications

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  • 2008-04-23. Cortical network dynamics with time delays reveals functional connectivity in the resting brain in COGNITIVE NEURODYNAMICS
  • 2008-04-30. A technicolour approach to the connectome in NATURE REVIEWS NEUROSCIENCE
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  • 2002-12. Long-term dendritic spine stability in the adult cortex in NATURE
  • Journal

    TITLE

    Nature Reviews Neuroscience

    ISSUE

    3

    VOLUME

    10

    Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1038/nrn2575

    DOI

    http://dx.doi.org/10.1038/nrn2575

    DIMENSIONS

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    PUBMED

    https://www.ncbi.nlm.nih.gov/pubmed/19190637


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    51 anatomical networks
    52 behavior
    53 brain
    54 brain anatomical networks
    55 brain network properties
    56 brain networks
    57 cellular scale
    58 cellular spatial scales
    59 challenges
    60 characterization
    61 cognitive behavior
    62 complex network properties
    63 complex networks
    64 computational data
    65 connection cost
    66 connectivity
    67 conservation
    68 cost
    69 data
    70 decades
    71 development
    72 different frequency scales
    73 diffusion tensor imaging (DTI) data
    74 disease
    75 disorders
    76 drug treatment
    77 dynamic properties
    78 dynamics
    79 dysconnectivity syndrome
    80 effect
    81 efficiency
    82 electrophysiological data
    83 emergent cognitive behaviours
    84 existence
    85 existence of hubs
    86 frequency scale
    87 functional MRI
    88 functional brain networks
    89 functional network properties
    90 functional networks
    91 functional systems
    92 future
    93 future work
    94 genetic risk
    95 graph theoretical analysis
    96 graph theory
    97 health
    98 hierarchical properties
    99 hub
    100 human brain anatomical networks
    101 hypothesis
    102 imaging data
    103 information transfer
    104 interdependence
    105 issues
    106 key issues
    107 lesions
    108 markers
    109 method
    110 metrics
    111 network
    112 network organization
    113 network properties
    114 network topology
    115 neuropsychiatric disorders
    116 neuroscience
    117 new method
    118 organization
    119 past decade
    120 properties
    121 relationship
    122 risk
    123 scale
    124 scales of space
    125 schizophrenia
    126 slow timescale
    127 small-world properties
    128 small-world topology
    129 space
    130 spatial scales
    131 species
    132 structural connectivity
    133 structural network topology
    134 synchronizability
    135 syndrome
    136 system
    137 tensor imaging data
    138 theoretical analysis
    139 theory
    140 therapeutic effect
    141 time
    142 timescales
    143 topology
    144 transfer
    145 treatment
    146 vulnerability
    147 wiring cost
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