Understanding brain networks and brain organization

Abstract

What is the relationship between brain and behavior? The answer to this question necessitates characterizing the mapping between structure and function. The aim of this paper is to discuss broad issues surrounding the link between structure and function in the brain that will motivate a network perspective to understanding this question. As others in the past, I argue that a network perspective should supplant the common strategy of understanding the brain in terms of individual regions. Whereas this perspective is needed for a fuller characterization of the mind-brain, it should not be viewed as panacea. For one, the challenges posed by the many-to-many mapping between regions and functions is not dissolved by the network perspective. Although the problem is ameliorated, one should not anticipate a one-to-one mapping when the network approach is adopted. Furthermore, decomposition of the brain network in terms of meaningful clusters of regions, such as the ones generated by community-finding algorithms, does not by itself reveal 'true' subnetworks. Given the hierarchical and multi-relational relationship between regions, multiple decompositions will offer different 'slices' of a broader landscape of networks within the brain. Finally, I described how the function of brain regions can be characterized in a multidimensional manner via the idea of diversity profiles. The concept can also be used to describe the way different brain regions participate in networks.

61 pages, 24 figures

References in corpus (15)

• Community structure in social and biological networks
• Uncovering the overlapping community structure of complex networks in nature and society
• Assortative mixing in networks
• Functional cartography of complex metabolic networks
• Identification of influential spreaders in complex networks
• Community Structure in Time-Dependent, Multiscale, and Multiplex Networks
• Hierarchical structure and the prediction of missing links in networks
• Link communities reveal multiscale complexity in networks
• Dynamic reconfiguration of human brain networks during learning
• Weak pairwise correlations imply strongly correlated network states in a neural population
• Detecting rich-club ordering in complex networks
• The performance of modularity maximization in practical contexts
• Multirelational Organization of Large-scale Social Networks in an Online World
• Communicability in complex networks
• Classes of complex networks defined by role-to-role connectivity profiles

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