Connectomics in Health and Disease: Graph Theoretical Analysis of Brain Networks
Prof. Ed Bullmore
Department of Psychiatry, University of Cambridge, UK
ImmunoPsychiatry, Immuno-Inflammation TAU, GlaxoSmithKline, Stevenage UK
By graph theoretical analysis of connectivity matrices derived from magnetic resonance imaging (MRI) it has been shown that human brain networks have a complex topology, characterised by small-worldness, modularity, and the existence of hub nodes and rich clubs. Human brains are also parsimoniously “wired”, with a strong bias towards short physical distances between connected regions, but wiring cost is not strictly minimized. It is suggested that brain networks have been selected by a trade-off between competitive pressures to minimize biological cost and to maximise cognitively valuable topological integration. High cost / high value network components, like connector hubs, are preferentially implicated by diverse clinical brain disorders. We show that analogous principles apply to brain networks of the nematode worm C elegans, and the mouse, and that new technologies, such as high-resolution micro-electrode arrays, will enable a more detailed analysis of the biological mechanisms driving network topology and development. It seems that network science is in a strong position to understand more completely both the nearly-universal principles and the specific biological details of the connectome.
Prof. Ed Bullmore
Department of Psychiatry, University of Cambridge, UK
ImmunoPsychiatry, Immuno-Inflammation TAU, GlaxoSmithKline, Stevenage UK
By graph theoretical analysis of connectivity matrices derived from magnetic resonance imaging (MRI) it has been shown that human brain networks have a complex topology, characterised by small-worldness, modularity, and the existence of hub nodes and rich clubs. Human brains are also parsimoniously “wired”, with a strong bias towards short physical distances between connected regions, but wiring cost is not strictly minimized. It is suggested that brain networks have been selected by a trade-off between competitive pressures to minimize biological cost and to maximise cognitively valuable topological integration. High cost / high value network components, like connector hubs, are preferentially implicated by diverse clinical brain disorders. We show that analogous principles apply to brain networks of the nematode worm C elegans, and the mouse, and that new technologies, such as high-resolution micro-electrode arrays, will enable a more detailed analysis of the biological mechanisms driving network topology and development. It seems that network science is in a strong position to understand more completely both the nearly-universal principles and the specific biological details of the connectome.
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