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Cost-efficiency of complex human brain networks

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Graph theoretical analysis can be applied to human neuroimaging data, such as structural and functional MRI or magnetoencephalography (MEG), to investigate complex properties of brain structural and functional networks. Over multiple scales of space and time, these networks have an economical small-world topology, characterised by high efficiency of information transfer for low connection cost, and critical synchronization dynamics. Greater cost-efficiency of high-frequency functional networks is associated with better cognitive performance. Brain networks in neuropsychiatric disorders, like schizophrenia and obsessive compulsive disorder, are abnormally configured. In the light of these results, I will discuss possible selection criteria that might have favoured the evolution of highly cost-efficient brain networks.

This talk is part of the Networks & Neuroscience series.

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