Characterising brain network topologies: A dynamic analysis approach using heat kernels.

摘要: Network theory provides a principled abstraction of the human brain: reducing complex system into simpler representation from which to investigate brain organisation. Recent advancement in neuroimaging field is towards representing connectivity as dynamic process order gain deeper understanding how organised for information transport. In this paper we propose network modelling approach based on heat kernel capture diffusion networks. By applying structural networks, define new features quantify change propagation. Identifying suitable can classify networks between cohorts useful effect disease architecture. We demonstrate discriminative power both synthetic and clinical preterm data. generating an extensive range with varying density randomisation, relation changes topology. that our proposed provide metric efficiency may be indicative organisational principles commonly associated with, example, small-world addition, show potential these characterise topologies. further methodology setting by it large cohort babies scanned at term equivalent age were computed. are able successfully predict motor function measured two years (sensitivity, specificity, F-score, accuracy = 75.0, 82.5, 78.6, 82.3%, respectively).