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23 April 2024 |
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Article overview
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Characterising brain network topologies: a dynamic analysis approach using heat kernels | | A.W. Chung
; M.D. Schirmer
; M.L. Krishna
; G. Ball
; P. Aljabar
; A.D. Edwards
; G. Montana
; | | Date: |
22 Mar 2016 | | Abstract: | Network theory provides a principled abstraction of the human brain: reducing
a complex system into a simpler representation from which to investigate brain
organisation. Recent advancement in the neuroimaging field are towards
representing brain connectivity as a dynamic process in order to gain a deeper
understanding of how the brain is organised for information transport. In this
paper we propose a network modelling approach based on the heat kernel to
capture the process of heat diffusion in complex networks. By applying the heat
kernel to structural brain networks, we define new features which quantify
change in energy flow. Identifying suitable features which can classify
networks between cohorts is useful towards understanding the effect of disease
on brain architecture. We demonstrate the discriminative power of heat kernel
features in both synthetic and clinical preterm data. By generating an
extensive range of synthetic networks with varying density and randomisation,
we investigate how heat flows in the networks in relation to changes in network
topology. We demonstrate that our proposed features provide a metric of network
efficiency and may be indicative of organisational principles commonly
associated with, for example, small-world architecture. In addition, we show
the potential of these features to characterise and classify between network
topologies. We further demonstrate our methodology in a clinical setting by
applying it to a large cohort of preterm babies scanned at term equivalent age
from which diffusion networks were computed. We show that our heat kernel
features are able to successfully predict motor function measured at two years
of age (sensitivity, specificity, F-score, accuracy = 75.0, 82.5, 78.6, 82.3%,
respectively. | | Source: | arXiv, 1603.6790 | | Services: | Forum | Review | PDF | Favorites | |
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