Imaging of nonlinear and dynamic functional brain connectivity based on EEG recordings with the application on the diagnosis of Alzheimer's disease

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dc.contributor.author Zhao, Yifan
dc.contributor.author Zhao, Yitian
dc.contributor.author Durongbhan, Pholpat
dc.contributor.author Chen, Liangyu
dc.contributor.author Liu, Jiang
dc.contributor.author Billings, S. A.
dc.contributor.author Zis, Panagiotis
dc.contributor.author Unwin, Zoe C.
dc.contributor.author De Marco, Matteo
dc.contributor.author Venneri, Annalena
dc.contributor.author Blackburn, Daniel J.
dc.contributor.author Sarrigiannis, Ptolemaios G.
dc.date.accessioned 2019-11-27T11:10:34Z
dc.date.available 2019-11-27T11:10:34Z
dc.date.issued 2019-11-14
dc.identifier.citation Zhao Y, Zhao Y, Durongbhan P, et al., Imaging of nonlinear and dynamic functional brain connectivity based on EEG recordings with the application on the diagnosis of Alzheimer's disease. IEEE Transactions on Medical Imaging, Available online 14 November 2019 en_UK
dc.identifier.issn 0278-0062
dc.identifier.uri https://doi.org/10.1109/TMI.2019.2953584
dc.identifier.uri https://dspace.lib.cranfield.ac.uk/handle/1826/14771
dc.description.abstract Since age is the most significant risk factor for the development of Alzheimer’s disease (AD), it is important to understand the effect of normal ageing on brain network characteristics before we can accurately diagnose the condition based on information derived from resting state electroencephalogram (EEG) recordings, aiming to detect brain network disruption. This paper proposes a novel brain functional connectivity imaging method, particularly targeting the contribution of nonlinear dynamics of functional connectivity, on distinguishing participants with AD from healthy controls (HC). We describe a parametric method established upon a Nonlinear Finite Impulse Response model, and a revised orthogonal least squares algorithm used to estimate the linear, nonlinear and combined connectivity between any two EEG channels without fitting a full model. This approach, where linear and non-linear interactions and their spatial distribution and dynamics can be estimated independently, offered us the means to dissect the dynamic brain network disruption in AD from a new perspective and to gain some insight into the dynamic behaviour of brain networks in two age groups (above and below 70) with normal cognitive function. Although linear and stationary connectivity dominates the classification contributions, quantitative results have demonstrated that nonlinear and dynamic connectivity can significantly improve the classification. en_UK
dc.language.iso en en_UK
dc.publisher IEEE en_UK
dc.rights Attribution-NonCommercial 4.0 International *
dc.rights.uri http://creativecommons.org/licenses/by-nc/4.0/ *
dc.subject Alzheimer’s disease en_UK
dc.subject dementia en_UK
dc.subject visualisation en_UK
dc.subject System identification en_UK
dc.subject Machine learning en_UK
dc.title Imaging of nonlinear and dynamic functional brain connectivity based on EEG recordings with the application on the diagnosis of Alzheimer's disease en_UK
dc.type Article en_UK


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