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.authorZhao, Yifan
dc.contributor.authorZhao, Yitian
dc.contributor.authorDurongbhan, Pholpat
dc.contributor.authorChen, Liangyu
dc.contributor.authorLiu, Jiang
dc.contributor.authorBillings, S. A.
dc.contributor.authorZis, Panagiotis
dc.contributor.authorUnwin, Zoe C.
dc.contributor.authorDe Marco, Matteo
dc.contributor.authorVenneri, Annalena
dc.contributor.authorBlackburn, Daniel J.
dc.contributor.authorSarrigiannis, Ptolemaios G.
dc.date.accessioned2019-11-27T11:10:34Z
dc.date.available2019-11-27T11:10:34Z
dc.date.issued2019-11-14
dc.description.abstractSince 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.identifier.citationZhao 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 2019en_UK
dc.identifier.issn0278-0062
dc.identifier.urihttps://doi.org/10.1109/TMI.2019.2953584
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/14771
dc.language.isoenen_UK
dc.publisherIEEEen_UK
dc.rightsAttribution-NonCommercial 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by-nc/4.0/*
dc.subjectAlzheimer’s diseaseen_UK
dc.subjectdementiaen_UK
dc.subjectvisualisationen_UK
dc.subjectSystem identificationen_UK
dc.subjectMachine learningen_UK
dc.titleImaging of nonlinear and dynamic functional brain connectivity based on EEG recordings with the application on the diagnosis of Alzheimer's diseaseen_UK
dc.typeArticleen_UK

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