Imaging of Nonlinear and Dynamic Functional Brain Connectivity Based on EEG Recordings With the Application on the Diagnosis of Alzheimer's Disease

• Published in: IEEE transactions on medical imaging 2020-05, Vol.39 (5), p.1571-1581
• Main Authors: Zhao, Yifan, Zhao, Yitian, Durongbhan, Pholpat, Chen, Liangyu, Liu, Jiang, Billings, S. A., Zis, Panagiotis, Unwin, Zoe C., De Marco, Matteo, Venneri, Annalena, Blackburn, Daniel J., Sarrigiannis, Ptolemaios G.
• Format: Article
• Language: English
• Subjects: Age; Aging; Algorithms; Alzheimer's disease; Brain; Brain modeling; Classification; Cognitive ability; Dementia; Disruption; Dynamical systems; EEG; Electroencephalography; Hospitals; Imaging; Impulse response; machine learning; Medical diagnosis; Medical imaging; Neural networks; Neurodegenerative diseases; Neuroimaging; Nonlinear dynamics; Nonlinear response; Nonlinear systems; Risk analysis; Risk factors; Spatial distribution; system identification; visualisation
• ISSN: 0278-0062; 1558-254X
• DOI: 10.1109/TMI.2019.2953584

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 article 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 accuracy, barring the group of participants below the age of 70, for resting state EEG recorded during eyes open. The developed approach is generic and can be used as a powerful tool to examine brain network characteristics and disruption in a user friendly and systematic way.