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

Yitian Zhao, Daniel J. Blackburn, Ptolemaios G. Sarrigiannis, Pholpat Durongbhan, Liangyu Chen, Jiang Liu, S. A. Billings, Panagiotis Zis, Zoe C. Unwin, Matteo De Marco, Annalena Venneri
2019 IEEE Transactions on Medical Imaging  
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
more » ... 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.
doi:10.1109/tmi.2019.2953584 pmid:31725372 fatcat:nr3juthasnhr3e2rwbywyk3yea