Characterising Neurological Disorders with Nonlinear System Identification and Network Analysis
Funder
Engineering and Physical Sciences Research Council (EPSRC)
Value
£303,583 Value to CU; £379,479Total Value
Project Team
(PI) Dr Fei He
Duration of Project
01/12/2023 - 31/05/2026
Project Overview
With an increasingly ageing population, neurological disorders (ND), including Alzheimer's and Parkinson's disease (AD and PD), are becoming the second leading cause of death and the world's largest cause of disability-adjusted life years. Currently, incurable ND have a devastating impact on individuals, families and a heavy economic burden on societies. Early diagnosis and longitudinal monitoring of ND, such as for AD, is extremely important for their treatment, care and ongoing research. However, current ND diagnosis approaches, such as cognitive and physical assessment, invasive tests, or neuroimaging scans, are often either very subjective and uncomfortable, or very capital intensive and time-consuming.
In this project, we propose a new computational framework that integrates novel nonlinear systems engineering and network analysis for the diagnosis and characterisation of ND based on electroencephalography (EEG) recordings. EEG measures brain electrical activity through small electrodes attached to the scalp. EEG has the advantage of a relatively low cost, better accessibility and portability, user-friendliness and, importantly, superior temporal resolution.
Current EEG approaches predominantly employ either the analysis of a single EEG channel or the analysis of pairs of channels using simple (linear) methods that cannot capture the full complexity of the information, and focus on a selected local brain region. Our new approach will be to characterise ND by analysing the brain as a network using non-linear (cross-frequency) methods. Emerging evidence suggests that cross-frequency coupling, between different frequency bands, is the key mechanism in the integration of communication in the brain across spatial-temporal scales, and thus this project seeks to investigate its role in the development and progression of ND.
Project Objectives
Our goal will be realised through the deliverables from four technical work packages, namely: (1) development of a unified framework to identify and quantify cross-frequency neural interactions from a systems engineering approach; (2) development of a novel multi-layer cross-frequency network approach and extraction of global network features; (3) identification of important brain regions for nonlinear dynamic analysis, and; (4) the integration of nonlinear and network features for diagnostic purposes.
