Information geometric theory of neural information processing and disorder

Funder

EPSRC

Value

£80,623

Team

Professor Eun-jin Kim 

Dr Fei He

Duration

February 2023 – February 2024

Project overview

Information processing is shared among seemingly different nonlinear complex dynamics. In particular, there has been a growing interest in information geometry that refers to the application of differential geometry to probability and statistics by defining the notion of metric (distance) in statistical manifolds. It provides us with a powerful method for understanding random stochastic processes for theoretical and practical purposes. Conceptually, assigning a metric to probability density functions (PDFs) enables us to quantify the difference among different PDFs and thus to make a beautiful link between a stochastic process, complexity, and geometry. This project aims to develop a new model-free information geometric theory of neural information processing for the practical purpose of improved disorder diagnosis by overcoming various current challenges.

Project objectives

1. To further develop a novel information geometric theory that can simultaneously accommodate non-stationary time varying effects, nonlinearity, and non-Gaussian stochasticity most effectively with potential wider applicability to different systems
2. To apply the theory to anonymised electroencephalogram (EEG) data from healthy control groups and patients with certain neurological disorders and test our new method against the existing method
3. To develop biomarkers to diagnose neurological disorders such as seizures, track disease progression, and explore clinical implications.

Impact statement

1. Offer new diagnostic opportunities for a whole range of neurological disorders while complementing the current strategic and research needs at clinics.
2. Benefit NHS clinical practice and research activities by clinicians working on complex brain processes.
3. Contribute to the development of future research tools in clinic practice to understand various elusive brain network functions.
4. Benefit researchers working in laboratory and astrophysical fluids/plasmas where causality quantification is crucial.
5. Provide a new interdisciplinary method across disciplines where information theory plays an increasingly important role.
6. Benefit other researchers working on stochastic processes, non-equilibrium phenomena, time-series analysis, data analysis, statistical analysis, and turbulence in diverse disciplines including physics, biomedical science, economics, engineering, and social science.