Computational Modelling of Neural Network Growth and Dynamics
Award Number
EP/G03950X/1Programme
Research GrantStatus / Stage
CompletedDates
1 March 2009 -1 July 2012
Duration (calculated)
03 years 04 monthsFunder(s)
EPSRC (UKRI)Funding Amount
£379,567.00Funder/Grant study page
EPSRCContracted Centre
Newcastle UniversityPrincipal Investigator
Marcus KaiserPI Contact
marcus.kaiser@ncl.ac.ukPI ORCID
0000-0002-3881-8399WHO Catergories
Understanding Underlying DiseaseDisease Type
Alzheimer's Disease (AD)Frontotemporal Demential (FTD)
CPEC Review Info
| Reference ID | 626 |
|---|---|
| Researcher | Reside Team |
| Published | 29/06/2023 |
Data
| Award Number | EP/G03950X/1 |
|---|---|
| Status / Stage | Completed |
| Start Date | 20090301 |
| End Date | 20120701 |
| Duration (calculated) | 03 years 04 months |
| Funder/Grant study page | EPSRC |
| Contracted Centre | Newcastle University |
| Funding Amount | £379,567.00 |
Abstract
Many brain diseases are caused by changes of neural development; e.g. schizophrenia, autism, and certain kinds of epilepsy. These changes of development result in neural network topologies that differ from those of healthy subjects. Recent studies of EEG (electroencephalography) synchronization networks, for example, found characteristic changes in network organisation for Alzheimer, schizophrenia, and epilepsy patients. To understand these diseases, it is essential to find out which developmental factors lead to altered network topologies and resulting functional changes such as waves or large-scale activations (as during epileptic seizures). As in vitro experiments are often limited and conditions are hard to control, I propose to develop the first in silico model of neural development in order to test hypotheses and inform future experiments.This proposal introduces a new direction of research in computational Neuroanatomy by determining the role of developmental factors through high-performance computer simulations and methods from network science and graph theory. These aims will be reached through the following two objectives:(1) Linking developmental factor and network topology: Studying the role of timing, spatial layout, and activity on the generated neural topology. Whereas methods for network generation have been investigated in the field of network science, very few take into account the spatial organisation and timing and therefore most models are not realistic for biological systems.(2) Linking network topology and dynamics: Linking the topology yielded by developmental simulations to experimentally observable features (waves, latencies, oscillations). As not all topologies can be tested, we will focus those with similar characteristics as the retina observing wave propagation and those with similar properties to cortical fibre tract networks for observing latencies and oscillations. These questions are also at the core of two grand challenges: understanding the architecture of brain and mind (GC5, UK Comp. Res. Comm.) and Building Brains (GC4, EPSRC network ‘Developing a Common Vision for UK research in Microelectronic Design’). Simply observing neural organisation might not be sufficient to understand how to translate this organisation to technical systems with different constraints. The proposed project can test which developmental configurations and resulting network topologies lead to equivalent dynamics and thus could identify configurations which lead to a scalable and feasible technical design. Insights into the relation between developmental factors and network behaviour will also help to explain developmental diseases potentially leading to new therapeutic treatments. We will discuss these applications with collaborators in engineering, developmental biology, and pharmaceutical research.
Aims
The proposed project can test which developmental configurations and resulting network topologies lead to equivalent dynamics and thus could identify configurations which lead to a scalable and feasible technical design. Insights into the relation between developmental factors and network behaviour will also help to explain developmental diseases potentially leading to new therapeutic treatments. We will discuss these applications with collaborators in engineering, developmental biology, and pharmaceutical research.