Details of Grant 

EPSRC Reference:	EP/J002909/1
Title:	The Human Brain as a Complex System: Investigating the Relationship between Structural and Functional Networks in the Thalamocortical System
Principal Investigator:	Bagshaw, Dr AP
Other Investigators:	Arvanitis, Professor T
Researcher Co-Investigators:
Project Partners:
Department:	School of Psychology
Organisation:	University of Birmingham
Scheme:	Standard Research
Starts:	03 September 2012	Ends:	31 December 2015	Value (£):	588,306
EPSRC Research Topic Classifications:	Complexity Science Image & Vision Computing Information & Knowledge Mgmt Networks & Distributed Systems
EPSRC Industrial Sector Classifications:	Healthcare
Related Grants:
Panel History:	Panel Date Panel Name Outcome 07 Dec 2011 EPSRC ICT Responsive Mode - Dec 2011 Announced
Summary on Grant Application Form
The majority of brain functions are performed not be single regions but by the combined, coordinated activity of networks distributed throughout the brain. Several neurological and psychiatric disorders may be caused by a breakdown of the ability of these regions to communicate effectively. While several different methods have been developed to understand how the component regions, or nodes, of a network interact, there is no comprehensive framework for combining the information from different techniques to give an overall picture of network function. Without such a framework, advances in neuroimaging techniques which allow the characterisation of anatomical and functional connections cannot be fully exploited. The purpose of this project is to develop such a framework, making use of intrinsic brain activity which can define well characterised model networks, thereby providing a natural validation of the results. The nodes of brain networks can be identified using three different definitions of connectivity between regions. Structural connectivity (SC) describes the anatomical connections between regions, functional connectivity (FC) identifies whether the activity of two regions increases and decreases coherently, while effective connectivity (EC) attempts to describe the brain not in terms of EEG or MRI signals, but the underlying neuronal populations which produce them. Each of these measures can be estimated using multiple different data acquisition and analysis techniques. For example, SC can be determined from diffusion tensor imaging (DTI) MRI scans, which are sensitive to the diffusion of water in white matter tracts, or from measurements of cortical thickness. Similarly, FC can be calculated from electroencephalography (EEG) or functional MRI (fMRI) measurements. Understanding how these different measures of connectivity are related, and how measurements of human brain function and structure can be combined to produce a unified picture, is not straightforward. Few studies have acquired the high quality data with multiple techniques that is required for such an undertaking. A further complication is that of defining model networks which are of sufficient complexity to provide a realistic test of any methodological developments, while being sufficiently well-characterised to allow developments to be validated. We will overcome this issue in a novel way by building on decades of invasive neurophysiological experiments which have characterised the networks responsible for the generation of thalamocortical oscillations (TCO), electrophysiological events that are generated by interactions between cortical and thalamic network nodes. TCO can be hallmarks of normal brain function (alpha rhythm, sleep spindles and K-complexes), or pathophysiology, of which the most obvious are generalised spike-wave discharges, characteristic of generalised epilepsy. This project will use the networks defined by TCO to investigate the relationships between different measures of brain connectivity, developing and optimising new methods to combine and fully exploit all of the information that can be extracted from non-invasive brain imaging data. Modelling and analysis of these networks will be based on graph theoretical approaches. By using these restricted and well-characterised model networks, we will be able to validate our work against previous neurophysiological data, and provide general tools for the neuroimaging community. In addition, we will shed light on the generation of normal and pathological brain activity and how this arises from network connectivity patterns.
Key Findings
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Organisation Website:	http://www.bham.ac.uk