Robust graph analysis of brain connectivity

A broad range of neurological diseases and disorders have been linked with pathological alterations in the connectivity of the brain. However, robust methods for identifying and characterising abnormalities in connectivity, their evolution over time and response to treatments, are lacking; and addressing this omission represents a pressing clinical need. In this proposal we aim to meet this need using novel methods based on medical imaging and graph theory, a well-established mathematical framework with which to describe and characterise interconnected systems. To establish a clear baseline, we will characterise the typical "normal" connectivity network, and model statistically its variability in a healthy population. We will also investigate approaches to classifying graphs into groups, and identify common factors underlying structural and functional connectivity, to be used as new biomarkers. We will use magnetic resonance imaging (MRI) and electroencephalography (EEG) to obtain connectivity information in the living brain; and childhood epilepsy and autism spectrum disorders will be investigated as neurological disorders which can display altered brain connectivity. Our developments will also be applicable to other data which can be represented graphically. This work will provide major novel tools for graph analysis of brain connectivity, thereby driving connectivity network methods towards reliable and routine clinical application. Through our close links with clinical colleagues we will ensure that our developments are well positioned for wide-ranging applicability, elucidating connectivity failures in disease and their recovery with treatment.

The primary opportunity for impact outside academia in the work described in this proposal lies in its contribution to the understanding of "disconnection syndromes". We highlight childhood epilepsy and autism spectrum disorders (ASDs) as two specific areas in which we expect a significant clinical impact, but disconnection between brain regions is thought to be a major contributing factor to a broad range of neurological deficits that collectively impose a huge health and economic burden on society. Since we also aim to elucidate the changes in connectivity in response to drug treatments, commercial exploitation of some of the developments arising from the project may also be a medium-to-long-term possibility in the pharmaceutical sector. Open access to the methods developed during the project, as well as clear summaries of the ideas embodied in it, will ensure that a very wide, international audience can benefit from them as quickly as possible.