Connectivity Modelling

Analysis Research

Clustering of brain networks using structural and functional connectivity

Understanding the functional and structural connectivity of the brain is a major goal of modern neuroscience. Techniques such as resting-state FMRI and MEG provide a non-invasive method of measuring dynamic, functional connectivity in-vivo. Diffusion imaging provides a way to measure the structural component of connectivity - the wiring of the brain - via tractography. The aim of our research in connectivity is to explore and combine the information provided by these different sources of information to gain a better understanding of the brain.

This research includes methods for exploratory analysis as well as the development of image-based biomarkers, and probing the mechanisms underlying the connectivity. One strand of this research pursues voxel-based methods, such as ICA, that can map the spatial and temporal characteristics of resting-state networks, whilst another strand explores connectivity between nodes or regions, using graph-based methods and network analysis. In addition, we have a strong interest in modelling and analysing changes in connectivity over time, as exemplified by our work on Temporal Functional Modes.

Our research is at the heart of major projects in brain imaging and connectomics such as the Human Connectome Project (HCP), the Developing Human Connectome Project (dHCP) and the UK Biobank. The tools resulting from our research are available via FSL and these tools are part of the analysis pipelines in HCP, dHCP and Biobank projects.