Computational Neuroimaging Lab 

The CoNI Lab is a dynamic research group which is part of the Precision maging Beacon and the Sir Peter Mansfield Imaging Centre. 

We work at the interface of computational modelling, MRI physics, and neuroscience. Broadly, our scientific goal is to achieve a better understanding of the structural and functional organisation of the brain at a systems level. We develop computational methodology and algorithmic/modelling frameworks for analysing neuroimaging data and specifically for mapping brain connections and microstructure using MRI. Our aim is to provide principled platforms for probing the brain organisation to untangle its complexity in health and disease. 

Development of modelling frameworks for mapping brain connections and microstructure 

We devise novel computational frameworks for analysing brain images. Particularly, we have focused on the analysis of multi-dimensional diffusion MRI data, which uniquely allow inference of microstructural tissue properties, as well as of macroscopic connections, non-invasively and in-vivo. Due to the indirect nature of the measurements, advanced signal/image processing techniques and modeling approaches are needed to obtain reliable estimates of the quantities of interest. Such methodology has been developed through our contributions, which allow biophysical information to be extracted from diffusion MRI that otherwise would have only been accessible in humans via post-mortem approaches. 

Pushing the boundaries of in-vivo brain MRI acquisitions and high-throughput analysis 

We have been heavily involved with the design and implementation of new technologies that aim to push the boundaries of in-vivo brain MRI acquisitions and analysis. For instance, we have been major contributors to the Human Connectome Project, which has contributed to changing the way neuroimaging data are acquired and processed. These developments have also set the paradigm for subsequent large-scale brain mapping projects in disease, development and aging. 

We also have a track record of releasing open-source software, impacting research methodology worldwide. We have developed frameworks that allow masive parallelisation in data processing for high-thoughput analysis for example using GP-GPUs. Finally, we have contributed to public data releases through a number of large collaborative projects, sharing publicly the wealth of collected data and contributing to Open Science. 

Personalised connectivity mapping and characterisation of individual variability 

We develop platforms to enable principled subject-specific mapping of brain connections and organisation and identification of personalised patterns. As the brain connections constraint what the humam mind can and cannot compute, we aim to link such patterns with function and behaviour to establish fingerprints of individual brains. These allow us to establish correspondences and frames of reference for studying brain changes throughout the lifespan, even across species. They also permit mapping variability at the population level for establishing measures of what is a healthy brain network. The latter could subsequently provide insight and offer quantitative assessments of disruptions linked to mental health disorders. 

Studying brain organisation in basic and clinical neuroscience 

Through collaborations with basic and clinical neuroscientists, we use brain connectivity mapping approaches to explore brain organisation and re-organisation (e.g. due to plasticity or non-invasive neuromodulation). We also work on extracting patterns of connectivity (even across species) that can be linked to the functional organisation of the brain. Such connectivity patterns have been used in collaboration with neuro-surgeons for pre-surgical planning and identification of efficacious targets for surgical interventions.