DiSCo MRI SFN
DiSCo MRI SFN
Total Funding€ 1 721 726
MRI is indispensable in the diagnosis of neurodegenerative diseases. These are poorly understood while their prevalence and socio-economic burden continue to rise. Structural and functional MRI can provide biomarkers for early diagnosis and potential therapeutic intervention. My research vision is to develop novel MRI methods for structural and functional mapping of tissue magnetic susceptibility and electrical conductivity as these show great promise for neuroimaging in diseases such as Alzheimer’s (AD). Susceptibility mapping (SM), which I pioneered, is uniquely sensitive to tissue composition including iron content affected in AD while conductivity mapping (CM) probably reflects cellular disruption in AD. Resting-state functional MRI (rsfMRI) reveals how AD affects brain networks without any tasks or stimulation equipment. However, each technique currently needs a separate time-consuming MRI scan. I will develop an integrated scan for simultaneous structural SM and CM, and rsfMRI functional connectivity characterisation. This efficient scan, ideal for AD patients, will reveal totally new resting-state networks based on electromagnetic properties: resting-state functional SM and resting-state functional CM for the first time. As changes in blood susceptibility underlie fMRI, rsfSM should measure functional connectivity more directly. This also makes it sensitive to physiological noise so I will develop noise removal methods building on fMRI techniques I established. Initial fSM studies have been at 7 Tesla but I will target the more widespread 3T field to maximise applicability. As a leader in both SM and rsfMRI physiological noise removal I have the ideal background to integrate SM and CM with fMRI and extend them for ground-breaking functional electromagnetic connectivity. This research will yield a rich set of novel, multimodal MRI contrasts to allow development of new combined structural and functional biomarkers for early diagnosis of AD and other diseases.
Project partnersUniversity College London