New methods for enhanced brain activity mapping through multi-modal data-fusion and deep learning

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Characterizing the human brain's function and structure is crucial to understand its physiology in health and pathology. There are complementary ways of measuring brain activity: magnetoencephalography (MEG), electroencephalography (EEG), and functional magnetic resonance imaging (fMRI). The first two are characterised by high temporal but poor spatial resolution, the latter by low temporal but high spatial resolution. Thus, fusing MEG/EEG and fMRI would clearly be advantageous, providing both high temporal and spatial resolution. Furthermore, structural information derived from diffusion MRI (dMRI) via tractography methods can be coupled with brain activity to allow detailed modelling of brain function.This PhD will focus on the challenge of fusing the complementary information from MEG, fMRI and dMRI to predict the human brain physiological activity with unprecedented spatial and temporal resolution. Towards this goal, the PhD student will collect a unique dataset by measuring brain activity while the same participants watch the same movies in both MRI and MEG scanners. When observers view film clips their brains show correlated responses in specific areas, making the information-fusion easier.