Our main research objective is to develop new methodologies for medical image processing. Most of our projects are related to neuroimaging, including functional magnetic resonance imaging (fMRI), laser Doppler imaging (LDI), and electroencephalography (EEG). Sophisticated tools in signal processing and statistics are required to fully exploit the potential of functional brain imaging data. Among those tools, the wavelet transform receives our particular attention. We develop multivariate analyses based on machine learning techniques that can take advantage of subtle coupling between voxels and lead to backward inference; so-called “mind reading” based on fMRI data. Another research axis pursues better integration of analysis methods for intrinsic and evoked brain activity. Our point-of-view is to consider intrinsic activity as an essential element that modulates evoked activity, for example through fluctuations in brain networks. One of our primary research goals is to bridge the gap between theoretical advances and applications in neurosciences and medical imaging.
Increased functional connectivity in the right dorsal auditory stream after a full year of piano training in healthy older adults.
Probing neurodynamics of experienced emotions-a Hitchhiker's guide to film fMRI.
Linking connectivity of deep brain stimulation of nucleus accumbens area with clinical depression improvements: a retrospective longitudinal case series.
Markers of limbic system damage following SARS-CoV-2 infection.
Institut de Bioengineering
Faculté de médecine
Université de Genève