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.
Supervised learning to quantify amyloidosis in whole brains of an Alzheimer's disease mouse model acquired with optical projection tomography.
Large-Scale Brain Network Dynamics Provide a Measure of Psychosis and Anxiety in 22q11.2 Deletion Syndrome.
Music in premature infants enhances high-level cognitive brain networks.
Resting brain dynamics at different timescales capture distinct aspects of human behavior.
Institut de Bioengineering
Faculté de médecine
Université de Genève