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.
When makes you unique: Temporality of the human brain fingerprint.
Mood disorders disrupt the functional dynamics, not spatial organization of brain resting state networks.
Characterization and prediction of clinical pathways of vulnerability to psychosis through graph signal processing.
Author Correction: Open-access quantitative MRI data of the spinal cord and reproducibility across participants, sites and manufacturers.
Institut de Bioengineering
Faculté de médecine
Université de Genève