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.
Brain functional connectivity dynamics at rest in the aftermath of affective and cognitive challenges.
Computational imaging during video game playing shows dynamic synchronization of cortical and subcortical networks of emotions.
Structural Neuroplastic Responses Preserve Functional Connectivity and Neurobehavioural Outcomes in Children Born Without Corpus Callosum.
Train the brain with music (TBM): brain plasticity and cognitive benefits induced by musical training in elderly people in Germany and Switzerland, a study protocol for an RCT comparing musical instrumental practice to sensitization to music.
Institut de Bioengineering
Faculté de médecine
Université de Genève