My main interest is to characterize the anatomo-functional architecture of individual brains using neuroimaging data, and in particular using resting-state fMRI. I am also interested in examining how brain connectivity can be used as a biomarker of neurodegenerative diseases such as Alzheimer's disease. These questions raise considerable methodological challenges, which feed the technical aspects of my work. To explore the resting-state networks in fMRI, I use some unsupervised pattern recognition techniques, i.e. various types of clustering and component analysis. To deal with the statistics associated with a stochastic clustering process, I have been working on non-parametric statistical methods, in particular based on the bootstrap. Besides the exploration of real data, my research also includes the development of fully synthetic neuroimaging databases which cover many aspects of the data-generating process, from neural activity and physiological noise to the physics of image acquisition, to provide a test bed for the evaluation and validation of neuroimaging analysis methods.

Karim Jerbi heads the Computational and Cognitive Neuroscience Lab (CoCo Lab) at UdeM. His research lies at the cross-roads between computational, systems and cognitive neuroscience, with an emphasis on exploring biological and artificial network dynamics. The research he leads seeks to elucidate the role of large-scale brain network dynamics in normal cognitive processes and their breakdown in psychiatric disorders. To this end, his research relies on a combination of invasive (intracranial electroencephalography, iEEG) and non-invasive (EEG and MEG) recordings, combined with advanced signal processing and artificial intelligence tools, including machine learning.

Karim Jerbi is interested in inter-disciplinary research questions such as the neural basis of attention, decision-making, states of consciousness and sleep, and has a keen interest in various forms of interaction between art, creativity and neuroscience.