Center for Multimodal Neuroimaging

Jonas Richiardi

Lausane University Hospital

Jonas Richiardi is Clinical Research Lead at the Department of Radiology, Lausanne University Hospital, Switzerland, with joint appointment at Siemens Healthineers Advanced Clinical Imaging Technology. He was previously a Marie Curie fellow at Stanford University (working with Mike Greicius in Neurology) and the University of Geneva (working with Patrik Vuilleumier in Fundamental Neurosciences), and a post-doctoral researcher in the Medical Image Processing Lab, a joint position between the Ecole Polytechnique Fédérale de Lausanne (EPFL), Institute of Bioengineering, and the University of Geneva's Department of Radiology and Medical Informatics. He obtained his Ph.D. in signal processing and pattern recognition in 2007 at EPFL in the Signal Processing Institute. His research interests include modelling and inference for complex multimodal biological data, in particular functional magnetic resonance imaging data and its combination with genomic data. He is working on graph-based statistical learning approaches, where all data is first represented as a graph, and machine learning approaches are applied to form prediction with graphs. Applications are early diagnosis and prognosis for individual subjects, for diseases from Alzheimer disease to multiple sclerosis and schizophrenia. A recent effort is to develop these techniques so that they can be applied to messy, hospital-scale data.

Recent Talks
Events in November 2017

Graph-based inference and prediction for neuroimaging

A network view of the brain has become ubiquitous in neuroscience. This has shifted focus from individual brain regions to macro-scale constructs including segregation and integration between remote regions. Complementing advances in multimodal non-invasive human imaging and signal processing, the mathematical tools that have allowed this perspective to flourish are graph theory and network science. By offering a constrained, yet expressive representation of large-scale, complex spatio-temporal data, graph approaches have enabled numerous systems-level insights into brain organization in health and disease. This talk will present mathematical tools that can be used to make sense of graph-structured brain data, both for group inference (graph-based statistics) and individual prediction (machine learning on brain graphs). We will particularly focus on applications in basic and clinical neuroscience, and show that graph-based approaches are particularly promising for inference across different levels and scales of brain biology.