Functional Brain Imaging: Signals, Imaging, and Graphs

Functional Brain Imaging: Signals, Imaging, and GraphsIn this episode of the IEEE Signal Processing Society Podcast, Professor Borbála Hunyadi from the Mental Health and Neuroscience Research Institute, Maastricht University, The Netherlands interviews Dr. Dimitri Van De Ville, Full Professor at the École Polytechnique Fédérale de Lausanne (EPFL) and the University of Geneva, Switzerland. Their conversation explores how modern neuroimaging modalities, combined with advanced signal processing and computational methods, are transforming our understanding of brain function in health and disorder. Dr. Dimitri Van De VilleDr. Dimitri Van De Ville received his M.S. and Ph.D. degrees from Ghent University, Belgium, in 1998 and 2002, respectively. He was a postdoctoral fellow at EPFL before leading the Signal Processing Unit at the University Hospital of Geneva as part of the CIBM Center for Biomedical Imaging. Since 2024, he has been a Full Professor at EPFL’s Neuro-X Institute with a joint appointment at the University of Geneva. His interdisciplinary research focuses on computational neuroimaging, wavelets, sparsity, and graph signal processing, applied to MRI and M/EEG data.In this episode, he discusses current and emerging neuroimaging modalities such as intracranial recordings, fMRI, fNIRS, M/EEG, and functional ultrasound (fUS). He highlights how signal processing plays a vital role in data formation, preprocessing, and analysis, enabling researchers to extract meaningful information about brain activity. The discussion also touches on innovations such as independent component analysis, connectomics, and the growing influence of AI and deep learning in neuroimaging. Dr. Van De Ville concludes by reflecting on the field’s future—emphasizing multimodal integration, brain–body connectivity, and targeted neuromodulation as key directions for advancing both neuroscience research and clinical applications.