Title and Abstract:
Recent developments in functional magnetic resonance imaging data analyses
Magnetic resonance imaging (MRI) has opened unprecedented avenues to observe the human brain non-invasively. In particular, for about two decades, functional MRI (fMRI) has enabled to monitor brain function using the blood-oxygen-level-dependent (BOLD) contrast as a proxy for neuronal activity. The impact of fMRI on neurosciences, medicine, and psychology is ever increasing and has been mainly focussing on (1) understanding brain organization in terms of segregation (i.e., localized processing) and integration (i.e., distributed processing), specifically, related to sensory processing and cognition; (2) exploring temporal characteristics of brain processes.
Conventional fMRI analysis is exploiting timing properties of a stimulation or task paradigm designed by the experimenter; i.e., evidence is sought for the presence of a hypothetical BOLD response. More recently, the community has shown increasing interest in spontaneous brain activity acquired during resting-state fMRI (RS-fMRI). In the absence of any task, data-driven or exploratory methods have found great use. First, I will talk about the state-of-the-art fMRI data analysis tools that integrate advanced signal processing techniques and neurosciences. Second, I will introduce our recent regularization strategy, termed "total activation", that allows deconvolving the fMRI signal to remove the hemodynamic blur and to improve spatial contrast of activation patterns by incorporating knowledge about meaningful brain regions. The method is able to readily recover plausible activation patterns for the visual stimuli without any prior knowledge about their timing.
F. Isik Karahanoglu received her B.S. in Electrical and Electronics Engineering in 2007 from Middle East Technical University. She received her M.S. in Communication Systems and Ph.D. in Electrical Engineering from Ecole Polytechinque Federale de Lausanne (EPFL) in 2009 and 2013. She is now a Postdoctoral Researcher at EPFL and University of Geneva. She received the Editor's Choice Award of Neuroimage Journal and Vasco Sanz Foundation Brain Research Award in 2013. Her research interests include signal processing, (functional) magnetic resonance imaging, inverse problems and regularization for fMRI.