Item

Abstract

Real-time fMRI neurofeedback (rtfMRI-nf) is an emerging technique that allows for a voluntary control over own brain signals, as well as targeting specific brain regions which
are unreachable by EEG. Despite the promising results of neurofeedback in a number of studies, it still faces substantial challenges mainly due to the unexplained inter-subject variability in self-regulation learning. Investigation of the underlying mechanism of neurofeedback
success and failure by looking at the simultaneous EEG/fMRI signal related to those conditions may help to improve neurofeedback learning and success rate. However, fusion of the data from different modalities requires a proper temporal model that links the underlying neuronal dynamics of interest (EEG) to the measured hemodynamic BOLD responses. In the current project, we focus on up- and downregulation of dorsal Anterior Cingulate Cortex (dACC) using rtfMRI neurofeedback because of its relevance in many important brain functions (e.g. resting state salience network) and the known alternation in several psychiatric disorders. We hypothesize that certain trains of EEG dynamics may be differentially linked to distinct subsequent slow BOLD signature (e.g. up- or downregulation of dACC) and
therefore, this information can be further used (in terms of a second feedback signal) to guide the mental strategy of the subjects to better control their brain metabolic activity.