Full course description
Building on the course “Neuroimaging: Functional MRI”, this course will examine advanced topics of fMRI methodology and applications. In the first week, students learn how knowledge about vascular effects on the MRI signal may help to detect BOLD artefacts. In the second week, principles of real-time fMRI will be presented. This is followed by an overview of fMRI neurofeedback studies and a discussion of its use as a new therapeutic tool. In addition, machine learning techniques for the real-time decoding of mental states and the application of these techniques in brain-computer interfaces will be discussed. In the third week, advanced cortical mapping techniques are examined, including estimation of population receptive fields for visual and cognitive topographic maps. Furthermore, deep neural networks will be discussed in the context of modeling responses of the visual system. During the last two meetings the possibilities of “mesoscopic” ultra-high field brain imaging will be discussed focusing on studies with sub-millimeter spatial resolution aiming to understand brain activity at the level of cortical columns and cortical layers.
Students are able to understand:
- effects of vascular system on the interpretability of the BOLD fMRI signal;
- real time fMRI data analysis during ongoing experiments;
- possibilities and limitations of fMRI-based brain-computer interfaces (BCIs);
- fMRI neurofeedback training as a new therapeutic tool;
- real-time decoding of mental states;
- Encoding and decoding representations using population receptive field mapping;
- learning about multivariate representational spaces using representational similarity analysis;
- principles of convolutional deep networks as a model of the visual cortex;
- opportunities and challenges of high-resolution fMRI at ultra-high magnetic field strengths to investigate the cortex at the columnar and laminar level.
Research master course ‘Neuroimaging’.