Real-Time fMRI and Neurofeedback

Full course description

Recent progress in computer hard- and software allows real-time analysis of functional magnetic resonance imaging (fMRI) data, which provides the basis for brain-computer interface (BCI) applications such as neurofeedback, control of external devices and motor-independent communication.

In fMRI-based neurofeedback studies, subjects can observe representations of their own brain activation while being measured in the MRI scanner. FMRI-based neurofeedback is performed by reading, analysing and visualising the hemodynamic brain signals in real-time during an ongoing experiment. This real-time approach is in contrast to the standard analysis approach in which the huge amount of incoming fMRI signals are recorded first and then analysed hours or days after the experiment.

During this workshop, there will be an introduction into the real-time fMRI methodology and a discussion of fMRI neurofeedback applications, which have demonstrated that with sufficient practice, subjects are indeed able to learn to modulate activity in certain brain areas. These results are extremely important for basic neuroscience research, because they allow researchers to study the degree to which humans can modulate their own brain activity and to potentially unravel the function of hitherto unknown brain areas. Neurofeedback research also touches on deep philosophical issues, such as the neural correlates of free will. It might also be possible in the future to help people with pain or depression by regulating at will neural activity in relevant brain areas. In fMRI-based communication studies, activation patterns evoked by participants are ‘decoded’ and interpreted online, e.g. as letters of the alphabet, offering the possibility for people with severe motor impairments to ‘write’ letters purely controlled by mental imagery.

In this workshop, a number of online analysis strategies will be discussed for decoding mental states, including analysis of the mean signal of regions-of-interest (ROIs) and the use of pattern classifiers operating at the voxel level.

The final assessment for this course is pass or fail - and not a numerical grade between 0,0 and 10,0.