Decoding fMRI Brain Activity Patterns in Real-Time: From Basic Research to Clinical Applications
published: Dec. 3, 2012, recorded: September 2012, views: 3592
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Recent progress in computer hard- and software allows the sophisticated analysis of fMRI data in real-time including "brain reading" methods such as multivariate pattern analysis. Advanced online fMRI data analysis provides the basis for brain-computer Interface (BCI) applications such as neurofeedback and motor-independent communication. In neurofeedback studies, subjects observe and learn to modulate their own brain activity during an ongoing fMRI measurement. Many neurofeedback studies have demonstrated that with sufficient practice, subjects are indeed able to learn to modulate brain activity in specific brain areas or networks using mental tasks. These results are important for basic neuroscience research, because they allow to study the degree to which the brain can modulate its own activity and to potentially unravel the function of hitherto unknown brain areas. Besides basic research applications, we have recently shown that fMRI neurofeedback may become a valuable therapeutic tool to help patients suffering from Parkinson's disease and mood disorders such as depression. Furthermore, we have shown that activation patterns evoked by participants can be ‘decoded’ and interpreted online as letters of the alphabet offering the possibility for people with severe motor impairments to ‘write’ letters purely controlled by mental imagery. In order to allow patients with severe motor impairments to use the developed communication tool at the bedside, we currently transfer our approach to functional near-infrared spectroscopy (fNIRS) that, like fMRI, measures hemodynamic brain signals. Finally we will present recent results from ultra-high field fMRI measurements (7 Tesla scanners) that achieve sub-millimeter functional spatial resolution allowing to crack the representational code within specialized brain areas at the level of cortical columns and cortical layers. These new possibilities are extremely important to advance our knowledge of brain organization but they will also enable more content-specific BCI applications.
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