Real-Time fMRI

Neurofeedback by real time functional MRI (rt-fMRI) has potential for behavioral research and treatment that will be realized only if the feedback given the subject is related meaningfully to the cognitive states that must be controlled. The mental operations of the brain are too distributed to be represented by the raw rt-fMRI signal in any one brain region or small group of regions. Our real time project aims are to: 1) Use computational machine learning to rapidly detect patterned activation in the rt-fMRI signal that better expresses cognitive state; 2) augment these data with concurrently-collected electroencephalographic (EEG) data; 3) develop an atlas of brain data that identifies brain patterns with cognitive states relevant to addiction and drug abuse research and 4) to explore rt-fMRI neurofeedback using this rt-fMRI/EEG machine learning method.

Our approach is to first create rapid algorithms for pattern matching that are fast compared with the imaging, thereby allowing “real-time” application. To do so we select features from the images that express the differences among state concisely (more technically, we use a method known as independent components analysis to reduce the data dimensionality.) We similarly condense the EEG features by studying them by the location of their sources within the brain, and by examining the frequencies that they contain.

- Software (scanSTAT)
- Real-time fMRI