A time series of image data is provided to a computer as the basis for performing an ICA to identify a number of candidate correlation patterns. The number of candidate correlation patterns includes a number of neurophysical events and false patterns due to noise. This is followed by a differentiation in the computer between the neurophysical events and the false patterns, for example based on a metric, which indicates the intensity of the candidate correlation patterns in a sub-region of the brain, or by a computer-implemented classifier. Such techniques can be used in conjunction with functional magnetic-resonance imaging.
Method and computer differentiating correlation patterns in functional magnetic resonance imaging