Slowly varying temporally correlated activity fluctuations between functionally related brain areas have been identified by functional magnetic resonance imaging (fMRI) research in recent years. These low-frequency oscillations of less than 0.08 Hz appear to play a major role in various dynamic functional brain networks, such as the so-called 'default mode' network. They also have been observed as a property of symmetric cortices, and they are known to be present in the motor cortex among others. These low-frequency data are difficult to detect and quantify in fMRI. Traditionally, user-based regions of interests (ROI) or 'seed clusters' have been the primary analysis method. In this paper, we propose unsupervised clustering algorithms based on various distance measures to detect functional connectivity in resting state fMRI. The achieved results are evaluated quantitatively for different distance measures. The Euclidian metric implemented by standard unsupervised clustering approaches is compared with a non-metric topographic mapping of proximities based on the the mutual prediction error between pixel-specific signal dynamics time-series. It is shown that functional connectivity in the motor cortex of the human brain can be detected based on such model-free analysis methods for resting state fMRI.
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