Scaling laws and persistence in human brain activity

We study the temporal variability of human brain activity in timeseries of functional magnetic resonance imaging (fMRI) data. We find that these timeseries show scaling behavior which is quantified by computing various scaling exponents. We demonstrate that mentally active zones are one-to-one related to large exponents, or equivalently, to highly temporally correlated processes, while mentally inactive zones are well described by a simple random walk model. Activation maps of scaling exponents are presented and compared to standard results in fMRI. In contrast to standard model-based activation analyses no prior knowledge of the experimental stimulation paradigm has to be assumed for extracting activation patterns from fMRI timeseries. We mention consequences of persistence in fMRI timeseries for standard statistical analysis.