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The aim of this work is to propose a multifractal analysis method for Multifractal Detrended Fluctuation analysis (MF-DFA) of Blood Oxygen Level Dependent (BOLD) functional Magnetic Resonance Imaging (fMRI). The fMRI signals exhibit a 1/f power spectrum, hence their structure has self-similarity and long memory, being usually successfully analyzed by different fractal analysis methods without a previous knowledge of haemodynamic models. Therefore, to validate activation detection using the MF-DFA method, a comparison study between images obtained using General Linear Model (GLM) and Independent Component Analysis (ICA) was conducted and evaluated by discrimination power, applying Receiver Operating Characteristic (ROC) analysis to the results.

This work proposes a multifractal analysis of the time series derived from ASL fMRI (Arterial Spin Labeling functional Magnetic Resonance Imaging) to detect brain activated regions in response to an unknown stimulus. In contrast to standard model-based activation analysis, no prior knowledge of the expected haemodynamic response has to be assumed for extracting activation patterns from fMRI. The ASL time series were analysed using MF-DFA (Multifractal Detrended Fluctuation Analysis). The results show clear differences between the multifractal spectra, in form and locus, with respect to voxels from activated and non-activated brain regions. These results are in line with known literature for BOLD (Blood Oxygenation Level Dependent) functional time series. MF-DFA reveals stronger activation in the motor cortex and in some other physiologically relevant activation areas, as the visual cortex. It is shown that the proposed framework is appropriate for studying the human brain function, based on recent approaches of the self-similarity formalism.