LEADER-BASED MULTIFRACTAL ANALYSIS FOR EVI fMRI TIME SERIES: ONGOING vs TASK-RELATED BRAIN ACTIVITY

Classical within-subject analysis in functional magnetic resonance imaging (fMRI) relies on a detection step to localize which parts of the brain are activated by a given stimulus type. This is usually achieved using model-based approaches. Here, we propose an alternative exploratory scaling analysis. By nature, scaling analysis requires the use of long enough signals, with high frequency sampling rates. Recently, 3D echo volumar imaging (EVI) techniques have emerged in fMRI allowing the very fast acquisition of successive brain volumes. The originality of this contribution is twofold: A new scaling analysis based on multifractal models instead of self-similarity ones and on wavelet Leaders instead of wavelet coefficients is introduced; it is applied to high temporal resolution EVI data acquired both in resting state and during a visual paradigm. We estimate voxel-based multifractal attributes for both kinds of data and bring evidence confirming the existence of true scaling as opposed to superimposed non- stationaries. Also, combining these estimates together with paired statistical tests, we observe significant scaling parameter changes between ongoing and evoked brain activity, which clearly validate an increase in long memory and suggest a global multifractality decrease effect.