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Scaling temporal dynamics in functional MRI (fMRI) signals have beenevidenced for a decade as intrinsic characteristics of ongoing brain activity(Zarahn et al., 1997). Recently, scaling properties were shown to fluctuateacross brain networks and to be modulated between rest and task (He, 2011):notably, Hurst exponent, quantifying long memory, decreases under task inactivating and deactivating brain regions. In most cases, such results wereobtained: First, from univariate (voxelwise or regionwise) analysis, hencefocusing on specific cognitive systems such as Resting-State Networks (RSNs)and raising the issue of the specificity of this scale-free dynamics modulationin RSNs. Second, using analysis tools designed to measure a single scalingexponent related to the second order statistics of the data, thus relying onmodels that either implicitly or explicitly assume Gaussianity and (asymptotic)self-similarity, while fMRI signals may significantly depart from those eitherof those two assumptions (Ciuciu et al., 2008; Wink et al., 2008). To addressthese issues, the present contribution elaborates on the analysis of thescaling properties of fMRI temporal dynamics by proposing two significantvariations. First, scaling properties are technically investigated using therecently introduced Wavelet Leader-based Multifractal formalism (WLMF; Wendt etal., 2007). This measures a collection of scaling exponents, thus enables aricher and more versatile description of scale invariance (beyond correlationand Gaussianity), referred to as multifractality. Also, it benefits fromimproved estimation performance compared to tools previously used in theliterature. Second, scaling properties are investigated in both RSN and non-RSNstructures (e.g., artifacts), at a broader spatial scale than the voxel one,using a multivariate approach, namely the Multi-Subject Dictionary Learning(MSDL) algorithm (Varoquaux et al., 2011) that produces a set of spatialcomponents that appear more sparse than their Independent Component Analysis(ICA) counterpart. These tools are combined and applied to a fMRI datasetcomprising 12 subjects with resting-state and activation runs (Sadaghiani etal., 2009). Results stemming from those analysis confirm the already reportedtask-related decrease of long memory in functional networks, but also show thatit occurs in artifacts, thus making this feature not specific to functionalnetworks. Further, results indicate that most fMRI signals appear multifractalat rest except in non-cortical regions. Task-related modulation ofmultifractality appears only significant in functional networks and thus can beconsidered as the key property disentangling functional networks fromartifacts. These finding are discussed in the light of the recent literaturereporting scaling dynamics of EEG microstate sequences at rest and addressingnon-stationarity issues in temporally independent fMRI modes.

Scale-Free and Multifractal Time Dynamics of fMRI Signals during Rest and Task