| Summary: | Scale-free (or 1/f spectral) properties in functional MRI (fMRI) signals have been evidenced for a decade as intrinsic characteristics of ongoing brain activity [46]. Recently, they were shown to fluctuate across brain networks and to be modulated between rest and task [26]: Notably, Hurst exponent, quantifying long memory, decreases during task in activating and deactivating brain regions. Such results were obtained using tools assuming Gaussianity and self-similar models, while fMRI signals may significantly depart from those two assumptions [16] and were often based on voxelwise or regionwise independent analysis, hence showing large inter-subject variability. The present article aims at contributing to the analysis of scale-free properties of fMRI signals in two respects. First, a recent statistical analysis tool, the Wavelet Leader-based Multifractal formalism (WLMF), is used [45,3]. It shows improved estimation performance, and it is framed within a richer and more versatile class of models, that of multifractal processes. Second, scale-free properties are not investigated at the voxel or region scale. Instead, resting-state brain networks are extracted using a Multi-Subject Dictionary Learning (MSDL) algorithm [40]: It produces both a set of spatial components, and a set of times series, for each component and each subject, that conveys ongoing dynamics in functional networks but also in artifacts.These combined tools are applied to an fMRI dataset comprising 12-subjects with resting-state and activation runs [36]. This dataset enabled us to confirm the task-related decrease of long memory in functional networks but also in artifacts making this feature not specific to functional networks. Also, most of fMRI signals appear multifractal at rest except in non-cortical regions. Task-related modulation of multifractality appears only significant in functional networks and thus becomes the key property to disentangle functional networks from artifacts.
|
|---|
