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Scaling temporal dynamics in functional MRI (fMRI) signals have been evidenced for a decade as intrinsic characteristics of ongoing brain activity (Zarahn et al., 1997). Recently, scaling properties were shown to fluctuate across brain networks and to be modulated between rest and task (He, 2011): notably, Hurst exponent, quantifying long memory, decreases under task in activating and deactivating brain regions. In most cases, such results were obtained: First, from univariate (voxelwise or regionwise) analysis, hence focusing on specific cognitive systems such as Resting-State Networks (RSNs) and raising the issue of the specificity of this scale-free dynamics modulation in RSNs. Second, using analysis tools designed to measure a single scaling exponent related to the second order statistics of the data, thus relying on models that either implicitly or explicitly assume Gaussianity and (asymptotic) self-similarity, while fMRI signals may significantly depart from those either of those two assumptions (Ciuciu et al., 2008; Wink et al., 2008). To address these issues, the present contribution elaborates on the analysis of the scaling properties of fMRI temporal dynamics by proposing two significant variations. First, scaling properties are technically investigated using the recently introduced Wavelet Leader-based Multifractal formalism (WLMF; Wendt et al., 2007). This measures a collection of scaling exponents, thus enables a richer and more versatile description of scale invariance (beyond correlation and Gaussianity), referred to as multifractality. Also, it benefits from improved estimation performance compared to tools previously used in the literature. Second, scaling properties are investigated in both RSN and non-RSN structures (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 spatial components that appear more sparse than their Independent Component Analysis (ICA) counterpart. These tools are combined and applied to a fMRI dataset comprising 12 subjects with resting-state and activation runs (Sadaghiani et al., 2009). Results stemming from those analysis confirm the already reported task-related decrease of long memory in functional networks, but also show that it occurs in artifacts, thus making this feature not specific to functional networks. Further, results indicate that most fMRI signals appear multifractal at rest except in non-cortical regions. Task-related modulation of multifractality appears only significant in functional networks and thus can be considered as the key property disentangling functional networks from artifacts. These finding are discussed in the light of the recent literature reporting scaling dynamics of EEG microstate sequences at rest and addressing non-stationarity issues in temporally independent fMRI modes. [ABSTRACT FROM AUTHOR]

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 [70]. Recently, they were shown to fluctuate across brain networks and to be modulated between rest and task [37]: 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 [24] 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 temporal scale-free properties of fMRI signals in two respects. First, a recent statistical analysis tool, the Wavelet Leader-based Multifractal formalism (WLMF), is used [68, 3]. It shows improved estimation performance, and it is framed within a rich and versatile class of models, that of multifractal processes. Second, scalefree 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 [63]: 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 [55]. 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 can be considered the key property to disentangle functional networks from artifacts. Our results are finally discussed in the light of the recent literature dealing with i) the scale-free dynamics of EEG microstate sequences at rest and ii) non-stationarities in temporally independent fMRI modes. [ABSTRACT FROM AUTHOR]

The present study examined the pathogenesis of interstitial inflammation and fibrosis in antihypertensively treated rats with two-kidney, one-clip hypertension. Hypertensive rats were randomized into four groups: no treatment and moderate, intermediate, and intensified lowering of blood pressure with increasing doses of a vasopeptidase inhibitor for 6 wk. The vasopeptidase inhibitor dose dependently lowered blood pressure. The tubulointerstitial damage was accompanied by a diffuse infiltration of mononuclear cells and circumscript mononuclear inflammatory cell cluster formation consisting mainly of T cells and to a lesser degree of macrophages and B cells. Real-time PCR analyses showed a dose-dependent induction of MCP-1 and the Th1-type chemokines IP10 and Mig as well as their receptor CXCR3 and the Th1 cytokine IFN-γ. In situ hybridization and laser microdissection revealed a strong expression of these Th1-associated transcripts in the clusters and, in the case of MCP-1, also diffusely in the interstitium. The inflammation was accompanied by the appearance of myofibroblasts and synthesis of the fibrogenic factor plasminogen activator inhibitor-1 as well as the collagenase matrix metalloproteinase-2, leading to collagen 1 upregulation and interstitial scarring. No inflammation or fibrosis was found in normotensive rats treated with the vasopeptidase inhibitor. The renal injury in the clipped kidney is accompanied by compartment-specific chemokine expression and cell cluster formation of Th1 specificity associated with upregulation of fibrogenic proteins and matrix metalloproteinases. These findings suggest that the Th1 chemokines IP10 and Mig as well as their receptor CXCR3 are potential targets for therapeutic interventions in ischemic nephropathy. [ABSTRACT FROM AUTHOR]