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

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.

A group model for stable multi-subject ICA on fMRI datasets.

Spatial Independent Component Analysis (ICA) is an increasingly used data-driven method to analyze functional Magnetic Resonance Imaging (fMRI) data. To date, it has been used to extract sets of mutually correlated brain regions without prior information on the time course of these regions. Some of these sets of regions, interpreted as functional networks, have recently been used to provide markers of brain diseases and open the road to paradigm-free population comparisons. Such group studies raise the question of modeling subject variability within ICA: how can the patterns representative of a group be modeled and estimated via ICA for reliable inter-group comparisons? In this paper, we propose a hierarchical model for patterns in multi-subject fMRI datasets, akin to mixed-effect group models used in linear-model-based analysis. We introduce an estimation procedure, CanICA (Canonical ICA), based on i) probabilistic dimension reduction of the individual data, ii) canonical correlation analysis to identify a data subspace common to the group iii) ICA-based pattern extraction. In addition, we introduce a procedure based on cross-validation to quantify the stability of ICA patterns at the level of the group. We compare our method with state-of-the-art multi-subject fMRI ICA methods and show that the features extracted using our procedure are more reproducible at the group level on two datasets of 12 healthy controls: a resting-state and a functional localizer study.

Antihypertensive therapy upregulates renin and (pro)renin receptor in the clipped kidney of Goldblatt hypertensive rats.

Recently, a (pro)renin receptor has been identified which mediates profibrotic effects independent of angiotensin II. Because antihypertensive therapy induces renal injury in the clipped kidney of two kidney-1-clip hypertensive rats, we examined the regulation of renin and the (pro)renin receptor in this model. Hypertensive Goldblatt rats were treated with increasing doses of the vasopeptidase inhibitor AVE 7688 after which the plasma renin and prorenin as well as the renal renin and (pro)renin receptor expression were measured. The vasopeptidase inhibitor dose-dependently lowered blood pressure, which was associated with a massive increase in plasma prorenin and renin as well as increased renal renin expression. The (pro)renin receptor was upregulated in the clipped kidney of the Goldblatt rat indicating a parallel upregulation of renin and its receptor in vivo. Immunohistochemistry showed a redistribution of renin upstream from the glomerulus in preglomerular vessels and renin staining in tubular cells. Expression of the (pro)renin receptor was increased in the vessels and tubules. This upregulation was associated with thickening of renin-positive vessels and tubulointerstitial damage. We propose that renin and the (pro)renin receptor may play a profibrotic role in the clipped kidney of Goldblatt rats treated for hypertension.

Antihypertensive therapy induces compartment-specific chemokine expression and a Th1 immune response in the clipped kidney of Goldblatt hypertensive rats.

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-gamma. 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 I 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.