In Vivo Inhibition of miR-155 Promotes Recovery after Experimental Mouse Stroke.

A multifunctional microRNA, miR-155, has been recently recognized as an important modulator of numerous biological processes. In our previous in vitro studies, miR-155 was identified as a potential regulator of the endothelial morphogenesis. The present study demonstrates that in vivo inhibition of miR-155 supports cerebral vasculature after experimental stroke. Intravenous injections of a specific miR-155 inhibitor were initiated at 48 h after mouse distal middle cerebral artery occlusion (dMCAO). Microvasculature in peri-infarct area, infarct size, and animal functional recovery were assessed at 1, 2, and 3 weeks after dMCAO. Using in vivo two-photon microscopy, we detected improved blood flow and microvascular integrity in the peri-infarct area of miR-155 inhibitor-injected mice. Electron microscopy revealed that, in contrast to the control group, these animals demonstrated well preserved capillary tight junctions (TJs). Western blot analysis data indicate that improved TJ integrity in the inhibitor-injected animals could be associated with stabilization of the TJ protein ZO-1 and mediated by the miR-155 target protein Rheb. MRI analysis showed significant (34%) reduction of infarct size in miR-155 inhibitor-injected animals at 21 d after dMCAO. Reduced brain injury was confirmed by electron microscopy demonstrating decreased neuronal damage in the peri-infarct area of stroke. Preservation of brain tissue was reflected in efficient functional recovery of inhibitor-injected animals. Based on our findings, we propose that in vivo miR-155 inhibition after ischemia supports brain microvasculature, reduces brain tissue damage, and improves the animal functional recovery. Significance statement: In the present study, we investigated an effect of the in vivo inhibition of a microRNA, miR-155, on brain recovery after experimental cerebral ischemia. To our knowledge, this is the first report describing the efficiency of intravenous anti-miRNA injections in a mouse model of ischemic stroke. The role of miRNAs in poststroke revascularization has been unexplored and in vivo regulation of miRNAs during the subacute phase of stroke has not yet been proposed. Our investigation introduces a new and unexplored approach to cerebral regeneration: regulation of poststroke angiogenesis and recovery through direct modulation of specific miRNA activity. We expect that our findings will lead to the development of novel strategies for regulating neurorestorative processes in the postischemic brain.

A trichotomy method for defining homogeneous subgroups in a dementia population.

INTRODUCTION: Diagnosis of dementia in the aging brain is confounded by the presence of multiple pathologies. Mixed dementia (MX), a combination of Alzheimer's disease (AD) proteins with vascular disease (VD), is frequently found at autopsy, and has been difficult to diagnose during life. This report develops a method for separating the MX group and defining preclinical AD (presence of AD factors with normal cognition) and preclinical VD subgroups (presence of white matter damage with normal cognition). METHODS: Clustering was based on three diagnostic axes: (1) AD factor (ADF) derived from cerebrospinal fluid proteins (Aß42 and pTau), (2) VD factor (VDF) calculated from mean free water and peak width of skeletonized mean diffusivity in the white matter, and (3) Cognition (Cog) based on memory and executive function. The trichotomy method was applied to an Alzheimer's Disease Neuroimaging Initiative cohort (N = 538). RESULTS: Eight biologically defined subgroups were identified which included the MX group with both high ADF and VDF (9.3%) and a preclinical VD group (3.9%), and a preclinical AD group (13.6%). Cog is significantly associated with both ADF and VDF, and the partial-correlation remains significant even when the effect of the other variable is removed (r(Cog, ADF/VDF removed) = 0.46, p < 10-28 and r(Cog, VDF/ADF removed) = 0.24, p < 10-7 ). DISCUSSION: The trichotomy method creates eight biologically characterized patient groups, which includes MX, preclinical AD, and preclinical VD subgroups. Further longitudinal studies are needed to determine the utility of the 3-way clustering method with multimodal biological biomarkers.

Comparison of multi-subject ICA methods for analysis of fMRI data.

Spatial independent component analysis (ICA) applied to functional magnetic resonance imaging (fMRI) data identifies functionally connected networks by estimating spatially independent patterns from their linearly mixed fMRI signals. Several multi-subject ICA approaches estimating subject-specific time courses (TCs) and spatial maps (SMs) have been developed, however, there has not yet been a full comparison of the implications of their use. Here, we provide extensive comparisons of four multi-subject ICA approaches in combination with data reduction methods for simulated and fMRI task data. For multi-subject ICA, the data first undergo reduction at the subject and group levels using principal component analysis (PCA). Comparisons of subject-specific, spatial concatenation, and group data mean subject-level reduction strategies using PCA and probabilistic PCA (PPCA) show that computationally intensive PPCA is equivalent to PCA, and that subject-specific and group data mean subject-level PCA are preferred because of well-estimated TCs and SMs. Second, aggregate independent components are estimated using either noise-free ICA or probabilistic ICA (PICA). Third, subject-specific SMs and TCs are estimated using back-reconstruction. We compare several direct group ICA (GICA) back-reconstruction approaches (GICA1-GICA3) and an indirect back-reconstruction approach, spatio-temporal regression (STR, or dual regression). Results show the earlier group ICA (GICA1) approximates STR, however STR has contradictory assumptions and may show mixed-component artifacts in estimated SMs. Our evidence-based recommendation is to use GICA3, introduced here, with subject-specific PCA and noise-free ICA, providing the most robust and accurate estimated SMs and TCs in addition to offering an intuitive interpretation.

A double-dichotomy clustering of dual pathology dementia patients.

INTRODUCTION: Subcortical ischemic vascular disease (SIVD) and Alzheimer's disease (AD) related dementia can coexist in older subjects, leading to mixed dementia (MX). Identification of dementia sub-groups is important for designing proper treatment plans and clinical trials. METHOD: An Alzheimer's disease severity (ADS) score and a vascular disease severity (VDS) score are calculated from CSF and MRI biomarkers, respectively. These scores, being sensitive to different Alzheimer's and vascular disease processes are combined orthogonally in a double-dichotomy plot. This formed an objective basis for clustering the subjects into four groups, consisting of AD, SIVD, MX and leukoaraiosis (LA). The relationship of these four groups is examined with respect to cognitive assessments and clinical diagnosis. RESULTS: Cluster analysis had at least 83% agreement with the clinical diagnosis for groups based either on Alzheimer's or on vascular sensitive biomarkers, and a combined agreement of 68.8% for clustering the four groups. The VDS score was correlated to executive function (r = -0.28, p < 0.01) and the ADS score to memory function (r = -0.35, p < 0.002) after adjusting for age, sex, and education. In the subset of patients for which the cluster scores and clinical diagnoses agreed, the correlations were stronger (VDS score-executive function: r = -0.37, p < 0.006 and ADS score-memory function: r = -0.58, p < 0.0001). CONCLUSIONS: The double-dichotomy clustering based on imaging and fluid biomarkers offers an unbiased method for identifying mixed dementia patients and selecting better defined sub-groups. Differential correlations with neuropsychological tests support the hypothesis that the categories of dementia represent different etiologies.