MRI evidence of past microbleeds may be found even in neurologically normal elderly individuals and is related, but not restricted, to other indicators of small vessel disease. The predictive potential of this finding regarding the risk of intracerebral bleeding requires further investigation.
Background and Purpose-MRI is known to detect clinically silent microbleeds (MBs) in patients with primary intracerebral hemorrhage (pICH), but the frequency and diagnostic and clinical significance of this finding are still debated. Therefore, we investigated a consecutive series of pICH patients and analyzed the patterns of MB distribution in the context of clinical variables and location of the symptomatic hematoma. Methods-The study population consisted of 109 patients with pICH. There were 59 women and 50 men aged 22 to 91 years (mean 64.6 years). MRI was obtained on a 1.5-T system with use of a gradient-echo T2*-weighted sequence. A cohort of 280 community-dwelling asymptomatic elderly individuals who underwent the same imaging protocol served for comparison. Results-MBs were seen in 59 (54%) patients and ranged in number from 1 to 90 lesions (mean 14, median 6). In the majority of patients, MBs were located simultaneously in various parts of the brain, with a preference for cortical-subcortical regions (39%) and the basal ganglia/thalami (38%). There was some tendency toward a regional association between MB location and the site of the symptomatic hematoma, but we could not discern specific patterns of MB distribution. Logistic regression analysis identified MBs, periventricular hyperintensity grades, and lacunes but not risk factors as independent variables contributing to a correct classification of pICH and control individuals. Conclusions-MBs can be detected in more than half of the patients with pICH and appear to be quite general markers of various types of bleeding-prone microangiopathy. (Stroke. 2000;31:2665-2669.)
Alzheimer's disease (AD) patients show altered patterns of functional connectivity (FC) on resting state functional magnetic resonance imaging (RSfMRI) scans. It is yet unclear which RSfMRI measures are most informative for the individual classification of AD patients. We investigated this using RSfMRI scans from 77 AD patients (MMSE = 20.4 ± 4.5) and 173 controls (MMSE = 27.5 ± 1.8). We calculated i) FC matrices between resting state components as obtained with independent component analysis (ICA), ii) the dynamics of these FC matrices using a sliding window approach, iii) the graph properties (e.g., connection degree, and clustering coefficient) of the FC matrices, and iv) we distinguished five FC states and administered how long each subject resided in each of these five states. Furthermore, for each voxel we calculated v) FC with 10 resting state networks using dual regression, vi) FC with the hippocampus, vii) eigenvector centrality, and viii) the amplitude of low frequency fluctuations (ALFF). These eight measures were used separately as predictors in an elastic net logistic regression, and combined in a group lasso logistic regression model. We calculated the area under the receiver operating characteristic curve plots (AUC) to determine classification performance. The AUC values ranged between 0.51 and 0.84 and the highest were found for the FC matrices (0.82), FC dynamics (0.84) and ALFF (0.82). The combination of all measures resulted in an AUC of 0.85. We show that it is possible to obtain moderate to good AD classification using RSfMRI scans. FC matrices, FC dynamics and ALFF are most discriminative and the combination of all the resting state measures improves classification accuracy slightly.
Magnetic resonance imaging (MRI) is sensitive to structural and functional changes in the brain caused by Alzheimer's disease (AD), and can therefore be used to help in diagnosing the disease. Improving classification of AD patients based on MRI scans might help to identify AD earlier in the disease's progress, which may be key in developing treatments for AD. In this study we used an elastic net classifier based on several measures derived from the MRI scans of mild to moderate AD patients (N = 77) from the prospective registry on dementia study and controls (N = 173) from the Austrian Stroke Prevention Family Study. We based our classification on measures from anatomical MRI, diffusion weighted MRI and resting state functional MRI. Our unimodal classification performance ranged from an area under the curve (AUC) of 0.760 (full correlations between functional networks) to 0.909 (grey matter density). When combining measures from multiple modalities in a stepwise manner, the classification performance improved to an AUC of 0.952. This optimal combination consisted of grey matter density, white matter density, fractional anisotropy, mean diffusivity, and sparse partial correlations between functional networks. Classification performance for mild AD as well as moderate AD also improved when using this multimodal combination. We conclude that different MRI modalities provide complementary information for classifying AD. Moreover, combining multiple modalities can substantially improve classification performance over unimodal classification.
We show here that increased oxidative stress is associated with migraine and contributes to migraine-related metabolic risk like nitrosative stress, an atherogenic lipid profile and hyperinsulinemia. Our data suggest that oxidative stress may represent a key event in the pathophysiology of migraine and a suitable therapeutic target.
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