Clinical Mini-Mental State Examination Research Articles

Relationship Between Vitamin D Status and Brain Perfusion in Neuropsychiatric Lupus.

Neuropsychiatric manifestation of lupus (NPSLE) is related with vitamin D (vit-D) deficiency which is possibly amenable to supplementation. This study was done to explore link of serum vit-D level and clinical mini-mental state examination (MMSE) with brain perfusion SPECT (BS) in patients with NPSLE. Patients who underwent BS with the diagnosis of NPSLE and had serum levels of vit-D and MMSE within a span of 1 month were retrospectively included. The BS DICOM data were used to generate 3D surface images of brain for visual identification of regional hypoperfusion, and the z-scores from eZIS software and then to perform voxel-based regression analysis in order to explore association between serum vit-D level and cerebral perfusion deficit using SPM8. Distribution of serum vit-D level was checked across MMSE and BS z-score using R. A total 19 patients with means ± SD age of 28.4 ± 9.2 years, having mean levels of serum vit-D of 18.7 ± 9.8 ng/ml and mean MMSE scores 24.2 ± 1.6, had undergone BS. The eZIS-derived z-score fall in the category of normal in six (31.6%), mild perfusion deficit (PD) in 10 (52.6%) and moderate PD in three (15.8%) with the means ± SD of z-score being 0.52 ± 0.2, 1.72 ± 0.2, and 2.33 ± 0.2. Voxel-based analysis revealed significant positive correlation between vit-D level and hypoperfusion in brain regions related to cognitive function (p<0.05). Serum vit-D levels were significantly lower in NPSLE patients with lower MMSE scores as well as in higher eZIS z-score (p < 0.01). Our results may support the utility of vit-D supplementation in NPSLE and applicability of BS as a clinical adjunct for monitoring response to vit-D supplementation.

Levels of Receptor for Advanced Glycation End Products and Glyoxalase-1 in the Total Circulating Extracellular Vesicles from Mild Cognitive Impairment and Different Stages of Alzheimer's Disease Patients.

Growing evidence supports that receptor for advanced glycation end products (RAGE) and glyoxalase-1 (GLO-1) are implicated in the pathophysiology of Alzheimer's disease (AD). Extracellular vesicles (EVs) are nanovesicles secreted by almost all cell types, contribute to cellular communication, and are implicated in AD pathology. Recently, EVs are considered as promising tools to identify reliable biomarkers in AD. The aim of our study was to determine the levels of RAGE and GLO-1 in circulating EVs from mild cognitive impairment (MCI) and AD patients and to analyze their correlation with the clinical Mini-Mental State Examination and Montreal Cognitive Assessment scores. We have studied the possibility that neuronal cells could release and transfer GLO-1 through EVs. RAGE and GLO-1 levels were measured in circulating EVs, respectively, by Luminex assay and western blot. Released-EVs from SK-N-SH neuronal cells were isolated and GLO-1 levels were determined by western blot. Our data showed higher levels of RAGE in EVs from late AD patients while GLO-1 levels in EVs from early AD were lower as compared to control and MCI patients. Interestingly, levels of RAGE and GLO-1 in EVs were correlated with the cognitive scores regardless of age. For the first time, we demonstrated that GLO-1 was released from neuronal cells through EVs. Although more samples will be needed, our preliminary results support the use of peripheral EVs cargo as new tools for the discovery of peripheral AD biomarkers.

Region-of-Interest based sparse feature learning method for Alzheimer’s disease identification

Background and ObjectiveIn recent years, some clinical parameters, such as the volume of gray matter (GM) and cortical thickness, have been used as anatomical features to identify Alzheimer’s disease (AD) from Healthy Controls (HC) in some feature-based machine learning methods. However, fewer image-based feature parameters have been proposed, which are equivalent to these clinical parameters, to describe the atrophy of regions-of-interest (ROIs) of the brain. In this study, we aim to extract effective image-based feature parameters to improve the diagnostic performance of AD with magnetic resonance imaging (MRI) data. MethodsA new subspace-based sparse feature learning method is proposed, which builds a union-of-subspace representation model to realize feature extraction and disease identification. Specifically, the proposed method estimates feature dimensions reasonably, at the same time, it protects local features for the specified ROIs of the brain, and realizes image-based feature extraction and classification automatically instead of computing the volume of GM or cortical thickness preliminarily. ResultsExperimental results illustrate the effectiveness and robustness of the proposed method on feature extraction and classification, which are based on the sampled clinical dataset from Peking University Third Hospital of China and the Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset. The extracted image-based feature parameters describe the atrophy of ROIs of the brain well as clinical parameters but show better performance in AD identification than clinical parameters. Based on them, the important ROIs for AD identification can be identified even for correlated variables. ConclusionThe extracted features and the proposed identification parameters show high correlation with the volume of GM and the clinical mini-mental state examination (MMSE) score respectively. The proposed method will be useful in denoting the changes of cerebral pathology and cognitive function in AD patients.