Background: Diffusion tensor imaging (DTI) estimates the microstructural alterations of the brain, as a magnetic resonance imaging (MRI)-based neuroimaging technique. Prior DTI studies reported decreased structural integrity of the superficial white matter (SWM) in the brain diseases. Objective: This study aimed to determine the diffusion characteristics of SWM in Alzheimer's disease (AD) and mild cognitive impairment (MCI) using tractography and region of interest (ROI) approaches. Methods: The diffusion MRI data were downloaded from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database on 24 patients with AD, 24 with MCI, and 24 normal control (NC) subjects. DTI processing was performed using DSI Studio software. First, for ROI-based analysis, The superficial white matter was divided into right and left frontal, parietal, temporal, insula, limbic and occipital regions by the Talairach Atlas, Then, for tractography-based analysis, the tractography of each of these regions was performed with 100000 seeds. Finally, the average diffusion values were extracted from voxels within the ROIs and tracts. Results: Both tractography and ROI analyses showed a significant difference in radial, axial and mean diffusivity values between the three groups (p < 0.05) across most of the SWM. Furthermore, The Mini-Mental State Examination was significantly correlated with radial, axial, and mean diffusivity values in parietal and temporal lobes SWM in the AD group (p < 0.05). Conclusion: DTI provided information indicating microstructural changes in the SWM of patients with AD and MCI. Therefore, assessment of the SWM using DTI may be helpful for the clinical diagnosis of patients with AD and MCI.

Background: With the development of medical imaging and processing tools, accurate diagnosis of diseases has been made possible by intelligent systems. Owing to the remarkable ability of support vector machines (SVMs) for diseases diagnosis, extensive research has been conducted using the SVM algorithm for the classification of Alzheimer's disease (AD) and mild cognitive impairment (MCI). Objectives: In this study, we applied an automated method to classify patients with AD and MCI and healthy control (HC) subjects based on the diffusion tensor imaging (DTI) features in the superficial white matter (SWM). Participants: For this purpose, DTI data were downloaded from the Alzheimer's Disease Neuroimaging Initiative (ADNI). This method employed DTI data from 72 subjects: 24 subjects as HC, 24 subjects with MCI, and 24 subjects with AD. Measure: ments: DTI processing was performed using DSI Studio software and all machine learning analyses were performed using MATLAB software. Results: The linear kernel of SVM was the best classifier, with an accuracy of 95.8% between the AD and HC groups, followed by the quadratic kernel of SVM with an accuracy of 83.3% between the MCI and HC groups and the Gaussian kernel of SVM with an accuracy of 83.3% between the AD and MCI groups. Conclusions: Given the importance of diagnosing AD and MCI as well as the role of superficial white matter in the diagnosis of neurodegenerative diseases, in this study, the features of different DTI methods of the SWM are discussed, which could be a useful tool to assist in the diagnosis of AD and MCI.

Introduction: Accurate, fast, and reliable diagnosis of Alzheimer's Disease (AD) from Mild Cognitive Impairment (MCI) is crucial for prescribing proper treatment and prevention of disease progression. At first glance, structural and diffusion MRI images, are affected by neurodegenerative proceedings in AD and MCI. In this study, we are looking for the most effective features to detect and differentiate between healthy normal control (NC), AD, and MCI groups by non-invasive Magnetic Resonance Imaging (MRI) method and propose the automatic multi-class classification using the structural and diffusion MRI Features of the brain. Methods: The structural and diffusion MRI data were downloaded from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database on three groups including AD, MCI, and NC subjects. Four famous classification models of machine learning were used to discover the best classification as a diagnostic tool for separation of the NC, AD and MCI groups. Results: Taken together, our results from this study lead to classify three groups for differentiation between the NC group and patients with MCI and AD, with average accuracy factor 89.9% for Support Vector Machine (SVM) and 91.9% for Artificial Neural Network (ANN) using selected features. Conclusions: Top 9 regions repetitive of WM based on four types of features are the caudate nucleus, corpus callosum, hippocampus, para hippocampus, temporal gyrus, putamen nucleus, cingulate gyrus, the region of 36 and 3 Brodmann. Therefore, these regions could be considered for identifying, monitoring, and future drug trials that could target this brain region to AD and MCI Management.

INTRODUCTION. The Coronavirus disease 2019 (COVID-19) outbreak is an increasing major global public health threat. Mortality rate varies across countries, therefore conducting studies on this disease in different countries is necessary, and will improve disease management worldwide. OBJECTIVE. This study aimed to investigate the COVID-19 disease course characteristics in Iran. METHODS. This is a retrospective study of 108 patients with confirmed COVID-19 from Feb 20 to June 20, 2020, at one Hospital in Iran. In summary, we obtained demographic data, clinical, laboratory, and chest CT findings of patients. The statistical analysis evaluated patients in two groups: recovered or died. RESULT. In brief, cough (70/108, 64.8%) and fever (69/108, 63.9%) were the most common symptoms. CT scan findings of patients with COVID-19 showed that bilateral lung involvement was more common in deceased patients than recovered ones (20/26, 76.9% vs. 30/70, 42.8%, p = 0.026). Laboratory findings of routine blood tests including Erythrocyte sedimentation rate (ESR), Fasting Blood Sugar (FBS), White Blood Cell (WBC), the number of platelets (PLTs) showed a significant difference between the two groups (p CONCLUSION. In this study, we described the features of deceased and recovered patients with COVID-19. Our findings suggest that levels of FBS, ESR, WBC, and PLTs, also patterns of lung involvement, existence of underlying disease, respiratory rate, and oxygen saturation can be predictors of mortality risk. Further studies are proposed to investigate these characteristics in different populations.