Magnetic resonance imaging manifestations of cerebral small vessel disease: automated quantification and clinical application

Is cerebral small vessel disease a central nervous system interstitial fluidopathy?

A typical anatomical congregate and functionally distinct multicellular cerebrovascular dynamic confer diverse blood-brain barrier (BBB) and microstructural permeabilities to conserve the health of brain parenchymal and its microenvironment. This equanimity presupposes the glymphatic system that governs the flow and clearance of metabolic waste and interstitial fluids (ISF) through venous circulation. Following the introduction of glymphatic system concept, various studies have been carried out on cerebrospinal fluid (CSF) and ISF dynamics. These studies reported that the onset of multiple diseases can be attributed to impairment in the glymphatic system, which is newly referred as central nervous system (CNS) interstitial fluidopathy. One such condition includes cerebral small vessel disease (CSVD) with poorly understood pathomechanisms. CSVD is an umbrella term to describe a chronic progressive disorder affecting the brain microvasculature (or microcirculation) involving small penetrating vessels that supply cerebral white and deep gray matter. This review article proposes CSVD as a form of "CNS interstitial fluidopathy". Linking CNS interstitial fluidopathy with CSVD will open a better insight pertaining to the perivascular space fluid dynamics in CSVD pathophysiology. This may lead to the development of treatment and therapeutic strategies to ameliorate the pathology and adverse effect of CSVD.

Assessment of cheese sign and its association with vascular risk factors: Data from PUMCH dementia cohort

BACKGROUND

In the clinic, practitioners encounter many patients with an abnormal pattern of dense punctate magnetic resonance imaging (MRI) signal in the basal ganglia, a phenomenon known as "cheese sign". This sign is reported as common in cerebrovascular diseases, dementia, and old age. Recently, cheese sign has been speculated to consist of dense perivascular space (PVS). This study aimed to assess the lesion types of cheese sign and analyze the correlation between this sign and vascular disease risk factors.

METHODS

A total of 812 patients from Peking Union Medical College Hospital (PUMCH) dementia cohort were enrolled. We analyzed the relationship between cheese sign and vascular risk. For assessing cheese sign and defining its degree, the abnormal punctate signals were classified into basal ganglia hyperintensity (BGH), PVS, lacunae/infarctions and microbleeds, and counted separately. Each type of lesion was rated on a four-level scale, and then the sum was calculated; this total was defined as the cheese sign score. Fazekas and Age-Related White Matter Changes (ARWMC) scores were used to evaluate the paraventricular, deep, and subcortical gray/white matter hyperintensities.

RESULTS

A total of 118 patients (14.5%) in this dementia cohort were found to have cheese sign. Age (odds ratio [OR]: 1.090, 95% confidence interval [CI]: 1.064-1.120, P <0.001), hypertension (OR: 1.828, 95% CI: 1.123-2.983, P = 0.014), and stroke (OR: 1.901, 95% CI: 1.092-3.259, P = 0.025) were risk factors for cheese sign. There was no significant relationship between diabetes, hyperlipidemia, and cheese sign. The main components of cheese sign were BGH, PVS, and lacunae/infarction. The proportion of PVS increased with cheese sign severity.

CONCLUSIONS

The risk factors for cheese sign were hypertension, age, and stroke. Cheese sign consists of BGH, PVS, and lacunae/infarction.

Chronic kidney disease and its association with cerebral small vessel disease in the general older hypertensive population

BACKGROUND

Cerebral small vessel disease can be identified using magnetic resonance imaging, and includes white matter hyperintensities, lacunar infarcts, cerebral microbleeds, and brain atrophy. Cerebral small vessel disease and chronic kidney disease share many risk factors, including hypertension. This study aims to explore an association between chronic kidney disease and cerebral small vessel disease, and also to explore the role of hypertension in this relationship.

METHODS

With a cross sectional study design, data from 390 older adults was retrieved from the general population study Good Aging in Skåne. Chronic kidney disease was defined as glomerular filtration rate < 60 ml/min/1,73m2. Associations between chronic kidney disease and magnetic resonance imaging markers of cerebral small vessel disease were explored using logistic regression models adjusted for age and sex. In a secondary analysis, the same calculations were performed with the study sample stratified based on hypertension status.

RESULTS

In the whole group, adjusted for age and sex, chronic kidney disease was not associated with any markers of cerebral small vessel disease. After stratification by hypertension status and adjusted for age and sex, we observed that chronic kidney disease was associated with cerebral microbleeds (OR 1.93, CI 1.04-3.59, p-value 0.037), as well as with cortical atrophy (OR 2.45, CI 1.34-4.48, p-value 0.004) only in the hypertensive group. In the non-hypertensive group, no associations were observed.

CONCLUSIONS

In this exploratory cross-sectional study, we observed that chronic kidney disease was associated with markers of cerebral small vessel disease only in the hypertensive subgroup of a general population of older adults. This might indicate that hypertension is an important link between chronic kidney disease and cerebral small vessel disease. Further studies investigating the relationship between CKD, CSVD, and hypertension are warranted.

A. SD. A diagnosis model for brain atrophy using deep learning and MRI of type 2 diabetes mellitus

Objective

Type 2 Diabetes Mellitus (T2DM) is linked to cognitive deterioration and anatomical brain abnormalities like cerebral brain atrophy and cerebral diseases. We aim to develop an automatic deep learning-based brain atrophy diagnosis model to detect, segment, classify, and predict the survival rate.

Methods

Two hundred thirty-five MRI images affected with brain atrophy due to prolonged T2DM were acquired. The dataset was divided into training and testing (80:20%; 188, 47, respectively). Pre-processing is done through a novel convolutional median filter, followed by segmentation of atrophy regions, i.e., the brain shrinkage, white and gray matter is done through the proposed TRAU-Net model (Transfer Residual Attention U-Net), classification with the proposed Multinomial Logistic regression with Attention Swin Transformer (MLAST), and prediction of chronological age is determined through Multivariate CoX Regression model (MCR). The classification of Brain Atrophy (BA) types is determined based on the features extracted from the segmented region. Performance measures like confusion matrix, specificity, sensitivity, accuracy, F1-score, and ROC-AUC curve are used to measure classification model performance, whereas, for the segmentation model, pixel accuracy and dice similarity coefficient are applied.

Results

The pixel accuracy and dice coefficient for segmentation were 98.25 and 96.41, respectively. Brain atrophy multi-class classification achieved overall training accuracy is 0.9632 ± 1.325, 0.9677 ± 1.912, 0.9682 ± 1.715, and 0.9521 ± 1.877 for FA, PA, R-MTA, and L-MTA, respectively. The overall AUC-ROC curve for the classification model is 0.9856. The testing and validation accuracy obtained for the proposed model are 0.9379 and 0.9694, respectively. The prediction model's performance is measured using correlation coefficient (r), coefficient determination r2, and Mean Square Error (MSE) and recorded 0.951, 0.904, and 0.5172, respectively.

Conclusion

The brain atrophy diagnosis model consists of sub-models to detect, segment, and classify the atrophy regions using novel deep learning and multivariate mathematical models. The proposed model has outperformed the existing models regarding multi-classification and segmentation; therefore, the automated diagnosis model can be deployed in healthcare centers to assist physicians.

Evaluation of grouped capsule network for intracranial hemorrhage segmentation in CT scans

Intracranial hemorrhage is a cerebral vascular disease with high mortality. Automotive diagnosing and segmentation of intracranial hemorrhage in Computed Tomography (CT) could assist the neurosurgeon in making treatment plans, which improves the survival rate. In this paper, we design a grouped capsule network named GroupCapsNet to segment the hemorrhage region from a Non-contract CT scan. In grouped capsule network, we constrain the prediction capsules for output capsules produced from different groups of input capsules with various types in each layer. This method can reduce the number of intermediate prediction capsules and accelerate the capsule network. In addition, we modify the squashing function to further accelerate the forward procedure without sacrificing its performance. We evaluate our proposed method with a collected dataset containing 210 intracranial hemorrhage CT scan slices. In experiments, our proposed method achieves competitive results in intracranial hemorrhage area segmentation compared to the existing methods.

Total Burden of Cerebral Small Vessel Disease on MRI May Predict Cognitive Impairment in Parkinson’s Disease

(1) Objective: to investigate the association between the total burden of cerebral small vessel disease (CSVD) and cognitive function in Parkinson’s disease (PD). (2) Methods: this retrospective study compared clinical and neuroimaging characteristics of 122 PD patients to determine the association between cognitive decline and total burden of CSVD in PD. All patients underwent brain MRI examinations, and their total CSVD burden scores were evaluated by silent lacunar infarction (SLI), cerebral microbleeds (CMB), white matter hyperintensities (WMH), and enlarged perivascular spaces (EPVS). The cognitive function was assessed by administering Mini-Mental State Examination (MMSE). Receiver-operating characteristic (ROC) curve and the area under the ROC curve (AUC) were performed to quantify the accuracy of the total burden of CSVD and PVH in discriminating PD patients with or without cognitive impairment. (3) Results: the PD patients with cognitive impairment had a significantly higher SLI, CMB, periventricular hyperintensities (PVH), deep white matter hyperintensities (DWMH), enlarged perivascular spaces of basal ganglia (BG-EPVS), and the total CSVD score compared with no cognitive impairment. Total CSVD score and MMSE had a significant negative correlation (r = −0. 483). Furthermore, total burden of CSVD and PVH were the independent risk factors of cognitive impairment in PD, and their good accuracy in discriminating PD patients with cognitive impairment from those with no cognitive impairment was confirmed by the results of ROC curves. (4) Conclusions: total burden of CSVD tightly linked to cognitive impairment in PD patients. The total burden of CSVD or PVH may predict the cognitive impairment in PD.

Fully Automatic Classification of Brain Atrophy on NCCT Images in Cerebral Small Vessel Disease: A Pilot Study Using Deep Learning Models

Objective

Brain atrophy is an important imaging characteristic of cerebral small vascular disease (CSVD). Our study explores the linear measurement application on CT images of CSVD patients and develops a fully automatic brain atrophy classification model. The second aim was to compare it with the end-to-end Convolutional Neural Networks (CNNs) model.

Methods

A total of 385 subjects such as 107 no-atrophy brain, 185 mild atrophy, and 93 severe atrophy were collected and randomly separated into training set (n = 308) and test set (n = 77). Key slices for linear measurement were manually identified and used to annotate nine linear measurements and a binary classification of cerebral sulci widening. A linear-measurement-based pipeline (2D model) was constructed for two-types (existence/non-existence brain atrophy) or three-types classification (no/mild atrophy/severe atrophy). For comparison, an end-to-end CNN model (3D-deep learning model) for brain atrophy classification was also developed. Furthermore, age and gender were integrated to the 2D and 3D models. The sensitivity, specificity, accuracy, average F1 score, receiver operating characteristics (ROC) curves for two-type classification and weighed kappa for three-type classification of the two models were compared.

Results

Automated measurement of linear measurements and cerebral sulci widening achieved moderate to almost perfect agreement with manual annotation. In two-type atrophy classification, area under the curves (AUCs) of the 2D model and 3D model were 0.953 and 0.941 with no significant difference (p = 0.250). The Weighted kappa of the 2D model and 3D model were 0.727 and 0.607 according to standard classification they displayed, mild atrophy and severe atrophy, respectively. Applying patient age and gender information improved classification performances of both 2D and 3D models in two-type and three-type classification of brain atrophy.

Conclusion

We provide a model composed of different modules that can classify CSVD-related brain atrophy on CT images automatically, using linear measurement. It has similar performance and better interpretability than the end-to-end CNNs model and may prove advantageous in the clinical setting.

Direct Rating Estimation of Enlarged Perivascular Spaces (EPVS) in Brain MRI Using Deep Neural Network

In this article, we propose a deep-learning-based estimation model for rating enlarged perivascular spaces (EPVS) in the brain’s basal ganglia region using T2-weighted magnetic resonance imaging (MRI) images. The proposed method estimates the EPVS rating directly from the T2-weighted MRI without using either the detection or the segmentation of EVPS. The model uses the cropped basal ganglia region on the T2-weighted MRI. We formulated the rating of EPVS as a multi-class classification problem. Model performance was evaluated using 96 subjects’ T2-weighted MRI data that were collected from two hospitals. The results show that the proposed method can automatically rate EPVS—demonstrating great potential to be used as a risk indicator of dementia to aid early diagnosis.

Molecular docking, binding mode analysis, molecular dynamics, and prediction of ADMET/toxicity properties of selective potential antiviral agents against SARS-CoV-2 main protease: an effort toward drug repurposing to combat COVID-19

The importance of the main protease (M^pro) enzyme of SARS-CoV-2 in the digestion of viral polyproteins introduces M^pro as an attractive drug target for antiviral drug design. This study aims to carry out the molecular docking, molecular dynamics studies, and prediction of ADMET properties of selected potential antiviral molecules. The study provides an insight into biomolecular interactions to understand the inhibitory mechanism and the spatial orientation of the tested ligands and further, identification of key amino acid residues within the substrate-binding pocket that can be applied for structure-based drug design. In this regard, we carried out molecular docking studies of chloroquine (CQ), hydroxychloroquine (HCQ), remdesivir (RDV), GS441524, arbidol (ARB), and natural product glycyrrhizin (GA) using AutoDock 4.2 tool. To study the drug-receptor complex's stability, selected docking possesses were further subjected to molecular dynamics studies with Schrodinger software. The prediction of ADMET/toxicity properties was carried out on ADMET Prediction™. The docking studies suggested a potential role played by CYS145, HIS163, and GLU166 in the interaction of molecules within the active site of COVID-19 M^pro. In the docking studies, RDV and GA exhibited superiority in binding with the crystal structure of M^pro over the other selected molecules in this study. Spatial orientations of the molecules at the active site of M^pro exposed the significance of S1–S4 subsites and surrounding amino acid residues. Among GA and RDV, RDV showed better and stable interactions with the protein, which is the reason for the lesser RMSD values for RDV. Overall, the present in silico study indicated the direction to combat COVID-19 using FDA-approved drugs as promising agents, which do not need much toxicity studies and could also serve as starting points for lead optimization in drug discovery.