Prevalence and Significance of the Vessel-Cluster Sign on Susceptibility-Weighted Imaging in Patients With Severe Small Vessel Disease

National and international collaborations to advance research into vascular contributions to cognitive decline

Cerebrovascular disease is the second most common cause of cognitive disorders, usually referred to as vascular contributions to cognitive impairment and dementia (VCID) and makes some contribution to about 70 % of all dementias. Despite its importance, research into VCID has lagged as compared to cognitive impairment due to Alzheimer's disease. There is an increasing appreciation that closing this gap requires large national and international collaborations. This paper highlights 24 notable large-scale national and international efforts to advance research into VCID (MarkVCID, DiverseVCID, DISCOVERY, COMPASS-ND, HBC, RHU SHIVA, UK DRI Vascular Theme, STROKOG, Meta VCI Map, ISGC, ENIGMA-Stroke Recovery, CHARGE, SVDs@target, BRIDGET, CADASIL Consortium, CADREA, AusCADASIL, DPUK, DPAU, STRIVE, HARNESS, FINESSE, VICCCS, VCD-CRE Delphi). These collaborations aim to investigate the effects on cognition from cerebrovascular disease or impaired cerebral blood flow, the mechanisms of action, means of prevention and avenues for treatment. Consensus groups have been developed to harmonise global approaches to VCID, standardise terminology and inform management and treatment, and data sharing is becoming the norm. VCID research is increasingly a global collaborative enterprise which bodes well for rapid advances in this field.

Cerebral Small Vessel Disease: a Review of the Pathophysiological Mechanisms

Cerebral small vessel disease (cSVD) refers to the age-dependent pathological processes involving the brain small vessels and leading to vascular cognitive impairment, intracerebral hemorrhage, and acute lacunar ischemic stroke. Despite the significant public health burden of cSVD, disease-specific therapeutics remain unavailable due to the incomplete understanding of the underlying pathophysiological mechanisms. Recent advances in neuroimaging acquisition and processing capabilities as well as findings from cSVD animal models have revealed critical roles of several age-dependent processes in cSVD pathogenesis including arterial stiffness, vascular oxidative stress, low-grade systemic inflammation, gut dysbiosis, and increased salt intake. These factors interact to cause a state of endothelial cell dysfunction impairing cerebral blood flow regulation and breaking the blood brain barrier. Neuroinflammation follows resulting in neuronal injury and cSVD clinical manifestations. Impairment of the cerebral waste clearance through the glymphatic system is another potential process that has been recently highlighted contributing to the cognitive decline. This review details these mechanisms and attempts to explain their complex interactions. In addition, the relevant knowledge gaps in cSVD mechanistic understanding are identified and a systematic approach to future translational and early phase clinical research is proposed in order to reveal new cSVD mechanisms and develop disease-specific therapeutics.

Deep learning based on susceptibility-weighted MR sequence for detecting cerebral microbleeds and classifying cerebral small vessel disease

BACKGROUND

Cerebral microbleeds (CMBs) serve as neuroimaging biomarkers to assess risk of intracerebral hemorrhage and diagnose cerebral small vessel disease (CSVD). Therefore, detecting CMBs can evaluate the risk of intracerebral hemorrhage and use its presence to support CSVD classification, both are conducive to optimizing CSVD management. This study aimed to develop and test a deep learning (DL) model based on susceptibility-weighted MR sequence (SWS) to detect CMBs and classify CSVD to assist neurologists in optimizing CSVD management. Patients with arteriolosclerosis (aSVD), cerebral amyloid angiopathy (CAA), and cerebral autosomal dominant arteriopathy with subcortical infarcts and leukoencephalopathy (CADASIL) treated at three centers were enrolled between January 2017 and May 2022 in this retrospective study. The SWSs of patients from two centers were used as the development set, and the SWSs of patients from the remaining center were used as the external test set. The DL model contains a Mask R-CNN for detecting CMBs and a multi-instance learning (MIL) network for classifying CSVD. The metrics for model performance included intersection over union (IoU), Dice score, recall, confusion matrices, receiver operating characteristic curve (ROC) analysis, accuracy, precision, and F1-score.

RESULTS

A total of 364 SWS were recruited, including 336 in the development set and 28 in the external test set. IoU for the model was 0.523 ± 0.319, Dice score 0.627 ± 0.296, and recall 0.706 ± 0.365 for CMBs detection in the external test set. For CSVD classification, the model achieved a weighted-average AUC of 0.908 (95% CI 0.895-0.921), accuracy of 0.819 (95% CI 0.768-0.870), weighted-average precision of 0.864 (95% CI 0.831-0.897), and weighted-average F1-score of 0.829 (95% CI 0.782-0.876) in the external set, outperforming the performance of the neurologist group.

CONCLUSION

The DL model based on SWS can detect CMBs and classify CSVD, thereby assisting neurologists in optimizing CSVD management.

Blood-brain barrier leakage hotspots collocating with brain lesions due to sporadic and monogenic small vessel disease

Blood-brain barrier (BBB) is known to be impaired in cerebral small vessel disease (SVD), and is measurable by dynamic-contrast enhancement (DCE)-MRI. In a cohort of 69 patients (42 sporadic, 27 monogenic SVD), who underwent 3T MRI, including DCE and cerebrovascular reactivity (CVR) sequences, we assessed the relationship of BBB-leakage hotspots to SVD lesions (lacunes, white matter hyperintensities (WMH), and microbleeds). We defined as hotspots the regions with permeability surface area product highest decile on DCE-derived maps within the white matter. We assessed factors associated with the presence and number of hotspots corresponding to SVD lesions in multivariable regression models adjusted for age, WMH volume, number of lacunes, and SVD type. We identified hotspots at lacune edges in 29/46 (63%) patients with lacunes, within WMH in 26/60 (43%) and at the WMH edges in 34/60 (57%) patients with WMH, and microbleed edges in 4/11 (36%) patients with microbleeds. In adjusted analysis, lower WMH-CVR was associated with presence and number of hotspots at lacune edges, and higher WMH volume with hotspots within WMH and at WMH edges, independently of the SVD type. In conclusion, SVD lesions frequently collocate with high BBB-leakage in patients with sporadic and monogenic forms of SVD.

An investigation of the level of stigma and the factors influencing it in the rehabilitation of young and middle-aged stroke patients-a cross-sectional study

BACKGROUND

There are few reported studies on stigma in young and middle-aged stroke patients during the rehabilitation period, however, the rehabilitation period plays a key role in the patients' disease regression. Exploring the level of stigma and the influencing factors in young and middle-aged stroke patients during the rehabilitation period is crucial for determining how to reduce the level of stigma and improve the patients' motivation for rehabilitation treatment. Therefore, this study investigated the level of stigma in young and middle-aged stroke patients and analyzed the factors influencing stigma in order to provide a reference or basis for healthcare professionals to develop effective and targeted stigma intervention programs.

METHODS

Using a convenience sampling method, 285 young and middle-aged stroke patients admitted to the rehabilitation medicine department of a tertiary care hospital in Shenzhen, China, from November 2021 to September 2022 were selected and surveyed using a general information questionnaire, the Stroke Stigma Scale(SSS), the Barthel Index(BI), and the Positive and Negative Emotions Scale(PANAS), and multiple linear regression and smoothed curve fitting were used to analyze the factors influencing the stigma of young and middle-aged stroke patients during the rehabilitation period.

RESULTS

SSS score of 45.08 ± 11.06, univariate analysis of age, occupation, education level, pre-stroke monthly income, insurance type, comorbid chronic disease status, primary caregiver, BI, positive and negative emotion as factors influencing stigma. Multiple linear regression showed that age, pre-stroke monthly income, BI, positive and negative emotions were independent influences on stigma in young and middle-aged stroke patients, explaining 58.0% of the total variance in stigma. A smoothed curve fit revealed a curvilinear relationship between the above influences and stigma.

CONCLUSION

Young and middle-aged stroke patients have a moderate level of stigma. Medical staff should focus on young patients aged 18-44 years, those with high monthly income before the stroke, those with poor self-care ability, and those with low positive and high negative emotion scores, and conduct early assessments and adopt targeted intervention programs according to the influencing factors to reduce the stigma of young and middle-aged stroke patients, improve their motivation for rehabilitation, and help them return to their families and society as soon as possible.

TRIAL REGISTRATION

Registration number of China Clinical Trials Registration Center: 20,220,328,004-FS01.

Microglial Priming in Infections and Its Risk to Neurodegenerative Diseases

Infectious diseases of different etiologies have been associated with acute and long-term neurological consequences. The primary cause of these consequences appears to be an inflammatory process characterized primarily by a pro-inflammatory microglial state. Microglial cells, the local effectors’ cells of innate immunity, once faced by a stimulus, alter their morphology, and become a primary source of inflammatory cytokines that increase the inflammatory process of the brain. This inflammatory scenario exerts a critical role in the pathogenesis of neurodegenerative diseases. In recent years, several studies have shown the involvement of the microglial inflammatory response caused by infections in the development of neurodegenerative diseases. This has been associated with a transitory microglial state subsequent to an inflammatory response, known as microglial priming, in which these cells are more responsive to stimuli. Thus, systemic inflammation and infections induce a transitory state in microglia that may lead to changes in their state and function, making priming them for subsequent immune challenges. However, considering that microglia are long-lived cells and are repeatedly exposed to infections during a lifetime, microglial priming may not be beneficial. In this review, we discuss the relationship between infections and neurodegenerative diseases and how this may rely on microglial priming.