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Cerebral small vessel disease (CSVD) is composed of several diseases affecting the small arteries, arterioles, venules, and capillaries of the brain, and refers to several pathological processes and etiologies. Neuroimaging features of CSVD include recent small subcortical infarcts, lacunes, white matter hyperintensities, perivascular spaces, microbleeds, and brain atrophy. The main clinical manifestations of CSVD include stroke, cognitive decline, dementia, psychiatric disorders, abnormal gait, and urinary incontinence. Currently, there are no specific preventive or therapeutic measures to improve this condition. In this review, we will discuss the pathophysiology, clinical aspects, neuroimaging, progress of research to treat and prevent CSVD and current treatment of this disease.

Cerebral small vessel disease (SVD) is a common accompaniment of ageing. Features seen on neuroimaging include recent small subcortical infarcts, lacunes, white matter hyperintensities, perivascular spaces, microbleeds, and brain atrophy. SVD can present as a stroke or cognitive decline, or can have few or no symptoms. SVD frequently coexists with neurodegenerative disease, and can exacerbate cognitive deficits, physical disabilities, and other symptoms of neurodegeneration. Terminology and definitions for imaging the features of SVD vary widely, which is also true for protocols for image acquisition and image analysis. This lack of consistency hampers progress in identifying the contribution of SVD to the pathophysiology and clinical features of common neurodegenerative diseases. We are an international working group from the Centres of Excellence in Neurodegeneration. We completed a structured process to develop definitions and imaging standards for markers and consequences of SVD. We aimed to achieve the following: first, to provide a common advisory about terms and definitions for features visible on MRI; second, to suggest minimum standards for image acquisition and analysis; third, to agree on standards for scientific reporting of changes related to SVD on neuroimaging; and fourth, to review emerging imaging methods for detection and quantification of preclinical manifestations of SVD. Our findings and recommendations apply to research studies, and can be used in the clinical setting to standardise image interpretation, acquisition, and reporting. This Position Paper summarises the main outcomes of this international effort to provide the STandards for ReportIng Vascular changes on nEuroimaging (STRIVE).

The heterogeneity of cerebral small vessel disease (CSVD) with mild cognitive impairment (MCI) presents a challenge for diagnosis and classification. This study aims to propose a multimodal magnetic resonance imaging (MRI)-based machine learning framework to effectively classify MCI and NCI in CSVD patients. We enrolled 165 CSVD patients, categorized into NCI ( n  = 81) and MCI ( n  = 84) groups based on neurocognitive assessments. Multimodal MRI data, including T1-weighted, resting-state functional MRI, and diffusion tensor images, were collected. Image preprocessing, feature extraction and selection were applied to obtain MRI features from three modalities. The AutoGluon platform was utilized for model development, and traditional machine learning algorithms were applied for comparison. The models were validated using a validation cohort of 83 CSVD patients, and their performance was assessed via receiver operating characteristic curve analysis. The AutoGluon model to distinguish MCI from NCI based on multimodal MRI features demonstrated high area under the curve (AUC), accuracy, sensitivity, specificity, precision, balanced accuracy, and F1-score in the training cohort (0.926, 88.48%, 88.10%, 88.89%, 89.16%, 88.50%, and 88.63%, respectively) and validation cohort (0.878, 81.93%, 86.36%, 76.92%, 80.85%, 81.64%, and 83.51%, respectively). Other traditional machine learning models had AUCs of 0.755–0.831, and their prediction accuracies were significantly lower than that of AutoGluon model ( P  < 0.001). Our study provides a multimodal MRI-based machine learning framework, utilizing the AutoGluon platform, that outperforms traditional algorithms in classifying MCI and NCI, offering a promising tool for the early prediction of MCI in CSVD.

The TOAST classification divides patients with ischemic stroke into five subgroups according to the presumed etiological mechanism. The aims of the present study were to evaluate the distribution of the different etiological stroke subtypes in a hospital-based sample of stroke patients, and to investigate the association between important risk factors and stroke subtypes. A total of 210 patients with a first-ever ischemic stroke admitted to the stroke unit of Asker and Bærum Hospital in Norway between February 2007 and July 2008 were enrolled in the study. Information on vascular risk factors was collected at admittance, examination of neurological deficits was carried out during their stay, and classification was made according to the TOAST criteria. According to the TOAST classification, 24 (11.4%) of the patients suffered from large vessel disease, 66 (31.4%) from cardioembolic disease, 66 (31.4%) from small vessel disease and 54 (25.7%) from a stroke of undetermined etiology. The presence of hyperlipidemia and atrial fibrillation varied significantly between the different subtypes. In multivariate analyses, hyperlipidemia [odds ratio (OR) 2.46, 95% confidence interval (CI) 1.32-4.60] and current smoking (OR 2.06, 95% CI 1.04-4.08) were the only variables that were related to small vessel disease. Small vessel disease was observed more frequently and large vessel disease less frequently than previously reported. Small vessel disease was significantly associated with hyperlipidemia and current smoking. Our study supports the view that the etiology of lacunar strokes is multifactorial.

Small deep infarcts might be classified into 2 types: lacunar and branchatheromatous infarcts. However, since their initial description, small deep infarcts were still regarded as the same category of the Trial of Org 10172 in Acute Stroke Treatment (TOAST) classification, small vessel occlusion (SVO). We hypothesized that the 2 types of small deep infarcts would be distinct clinical entities. This study was conducted to investigate the clinical characteristics in the 2 groups of patients according to lesion pattern and combined atherosclerotic diseases. We included patients with small deep infarcts in the subcortical area. The patients were divided into 2 groups: (1) island lesions and (2) linear lesions on coronal diffusion weighted imaging. The status of the relevant artery was categorized as no stenosis, non-significant (<50% of luminal narrowing) and significant (≥50% of luminal narrowing). We compared the clinical and imaging characteristics of two lesion types according to various arterial status. This study analyzed a total of 248 patients. Independent factors for island lesions on coronal DWI were male, severe leukoaraiosis, microbleeds, abnormal glycated hemoglobin (HbA1C), and abnormal estimated glomerular filtration ratio (eGFR) adjusted by age, sex, and inititial National Institutes of Health Stroke Scale. In addition, in patients without significant relevant arterial stenosis, island lesion patterns were more frequently associated with severe periventricular white matter hyperintensity, diabetes mellitus, abnormal eGFR and abnormal HbA1C than linear lesion patterns. This study demonstrated that SVO of TOAST classifications had different imaging and clinical characteristics according to the lesion patterns of coronal imaging. It suggests that two types of SVO should be regarded as the different categories of stroke classification.

Background Cerebral small vessel disease (CSVD) often coexists with neurodegenerative pathologies, yet their role remains underexplored. This study aims to determine their prevalence, risk factors, and cognitive effects in patients with deep perforator arteriopathy (DPA) or cerebral amyloid angiopathy (CAA) using the biomarker-based ATN classification. Methods In this cross-sectional study 186 patients (median age 75 years, 41% females, 111 with probable CAA, 75 with DPA) underwent MRI for analysis of CSVD severity and etiology, and lumbar puncture for analysis of cerebrospinal fluid amyloid-β 42/40 ratio, phosphorylated-tau, total-tau and neurofilament light. ATN profiles were related to clinical characteristics, MRI markers and cognitive performance in multivariate regression models. Results Among CSVD patients, 30% had normal biomarkers (A-T-N-), 33% were within the AD pathology continuum (A + T ± N ± : 47% in CAA vs. 13% in DPA, p < .001), and 37% showed non-AD pathological changes (A-T ± N + : 53% in DPA vs. 25% in CAA, p < .001). The AD pathology continuum was associated with a severe lobar hemorrhagic phenotype and cognitive impairment, while non-AD pathological change was related to CSVD severity, history of stroke and similarly cognitive impairment. Both pathological ATN profiles were further related to lower MMSE scores (A + T ± N ± : B = − 3.3, p = .006; A-T ± N + : B = − 2.7, p = .021). Conclusions Using biomarkers, this study confirms in vivo that CSVD frequently co-occurs with neurodegenerative pathologies, exerting detrimental effects on cognitive health.

Cerebral small vessel disease (CSVD) is an important cause of stroke, cognitive impairment, and other diseases, and its early quantitative evaluation can significantly improve patient prognosis. Magnetic resonance imaging (MRI) is an important method to evaluate the occurrence, development, and severity of CSVD. However, the diagnostic process lacks quantitative evaluation criteria and is limited by experience, which may easily lead to missed diagnoses and misdiagnoses. With the development of artificial intelligence technology based on deep learning, the extraction of high‐dimensional features in imaging can assist doctors in clinical decision‐making, and it has been widely used in brain function and mental disorders, and cardiovascular and cerebrovascular diseases. This paper summarizes the global research results in recent years and briefly describes the application of deep learning in evaluating CSVD signs in MRI imaging, including recent small subcortical infarcts, lacunes of presumed vascular origin, vascular white matter hyperintensity, enlarged perivascular spaces, cerebral microbleeds, brain atrophy, cortical superficial siderosis, and cortical cerebral microinfarct. Recent advancements in artificial intelligence, particularly deep learning, have revolutionized the detection and evaluation of cerebral small vessel disease (CSVD) through magnetic resonance imaging (MRI). This study demonstrates how deep learning can extract high‐dimensional imaging features, improving the quantitative assessment of CSVD, and leading to earlier diagnosis and better patient outcomes.

Background The vascular contributions to neurodegeneration and neuroinflammation may be assessed by magnetic resonance imaging (MRI) and ultrasonography (US). This review summarises the methodology for these widely available, safe and relatively low cost tools and analyses recent work highlighting their potential utility as biomarkers for differentiating subtypes of cognitive impairment and dementia, tracking disease progression and evaluating response to treatment in various neurocognitive disorders. Methods At the 9th International Congress on Vascular Dementia (Ljubljana, Slovenia, October 2015) a writing group of experts was formed to review the evidence on the utility of US and arterial spin labelling (ASL) as neurophysiological markers of normal ageing, vascular cognitive impairment (VCI) and Alzheimer’s disease (AD). Original articles, systematic literature reviews, guidelines and expert opinions published until September 2016 were critically analysed to summarise existing evidence, indicate gaps in current knowledge and, when appropriate, suggest standards of use for the most widely used US and ASL applications. Results Cerebral hypoperfusion has been linked to cognitive decline either as a risk or an aggravating factor. Hypoperfusion as a consequence of microangiopathy, macroangiopathy or cardiac dysfunction can promote or accelerate neurodegeneration, blood-brain barrier disruption and neuroinflammation. US can evaluate the cerebrovascular tree for pathological structure and functional changes contributing to cerebral hypoperfusion. Microvascular pathology and hypoperfusion at the level of capillaries and small arterioles can also be assessed by ASL, an MRI signal. Despite increasing evidence supporting the utility of these methods in detection of microvascular pathology, cerebral hypoperfusion, neurovascular unit dysfunction and, most importantly, disease progression, incomplete standardisation and missing validated cut-off values limit their use in daily routine. Conclusions US and ASL are promising tools with excellent temporal resolution, which will have a significant impact on our understanding of the vascular contributions to VCI and AD and may also be relevant for assessing future prevention and therapeutic strategies for these conditions. Our work provides recommendations regarding the use of non-invasive imaging techniques to investigate the functional consequences of vascular burden in dementia.

Cerebral small vessel disease (SVD) commonly coexists with large artery atherosclerosis (LAA). We evaluate the effect of SVD on stroke recurrence in patients for ischemic stroke with LAA. We consecutively collected first-ever ischemic stroke patients who were classified as LAA mechanism between Jan 2010 and Dec 2013. Univariate and multivariate Cox analyses were performed to evaluate the association between the 2-year recurrence and demographic, clinical, and radiological factors. To evaluate the impact of SVD and its components on recurrent stroke, we used the Kaplan-Meier analysis. SVD was defined as the presence of severe white matter hyperintensity (WMH) or old lacunar infarction (OLI) or cerebral microbleeds (CMB). We also compared frequency and burden of SVD among recurrent stroke groups with different mechanisms. Among a total of 956 participants, 92 patients had recurrent events. Recurrence group showed a higher frequency of severe WMH, OLI, asymptomatic territorial infarction, and severe stenosis on the relevant vessel in multivariate analysis. The impact of SVD and its components on recurrent stroke was significant in any ischemic recurrent stroke, and the presence of SVD was continuously important in stroke recurrence regardless of its mechanism, including recurrent LAA stroke, recurrent small vessel occlusion stroke, and even recurrent cardioembolic stroke. Additionally, the recurrence rate increased in dose-response manner with the increased number of SVD components. Cerebral SVD is associated with recurrent stroke in patients with LAA. Additionally, it may affect any mechanisms of recurrent stroke and even with a dose response manner.

The present study aimed to determine whether a recently proposed cerebral small vessel disease (CSVD) classification scheme could differentiate the 5-year all-cause mortality in middle-to-old aged asymptomatic CSVD. Stroke-free and non-demented participants recruited from the community-based I-Lan Longitudinal Aging Study underwent baseline brain magnetic resonance imaging (MRI) between 2011 and 2014 and were followed-up between 2018 and 2019. The study population was classified into control (non-CSVD) and CSVD type 1–4 groups based on MRI markers. We determined the association with mortality using Cox regression models, adjusting for the age, sex, and vascular risk factors. A total of 735 participants were included. During a mean follow-up of 5.7 years, 62 (8.4%) died. There were 335 CSVD type 1 (57.9 ± 5.9 years), 249 type 2 (65.6 ± 8.1 years), 52 type 3 (67.8 ± 9.2 years), and 38 type 4 (64.3 ± 9.0 years). Among the four CSVD types, CSVD type 4 individuals had significantly higher all-cause mortality (adjusted hazard ratio = 5.0, 95% confidence interval 1.6–15.3) compared to controls. This novel MRI-based CSVD classification scheme was able to identify individuals at risk of mortality at an asymptomatic, early stage of disease and might be applied for future community-based health research and policy.