Advanced Neuroimaging of Cerebral Small Vessel Disease

Abstract

Opinion statementCerebral small vessel disease (SVD) is characterised by damage to deep grey and white matter structures of the brain and is responsible for a diverse range of clinical problems that include stroke and dementia. In this review, we describe advances in neuroimaging published since January 2015, mainly with magnetic resonance imaging (MRI), that, in general, are improving quantification, observation and investigation of SVD focussing on three areas: quantifying the total SVD burden, imaging brain microstructural integrity and imaging vascular malfunction. Methods to capture ‘whole brain SVD burden’ across the spectrum of SVD imaging changes will be useful for patient stratification in clinical trials, an approach that we are already testing. More sophisticated imaging measures of SVD microstructural damage are allowing the disease to be studied at earlier stages, will help identify specific factors that are important in development of overt SVD imaging features and in understanding why specific clinical consequences may occur. Imaging vascular function will help establish the precise blood vessel and blood flow alterations at early disease stages and, together with microstructural integrity measures, may provide important surrogate endpoints in clinical trials testing new interventions. Better knowledge of SVD pathophysiology will help identify new treatment targets, improve patient stratification and may in future increase efficiency of clinical trials through smaller sample sizes or shorter follow-up periods. However, most of these methods are not yet sufficiently mature to use with confidence in clinical trials, although rapid advances in the field suggest that reliable quantification of SVD lesion burden, tissue microstructural integrity and vascular dysfunction are imminent.Electronic supplementary materialThe online version of this article (doi:10.1007/s11936-017-0555-1) contains supplementary material, which is available to authorized users.

Intracranial functional haemodynamic relationships in patients with cerebral small vessel disease

“…Reduced CSF stroke volume could indicate reduced CSF flux around the base of the brain with reduced CSF movement linked to impaired PVS flushing. Caution is required, however, since the PVS-CSF stroke volume and CVR associations could be a co-association with other SVD features, a common problem in SVD research(6) and should be evaluated further.…”

“…Additionally we adjusted for WMH volume in some models to assess if relationships were independent of a co-association, since we found previously that failure to control for WMH was a potential confound in assessments of CVR in patients with SVD. (6) We examined the normality of residuals (QQ plots and histograms) and heteroscedascity (residual versus fitted values) to assess modelling assumptions. We checked variable inflation factors to assess for collinearity between variables, with a limit of two applied.…”

“…Reliable measures of vascular function would help screen potential treatments in early phase trials(5) prior to definitive but costly trials with clinical endpoints that require large sample sizes and long follow up periods. (6) Sensitive measures of early cerebrovascular dysfunction might also help personalise therapies.…”

Blood–brain barrier breakdown in Alzheimer disease and other neurodegenerative disorders

“…Microbleeds are often used as a criterion to define small vessel disease in the brain 103 . Small hypointense regions on T2*-weighted and susceptibility-weighted imaging (SWI) MRI are thought to representing blood-derived hemosiderin deposits, probably phagocytosed by macrophages in the perivascular spaces, after microbleeding events 96 .…”

Deep Learning in Cardiology

“…Other challenges of applying deep learning in medicine that previous literature has identified are data standardization/availability/dimensionality/volume/quality issues, difficulty in acquiring the corresponding annotations and noise in annotations [239], [240], [242], [246]. More specifically, in [245] the authors note that deep learning applications on small vessel disease have been developed using only a few representative datasets and they need to be evaluated in large multi-center datasets. Kikuchi et al [256] mention that compared with CT and MRI, nuclear cardiology imaging modalities have limited number of images per patient and only specific number of organs are depicted.…”