Stroke subtype, vascular risk factors, and total MRI brain small-vessel disease burden

Staals, Julie; Makin, Stephen; Doubal, Fergus; Dennis, Martin; Wardlaw, Joanna M.

doi:10.1212/wnl.0000000000000837

Cited by 670 publications

(747 citation statements)

References 35 publications

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“…It is the most common cause of vascular dementia and causes approximately a fifth of the strokes worldwide [9], proven to have significant and strong associations with vascular risk factors [10]. A moderate-to-severe burden of PVS in the basal ganglia (BG) is one of the markers of SVD [9], along with lacunes, cerebral microbleeds and white matter hyperintensities (WMH).…”

Section: Introductionmentioning

confidence: 99%

Reliability of an automatic classifier for brain enlarged perivascular spaces burden and comparison with human performance

et al. 2017

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Correspondence: Víctor González-Castro (victor.gonzalez@ed.ac.uk) or María del C. Valdés Hernández (M. Valdes-Hernan@ed.ac.uk) In the brain, enlarged perivascular spaces (PVS) relate to cerebral small vessel disease (SVD), poor cognition, inflammation and hypertension. We propose a fully automatic scheme that uses a support vector machine (SVM) to classify the burden of PVS in the basal ganglia (BG) region as low or high. We assess the performance of three different types of descriptors extracted from the BG region in T2-weighted MRI images: (i) statistics obtained from Wavelet transform's coefficients, (ii) local binary patterns and (iii) bag of visual words (BoW) based descriptors characterizing local keypoints obtained from a dense grid with the scale-invariant feature transform (SIFT) characteristics. When the latter were used, the SVM classifier achieved the best accuracy (81.16%). The output from the classifier using the BoW descriptors was compared with visual ratings done by an experienced neuroradiologist (Observer 1) and by a trained image analyst (Observer 2). The agreement and cross-correlation between the classifier and Observer 2 (κ = 0.67 (0.58-0.76)) were slightly higher than between the classifier and Observer 1 (κ = 0.62 (0.53-0.72)) and comparable between both the observers (κ = 0.68 (0.61-0.75)). Finally, three logistic regression models using clinical variables as independent variable and each of the PVS ratings as dependent variable were built to assess how clinically meaningful were the predictions of the classifier. The goodness-of-fit of the model for the classifier was good (area under the curve (AUC) values: 0.93 (model 1), 0.90 (model 2) and 0.92 (model 3)) and slightly better (i.e. AUC values: 0.02 units higher) than that of the model for Observer 2. These results suggest that, although it can be improved, an automatic classifier to assess PVS burden from brain MRI can provide clinically meaningful results close to those from a trained observer.

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Section: Introductionmentioning

confidence: 99%

Reliability of an automatic classifier for brain enlarged perivascular spaces burden and comparison with human performance

et al. 2017

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“…Moreover, a recent study using an unweighted SVD scale recommended that different cutoff points and weighting for MRI features should be investigated. 10 Furthermore, it is important to consider whether each measure of CeVD, in any proposed scale, carries the same importance to patients 11 and whether differential weighting of each MRI feature are needed to optimally predict outcomes of clinical importance, such as cognition. Therefore, we have explored whether a weighted assessment of total CeVD burden can improve understanding of the cognitive consequences of CeVD.…”

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confidence: 99%

Association of Magnetic Resonance Imaging Markers of Cerebrovascular Disease Burden and Cognition

Hilal

Collinson

et al. 2015

Stroke

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Background and Purpose-The present study sought to examine the association between the burden of cerebrovascular disease (CeVD) as assessed by multimodal magnetic resonance imaging and neurocognitive function. Methods-Cognitively impaired patients and controls were tested on an extensive neuropsychological battery and underwent multimodal brain magnetic resonance imaging. CeVD markers determined from magnetic resonance imaging included the presence of multiple lacunes, multiple cerebral microbleeds, and moderate or severe white matter hyperintensities as markers for small-vessel disease and cortical stroke and intracranial stenosis as markers for large-vessel disease. A weighted CeVD burden score was constructed, and its association with global and domain-specific cognitive performance was investigated. Results-A total of 305 cases and 94 controls were included in the analysis. A graded association of CeVD burden with neurocognitive function was found. Moreover, a clear threshold of CeVD burden was associated with severe impairment. White matter hyperintensities was associated with global neurocognitive deficits, whereas microbleeds were associated with domain-specific impairments. Conclusions-The weighted CeVD burden score comprising markers of both small-and large-vessel diseases were associated with deficits in both global and domain-specific neurocognitive function. Additional studies are needed to validate the use of this CeVD burden score for the prediction of dementia.

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“…One difference between the present study and previous investigations presenting derivations and investigation of total SVD burden scores is that we present a single measure derived from a single imaging modality rather than measures obtained from different MRI markers of SVD. For example, (Huijts et al, 2013;Klarenbeek et al, 2013, and Staals et al (2014 previously used information from lacunar infarcts, WMH, CMB and enlarged perivascular spaces to obtain SVD burden scores. Our SVD burden score, DSEG θ, was related to all four conventional MRI markers of SVD used in this study (WMH load, TCVrepresenting cerebral atrophy, new lacunes and CMB), with an increase in DSEG θ being associated with higher SVD burden as measured by these markers.…”

Section: Discussionmentioning

confidence: 99%

“…Supporting this, Jokinen et al (2011) reported independent contributions of WMH and incident lacunes in predicting decline in EF and IPS. As MRI markers of SVD are often co-occur in individuals, recent efforts have been made to combine them into a unitary score of SVD burden (Huijts et al, 2013;Klarenbeek et al, 2013;Staals et al, 2015;Staals et al, 2014). Huijts et al (2013) and Staals et al (2014) used a score of SVD burden in which the presence of WMH, CMB, perivascular spaces and lacunar infarcts was summed to create a score between 0 and 4.…”

Section: Introductionmentioning

confidence: 99%

“…As MRI markers of SVD are often co-occur in individuals, recent efforts have been made to combine them into a unitary score of SVD burden (Huijts et al, 2013;Klarenbeek et al, 2013;Staals et al, 2015;Staals et al, 2014). Huijts et al (2013) and Staals et al (2014) used a score of SVD burden in which the presence of WMH, CMB, perivascular spaces and lacunar infarcts was summed to create a score between 0 and 4. Huijts et al found that increased SVD burden as measured by this accumulation score was related to EF, IPS, EM and global cognition in participants at risk of SVD.…”

Section: Introductionmentioning

confidence: 99%

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[P1–450]: Diffusion Tensor Image Segmentation of the Cerebrum Provides a Single Measure of Cerebral Small‐vessel Disease Severity Related to Cognitive Change

Williams

Zeestraten

Benjamin³

et al. 2017

Alzheimer's & Dementia

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A B S T R A C TCerebral small vessel disease (SVD) is the primary cause of vascular cognitive impairment and is associated with decline in executive function (EF) and information processing speed (IPS). Imaging biomarkers are needed that can monitor and identify individuals at risk of severe cognitive decline. Recently there has been interest in combining several magnetic resonance imaging (MRI) markers of SVD into a unitary score to describe disease severity. Here we apply a diffusion tensor image (DTI) segmentation technique (DSEG) to describe SVD related changes in a single unitary score across the whole cerebrum, to investigate its relationship with cognitive change over a three-year period.98 patients (aged 43-89) with SVD underwent annual MRI scanning and cognitive testing for up to three years. DSEG provides a vector of 16 discrete segments describing brain microstructure of healthy and/or damaged tissue. By calculating the scalar product of each DSEG vector in reference to that of a healthy ageing control we generate an angular measure (DSEG θ) describing the patients' brain tissue microstructural similarity to a disease free model of a healthy ageing brain. Conventional MRI markers of SVD brain change were also assessed including white matter hyperintensities, cerebral atrophy, incident lacunes, cerebral-microbleeds, and white matter microstructural damage measured by DTI histogram parameters. The impact of brain change on cognition was explored using linear mixed-effects models. Post-hoc sample size analysis was used to assess the viability of DSEG θ as a tool for clinical trials. Changes in brain structure described by DSEG θ were related to change in EF and IPS (p < 0.001) and remained significant in multivariate models including other MRI markers of SVD as well as age, gender and premorbid IQ. Of the conventional markers, presence of new lacunes was the only marker to remain a significant predictor of change in EF and IPS in the multivariate models (p = 0.002). Change in DSEG θ was also related to change in all other MRI markers (p < 0.017), suggesting it may be used as a surrogate marker of SVD damage across the cerebrum. Sample size estimates indicated that fewer patients would be required to detect treatment effects using DSEG θ compared to conventional MRI and DTI markers of SVD severity.DSEG θ is a powerful tool for characterising subtle brain change in SVD that has a negative impact on cognition and remains a significant predictor of cognitive change when other MRI markers of brain change are accounted for. DSEG provides an automatic segmentation of the whole cerebrum that is sensitive to a range of SVD related structural changes and successfully predicts cognitive change. Power analysis shows DSEG θ has potential as a monitoring tool in clinical trials. As such it may provide a marker of SVD severity from a single imaging modality (i.e. DTIs). Abbreviations: SVD, cerebral small vessel disease; DSEG, diffusion tensor image segmentation algorithm; EF, executive functions; IPS, information proce...

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Stroke subtype, vascular risk factors, and total MRI brain small-vessel disease burden

Cited by 670 publications

References 35 publications

Reliability of an automatic classifier for brain enlarged perivascular spaces burden and comparison with human performance

Reliability of an automatic classifier for brain enlarged perivascular spaces burden and comparison with human performance

Association of Magnetic Resonance Imaging Markers of Cerebrovascular Disease Burden and Cognition

[P1–450]: Diffusion Tensor Image Segmentation of the Cerebrum Provides a Single Measure of Cerebral Small‐vessel Disease Severity Related to Cognitive Change

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