A comparison of location of acute symptomatic vs. 'silent' small vessel lesions

Background: Acute lacunar ischaemic stroke, white matter hyperintensities, and lacunes are all features of cerebral small vessel disease. It is unclear why some small vessel disease lesions present with acute stroke symptoms, whereas others typically do not. Aim: To test if lesion location could be one reason why some small vessel disease lesions present with acute stroke, whereas others accumulate covertly. Methods: We identified prospectively patients who presented with acute lacunar stroke symptoms with a recent small subcortical infarct confirmed on magnetic resonance diffusion imaging. We compared the distribution of the acute infarcts with that of white matter hyperintensity and lacunes using computational image mapping methods. Results: In 188 patients, mean age 67 ± standard deviation 12 years, the lesions that presented with acute lacunar ischaemic stroke were located in or near the main motor and sensory tracts in (descending order): posterior limb of the internal capsule (probability density 0·2/mm3), centrum semiovale (probability density = 0·15/mm3), medial lentiform nucleus/lateral thalamus (probability density = 0·09/mm3), and pons (probability density = 0·02/mm3). Most lacunes were in the lentiform nucleus (probability density = 0·01–0·04/mm3) or external capsule (probability density = 0·05/mm3). Most white matter hyperintensities were in centrum semiovale (except for the area affected by the acute symptomatic infarcts), external capsules, basal ganglia, and brainstem, with little overlap with the acute symptomatic infarcts (analysis of variance, P < 0·01). Conclusions: Lesions that present with acute lacunar ischaemic stroke symptoms may be more likely noticed by the patient through affecting the main motor and sensory tracts, whereas white matter hyperintensity and asymptomatic lacunes mainly affect other areas. Brain location could at least partly explain the symptomatic vs. covert development of small vessel disease

How Much Do Focal Infarcts Distort White Matter Lesions and Global Cerebral Atrophy Measures?

BACKGROUND: White matter lesions (WML) and brain atrophy are important biomarkers in stroke and dementia. Stroke lesions, either acute or old, symptomatic or silent, are common in older people. Such stroke lesions can have similar signals to WML and cerebrospinal fluid (CSF) on magnetic resonance (MR) images, and may be classified accidentally as WML or CSF by MR image processing algorithms, distorting WML and brain atrophy volume from the true volume. We evaluated the effect that acute or old stroke lesions at baseline, and new stroke lesions occurring during follow-up, could have on measurement of WML volume, cerebral atrophy and their longitudinal progression. METHODS: We used MR imaging data from patients who had originally presented with acute lacunar or minor cortical ischaemic stroke symptoms, recruited prospectively, who were scanned at baseline and about 3 years later. We measured WML and CSF volumes (ml) semi-automatically. We manually outlined the acute index stroke lesion (ISL), any old stroke lesions present at baseline, and new lesions appearing de novo during follow-up. We compared baseline and follow-up WML volume, cerebral atrophy and their longitudinal progression excluding and including the acute ISL, old and de novo stroke lesions. A non-parametric test (Wilcoxon's signed rank test) was used to compare the effects. RESULTS: Among 46 patients (mean age 72 years), 33 had an ISL visible on MR imaging (median volume 2.05 ml, IQR 0.88–8.88) and 7 of the 33 had old lacunes at baseline: WML volume was 8.54 ml (IQR 5.86–15.80) excluding versus 10.98 ml (IQR 6.91–24.86) including ISL (p < 0.001). At follow-up, median 39 months later (IQR 30–45), 3 patients had a de novo stroke lesion; total stroke lesion volume had decreased in 11 and increased in 22 patients: WML volume was 12.17 ml (IQR 8.54–19.86) excluding versus 14.79 ml (IQR 10.02–38.03) including total stroke lesions (p < 0.001). Including/excluding lacunes at baseline or follow-up also made small differences. Twenty-two of the 33 patients had tissue loss due to stroke lesions between baseline and follow-up, resulting in a net median brain tissue volume loss (i.e. atrophy) during follow-up of 24.49 ml (IQR 12.87–54.01) excluding versus 24.61 ml (IQR 15.54–54.04) including tissue loss due to stroke lesions (p < 0.001). Including stroke lesions in the WML volume added substantial noise, reduced statistical power, and thus increased sample size estimated for a clinical trial. CONCLUSIONS: Failure to exclude even small stroke lesions distorts WML volume, cerebral atrophy and their longitudinal progression measurements. This has important implications for design and sample size calculations for observational studies and randomised trials using WML volume, WML progression or brain atrophy as outcome measures. Improved methods of discriminating between stroke lesions and WML, and between tissue loss due to stroke lesions and true brain atrophy are required

Perivascular Spaces Segmentation in Brain MRI Using Optimal 3D Filtering

Perivascular Spaces (PVS) are a recently recognised feature of Small Vessel Disease (SVD), also indicating neuroinflammation, and are an important part of the brain's circulation and glymphatic drainage system. Quantitative analysis of PVS on Magnetic Resonance Images (MRI) is important for understanding their relationship with neurological diseases. In this work, we propose a segmentation technique based on the 3D Frangi filtering for extraction of PVS from MRI. Based on prior knowledge from neuroradiological ratings of PVS, we used ordered logit models to optimise Frangi filter parameters in response to the variability in the scanner's parameters and study protocols. We optimized and validated our proposed models on two independent cohorts, a dementia sample (N=20) and patients who previously had mild to moderate stroke (N=48). Results demonstrate the robustness and generalisability of our segmentation method. Segmentation-based PVS burden estimates correlated with neuroradiological assessments (Spearman's $\rho$ = 0.74, p $<$ 0.001), suggesting the great potential of our proposed metho

The Brain Health Index: Towards a combined measure of neurovascular and neurodegenerative structural brain injury

Background: A structural magnetic resonance imaging measure of combined neurovascular and neurodegenerative burden may be useful as these features often coexist in older people, stroke and dementia. Aim: We aimed to develop a new automated approach for quantifying visible brain injury from small vessel disease and brain atrophy in a single measure, the brain health index. Materials and methods: We computed brain health index in N = 288 participants using voxel-based Gaussian mixture model cluster analysis of T1, T2, T2*, and FLAIR magnetic resonance imaging. We tested brain health index against a validated total small vessel disease visual score and white matter hyperintensity volumes in two patient groups (minor stroke, N = 157; lupus, N = 51) and against measures of brain atrophy in healthy participants (N = 80) using multiple regression. We evaluated associations with Addenbrooke’s Cognitive Exam Revised in patients and with reaction time in healthy participants. Results: The brain health index (standard beta = 0.20–0.59, P &#60; 0.05) was significantly and more strongly associated with Addenbrooke’s Cognitive Exam Revised, including at one year follow-up, than white matter hyperintensity volume (standard beta = 0.04–0.08, P &#62; 0.05) and small vessel disease score (standard beta = 0.02–0.27, P &#62; 0.05) alone in both patient groups. Further, the brain health index (standard beta = 0.57–0.59, P &#60; 0.05) was more strongly associated with reaction time than measures of brain atrophy alone (standard beta = 0.04–0.13, P &#62; 0.05) in healthy participants. Conclusions: The brain health index is a new image analysis approach that may usefully capture combined visible brain damage in large-scale studies of ageing, neurovascular and neurodegenerative disease

Sample size considerations for trials using cerebral white matter hyperintensity progression as an intermediate outcome at 1 year after mild stroke: Results of a prospective cohort study

Background: White matter hyperintensities (WMHs) are commonly seen on in brain imaging and are associated with stroke and cognitive decline. Therefore, they may provide a relevant intermediate outcome in clinical trials. WMH can be measured as a volume or visually on the Fazekas scale. We investigated predictors of WMH progression and design of efficient studies using WMH volume and Fazekas score as an intermediate outcome. Methods: We prospectively recruited 264 patients with mild ischaemic stroke and measured WMH volume, Fazekas score, age and cardiovascular risk factors at baseline and 1 year. We modelled predictors of WMH burden at 1 year and used the results in sample size calculations for hypothetical randomised controlled trials with different analysis plans and lengths of follow-up. Results: Follow-up WMH volume was predicted by baseline WMH: a 0.73-ml (95% CI 0.65-0.80, p < 0.0001) increase per 1-ml baseline volume increment, and a 2.93-ml increase (95% CI 1.76-4.10, p < 0.0001) per point on the Fazekas scale. Using a mean difference of 1 ml in WMH volume between treatment groups, 80% power and 5% alpha, adjusting for all predictors and 2-year follow-up produced the smallest sample size (n = 642). Other study designs produced samples sizes from 2054 to 21,270. Sample size calculations using Fazekas score as an outcome with the same power and alpha, as well as an OR corresponding to a 1-ml difference, were sensitive to assumptions and ranged from 2504 to 18,886. Conclusions: Baseline WMH volume and Fazekas score predicted follow-up WMH volume. Study size was smallest using volumes and longer-term follow-up, but this must be balanced against resources required to measure volumes versus Fazekas scores, bias due to dropout and scanner drift. Samples sizes based on Fazekas scores may be best estimated with simulation studies

Physicists attempt to scale the ivory towers of finance

Physicists have recently begun doing research in finance, and even though this movement is less than five years old, interesting and useful contributions have already emerged. This article reviews these developments in four areas, including empirical statistical properties of prices, random-process models for price dynamics, agent-based modeling, and practical applications.Comment: 13 pages, 5 figure

The relationship between educational level and bone mineral density in postmenopausal women

BACKGROUND: This study describes the influence of educational level on bone mineral density (BMD) and investigating the relationship between educational level and bone mineral density in postmenopausal women. METHODS: A total of 569 postmenopausal women, from 45 to 86 years of age (mean age of 60.43 ± 7.19 years) were included in this study. A standardized interview was used at the follow-up visit to obtain information on demographic, life-style, reproductive and menstrual histories such as age at menarche, age at menopause, number of pregnancies, number of abortions, duration of menopause, duration of fertility, and duration of lactation. Patients were separated into four groups according to the level of education, namely no education (Group 1 with 209 patients), elementary (Group 2 with 222 patients), high school (Group 3 with 79 patients), and university (Group 4 with 59 patients). RESULTS: The mean ages of groups were 59.75 ± 7.29, 61.42 ± 7.50, 60.23 ± 7.49, and 58.72 ± 7.46, respectively. Spine BMD was significant lower in Group 1 than that of other groups (p < 0.05). Trochanter and ward's triangle BMD were the highest in Group 4 and there was a significant difference between Group 1 and 4 (p < 0.05). The prevalence of osteoporosis showed an inverse relationship with level of education, ranging from 18.6% for the most educated to 34.4% for the no educated women (p < 0.05). Additionally, there was a significant correlation between educational level and spine BMD (r = 0.20, p < 0.01), trochanter BMD (r = 0.13, p < 0.01), and ward's BMD (r = 0.14, p < 0.01). CONCLUSIONS: The results of the study suggest that there is a significant correlation between educational level and BMD. Losses in BMD for women of lower educational level tend to be relatively high, and losses in spine and femur BMD showed a decrease with increasing educational level