Clinical significance of cerebral microbleeds on MRI

__Background:__ Cerebral microbleeds can confer a high risk of intracerebral hemorrhage, ischemic stroke, death and dementia, but estimated risks remain imprecise and often conflicting. We investigated the association between cerebral microbleeds presence and these outcomes in a large meta-analysis of all published cohorts including: ischemic stroke/TIA, memory clinic, “high risk” elderly populations, and healthy individuals in population-based studies. __Methods:__ Cohorts (with > 100 participants) that assessed cerebral microbleeds presence on MRI, with subsequent follow-up (≥3 months) were identified. The association between cerebral microbleeds and each of the outcomes (ischemic stroke, intracerebral hemorrhage, death, and dementia) was quantified using random effects models of (a) unadjusted crude odds ratios and (b) covariate-adjusted hazard rations. Results: We identified 31 cohorts (n = 20,368): 19 ischemic stroke/TIA (n = 7672), 4 memory clinic (n = 1957), 3 high risk elderly (n = 1458) and 5 population-based cohorts (n = 11,722). Cerebral microbleeds were associated with an increased risk of ischemic stroke (OR: 2.14; 95% CI: 1.58–2.89 and adj-HR: 2.09; 95% CI: 1.71–2.57), but the relative increase in future intracerebral hemorrhage risk was greater (OR: 4.65; 95% CI: 2.68–8.08 and adj-HR: 3.93; 95% CI: 2.71–5.69). Cerebral microbleeds were an independent predictor of all-cause mortality (adj-HR: 1.36; 95% CI: 1.24–1.48). In three population-based studies, cerebral microbleeds were independently associated with incident dementia (adj-HR: 1.35; 95% CI: 1.00–1.82). Results were overall consistent in analyses stratified by different populations, but with different degrees of heterogeneity. __Conclusions:__ Our meta-analysis shows that cerebral microbleeds predict an increased risk of stroke, death, and dementia and provides up-to-date effect sizes across different clinical settings. These pooled estimates can inform clinical decisions and trials, further supporting cerebral microbleeds role as biomarkers of underlying subclinical brain pathology in research and clinical settings

Harmonizing brain magnetic resonance imaging methods for vascular contributions to neurodegeneration

Introduction Many consequences of cerebrovascular disease are identifiable by magnetic resonance imaging (MRI), but variation in methods limits multicenter studies and pooling of data. The European Union Joint Program on Neurodegenerative Diseases (EU JPND) funded the HARmoNizing Brain Imaging MEthodS for VaScular Contributions to Neurodegeneration (HARNESS) initiative, with a focus on cerebral small vessel disease. Methods Surveys, teleconferences, and an in-person workshop were used to identify gaps in knowledge and to develop tools for harmonizing imaging and analysis. Results A framework for neuroimaging biomarker development was developed based on validating repeatability and reproducibility, biological principles, and feasibility of implementation. The status of current MRI biomarkers was reviewed. A website was created at www.harness-neuroimaging.org with acquisition protocols, a software database, rating scales and case report forms, and a deidentified MRI repository. Conclusions The HARNESS initiative provides resources to reduce variability in measurement in MRI studies of cerebral small vessel disease

Simple CART Based Real-Time Traffic Classification Engine on FPGAs

International Conference on Reconfigurable Computing and FPGAs (ReConFig) -- DEC 04-06, 2017 -- Cancun, MEXICO -- Natl Inst Astrophy Opt & Elect Mexico, Virginia Tech, Univ N Carolina Charlotte, IEEE, IEEE Circuits & Syst Soc, XILINXTraffic classification is a process which assorts computer network traffic into predefined traffic classes by utilizing packet header information or network packet statistics. Real-time traffic classification is mainly used in network management tasks comprising traffic shaping and flow prioritization as well as in network security applications for intrusion detection. Machine Learning (ML) based traffic classification that exploits statistical characteristics of traffic, has come into prominence recently, due to its ability to cope with encrypted traffic and newly emerging network applications utilizing non-standard ports to circumvent firewalls. To meet high data rates and achieve online classification with ML-based techniques, Field Programmable Gate Arrays (FPGAs) providing abundant parallelism and high operating frequency is the most appropriate platform. In this paper, we propose to use Simple Classification and Regression Trees (Simple CART) machine learning algorithm for traffic classification. However, the variations in node sizes of Simple CART decision tree caused by discretization pre-process incur memory and resource inefficiency problems when the tree is directly mapped onto the hardware. To resolve these problems, we propose to represent Simple CART decision tree by two stage hybrid data structure (Extended-Simple CART) that comprises multiple range trees in Stage 1 and a Simple CART decision tree enriched with bitmaps at its nodes in Stage 2. Our design is implemented on parallel and pipelined architectures using Field Programmable Gate Arrays (FPGAs) to acquire high throughput. Extended-Simple CART architecture can sustain 557 Gbps or 1741 million classification per second (MCPS) (for the minimum packet size of 40 Bytes) on a state-of-the-art FPGA and achieve an accuracy of 96.8% while classifying an internet traffic trace including eight application classes.WOS:0004265297000422-s2.0-8504695262

Real-Time Traffic Classification using Simple CART Forest on FPGAs

IEEE 19th International Conference on High Performance Switching and Routing (HPSR) -- JUN 17-20, 2018 -- Bucharest, ROMANIA -- IEEE, IEEE Commun SocTraffic classification process categorizes internet traffic into application classes by exploiting packet header data or collected packet statistics. Real-time internet traffic classification is mostly required for network management and security applications. Machine Learning (ML) based traffic classification approaches which utilize statistical properties of traffic flows, have recently attracted great deal of attention from the researches due to its operability under encrypted traffic conditions. In this paper, we propose to use Simple Classification and Regression Trees Forest (SCF) for internet traffic classification. Our proposed scheme comprising multiple parallel trees demonstrates a substantial improvement in search delay and throughput as well as in the memory footprint when compared to a traditional single Simple CART structure. To reach high data rates for real-time classification, we also proposed a parallel and pipelined architecture on Field Programmable Gate Arrays (FPGAs) that support SCF data structure. The post place-and-route FPGA results demonstrate that our design can sustain 854 Gbps or 2669 million classification per second (MCPS) for the minimum packet size of 40 Bytes. Furthermore, our architecture shows an accuracy of 96.6719% for real internet traffic with eight application classes.WOS:00051661800001

Updates on Prevention of Hemorrhagic and Lacunar Strokes

Intracerebral hemorrhage (ICH) and lacunar infarction (LI) are the major acute clinical manifestations of cerebral small vessel diseases (cSVDs). Hypertensive small vessel disease, cerebral amyloid angiopathy, and hereditary causes, such as Cerebral Autosomal Dominant Arteriopathy with Subcortical Infarcts and Leukoencephalopathy (CADASIL), constitute the three common cSVD categories. Diagnosing the underlying vascular pathology in these patients is important because the risk and types of recurrent strokes show significant differences. Recent advances in our understanding of the cSVD-related radiological markers have improved our ability to stratify ICH risk in individual patients, which helps guide antithrombotic decisions. There are general good-practice measures for stroke prevention in patients with cSVD, such as optimal blood pressure and glycemic control, while individualized measures tailored for particular patients are often needed. Antithrombotic combinations and anticoagulants should be avoided in cSVD treatment, as they increase the risk of potentially fatal ICH without necessarily lowering LI risk in these patients. Even when indicated for a concurrent pathology, such as nonvalvular atrial fibrillation, nonpharmacological approaches should be considered in the presence of cSVD. More data are emerging regarding the presentation, clinical course, and diagnostic markers of hereditary cSVD, allowing accurate diagnosis, and therefore, guiding management of symptomatic patients. When suspicion for asymptomatic hereditary cSVD exists, the pros and cons of prescribing genetic testing should be discussed in detail in the absence of any curative treatment. Recent data regarding diagnosis, risk stratification, and specific preventive approaches for both sporadic and hereditary cSVDs are discussed in this review article

Total small vessel disease burden and brain network efficiency in cerebral amyloid angiopathy

BACKGROUND: Cerebral amyloid angiopathy (CAA) is associated with hemorrhagic and nonhemorrhagic markers small vessel disease (SVD). A composite score to quantify the total burden of SVD on MRI specifically for CAA patients was recently developed. Brain network alterations related to individual MRI markers of SVD in CAA were demonstrated. OBJECTIVES: Considering diffusion based network measures sensitive to detect different relevant SVD-related brain injury, we investigated if increased overall SVD injury on MRI corresponds to worse global brain connectivity in CAA. METHODS: Seventy-three patients (79.5% male, mean age 70.58\ub18.22years) with a diagnosis CAA were considered. SVD markers in total MRI SVD score included: lobar cerebral microbleeds, cortical superficial siderosis (cSS), white matter hyperintensities (WMH) and centrum semiovale-enlarged perivascular spaces. Diffusion imaging based network reconstruction was made. The associations between total MRI SVD score and global network efficiency (GNE) were analyzed. RESULTS: A modest significant inverse correlation between total MRI SVD score and GNE existed (p=0.013; R2=0.07). GNE was related with the presence of cSS and moderate-severe WMHs. CONCLUSIONS: An increased burden of SVD neuroimaging markers corresponds to more reductions in global brain connectivity, implying a possible cumulative effect of overall SVD markers on disrupted physiology. GNE was related with some components of the score, specifically cSS and moderate-severe WMHs