Статті в журналах: "Cerebrovascular network"

1

Yu, Qifeng, Yuming Jiao, Ran Huo, Hongyuan Xu, Jie Wang, Shaozhi Zhao, Qiheng He, et al. "Application of the concept of neural networks surgery in cerebrovascular disease treatment." Brain & Heart 1, no. 1 (December 30, 2022): 223. http://dx.doi.org/10.36922/bh.v1i1.223.

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Based on advanced techniques, both the brain structural network and functional network can be reflected, giving rise to a new field: neural networks. Entering the 21st century, along with the extensive research on neural networks and the digital brain imaging field of neuromodulation, the neurosurgical field has entered into a novel stage: neural networks surgery. Neural networks surgery was developed to devote to protecting the cognitive function of patients with central nervous system diseases. By lucubrate, multiple new views of cerebrovascular disease have emerged. In this paper, we review the applications of this novel concept in treating cerebrovascular diseases, primarily through three aspects: disease mechanism, progression, and treatment strategy. Based on recent research, the development of a novel treatment system for cerebrovascular diseases might help clarify the course of these diseases, provide optimal treatment strategies, and protect the cognitive function of patients to the greatest extent.

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Marshall, Olga, Sanjeev Chawla, Hanzhang Lu, Louise Pape, and Yulin Ge. "Cerebral blood flow modulation insufficiency in brain networks in multiple sclerosis: A hypercapnia MRI study." Journal of Cerebral Blood Flow & Metabolism 36, no. 12 (July 20, 2016): 2087–95. http://dx.doi.org/10.1177/0271678x16654922.

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Cerebrovascular reactivity measures vascular regulation of cerebral blood flow and is responsible for maintaining healthy neurovascular coupling. Multiple sclerosis exhibits progressive neurodegeneration and global cerebrovascular reactivity deficits. This study investigates varied degrees of cerebrovascular reactivity impairment in different brain networks, which may be an underlying cause for functional changes in the brain, affecting long-distance projection integrity and cognitive function; 28 multiple sclerosis and 28 control subjects underwent pseudocontinuous arterial spin labeling perfusion MRI to measure cerebral blood flow under normocapnia (room air) and hypercapnia (5% carbon dioxide gas mixture) breathing. Cerebrovascular reactivity, measured as normocapnic to hypercapnic cerebral blood flow percent increase normalized by end-tidal carbon dioxide change, was determined from seven functional networks (default mode, frontoparietal, somatomotor, visual, limbic, dorsal, and ventral attention networks). Group analysis showed significantly decreased cerebrovascular reactivity in patients compared to controls within the default mode, frontoparietal, somatomotor, and ventral attention networks after multiple comparison correction. Regression analysis showed a significant correlation of cerebrovascular reactivity with lesion load in the default mode and ventral attention networks and with gray matter atrophy in the default mode network. Functional networks in multiple sclerosis patients exhibit varied amounts of cerebrovascular reactivity deficits. Such blood flow regulation abnormalities may contribute to functional communication disruption in multiple sclerosis.

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Yang, Zhengfei, Ping Li, and Rui Wang. "Prediction of Metabolic Characteristics of Cardiovascular and Cerebrovascular Diseases Based on Convolutional Neural Network." Computational and Mathematical Methods in Medicine 2022 (July 27, 2022): 1–13. http://dx.doi.org/10.1155/2022/3206378.

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As a typical disease, cardiovascular and cerebrovascular diseases cause great damage to the human body. In view of the problem that the existing models failed to describe and represent the characteristics of cardiovascular and cerebrovascular indicators, convolution neural network was used to analyze the metabolic factors of cardiovascular and cerebrovascular. Based on convolutional neural network theory, feature extraction was carried out on the relevant parameters of the model, and the change trend of different cardiovascular and cerebrovascular indicators was studied by model optimization, theoretical analysis, and experimental verification. Relevant studies show that the value of neurons increases slowly at first and then rapidly with the increase of bias term b . And with the increase of computing time, the corresponding nonlinear characteristics are gradually reflected; so, the influence of computing time on neuron results should be considered when selecting bias term b . The gradient changes under different functions have typical symmetry, which indicates that the effects of functions on model parameters have certain cyclic characteristics. Among them, ReLU function has the largest variation range, tanh function has a relatively small curve variation range, and sigmoid function has the smallest variation range. Five indicators are selected to describe the metabolic characteristics of the disease through characteristic analysis of cardiovascular and cerebrovascular diseases. The onset signs have the greatest impact on cardiovascular and cerebrovascular diseases, while the corresponding metabolic characteristics have the least impact on cardiovascular and cerebrovascular diseases. The study showed that the influence of different indicators on the model had typical stage characteristics, and relevant data were used to verify the accuracy of the model. Finally, the optimization model based on convolutional neural network was used to predict the metabolic characteristics of cardiovascular and cerebrovascular diseases. Relevant studies show that the optimization model can better analyze the metabolic characteristics of cardiovascular and cerebrovascular diseases. This research can provide theoretical support for the application of convolutional neural networks in other fields.

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Tay, Jonathan, Danuta M. Lisiecka-Ford, Matthew J. Hollocks, Anil M. Tuladhar, Thomas R. Barrick, Anne Forster, Michael J. O’Sullivan, et al. "Network neuroscience of apathy in cerebrovascular disease." Progress in Neurobiology 188 (May 2020): 101785. http://dx.doi.org/10.1016/j.pneurobio.2020.101785.

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5

Liu, Hanqing, Xiaojun Li, Jin Wei, and Xiaodong Kang. "Cerebral Arterial Stenosis Detection Based on a Retained Two-Stage Detection Algorithm." Discrete Dynamics in Nature and Society 2022 (April 26, 2022): 1–12. http://dx.doi.org/10.1155/2022/4494411.

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Stroke is one of the fatal diseases worldwide, and its primary mechanism is produced by cerebrovascular stenosis, blockages, or embolisms. Computer-aided diagnosis can assist clinical practitioners in identifying cerebrovascular anomalies, elucidating the precise lesions’ location in the patients, and providing guidance for clinical therapy. Due to different portions of the cerebrovascular possessing diverse morphological properties and the limited narrow area, the detection effect is unsatisfactory. A retrained two-stage algorithm for detecting cerebral arterial stenosis in CTA images is proposed to solve these problems by further fusing image features and improving the quality of regions of interest. In Faster R-CNN and Libra R-CNN, the backbone network was Resnet50, with deformable convolutional and nonlocal neural networks introduced in the third, fourth, and fifth stages of the backbone network. Deformable convolutional networks learned offsets to extract morphological features of blood vessels in different tomographic planes. Nonlocal neural networks fused global information and extracted global features from location information of feature maps. A cascade detector refined object classification and bounding box regression before prediction. The experimental results show that the retained algorithm increases mAP by 7.3% and 7.5%, respectively, compared with Faster R-CNN and Libra R-CNN. Deformable convolutional networks, nonlocal neural networks, and cascade detectors are incorporated into further feature fusion; thus, semantic information about the cerebrovascular structure is learned, demonstrating more accurate stenotic region detection and demonstrating generalizability across different two-stage algorithms.

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6

Liu, Hanqing, Xiaojun Li, Jin Wei, and Xiaodong Kang. "Cerebral Arterial Stenosis Detection Based on a Retained Two-Stage Detection Algorithm." Discrete Dynamics in Nature and Society 2022 (April 26, 2022): 1–12. http://dx.doi.org/10.1155/2022/4494411.

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Stroke is one of the fatal diseases worldwide, and its primary mechanism is produced by cerebrovascular stenosis, blockages, or embolisms. Computer-aided diagnosis can assist clinical practitioners in identifying cerebrovascular anomalies, elucidating the precise lesions’ location in the patients, and providing guidance for clinical therapy. Due to different portions of the cerebrovascular possessing diverse morphological properties and the limited narrow area, the detection effect is unsatisfactory. A retrained two-stage algorithm for detecting cerebral arterial stenosis in CTA images is proposed to solve these problems by further fusing image features and improving the quality of regions of interest. In Faster R-CNN and Libra R-CNN, the backbone network was Resnet50, with deformable convolutional and nonlocal neural networks introduced in the third, fourth, and fifth stages of the backbone network. Deformable convolutional networks learned offsets to extract morphological features of blood vessels in different tomographic planes. Nonlocal neural networks fused global information and extracted global features from location information of feature maps. A cascade detector refined object classification and bounding box regression before prediction. The experimental results show that the retained algorithm increases mAP by 7.3% and 7.5%, respectively, compared with Faster R-CNN and Libra R-CNN. Deformable convolutional networks, nonlocal neural networks, and cascade detectors are incorporated into further feature fusion; thus, semantic information about the cerebrovascular structure is learned, demonstrating more accurate stenotic region detection and demonstrating generalizability across different two-stage algorithms.

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7

Qin, Qiuli, Xing Yang, Runtong Zhang, Manlu Liu, and Yuhan Ma. "An Application of Deep Belief Networks in Early Warning for Cerebrovascular Disease Risk." Journal of Organizational and End User Computing 34, no. 4 (July 2022): 1–14. http://dx.doi.org/10.4018/joeuc.287574.

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To reduce the incidence of cerebrovascular disease and mortality, identifying the risks of cerebrovascular disease in advance and taking certain preventive measures are significant. This article was aimed to investigate the risk factors of cerebrovascular disease (CVD) in the primary prevention, and to build an early warning model based on the existing technology. The authors use the information entropy algorithm of rough set theory to establish the index system suitable for early warning model. Then, using the limited Boltzmann machine and direction propagation algorithm, the depth trust network is established by building and stacking RBM, and the back propagation is used to fine-tune the parameters of the network at the top layer. Compared with the LM-BP early-warning model, the deep confidence network model is more effective than traditional artificial neural network, which can help to identify the risk of cerebrovascular disease in advance and promote the primary prevention.

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8

Lin, Wei-Wei, Lin-Tao Xu, Yi-Sheng Chen, Ken Go, Chenyu Sun, and Yong-Jian Zhu. "Single-Cell Transcriptomics-Based Study of Transcriptional Regulatory Features in the Mouse Brain Vasculature." BioMed Research International 2021 (July 23, 2021): 1–15. http://dx.doi.org/10.1155/2021/7643209.

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Background. The critical role of vascular health on brain function has received much attention in recent years. At the single-cell level, studies on the developmental processes of cerebral vascular growth are still relatively few. Techniques for constructing gene regulatory networks (GRNs) based on single-cell transcriptome expression data have made significant progress in recent years. Herein, we constructed a single-cell transcriptional regulatory network of mouse cerebrovascular cells. Methods. The single-cell RNA-seq dataset of mouse brain vessels was downloaded from GEO (GSE98816). This cell clustering was annotated separately using singleR and CellMarker. We then used a modified version of the SCENIC method to construct GRNs. Next, we used a mouse version of SEEK to assess whether genes in the regulon were coexpressed. Finally, regulatory module analysis was performed to complete the cell type relationship quantification. Results. Single-cell RNA-seq data were used to analyze the heterogeneity of mouse cerebrovascular cells, whereby four cell types including endothelial cells, fibroblasts, microglia, and oligodendrocytes were defined. These subpopulations of cells and marker genes together characterize the molecular profile of mouse cerebrovascular cells. Through these signatures, key transcriptional regulators that maintain cell identity were identified. Our findings identified genes like Lmo2, which play an important role in endothelial cells. The same cell type, for instance, fibroblasts, was found to have different regulatory networks, which may influence the functional characteristics of local tissues. Conclusions. In this study, a transcriptional regulatory network based on single-cell analysis was constructed. Additionally, the study identified and profiled mouse cerebrovascular cells using single-cell transcriptome data as well as defined TFs that affect the regulatory network of the mouse brain vasculature.

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Cabrera DeBuc, Delia, Gabor Mark Somfai, and Akos Koller. "Retinal microvascular network alterations: potential biomarkers of cerebrovascular and neural diseases." American Journal of Physiology-Heart and Circulatory Physiology 312, no. 2 (February 1, 2017): H201—H212. http://dx.doi.org/10.1152/ajpheart.00201.2016.

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Increasing evidence suggests that the conditions of retinal microvessels are indicators to a variety of cerebrovascular, neurodegenerative, psychiatric, and developmental diseases. Thus noninvasive visualization of the human retinal microcirculation offers an exceptional opportunity for the investigation of not only the retinal but also cerebral microvasculature. In this review, we show how the conditions of the retinal microvessels could be used to assess the conditions of brain microvessels because the microvascular network of the retina and brain share, in many aspects, standard features in development, morphology, function, and pathophysiology. Recent techniques and imaging modalities, such as optical coherence tomography (OCT), allow more precise visualization of various layers of the retina and its microcirculation, providing a “microscope” to brain microvessels. We also review the potential role of retinal microvessels in the risk identification of cerebrovascular and neurodegenerative diseases. The association between vision problems and cerebrovascular and neurodegenerative diseases, as well as the possible role of retinal microvascular imaging biomarkers in cerebrovascular and neurodegenerative screening, their potentials, and limitations, are also discussed.

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10

Liu, Yongwei, Hyo-Sung Kwak, and Il-Seok Oh. "Cerebrovascular Segmentation Model Based on Spatial Attention-Guided 3D Inception U-Net with Multi-Directional MIPs." Applied Sciences 12, no. 5 (February 22, 2022): 2288. http://dx.doi.org/10.3390/app12052288.

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The segmentation algorithm of cerebrovascular magnetic resonance angiography (MRA) images based on deep learning plays an essential role in medical study. Traditional segmentation algorithms face poor segmentation results and poor connectivity when the cerebrovascular vessels are thinner. An improved segmentation algorithm based on deep convolutional networks is proposed in this research. The proposed segmentation network combines the original 3D U-Net with the maximum intensity projection (MIP), which was transformed from the corresponding patch of a 3D MRA image. The MRA dataset provided by Jeonbuk National University Hospital was used to evaluate the experimental results in comparison with traditional 3D cerebrovascular segmentation methods and other state–of–the–art deep learning methods. The experimental results showed that our method achieved the best test performance among the compared methods in terms of the Dice score when Inception blocks and attention modules were placed in the proposed dual-path networks.

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11

Zhao, Fengjun, Yibing Chen, Fei Chen, Xuelei He, Xin Cao, Yuqing Hou, Huangjian Yi, Xiaowei He, and Jimin Liang. "Semi-Supervised Cerebrovascular Segmentation by Hierarchical Convolutional Neural Network." IEEE Access 6 (2018): 67841–52. http://dx.doi.org/10.1109/access.2018.2879521.

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12

Meng, Cai, Kai Sun, Shaoya Guan, Qi Wang, Rui Zong, and Lei Liu. "Multiscale dense convolutional neural network for DSA cerebrovascular segmentation." Neurocomputing 373 (January 2020): 123–34. http://dx.doi.org/10.1016/j.neucom.2019.10.035.

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13

Pokhilko, Alexandra, Gaia Brezzo, Lahiru Handunnetthi, Raphael Heilig, Rachel Lennon, Colin Smith, Stuart M. Allan, et al. "Global proteomic analysis of extracellular matrix in mouse and human brain highlights relevance to cerebrovascular disease." Journal of Cerebral Blood Flow & Metabolism 41, no. 9 (March 17, 2021): 2423–38. http://dx.doi.org/10.1177/0271678x211004307.

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The extracellular matrix (ECM) is a key interface between the cerebrovasculature and adjacent brain tissues. Deregulation of the ECM contributes to a broad range of neurological disorders. However, despite this importance, our understanding of the ECM composition remains very limited mainly due to difficulties in its isolation. To address this, we developed an approach to extract the cerebrovascular ECM from mouse and human post-mortem normal brain tissues. We then used mass spectrometry with off-line high-pH reversed-phase fractionation to increase the protein detection. This identified more than 1000 proteins in the ECM-enriched fraction, with > 66% of the proteins being common between the species. We report 147 core ECM proteins of the human brain vascular matrisome, including collagens, laminins, fibronectin and nidogens. We next used network analysis to identify the connection between the brain ECM proteins and cerebrovascular diseases. We found that genes related to cerebrovascular diseases, such as COL4A1, COL4A2, VCAN and APOE were significantly enriched in the cerebrovascular ECM network. This provides unique mechanistic insight into cerebrovascular disease and potential drug targets. Overall, we provide a powerful resource to study the functions of brain ECM and highlight a specific role for brain vascular ECM in cerebral vascular disease.

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Chong, Joanna Su Xian, Hyemin Jang, Hee Jin Kim, Kwun Kei Ng, Duk L. Na, Jae Hong Lee, Sang Won Seo, and Juan Zhou. "Amyloid and cerebrovascular burden divergently influence brain functional network changes over time." Neurology 93, no. 16 (September 11, 2019): e1514-e1525. http://dx.doi.org/10.1212/wnl.0000000000008315.

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ObjectiveTo examine the effects of baseline Alzheimer disease and cerebrovascular disease markers on longitudinal default mode network (DMN) and executive control network (ECN) functional connectivity (FC) changes in mild cognitive impairment (MCI).MethodsWe studied 30 patients with amnestic MCI (aMCI) and 55 patients with subcortical vascular MCI (svMCI) with baseline Pittsburgh Compound B (PiB)–PET scans and longitudinal MRI scans. Participants were followed up clinically with annual MRI for up to 4 years (aMCI: 26 with 2 timepoints, 4 with 3 timepoints; svMCI: 13 with 2 timepoints, 16 with 3 timepoints, 26 with 4 timepoints).Resultsβ-Amyloid (Aβ) burden was associated with longitudinal DMN FC declines, while cerebrovascular burden was associated with longitudinal ECN FC changes. When patients were divided into PiB+ and PiB− groups, PiB+ patients showed longitudinal DMN FC declines, while patients with svMCI showed longitudinal ECN FC increases. Direct comparisons between the 2 groups without mixed pathology (aMCI PiB+ and svMCI PiB−) recapitulated this divergent pattern: aMCI PiB+ patients showed steeper longitudinal DMN FC declines, while svMCI PiB− patients showed steeper longitudinal ECN FC increases. Finally, using baseline PiB uptake and lacune numbers as continuous variables, baseline PiB uptake showed inverse U-shape associations with longitudinal DMN FC changes in both MCI subtypes, while baseline lacune numbers showed mainly inverse U-shape relationships with longitudinal ECN FC changes in patients with svMCI.ConclusionsOur findings underscore the divergent effects of Aβ and cerebrovascular burden on longitudinal FC changes in the DMN and ECN in the predementia stage, which reflect the underlying pathology and may be used to track early changes in Alzheimer disease and cerebrovascular disease.

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Xu, Yan, and Lingwei Meng. "Deconstruction of Risk Prediction of Ischemic Cardiovascular and Cerebrovascular Diseases Based on Deep Learning." Contrast Media & Molecular Imaging 2022 (September 30, 2022): 1–10. http://dx.doi.org/10.1155/2022/8478835.

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Over the years, with the widespread use of computer technology and the dramatic increase in electronic medical data, data-driven approaches to medical data analysis have emerged. However, the analysis of medical data remains challenging due to the mixed nature of the data, the incompleteness of many records, and the high level of noise. This paper proposes an improved neural network DBN-LSTM that combines a deep belief network (DBN) with a long short-term memory (LSTM) network. The subset of feature attributes processed by CFS-EGA is used for training, and the optimal selection test of the number of hidden layers is performed on the upper DBN in the process of training DBN-LSTM. At the same time, the validation set is combined to determine the hyperparameters of the LSTM. Construct the DNN, CNN, and long short-term memory (LSTM) network for comparative analysis with DBN-LSTM. Use the classification method to compare the average of the final results of the two experiments. The results show that the prediction accuracy of DBN-LSTM for cardiovascular and cerebrovascular diseases reaches 95.61%, which is higher than the three traditional neural networks.

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Huang, Lingsong, and Haoquan Wang. "Brain Blood Vessel Segmentation based on Region Growing and U-net Neural Network." Journal of Medicine and Health Science 2, no. 2 (June 2024): 63–70. http://dx.doi.org/10.62517/jmhs.202405212.

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Aiming at the problems of insufficient feature extraction in existing deep learning-based cerebrovascular segmentation methods and uneven segmentation results when the original imaging quality is poor, a cerebrovascular segmentation method that integrates region growing and an improved U-net neural network is proposed. Based on the deep learning segmentation results, automatic seed point selection for region growing is performed and fused to improve the segmentation accuracy at the pixel level. In this work, the integrated segmentation method improves the Dice coefficient from 0.805 to 0.878 and the Average Hausdorff Distance (AHD) from 3.949 to 0.903 compared to the deep learning segmentation method.

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Almasi, Sepideh, Alexandra Lauric, Adel Malek, and Eric L. Miller. "Cerebrovascular network registration via an efficient attributed graph matching technique." Medical Image Analysis 46 (May 2018): 118–29. http://dx.doi.org/10.1016/j.media.2018.02.007.

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Zhou, Dejia, Liya Wang, Shuhan Ding, Minghui Shen, and Hang Qiu. "Phenotypic Disease Network Analysis to Identify Comorbidity Patterns in Hospitalized Patients with Ischemic Heart Disease Using Large-Scale Administrative Data." Healthcare 10, no. 1 (January 1, 2022): 80. http://dx.doi.org/10.3390/healthcare10010080.

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Ischemic heart disease (IHD) exhibits elevated comorbidity. However, few studies have systematically analyzed the comorbid status of IHD patients with respect to the entire spectrum of chronic diseases. This study applied network analysis to provide a complete picture of physical and mental comorbidities in hospitalized patients with IHD using large-scale administrative data. Hospital discharge records from a provincial healthcare database of IHD inpatients (n = 1,035,338) and one-to-one matched controls were included in this retrospective analysis. We constructed the phenotypic disease networks in IHD and control patients and further assessed differences in comorbidity patterns. The community detection method was applied to cluster diagnoses within the comorbidity network. Age- and sex-specific patterns of IHD comorbidities were also analyzed. IHD inpatients showed 50% larger comorbid burden when compared to controls. The IHD comorbidity network consisted of 1941 significant associations between 71 chronic conditions. Notably, the more densely connected comorbidities in IHD patients were not within the highly prevalent ones but the rarely prevalent ones. Two highly interlinked communities were detected in the IHD comorbidity network, where one included hypertension with heart and multi-organ failures, and another included cerebrovascular diseases, cerebrovascular risk factors and anxiety. Males exhibited higher comorbid burden than females, and thus more complex comorbidity relationships were found in males. Sex-specific disease pairs were detected, e.g., 106 and 30 disease pairs separately dominated in males and females. Aging accounts for the majority of comorbid burden, and the complexity of the comorbidity network increased with age. The network-based approach improves our understanding of IHD-related comorbidities and enhances the integrated management of patients with IHD.

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Taher, Fatma, and Neema Prakash. "Automatic cerebrovascular segmentation methods-a review." IAES International Journal of Artificial Intelligence (IJ-AI) 10, no. 3 (September 1, 2021): 576. http://dx.doi.org/10.11591/ijai.v10.i3.pp576-583.

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Cerebrovascular diseases are one of the serious causes for the increase in mortality rate in the world which affect the blood vessels and blood supply to the brain. In order, diagnose and study the abnormalities in the cerebrovascular system, accurate segmentation methods can be used. The shape, direction and distribution of blood vessels can be studied using automatic segmentation. This will help the doctors to envisage the cerebrovascular system. Due to the complex shape and topology, automatic segmentation is still a challenge to the clinicians. In this paper, some of the latest approaches used for segmentation of magnetic resonance angiography images are explained. Some of such methods are deep convolutional neural network (CNN), 3dimentional-CNN (3D-CNN) and 3D U-Net. Finally, these methods are compared for evaluating their performance. 3D U-Net is the better performer among the described methods.

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Bentham, Charlotte, Matteo De Marco, and Annalena Venneri. "The Modulatory Effect of Cerebrovascular Burden in Response to Cognitive Stimulation in Healthy Ageing and Mild Cognitive Impairment." Neural Plasticity 2019 (August 6, 2019): 1–12. http://dx.doi.org/10.1155/2019/2305318.

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Background. Cerebrovascular burden is a common pathology in mild cognitive impairment (MCI) and Alzheimer’s disease (AD), with an additive impact on cognitive functioning. Despite being proposed as a potential moderator of cholinesterase inhibiting drug therapy, there is a paucity of evidence investigating the impact of cerebrovascular pathology on responsiveness to cognitive interventions. Method. The current study uses neuropsychological, neurostructural, and functional connectivity indices to characterise response to a cognitive stimulation paradigm in 25 healthy ageing and 22 MCI participants, to examine the hypothesised detrimental effects of concurrent vascular pathology. Results. In both healthy ageing and MCI, increased levels of vascular pathology limited the potential for a neuroplastic response to cognitive stimulation. In healthy ageing, participants with lower levels of vascular burden had greater functional connectivity response in the target posterior default mode network. Those with low levels of vascular pathology in the MCI cohort had increased functional connectivity of the right insula and claustrum within the salience network. Burden did not, however, predict cognitive or neuroanatomical changes. Conclusions. The current research evidences the modulatory effect of cerebrovascular pathology in interventions aimed at re-establishing network connectivity to prevent cognitive deterioration and delay the transition to the dementia stage of AD. Examination of co-occurring vascular pathology may improve precision in targeting treatment to MCI candidates who may respond optimally to such cognitive interventions.

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Wu, Yuan-ting, Hannah C. Bennett, Uree Chon, Daniel J. Vanselow, Qingguang Zhang, Rodrigo Muñoz-Castañeda, Keith C. Cheng, Pavel Osten, Patrick J. Drew, and Yongsoo Kim. "Quantitative relationship between cerebrovascular network and neuronal cell types in mice." Cell Reports 39, no. 12 (June 2022): 110978. http://dx.doi.org/10.1016/j.celrep.2022.110978.

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Haight, Thaddeus J., R. Nick Bryan, Guray Erus, Christos Davatzikos, David R. Jacobs, Mark D'Esposito, Cora E. Lewis, and Lenore J. Launer. "Vascular risk factors, cerebrovascular reactivity, and the default-mode brain network." NeuroImage 115 (July 2015): 7–16. http://dx.doi.org/10.1016/j.neuroimage.2015.04.039.

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Ruigómez, Ana, Elisa Martín-Merino, and Luis Alberto García Rodríguez. "Validation of ischemic cerebrovascular diagnoses in the health improvement network (THIN)." Pharmacoepidemiology and Drug Safety 19, no. 6 (February 3, 2010): 579–85. http://dx.doi.org/10.1002/pds.1919.

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Zhang, Liwen, Geert Jan Biessels, Saima Hilal, Joanna Su Xian Chong, Siwei Liu, Hee Youn Shim, Xin Xu, et al. "Cerebral microinfarcts affect brain structural network topology in cognitively impaired patients." Journal of Cerebral Blood Flow & Metabolism 41, no. 1 (January 27, 2020): 105–15. http://dx.doi.org/10.1177/0271678x20902187.

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Cerebral microinfarcts (CMIs), a novel cerebrovascular marker, are prevalent in Alzheimer’s disease (AD) and associated with cognitive impairment. Nonetheless, the underlying mechanism of how CMIs influence cognition remains uncertain. We hypothesized that cortical-CMIs disrupted structural connectivity in the higher-order cognitive networks, leading to cognitive impairment. We analyzed diffusion-MRI data of 92 AD (26 with cortical-CMIs) and 110 cognitive impairment no dementia patients (CIND, 28 with cortical-CMIs). We compared structural network topology between groups with and without cortical-CMIs in AD/CIND, and tested whether structural connectivity mediated the association between cortical-CMIs and cognition. Cortical-CMIs correlated with impaired structural network topology (i.e. lower efficiency/degree centrality in the executive control/dorsal attention networks in CIND, and lower clustering coefficient in the default mode/dorsal attention networks in AD), which mediated the association of cortical-CMIs with visuoconstruction dysfunction. Our findings provide the first in vivo human evidence that cortical-CMIs impair cognition in elderly via disrupting structural connectivity.

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Sui, Jin Xue, Li Yang, Yun An Hu, and Zhi Lin Zhu. "Cerebral Circulation Network Modeling and Averaging Pathological Analysis." Applied Mechanics and Materials 40-41 (November 2010): 133–39. http://dx.doi.org/10.4028/www.scientific.net/amm.40-41.133.

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Based on the periodically forced dynamic qualities of the blood circulation system, this paper considered from network and hemodynamics, establishes plane structure diagram of the cerebral circulation. According to the electric circuit graph theory and blood dynamics equation, applies the averaging method to find an approximate solution of this equation. We apply this method in the cerebrovascular network that may help to explain the development processes of venous diseases. Simulation shows that computing result is consistent with blood flow change phenomenon of the clinical observation cerebral infarction.

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Kantorová, Ema, Ľubica Jesenská, Daniel Čierny, Kamil Zeleňák, Štefan Sivák, Matej Stančík, Peter Galajda, Vladimír Nosáľ, and Egon Kurča. "The Intricate Network of Adipokines and Stroke." International Journal of Endocrinology 2015 (2015): 1–8. http://dx.doi.org/10.1155/2015/967698.

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Cerebrovascular disorders, particularly ischemic stroke, are one of the most common neurological disorders. High rates of overweight and obesity support an interest in the role of adipose tissue and adipose tissue releasing cytokines in inducing associated comorbidities. Adipokines can serve as a key messenger to central energy homeostasis and metabolic homeostasis. They can contribute to the crosstalk between adipose tissue and brain. However recent research has offered ambiguous data on the network of adipose tissue, adipokines, and vascular disorders. In our paper we provide a critical insight into the role of adipokines in evolution of ischemic stroke.

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Dasari, Yashesh, James Duffin, Ece Su Sayin, Harrison T. Levine, Julien Poublanc, Andrea E. Para, David J. Mikulis, Joseph A. Fisher, Olivia Sobczyk, and Mir Behrad Khamesee. "Convolutional Neural Networks to Assess Steno-Occlusive Disease Using Cerebrovascular Reactivity." Healthcare 11, no. 16 (August 8, 2023): 2231. http://dx.doi.org/10.3390/healthcare11162231.

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Cerebrovascular Reactivity (CVR) is a provocative test used with Blood oxygenation level-dependent (BOLD) Magnetic Resonance Imaging (MRI) studies, where a vasoactive stimulus is applied and the corresponding changes in the cerebral blood flow (CBF) are measured. The most common clinical application is the assessment of cerebral perfusion insufficiency in patients with steno-occlusive disease (SOD). Globally, millions of people suffer from cerebrovascular diseases, and SOD is the most common cause of ischemic stroke. Therefore, CVR analyses can play a vital role in early diagnosis and guiding clinical treatment. This study develops a convolutional neural network (CNN)-based clinical decision support system to facilitate the screening of SOD patients by discriminating between healthy and unhealthy CVR maps. The networks were trained on a confidential CVR dataset with two classes: 68 healthy control subjects, and 163 SOD patients. This original dataset was distributed in a ratio of 80%-10%-10% for training, validation, and testing, respectively, and image augmentations were applied to the training and validation sets. Additionally, some popular pre-trained networks were imported and customized for the objective classification task to conduct transfer learning experiments. Results indicate that a customized CNN with a double-stacked convolution layer architecture produces the best results, consistent with expert clinical readings.

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Shi, Dan, Man Qi, Liping Zhou, Xiang Li, Le Ni, Changming Li, Tianyou Yuan, et al. "Endothelial Mitochondrial Preprotein Translocase Tomm7-Rac1 Signaling Axis Dominates Cerebrovascular Network Homeostasis." Arteriosclerosis, Thrombosis, and Vascular Biology 38, no. 11 (November 2018): 2665–77. http://dx.doi.org/10.1161/atvbaha.118.311538.

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Kiselev, V. G., and S. Posse. "Analytical Theory of Susceptibility Induced NMR Signal Dephasing in a Cerebrovascular Network." Physical Review Letters 81, no. 25 (December 21, 1998): 5696–99. http://dx.doi.org/10.1103/physrevlett.81.5696.

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Kurokawa, T., Y. Chen, S. Tomita, T. Kishikawa, and K. Kitamura. "Cerebrovascular Occlusive Disease with and without the Moyamoya Vascular Network in Children." Neuropediatrics 16, no. 01 (February 1985): 29–32. http://dx.doi.org/10.1055/s-2008-1052540.

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Zeng, Xueqiang, Yingwei Guo, Asim Zaman, Haseeb Hassan, Jiaxi Lu, Jiaxuan Xu, Huihui Yang, et al. "Tubular Structure Segmentation via Multi-Scale Reverse Attention Sparse Convolution." Diagnostics 13, no. 13 (June 25, 2023): 2161. http://dx.doi.org/10.3390/diagnostics13132161.

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Cerebrovascular and airway structures are tubular structures used for transporting blood and gases, respectively, providing essential support for the normal activities of the human body. Accurately segmenting these tubular structures is the basis of morphology research and pathological detection. Nevertheless, accurately segmenting these structures from images presents great challenges due to their complex morphological and topological characteristics. To address this challenge, this paper proposes a framework UARAI based on the U-Net multi-scale reverse attention network and sparse convolution network. The framework utilizes a multi-scale structure to effectively extract the global and deep detail features of vessels and airways. Further, it enhances the extraction ability of fine-edged features by a joint reverse attention module. In addition, the sparse convolution structure is introduced to improve the features’ expression ability without increasing the model’s complexity. Finally, the proposed training sample cropping strategy reduces the influence of block boundaries on the accuracy of tubular structure segmentation. The experimental findings demonstrate that the UARAI-based metrics, namely Dice and IoU, achieve impressive scores of 90.31% and 82.33% for cerebrovascular segmentation and 93.34% and 87.51% for airway segmentation, respectively. Compared to commonly employed segmentation techniques, the proposed method exhibits remarkable accuracy and robustness in delineating tubular structures such as cerebrovascular and airway structures. These results hold significant promise in facilitating medical image analysis and clinical diagnosis, offering invaluable support to healthcare professionals.

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Bogorad, Max I., Jackson G. DeStefano, Raleigh M. Linville, Andrew D. Wong, and Peter C. Searson. "Cerebrovascular plasticity: Processes that lead to changes in the architecture of brain microvessels." Journal of Cerebral Blood Flow & Metabolism 39, no. 8 (June 17, 2019): 1413–32. http://dx.doi.org/10.1177/0271678x19855875.

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The metabolic demands of the brain are met by oxygen and glucose, supplied by a complex hierarchical network of microvessels (arterioles, capillaries, and venules). Transient changes in neural activity are accommodated by local dilation of arterioles or capillaries to increase cerebral blood flow and hence nutrient availability. Transport and communication between the circulation and the brain is regulated by the brain microvascular endothelial cells that form the blood–brain barrier. Under homeostatic conditions, there is very little turnover in brain microvascular endothelial cells, and the cerebrovascular architecture is largely static. However, changes in the brain microenvironment, due to environmental factors, disease, or trauma, can result in additive or subtractive changes in cerebrovascular architecture. Additions occur by angiogenesis or vasculogenesis, whereas subtractions occur by vascular pruning, injury, or endothelial cell death. Here we review the various processes that lead to changes in the cerebrovascular architecture, including sustained changes in the brain microenvironment, development and aging, and injury, disease, and repair.

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Kong, Tania S., Caterina Gratton, Kathy A. Low, Chin Hong Tan, Antonio M. Chiarelli, Mark A. Fletcher, Benjamin Zimmerman, et al. "Age-related differences in functional brain network segregation are consistent with a cascade of cerebrovascular, structural, and cognitive effects." Network Neuroscience 4, no. 1 (January 2020): 89–114. http://dx.doi.org/10.1162/netn_a_00110.

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Age-related declines in cognition are associated with widespread structural and functional brain changes, including changes in resting-state functional connectivity and gray and white matter status. Recently we have shown that the elasticity of cerebral arteries also explains some of the variance in cognitive and brain health in aging. Here, we investigated how network segregation, cerebral arterial elasticity (measured with pulse-DOT—the arterial pulse based on diffuse optical tomography) and gray and white matter status jointly account for age-related differences in cognitive performance. We hypothesized that at least some of the variance in brain and cognitive aging is linked to reduced cerebrovascular elasticity, leading to increased cortical atrophy and white matter abnormalities, which, in turn, are linked to reduced network segregation and decreases in cognitive performance. Pairwise comparisons between these variables are consistent with an exploratory hierarchical model linking them, especially when focusing on association network segregation (compared with segregation in sensorimotor networks). These findings suggest that preventing or slowing age-related changes in one or more of these factors may induce a neurophysiological cascade beneficial for preserving cognition in aging.

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Bayona, Hernán, Brenda Ropero, Antonio José Salazar, Juan Camilo Pérez, Manuel Felipe Granja, Carlos Fernando Martínez, and Juan Nicolás Useche. "Comprehensive Telestroke Network to Optimize Health Care Delivery for Cerebrovascular Diseases: Algorithm Development." Journal of Medical Internet Research 22, no. 7 (July 27, 2020): e18058. http://dx.doi.org/10.2196/18058.

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Background Health care delivery for cerebrovascular diseases is a complex process, which may be improved using telestroke networks. Objective The purpose of this work was to establish and implement a protocol for the management of patients with acute stroke symptoms according to the available treatment alternatives at the initial point of care and the transfer possibilities. Methods The review board of our institutions approved this work. The protocol was based on the latest guidelines of the American Heart Association and American Stroke Association. Stroke care requires human and technological resources, which may differ according to the patient’s point of entry into the health care system. Three health care settings were identified to define the appropriate protocols: primary health care setting, intermediate health care setting, and advanced health care setting. Results A user-friendly web-based telestroke solution was developed. The predictors, scales, and scores implemented in this system allowed the assessment of the vascular insult severity and neurological status of the patient. The total number of possible pathways implemented was as follows: 10 in the primary health care setting, 39 in the intermediate health care setting, and 1162 in the advanced health care setting. Conclusions The developed comprehensive telestroke platform is the first stage in optimizing health care delivery for patients with stroke symptoms, regardless of the entry point into the emergency network, in both urban and rural regions. This system supports health care personnel by providing adequate inpatient stroke care and facilitating the prompt transfer of patients to a more appropriate health care setting if necessary, especially for patients with acute ischemic stroke within the therapeutic window who are candidates for reperfusion therapies, ultimately contributing to mitigating the mortality and morbidity associated with stroke.

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Gul, Aman, Mutalifu Aimaiti, Tuerhong Tuerxun, Raziye Amat, Ayinuer Reheman, Min Fang Zhang, and Nassirhadjy Memtily. "Study on the Mechanism of Üstikuddus Sherbiti in Ischemic Cerebrovascular Diseases: Based on Network Pharmacology." Evidence-Based Complementary and Alternative Medicine 2022 (April 8, 2022): 1–16. http://dx.doi.org/10.1155/2022/5581864.

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This paper aims to study the potential biological mechanism of Üstikuddus Sherbiti (ÜS) in the treatment of ischemic cerebrovascular diseases (ICVD) by the network pharmacology method. Traditional Chinese Medicine Systems Pharmacology (TCMSP) database was used to obtain effective constituents of ÜS by screening eligible oral utilization, drug similarity, and blood-brain barrier permeability threshold. By drug target prediction and stroke treatment target mining, 2 target data sets were analyzed to find intersection targets and the corresponding constituents were used as active constituents. An active constituent target network and an effective constituent target network were constructed by using Cytoscape 3.7.2 software. Degree parameters of the effective constituent target network were analyzed to find important effective constituents and targets. Through protein-protein interaction (PPI) analysis/Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis, potential signaling pathways of ÜS in ischemic stroke were found out. AutoDock was used for molecular docking verification. A total of 90 active constituents of ÜS were screened out. There were 10 active constituents against ICVD, including quercetin, luteolin, kaempferol, and naringenin, and 10 important targets for anticerebral ischemia, namely, PIK3CA, APP, PIK3R1, MAPK1, MAPK3, AKT1, PRKCD, Fyn, RAC1, and NF-κB1. Based on the protein interaction network, the important targets of ÜS were significantly enriched in PI3K-Akt signaling pathway, neuroactive ligand-receptor interaction pathway, Ras signaling pathway, etc. ÜS in ICVD has characteristics like multiple targets, multiple approaches, and multiple pathways. Results of molecular docking showed that the active components in ICVD had a good binding ability with the key targets. Its main biological mechanism may be related to the PI3K-Akt and Ras-MAPK centered signaling pathway. Our study demonstrated that ÜS exerted the effect of treating ICVD by regulating multiple targets and multiple channels with multiple components through the method of network pharmacology and molecular docking.

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Gandrakota, Rohit, V. S. Chakravarthy, and Ranjan K. Pradhan. "A Model of Indispensability of a Large Glial Layer in Cerebrovascular Circulation." Neural Computation 22, no. 4 (April 2010): 949–68. http://dx.doi.org/10.1162/neco.2009.01-09-945.

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We formulate the problem of oxygen delivery to neural tissue as a problem of association. Input to a pool of neurons in one brain area must be matched in space and time with metabolic inputs from the vascular network via the glial network. We thus have a model in which neural, glial, and vascular layers are connected bidirectionally, in that order. Connections between neuro-glial and glial-vascular stages are trained by an unsupervised learning mechanism such that input to the neural layer is sustained by the precisely patterned delivery of metabolic inputs from the vascular layer via the glial layer. Simulations show that the capacity of such a system to sustain patterns is weak when the glial layer is absent. Capacity is higher when a glial layer is present and increases with the layer size. The proposed formulation of neurovascular interactions raises many intriguing questions about the role of glial cells in cerebral circulation.

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Zechariah, Anil, Cam Ha T. Tran, Bjorn O. Hald, Shaun L. Sandow, Maria Sancho, Michelle Sun Mi Kim, Sergio Fabris, Ursula I. Tuor, Grant R. J. Gordon, and Donald G. Welsh. "Intercellular Conduction Optimizes Arterial Network Function and Conserves Blood Flow Homeostasis During Cerebrovascular Challenges." Arteriosclerosis, Thrombosis, and Vascular Biology 40, no. 3 (March 2020): 733–50. http://dx.doi.org/10.1161/atvbaha.119.313391.

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Objective: Cerebral arterial networks match blood flow delivery with neural activity. Neurovascular response begins with a stimulus and a focal change in vessel diameter, which by themselves is inconsequential to blood flow magnitude, until they spread and alter the contractile status of neighboring arterial segments. We sought to define the mechanisms underlying integrated vascular behavior and considered the role of intercellular electrical signaling in this phenomenon. Approach and Results: Electron microscopic and histochemical analysis revealed the structural coupling of cerebrovascular cells and the expression of gap junctional subunits at the cell interfaces, enabling intercellular signaling among vascular cells. Indeed, robust vasomotor conduction was detected in human and mice cerebral arteries after focal vessel stimulation: a response attributed to endothelial gap junctional communication, as its genetic alteration attenuated this behavior. Conducted responses were observed to ascend from the penetrating arterioles, influencing the contractile status of cortical surface vessels, in a simulated model of cerebral arterial network. Ascending responses recognized in vivo after whisker stimulation were significantly attenuated in mice with altered endothelial gap junctional signaling confirming that gap junctional communication drives integrated vessel responses. The diminishment in vascular communication also impaired the critical ability of the cerebral vasculature to maintain blood flow homeostasis and hence tissue viability after stroke. Conclusions: Our findings highlight the integral role of intercellular electrical signaling in transcribing focal stimuli into coordinated changes in cerebrovascular contractile activity and expose, a hitherto unknown mechanism for flow regulation after stroke.

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Kiernan, Terri-Ellen J., and Bart M. Demaerschalk. "Nursing Roles within a Stroke Telemedicine Network." Journal of Central Nervous System Disease 2 (January 2010): JCNSD.S4284. http://dx.doi.org/10.4137/jcnsd.s4284.

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Time sensitive acute stroke treatments and the growing shortage of vascular neurologists compound to create a gap in the delivery of care to meet the American Stroke Association guidelines in underserviced regions. Audio/video consultation (telemedicine), which has been evolving since the late 1990's, is a putative solution. While telemedicine can serve as a valuable facilitative tool, the telestroke consultation is only one piece of a complex collaboration between hub and spoke environments and clinical personnel. The growing use of telemedicine in stroke offers more opportunities for all nurses to participate in the continuum of cerebrovascular disease care. A review of this collaboration will include but will not be limited to: algorithms of the acute stroke evaluation, hub and spoke staff meetings, stroke education for spoke staff, and patient follow–up post acute treatment. Our team's telemedicine experience, utilizing research, education, and clinical practice, will be described.

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Yao, Jia Xin, Dan Fei Huang, and Jun Qiang Chen. "The ECG Monitoring Terminal Design of Family Used in Telemedicine." Applied Mechanics and Materials 738-739 (March 2015): 797–800. http://dx.doi.org/10.4028/www.scientific.net/amm.738-739.797.

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With the improvement of people's living standards and life rhythm speeding up, the cardiovascular and cerebrovascular diseases have become a danger to the top killer of the elderly health and longevity.This paper designed an ECG monitoring terminal of family used in telemedicine for cardiovascular and cerebrovascular diseases.The terminal adopted ADS1298 chip as collection simulation front-end, AVR as core processor ,collected the ECG signal and wireless transmission .The monitoring terminal had high common mode rejection ratio, high signal to noise ratio, low energy consumption, small volume, etc .And can real-time transfer the ECG data to the local area network through wireless transmission technology,convenient medical staff grasp patient's ECG data real-time and treatment it in time,this design meets the basic requirement of the portable monitoring.

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Giese, Anne-Katrin, Markus D. Schirmer, Kathleen L. Donahue, Lisa Cloonan, Robert Irie, Stefan Winzeck, Mark J. R. J. Bouts, et al. "Design and rationale for examining neuroimaging genetics in ischemic stroke." Neurology Genetics 3, no. 5 (August 24, 2017): e180. http://dx.doi.org/10.1212/nxg.0000000000000180.

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Objective:To describe the design and rationale for the genetic analysis of acute and chronic cerebrovascular neuroimaging phenotypes detected on clinical MRI in patients with acute ischemic stroke (AIS) within the scope of the MRI–GENetics Interface Exploration (MRI-GENIE) study.Methods:MRI-GENIE capitalizes on the existing infrastructure of the Stroke Genetics Network (SiGN). In total, 12 international SiGN sites contributed MRIs of 3,301 patients with AIS. Detailed clinical phenotyping with the web-based Causative Classification of Stroke (CCS) system and genome-wide genotyping data were available for all participants. Neuroimaging analyses include the manual and automated assessments of established MRI markers. A high-throughput MRI analysis pipeline for the automated assessment of cerebrovascular lesions on clinical scans will be developed in a subset of scans for both acute and chronic lesions, validated against gold standard, and applied to all available scans. The extracted neuroimaging phenotypes will improve characterization of acute and chronic cerebrovascular lesions in ischemic stroke, including CCS subtypes, and their effect on functional outcomes after stroke. Moreover, genetic testing will uncover variants associated with acute and chronic MRI manifestations of cerebrovascular disease.Conclusions:The MRI-GENIE study aims to develop, validate, and distribute the MRI analysis platform for scans acquired as part of clinical care for patients with AIS, which will lead to (1) novel genetic discoveries in ischemic stroke, (2) strategies for personalized stroke risk assessment, and (3) personalized stroke outcome assessment.

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Chen, Zan, Lei Xie, Yukai Chen, Qingrun Zeng, Qichuan ZhuGe, Jiakai Shen, Caiyun Wen, and Yuanjing Feng. "Generative adversarial network based cerebrovascular segmentation for time-of-flight magnetic resonance angiography image." Neurocomputing 488 (June 2022): 657–68. http://dx.doi.org/10.1016/j.neucom.2021.11.075.

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Barbeau‐Meunier, Charles‐Antoine, Michaël Bernier, Samantha Côté, Guillaume Gilbert, Christian Bocti, and Kevin Whittingstall. "Sexual dimorphism in the cerebrovascular network: Brain MRI shows lower arterial density in women." Journal of Neuroimaging 32, no. 2 (December 3, 2021): 337–44. http://dx.doi.org/10.1111/jon.12951.

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Cissom, Cody, Jason J. Paris, and Zia Shariat-Madar. "Dynorphins in Development and Disease: Implications for Cardiovascular Disease." Current Molecular Medicine 20, no. 4 (March 20, 2020): 259–74. http://dx.doi.org/10.2174/1566524019666191028122559.

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It is well-established that cardiovascular disease continues to represent a growing health problem and significant effort has been made to elucidate the underlying mechanisms. In this review, we report on past and recent high impact publications in the field of intracrine network signaling, focusing specifically on opioids and their interrelation with key modulators of the cardiovascular system and the onset of related disease. We present an overview of studies outlining the scope of cardiovascular and cerebrovascular processes that are affected by opioids, including heart function, ischemia, reperfusion, and blood flow. Specific emphasis is placed on the importance of dynorphin molecules in cerebrovascular and cardiovascular regulation. Evidence suggests that excessive or insufficient dynorphin could make an important contribution to cardiovascular physiology, yet numerous paradoxical observations frequently impede a clear understanding of the role of dynorphin. Thus, we argue that dynorphin-mediated signaling events for which an immediate regulatory effect is disputed should not be dismissed as unimportant, as they may play a role in cross-talk with other signaling networks. Finally, we consider the most recent evidence on the role of dynorphin during cardiovascular-related inflammation and on the potential value of endogenous and exogenous inhibitors of kappa-opioid receptor, a major dynorphin A receptor, to limit or prevent cardiovascular disease and its related sequelae.

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Herwadkar, A. "A Case of Carotid Rete Mirabile Associated with Basilar Tip Aneurysm." Interventional Neuroradiology 12, no. 2 (June 2006): 161–64. http://dx.doi.org/10.1177/159101990601200211.

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Carotid rete mirabile (CRM) is a rare condition consisting of a physiological network between the external and internal carotid arteries in lower vertebrates. Only a few cases have been reported in humans presenting as hemorrhagic or ischemic cerebrovascular disorder. We present a case of CRM presenting as an aneurysmal subarachnoid hemorrhage and discuss the embryologic, anatomic, hemodynamic and clinical significance of this rare entity.

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Schnurman, Zane, Gustavo Chagoya, Jan O. Jansen, and Mark R. Harrigan. "Existence of knowledge silos in the adult blunt cerebrovascular injury literature." Trauma Surgery & Acute Care Open 6, no. 1 (December 2021): e000741. http://dx.doi.org/10.1136/tsaco-2021-000741.

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BackgroundBlunt cerebrovascular injuries (BCVI) remain a significant source of disability and mortality among trauma patients. The purpose of the present study was to determine whether knowledge silos exist in the overall BCVI literature.MethodsAn object-oriented programmatic script written in Python programming language was used to extract and categorize articles and references on the topic of BCVI. Additionally, each BCVI article was searched for by digital object identifier in the other BCVI references to build a network analysis and visualize topic reference patterns. Analyses were performed using Stata V.14.2 (StataCorp).ResultsA total of 306 articles with 10 282 references were included for analysis. Of these, 24% (74) were published in neurosurgery journals, 45% (137) were published in trauma journals, and 31% (95) were published in a journal of another specialty. Similar proportions were found when categorized by author departmental affiliation. Trauma surgery authors disproportionately referenced articles in the trauma literature, compared with neurosurgeons (73.5% vs. 48.0%, p<0.0001), and other authors. The biggest factor influencing reference proportions was the specialty of the publishing journal. Finally, a network analysis revealed that there are more trauma BCVI articles, and there are more frequently cited trauma BCVI articles by all specialties.ConclusionsThis study revealed the existence of a one-way knowledge silo in the BCVI literature. However, a robust preference by both trauma and neurosurgery to cite trauma references when publishing in trauma journals may indicate a possible conscious curating of citations by authors to increase the likelihood of publication. These observations highlight the need for an active role by journal editors, peer reviewers, and authors to actively foster diversity of citations and cross-specialty collaboration to improve dissemination of information between these specialties.Level of evidenceLevel IV. Observational study.

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Rodrigues, Rosalina Aparecida Partezani, Sueli Marques, Luciana Kusumota, Emanuella Barros dos Santos, Jack Roberto da Silva Fhon, and Suzele Cristina Coelho Fabrício-Wehbe. "Transition of care for the elderly after cerebrovascular accidents - from hospital to the home." Revista Latino-Americana de Enfermagem 21, spe (February 2013): 216–24. http://dx.doi.org/10.1590/s0104-11692013000700027.

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OBJECTIVE: to examine the transition of care in families caring for elderly persons who suffered the first episode of a cerebrovascular accident. METHODOLOGY: an instrumental ethnographic case study was used. The sample comprised 20 subjects: 10 caregivers and 10 elderly persons aged 65 or over, of both sexes, with diagnoses of first episode of cerebrovascular accident, capable of communicating, and requiring care from a main carer in their family. The data was collected through interviews, observation, existing documentation and field notes. Qualitative analysis techniques were used to codify and classify the data and to formulate significant categories, which generated typologies of care. RESULTS AND DISCUSSION: The central idea was the Transition of Care and showed the context in three typologies: The care process for the dependent elderly person, Strategies for the care process and Impact and acceptance of the limitations. CONCLUSION: The data indicates that caring for an elderly person after a cerebrovascular accident is a challenge for the family. The data permitted it possible to elaborate a proposal for a model for the organization of the work, with a view to holistic care delivery in the health services, forming a care network, which constitutes an advance for the area of nursing.

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Kennedy, Richard E., Virginia G. Wadley, Leslie A. McClure, Abraham J. Letter, Frederick W. Unverzagt, Michael Crowe, David Nyenhius, et al. "Performance of the NINDS-CSN 5-Minute Protocol in a National Population-Based Sample." Journal of the International Neuropsychological Society 20, no. 8 (August 27, 2014): 856–67. http://dx.doi.org/10.1017/s1355617714000733.

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AbstractIn 2006, the National Institute of Neurological Disorders and Stroke-Canadian Stroke Network (NINDS-CSN) Vascular Cognitive Impairment Harmonization Standards recommended a 5-Minute Protocol as a brief screening instrument for vascular cognitive impairment (VCI). We report demographically adjusted norms for the 5-Minute Protocol and its relation to other measures of cognitive function and cerebrovascular risk factors. We performed a cross-sectional analysis of 7199 stroke-free adults in the REasons for Geographic And Racial Differences in Stroke (REGARDS) study on the NINDS-CSN 5-Minute Protocol score. Total scores on the 5-Minute Protocol were inversely correlated with age and positively correlated with years of education, and performance on the Six-Item Screener, Word List Learning, and Animal Fluency (all p-values <.001). Higher cerebrovascular risk on the Framingham Stroke Risk Profile (FSRP) was associated with lower total 5-Minute Protocol scores (p <.001). The 5-Minute Protocol also differentiated between participants with and without confirmed stroke and with and without stroke symptom histories (p <.001). The NINDS-CSN 5-Minute Protocol is a brief, easily administered screening measure that is sensitive to cerebrovascular risk and offers a valid method of screening for cognitive impairment in populations at risk for VCI. (JINS, 2014, 20, 1–12)

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Zedde, Marialuisa, and Rosario Pascarella. "The Cerebrovascular Side of Plasticity: Microvascular Architecture across Health and Neurodegenerative and Vascular Diseases." Brain Sciences 14, no. 10 (September 28, 2024): 983. http://dx.doi.org/10.3390/brainsci14100983.

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The delivery of nutrients to the brain is provided by a 600 km network of capillaries and microvessels. Indeed, the brain is highly energy demanding and, among a total amount of 100 billion neurons, each neuron is located just 10–20 μm from a capillary. This vascular network also forms part of the blood–brain barrier (BBB), which maintains the brain’s stable environment by regulating chemical balance, immune cell transport, and blocking toxins. Typically, brain microvascular endothelial cells (BMECs) have low turnover, indicating a stable cerebrovascular structure. However, this structure can adapt significantly due to development, aging, injury, or disease. Temporary neural activity changes are managed by the expansion or contraction of arterioles and capillaries. Hypoxia leads to significant remodeling of the cerebrovascular architecture and pathological changes have been documented in aging and in vascular and neurodegenerative conditions. These changes often involve BMEC proliferation and the remodeling of capillary segments, often linked with local neuronal changes and cognitive function. Cerebrovascular plasticity, especially in arterioles, capillaries, and venules, varies over different time scales in development, health, aging, and diseases. Rapid changes in cerebral blood flow (CBF) occur within seconds due to increased neural activity. Prolonged changes in vascular structure, influenced by consistent environmental factors, take weeks. Development and aging bring changes over months to years, with aging-associated plasticity often improved by exercise. Injuries cause rapid damage but can be repaired over weeks to months, while neurodegenerative diseases cause slow, varied changes over months to years. In addition, if animal models may provide useful and dynamic in vivo information about vascular plasticity, humans are more complex to investigate and the hypothesis of glymphatic system together with Magnetic Resonance Imaging (MRI) techniques could provide useful clues in the future.

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Choi, Woo June, Bjorn Paulson, Sungwook Yu, Ruikang K. Wang, and Jun Ki Kim. "Mean-Subtraction Method for De-Shadowing of Tail Artifacts in Cerebral OCTA Images: A Proof of Concept." Materials 13, no. 9 (April 26, 2020): 2024. http://dx.doi.org/10.3390/ma13092024.

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When imaging brain vasculature with optical coherence tomography angiography (OCTA), volumetric analysis of cortical vascular networks in OCTA datasets is frequently challenging due to the presence of artifacts, which appear as multiple-scattering tails beneath superficial large vessels in OCTA images. These tails shadow underlying small vessels, making the assessment of vascular morphology in the deep cortex difficult. In this work, we introduce an image processing technique based on mean subtraction of the depth profile that can effectively reduce these tails to better reveal small hidden vessels compared to the current tail removal approach. With the improved vascular image quality, we demonstrate that this simple method can provide better visualization of three-dimensional vascular network topology for quantitative cerebrovascular studies.

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Carnevale, Lorenzo, and Giuseppe Lembo. "Innovative MRI Techniques in Neuroimaging Approaches for Cerebrovascular Diseases and Vascular Cognitive Impairment." International Journal of Molecular Sciences 20, no. 11 (May 30, 2019): 2656. http://dx.doi.org/10.3390/ijms20112656.

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Cognitive impairment and dementia are recognized as major threats to public health. Many studies have shown the important role played by challenges to the cerebral vasculature and the neurovascular unit. To investigate the structural and functional characteristics of the brain, MRI has proven an invaluable tool for visualizing the internal organs of patients and analyzing the parameters related to neuronal activation and blood flow in vivo. Different strategies of imaging can be combined to obtain various parameters: (i) measures of cortical and subcortical structures (cortical thickness, subcortical structures volume); (ii) evaluation of microstructural characteristics of the white matter (fractional anisotropy, mean diffusivity); (iii) neuronal activation and synchronicity to identify functional networks across different regions (functional connectivity between specific regions, graph measures of specific nodes); and (iv) structure of the cerebral vasculature and its efficacy in irrorating the brain (main vessel diameter, cerebral perfusion). The high amount of data obtainable from multi-modal sources calls for methods of advanced analysis, like machine-learning algorithms that allow the discrimination of the most informative features, to comprehensively characterize the cerebrovascular network into specific and sensitive biomarkers. By using the same techniques of human imaging in pre-clinical research, we can also investigate the mechanisms underlying the pathophysiological alterations identified in patients by imaging, with the chance of looking for molecular mechanisms to recover the pathology or hamper its progression.

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