Relevant bibliographies by topics / Cerebrovascular network

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers

Academic literature on the topic 'Cerebrovascular network'

Author: Grafiati
Published: 12 April 2025

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Journal articles on the topic "Cerebrovascular network"

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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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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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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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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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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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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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Dissertations / Theses on the topic "Cerebrovascular network"

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Åström, Monica. "Depression after stroke." Doctoral thesis, Umeå universitet, Psykiatri, 1993. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-96912.

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Both stroke and depression are major health problems in the elderly. In this study, the prevalence of major depression after stroke was investigated in a well-defined sample of acute stroke patients (n=80), followed up at 3 months, 1 year, 2 and 3 years after the stroke event. Links to biological and psychosocial factors were examined. Hypercortisolism was studied by the dexamethasone suppression test and compared with healthy elderly. Living conditions (including demographic caracteristics, economic resources, health, functional ability, activity/leisure, social network) and life satisfaction were described before and after stroke in relation to a general elderly population. Demographic caracteristics, economic resources, social network and psychiatric morbidity prestroke did not differ from the general elderly population. Already prior to the stroke, patients reported more health problems and lower functional ability in many aspects of daily life, more passive leisure time and a lower global life satisfaction. After stroke, contacts with children were maintained, whilst contacts outside the family declined and remained lower than in the general elderly population. Stroke involved a marked reduction in global life satisfaction. Poor life satisfaction at 1 year remained poor for the entire three years; these stroke victims had a higher frequency of major depression early after stroke. The prevalence of major depression was 25% at the acute stage, 31% at 3 months, decreased to 16% at 1 year, was 19% at 2 years and increased to 29% at 3 years. The most important predictors of immediate major depression were left anterior brain lesion, dysphasia, and living alone. Dependence in self-care ability and loss of social contacts outside the family were the most important predictors at 3 months. From 1 year onwards, loss of social contacts contributed most to depression and at 3 years also cerebral atrophy. Sixty percent of patients with early depression (0-3 months) had recovered at 1 year; those not recovered at 1 year had a high risk of chronicitation. Hypercortisolism as measured by the dexamethasone suppression test was associated with major depression late (3 years) but not early (0-3 months) after stroke. At 3 years, the dexamethasone suppression test had a sensitivity of 70%, a specificity of 97%, a positive predictive value of 88%, a negative predicitive value of 91%, and a diagnostic accuracy of 90%. Nonsuppression of dexamethasone at 3 months was a significant predictor of major depression at 3 years.

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Rougé, Pierre. "Segmentation et modélisation du réseau vasculaire cérébral à partir d'images IRM." Electronic Thesis or Diss., Reims, 2025. http://www.theses.fr/2025REIMS001.

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Les maladies cardio-neurovasculaires sont la première cause de mortalité dans le monde et représentent un enjeu majeur de santé publique. L'imagerie du réseau vasculaire cérébral a considérablement amélioré le diagnostic de ces pathologies, et les algorithmes de traitement d'images automatiques jouent désormais un rôle clé en assistant les médecins. Ces algorithmes reposent généralement sur la segmentation du réseau vasculaire cérébral. Pour cette raison, l'automatisation de cette tâche suscite aujourd'hui un intérêt majeur.Malgré des avancées, les méthodes actuelles de segmentation automatique présentent encore des limitations majeures. Elles peinent à préserver la topologie et notamment la connectivité des réseaux vasculaires, et les métriques de segmentation traditionnelles ne sont pas adaptées à la complexité géométrique du réseau cérébrovasculaire. De plus, l'annotation manuelle nécessaire pour entraîner ces modèles demeure une tâche chronophage et fastidieuse, freinant la création de jeux de données annotés.Dans cette thèse, nous nous intéressons à la segmentation cérébrovasculaire à partir d'images ARM TOF. Nous proposons en premier lieu un modèle multitâche basé sur une fonction de coût topologique permettant d'améliorer la connectivité des segmentations. Puis, nous proposons une nouvelle métrique, appelée ccDice, permettant de quantifier les erreurs topologiques. Enfin, nous étudions l'impact du manque et du bruit des annotations, et formulons des recommandations à destination des cliniciens pour améliorer la qualité des annotations, favorisant ainsi le développement de modèles d'apprentissage plus performants à l'avenir
Cardio-neurovascular diseases are the leading cause of death worldwide and represent a major public health challenge. Imaging of the cerebral vascular network has significantly improved the diagnosis of these pathologies, and automated image processing algorithms now play a key role in assisting physicians. These algorithms generally rely on the segmentation of the cerebral vascular network. For this reason, automating this task has garnered significant interest.Despite advances, current automatic segmentation methods still suffer from major limitations. They struggle to preserve the topology and connectivity of vascular networks, and traditional segmentation metrics are not well-suited to the geometric complexity of the cerebrovascular network. Additionally, manual annotation, necessary for training these models, remains a time-consuming and tedious task, hindering the creation of annotated datasets.In this thesis, we focus on cerebrovascular segmentation from TOF MRA images. First, we propose a multitask model based on a topological cost function to improve the connectivity of segmentations. Additionally, we introduce a new metric, called ccDice, to quantify topological errors. Finally, we study the impact of annotation scarcity and noise, and we formulate recommendations for clinicians to improve annotation quality, thereby fostering the development of more efficient learning models in the future
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Kleineibst, Lynn Jill. "The effectiveness of a caregiver support programme to address the needs of primary caregivers of stroke patients in a low socio economic community." Thesis, Link to the online version, 2007. http://hdl.handle.net/10019/432.

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Mendes, Luciana Moura. "Modelo de apoio à decisão no acesso aos serviços de fisioterapia para reabilitação de pacientes com acidente vascular encefálico." Universidade Federal da Paraíba, 2015. http://tede.biblioteca.ufpb.br:8080/handle/tede/7551.

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Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - CAPES
Cerebrovascular Accident (CVA) is a disease characterized by an interruption of blood flow to the encephalon, which represents the leading cause of long-term disability and functional impairment in adult population. Therefore, the individual who had suffered CVA needs to access health services that offer rehabilitation assistance as they promote a better physical, functional, and mental capacity, helping the reinsertion and reintegration of this individual into society. Thus, this study aims to develop a decision-making model to determine the access to physiotherapy services for rehabilitation of patients who had suffered acute CVA in the cities of João Pessoa and Cabedelo. This is an observational-longitudinal study among man and women who were admitted at a public hospital in João Pessoa and live in its metro area, who had presented CVA as primary cause of hospitalization. A questionnaire was used containing items related to socioeconomic, demographic, and clinical data from this person, such as general health conditions, risk factors, functionality evaluation, and access to physiotherapy services. Interviews were conducted between 15 and 21 days after hospital discharge (T0) and between 90 and 105 days after the first interview (T1). There was a homogeneous distribution of sexes, group age over 60 years (mean age= 61.6 years; standard deviation= 15.7 years). Most of the subjects (69.2%) have had a ischemic CVA, which the right side was more affected (46.2%) and 89.7% have had up to two CVA episodes. From interviewed patients, 69.2% have not had access to physiotherapy services after three months from the first interview. For utilization of decision model, 16 variables were selected helped by WEKA software, generating a feedfoward Artificial Neural Network model composed by 16 neurons in the input layer, followed by two hidden layers with two hidden neurons in each layer and an output layer with 2 neurons with backpropagation learning. This decision model allowed classifying correctly almost all subjects that accessed or not the physiotherapy services, achieving 97.4% of successes, representing a greater reliability. Therefore, this model is constituted as an important tool in the visibility of the problem, helping in the decision-making process, planning, and reorganization of public health system and its several attention levels.
O Acidente Vascular Encefálico (AVE) é uma doença causada pela interrupção no suprimento sanguíneo ao encéfalo, representando a primeira causa de incapacidade prolongada e o comprometimento funcional em adultos. Assim, o indivíduo com AVE necessita acessar os serviços de saúde que oferecem assistência de reabilitação, pois promovem uma melhora na capacidade física, funcional e/ou mental, proporcionando a reinserção e a reintegração à sociedade. Portanto, o objetivo deste estudo foi elaborar um modelo de tomada de decisão para averiguar o acesso aos serviços de fisioterapia para reabilitação de pacientes com AVE agudo dos municípios de João Pessoa e Cabedelo. Trata-se de um estudo longitudinal observacional com indivíduos de ambos os sexos, admitidos em um hospital público de João Pessoa/PB e residentes na região metropolitana de João Pessoa, que apresentaram como causa primária da internação o AVE. Para tanto, foi utilizado um questionário contendo itens referentes aos dados socioeconômicos, demográficos e clínicos do sujeito, condições gerais de saúde, fatores de risco, avaliação da funcionalidade e do acesso aos serviços de fisioterapia. As entrevistas foram realizadas entre 15 e 21 dias após a alta hospitalar (T0) e entre 90 e 105 dias após a realização da primeira entrevista (T1). Verificou-se uma distribuição homogênea dos sexos, com faixa etária acima de 60 anos (média de idade=61,6 anos, dp=15,7). A maioria dos sujeitos (69,2%) tiveram um AVE do tipo isquêmico, sendo o lado direito mais afetado (46,2%) e 89,7% tiveram até dois episódios de AVE. Dos pacientes entrevistados, 69,2% não tiveram acesso aos serviços de fisioterapia após três meses da primeira entrevista. Para a utilização do modelo de decisão, selecionou-se 16 variáveis com auxílio do software WEKA, gerando um modelo de Redes Neurais Artificiais do tipo feedforward composta por 16 neurônios na camada de entrada, seguido por duas camadas ocultas com dois neurônios ocultos em cada e uma camada de saída com 2 neurônios com aprendizagem por backpropagation. Este modelo de decisão permitiu classificar corretamente quase todos os sujeitos que acessaram ou não os serviços de fisioterapia, obtendo 97,4% de acertos, representando uma maior confiabilidade. Portanto, este modelo constitui-se como uma ferramenta importante na visibilidade do problema, auxiliando no processo de tomada de decisão, no planejamento e na reorganização da rede de saúde em seus diversos níveis de atenção.
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Earnheart, Kristie. "Cardiovascular Problems as a Predictor of Later Cognitive Decline: Moderating Effect of General and Spousal Social Support." Thesis, University of North Texas, 2006. https://digital.library.unt.edu/ark:/67531/metadc5377/.

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Individuals are living longer now than they have in the past. As a result, there is an increased incidence in illnesses that are more prevalent in later life. One group of illnesses that is more prevalent is age related dementia. Alzheimer's disease (AD) and vascular dementia (VaD) are two common types of dementia found in the older adult population. Recent research suggests that these two types of dementia may both have a vascular component that is instrumental in their development. Not only may this vascular component be present in both these illnesses, but also it may be related to a more severe cognitive decline in the aging process. Results indicate that both cardiovascular disease and general and spousal social support in middle age are all three independent significant predictors of mild cognitive impairment and other non-normative cognitive impairment in later life. However, results do not indicate that social support moderates the relationship between cardiovascular disease and cognition.
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Books on the topic "Cerebrovascular network"

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Publications, ICON Health. Cerebral Vascular Accident: A Medical Dictionary, Bibliography, And Annotated Research Guide To Internet References. Icon Health Publications, 2004.

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Book chapters on the topic "Cerebrovascular network"

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Zhang, Hao, Likun Xia, Ran Song, Jianlong Yang, Huaying Hao, Jiang Liu, and Yitian Zhao. "Cerebrovascular Segmentation in MRA via Reverse Edge Attention Network." In Medical Image Computing and Computer Assisted Intervention – MICCAI 2020, 66–75. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-59725-2_7.

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Yang, Chaozhi, Yachuan Li, Yun Bai, Qian Xiao, Zongmin Li, Hongyi Li, and Hua Li. "SS-Net: 3D Spatial-Spectral Network for Cerebrovascular Segmentation in TOF-MRA." In Artificial Neural Networks and Machine Learning – ICANN 2023, 149–59. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-44213-1_13.

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Xie, Qihang, Dan Zhang, Lei Mou, Shanshan Wang, Yitian Zhao, Mengguo Guo, and Jiong Zhang. "DSNet: A Spatio-Temporal Consistency Network for Cerebrovascular Segmentation in Digital Subtraction Angiography Sequences." In Lecture Notes in Computer Science, 199–208. Cham: Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-72111-3_19.

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Qin, Qiuli, Chunxiao Yao, and Yong Jiang. "Research on Cerebrovascular Disease Prediction Model Based on the Long Short Term Memory Neural Network." In Smart Health, 247–56. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-34482-5_22.

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Wang, Yifan, Guoli Yan, Haikuan Zhu, Sagar Buch, Ying Wang, Ewart Mark Haacke, Jing Hua, and Zichun Zhong. "JointVesselNet: Joint Volume-Projection Convolutional Embedding Networks for 3D Cerebrovascular Segmentation." In Medical Image Computing and Computer Assisted Intervention – MICCAI 2020, 106–16. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-59725-2_11.

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Razumnikova, Olga, and Vladislav Kagan. "Aging Associated Specificity in Training Visual Short-Term Memory." In Cerebrovascular Diseases [Working Title]. IntechOpen, 2021. http://dx.doi.org/10.5772/intechopen.101669.

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There are numerous data in existence, the computerized cognitive training programs (CCTP) maintain or improve the plasticity of the neural networks in the brain. It is known as well that CCTP reduces the probability of cognitive dysfunctions associated with aging. In the chapter, the age-associated specificity in the temporal dynamics of changes in the visuospatial short-term memory (VSWM, also called visuospatial working memory) is presented. VSWM has been analyzed as there are evidence for age-related decline in visuospatial memory associated with hippocampus atrophy in aging. Memory retrieval decline in older women in comparison with young women while computerized training at home is shown. The elderly achieving results which are comparable to the youngs are determined by significantly increased duration while performing the memory tasks. To reveal factors of the CCTP’s efficiency, age-related differences in the attention systems using the Attention Network Test were resolved. In the group of older women, VSWM efficiency is negatively related to the errors of incongruent information selection whereas in young women—to the reaction time while testing. Thus, the success of long-term systematic training of visuospatial memory in old age is strongly related to the high level of executive control.
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Forkert Nils Daniel, Suniaga Santiago, Fiehler Jens, Wersching Heike, Knecht Stefan, and Kemmling Andre. "Generation of a Probabilistic Arterial Cerebrovascular Atlas Derived from 700 Time-of-Flight MRA Datasets." In Studies in Health Technology and Informatics. IOS Press, 2012. https://doi.org/10.3233/978-1-61499-101-4-148.

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The cerebral vasculature is a complex vessel network with high variations among human subjects. Although the coarse structure and spatial relationships of the main cerebrovascular branches are well known, not much knowledge about inter-individual vessel variability of humans at a finer level is available. The aim of this work is to present a probabilistic atlas of cerebral arterial vascular structures derived from 700 Time-of-Flight (TOF) magnetic resonance angiography (MRA) datasets of healthy subjects. Therefore, the cerebrovascular system was automatically segmented in each TOF datasets. In a following step, each TOF dataset and corresponding segmentation was registered to the MNI brain atlas. The registered datasets were then used for generation of a probabilistic cerebrovascular atlas. The generated atlas was evaluated with respect to three possible applications. The results suggest that the atlas is especially helpful to obtain knowledge about the cerebrovascular anatomy and its variations in terms of vessel occurrence probability. Furthermore, it appears useful for initialization of automatic cerebrovascular segmentation methods while an application for detection of vessel pathologies seems only feasible for large malformations.
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Coelho Silva, Henrique, Rafael Costa Lima Maia, Paulo Roberto Leitao de Vasconcelos, and Orleancio Gomes Ripardo de Azevedo. "The Pathophysiological Aspects of Cerebral Diseases." In Cerebrovascular Diseases [Working Title]. IntechOpen, 2022. http://dx.doi.org/10.5772/intechopen.101218.

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Introduction. Cerebrovascular disorders are the main causes of heavy burden health worldwide, also, it is critical to understand the pathophysiological mechanism and then trying to prevent the neurological sequels. Objective. To discuss the inflammatory and oxidative stress aspects associated to the cerebrovascular diseases, focusing on biomarkers, also the role of omega oils, and the intracellular molecular network associated to the tissue burden on those conditions. Results. One of the most promising biomarkers it is Neuron-Specific Enolase (NSE). Serum NSE levels were elevated in stroke-patients compared to the non-stroke controls. Also, studies have demonstrated that in specific ratio omega oils 3, 6 and 9 can ameliorate the inflammatory and oxidative stress in nervous tissue and could be useful to the inflammatory and oxidative stress negative effects of cerebrovascular diseases. In addition, the study of the molecular mechanisms is essential to understand which molecules could be addressed in cascade of events preventing the permanent damage on the nervous tissue. Final considerations. The studies on cerebrovascular disorders must precisely identify the mechanisms and key molecules involved and improve the time of diagnostics and prognostics reducing the negative impacts of those conditions.
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Guozheng Qian, Youfa Li, Guiqing Wang, and Yifeng Cao. "Studies on Improved Model for Cerebrovascular Blood Circulation." In Studies in Health Technology and Informatics. IOS Press, 2001. https://doi.org/10.3233/978-1-60750-928-8-1339.

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For the elastic cavity model adopted in the existing cerebrovascular hemodynamics analyzers was too simple, we developed the improved model for cerebrovascular blood circulation to describe more exactly the physiologic nature of both sides cerebrovascular blood circulation and the relation between them (Willis Circle). It was a network consisting of 2 inputs, 9 arteries and 4 terminal flow resistances. For two cases, constantflow and pulsating flow, the further improved models for calculability were analyzed and the mathematical equations describing the model was obtained; some algorithms were given to solve the positive and negative problems to the mathematical functions; the physiologic means to parameters in the model were defined; at last, the possible applications of the models in basic research and clinic about cerebrovascular disease were stated as well. In the paper, we compared our portable cerebrovascular hemodynamics analyzer with the improved model to other analyzers with the elastic cavity model, and concluded that because of providing additional M-style ultrasonic detector for measuring the diameters of blood vessels and improving the Doppler ultrasonic detector for measuring the velocity of blood flow of the internal carotid artery we could accept the improved model to obtain more exact and more details and the relation about cerebrovascular blood circulation and hemodynamics.
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Marchi, Nicola, and Amy L. Brewster. "Pericytes and Microglia." In Jasper's Basic Mechanisms of the Epilepsies, edited by Annamaria Vezzani and Helen E. Scharfman, 589–610. 5th ed. Oxford University PressNew York, 2024. http://dx.doi.org/10.1093/med/9780197549469.003.0029.

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Abstract In seizure conditions, neuro-glio-vascular immune communications, blood–brain barrier (BBB) damage, and synaptic circuitry functional remodeling are dynamic processes that unfold over time and by brain region. These processes are associated with the generation and maintenance of epileptic networks. Accumulating evidence suggests that pericytes and microglial cells play critical roles in contributing to epileptogenic events, with a specific and renewed attention to pro- and anti-inflammatory equilibriums that strictly depend on the disease stage. Implications for pericytes include BBB dysfunction during and after seizures, while microglia is involved in cerebrovascular and neural network remodeling through altered surveillance, phagocytic, and inflammatory responses. These cellular modifications can span from the epileptogenic foci to seizure-propagating regions or networks. Understanding how pericyte and microglial cells interact with neuronal and cerebrovascular structures will facilitate the development of multitarget pharmacological strategies for epilepsy. Within this framework, the timing of pharmacological intervention could dictate therapeutic success, in the light of the varying and contrasting inflammatory and neurovascular trajectories that are cell-specific and unfold after a seizure or during epilepsy.
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Conference papers on the topic "Cerebrovascular network"

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Shan, Wenqi, Qiang Li, and Zhiwei Wang. "SPNet: Sparse-mask Prompt-learning Network for Cerebrovascular Segmentation." In ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 1–5. IEEE, 2025. https://doi.org/10.1109/icassp49660.2025.10889326.

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Sanchesa, Pedro, Cyril Meyer, Vincent Vigon, and Benoit Naegel. "Cerebrovascular Network Segmentation of MRA Images With Deep Learning." In 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI). IEEE, 2019. http://dx.doi.org/10.1109/isbi.2019.8759569.

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Yan, Songlin, Weijing Xu, Wentao Liu, Huihua Yang, Lemeng Wang, Yiming Deng, and Feng Gao. "TBENet:A two-branch boundary enhancement Network for cerebrovascular segmentation." In 2023 45th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC). IEEE, 2023. http://dx.doi.org/10.1109/embc40787.2023.10340540.

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Du, Chencheng, Ping'an Li, and Kehao Wang. "An automatic extraction method of cerebrovascular centerline for MRA." In 2016 5th International Conference on Computer Science and Network Technology (ICCSNT). IEEE, 2016. http://dx.doi.org/10.1109/iccsnt.2016.8070254.

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Wu, Qian, Yufei Chen, Ning Huang, and Xiaodong Yue. "Weakly-supervised Cerebrovascular Segmentation Network with Shape Prior and Model Indicator." In ICMR '22: International Conference on Multimedia Retrieval. New York, NY, USA: ACM, 2022. http://dx.doi.org/10.1145/3512527.3531377.

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Fiona Mary, M., M. Rajeswari, and M. Amalasweena. "Neural Network-based Prognostic Model for Cerebrovascular Accident using CT Scans." In 2023 International Conference on Sustainable Computing and Data Communication Systems (ICSCDS). IEEE, 2023. http://dx.doi.org/10.1109/icscds56580.2023.10104728.

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Malykhina, Galina, Vyacheslav Salnikov, Vladimir Semenyutin, and Dmitriy Tarkhov. "Digitalization of medical services for detecting violations of cerebrovascular regulation based on a neural network signal analysis algorithm." In SPBPU IDE '20: SPBPU IDE-2020. New York, NY, USA: ACM, 2020. http://dx.doi.org/10.1145/3444465.3444526.

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Pacheco Pachado, Mayra, Alexandra Petraina, Cristian Nogales, Theodora Saridaki, Harald H. H. W. Schmidt, and Ana I. Casas. "An organ-agnostic drug repurposing strategy for dementia: Pre-clinical validation of network pharmacology to treat cerebrovascular dysfunction and cognitive impairment." In RExPO22. ScienceOpen, 2022. http://dx.doi.org/10.14293/s2199-1006.1.sor-.ppplken3.v1.

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Tokuda, Shigefumi, Takeshi Unemura, and Marie Oshima. "Computational Study on the Effects of Peripheral Vessel Network on Blood Flow in the Arterial Circle of Willis." In ASME 2007 Summer Bioengineering Conference. American Society of Mechanical Engineers, 2007. http://dx.doi.org/10.1115/sbc2007-176706.

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Cerebrovascular disorder such as subarachnoid hemorrhage (SAH) is 3rd position of the cause of death in Japan [1]. Its initiation and growth are reported to depend on hemodynamic factors, particularly on wall shear stress or blood pressure induced by blood flow. In order to investigate the information on the hemodynamic quantities in the cerebral vascular system, the authors have been developing a computational tool using patient-specific modeling and numerical simulation [2]. In order to achieve an in vivo simulation of living organisms, it is important to apply appropriate physiological conditions such as physical properties, models, and boundary conditions. Generally, the numerical simulation using a patient-specific model is conducted for a localized region near the research target. Although the analysis region is only a part of the circulatory system, the simulation has to include the effects from the entire circulatory system. Many studies have carried out to derive the boundary conditions to model in vivo environment [3–5]. However, it is not easy to obtain the biological data of cerebral arteries due to head capsule.
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Faes, Luca, Gorana Mijatovic, Laura Sparacino, Riccardo Pernice, Yuri Antonacci, Alberto Porta, and Sebastiano Stramaglia. "Quantifying High-Order Interactions in Cardiovascular and Cerebrovascular Networks." In 2022 12th Conference of the European Study Group on Cardiovascular Oscillations (ESGCO). IEEE, 2022. http://dx.doi.org/10.1109/esgco55423.2022.9931385.

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