Network Neuroscience最新文献_第10页

A parcellation scheme of mouse isocortex based on reversals in connectivity gradients 基于连接梯度反转的小鼠同皮层分割方案

IF 4.7 3区医学

Network Neuroscience Pub Date : 2023-02-23 DOI: 10.1101/2022.08.30.505842

Michael W. Reimann, Timothé Guyonnet-Hencke

{"title":"A parcellation scheme of mouse isocortex based on reversals in connectivity gradients","authors":"Michael W. Reimann, Timothé Guyonnet-Hencke","doi":"10.1101/2022.08.30.505842","DOIUrl":"https://doi.org/10.1101/2022.08.30.505842","url":null,"abstract":"The brain is composed of several anatomically clearly separated structures. This parcellation is often extended into the isocortex, based on anatomical, physiological or functional differences. Here, we derive a parcellation scheme based purely on the spatial structure of long-range synaptic connections within the cortex. To that end, we analyzed a publicly available dataset of average mouse brain connectivity, and split the isocortex into disjunct regions. Instead of clustering connectivity based on modularity, our scheme is inspired by methods that split sensory cortices into subregions where gradients of neuronal response properties, such as the location of the receptive field, reverse. We calculated comparable gradients from voxelized brain connectivity data and automatically detected reversals in them. This approach better respects the known presence of functional gradients within brain regions than clustering-based approaches. Placing borders at the reversals resulted in a parcellation into 41 subregions that differs significantly from an established scheme in nonrandom ways, but is comparable in terms of the modularity of connectivity between regions. It reveals unexpected trends of connectivity, such as a tripartite split of somatomotor regions along an anterior to posterior gradient. The method can be readily adapted to other organisms and data sources, such as human functional connectivity.","PeriodicalId":48520,"journal":{"name":"Network Neuroscience","volume":"7 1","pages":"999 - 1021"},"PeriodicalIF":4.7,"publicationDate":"2023-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44430231","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

State switching and high-order spatiotemporal organization of dynamic Functional Connectivity are disrupted by Alzheimer's Disease 阿尔茨海默病破坏了动态功能连接的状态转换和高阶时空组织

IF 4.7 3区医学

Network Neuroscience Pub Date : 2023-02-22 DOI: 10.1101/2023.02.19.23285768

Lucas Arbabyazd, S. Petkoski, M. Breakspear, A. Solodkin, Demian Battaglia, V. Jirsa

{"title":"State switching and high-order spatiotemporal organization of dynamic Functional Connectivity are disrupted by Alzheimer's Disease","authors":"Lucas Arbabyazd, S. Petkoski, M. Breakspear, A. Solodkin, Demian Battaglia, V. Jirsa","doi":"10.1101/2023.02.19.23285768","DOIUrl":"https://doi.org/10.1101/2023.02.19.23285768","url":null,"abstract":"Spontaneous activity during the resting state, tracked by BOLD fMRI imaging, or shortly rsfMRI, gives rise to brain-wide dynamic patterns of inter-regional correlations, whose structured flexibility relates to cognitive performance. Here we analyze resting state dynamic Functional Connectivity (dFC) in a cohort of older adults, including amnesic Mild Cognitive Impairment (aMCI, N = 34) and Alzheimer's Disease (AD, N = 13) patients, as well as normal control (NC, N = 16) and cognitively \"super-normal\" (SN, N = 10) subjects. Using complementary state-based and state-free approaches, we find that resting state fluctuations of different functional links are not independent but are constrained by high-order correlations between triplets or quadruplets of functionally connected regions. When contrasting patients with healthy subjects, we find that dFC between cingulate and other limbic regions is increasingly bursty and intermittent when ranking the four groups from SNC to NC, aMCI and AD. Furthermore, regions affected at early stages of AD pathology are less involved in higher-order interactions in patient than in control groups, while pairwise interactions are not significantly reduced. Our analyses thus suggest that the spatiotemporal complexity of dFC organization is precociously degraded in AD and provides a richer window into the underlying neurobiology than time-averaged FC connections.","PeriodicalId":48520,"journal":{"name":"Network Neuroscience","volume":" ","pages":""},"PeriodicalIF":4.7,"publicationDate":"2023-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44049220","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Improving Bundle Routing in a Space DTN by Approximating the Transmission Time of the Reliable LTP 通过逼近可靠LTP的传输时间改进空间DTN中的分组路由

IF 4.7 3区医学

Network Neuroscience Pub Date : 2023-02-03 DOI: 10.3390/network3010009

R. Lent

{"title":"Improving Bundle Routing in a Space DTN by Approximating the Transmission Time of the Reliable LTP","authors":"R. Lent","doi":"10.3390/network3010009","DOIUrl":"https://doi.org/10.3390/network3010009","url":null,"abstract":"Because the operation of space networks is carefully planned, it is possible to predict future contact opportunities from link budget analysis using the anticipated positions of the nodes over time. In the standard approach to space delay-tolerant networking (DTN), such knowledge is used by contact graph routing (CGR) to decide the paths for data bundles. However, the computation assumes nearly ideal channel conditions, disregarding the impact of the convergence layer retransmissions (e.g., as implemented by the Licklider transmission protocol (LTP)). In this paper, the effect of the bundle forwarding time estimation (i.e., the link service time) to routing optimality is analyzed, and an accurate expression for lossy channels is discussed. The analysis is performed first from a general and protocol-agnostic perspective, assuming knowledge of the statistical properties and general features of the contact opportunities. Then, a practical case is studied using the standard space DTN protocol, evaluating the performance improvement of CGR under the proposed forwarding time estimation. The results of this study provide insight into the optimal routing problem for a space DTN and a suggested improvement to the current routing standard.","PeriodicalId":48520,"journal":{"name":"Network Neuroscience","volume":"1 1","pages":"180-198"},"PeriodicalIF":4.7,"publicationDate":"2023-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89711553","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Formal Algebraic Model of an Edge Data Center with a Redundant Ring Topology 冗余环拓扑边缘数据中心的形式化代数模型

IF 4.7 3区医学

Network Neuroscience Pub Date : 2023-01-30 DOI: 10.3390/network3010007

P. Roig, S. Alcaraz, K. Gilly, Cristina Bernad, C. Juiz

引用次数: 2

A Federated Learning-Based Approach for Improving Intrusion Detection in Industrial Internet of Things Networks 基于联邦学习的工业物联网入侵检测改进方法

IF 4.7 3区医学

Network Neuroscience Pub Date : 2023-01-30 DOI: 10.3390/network3010008

Md Mamunur Rashid, Shahriar Usman Khan, Fariha Eusufzai, Md. Azharuddin Redwan, S. R. Sabuj, Mahmoud Elsharief

{"title":"A Federated Learning-Based Approach for Improving Intrusion Detection in Industrial Internet of Things Networks","authors":"Md Mamunur Rashid, Shahriar Usman Khan, Fariha Eusufzai, Md. Azharuddin Redwan, S. R. Sabuj, Mahmoud Elsharief","doi":"10.3390/network3010008","DOIUrl":"https://doi.org/10.3390/network3010008","url":null,"abstract":"The Internet of Things (IoT) is a network of electrical devices that are connected to the Internet wirelessly. This group of devices generates a large amount of data with information about users, which makes the whole system sensitive and prone to malicious attacks eventually. The rapidly growing IoT-connected devices under a centralized ML system could threaten data privacy. The popular centralized machine learning (ML)-assisted approaches are difficult to apply due to their requirement of enormous amounts of data in a central entity. Owing to the growing distribution of data over numerous networks of connected devices, decentralized ML solutions are needed. In this paper, we propose a Federated Learning (FL) method for detecting unwanted intrusions to guarantee the protection of IoT networks. This method ensures privacy and security by federated training of local IoT device data. Local IoT clients share only parameter updates with a central global server, which aggregates them and distributes an improved detection algorithm. After each round of FL training, each of the IoT clients receives an updated model from the global server and trains their local dataset, where IoT devices can keep their own privacy intact while optimizing the overall model. To evaluate the efficiency of the proposed method, we conducted exhaustive experiments on a new dataset named Edge-IIoTset. The performance evaluation demonstrates the reliability and effectiveness of the proposed intrusion detection model by achieving an accuracy (92.49%) close to that offered by the conventional centralized ML models’ accuracy (93.92%) using the FL method.","PeriodicalId":48520,"journal":{"name":"Network Neuroscience","volume":"3 1","pages":"158-179"},"PeriodicalIF":4.7,"publicationDate":"2023-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84611867","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 7

IoT and Blockchain Integration: Applications, Opportunities, and Challenges 物联网和区块链整合:应用、机遇和挑战

IF 4.7 3区医学

Network Neuroscience Pub Date : 2023-01-24 DOI: 10.3390/network3010006

Naresh Adhikari, M. Ramkumar

{"title":"IoT and Blockchain Integration: Applications, Opportunities, and Challenges","authors":"Naresh Adhikari, M. Ramkumar","doi":"10.3390/network3010006","DOIUrl":"https://doi.org/10.3390/network3010006","url":null,"abstract":"During the recent decade, two variants of evolving computing networks have augmented the Internet: (i) The Internet of Things (IoT) and (ii) Blockchain Network(s) (BCNs). The IoT is a network of heterogeneous digital devices embedded with sensors and software for various automation and monitoring purposes. A Blockchain Network is a broadcast network of computing nodes provisioned for validating digital transactions and recording the “well-formed” transactions in a unique data storage called a blockchain ledger. The power of a blockchain network is that (ideally) every node maintains its own copy of the ledger and takes part in validating the transactions. Integrating IoT and BCNs brings promising applications in many areas, including education, health, finance, agriculture, industry, and the environment. However, the complex, dynamic and heterogeneous computing and communication needs of IoT technologies, optionally integrated by blockchain technologies (if mandated), draw several challenges on scaling, interoperability, and security goals. In recent years, numerous models integrating IoT with blockchain networks have been proposed, tested, and deployed for businesses. Numerous studies are underway to uncover the applications of IoT and Blockchain technology. However, a close look reveals that very few applications successfully cater to the security needs of an enterprise. Needless to say, it makes less sense to integrate blockchain technology with an existing IoT that can serve the security need of an enterprise. In this article, we investigate several frameworks for IoT operations, the applicability of integrating them with blockchain technology, and due security considerations that the security personnel must make during the deployment and operations of IoT and BCN. Furthermore, we discuss the underlying security concerns and recommendations for blockchain-integrated IoT networks.","PeriodicalId":48520,"journal":{"name":"Network Neuroscience","volume":"8 1","pages":"115-141"},"PeriodicalIF":4.7,"publicationDate":"2023-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89786814","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 5

Edge Data Center Organization and Optimization by Using Cage Graphs 基于笼图的边缘数据中心组织与优化

IF 4.7 3区医学

Network Neuroscience Pub Date : 2023-01-18 DOI: 10.3390/network3010005

P. Roig, S. Alcaraz, K. Gilly, Cristina Bernad, C. Juiz

引用次数: 1

Acknowledgment to the Reviewers of Network in 2022 感谢2022年网络审稿人

IF 4.7 3区医学

Network Neuroscience Pub Date : 2023-01-17 DOI: 10.3390/network3010004

引用次数: 0

Can hubs of the human connectome be identified consistently with diffusion MRI? 人类连接组的枢纽能否与扩散MRI一致地识别?

3区医学

Network Neuroscience Pub Date : 2023-01-01 DOI: 10.1162/netn_a_00324

Mehul Gajwani, Stuart J. Oldham, James C. Pang, Aurina Arnatkevičiūtė, Jeggan Tiego, Mark A. Bellgrove, Alex Fornito

{"title":"Can hubs of the human connectome be identified consistently with diffusion MRI?","authors":"Mehul Gajwani, Stuart J. Oldham, James C. Pang, Aurina Arnatkevičiūtė, Jeggan Tiego, Mark A. Bellgrove, Alex Fornito","doi":"10.1162/netn_a_00324","DOIUrl":"https://doi.org/10.1162/netn_a_00324","url":null,"abstract":"Abstract Recent years have seen a surge in the use of diffusion MRI to map connectomes in humans, paralleled by a similar increase in processing and analysis choices. Yet these different steps and their effects are rarely compared systematically. Here, in a healthy young adult population (n = 294), we characterized the impact of a range of analysis pipelines on one widely studied property of the human connectome: its degree distribution. We evaluated the effects of 40 pipelines (comparing common choices of parcellation, streamline seeding, tractography algorithm, and streamline propagation constraint) and 44 group-representative connectome reconstruction schemes on highly connected hub regions. We found that hub location is highly variable between pipelines. The choice of parcellation has a major influence on hub architecture, and hub connectivity is highly correlated with regional surface area in most of the assessed pipelines (ρ &gt; 0.70 in 69% of the pipelines), particularly when using weighted networks. Overall, our results demonstrate the need for prudent decision-making when processing diffusion MRI data, and for carefully considering how different processing choices can influence connectome organization.","PeriodicalId":48520,"journal":{"name":"Network Neuroscience","volume":"215 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135686179","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

On Attacking Future 5G Networks with Adversarial Examples: Survey 用对抗性例子攻击未来5G网络:调查

IF 4.7 3区医学

Network Neuroscience Pub Date : 2022-12-30 DOI: 10.3390/network3010003

M. Zolotukhin, Di Zhang, Timo Hämäläinen, Parsa Miraghaei

{"title":"On Attacking Future 5G Networks with Adversarial Examples: Survey","authors":"M. Zolotukhin, Di Zhang, Timo Hämäläinen, Parsa Miraghaei","doi":"10.3390/network3010003","DOIUrl":"https://doi.org/10.3390/network3010003","url":null,"abstract":"The introduction of 5G technology along with the exponential growth in connected devices is expected to cause a challenge for the efficient and reliable network resource allocation. Network providers are now required to dynamically create and deploy multiple services which function under various requirements in different vertical sectors while operating on top of the same physical infrastructure. The recent progress in artificial intelligence and machine learning is theorized to be a potential answer to the arising resource allocation challenges. It is therefore expected that future generation mobile networks will heavily depend on its artificial intelligence components which may result in those components becoming a high-value attack target. In particular, a smart adversary may exploit vulnerabilities of the state-of-the-art machine learning models deployed in a 5G system to initiate an attack. This study focuses on the analysis of adversarial example generation attacks against machine learning based frameworks that may be present in the next generation networks. First, various AI/ML algorithms and the data used for their training and evaluation in mobile networks is discussed. Next, multiple AI/ML applications found in recent scientific papers devoted to 5G are overviewed. After that, existing adversarial example generation based attack algorithms are reviewed and frameworks which employ these algorithms for fuzzing stat-of-art AI/ML models are summarised. Finally, adversarial example generation attacks against several of the AI/ML frameworks described are presented.","PeriodicalId":48520,"journal":{"name":"Network Neuroscience","volume":"8 1","pages":"39-90"},"PeriodicalIF":4.7,"publicationDate":"2022-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88754032","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1