Pub Date : 2023-02-23DOI: 10.1101/2022.08.30.505842
Michael W. Reimann, Timothé Guyonnet-Hencke
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.
{"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}
Pub Date : 2023-02-22DOI: 10.1101/2023.02.19.23285768
Lucas Arbabyazd, S. Petkoski, M. Breakspear, A. Solodkin, Demian Battaglia, V. Jirsa
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.
通过BOLD功能磁共振成像(简称rsfMRI)追踪静息状态下的自发活动，产生了全脑区域间相关性的动态模式，其结构灵活性与认知表现有关。在此，我们分析了一组老年人的静息状态动态功能连接(dFC)，包括遗忘性轻度认知障碍(aMCI, N = 34)和阿尔茨海默病(AD, N = 13)患者，以及正常对照(NC, N = 16)和认知“超常”(SN, N = 10)受试者。利用基于状态和无状态的互补方法，我们发现不同功能链接的静息状态波动不是独立的，而是受到功能连接区域的三联体或四联体之间的高阶相关性的约束。在与健康受试者对比时，我们发现从SNC到NC、aMCI和AD四组，扣带区与其他边缘区之间的dFC越来越突发性和间歇性。此外，与对照组相比，患者在阿尔茨海默病病理早期受影响的区域较少参与高阶相互作用，而成对相互作用并没有显著减少。因此，我们的分析表明，dFC组织的时空复杂性在AD中提前退化，并提供了一个比时间平均FC连接更丰富的潜在神经生物学窗口。
{"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}
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.
{"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}
P. Roig, S. Alcaraz, K. Gilly, Cristina Bernad, C. Juiz
Data center organization and optimization presents the opportunity to try and design systems with specific characteristics. In this sense, the combination of artificial intelligence methodology and sustainability may lead to achieve optimal topologies with enhanced feature, whilst taking care of the environment by lowering carbon emissions. In this paper, a model for a field monitoring system has been proposed, where an edge data center topology in the form of a redundant ring has been designed for redundancy purposes to join together nodes spread apart. Additionally, a formal algebraic model of such a design has been exposed and verified.
{"title":"Formal Algebraic Model of an Edge Data Center with a Redundant Ring Topology","authors":"P. Roig, S. Alcaraz, K. Gilly, Cristina Bernad, C. Juiz","doi":"10.3390/network3010007","DOIUrl":"https://doi.org/10.3390/network3010007","url":null,"abstract":"Data center organization and optimization presents the opportunity to try and design systems with specific characteristics. In this sense, the combination of artificial intelligence methodology and sustainability may lead to achieve optimal topologies with enhanced feature, whilst taking care of the environment by lowering carbon emissions. In this paper, a model for a field monitoring system has been proposed, where an edge data center topology in the form of a redundant ring has been designed for redundancy purposes to join together nodes spread apart. Additionally, a formal algebraic model of such a design has been exposed and verified.","PeriodicalId":48520,"journal":{"name":"Network Neuroscience","volume":"4 1","pages":"142-157"},"PeriodicalIF":4.7,"publicationDate":"2023-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77159468","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}
Md Mamunur Rashid, Shahriar Usman Khan, Fariha Eusufzai, Md. Azharuddin Redwan, S. R. Sabuj, Mahmoud Elsharief
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.
{"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}
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.
{"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}
P. Roig, S. Alcaraz, K. Gilly, Cristina Bernad, C. Juiz
Data center organization and optimization are increasingly receiving attention due to the ever-growing deployments of edge and fog computing facilities. The main aim is to achieve a topology that processes the traffic flows as fast as possible and that does not only depend on AI-based computing resources, but also on the network interconnection among physical hosts. In this paper, graph theory is introduced, due to its features related to network connectivity and stability, which leads to more resilient and sustainable deployments, where cage graphs may have an advantage over the rest. In this context, the Petersen graph cage is studied as a convenient candidate for small data centers due to its small number of nodes and small network diameter, thus providing an interesting solution for edge and fog data centers.
{"title":"Edge Data Center Organization and Optimization by Using Cage Graphs","authors":"P. Roig, S. Alcaraz, K. Gilly, Cristina Bernad, C. Juiz","doi":"10.3390/network3010005","DOIUrl":"https://doi.org/10.3390/network3010005","url":null,"abstract":"Data center organization and optimization are increasingly receiving attention due to the ever-growing deployments of edge and fog computing facilities. The main aim is to achieve a topology that processes the traffic flows as fast as possible and that does not only depend on AI-based computing resources, but also on the network interconnection among physical hosts. In this paper, graph theory is introduced, due to its features related to network connectivity and stability, which leads to more resilient and sustainable deployments, where cage graphs may have an advantage over the rest. In this context, the Petersen graph cage is studied as a convenient candidate for small data centers due to its small number of nodes and small network diameter, thus providing an interesting solution for edge and fog data centers.","PeriodicalId":48520,"journal":{"name":"Network Neuroscience","volume":"279 1","pages":"93-114"},"PeriodicalIF":4.7,"publicationDate":"2023-01-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77493023","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}
High-quality academic publishing is built on rigorous peer review [...]
高质量的学术出版建立在严格的同行评审的基础上[…]
{"title":"Acknowledgment to the Reviewers of Network in 2022","authors":"","doi":"10.3390/network3010004","DOIUrl":"https://doi.org/10.3390/network3010004","url":null,"abstract":"High-quality academic publishing is built on rigorous peer review [...]","PeriodicalId":48520,"journal":{"name":"Network Neuroscience","volume":"10 1","pages":""},"PeriodicalIF":4.7,"publicationDate":"2023-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89645828","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}
Mehul Gajwani, Stuart J. Oldham, James C. Pang, Aurina Arnatkevičiūtė, Jeggan Tiego, Mark A. Bellgrove, Alex Fornito
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 > 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.
{"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 > 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}
M. Zolotukhin, Di Zhang, Timo Hämäläinen, Parsa Miraghaei
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.
{"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}
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