The Experts below are selected from a list of 218724 Experts worldwide ranked by ideXlab platform

Carlo Vittorio Cannistraci - One of the best experts on this subject based on the ideXlab platform.

  • modular gateway ness connectivity and structural core organization in maritime Network Science
    Nature Communications, 2020
    Co-Authors: Qian Pan, Alessandro Muscoloni, Haoxiang Xia, Carlo Vittorio Cannistraci
    Abstract:

    Around 80% of global trade by volume is transported by sea, and thus the maritime transportation system is fundamental to the world economy. To better exploit new international shipping routes, we need to understand the current ones and their complex systems association with international trade. We investigate the structure of the global liner shipping Network (GLSN), finding it is an economic small-world Network with a trade-off between high transportation efficiency and low wiring cost. To enhance understanding of this trade-off, we examine the modular segregation of the GLSN; we study provincial-, connector-hub ports and propose the definition of gateway-hub ports, using three respective structural measures. The gateway-hub structural-core organization seems a salient property of the GLSN, which proves importantly associated to Network integration and function in realizing the cargo transportation of international trade. This finding offers new insights into the GLSN’s structural organization complexity and its relevance to international trade. It is crucial to understand the evolving structure of global liner shipping system. Here the authors unveiled the architecture of a recent global liner shipping Network (GLSN) and show that the structure of global liner shipping system has evolved to be self-organized with a trade-off between high transportation efficiency and low wiring cost and ports’ gateway-ness is most highly associated with ports’ economic performance.

  • modular gateway ness connectivity and structural core organization in maritime Network Science
    arXiv: Physics and Society, 2020
    Co-Authors: Qian Pan, Alessandro Muscoloni, Haoxiang Xia, Carlo Vittorio Cannistraci
    Abstract:

    Around 80% of global trade by volume is transported by sea, and thus the maritime transportation system is fundamental to the world economy. To better exploit new international shipping routes, we need to understand the current ones and their complex systems association with international trade. We investigate the structure of the global liner shipping Network (GLSN), finding it is an economic small-world Network with a trade-off between high transportation efficiency and low wiring cost. To enhance understanding of this trade-off, we examine the modular segregation of the GLSN; we study provincial-, connector-hub ports and propose the definition of gateway-hub ports, using three respective structural measures. The gateway-hub structural-core organization seems a salient property of the GLSN, which proves importantly associated to Network integration and function in realizing the cargo transportation of international trade. This finding offers new insights into the GLSN's structural organization complexity and its relevance to international trade.

Paul J Laurienti - One of the best experts on this subject based on the ideXlab platform.

  • aging brain from a Network Science perspective something to be positive about
    PLOS ONE, 2013
    Co-Authors: Michelle W Voss, Paul J Laurienti, Jonathan H Burdette, Chelsea N Wong, Pauline L Baniqued, Kirk I Erickson, Ruchika Shaurya Prakash, Edward Mcauley, Arthur F Kramer
    Abstract:

    To better understand age differences in brain function and behavior, the current study applied Network Science to model functional interactions between brain regions. We observed a shift in Network topology whereby for older adults subcortical and cerebellar structures overlapping with the Salience Network had more connectivity to the rest of the brain, coupled with fragmentation of large-scale cortical Networks such as the Default and Fronto-Parietal Networks. Additionally, greater integration of the dorsal medial thalamus and red nucleus in the Salience Network was associated with greater satisfaction with life for older adults, which is consistent with theoretical predictions of age-related increases in emotion regulation that are thought to help maintain well-being and life satisfaction in late adulthood. In regard to cognitive abilities, greater ventral medial prefrontal cortex coherence with its topological neighbors in the Default Network was associated with faster processing speed. Results suggest that large-scale organizing properties of the brain differ with normal aging, and this perspective may offer novel insight into understanding age-related differences in cognitive function and well-being.

  • analyzing complex functional brain Networks fusing statistics and Network Science to understand the brain
    Statistics Surveys, 2013
    Co-Authors: Sean L Simpson, Dubois F Bowman, Paul J Laurienti
    Abstract:

    Complex functional brain Network analyses have exploded over the last decade, gaining traction due to their profound clinical implications. The application of Network Science (an interdisciplinary offshoot of graph theory) has facilitated these analyses and enabled examining the brain as an integrated system that produces complex behaviors. While the field of statistics has been integral in advancing activation analyses and some connectivity analyses in functional neuroimaging research, it has yet to play a commensurate role in complex Network analyses. Fusing novel statistical methods with Network-based functional neuroimage analysis will engender powerful analytical tools that will aid in our understanding of normal brain function as well as alterations due to various brain disorders. Here we survey widely used statistical and Network Science tools for analyzing fMRI Network data and discuss the challenges faced in filling some of the remaining methodological gaps. When applied and interpreted correctly, the fusion of Network scientific and statistical methods has a chance to revolutionize the understanding of brain function.

  • the brain as a complex system using Network Science as a tool for understanding the brain
    Brain connectivity, 2011
    Co-Authors: Qawi K Telesford, Sean L Simpson, Jonathan H Burdette, Satoru Hayasaka, Paul J Laurienti
    Abstract:

    Abstract Although graph theory has been around since the 18th century, the field of Network Science is more recent and continues to gain popularity, particularly in the field of neuroimaging. The field was propelled forward when Watts and Strogatz introduced their small-world Network model, which described a Network that provided regional specialization with efficient global information transfer. This model is appealing to the study of brain connectivity, as the brain can be viewed as a system with various interacting regions that produce complex behaviors. In practice, graph metrics such as clustering coefficient, path length, and efficiency measures are often used to characterize system properties. Centrality metrics such as degree, betweenness, closeness, and eigenvector centrality determine critical areas within the Network. Community structure is also essential for understanding Network organization and topology. Network Science has led to a paradigm shift in the neuroscientific community, but it sho...

  • using Network Science to evaluate exercise associated brain changes in older adults
    Frontiers in Aging Neuroscience, 2010
    Co-Authors: Jonathan H Burdette, Paul J Laurienti, Qawi K Telesford, Mark A Espeland, Ashley R Morgan, Crystal D Vechlekar, Satoru Hayaska, Janine J Jennings, Jeffrey A Katula, Robert A Kraft
    Abstract:

    Literature has shown that exercise is beneficial for cognitive function in older adults and that aerobic fitness is associated with increased hippocampal tissue and blood volumes. The current study used novel Network Science methods to shed light on the neurophysiological implications of exercise-induced changes in the hippocampus of older adults. Participants represented a volunteer subgroup of older adults that were part of either the exercise training (ET) or healthy aging educational control (HAC) treatment arms from the Seniors Health and Activity Research Program Pilot (SHARP-P) trial. Following the 4-month interventions, MRI measures of resting brain blood flow and connectivity were performed. The ET group's hippocampal cerebral blood flow (CBF) exhibited statistically significant increases compared to the HAC group. Novel whole-brain Network connectivity analyses showed greater connectivity in the hippocampi of the ET participants compared to HAC. Furthermore, the hippocampus was consistently shown to be within the same Network neighborhood (module) as the anterior cingulate cortex only within the ET group. Thus, within the ET group, the hippocampus and anterior cingulate were highly interconnected and localized to the same Network neighborhood. This project shows the power of Network Science to investigate potential mechanisms for exercise-induced benefits to the brain in older adults. We show a link between neurological Network features and CBF, and it is possible that this alteration of functional brain Networks may lead to the known improvement in cognitive function among older adults following exercise.

Cynthia S Q Siew - One of the best experts on this subject based on the ideXlab platform.

  • contributions of modern Network Science to the cognitive Sciences revisiting research spirals of representation and process
    Proceedings of The Royal Society A: Mathematical Physical and Engineering Sciences, 2020
    Co-Authors: Nichol Castro, Cynthia S Q Siew
    Abstract:

    Modelling the structure of cognitive systems is a central goal of the cognitive Sciencesa goal that has greatly benefitted from the application of Network Science approaches. This paper provides an...

  • cognitive Network Science a review of research on cognition through the lens of Network representations processes and dynamics
    Complexity, 2019
    Co-Authors: Cynthia S Q Siew, Dirk U Wulff, Nicole Beckage, Yoed N Kenett
    Abstract:

    Network Science provides a set of quantitative methods to investigate complex systems, including human cognition. Although cognitive theories in different domains are strongly based on a Network perspective, the application of Network Science methodologies to quantitatively study cognition has so far been limited in scope. This review demonstrates how Network Science approaches have been applied to the study of human cognition and how Network Science can uniquely address and provide novel insight on important questions related to the complexity of cognitive systems and the processes that occur within those systems. Drawing on the literature in cognitive Network Science, with a focus on semantic and lexical Networks, we argue three key points. (i) Network Science provides a powerful quantitative approach to represent cognitive systems. (ii) The Network Science approach enables cognitive scientists to achieve a deeper understanding of human cognition by capturing how the structure, i.e., the underlying Network, and processes operating on a Network structure interact to produce behavioral phenomena. (iii) Network Science provides a quantitative framework to model the dynamics of cognitive systems, operationalized as structural changes in cognitive systems on different timescales and resolutions. Finally, we highlight key milestones that the field of cognitive Network Science needs to achieve as it matures in order to provide continued insights into the nature of cognitive structures and processes.

  • the phonographic language Network using Network Science to investigate the phonological and orthographic similarity structure of language
    Journal of Experimental Psychology: General, 2019
    Co-Authors: Cynthia S Q Siew, Michael S Vitevitch
    Abstract:

    Orthographic effects in spoken word recognition and phonological effects in visual word recognition have been observed in a variety of experimental tasks, strongly suggesting that a close interrelationship exists between phonology and orthography. However, the metrics used to investigate these effects, such as consistency and neighborhood size, fail to generalize to words of various lengths or syllable structures, and do not take into account the more global similarity structure that exists between phonological and orthographic representations in the language. To address these limitations, the tools of Network Science were used to simultaneously characterize the phonological as well as orthographic similarity structure of words in English. In the phonographic Network of language, links are placed between words that are both phonologically and orthographically similar to each other (e.g., words such as pant (/paent/) and punt (/pʌnt/)). Conventional psycholinguistic experiments (auditory naming and auditory lexical decision) and an archival analysis of the English Lexicon Project (visual naming and visual lexical decision) were conducted to investigate the influence of 2 Network Science metrics derived from the phonographic Network-phonographic degree and phonographic clustering coefficient-on spoken and visual word recognition. Results indicated a facilitatory effect of phonographic degree on visual word recognition, and a facilitatory effect of phonographic clustering coefficient on spoken word recognition. Implications of the present findings for theoretical models of spoken and visual word recognition are discussed. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

  • the orthographic similarity structure of english words insights from Network Science
    Applied Network Science, 2018
    Co-Authors: Cynthia S Q Siew
    Abstract:

    Network Science has been applied to study the structure of the mental lexicon, the part of long-term memory where all the words a person knows are stored. Here the tools of Network Science are used to study the organization of orthographic word-forms in the mental lexicon and how that might influence visual word recognition. An orthographic similarity Network of the English language was constructed such that each node represented an English word, and undirected, unweighted edges were placed between words that differed by an edit distance of 1, a commonly used operationalization of orthographic similarity in psycholinguistics. The largest connected component of the orthographic language Network had a small-world structure and a long-tailed degree distribution. Additional analyses were conducted using behavioral data obtained from a psycholinguistic database to determine if Network Science measures obtained from the orthographic language Network could be used to predict how quickly and accurately people process written words. The present findings show that the structure of the mental lexicon influences lexical access in visual word recognition.

  • spoken word recognition and serial recall of words from components in the phonological Network
    Journal of Experimental Psychology: Learning Memory and Cognition, 2016
    Co-Authors: Cynthia S Q Siew, Michael S Vitevitch
    Abstract:

    Network Science uses mathematical techniques to study complex systems such as the phonological lexicon (Vitevitch, 2008). The phonological Network consists of a giant component (the largest connected component of the Network) and lexical islands (smaller groups of words that are connected to each other, but not to the giant component). To determine if the component that a word resided in influenced lexical processing, language-related tasks (naming, lexical decision, and serial recall) were used to compare the processing of words from the giant component and from lexical islands. Results showed that words from lexical islands were recognized more quickly and recalled more accurately than words from the giant component. These findings can be accounted for via the diffusion of activation across a Network. Implications for models of spoken word recognition and Network Science are also discussed.

Danielle S Bassett - One of the best experts on this subject based on the ideXlab platform.

  • emerging frontiers of neuroengineering a Network Science of brain connectivity
    Annual Review of Biomedical Engineering, 2017
    Co-Authors: Danielle S Bassett, Ankit N Khambhati, Scott T Grafton
    Abstract:

    Neuroengineering is faced with unique challenges in repairing or replacing complex neural systems that are composed of many interacting parts. These interactions form intricate patterns over large spatiotemporal scales and produce emergent behaviors that are difficult to predict from individual elements. Network Science provides a particularly appropriate framework in which to study and intervene in such systems by treating neural elements (cells, volumes) as nodes in a graph and neural interactions (synapses, white matter tracts) as edges in that graph. Here, we review the emerging discipline of Network neuroScience, which uses and develops tools from graph theory to better understand and manipulate neural systems from micro- to macroscales. We present examples of how human brain imaging data are being modeled with Network analysis and underscore potential pitfalls. We then highlight current computational and theoretical frontiers and emphasize their utility in informing diagnosis and monitoring, brain-machine interfaces, and brain stimulation. A flexible and rapidly evolving enterprise, Network neuroScience provides a set of powerful approaches and fundamental insights that are critical for the neuroengineer's tool kit.

  • emerging frontiers of neuroengineering a Network Science of brain connectivity
    arXiv: Neurons and Cognition, 2016
    Co-Authors: Danielle S Bassett, Ankit N Khambhati, Scott T Grafton
    Abstract:

    Neuroengineering is faced with unique challenges in repairing or replacing complex neural systems that are composed of many interacting parts. These interactions form intricate patterns over large spatiotemporal scales, and produce emergent behaviors that are difficult to predict from individual elements. Network Science provides a particularly appropriate framework in which to study and intervene in such systems, by treating neural elements (cells, volumes) as nodes in a graph and neural interactions (synapses, white matter tracts) as edges in that graph. Here, we review the emerging discipline of Network neuroScience, which uses and develops tools from graph theory to better understand and manipulate neural systems, from micro- to macroscales. We present examples of how human brain imaging data is being modeled with Network analysis and underscore potential pitfalls. We then highlight current computational and theoretical frontiers, and emphasize their utility in informing diagnosis and monitoring, brain-machine interfaces, and brain stimulation. A flexible and rapidly evolving enterprise, Network neuroScience provides a set of powerful approaches and fundamental insights critical to the neuroengineer's toolkit.

  • review cognitive Network neuroScience
    Journal of Cognitive Neuroscience, 2015
    Co-Authors: John D Medaglia, Maryellen Lynall, Danielle S Bassett
    Abstract:

    Network Science provides theoretical, computational, and empirical tools that can be used to understand the structure and function of the human brain in novel ways using simple concepts and mathematical representations. Network neuroScience is a rapidly growing field that is providing considerable insight into human structural connectivity, functional connectivity while at rest, changes in functional Networks over time dynamics, and how these properties differ in clinical populations. In addition, a number of studies have begun to quantify Network characteristics in a variety of cognitive processes and provide a context for understanding cognition from a Network perspective. In this review, we outline the contributions of Network Science to cognitive neuroScience. We describe the methodology of Network Science as applied to the particular case of neuroimaging data and review its uses in investigating a range of cognitive functions including sensory processing, language, emotion, attention, cognitive control, learning, and memory. In conclusion, we discuss current frontiers and the specific challenges that must be overcome to integrate these complementary disciplines of Network Science and cognitive neuroScience. Increased communication between cognitive neuroscientists and Network scientists could lead to significant discoveries under an emerging scientific intersection known as cognitive Network neuroScience.

Olaf Sporns - One of the best experts on this subject based on the ideXlab platform.