The Experts below are selected from a list of 12720 Experts worldwide ranked by ideXlab platform
Fahui Wang - One of the best experts on this subject based on the ideXlab platform.
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evolution of air transport network of china 1930 2012
Journal of Transport Geography, 2014Co-Authors: Jiaoe Wang, Huihui Mo, Fahui WangAbstract:This paper analyzes the evolution process of the air transport network of China (ATNC) since 1930. Based on the network analysis results, the ATNC has significantly improved in connectivity based on (1) rising alpha, beta and gamma indices, (2) declining diameter and centre index and (3) decreasing Average Path Length and increasing clustering coefficient. The network centralization index reveals a fluctuation phase before 1952, a pre-1980 centralization phase before the economic reform era, a centralization phase after the mid-1990s deregulation, and a decentralization phase between. The k-core decomposition method helps identify the evolution of core network and hierarchy of the ATNC over time. The spatial development model characterizes its structure change in six stages: (1) scattered development, (2) trunk line connection, (3) circular linkage, (4) hub formation, (5) a complex network structure, and (6) emerging multi-airport systems.
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exploring the network structure and nodal centrality of china s air transport network a complex network approach
Journal of Transport Geography, 2011Co-Authors: Jiaoe Wang, Huihui Mo, Fahui WangAbstract:This paper uses a complex network approach to examine the network structure and nodal centrality of individual cities in the air transport network of China (ATNC). Measures for overall network structure include degree distribution, Average Path Length and clustering coefficient. Centrality metrics for individual cities are degree, closeness and betweenness, representing a node’s location advantage as being directly connected to others, being accessible to others, and being the intermediary between others, respectively. Results indicate that the ATNC has a cumulative degree distribution captured by an exponential function, and displays some small-world (SW) network properties with an Average Path Length of 2.23 and a clustering coefficient of 0.69. All three centrality indices are highly correlated with socio-economic indicators of cities such as air passenger volume, population, and gross regional domestic product (GRDP). This confirms that centrality captures a crucial aspect of location advantage in the ATNC and has important implications in shaping the spatial pattern of economic activities. Most small and low-degree airports are directly connected to the largest cities with the best centrality and bypass their regional centers, and therefore sub-networks in the ATNC are less developed except for Kunming in the southwest and Urumchi in the northwest because of their strategic locations for geographic and political reasons. The ANTC is relatively young, and not as efficient and well-developed as that of the US.
Eco J C De Geus - One of the best experts on this subject based on the ideXlab platform.
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heritability of small world networks in the brain a graph theoretical analysis of resting state eeg functional connectivity
Human Brain Mapping, 2008Co-Authors: Dirk J A Smit, Cornelis J Stam, Danielle Posthuma, Dorret I Boomsma, Eco J C De GeusAbstract:Recent studies have shown that resting-state functional networks as studied with fMRI, EEG, and MEG may be so-called small-world networks. We investigated to what extent the characteristic features of small-world networks are genetically determined. To represent functional connectivity between brain areas, we measured resting EEG in 574 twins and their siblings and calculated the synchronization likelihood between each pair of electrodes. We applied a threshold to obtain a binary graph from which we calculated the clustering coefficient C (describing local interconnectedness) and Average Path Length L (describing global interconnectedness) for each individual. Modeling of MZ and DZ twin and sibling resemblance indicated that across various frequency bands 46-89% of the individual differences in C and 37-62% of the individual differences in L are heritable. It is asserted that C, L, and a small-world organization are viable markers of genetic differences in brain organization.
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heritability of small world networks in the brain a graph theoretical analysis of resting state eeg functional connectivity
Human Brain Mapping, 2008Co-Authors: Dirk J A Smit, Cornelis J Stam, Danielle Posthuma, Dorret I Boomsma, Eco J C De GeusAbstract:Recent studies have shown that resting-state functional networks as studied with fMRI, EEG, and MEG may be so-called small-world networks. We investigated to what extent the characteristic fea- tures of small-world networks are genetically determined. To represent functional connectivity between brain areas, we measured resting EEG in 574 twins and their siblings and calculated the synchroniza- tion likelihood between each pair of electrodes. We applied a threshold to obtain a binary graph from which we calculated the clustering coefficient C (describing local interconnectedness) and Average Path Length L (describing global interconnectedness) for each individual. Modeling of MZ and DZ twin and sibling resemblance indicated that across various frequency bands 46-89% of the individual differences in C and 37-62% of the individual differences in L are heritable. It is asserted that C, L, and a small- world organization are viable markers of genetic differences in brain organization. Hum Brain Mapp 29:1368-1378, 2008. V C 2007 Wiley-Liss, Inc.
Dirk J A Smit - One of the best experts on this subject based on the ideXlab platform.
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heritability of small world networks in the brain a graph theoretical analysis of resting state eeg functional connectivity
Human Brain Mapping, 2008Co-Authors: Dirk J A Smit, Cornelis J Stam, Danielle Posthuma, Dorret I Boomsma, Eco J C De GeusAbstract:Recent studies have shown that resting-state functional networks as studied with fMRI, EEG, and MEG may be so-called small-world networks. We investigated to what extent the characteristic features of small-world networks are genetically determined. To represent functional connectivity between brain areas, we measured resting EEG in 574 twins and their siblings and calculated the synchronization likelihood between each pair of electrodes. We applied a threshold to obtain a binary graph from which we calculated the clustering coefficient C (describing local interconnectedness) and Average Path Length L (describing global interconnectedness) for each individual. Modeling of MZ and DZ twin and sibling resemblance indicated that across various frequency bands 46-89% of the individual differences in C and 37-62% of the individual differences in L are heritable. It is asserted that C, L, and a small-world organization are viable markers of genetic differences in brain organization.
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heritability of small world networks in the brain a graph theoretical analysis of resting state eeg functional connectivity
Human Brain Mapping, 2008Co-Authors: Dirk J A Smit, Cornelis J Stam, Danielle Posthuma, Dorret I Boomsma, Eco J C De GeusAbstract:Recent studies have shown that resting-state functional networks as studied with fMRI, EEG, and MEG may be so-called small-world networks. We investigated to what extent the characteristic fea- tures of small-world networks are genetically determined. To represent functional connectivity between brain areas, we measured resting EEG in 574 twins and their siblings and calculated the synchroniza- tion likelihood between each pair of electrodes. We applied a threshold to obtain a binary graph from which we calculated the clustering coefficient C (describing local interconnectedness) and Average Path Length L (describing global interconnectedness) for each individual. Modeling of MZ and DZ twin and sibling resemblance indicated that across various frequency bands 46-89% of the individual differences in C and 37-62% of the individual differences in L are heritable. It is asserted that C, L, and a small- world organization are viable markers of genetic differences in brain organization. Hum Brain Mapp 29:1368-1378, 2008. V C 2007 Wiley-Liss, Inc.
Cornelis J Stam - One of the best experts on this subject based on the ideXlab platform.
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brain network organization in focal epilepsy a systematic review and meta analysis
PLOS ONE, 2014Co-Authors: Eric Van Diessen, Willemiek Zweiphenning, Floor E Jansen, Cornelis J Stam, Kees P J Braun, Willem M OtteAbstract:Normal brain functioning is presumed to depend upon interacting regions within large-scale neuronal networks. Increasing evidence exists that interictal network alterations in focal epilepsy are associated with cognitive and behavioral deficits. Nevertheless, the reported network alterations are inconclusive and prone to low statistical power due to small sample sizes as well as modest effect sizes. We therefore systematically reviewed the existing literature and conducted a meta-analysis to characterize the changes in whole-brain interictal focal epilepsy networks at sufficient power levels. We focused on the two most commonly used metrics in whole-brain networks: Average Path Length and Average clustering coefficient. Twelve studies were included that reported whole-brain network Average Path Length and Average clustering coefficient characteristics in patients and controls. The overall group difference, quantified as the standardized mean Average Path Length difference between epilepsy and control groups, corresponded to a significantly increased Average Path Length of 0.29 (95% confidence interval (CI): 0.12 to 0.45, p = 0.0007) in the epilepsy group. This suggests a less integrated interictal whole-brain network. Similarly, a significantly increased standardized mean Average clustering coefficient of 0.35 (CI: 0.05 to 0.65, p = 0.02) was found in the epilepsy group in comparison with controls, pointing towards a more segregated interictal network. Sub-analyses revealed similar results for functional and structural networks in terms of effect size and directionality for both metrics. In addition, we found individual network studies to be prone to low power due to the relatively small group differences in Average Path Length and Average clustering coefficient in combination with small sample sizes. The pooled network characteristics support the hypothesis that focal epilepsy has widespread detrimental effects, that is, reduced integration and increased segregation, on whole brain interictal network organization, which may relate to the co-morbid cognitive and behavioral impairments often reported in patients with focal epilepsy.
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heritability of small world networks in the brain a graph theoretical analysis of resting state eeg functional connectivity
Human Brain Mapping, 2008Co-Authors: Dirk J A Smit, Cornelis J Stam, Danielle Posthuma, Dorret I Boomsma, Eco J C De GeusAbstract:Recent studies have shown that resting-state functional networks as studied with fMRI, EEG, and MEG may be so-called small-world networks. We investigated to what extent the characteristic features of small-world networks are genetically determined. To represent functional connectivity between brain areas, we measured resting EEG in 574 twins and their siblings and calculated the synchronization likelihood between each pair of electrodes. We applied a threshold to obtain a binary graph from which we calculated the clustering coefficient C (describing local interconnectedness) and Average Path Length L (describing global interconnectedness) for each individual. Modeling of MZ and DZ twin and sibling resemblance indicated that across various frequency bands 46-89% of the individual differences in C and 37-62% of the individual differences in L are heritable. It is asserted that C, L, and a small-world organization are viable markers of genetic differences in brain organization.
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heritability of small world networks in the brain a graph theoretical analysis of resting state eeg functional connectivity
Human Brain Mapping, 2008Co-Authors: Dirk J A Smit, Cornelis J Stam, Danielle Posthuma, Dorret I Boomsma, Eco J C De GeusAbstract:Recent studies have shown that resting-state functional networks as studied with fMRI, EEG, and MEG may be so-called small-world networks. We investigated to what extent the characteristic fea- tures of small-world networks are genetically determined. To represent functional connectivity between brain areas, we measured resting EEG in 574 twins and their siblings and calculated the synchroniza- tion likelihood between each pair of electrodes. We applied a threshold to obtain a binary graph from which we calculated the clustering coefficient C (describing local interconnectedness) and Average Path Length L (describing global interconnectedness) for each individual. Modeling of MZ and DZ twin and sibling resemblance indicated that across various frequency bands 46-89% of the individual differences in C and 37-62% of the individual differences in L are heritable. It is asserted that C, L, and a small- world organization are viable markers of genetic differences in brain organization. Hum Brain Mapp 29:1368-1378, 2008. V C 2007 Wiley-Liss, Inc.
Jiaoe Wang - One of the best experts on this subject based on the ideXlab platform.
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evolution of air transport network of china 1930 2012
Journal of Transport Geography, 2014Co-Authors: Jiaoe Wang, Huihui Mo, Fahui WangAbstract:This paper analyzes the evolution process of the air transport network of China (ATNC) since 1930. Based on the network analysis results, the ATNC has significantly improved in connectivity based on (1) rising alpha, beta and gamma indices, (2) declining diameter and centre index and (3) decreasing Average Path Length and increasing clustering coefficient. The network centralization index reveals a fluctuation phase before 1952, a pre-1980 centralization phase before the economic reform era, a centralization phase after the mid-1990s deregulation, and a decentralization phase between. The k-core decomposition method helps identify the evolution of core network and hierarchy of the ATNC over time. The spatial development model characterizes its structure change in six stages: (1) scattered development, (2) trunk line connection, (3) circular linkage, (4) hub formation, (5) a complex network structure, and (6) emerging multi-airport systems.
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exploring the network structure and nodal centrality of china s air transport network a complex network approach
Journal of Transport Geography, 2011Co-Authors: Jiaoe Wang, Huihui Mo, Fahui WangAbstract:This paper uses a complex network approach to examine the network structure and nodal centrality of individual cities in the air transport network of China (ATNC). Measures for overall network structure include degree distribution, Average Path Length and clustering coefficient. Centrality metrics for individual cities are degree, closeness and betweenness, representing a node’s location advantage as being directly connected to others, being accessible to others, and being the intermediary between others, respectively. Results indicate that the ATNC has a cumulative degree distribution captured by an exponential function, and displays some small-world (SW) network properties with an Average Path Length of 2.23 and a clustering coefficient of 0.69. All three centrality indices are highly correlated with socio-economic indicators of cities such as air passenger volume, population, and gross regional domestic product (GRDP). This confirms that centrality captures a crucial aspect of location advantage in the ATNC and has important implications in shaping the spatial pattern of economic activities. Most small and low-degree airports are directly connected to the largest cities with the best centrality and bypass their regional centers, and therefore sub-networks in the ATNC are less developed except for Kunming in the southwest and Urumchi in the northwest because of their strategic locations for geographic and political reasons. The ANTC is relatively young, and not as efficient and well-developed as that of the US.
