The Experts below are selected from a list of 321 Experts worldwide ranked by ideXlab platform
Yufeng Zang - One of the best experts on this subject based on the ideXlab platform.
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parcellation dependent small world brain functional networks a resting state fmri study
Human Brain Mapping, 2009Co-Authors: Jinhui Wang, Liang Wang, Zhang Chen, Hong Yang, Hehan Tang, Qiyong Gong, Yufeng Zang, Yong HeAbstract:Recent studies have demonstrated small-world properties in both functional and structural brain networks that are constructed based on different parcellation approaches. However, one fundamental but vital issue of the impact of different brain parcellation schemes on the network Topological Architecture remains unclear. Here, we used resting-state functional MRI (fMRI) to investigate the influences of different brain parcellation atlases on the Topological organization of brain functional networks. Whole-brain fMRI data were divided into ninety and seventy regions of interest according to two predefined anatomical atlases, respectively. Brain functional networks were constructed by thresholding the correlation matrices among the parcellated regions and further analyzed using graph theoretical approaches. Both atlas-based brain functional networks were found to show robust small-world properties and truncated power-law connectivity degree distributions, which are consistent with previous brain functional and structural networks studies. However, more importantly, we found that there were significant differences in multiple Topological parameters (e.g., small-worldness and degree distribution) between the two groups of brain functional networks derived from the two atlases. This study provides quantitative evidence on how the Topological organization of brain networks is affected by the different parcellation strategies applied. Hum Brain Mapp 2009. © 2008 Wiley-Liss, Inc.
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uncovering intrinsic modular organization of spontaneous brain activity in humans
PLOS ONE, 2009Co-Authors: Jinhui Wang, Liang Wang, Zhang Chen, Chaogan Yan, Hong Yang, Hehan Tang, Chaozhe Zhu, Qiyong Gong, Yufeng ZangAbstract:The characterization of Topological Architecture of complex brain networks is one of the most challenging issues in neuroscience. Slow (<0.1 Hz), spontaneous fluctuations of the blood oxygen level dependent (BOLD) signal in functional magnetic resonance imaging are thought to be potentially important for the reflection of spontaneous neuronal activity. Many studies have shown that these fluctuations are highly coherent within anatomically or functionally linked areas of the brain. However, the underlying Topological mechanisms responsible for these coherent intrinsic or spontaneous fluctuations are still poorly understood. Here, we apply modern network analysis techniques to investigate how spontaneous neuronal activities in the human brain derived from the resting-state BOLD signals are Topologically organized at both the temporal and spatial scales. We first show that the spontaneous brain functional networks have an intrinsically cohesive modular structure in which the connections between regions are much denser within modules than between them. These identified modules are found to be closely associated with several well known functionally interconnected subsystems such as the somatosensory/motor, auditory, attention, visual, subcortical, and the “default” system. Specifically, we demonstrate that the module-specific Topological features can not be captured by means of computing the corresponding global network parameters, suggesting a unique organization within each module. Finally, we identify several pivotal network connectors and paths (predominantly associated with the association and limbic/paralimbic cortex regions) that are vital for the global coordination of information flow over the whole network, and we find that their lesions (deletions) critically affect the stability and robustness of the brain functional system. Together, our results demonstrate the highly organized modular Architecture and associated Topological properties in the temporal and spatial brain functional networks of the human brain that underlie spontaneous neuronal dynamics, which provides important implications for our understanding of how intrinsically coherent spontaneous brain activity has evolved into an optimal neuronal Architecture to support global computation and information integration in the absence of specific stimuli or behaviors.
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Uncovering Intrinsic Modular Organization of Spontaneous Brain Activity in Humans
PloS one, 2009Co-Authors: Jinhui Wang, Liang Wang, Zhang Chen, Chaogan Yan, Hong Yang, Hehan Tang, Chaozhe Zhu, Qiyong Gong, Yufeng ZangAbstract:The characterization of Topological Architecture of complex brain networks is one of the most challenging issues in neuroscience. Slow (
Jinhui Wang - One of the best experts on this subject based on the ideXlab platform.
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disrupted functional brain connectome in individuals at risk for alzheimer s disease
Biological Psychiatry, 2013Co-Authors: Jinhui Wang, Xinian Zuo, Zhengjia Dai, Mingrui Xia, Zhilian Zhao, Xiaoling Zhao, Jianping Jia, Ying HanAbstract:Background: Alzheimer's disease disrupts the Topological Architecture of whole-brain connectivity (i.e., the connectome); however, whether this disruption is present in amnestic mild cognitive impairment (aMCI), the prodromal stage of Alzheimer's disease, remains largely unknown. Methods: We employed resting-state functional magnetic resonance imaging and graph theory approaches to systematically investigate the Topological organization of the functional connectome of 37 patients with aMCI and 47 healthy control subjects. Frequency-dependent brain networks were derived from wavelet-based correlations of both high-and low-resolution parcellation units. Results: In the frequency interval .031-.063 Hz, the aMCI patients showed an overall decreased functional connectivity of their brain connectome compared with control subjects. Further graph theory analyses of this frequency band revealed an increased path length of the connectome in the aMCI group. Moreover, the disease targeted several key nodes predominantly in the default-mode regions and key links primarily in the intramodule connections within the default-mode network and the intermodule connections among different functional systems. Intriguingly, the Topological aberrations correlated with the patients' memory performance and differentiated individuals with aMCI from healthy elderly individuals with a sensitivity of 86.5% and a specificity of 85.1%. Finally, we demonstrated a high reproducibility of our findings across different large-scale parcellation schemes and validated the test-retest reliability of our network-based approaches. Conclusions: This study demonstrates a disruption of whole-brain Topological organization of the functional connectome in aMCI. Our finding provides novel insights into the pathophysiological mechanism of aMCI and highlights the potential for using connectome-based metrics as a disease biomarker.
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parcellation dependent small world brain functional networks a resting state fmri study
Human Brain Mapping, 2009Co-Authors: Jinhui Wang, Liang Wang, Zhang Chen, Hong Yang, Hehan Tang, Qiyong Gong, Yufeng Zang, Yong HeAbstract:Recent studies have demonstrated small-world properties in both functional and structural brain networks that are constructed based on different parcellation approaches. However, one fundamental but vital issue of the impact of different brain parcellation schemes on the network Topological Architecture remains unclear. Here, we used resting-state functional MRI (fMRI) to investigate the influences of different brain parcellation atlases on the Topological organization of brain functional networks. Whole-brain fMRI data were divided into ninety and seventy regions of interest according to two predefined anatomical atlases, respectively. Brain functional networks were constructed by thresholding the correlation matrices among the parcellated regions and further analyzed using graph theoretical approaches. Both atlas-based brain functional networks were found to show robust small-world properties and truncated power-law connectivity degree distributions, which are consistent with previous brain functional and structural networks studies. However, more importantly, we found that there were significant differences in multiple Topological parameters (e.g., small-worldness and degree distribution) between the two groups of brain functional networks derived from the two atlases. This study provides quantitative evidence on how the Topological organization of brain networks is affected by the different parcellation strategies applied. Hum Brain Mapp 2009. © 2008 Wiley-Liss, Inc.
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uncovering intrinsic modular organization of spontaneous brain activity in humans
PLOS ONE, 2009Co-Authors: Jinhui Wang, Liang Wang, Zhang Chen, Chaogan Yan, Hong Yang, Hehan Tang, Chaozhe Zhu, Qiyong Gong, Yufeng ZangAbstract:The characterization of Topological Architecture of complex brain networks is one of the most challenging issues in neuroscience. Slow (<0.1 Hz), spontaneous fluctuations of the blood oxygen level dependent (BOLD) signal in functional magnetic resonance imaging are thought to be potentially important for the reflection of spontaneous neuronal activity. Many studies have shown that these fluctuations are highly coherent within anatomically or functionally linked areas of the brain. However, the underlying Topological mechanisms responsible for these coherent intrinsic or spontaneous fluctuations are still poorly understood. Here, we apply modern network analysis techniques to investigate how spontaneous neuronal activities in the human brain derived from the resting-state BOLD signals are Topologically organized at both the temporal and spatial scales. We first show that the spontaneous brain functional networks have an intrinsically cohesive modular structure in which the connections between regions are much denser within modules than between them. These identified modules are found to be closely associated with several well known functionally interconnected subsystems such as the somatosensory/motor, auditory, attention, visual, subcortical, and the “default” system. Specifically, we demonstrate that the module-specific Topological features can not be captured by means of computing the corresponding global network parameters, suggesting a unique organization within each module. Finally, we identify several pivotal network connectors and paths (predominantly associated with the association and limbic/paralimbic cortex regions) that are vital for the global coordination of information flow over the whole network, and we find that their lesions (deletions) critically affect the stability and robustness of the brain functional system. Together, our results demonstrate the highly organized modular Architecture and associated Topological properties in the temporal and spatial brain functional networks of the human brain that underlie spontaneous neuronal dynamics, which provides important implications for our understanding of how intrinsically coherent spontaneous brain activity has evolved into an optimal neuronal Architecture to support global computation and information integration in the absence of specific stimuli or behaviors.
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Uncovering Intrinsic Modular Organization of Spontaneous Brain Activity in Humans
PloS one, 2009Co-Authors: Jinhui Wang, Liang Wang, Zhang Chen, Chaogan Yan, Hong Yang, Hehan Tang, Chaozhe Zhu, Qiyong Gong, Yufeng ZangAbstract:The characterization of Topological Architecture of complex brain networks is one of the most challenging issues in neuroscience. Slow (
Hong Yang - One of the best experts on this subject based on the ideXlab platform.
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parcellation dependent small world brain functional networks a resting state fmri study
Human Brain Mapping, 2009Co-Authors: Jinhui Wang, Liang Wang, Zhang Chen, Hong Yang, Hehan Tang, Qiyong Gong, Yufeng Zang, Yong HeAbstract:Recent studies have demonstrated small-world properties in both functional and structural brain networks that are constructed based on different parcellation approaches. However, one fundamental but vital issue of the impact of different brain parcellation schemes on the network Topological Architecture remains unclear. Here, we used resting-state functional MRI (fMRI) to investigate the influences of different brain parcellation atlases on the Topological organization of brain functional networks. Whole-brain fMRI data were divided into ninety and seventy regions of interest according to two predefined anatomical atlases, respectively. Brain functional networks were constructed by thresholding the correlation matrices among the parcellated regions and further analyzed using graph theoretical approaches. Both atlas-based brain functional networks were found to show robust small-world properties and truncated power-law connectivity degree distributions, which are consistent with previous brain functional and structural networks studies. However, more importantly, we found that there were significant differences in multiple Topological parameters (e.g., small-worldness and degree distribution) between the two groups of brain functional networks derived from the two atlases. This study provides quantitative evidence on how the Topological organization of brain networks is affected by the different parcellation strategies applied. Hum Brain Mapp 2009. © 2008 Wiley-Liss, Inc.
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uncovering intrinsic modular organization of spontaneous brain activity in humans
PLOS ONE, 2009Co-Authors: Jinhui Wang, Liang Wang, Zhang Chen, Chaogan Yan, Hong Yang, Hehan Tang, Chaozhe Zhu, Qiyong Gong, Yufeng ZangAbstract:The characterization of Topological Architecture of complex brain networks is one of the most challenging issues in neuroscience. Slow (<0.1 Hz), spontaneous fluctuations of the blood oxygen level dependent (BOLD) signal in functional magnetic resonance imaging are thought to be potentially important for the reflection of spontaneous neuronal activity. Many studies have shown that these fluctuations are highly coherent within anatomically or functionally linked areas of the brain. However, the underlying Topological mechanisms responsible for these coherent intrinsic or spontaneous fluctuations are still poorly understood. Here, we apply modern network analysis techniques to investigate how spontaneous neuronal activities in the human brain derived from the resting-state BOLD signals are Topologically organized at both the temporal and spatial scales. We first show that the spontaneous brain functional networks have an intrinsically cohesive modular structure in which the connections between regions are much denser within modules than between them. These identified modules are found to be closely associated with several well known functionally interconnected subsystems such as the somatosensory/motor, auditory, attention, visual, subcortical, and the “default” system. Specifically, we demonstrate that the module-specific Topological features can not be captured by means of computing the corresponding global network parameters, suggesting a unique organization within each module. Finally, we identify several pivotal network connectors and paths (predominantly associated with the association and limbic/paralimbic cortex regions) that are vital for the global coordination of information flow over the whole network, and we find that their lesions (deletions) critically affect the stability and robustness of the brain functional system. Together, our results demonstrate the highly organized modular Architecture and associated Topological properties in the temporal and spatial brain functional networks of the human brain that underlie spontaneous neuronal dynamics, which provides important implications for our understanding of how intrinsically coherent spontaneous brain activity has evolved into an optimal neuronal Architecture to support global computation and information integration in the absence of specific stimuli or behaviors.
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Uncovering Intrinsic Modular Organization of Spontaneous Brain Activity in Humans
PloS one, 2009Co-Authors: Jinhui Wang, Liang Wang, Zhang Chen, Chaogan Yan, Hong Yang, Hehan Tang, Chaozhe Zhu, Qiyong Gong, Yufeng ZangAbstract:The characterization of Topological Architecture of complex brain networks is one of the most challenging issues in neuroscience. Slow (
Yuanyuan Yang - One of the best experts on this subject based on the ideXlab platform.
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GLOBECOM - Adaptively Fitting Network Topologies to Traffic Locality in Clos-Type Data Center Networks
2018 IEEE Global Communications Conference (GLOBECOM), 2018Co-Authors: Jun Duan, Yuanyuan YangAbstract:Localized traffic is ubiquitous in today's data centers. In this context, we propose to fit the topologies of the underlying network into the traffic locality, so that we can improve the efficiency of network resource utilization. We make our network infrastructure to be versatile, in the sense that its topology can be fitted into the profile of the traffic. We describe our network's Topological Architecture, design its addressing and routing schemes, and validate its adaptively fitting capability. We also evaluate its performance and compare it with fat-tree, a representative Clos-type data center network Architecture. The evaluation results demonstrate that the our network can deliver the same throughput as fat-tree, but use significantly reduced network resources.
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vcn versatile clos type networks for traffic locality in data centers
International Workshop on Quality of Service, 2018Co-Authors: Jun Duan, Yuanyuan YangAbstract:Traffic locality is ubiquitously exploited in today's data centers. It allows network resources to be used more efficiently because traffic in data centers is adapted to the underlying network infrastructures. In this paper, we approach this problem from another direction, which is to adapt network infrastructure to the traffic. Towards this direction, we adopt two approaches to boost the efficiency of network resource utilization. First, we improve the topology of Clos-type data center networks by introducing horizontal connections, which facilitates the routing of local traffic. Second, based on the improved topology, we make the network infrastructure to be versatile, in the sense that it can be fitted into the characteristics of the traffic. We name this design as Versatile Clos-type Networks (VCN). We describe the Topological Architecture of the VCN, design its addressing and routing schemes, and demonstrate its various properties. We also evaluate its performance and compare it with fat-tree, a representative Clos-type data center network Architecture. The evaluation results show that the VCN can deliver the same throughput as fat-tree, but use significantly reduced network resources.
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Adaptively Fitting Network Topologies to Traffic Locality in Clos-Type Data Center Networks
2018 IEEE Global Communications Conference (GLOBECOM), 2018Co-Authors: Jun Duan, Yuanyuan YangAbstract:Localized traffic is ubiquitous in today's data centers. In this context, we propose to fit the topologies of the underlying network into the traffic locality, so that we can improve the efficiency of network resource utilization. We make our network infrastructure to be versatile, in the sense that its topology can be fitted into the profile of the traffic. We describe our network's Topological Architecture, design its addressing and routing schemes, and validate its adaptively fitting capability. We also evaluate its performance and compare it with fat-tree, a representative Clos-type data center network Architecture. The evaluation results demonstrate that the our network can deliver the same throughput as fat-tree, but use significantly reduced network resources.
Liang Wang - One of the best experts on this subject based on the ideXlab platform.
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parcellation dependent small world brain functional networks a resting state fmri study
Human Brain Mapping, 2009Co-Authors: Jinhui Wang, Liang Wang, Zhang Chen, Hong Yang, Hehan Tang, Qiyong Gong, Yufeng Zang, Yong HeAbstract:Recent studies have demonstrated small-world properties in both functional and structural brain networks that are constructed based on different parcellation approaches. However, one fundamental but vital issue of the impact of different brain parcellation schemes on the network Topological Architecture remains unclear. Here, we used resting-state functional MRI (fMRI) to investigate the influences of different brain parcellation atlases on the Topological organization of brain functional networks. Whole-brain fMRI data were divided into ninety and seventy regions of interest according to two predefined anatomical atlases, respectively. Brain functional networks were constructed by thresholding the correlation matrices among the parcellated regions and further analyzed using graph theoretical approaches. Both atlas-based brain functional networks were found to show robust small-world properties and truncated power-law connectivity degree distributions, which are consistent with previous brain functional and structural networks studies. However, more importantly, we found that there were significant differences in multiple Topological parameters (e.g., small-worldness and degree distribution) between the two groups of brain functional networks derived from the two atlases. This study provides quantitative evidence on how the Topological organization of brain networks is affected by the different parcellation strategies applied. Hum Brain Mapp 2009. © 2008 Wiley-Liss, Inc.
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uncovering intrinsic modular organization of spontaneous brain activity in humans
PLOS ONE, 2009Co-Authors: Jinhui Wang, Liang Wang, Zhang Chen, Chaogan Yan, Hong Yang, Hehan Tang, Chaozhe Zhu, Qiyong Gong, Yufeng ZangAbstract:The characterization of Topological Architecture of complex brain networks is one of the most challenging issues in neuroscience. Slow (<0.1 Hz), spontaneous fluctuations of the blood oxygen level dependent (BOLD) signal in functional magnetic resonance imaging are thought to be potentially important for the reflection of spontaneous neuronal activity. Many studies have shown that these fluctuations are highly coherent within anatomically or functionally linked areas of the brain. However, the underlying Topological mechanisms responsible for these coherent intrinsic or spontaneous fluctuations are still poorly understood. Here, we apply modern network analysis techniques to investigate how spontaneous neuronal activities in the human brain derived from the resting-state BOLD signals are Topologically organized at both the temporal and spatial scales. We first show that the spontaneous brain functional networks have an intrinsically cohesive modular structure in which the connections between regions are much denser within modules than between them. These identified modules are found to be closely associated with several well known functionally interconnected subsystems such as the somatosensory/motor, auditory, attention, visual, subcortical, and the “default” system. Specifically, we demonstrate that the module-specific Topological features can not be captured by means of computing the corresponding global network parameters, suggesting a unique organization within each module. Finally, we identify several pivotal network connectors and paths (predominantly associated with the association and limbic/paralimbic cortex regions) that are vital for the global coordination of information flow over the whole network, and we find that their lesions (deletions) critically affect the stability and robustness of the brain functional system. Together, our results demonstrate the highly organized modular Architecture and associated Topological properties in the temporal and spatial brain functional networks of the human brain that underlie spontaneous neuronal dynamics, which provides important implications for our understanding of how intrinsically coherent spontaneous brain activity has evolved into an optimal neuronal Architecture to support global computation and information integration in the absence of specific stimuli or behaviors.
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Uncovering Intrinsic Modular Organization of Spontaneous Brain Activity in Humans
PloS one, 2009Co-Authors: Jinhui Wang, Liang Wang, Zhang Chen, Chaogan Yan, Hong Yang, Hehan Tang, Chaozhe Zhu, Qiyong Gong, Yufeng ZangAbstract:The characterization of Topological Architecture of complex brain networks is one of the most challenging issues in neuroscience. Slow (