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Alle Meije Wink - One of the best experts on this subject based on the ideXlab platform.

  • Eigenvector Centrality Dynamics From Resting-State fMRI : Gender and Age Differences in Healthy Subjects
    Frontiers in neuroscience, 2019
    Co-Authors: Alle Meije Wink
    Abstract:

    With the increasing use of functional brain network properties as markers of brain disorders, efficient visualization and evaluation methods have become essential. Eigenvector Centrality mapping (ECM) of functional MRI (fMRI) data enables the representation of per-node graph theoretical measures as brain maps. This paper studies the use of Centrality dynamics for measuring group differences in imaging studies. Imaging data were used from a publicly available imaging study, which included resting fMRI data. After warping the images to a standard space and masking cortical regions, ECM were computed in a sliding window. The dual regression method was used to identify dynamic Centrality differences inside well-known resting-state networks between gender and age groups. Gender-related differences were found in the medial and lateral visual, motor, default mode, and executive control RSN, where male subjects had more consistent Centrality variations within the network. Age-related differences between the youngest and oldest subjects, based on a median split, were found in the medial visual, executive control and left frontoparietal networks, where younger subjects had more consistent Centrality variations within the network. Our findings show that Centrality dynamics can be used to identify between-group functional brain network Centrality differences, and that age and gender distributions studies need to be taken into account in functional imaging studies.

  • altered Eigenvector Centrality is related to local resting state network functional connectivity in patients with longstanding type 1 diabetes mellitus
    Human Brain Mapping, 2017
    Co-Authors: Eelco Van Duinkerken, Frederik Barkhof, Menno M Schoonheim, Richard G Ijzerman, Annette C Moll, J Landeirafernandez, Martin Klein, Michaela Diamant, Frank J Snoek, Alle Meije Wink
    Abstract:

    Introduction: Longstanding type 1 diabetes (T1DM) is associated with microangiopathy and poorer cognition. In the brain, T1DM is related to increased functional resting-state network (RSN) connectivity in patients without, which was decreased in patients with clinically evident microangiopathy. Subcortical structure seems affected in both patient groups. How these localized alterations affect the hierarchy of the functional network in T1DM is unknown. Eigenvector Centrality mapping (ECM) and degree Centrality are graph theoretical methods that allow determining the relative importance (ECM) and connectedness (degree Centrality) of regions within the whole-brain network hierarchy. Methods: Therefore, ECM and degree Centrality of resting-state functional MRI-scans was compared between 51 patients with, 53 patients without proliferative retinopathy, and 49 controls, and associated with RSN connectivity, subcortical gray matter volume, and cognition. Results: In all patients versus controls, ECM and degree Centrality were lower in the bilateral thalamus and the dorsal striatum, with lowest values in patients without proliferative retinopathy (PFWE<0.05). Increased ECM in this group versus patients with proliferative retinopathy was seen in the bilateral lateral occipital cortex, and in the right lateral cortex versus controls (PFWE<0.05). In all patients, ECM and degree Centrality were related to altered visual, sensorimotor, and auditory and language RSN connectivity (PFWE 0.05). Conclusion: Our findings suggest reorganization of the hierarchy of the cortical connectivity network in patients without proliferative retinopathy, which is lost with disease progression. Centrality seems sensitive to capture early T1DM-related functional connectivity alterations, but not disease progression.

  • Altered Eigenvector Centrality is related to local resting-state network functional connectivity in patients with longstanding type 1 diabetes mellitus
    Human brain mapping, 2017
    Co-Authors: Eelco Van Duinkerken, Frederik Barkhof, Menno M Schoonheim, Richard G Ijzerman, Annette C Moll, Martin Klein, Michaela Diamant, Frank J Snoek, Jesus Landeira-fernandez, Alle Meije Wink
    Abstract:

    Longstanding type 1 diabetes (T1DM) is associated with microangiopathy and poorer cognition. In the brain, T1DM is related to increased functional resting-state network (RSN) connectivity in patients without, which was decreased in patients with clinically evident microangiopathy. Subcortical structure seems affected in both patient groups. How these localized alterations affect the hierarchy of the functional network in T1DM is unknown. Eigenvector Centrality mapping (ECM) and degree Centrality are graph theoretical methods that allow determining the relative importance (ECM) and connectedness (degree Centrality) of regions within the whole-brain network hierarchy. Therefore, ECM and degree Centrality of resting-state functional MRI-scans were compared between 51 patients with, 53 patients without proliferative retinopathy, and 49 controls, and associated with RSN connectivity, subcortical gray matter volume, and cognition. In all patients versus controls, ECM and degree Centrality were lower in the bilateral thalamus and the dorsal striatum, with lowest values in patients without proliferative retinopathy (PFWE  < 0.05). Increased ECM in this group versus patients with proliferative retinopathy was seen in the bilateral lateral occipital cortex, and in the right cuneus and occipital fusiform gyrus versus controls (PFWE  < 0.05). In all patients, ECM and degree Centrality were related to altered visual, sensorimotor, and auditory and language RSN connectivity (PFWE  < 0.05), but not to subcortical gray matter volume or cognition (PFDR  > 0.05). The findings suggested reorganization of the hierarchy of the cortical connectivity network in patients without proliferative retinopathy, which is lost with disease progression. Centrality seems sensitive to capture early T1DM-related functional connectivity alterations, but not disease progression. Hum Brain Mapp 38:3623-3636, 2017. © 2017 Wiley Periodicals, Inc. © 2017 Wiley Periodicals, Inc.

  • The Association of Glucose Metabolism and Eigenvector Centrality in Alzheimer's Disease
    Brain connectivity, 2015
    Co-Authors: Sofie Adriaanse, Alle Meije Wink, Betty M. Tijms, Rik Ossenkoppele, Sander C.j. Verfaillie, Adriaan A. Lammertsma, Ronald Boellaard, Philip Scheltens, Bart N.m. Van Berckel, Frederik Barkhof
    Abstract:

    Both fluorine-18-labeled fluorodeoxyglucose ([(18)F]FDG) positron emission tomography, examining glucose metabolism, and resting-state functional magnetic resonance imaging (rs-fMRI), using covarying blood oxygen levels, can be used to explore neuronal dysfunction in Alzheimer's disease (AD). Both measures are reported to identify similar brain regions affected in AD patients. The spatial overlap and association of [(18)F]FDG with rs-fMRI in AD patients and controls were examined to investigate whether these two measures are associated, and if so, to what extent. For 24 AD patients and 18 controls, [(18)F]FDG and rs-fMRI data were available. [(18)F]FDG standardized uptake value ratios (SUVr), with cerebellar gray matter (GM) as reference tissue, were calculated. Eigenvector Centrality (EC) mapping was used to spatially analyze the functional brain network. Group differences were calculated for [(18)F]FDG and Eigenvector Centrality mapping (ECM) values in four cortical regions (occipital, parietal, frontal, and temporal) and across voxels, with age, gender, and GM as covariates. Correlation of [(18)F]FDG with ECM was calculated within groups. Both lowered [(18)F]FDG SUVr and EC values were seen in the parietal and occipital cortex of AD patients. However, [(18)F]FDG yielded more robust and widespread brain areas affected in AD patients; hypometabolism was also observed in the temporal cortex and regions within frontal brain areas. Poor spatial overlap of both measures was observed. No associations were found between local [(18)F]FDG SUVr and ECM. In conclusion, agreement of [(18)F]FDG and ECM in AD patients seems moderate at best. [(18)F]FDG was most accurate in distinguishing AD patients from controls.

  • Brain network alterations in Alzheimer's disease measured by Eigenvector Centrality in fMRI are related to cognition and CSF biomarkers.
    Human brain mapping, 2013
    Co-Authors: Maja Binnewijzend, Sofie Adriaanse, Philip Scheltens, Bart N.m. Van Berckel, Frederik Barkhof, Jan C. De Munck, Wiesje M. Van Der Flier, Charlotte E. Teunissen, Cornelis J. Stam, Alle Meije Wink
    Abstract:

    Recent imaging studies have demonstrated functional brain network changes in patients with Alzheimer's disease (AD). Eigenvector Centrality (EC) is a graph analytical measure that identifies prominent regions in the brain network hierarchy and detects localized differences between patient populations. This study used voxel-wise EC mapping (ECM) to analyze individual whole-brain resting-state functional magnetic resonance imaging (MRI) scans in 39 AD patients (age 67 ± 8) and 43 healthy controls (age 69 ± 7). Between-group differences were assessed by a permutation-based method. Associations of EC with biomarkers for AD pathology in cerebrospinal fluid (CSF) and Mini Mental State Examination (MMSE) scores were assessed using Spearman correlation analysis. Decreased EC was found bilaterally in the occipital cortex in AD patients compared to controls. Regions of increased EC were identified in the anterior cingulate and paracingulate gyrus. Across groups, frontal and occipital EC changes were associated with pathological concentrations of CSF biomarkers and with cognition. In controls, decreased EC values in the occipital regions were related to lower MMSE scores. Our main finding is that ECM, a hypothesis-free and computationally efficient analysis method of functional MRI (fMRI) data, identifies changes in brain network organization in AD patients that are related to cognition and underlying AD pathology. The relation between AD-like EC changes and cognitive performance suggests that resting-state fMRI measured EC is a potential marker of disease severity for AD.

Seung-hwan Yoo - One of the best experts on this subject based on the ideXlab platform.

  • Analysis of the characteristics of the global virtual water trade networkusing degree and Eigenvector Centrality, with a focus on food and feed crops
    Hydrology and Earth System Sciences, 2016
    Co-Authors: Sanghyun Lee, Rabi H. Mohtar, Jin-yong Choi, Seung-hwan Yoo
    Abstract:

    Abstract. This study aims to analyze the characteristics of global virtual water trade (GVWT), such as the connectivity of each trader, vulnerable importers, and influential countries, using degree and Eigenvector Centrality during the period 2006–2010. The degree Centrality was used to measure the connectivity, and Eigenvector Centrality was used to measure the influence on the entire GVWT network. Mexico, Egypt, China, the Republic of Korea, and Japan were classified as vulnerable importers, because they imported large quantities of virtual water with low connectivity. In particular, Egypt had a 15.3 Gm3 year−1 blue water saving effect through GVWT: the vulnerable structure could cause a water shortage problem for the importer. The entire GVWT network could be changed by a few countries, termed "influential traders". We used Eigenvector Centrality to identify those influential traders. In GVWT for food crops, the USA, Russian Federation, Thailand, and Canada had high Eigenvector Centrality with large volumes of green water trade. In the case of blue water trade, western Asia, Pakistan, and India had high Eigenvector Centrality. For feed crops, the green water trade in the USA, Brazil, and Argentina was the most influential. However, Argentina and Pakistan used high proportions of internal water resources for virtual water export (32.9 and 25.1 %); thus other traders should carefully consider water resource management in these exporters.

  • Analysis of the characteristics of global virtual water trade network using degree and Eigenvector Centrality, with a focus on food and feed crops
    2016
    Co-Authors: Sanghyun Lee, Rabi H. Mohtar, Jin-yong Choi, Seung-hwan Yoo
    Abstract:

    Abstract. This study aims to analyse the characteristics of global virtual water trade (GVWT) such as connectivity of each trader, vulnerable importers, and influential countries using degree and Eigenvector Centrality during the period 2006–2010. The degree Centrality was used to measure the connectivity and Eigenvector Centrality was used to measure the influence on entire GVWT network. Mexico, Egypt, China, Korea Rep., and Japan were classified to vulnerable importers because they imported a lot of virtual water with the low connectivity. Especially, Egypt had 15.3 Gm³ year-1 blue water savings effects through GVWT, thus the vulnerable structure could cause the water shortage problem in importer. The entire GVWT network could be changed by a few nodes which call influential traders, and we figured out the influential traders using Eigenvector Centrality. In GVWT for food crops, the USA, Russian Federation, Thailand, and Canada had high Eigenvector with a large volume of green water trade. In case of blue water trade, western Asia, Pakistan, and India had high Eigenvector Centrality. For feed crops, the green water trade in the USA, Brazil, and Argentina was the most influential. However, Argentina and Pakistan used the high proportion of internal water resource for virtual water export (32.9 and 25.1 %), thus rest of traders should consider the water resource management in these exporters carefully.

Frederik Barkhof - One of the best experts on this subject based on the ideXlab platform.

  • altered Eigenvector Centrality is related to local resting state network functional connectivity in patients with longstanding type 1 diabetes mellitus
    Human Brain Mapping, 2017
    Co-Authors: Eelco Van Duinkerken, Frederik Barkhof, Menno M Schoonheim, Richard G Ijzerman, Annette C Moll, J Landeirafernandez, Martin Klein, Michaela Diamant, Frank J Snoek, Alle Meije Wink
    Abstract:

    Introduction: Longstanding type 1 diabetes (T1DM) is associated with microangiopathy and poorer cognition. In the brain, T1DM is related to increased functional resting-state network (RSN) connectivity in patients without, which was decreased in patients with clinically evident microangiopathy. Subcortical structure seems affected in both patient groups. How these localized alterations affect the hierarchy of the functional network in T1DM is unknown. Eigenvector Centrality mapping (ECM) and degree Centrality are graph theoretical methods that allow determining the relative importance (ECM) and connectedness (degree Centrality) of regions within the whole-brain network hierarchy. Methods: Therefore, ECM and degree Centrality of resting-state functional MRI-scans was compared between 51 patients with, 53 patients without proliferative retinopathy, and 49 controls, and associated with RSN connectivity, subcortical gray matter volume, and cognition. Results: In all patients versus controls, ECM and degree Centrality were lower in the bilateral thalamus and the dorsal striatum, with lowest values in patients without proliferative retinopathy (PFWE<0.05). Increased ECM in this group versus patients with proliferative retinopathy was seen in the bilateral lateral occipital cortex, and in the right lateral cortex versus controls (PFWE<0.05). In all patients, ECM and degree Centrality were related to altered visual, sensorimotor, and auditory and language RSN connectivity (PFWE 0.05). Conclusion: Our findings suggest reorganization of the hierarchy of the cortical connectivity network in patients without proliferative retinopathy, which is lost with disease progression. Centrality seems sensitive to capture early T1DM-related functional connectivity alterations, but not disease progression.

  • Altered Eigenvector Centrality is related to local resting-state network functional connectivity in patients with longstanding type 1 diabetes mellitus
    Human brain mapping, 2017
    Co-Authors: Eelco Van Duinkerken, Frederik Barkhof, Menno M Schoonheim, Richard G Ijzerman, Annette C Moll, Martin Klein, Michaela Diamant, Frank J Snoek, Jesus Landeira-fernandez, Alle Meije Wink
    Abstract:

    Longstanding type 1 diabetes (T1DM) is associated with microangiopathy and poorer cognition. In the brain, T1DM is related to increased functional resting-state network (RSN) connectivity in patients without, which was decreased in patients with clinically evident microangiopathy. Subcortical structure seems affected in both patient groups. How these localized alterations affect the hierarchy of the functional network in T1DM is unknown. Eigenvector Centrality mapping (ECM) and degree Centrality are graph theoretical methods that allow determining the relative importance (ECM) and connectedness (degree Centrality) of regions within the whole-brain network hierarchy. Therefore, ECM and degree Centrality of resting-state functional MRI-scans were compared between 51 patients with, 53 patients without proliferative retinopathy, and 49 controls, and associated with RSN connectivity, subcortical gray matter volume, and cognition. In all patients versus controls, ECM and degree Centrality were lower in the bilateral thalamus and the dorsal striatum, with lowest values in patients without proliferative retinopathy (PFWE  < 0.05). Increased ECM in this group versus patients with proliferative retinopathy was seen in the bilateral lateral occipital cortex, and in the right cuneus and occipital fusiform gyrus versus controls (PFWE  < 0.05). In all patients, ECM and degree Centrality were related to altered visual, sensorimotor, and auditory and language RSN connectivity (PFWE  < 0.05), but not to subcortical gray matter volume or cognition (PFDR  > 0.05). The findings suggested reorganization of the hierarchy of the cortical connectivity network in patients without proliferative retinopathy, which is lost with disease progression. Centrality seems sensitive to capture early T1DM-related functional connectivity alterations, but not disease progression. Hum Brain Mapp 38:3623-3636, 2017. © 2017 Wiley Periodicals, Inc. © 2017 Wiley Periodicals, Inc.

  • The Association of Glucose Metabolism and Eigenvector Centrality in Alzheimer's Disease
    Brain connectivity, 2015
    Co-Authors: Sofie Adriaanse, Alle Meije Wink, Betty M. Tijms, Rik Ossenkoppele, Sander C.j. Verfaillie, Adriaan A. Lammertsma, Ronald Boellaard, Philip Scheltens, Bart N.m. Van Berckel, Frederik Barkhof
    Abstract:

    Both fluorine-18-labeled fluorodeoxyglucose ([(18)F]FDG) positron emission tomography, examining glucose metabolism, and resting-state functional magnetic resonance imaging (rs-fMRI), using covarying blood oxygen levels, can be used to explore neuronal dysfunction in Alzheimer's disease (AD). Both measures are reported to identify similar brain regions affected in AD patients. The spatial overlap and association of [(18)F]FDG with rs-fMRI in AD patients and controls were examined to investigate whether these two measures are associated, and if so, to what extent. For 24 AD patients and 18 controls, [(18)F]FDG and rs-fMRI data were available. [(18)F]FDG standardized uptake value ratios (SUVr), with cerebellar gray matter (GM) as reference tissue, were calculated. Eigenvector Centrality (EC) mapping was used to spatially analyze the functional brain network. Group differences were calculated for [(18)F]FDG and Eigenvector Centrality mapping (ECM) values in four cortical regions (occipital, parietal, frontal, and temporal) and across voxels, with age, gender, and GM as covariates. Correlation of [(18)F]FDG with ECM was calculated within groups. Both lowered [(18)F]FDG SUVr and EC values were seen in the parietal and occipital cortex of AD patients. However, [(18)F]FDG yielded more robust and widespread brain areas affected in AD patients; hypometabolism was also observed in the temporal cortex and regions within frontal brain areas. Poor spatial overlap of both measures was observed. No associations were found between local [(18)F]FDG SUVr and ECM. In conclusion, agreement of [(18)F]FDG and ECM in AD patients seems moderate at best. [(18)F]FDG was most accurate in distinguishing AD patients from controls.

  • Brain network alterations in Alzheimer's disease measured by Eigenvector Centrality in fMRI are related to cognition and CSF biomarkers.
    Human brain mapping, 2013
    Co-Authors: Maja Binnewijzend, Sofie Adriaanse, Philip Scheltens, Bart N.m. Van Berckel, Frederik Barkhof, Jan C. De Munck, Wiesje M. Van Der Flier, Charlotte E. Teunissen, Cornelis J. Stam, Alle Meije Wink
    Abstract:

    Recent imaging studies have demonstrated functional brain network changes in patients with Alzheimer's disease (AD). Eigenvector Centrality (EC) is a graph analytical measure that identifies prominent regions in the brain network hierarchy and detects localized differences between patient populations. This study used voxel-wise EC mapping (ECM) to analyze individual whole-brain resting-state functional magnetic resonance imaging (MRI) scans in 39 AD patients (age 67 ± 8) and 43 healthy controls (age 69 ± 7). Between-group differences were assessed by a permutation-based method. Associations of EC with biomarkers for AD pathology in cerebrospinal fluid (CSF) and Mini Mental State Examination (MMSE) scores were assessed using Spearman correlation analysis. Decreased EC was found bilaterally in the occipital cortex in AD patients compared to controls. Regions of increased EC were identified in the anterior cingulate and paracingulate gyrus. Across groups, frontal and occipital EC changes were associated with pathological concentrations of CSF biomarkers and with cognition. In controls, decreased EC values in the occipital regions were related to lower MMSE scores. Our main finding is that ECM, a hypothesis-free and computationally efficient analysis method of functional MRI (fMRI) data, identifies changes in brain network organization in AD patients that are related to cognition and underlying AD pathology. The relation between AD-like EC changes and cognitive performance suggests that resting-state fMRI measured EC is a potential marker of disease severity for AD.

  • Fast Eigenvector Centrality Mapping of Voxel-Wise Connectivity in Functional Magnetic Resonance Imaging: Implementation, Validation, and Interpretation
    Brain connectivity, 2012
    Co-Authors: Alle Meije Wink, Jan C. De Munck, Ysbrand D. Van Der Werf, Odile A. Van Den Heuvel, Frederik Barkhof
    Abstract:

    Eigenvector Centrality mapping (ECM) has recently emerged as a measure to spatially characterize connectivity in functional brain imaging by attributing network properties to voxels. The main obstacle for widespread use of ECM in functional magnetic resonance imaging (fMRI) is the cost of computing and storing the connectivity matrix. This article presents fast ECM (fECM), an efficient algorithm to estimate voxel-wise Eigenvector centralities from fMRI time series. Instead of explicitly storing the connectivity matrix, fECM computes matrix-vector products directly from the data, achieving high accelerations for computing voxel-wise centralities in fMRI at standard resolutions for multivariate analyses, and enabling high-resolution analyses performed on standard hardware. We demonstrate the validity of fECM at cluster and voxel levels, using synthetic and in vivo data. Results from synthetic data are compared to the theoretical gold standard, and local Centrality changes in fMRI data are measured after experimental intervention. A simple scheme is presented to generate time series with prescribed covariances that represent a connectivity matrix. These time series are used to construct a 4D dataset whose volumes consist of separate regions with known intra- and inter-regional connectivities. The fECM method is tested and validated on these synthetic data. Resting-state fMRI data acquired after real-versus-sham repetitive transcranial magnetic stimulation show fECM connectivity changes in resting-state network regions. A comparison of analyses with and without accounting for motion parameters demonstrates a moderate effect of these parameters on the Centrality estimates. Its computational speed and statistical sensitivity make fECM a good candidate for connectivity analyses of multimodality and high-resolution functional neuroimaging data.

Sanghyun Lee - One of the best experts on this subject based on the ideXlab platform.

  • Analysis of the characteristics of the global virtual water trade networkusing degree and Eigenvector Centrality, with a focus on food and feed crops
    Hydrology and Earth System Sciences, 2016
    Co-Authors: Sanghyun Lee, Rabi H. Mohtar, Jin-yong Choi, Seung-hwan Yoo
    Abstract:

    Abstract. This study aims to analyze the characteristics of global virtual water trade (GVWT), such as the connectivity of each trader, vulnerable importers, and influential countries, using degree and Eigenvector Centrality during the period 2006–2010. The degree Centrality was used to measure the connectivity, and Eigenvector Centrality was used to measure the influence on the entire GVWT network. Mexico, Egypt, China, the Republic of Korea, and Japan were classified as vulnerable importers, because they imported large quantities of virtual water with low connectivity. In particular, Egypt had a 15.3 Gm3 year−1 blue water saving effect through GVWT: the vulnerable structure could cause a water shortage problem for the importer. The entire GVWT network could be changed by a few countries, termed "influential traders". We used Eigenvector Centrality to identify those influential traders. In GVWT for food crops, the USA, Russian Federation, Thailand, and Canada had high Eigenvector Centrality with large volumes of green water trade. In the case of blue water trade, western Asia, Pakistan, and India had high Eigenvector Centrality. For feed crops, the green water trade in the USA, Brazil, and Argentina was the most influential. However, Argentina and Pakistan used high proportions of internal water resources for virtual water export (32.9 and 25.1 %); thus other traders should carefully consider water resource management in these exporters.

  • Analysis of the characteristics of global virtual water trade network using degree and Eigenvector Centrality, with a focus on food and feed crops
    2016
    Co-Authors: Sanghyun Lee, Rabi H. Mohtar, Jin-yong Choi, Seung-hwan Yoo
    Abstract:

    Abstract. This study aims to analyse the characteristics of global virtual water trade (GVWT) such as connectivity of each trader, vulnerable importers, and influential countries using degree and Eigenvector Centrality during the period 2006–2010. The degree Centrality was used to measure the connectivity and Eigenvector Centrality was used to measure the influence on entire GVWT network. Mexico, Egypt, China, Korea Rep., and Japan were classified to vulnerable importers because they imported a lot of virtual water with the low connectivity. Especially, Egypt had 15.3 Gm³ year-1 blue water savings effects through GVWT, thus the vulnerable structure could cause the water shortage problem in importer. The entire GVWT network could be changed by a few nodes which call influential traders, and we figured out the influential traders using Eigenvector Centrality. In GVWT for food crops, the USA, Russian Federation, Thailand, and Canada had high Eigenvector with a large volume of green water trade. In case of blue water trade, western Asia, Pakistan, and India had high Eigenvector Centrality. For feed crops, the green water trade in the USA, Brazil, and Argentina was the most influential. However, Argentina and Pakistan used the high proportion of internal water resource for virtual water export (32.9 and 25.1 %), thus rest of traders should consider the water resource management in these exporters carefully.

Wang Jiandong - One of the best experts on this subject based on the ideXlab platform.