The Experts below are selected from a list of 188118 Experts worldwide ranked by ideXlab platform
Jingjing Zhao - One of the best experts on this subject based on the ideXlab platform.
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White matter Network Connectivity deficits in developmental dyslexia
Human Brain Mapping, 2019Co-Authors: Chenglin Lou, Xiting Duan, Irene Altarelli, John Sweeney, Franck Ramus, Jingjing ZhaoAbstract:A number of studies have shown an abnormal Connectivity of certain white matter pathways in developmental dyslexia, as well as correlations between these white matter pathways and behavioral deficits. However, whether developmental dyslexia presents broader white matter Network Connectivity disruption is currently unknown. The present study reconstructed white matter Networks for 26 dyslexic children (11.61 AE 1.31 years) and 31 age-matched controls (11.49 AE 1.36 years) using constrained spherical deconvolution tractography. Network-based statistics (NBS) analysis was performed to identify Network Connectivity deficits in dyslexic individuals. Network topological features were measured based on graph theory to examine whether these parameters correlate with literacy skills, and whether they explain additional variance over previously established white matter Connectivity abnormalities in dyslexic children. The NBS analysis identified a Network connecting the left-occipital-temporal cortex and temporo-parietal cortex that had decreased streamlines in dyslexic children. Four Network topo-logical parameters (clustering coefficient, local efficiency, transitivity, and global efficiency) were positively correlated with literacy skills of dyslexic children, and explained a substantial proportion of additional variance in literacy skills beyond Connectivity measures of white matter pathways. This study for the first time reports a disconnection in a local subNetwork in the left hemisphere in dyslexia and shows that the global white matter Network topological properties contribute to reduced literacy skills in dyslexic children. K E Y W O R D S dyslexia, graph theory, literacy, NBS, white matter Network
Stijn Michielse - One of the best experts on this subject based on the ideXlab platform.
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Microstructural white matter Network-Connectivity in individuals with psychotic disorder, unaffected siblings and controls.
NeuroImage. Clinical, 2019Co-Authors: Stijn Michielse, Kimberley Rakijo, Sanne Peeters, Wolfgang Viechtbauer, Jim Van Os, Machteld MarcelisAbstract:Abstract Background Altered structural Network-Connectivity has been reported in psychotic disorder but whether these alterations are associated with genetic vulnerability, and/or with phenotypic variation, has been less well examined. This study examined i) whether differences in Network-Connectivity exist between patients with psychotic disorder, siblings of patients with psychotic disorder and controls, and ii) whether Network-Connectivity alterations vary with (subclinical) symptomatology. Methods Network-Connectivity measures (global efficiency (GE), density, local efficiency (LE), clustering coefficient (CC)) were derived from diffusion weighted imaging (DWI) and were compared between 85 patients with psychotic disorder, 93 siblings without psychotic disorder and 80 healthy comparison subjects using multilevel regression models. In patients, associations between Positive and Negative Syndrome Scale (PANSS) symptoms and topological measures were examined. In addition, interactions between subclinical psychopathology and sibling/healthy comparison subject status were examined in models of topological measures. Results While there was no main effect of group with respect to GE, density, LE and CC, siblings had a significantly higher CC compared to patients ( B = 0.0039, p = .002). In patients, none of the PANSS symptom domains were significantly associated with any of the four Network-Connectivity measures. The two-way interaction between group and SIR-r positive score in the model of LE was significant ( χ 2 = 6.24, p = .01, df = 1 ). In the model of CC, the interactions between group and respectively SIS-r positive ( χ 2 = 5.59, p = .02, df = 1 ) and negative symptom scores ( χ 2 = 4.71, p = .03, df = 1 ) were significant. Stratified analysis showed that, in siblings, decreased LE and CC was significantly associated with increased SIS-r positive scores (LE: B = −0.0049, p = .003, CC: B = −0.0066, p = .01 ) and that decreased CC was significantly associated with increased SIS-r negative scores ( B = −0.012, p = .003). There were no significant interactions between group and SIS-r scores in the models of GE and density. Conclusion The findings indicate absence of structural Network-Connectivity alterations in individuals with psychotic disorder and in individuals at higher than average genetic risk for psychotic disorder, in comparison with healthy subjects. The differential subclinical symptom-Network Connectivity associations in siblings with respect to controls may be a sign of psychosis vulnerability in the siblings.
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White matter microstructure and Network-Connectivity in emerging adults with subclinical psychotic experiences
Brain Imaging and Behavior, 2019Co-Authors: Stijn Michielse, Iris Lange, Jindra Bakker, Liesbet Goossens, Simone Verhagen, Marieke Wichers, Ritsaert Lieverse, Koen Schruers, Therese Amelsvoort, Jim OsAbstract:Group comparisons of individuals with psychotic disorder and controls have shown alterations in white matter microstructure. Whether white matter microstructure and Network Connectivity is altered in adolescents with subclinical psychotic experiences (PE) at the lowest end of the psychosis severity spectrum is less clear. DWI scan were acquired in 48 individuals with PE and 43 healthy controls (HC). Traditional tensor-derived indices: Fractional Anisotropy, Axial Diffusivity, Mean Diffusivity and Radial Diffusivity, as well as Network Connectivity measures (global/local efficiency and clustering coefficient) were compared between the groups. Subclinical psychopathology was assessed with the Community Assessment of Psychic Experiences (CAPE) and Montgomery–Åsberg Depression Rating Scale (MADRS) questionnaires and, in order to capture momentary subclinical expression of psychosis, the Experience Sampling Method (ESM) questionnaires. Within the PE-group, interactions between subclinical (momentary) symptoms and brain regions in the model of tensor-derived indices and Network Connectivity measures were investigated in a hypothesis-generating fashion. Whole brain analyses showed no group differences in tensor-derived indices and Network Connectivity measures. In the PE-group, a higher positive symptom distress score was associated with both higher local efficiency and clustering coefficient in the right middle temporal pole. The findings indicate absence of microstructural white matter differences between emerging adults with subclinical PE and controls. In the PE-group, attenuated symptoms were positively associated with Network efficiency/cohesion, which requires replication and may indicate Network alterations in emerging mild psychopathology.
Chenglin Lou - One of the best experts on this subject based on the ideXlab platform.
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White matter Network Connectivity deficits in developmental dyslexia
Human Brain Mapping, 2019Co-Authors: Chenglin Lou, Xiting Duan, Irene Altarelli, John Sweeney, Franck Ramus, Jingjing ZhaoAbstract:A number of studies have shown an abnormal Connectivity of certain white matter pathways in developmental dyslexia, as well as correlations between these white matter pathways and behavioral deficits. However, whether developmental dyslexia presents broader white matter Network Connectivity disruption is currently unknown. The present study reconstructed white matter Networks for 26 dyslexic children (11.61 AE 1.31 years) and 31 age-matched controls (11.49 AE 1.36 years) using constrained spherical deconvolution tractography. Network-based statistics (NBS) analysis was performed to identify Network Connectivity deficits in dyslexic individuals. Network topological features were measured based on graph theory to examine whether these parameters correlate with literacy skills, and whether they explain additional variance over previously established white matter Connectivity abnormalities in dyslexic children. The NBS analysis identified a Network connecting the left-occipital-temporal cortex and temporo-parietal cortex that had decreased streamlines in dyslexic children. Four Network topo-logical parameters (clustering coefficient, local efficiency, transitivity, and global efficiency) were positively correlated with literacy skills of dyslexic children, and explained a substantial proportion of additional variance in literacy skills beyond Connectivity measures of white matter pathways. This study for the first time reports a disconnection in a local subNetwork in the left hemisphere in dyslexia and shows that the global white matter Network topological properties contribute to reduced literacy skills in dyslexic children. K E Y W O R D S dyslexia, graph theory, literacy, NBS, white matter Network
Machteld Marcelis - One of the best experts on this subject based on the ideXlab platform.
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Microstructural white matter Network-Connectivity in individuals with psychotic disorder, unaffected siblings and controls.
NeuroImage. Clinical, 2019Co-Authors: Stijn Michielse, Kimberley Rakijo, Sanne Peeters, Wolfgang Viechtbauer, Jim Van Os, Machteld MarcelisAbstract:Abstract Background Altered structural Network-Connectivity has been reported in psychotic disorder but whether these alterations are associated with genetic vulnerability, and/or with phenotypic variation, has been less well examined. This study examined i) whether differences in Network-Connectivity exist between patients with psychotic disorder, siblings of patients with psychotic disorder and controls, and ii) whether Network-Connectivity alterations vary with (subclinical) symptomatology. Methods Network-Connectivity measures (global efficiency (GE), density, local efficiency (LE), clustering coefficient (CC)) were derived from diffusion weighted imaging (DWI) and were compared between 85 patients with psychotic disorder, 93 siblings without psychotic disorder and 80 healthy comparison subjects using multilevel regression models. In patients, associations between Positive and Negative Syndrome Scale (PANSS) symptoms and topological measures were examined. In addition, interactions between subclinical psychopathology and sibling/healthy comparison subject status were examined in models of topological measures. Results While there was no main effect of group with respect to GE, density, LE and CC, siblings had a significantly higher CC compared to patients ( B = 0.0039, p = .002). In patients, none of the PANSS symptom domains were significantly associated with any of the four Network-Connectivity measures. The two-way interaction between group and SIR-r positive score in the model of LE was significant ( χ 2 = 6.24, p = .01, df = 1 ). In the model of CC, the interactions between group and respectively SIS-r positive ( χ 2 = 5.59, p = .02, df = 1 ) and negative symptom scores ( χ 2 = 4.71, p = .03, df = 1 ) were significant. Stratified analysis showed that, in siblings, decreased LE and CC was significantly associated with increased SIS-r positive scores (LE: B = −0.0049, p = .003, CC: B = −0.0066, p = .01 ) and that decreased CC was significantly associated with increased SIS-r negative scores ( B = −0.012, p = .003). There were no significant interactions between group and SIS-r scores in the models of GE and density. Conclusion The findings indicate absence of structural Network-Connectivity alterations in individuals with psychotic disorder and in individuals at higher than average genetic risk for psychotic disorder, in comparison with healthy subjects. The differential subclinical symptom-Network Connectivity associations in siblings with respect to controls may be a sign of psychosis vulnerability in the siblings.
Arnaud Dragicevic - One of the best experts on this subject based on the ideXlab platform.
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SPACETIME DISCOUNTED VALUE OF Network Connectivity
Advances in Complex Systems, 2018Co-Authors: Arnaud DragicevicAbstract:In order to unveil the value of Network Connectivity, discounted both in space and time, we formalize the construction of Networks as an optimal control dynamic graph-theoretic problem. The Network...
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Spacetime discounted value of Network Connectivity
Advances in Complex Systems, 2018Co-Authors: Arnaud DragicevicAbstract:In order to unveil the value of Network Connectivity, discounted both in space and time, we formalize the construction of Networks as an optimal control dynamic graph-theoretic problem. The Network is based on a set of leaders and followers linked through edges. The node dynamics, built upon the consensus protocol, form a time evolutive Mahalanobis distance weighted by the opportunity costs. The results show that the Network equilibrium depends on the influence of leader nodes, while the Network Connectivity depends on the cohesiveness among followers. Through numerical simulations, we find that
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Network Connectivity value
Journal of Theoretical Biology, 2017Co-Authors: Arnaud Dragicevic, Vincent Boulanger, Max Bruciamacchie, Sandrine Chauchard, Jean-luc Dupouey, Anne StengerAbstract:In order to unveil the value of Network Connectivity, we formalize the construction of ecological Networks in forest environments as an optimal control dynamic graph-theoretic problem. The Network is based on a set of bioreserves and patches linked by ecological corridors. The node dynamics, built upon the consensus protocol, form a time evolutive Mahalanobis distance weighted by the opportunity costs of timber production. We consider a case of complete graph, where the ecological Network is fully connected, and a case of incomplete graph, where the ecological Network is partially connected. The results show that the Network equilibrium depends on the size of the reception zone, while the Network Connectivity depends on the environmental compatibility between the ecological areas. Through shadow prices, we find that securing Connectivity in partially connected Networks is more expensive than in fully connected Networks, but should be undertaken when the opportunity costs are significant.
