The Experts below are selected from a list of 360 Experts worldwide ranked by ideXlab platform
Xiaojin Liu - One of the best experts on this subject based on the ideXlab platform.
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intrinsic connectivity patterns of task defined brain networks allow individual prediction of cognitive symptom dimension of schizophrenia and are linked to Molecular Architecture
Biological Psychiatry, 2021Co-Authors: Ji Chen, Juergen Dukart, Felix Hoffstaedter, Justin T Baker, Avram J Holmes, Deniz Vatansever, Thomas Nickljockschat, Xiaojin Liu, Veronika I Muller, Birgit DerntlAbstract:Abstract Background Despite the marked interindividual variability in the clinical presentation of schizophrenia, the extent to which individual dimensions of psychopathology relate to the functional variability in brain networks among patients remains unclear. Here, we address this question using network-based predictive modeling of individual psychopathology along 4 data-driven symptom dimensions. Follow-up analyses assess the Molecular underpinnings of predictive networks by relating them to neurotransmitter-receptor distribution patterns. Methods We investigated resting-state functional magnetic resonance imaging data from 147 patients with schizophrenia recruited at 7 sites. Individual expression along negative, positive, affective, and cognitive symptom dimensions was predicted using a relevance vector machine based on functional connectivity within 17 meta-analytic task networks following repeated 10-fold cross-validation and leave-one-site-out analyses. Results were validated in an independent sample. Networks robustly predicting individual symptom dimensions were spatially correlated with density maps of 9 receptors/transporters from prior Molecular imaging in healthy populations. Results Tenfold and leave-one-site-out analyses revealed 5 predictive network-symptom associations. Connectivity within theory of mind, cognitive reappraisal, and mirror neuron networks predicted negative, positive, and affective symptom dimensions, respectively. Cognitive dimension was predicted by theory of mind and socioaffective default networks. Importantly, these predictions generalized to the independent sample. Intriguingly, these two networks were positively associated with D1 receptor and serotonin reuptake transporter densities as well as dopamine synthesis capacity. Conclusions We revealed a robust association between intrinsic functional connectivity within networks for socioaffective processes and the cognitive dimension of psychopathology. By investigating the Molecular Architecture, this work links dopaminergic and serotonergic systems with the functional topography of brain networks underlying cognitive symptoms in schizophrenia.
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intrinsic connectivity patterns of task defined brain networks allow individual prediction of cognitive symptom dimension of schizophrenia and are linked to Molecular Architecture
Biological Psychiatry, 2020Co-Authors: Ji Chen, Juergen Dukart, Felix Hoffstaedter, Justin T Baker, Avram J Holmes, Deniz Vatansever, Thomas Nickljockschat, Veronika I Muller, Xiaojin LiuAbstract:Abstract Background Despite the marked inter-individual variability in the clinical presentation of schizophrenia, it remains unclear the extent to which individual dimensions of psychopathology relate to the functional variability in brain networks among patients. Here, we address this question using network-based predictive modeling of individual psychopathology along four data-driven symptom dimensions. Follow-up analyses assess the Molecular underpinnings of predictive networks by relating them to neurotransmitter-receptor distribution patterns. Methods We investigated resting-state fMRI data from 147 schizophrenia patients recruited at seven sites. Individual expression along negative, positive, affective, and cognitive symptom dimensions was predicted using relevance vector machine based on functional connectivity within 17 meta-analytic task-networks following a repeated 10-fold cross-validation and leave-one-site-out analyses. Results were validated in an independent sample. Networks robustly predicting individual symptom dimensions were spatially correlated with density maps of nine receptors/transporters from prior Molecular imaging in healthy populations. Results Ten-fold and leave-one-site-out analyses revealed five predictive network-symptom associations. Connectivity within theory-of-mind, cognitive reappraisal, and mirror neuron networks predicted negative, positive, and affective symptom dimensions, respectively. Cognitive dimension was predicted by theory-of-mind and socio-affective-default networks. Importantly, these predictions generalized to the independent sample. Intriguingly, these two networks were positively associated with D1 dopamine receptor and serotonin reuptake transporter densities as well as dopamine-synthesis-capacity. Conclusions We revealed a robust association between intrinsic functional connectivity within networks for socio-affective processes and the cognitive dimension of psychopathology. By investigating the Molecular Architecture, this work links dopaminergic and serotonergic systems with the functional topography of brain networks underlying cognitive symptoms in schizophrenia.
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connectivity patterns of task specific brain networks allow individual prediction of cognitive symptom dimension of schizophrenia and link to Molecular Architecture
bioRxiv, 2020Co-Authors: Ji Chen, Veronika I Mueller, Juergen Dukart, Felix Hoffstaedter, Justin T Baker, Avram J Holmes, Deniz Vatansever, Thomas Nickljockschat, Xiaojin Liu, Birgit DerntlAbstract:Background: Despite the marked inter-individual variability in the clinical presentation of schizophrenia, it remains unclear the extent to which individual dimensions of psychopathology may be reflected in variability across the collective set of functional brain connections. Here, we address this question using network-based predictive modeling of individual psychopathology along four data-driven symptom dimensions. Follow-up analyses assess the Molecular underpinnings of predictive networks by relating them to neurotransmitter-receptor distribution patterns. Methods: We investigated resting-state fMRI data from 147 schizophrenia patients recruited at seven sites. Individual expression along negative, positive, affective, and cognitive symptom dimensions was predicted using relevance vector machine based on functional connectivity within 17 meta-analytic task-networks following a repeated 10-fold cross-validation and leave-one-site-out analyses. Results were validated in an independent sample. Networks robustly predicting individual symptom dimensions were spatially correlated with density maps of nine receptors/transporters from prior Molecular imaging in healthy populations. Results: Ten-fold and leave-one-site-out analyses revealed five predictive network-symptom associations. Connectivity within theory-of-mind, cognitive reappraisal, and mirror neuron networks predicted negative, positive, and affective symptom dimensions, respectively. Cognitive dimension was predicted by theory-of-mind and socio-affective-default networks. Importantly, these predictions generalized to the independent sample. Intriguingly, these two networks were positively associated with D1 dopamine receptor and serotonin reuptake transporter densities as well as dopamine-synthesis-capacity. Conclusions: We revealed a robust association between intrinsic functional connectivity within networks for socio-affective processes and the cognitive dimension of psychopathology. By investigating the Molecular Architecture, the present work links dopaminergic and serotonergic systems with the functional topography of brain networks underlying cognitive symptoms in schizophrenia.
Jianlin Lei - One of the best experts on this subject based on the ideXlab platform.
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Molecular Architecture of the sars cov 2 virus
Social Science Research Network, 2020Co-Authors: Hangping Yao, Yutong Song, Chujie Sun, Jiaxing Zhang, Tian-hao Weng, Zheyuan Zhang, Yong Chen, Linfang Cheng, Danrong Shi, Jianlin LeiAbstract:SARS-CoV-2 is an enveloped virus responsible for the COVID-19 pandemic. Despite recent advances in the structural elucidation of SARS-CoV-2 proteins and the complexes of the spike with the cellular receptor ACE2 or neutralizing antibodies, detailed Architecture of the intact virus remains to be unveiled. Here we report the Molecular assembly of the authentic SARS-CoV-2 virus using cryo-electron tomography and subtomogram averaging. Native structures of the S proteins in both pre- and postfusion conformations were determined to average resolutions of 9-11 A. Compositions of the N-linked glycans from the native spikes were analyzed by mass-spectrometry. The native Architecture of the ribonucleoproteins and its higher-order assemblies were revealed. These characterizations have revealed the Architecture of the SARS-CoV-2 virus to an unprecedented resolution, and shed lights on how the virus packs its ~30 Kb long single-segmented RNA in its lumen. Overall, the results unveiled the Molecular Architecture of the SARS-CoV-2 in native context. Funding: This work was supported in part by funds from Major Project of Zhejiang Provincial Science and Technology Department #2020C03123-1, and National Science and Technology Major Project for the Control and Prevention of Major Infectious Diseases in China (#2018ZX10711001, #2018ZX10102001). Conflict of Interest: The authors declare no competing interests.
Justin T Baker - One of the best experts on this subject based on the ideXlab platform.
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intrinsic connectivity patterns of task defined brain networks allow individual prediction of cognitive symptom dimension of schizophrenia and are linked to Molecular Architecture
Biological Psychiatry, 2021Co-Authors: Ji Chen, Juergen Dukart, Felix Hoffstaedter, Justin T Baker, Avram J Holmes, Deniz Vatansever, Thomas Nickljockschat, Xiaojin Liu, Veronika I Muller, Birgit DerntlAbstract:Abstract Background Despite the marked interindividual variability in the clinical presentation of schizophrenia, the extent to which individual dimensions of psychopathology relate to the functional variability in brain networks among patients remains unclear. Here, we address this question using network-based predictive modeling of individual psychopathology along 4 data-driven symptom dimensions. Follow-up analyses assess the Molecular underpinnings of predictive networks by relating them to neurotransmitter-receptor distribution patterns. Methods We investigated resting-state functional magnetic resonance imaging data from 147 patients with schizophrenia recruited at 7 sites. Individual expression along negative, positive, affective, and cognitive symptom dimensions was predicted using a relevance vector machine based on functional connectivity within 17 meta-analytic task networks following repeated 10-fold cross-validation and leave-one-site-out analyses. Results were validated in an independent sample. Networks robustly predicting individual symptom dimensions were spatially correlated with density maps of 9 receptors/transporters from prior Molecular imaging in healthy populations. Results Tenfold and leave-one-site-out analyses revealed 5 predictive network-symptom associations. Connectivity within theory of mind, cognitive reappraisal, and mirror neuron networks predicted negative, positive, and affective symptom dimensions, respectively. Cognitive dimension was predicted by theory of mind and socioaffective default networks. Importantly, these predictions generalized to the independent sample. Intriguingly, these two networks were positively associated with D1 receptor and serotonin reuptake transporter densities as well as dopamine synthesis capacity. Conclusions We revealed a robust association between intrinsic functional connectivity within networks for socioaffective processes and the cognitive dimension of psychopathology. By investigating the Molecular Architecture, this work links dopaminergic and serotonergic systems with the functional topography of brain networks underlying cognitive symptoms in schizophrenia.
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intrinsic connectivity patterns of task defined brain networks allow individual prediction of cognitive symptom dimension of schizophrenia and are linked to Molecular Architecture
Biological Psychiatry, 2020Co-Authors: Ji Chen, Juergen Dukart, Felix Hoffstaedter, Justin T Baker, Avram J Holmes, Deniz Vatansever, Thomas Nickljockschat, Veronika I Muller, Xiaojin LiuAbstract:Abstract Background Despite the marked inter-individual variability in the clinical presentation of schizophrenia, it remains unclear the extent to which individual dimensions of psychopathology relate to the functional variability in brain networks among patients. Here, we address this question using network-based predictive modeling of individual psychopathology along four data-driven symptom dimensions. Follow-up analyses assess the Molecular underpinnings of predictive networks by relating them to neurotransmitter-receptor distribution patterns. Methods We investigated resting-state fMRI data from 147 schizophrenia patients recruited at seven sites. Individual expression along negative, positive, affective, and cognitive symptom dimensions was predicted using relevance vector machine based on functional connectivity within 17 meta-analytic task-networks following a repeated 10-fold cross-validation and leave-one-site-out analyses. Results were validated in an independent sample. Networks robustly predicting individual symptom dimensions were spatially correlated with density maps of nine receptors/transporters from prior Molecular imaging in healthy populations. Results Ten-fold and leave-one-site-out analyses revealed five predictive network-symptom associations. Connectivity within theory-of-mind, cognitive reappraisal, and mirror neuron networks predicted negative, positive, and affective symptom dimensions, respectively. Cognitive dimension was predicted by theory-of-mind and socio-affective-default networks. Importantly, these predictions generalized to the independent sample. Intriguingly, these two networks were positively associated with D1 dopamine receptor and serotonin reuptake transporter densities as well as dopamine-synthesis-capacity. Conclusions We revealed a robust association between intrinsic functional connectivity within networks for socio-affective processes and the cognitive dimension of psychopathology. By investigating the Molecular Architecture, this work links dopaminergic and serotonergic systems with the functional topography of brain networks underlying cognitive symptoms in schizophrenia.
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connectivity patterns of task specific brain networks allow individual prediction of cognitive symptom dimension of schizophrenia and link to Molecular Architecture
bioRxiv, 2020Co-Authors: Ji Chen, Veronika I Mueller, Juergen Dukart, Felix Hoffstaedter, Justin T Baker, Avram J Holmes, Deniz Vatansever, Thomas Nickljockschat, Xiaojin Liu, Birgit DerntlAbstract:Background: Despite the marked inter-individual variability in the clinical presentation of schizophrenia, it remains unclear the extent to which individual dimensions of psychopathology may be reflected in variability across the collective set of functional brain connections. Here, we address this question using network-based predictive modeling of individual psychopathology along four data-driven symptom dimensions. Follow-up analyses assess the Molecular underpinnings of predictive networks by relating them to neurotransmitter-receptor distribution patterns. Methods: We investigated resting-state fMRI data from 147 schizophrenia patients recruited at seven sites. Individual expression along negative, positive, affective, and cognitive symptom dimensions was predicted using relevance vector machine based on functional connectivity within 17 meta-analytic task-networks following a repeated 10-fold cross-validation and leave-one-site-out analyses. Results were validated in an independent sample. Networks robustly predicting individual symptom dimensions were spatially correlated with density maps of nine receptors/transporters from prior Molecular imaging in healthy populations. Results: Ten-fold and leave-one-site-out analyses revealed five predictive network-symptom associations. Connectivity within theory-of-mind, cognitive reappraisal, and mirror neuron networks predicted negative, positive, and affective symptom dimensions, respectively. Cognitive dimension was predicted by theory-of-mind and socio-affective-default networks. Importantly, these predictions generalized to the independent sample. Intriguingly, these two networks were positively associated with D1 dopamine receptor and serotonin reuptake transporter densities as well as dopamine-synthesis-capacity. Conclusions: We revealed a robust association between intrinsic functional connectivity within networks for socio-affective processes and the cognitive dimension of psychopathology. By investigating the Molecular Architecture, the present work links dopaminergic and serotonergic systems with the functional topography of brain networks underlying cognitive symptoms in schizophrenia.
Ji Chen - One of the best experts on this subject based on the ideXlab platform.
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intrinsic connectivity patterns of task defined brain networks allow individual prediction of cognitive symptom dimension of schizophrenia and are linked to Molecular Architecture
Biological Psychiatry, 2021Co-Authors: Ji Chen, Juergen Dukart, Felix Hoffstaedter, Justin T Baker, Avram J Holmes, Deniz Vatansever, Thomas Nickljockschat, Xiaojin Liu, Veronika I Muller, Birgit DerntlAbstract:Abstract Background Despite the marked interindividual variability in the clinical presentation of schizophrenia, the extent to which individual dimensions of psychopathology relate to the functional variability in brain networks among patients remains unclear. Here, we address this question using network-based predictive modeling of individual psychopathology along 4 data-driven symptom dimensions. Follow-up analyses assess the Molecular underpinnings of predictive networks by relating them to neurotransmitter-receptor distribution patterns. Methods We investigated resting-state functional magnetic resonance imaging data from 147 patients with schizophrenia recruited at 7 sites. Individual expression along negative, positive, affective, and cognitive symptom dimensions was predicted using a relevance vector machine based on functional connectivity within 17 meta-analytic task networks following repeated 10-fold cross-validation and leave-one-site-out analyses. Results were validated in an independent sample. Networks robustly predicting individual symptom dimensions were spatially correlated with density maps of 9 receptors/transporters from prior Molecular imaging in healthy populations. Results Tenfold and leave-one-site-out analyses revealed 5 predictive network-symptom associations. Connectivity within theory of mind, cognitive reappraisal, and mirror neuron networks predicted negative, positive, and affective symptom dimensions, respectively. Cognitive dimension was predicted by theory of mind and socioaffective default networks. Importantly, these predictions generalized to the independent sample. Intriguingly, these two networks were positively associated with D1 receptor and serotonin reuptake transporter densities as well as dopamine synthesis capacity. Conclusions We revealed a robust association between intrinsic functional connectivity within networks for socioaffective processes and the cognitive dimension of psychopathology. By investigating the Molecular Architecture, this work links dopaminergic and serotonergic systems with the functional topography of brain networks underlying cognitive symptoms in schizophrenia.
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intrinsic connectivity patterns of task defined brain networks allow individual prediction of cognitive symptom dimension of schizophrenia and are linked to Molecular Architecture
Biological Psychiatry, 2020Co-Authors: Ji Chen, Juergen Dukart, Felix Hoffstaedter, Justin T Baker, Avram J Holmes, Deniz Vatansever, Thomas Nickljockschat, Veronika I Muller, Xiaojin LiuAbstract:Abstract Background Despite the marked inter-individual variability in the clinical presentation of schizophrenia, it remains unclear the extent to which individual dimensions of psychopathology relate to the functional variability in brain networks among patients. Here, we address this question using network-based predictive modeling of individual psychopathology along four data-driven symptom dimensions. Follow-up analyses assess the Molecular underpinnings of predictive networks by relating them to neurotransmitter-receptor distribution patterns. Methods We investigated resting-state fMRI data from 147 schizophrenia patients recruited at seven sites. Individual expression along negative, positive, affective, and cognitive symptom dimensions was predicted using relevance vector machine based on functional connectivity within 17 meta-analytic task-networks following a repeated 10-fold cross-validation and leave-one-site-out analyses. Results were validated in an independent sample. Networks robustly predicting individual symptom dimensions were spatially correlated with density maps of nine receptors/transporters from prior Molecular imaging in healthy populations. Results Ten-fold and leave-one-site-out analyses revealed five predictive network-symptom associations. Connectivity within theory-of-mind, cognitive reappraisal, and mirror neuron networks predicted negative, positive, and affective symptom dimensions, respectively. Cognitive dimension was predicted by theory-of-mind and socio-affective-default networks. Importantly, these predictions generalized to the independent sample. Intriguingly, these two networks were positively associated with D1 dopamine receptor and serotonin reuptake transporter densities as well as dopamine-synthesis-capacity. Conclusions We revealed a robust association between intrinsic functional connectivity within networks for socio-affective processes and the cognitive dimension of psychopathology. By investigating the Molecular Architecture, this work links dopaminergic and serotonergic systems with the functional topography of brain networks underlying cognitive symptoms in schizophrenia.
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connectivity patterns of task specific brain networks allow individual prediction of cognitive symptom dimension of schizophrenia and link to Molecular Architecture
bioRxiv, 2020Co-Authors: Ji Chen, Veronika I Mueller, Juergen Dukart, Felix Hoffstaedter, Justin T Baker, Avram J Holmes, Deniz Vatansever, Thomas Nickljockschat, Xiaojin Liu, Birgit DerntlAbstract:Background: Despite the marked inter-individual variability in the clinical presentation of schizophrenia, it remains unclear the extent to which individual dimensions of psychopathology may be reflected in variability across the collective set of functional brain connections. Here, we address this question using network-based predictive modeling of individual psychopathology along four data-driven symptom dimensions. Follow-up analyses assess the Molecular underpinnings of predictive networks by relating them to neurotransmitter-receptor distribution patterns. Methods: We investigated resting-state fMRI data from 147 schizophrenia patients recruited at seven sites. Individual expression along negative, positive, affective, and cognitive symptom dimensions was predicted using relevance vector machine based on functional connectivity within 17 meta-analytic task-networks following a repeated 10-fold cross-validation and leave-one-site-out analyses. Results were validated in an independent sample. Networks robustly predicting individual symptom dimensions were spatially correlated with density maps of nine receptors/transporters from prior Molecular imaging in healthy populations. Results: Ten-fold and leave-one-site-out analyses revealed five predictive network-symptom associations. Connectivity within theory-of-mind, cognitive reappraisal, and mirror neuron networks predicted negative, positive, and affective symptom dimensions, respectively. Cognitive dimension was predicted by theory-of-mind and socio-affective-default networks. Importantly, these predictions generalized to the independent sample. Intriguingly, these two networks were positively associated with D1 dopamine receptor and serotonin reuptake transporter densities as well as dopamine-synthesis-capacity. Conclusions: We revealed a robust association between intrinsic functional connectivity within networks for socio-affective processes and the cognitive dimension of psychopathology. By investigating the Molecular Architecture, the present work links dopaminergic and serotonergic systems with the functional topography of brain networks underlying cognitive symptoms in schizophrenia.
James M Anderson - One of the best experts on this subject based on the ideXlab platform.
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Molecular Architecture of tight junctions
Annual Review of Physiology, 1998Co-Authors: Laura L Mitic, James M AndersonAbstract:▪ Abstract The tight junction creates a regulated barrier in the paracellular pathway and, together with the actin-rich adherens junction, forms a functional unit called the apical junction complex. A growing number of tight junction–associated proteins have been identified, but functions are defined for only a few. The intercellular barrier is formed by rows of the transmembrane protein occludin, which is bound on the cytoplasmic surface to ZO-1 and ZO-2. These proteins are members of the membrane-associated guanylate kinase (MAGUK) protein family and are likely to have both structural and signaling roles. Junctional plaque proteins without known functions include cingulin, p130, and 7H6; single reports describe ZA-1TJ and symplekin. Many cellular signaling pathways affect assembly and sealing of junctions. Transducing proteins, which localize within the junction, include both heterotrimeric and rho-related GTP-binding proteins, PKC-ζ and nonreceptor tyrosine kinases. Control of perijunctional actin may ...
