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10.1089/brain.2015.0360 http://scihub22266oqcxt.onion/10.1089/brain.2015.0360 C4779965!4779965!26596775     free     free     free Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 245.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Brain+Connect 2016 ; 6 (2): 152-63 Nephropedia Template TP {{tp|p=26596775|t=2016. Scale-Dependent Variability and Quantitative Regimes in Graph-Theoretic Representations of Human Cortical Networks |pdf=|usr=}}{{26596775}} gab.com Text Scale-Dependent Variability and Quantitative Regimes in Graph-Theoretic Representations of Human Cortical Networks JO: Brain Connect http://www.kidney.de/pget.php?&tw=1&pmid=26596775 #FOAvip Studying brain connectivity is important due to potential differences in brain circuitry between health and disease. One drawback of graph-theoretic approaches to this is that their results are dependent on the spatial scale at which brain circuitry is examined and explicitly on how vertices and edges are defined in network models. To investigate this, magnetic resonance and diffusion tensor images were acquired from 136 healthy adults, and each subject's cortex was parceled into as many as 50,000 regions. Regions were represented as nodes in a reconstructed network representation, and interregional connectivity was inferred via deterministic tractography. Network model behavior was explored as a function of nodal number and connectivity weighing. Three distinct regimes of quantitative behavior assumed by network models as a function of spatial scale are identified, and their existence may be modulated by the spatial folding scale of the cortex. The maximum number of network nodes used to model human brain circuitry in this study (?50,000) is larger than in previous macroscale neuroimaging studies. Results suggest that network model properties vary appreciably as a function of vertex assignment convention and edge weighing scheme and that graph-theoretic analysis results should not be compared across spatial scales without appropriate understanding of how spatial scale and model topology modulate network model properties. These findings have implications for comparing macro- to mesoscale studies of brain network models and understanding how choosing network-theoretic parameters affects the interpretation of brain connectivity studies. Twit Text FOAVip Scale-Dependent Variability and Quantitative Regimes in Graph-Theoretic Representations of Human Cortical Networks ????Brain Connect http://www.kidney.de/pget.php?&tw=1&pmid=26596775 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=26596775 #FOAvip English Wikipedia <ref>{{Cite pmid|26596775}}</ref> Scale-Dependent Variability and Quantitative Regimes in Graph-Theoretic Representations of Human Cortical Networks #MMPMID26596775 Irimia A; Van Horn JD Brain Connect 2016[Mar]; 6 (2): 152-63 PMID26596775show ga Studying brain connectivity is important due to potential differences in brain circuitry between health and disease. One drawback of graph-theoretic approaches to this is that their results are dependent on the spatial scale at which brain circuitry is examined and explicitly on how vertices and edges are defined in network models. To investigate this, magnetic resonance and diffusion tensor images were acquired from 136 healthy adults, and each subject's cortex was parceled into as many as 50,000 regions. Regions were represented as nodes in a reconstructed network representation, and interregional connectivity was inferred via deterministic tractography. Network model behavior was explored as a function of nodal number and connectivity weighing. Three distinct regimes of quantitative behavior assumed by network models as a function of spatial scale are identified, and their existence may be modulated by the spatial folding scale of the cortex. The maximum number of network nodes used to model human brain circuitry in this study (?50,000) is larger than in previous macroscale neuroimaging studies. Results suggest that network model properties vary appreciably as a function of vertex assignment convention and edge weighing scheme and that graph-theoretic analysis results should not be compared across spatial scales without appropriate understanding of how spatial scale and model topology modulate network model properties. These findings have implications for comparing macro- to mesoscale studies of brain network models and understanding how choosing network-theoretic parameters affects the interpretation of brain connectivity studies. äScale-Dependent Variability and Quantitative Regimes in Graph-Theoreti(epmc).pdf DeepDyve Pubget Overpricing