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10.1038/s41598-017-14613-4 http://scihub22266oqcxt.onion/10.1038/s41598-017-14613-4 C5663902!5663902!29089582     free     free     free Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Sci+Rep 2017 ; 7 (ä): ä Nephropedia Template TP {{tp|p=29089582|t=2017. Graph theory reveals amygdala modules consistent with its anatomical subdivisions |pdf=|usr=}}{{29089582}} gab.com Text Graph theory reveals amygdala modules consistent with its anatomical subdivisions JO: Sci Rep http://www.kidney.de/pget.php?&tw=1&pmid=29089582 #FOAvip Similarities on the cellular and neurochemical composition of the amygdaloid subnuclei suggests their clustering into subunits that exhibit unique functional organization. The topological principle of community structure has been used to identify functional subnetworks in neuroimaging data that reflect the brain effective organization. Here we used modularity to investigate the organization of the amygdala using resting state functional magnetic resonance imaging (rsfMRI) data. Our goal was to determine whether such topological organization would reliably reflect the known neurobiology of individual amygdaloid nuclei, allowing for human imaging studies to accurately reflect the underlying neurobiology. Modularity analysis identified amygdaloid elements consistent with the main anatomical subdivisions of the amygdala that embody distinct functional and structural properties. Additionally, functional connectivity pathways of these subunits and their correlation with task-induced amygdala activation revealed distinct functional profiles consistent with the neurobiology of the amygdala nuclei. These modularity findings corroborate the structure?function relationship between amygdala anatomical substructures, supporting the use of network analysis techniques to generate biologically meaningful partitions of brain structures. Twit Text FOAVip Graph theory reveals amygdala modules consistent with its anatomical subdivisions ????Sci Rep http://www.kidney.de/pget.php?&tw=1&pmid=29089582 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=29089582 #FOAvip English Wikipedia <ref>{{Cite pmid|29089582}}</ref> Graph theory reveals amygdala modules consistent with its anatomical subdivisions #MMPMID29089582 Caparelli EC; Ross TJ; Gu H; Liang X; Stein EA; Yang Y Sci Rep 2017[]; 7 (ä): ä PMID29089582show ga Similarities on the cellular and neurochemical composition of the amygdaloid subnuclei suggests their clustering into subunits that exhibit unique functional organization. The topological principle of community structure has been used to identify functional subnetworks in neuroimaging data that reflect the brain effective organization. Here we used modularity to investigate the organization of the amygdala using resting state functional magnetic resonance imaging (rsfMRI) data. Our goal was to determine whether such topological organization would reliably reflect the known neurobiology of individual amygdaloid nuclei, allowing for human imaging studies to accurately reflect the underlying neurobiology. Modularity analysis identified amygdaloid elements consistent with the main anatomical subdivisions of the amygdala that embody distinct functional and structural properties. Additionally, functional connectivity pathways of these subunits and their correlation with task-induced amygdala activation revealed distinct functional profiles consistent with the neurobiology of the amygdala nuclei. These modularity findings corroborate the structure?function relationship between amygdala anatomical substructures, supporting the use of network analysis techniques to generate biologically meaningful partitions of brain structures. äGraph theory reveals amygdala modules consistent with its anatomical s(epmc).pdf DeepDyve Pubget Overpricing