{"id":1912,"date":"2023-11-28T18:12:04","date_gmt":"2023-11-28T18:12:04","guid":{"rendered":"https:\/\/mappingcomplexity.net\/mainpage\/?page_id=1912"},"modified":"2024-02-06T16:27:31","modified_gmt":"2024-02-06T16:27:31","slug":"brain-networks","status":"publish","type":"page","link":"https:\/\/mappingcomplexity.net\/mainpage\/brain-networks\/","title":{"rendered":"Brain Networks"},"content":{"rendered":"\t\t<div data-elementor-type=\"wp-page\" data-elementor-id=\"1912\" class=\"elementor elementor-1912\">\n\t\t\t\t<div class=\"elementor-element elementor-element-ae52721 e-flex e-con-boxed wpr-particle-no wpr-jarallax-no wpr-parallax-no wpr-sticky-section-no e-con e-parent\" data-id=\"ae52721\" data-element_type=\"container\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-de4186c elementor-widget elementor-widget-heading\" data-id=\"de4186c\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<style>\/*! elementor - v3.21.0 - 26-05-2024 *\/\n.elementor-heading-title{padding:0;margin:0;line-height:1}.elementor-widget-heading .elementor-heading-title[class*=elementor-size-]>a{color:inherit;font-size:inherit;line-height:inherit}.elementor-widget-heading .elementor-heading-title.elementor-size-small{font-size:15px}.elementor-widget-heading .elementor-heading-title.elementor-size-medium{font-size:19px}.elementor-widget-heading .elementor-heading-title.elementor-size-large{font-size:29px}.elementor-widget-heading .elementor-heading-title.elementor-size-xl{font-size:39px}.elementor-widget-heading .elementor-heading-title.elementor-size-xxl{font-size:59px}<\/style><h2 class=\"elementor-heading-title elementor-size-default\">Brain Networks<\/h2>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-d578ff2 e-flex e-con-boxed wpr-particle-no wpr-jarallax-no wpr-parallax-no wpr-sticky-section-no e-con e-parent\" data-id=\"d578ff2\" data-element_type=\"container\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-4989cc0 elementor-widget elementor-widget-text-editor\" data-id=\"4989cc0\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<style>\/*! elementor - v3.21.0 - 26-05-2024 *\/\n.elementor-widget-text-editor.elementor-drop-cap-view-stacked .elementor-drop-cap{background-color:#69727d;color:#fff}.elementor-widget-text-editor.elementor-drop-cap-view-framed .elementor-drop-cap{color:#69727d;border:3px solid;background-color:transparent}.elementor-widget-text-editor:not(.elementor-drop-cap-view-default) .elementor-drop-cap{margin-top:8px}.elementor-widget-text-editor:not(.elementor-drop-cap-view-default) .elementor-drop-cap-letter{width:1em;height:1em}.elementor-widget-text-editor .elementor-drop-cap{float:left;text-align:center;line-height:1;font-size:50px}.elementor-widget-text-editor .elementor-drop-cap-letter{display:inline-block}<\/style>\t\t\t\t<p><img fetchpriority=\"high\" decoding=\"async\" class=\"alignleft wp-image-1415\" src=\"https:\/\/mappingcomplexity.net\/mainpage\/wp-content\/uploads\/2023\/10\/project3.png\" alt=\"\" width=\"400\" height=\"400\" \/><\/p><div><div><div><div><p>The architecture of the human brain underlies human behavior and is extremely complex with multiple scales interacting with one another. However, research efforts are typically focused on simplified representations and a single spatial scale.<\/p><p>The architecture of the brain is intimately related with it spatial embedding, typically assumed to be the Euclidean space in which it has evolved and develops. Recent advances in network science, however, include the discovery that complex networks have a hidden geometry and that this geometry is hyperbolic. We showed that this methodology can also be used to infer high-quality maps of connectomes, where brain regions are given coordinates in hyperbolic space such that the closer they are the more likely that they are connected. Even if Euclidean space is assumed as the natural geometry of the brain, distances in hyperbolic space offer a more accurate interpretation of the structure of connectomes, which suggests a new perspective for the mapping of the organization of the brain\u2019s neuroanatomical regions.<br \/><br \/>We are also interested in the multiscale spatial organization of the brain. Using two high-quality datasets with connectomes of 84 healthy human subjects with five anatomical resolutions for each, we found that the zoomed-out layers remain self-similar and that the geometric network model, where distances are not Euclidean but hyperbolic, predicts the observations by application of a renormalization protocol. Our results prove that the same principle explains brain connectivity, within the rank of length scales that cover the used datasets, and leads to efficient decentralized communication.<\/p><\/div><\/div><\/div><div>The implications of our discoveries are several. On the one hand, they can be useful in fundamental debates, such as whether the brain is working at the edge of criticality. On the other hand, they can have applications for advanced tools to simulate brain function.<\/div><\/div>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-eda7d61 e-flex e-con-boxed wpr-particle-no wpr-jarallax-no wpr-parallax-no wpr-sticky-section-no e-con e-parent\" data-id=\"eda7d61\" data-element_type=\"container\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-fb9fb6b elementor-widget elementor-widget-heading\" data-id=\"fb9fb6b\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">Related Papers<\/h2>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-3788973 e-flex e-con-boxed wpr-particle-no wpr-jarallax-no wpr-parallax-no wpr-sticky-section-no e-con e-parent\" data-id=\"3788973\" data-element_type=\"container\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-f506fb9 elementor-widget elementor-widget-shortcode\" data-id=\"f506fb9\" data-element_type=\"widget\" data-widget_type=\"shortcode.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<style>\n                .publications-list {\n                    color: #404040;\n                    font-size: 20px;\n                    line-height: 30px;\n                }\n                .publication-item {\n                }\n                .italic {\n                    font-style: italic;\n                }\n                .bold {\n                    font-weight: 600;\n                }\n                .pdf-link {\n                    font-weight: 400;\n                }\n            <\/style><div class=\"publications-list\"><div class='publication-item'>L. Barjuan, J. Soriano, M. &Aacute;ngeles Serrano\r\n<div class='italic'>Optimal navigability of weighted human brain connectomes in physical space\r\n<\/div><div class='bold'>NeuroImage, 297 , 120703 (2024) <span class='pdf-link'>[<a href='https:\/\/arxiv.org\/abs\/2311.10669' target='_blank'>PDF<\/a>]<\/span><\/div><br><div class='publication-item'>Muhua Zheng, Antoine Allard, Patric Hagmann, Yasser Alem&aacute;n-G&oacute;mez, M. &Aacute;ngeles Serrano\r\n<div class='italic'>Geometric renormalization unravels self-similarity of the multiscale human connectome\r\n<\/div><div class='bold'>Proceedings of the National Academy of Sciences USA, 117, 20244-20253 (2020) <span class='pdf-link'>[<a href='https:\/\/www-pnas-org.sire.ub.edu\/content\/117\/33\/20244.abstract' target='_blank'>PDF<\/a>]<\/span><\/div><br><div class='publication-item'>Antoine Allard, M. &Aacute;ngeles Serrano\r\n<div class='italic'>Navigable maps of structural brain networks across species\r\n<\/div><div class='bold'>PLoS Computational Biology, 16, e1007584 (2020) <span class='pdf-link'>[<a href='https:\/\/journals.plos.org\/ploscompbiol\/article?id=10.1371\/journal.pcbi.1007584' target='_blank'>PDF<\/a>]<\/span><\/div><br><\/div>\t\t<div class=\"elementor-shortcode\"><\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t","protected":false},"excerpt":{"rendered":"<p>Brain Networks The architecture of the human brain underlies human behavior and is extremely complex with multiple scales interacting with one another. However, research efforts are typically focused on simplified representations and a single spatial scale. The architecture of the brain is intimately related with it spatial embedding, typically assumed to be the Euclidean space&hellip;<\/p>\n","protected":false},"author":1,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"footnotes":""},"class_list":["post-1912","page","type-page","status-publish","hentry"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v22.8 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Brain Networks - Mapping Complexity Lab<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/mappingcomplexity.net\/mainpage\/brain-networks\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Brain Networks - Mapping Complexity Lab\" \/>\n<meta property=\"og:description\" content=\"Brain Networks The architecture of the human brain underlies human behavior and is extremely complex with multiple scales interacting with one another. 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