{"id":24941,"date":"2025-07-14T15:53:44","date_gmt":"2025-07-14T13:53:44","guid":{"rendered":"https:\/\/sano.science\/?post_type=research&#038;p=24941"},"modified":"2025-07-14T15:54:18","modified_gmt":"2025-07-14T13:54:18","slug":"comparing-structure-function-relationships-in-brain-networks-using-eeg-and-fnirs","status":"publish","type":"research","link":"https:\/\/sano.science\/research\/comparing-structure-function-relationships-in-brain-networks-using-eeg-and-fnirs\/","title":{"rendered":"Comparing structure\u2013function relationships in brain networks using EEG and fNIRS"},"content":{"rendered":"\n<h2 class=\"wp-block-heading eplus-wrapper\" id=\"h-rosmary-blanco-maria-giulia-preti-cemal-koba-dimitri-van-de-ville-alessandro-crimi\">Rosmary Blanco,\u00a0Maria Giulia Preti,\u00a0Cemal Koba,\u00a0 Dimitri Van De Ville, Alessandro Crimi\u00a0<\/h2>\n\n\n\n<div style=\"height:50px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n\n\n\n<p class=\" eplus-wrapper\">Understanding how structural and functional brain networks interact is key to uncovering the principles behind large-scale brain organization. While techniques like functional near-infrared spectroscopy (fNIRS) hold promise for studying these relationships, their full potential remains largely untapped. In this research, we analyzed data from 18 participants using simultaneous EEG and fNIRS recordings to examine how structural and functional connectivity align at different timescales, both at rest and during motor imagery tasks\u2014an area still not fully explored. By applying graph signal processing methods, we evaluated differences in structure\u2013function coupling between hemodynamic (fNIRS) and electrical (EEG) signals under varying brain states. TO: We evaluated differences in the structure\u2013function relationship between hemodynamic (fNIRS) and electrical (EEG) networks by applying graph signal processing. Results show that fNIRS structure\u2013function coupling resembles slower-frequency EEG coupling at rest, with variations across brain states and oscillations. Locally, the relationship is heterogeneous, following the unimodal to transmodal gradient. Discrepancies between EEG and fNIRS are noted, particularly in the frontoparietal network. Cross-band representations of neural activity revealed lower correspondence between electrical and hemodynamic activity in the transmodal cortex, irrespective of brain state, while showing specificity for the somatomotor network during a motor imagery task. Overall, these findings initiate a multimodal comprehension of structure\u2013function relationship and brain organization when using affordable functional brain imaging.\u00a0<\/p>\n\n\n\n<div style=\"height:105px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n\n\n\n<p class=\" eplus-wrapper\"><strong>Autors<\/strong>: <a href=\"https:\/\/sano.science\/people\/rosmary-blanco\/\">Rosmary Blanco<\/a>,\u00a0Maria Giulia Preti,\u00a0<a href=\"https:\/\/sano.science\/people\/cemal-koba\/\">Cemal Koba<\/a>,\u00a0 Dimitri Van De Ville, <a href=\"https:\/\/sano.science\/people\/alessandro-crimi\/\">Alessandro Crimi<\/a>\u00a0<\/p>\n\n\n\n<p class=\" eplus-wrapper\"><strong>DOI<\/strong>: 10.1038\/s41598-024-79817-x\u00a0<\/p>\n\n\n\n<div style=\"height:105px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n\n\n\n\t\n    \n        \n\t\t\t<a href=\"https:\/\/www.nature.com\/articles\/s41598-024-79817-x\" target=\"_blank\" rel= \"noopener noreferrer nofollow\" class=\"button primary \">\n\n\t\t\t\t<span>\n\t\t\t\t\tREAD HERE\n\t\t\t\t<\/span>\n\n\t\t\t<\/a>\n\n        \n    \n","protected":false},"excerpt":{"rendered":"<p>article in journal: Scientific Reports, 2024<\/p>\n","protected":false},"featured_media":0,"template":"","research_type":[8],"research_team":[15],"class_list":["post-24941","research","type-research","status-publish","hentry","research_type-publications","research_team-computational-neuroscience"],"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v27.4 (Yoast SEO v27.4) - 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While techniques like functional near-infrared spectroscopy (fNIRS) hold promise for studying these relationships, their full potential remains largely untapped. In this research, we analyzed data from 18 participants using simultaneous EEG and fNIRS recordings to examine how structural and functional connectivity align at different timescales, both at rest and during motor imagery tasks\u2014an area still not fully explored. By applying graph signal processing methods, we evaluated differences in structure\u2013function coupling between hemodynamic (fNIRS) and electrical (EEG) signals under varying brain states. TO: We evaluated differences in the structure\u2013function relationship between hemodynamic (fNIRS) and electrical (EEG) networks by applying graph signal processing. Results show that fNIRS structure\u2013function coupling resembles slower-frequency EEG coupling at rest, with variations across brain states and oscillations. Locally, the relationship is heterogeneous, following the unimodal to transmodal gradient. Discrepancies between EEG and fNIRS are noted, particularly in the frontoparietal network. Cross-band representations of neural activity revealed lower correspondence between electrical and hemodynamic activity in the transmodal cortex, irrespective of brain state, while showing specificity for the somatomotor network during a motor imagery task. Overall, these findings initiate a multimodal comprehension of structure\u2013function relationship and brain organization when using affordable functional brain imaging.\u00a0<\/p>\n","innerContent":["\n<p class=\" eplus-wrapper\">Understanding how structural and functional brain networks interact is key to uncovering the principles behind large-scale brain organization. While techniques like functional near-infrared spectroscopy (fNIRS) hold promise for studying these relationships, their full potential remains largely untapped. In this research, we analyzed data from 18 participants using simultaneous EEG and fNIRS recordings to examine how structural and functional connectivity align at different timescales, both at rest and during motor imagery tasks\u2014an area still not fully explored. By applying graph signal processing methods, we evaluated differences in structure\u2013function coupling between hemodynamic (fNIRS) and electrical (EEG) signals under varying brain states. TO: We evaluated differences in the structure\u2013function relationship between hemodynamic (fNIRS) and electrical (EEG) networks by applying graph signal processing. Results show that fNIRS structure\u2013function coupling resembles slower-frequency EEG coupling at rest, with variations across brain states and oscillations. Locally, the relationship is heterogeneous, following the unimodal to transmodal gradient. Discrepancies between EEG and fNIRS are noted, particularly in the frontoparietal network. Cross-band representations of neural activity revealed lower correspondence between electrical and hemodynamic activity in the transmodal cortex, irrespective of brain state, while showing specificity for the somatomotor network during a motor imagery task. Overall, these findings initiate a multimodal comprehension of structure\u2013function relationship and brain organization when using affordable functional brain imaging.\u00a0<\/p>\n"]},{"blockName":"core\/spacer","attrs":{"height":"105px","epAnimationGeneratedClass":"edplus_anim-kBMl6x","epGeneratedClass":"eplus-wrapper"},"innerBlocks":[],"innerHTML":"\n<div style=\"height:105px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n","innerContent":["\n<div style=\"height:105px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n"]},{"blockName":"core\/paragraph","attrs":{"epAnimationGeneratedClass":"edplus_anim-f2GF6j","epGeneratedClass":"eplus-wrapper"},"innerBlocks":[],"innerHTML":"\n<p class=\" eplus-wrapper\"><strong>Autors<\/strong>: <a href=\"https:\/\/sano.science\/people\/rosmary-blanco\/\">Rosmary Blanco<\/a>,\u00a0Maria Giulia Preti,\u00a0<a href=\"https:\/\/sano.science\/people\/cemal-koba\/\">Cemal Koba<\/a>,\u00a0 Dimitri Van De Ville, <a href=\"https:\/\/sano.science\/people\/alessandro-crimi\/\">Alessandro Crimi<\/a>\u00a0<\/p>\n","innerContent":["\n<p class=\" eplus-wrapper\"><strong>Autors<\/strong>: <a href=\"https:\/\/sano.science\/people\/rosmary-blanco\/\">Rosmary Blanco<\/a>,\u00a0Maria Giulia Preti,\u00a0<a href=\"https:\/\/sano.science\/people\/cemal-koba\/\">Cemal Koba<\/a>,\u00a0 Dimitri Van De Ville, <a href=\"https:\/\/sano.science\/people\/alessandro-crimi\/\">Alessandro Crimi<\/a>\u00a0<\/p>\n"]},{"blockName":"core\/paragraph","attrs":{"epAnimationGeneratedClass":"edplus_anim-f2GF6j","epGeneratedClass":"eplus-wrapper"},"innerBlocks":[],"innerHTML":"\n<p class=\" eplus-wrapper\"><strong>DOI<\/strong>: 10.1038\/s41598-024-79817-x\u00a0<\/p>\n","innerContent":["\n<p class=\" eplus-wrapper\"><strong>DOI<\/strong>: 10.1038\/s41598-024-79817-x\u00a0<\/p>\n"]},{"blockName":"core\/spacer","attrs":{"height":"105px","epAnimationGeneratedClass":"edplus_anim-kBMl6x","epGeneratedClass":"eplus-wrapper"},"innerBlocks":[],"innerHTML":"\n<div style=\"height:105px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n","innerContent":["\n<div style=\"height:105px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n"]},{"blockName":"acf\/button","attrs":{"title":"READ HERE","button_type":"link","url":"https:\/\/www.nature.com\/articles\/s41598-024-79817-x","button_style":"primary","target":"_blank","button_extra_classes":""},"innerBlocks":[],"innerHTML":"","innerContent":[]}],"meta_data":{"is_automatically_other_posts":true,"number_of_posts":"3","is_automatically_check_also_posts":true},"_links":{"self":[{"href":"https:\/\/sano.science\/index.php\/wp-json\/wp\/v2\/research\/24941","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/sano.science\/index.php\/wp-json\/wp\/v2\/research"}],"about":[{"href":"https:\/\/sano.science\/index.php\/wp-json\/wp\/v2\/types\/research"}],"version-history":[{"count":7,"href":"https:\/\/sano.science\/index.php\/wp-json\/wp\/v2\/research\/24941\/revisions"}],"predecessor-version":[{"id":24948,"href":"https:\/\/sano.science\/index.php\/wp-json\/wp\/v2\/research\/24941\/revisions\/24948"}],"wp:attachment":[{"href":"https:\/\/sano.science\/index.php\/wp-json\/wp\/v2\/media?parent=24941"}],"wp:term":[{"taxonomy":"research_type","embeddable":true,"href":"https:\/\/sano.science\/index.php\/wp-json\/wp\/v2\/research_type?post=24941"},{"taxonomy":"research_team","embeddable":true,"href":"https:\/\/sano.science\/index.php\/wp-json\/wp\/v2\/research_team?post=24941"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}