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This pre-print is available under a Creative Commons License (Attribution-NonCommercial-NoDerivs 4.0 International), CC BY-NC-ND 4.0, as described at http://creativecommons.org/licenses/by-nc-nd/4.0/" /> <meta name="DC.AccessRights" content="restricted" /> <meta name="DC.Description" content="Higher-Order Interaction (HOI) theory offers a powerful framework for capturing complex, non-linear relationships within multidimensional systems, moving beyond traditional pairwise graph methods to encompass multi-way interactions. This study applies HOI analysis, specifically using hypergraph theory, to explore intricate connectivity patterns in electrophysiological signals from neuroscience. Hypergraphs were constructed from connectivity data across various frequency bands, characterized through metrics such as spectral entropy, hyperedge centrality, and vertex centrality, and compared using spectral and centrality distance measures. Three distinct neurophysiological datasets were analyzed: intracranial EEG signals from rats during different sleep stages, scalp EEG data to distinguish between epilepsy types, and MEG recordings of seizure dynamics. The findings highlight the effectiveness of hypergraph-based HOI analysis in mapping neural dynamics across normal and pathological brain states. In sleep studies, it reveals distinct connectivity patterns between REM and NREM stages, while in epilepsy, it differentiates seizure types and stages, identifying spectral entropy as a potential marker for seizure onset. Notably, HOI analysis captures differences between primary and secondary generalized epilepsy, suggesting enhanced diagnostic accuracy. This approach provides a powerful tool for understanding complex neural interactions in high-dimensional data. ### Competing Interest Statement The authors have declared no competing interest." /> <meta name="DC.Contributor" content="Dalma Bilbao" /> <meta name="DC.Contributor" content="Hugo Aimar" /> <meta name="DC.Contributor" content="Pablo Torterolo" /> <meta name="DC.Contributor" content="Diego M. Mateos" /> <meta name="article:published_time" content="2024-11-22" /> <meta name="article:section" content="New Results" /> <meta name="citation_title" content="Higher-Order Interaction Analysis via Hypergraph Models for Studying Multidimensional Neuroscience Data" /> <meta name="citation_abstract" lang="en" content="<p>Higher-Order Interaction (HOI) theory offers a powerful framework for capturing complex, non-linear relationships within multidimensional systems, moving beyond traditional pairwise graph methods to encompass multi-way interactions. This study applies HOI analysis, specifically using hypergraph theory, to explore intricate connectivity patterns in electrophysiological signals from neuroscience. Hypergraphs were constructed from connectivity data across various frequency bands, characterized through metrics such as spectral entropy, hyperedge centrality, and vertex centrality, and compared using spectral and centrality distance measures. Three distinct neurophysiological datasets were analyzed: intracranial EEG signals from rats during different sleep stages, scalp EEG data to distinguish between epilepsy types, and MEG recordings of seizure dynamics. The findings highlight the effectiveness of hypergraph-based HOI analysis in mapping neural dynamics across normal and pathological brain states. In sleep studies, it reveals distinct connectivity patterns between REM and NREM stages, while in epilepsy, it differentiates seizure types and stages, identifying spectral entropy as a potential marker for seizure onset. Notably, HOI analysis captures differences between primary and secondary generalized epilepsy, suggesting enhanced diagnostic accuracy. This approach provides a powerful tool for understanding complex neural interactions in high-dimensional data.</p>" /> <meta name="citation_journal_title" content="bioRxiv" /> <meta name="citation_publisher" content="Cold Spring Harbor Laboratory" /> <meta name="citation_publication_date" content="2024/01/01" /> <meta name="citation_mjid" content="biorxiv;2024.11.22.624800v1" /> <meta name="citation_id" content="2024.11.22.624800v1" /> <meta name="citation_public_url" content="https://www.biorxiv.org/content/10.1101/2024.11.22.624800v1" /> <meta name="citation_abstract_html_url" content="https://www.biorxiv.org/content/10.1101/2024.11.22.624800v1.abstract" /> <meta name="citation_full_html_url" content="https://www.biorxiv.org/content/10.1101/2024.11.22.624800v1.full" /> <meta name="citation_pdf_url" content="https://www.biorxiv.org/content/biorxiv/early/2024/11/22/2024.11.22.624800.full.pdf" /> <meta name="citation_doi" content="10.1101/2024.11.22.624800" /> <meta name="citation_num_pages" content="14" /> <meta name="citation_article_type" content="Article" /> <meta name="citation_section" content="New Results" /> <meta name="citation_firstpage" content="2024.11.22.624800" /> <meta name="citation_author" content="Dalma Bilbao" /> <meta name="citation_author_institution" content="Instituto de Matemática Aplicada del Litoral (IMAL-CONICET-UNL)" /> <meta name="citation_author" content="Hugo Aimar" /> <meta name="citation_author_institution" content="Instituto de Matemática Aplicada del Litoral (IMAL-CONICET-UNL)" /> <meta name="citation_author" content="Pablo Torterolo" /> <meta name="citation_author_institution" content="Laboratorio de Neurobiología del Sueño, Departamento de Fisiología, Facultad de Medicina" /> <meta name="citation_author_orcid" content="https://orcid.org/0000-0002-3531-5008" /> <meta name="citation_author" content="Diego M. 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This study applies HOI analysis, specifically using hypergraph theory, to explore intricate connectivity patterns in electrophysiological signals from neuroscience. Hypergraphs were constructed from connectivity data across various frequency bands, characterized through metrics such as spectral entropy, hyperedge centrality, and vertex centrality, and compared using spectral and centrality distance measures. Three distinct neurophysiological datasets were analyzed: intracranial EEG signals from rats during different sleep stages, scalp EEG data to distinguish between epilepsy types, and MEG recordings of seizure dynamics. The findings highlight the effectiveness of hypergraph-based HOI analysis in mapping neural dynamics across normal and pathological brain states. In sleep studies, it reveals distinct connectivity patterns between REM and NREM stages, while in epilepsy, it differentiates seizure types and stages, identifying spectral entropy as a potential marker for seizure onset. Notably, HOI analysis captures differences between primary and secondary generalized epilepsy, suggesting enhanced diagnostic accuracy. This approach provides a powerful tool for understanding complex neural interactions in high-dimensional data. ### Competing Interest Statement The authors have declared no competing interest." /> <meta name="og-title" property="og:title" content="Higher-Order Interaction Analysis via Hypergraph Models for Studying Multidimensional Neuroscience Data" /> <meta name="og-url" property="og:url" content="https://www.biorxiv.org/content/10.1101/2024.11.22.624800v1" /> <meta name="og-site-name" property="og:site_name" content="bioRxiv" /> <meta name="og-description" property="og:description" content="Higher-Order Interaction (HOI) theory offers a powerful framework for capturing complex, non-linear relationships within multidimensional systems, moving beyond traditional pairwise graph methods to encompass multi-way interactions. This study applies HOI analysis, specifically using hypergraph theory, to explore intricate connectivity patterns in electrophysiological signals from neuroscience. Hypergraphs were constructed from connectivity data across various frequency bands, characterized through metrics such as spectral entropy, hyperedge centrality, and vertex centrality, and compared using spectral and centrality distance measures. Three distinct neurophysiological datasets were analyzed: intracranial EEG signals from rats during different sleep stages, scalp EEG data to distinguish between epilepsy types, and MEG recordings of seizure dynamics. The findings highlight the effectiveness of hypergraph-based HOI analysis in mapping neural dynamics across normal and pathological brain states. In sleep studies, it reveals distinct connectivity patterns between REM and NREM stages, while in epilepsy, it differentiates seizure types and stages, identifying spectral entropy as a potential marker for seizure onset. Notably, HOI analysis captures differences between primary and secondary generalized epilepsy, suggesting enhanced diagnostic accuracy. This approach provides a powerful tool for understanding complex neural interactions in high-dimensional data. ### Competing Interest Statement The authors have declared no competing interest." /> <meta name="og-type" property="og:type" content="article" /> <meta name="og-image" property="og:image" content="https://www.biorxiv.org/sites/default/files/images/biorxiv_logo_homepage7-5-small.png" /> <meta name="citation_date" content="2024-11-22" /> <link rel="alternate" type="application/vnd.ms-powerpoint" title="Powerpoint" href="/content/10.1101/2024.11.22.624800v1.ppt" /> <meta name="description" content="bioRxiv - the preprint server for biology, operated by Cold Spring Harbor Laboratory, a research and educational institution" /> <meta name="generator" content="Drupal 7 (http://drupal.org)" /> <link rel="canonical" href="https://www.biorxiv.org/content/10.1101/2024.11.22.624800v1" /> <link rel="shortlink" href="https://www.biorxiv.org/node/4240610" /> <title>Higher-Order Interaction 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class="highwire-article-citation highwire-citation-type-highwire-article node4240610" data-node-nid="4240610" id="node-4240610--2780488982" data-pisa="biorxiv;2024.11.22.624800v1" data-pisa-master="biorxiv;2024.11.22.624800" data-apath="/biorxiv/early/2024/11/22/2024.11.22.624800.atom" data-hw-author-tooltip-instance="highwire_author_tooltip"><div class="highwire-cite highwire-cite-highwire-article highwire-citation-biorxiv-article-top clearfix has-author-tooltip" > <span class="biorxiv-article-type"> New Results </span> <h1 class="highwire-cite-title" id="page-title">Higher-Order Interaction Analysis via Hypergraph Models for Studying Multidimensional Neuroscience Data</h1> <div class="highwire-cite-authors" ><span class="highwire-citation-authors"><span class="highwire-citation-author first" data-delta="0"><span class="nlm-given-names">Dalma</span> <span class="nlm-surname">Bilbao</span></span>, <span class="highwire-citation-author" data-delta="1"><span class="nlm-given-names">Hugo</span> <span class="nlm-surname">Aimar</span></span>, <span class="highwire-citation-author hw-author-orcid-logo-wrapper" data-delta="2"><a href="https://orcid.org/0000-0002-3531-5008" target="_blank" class="hw-author-orcid-logo link-icon-only link-icon"><span class="hw-icon-orcid hw-icon-color-orcid"></span> <span class="title element-invisible">View ORCID Profile</span></a><span class="nlm-given-names">Pablo</span> <span class="nlm-surname">Torterolo</span></span>, <span class="highwire-citation-author hw-author-orcid-logo-wrapper" data-delta="3"><a href="https://orcid.org/0000-0002-1953-0875" target="_blank" class="hw-author-orcid-logo link-icon-only link-icon"><span class="hw-icon-orcid hw-icon-color-orcid"></span> <span class="title element-invisible">View ORCID Profile</span></a><span class="nlm-given-names">Diego M.</span> <span class="nlm-surname">Mateos</span></span></span></div> <div class="highwire-cite-metadata" ><span class="highwire-cite-metadata-doi highwire-cite-metadata"><span class="label">doi:</span> https://doi.org/10.1101/2024.11.22.624800 </span></div> </div> <div id="hw-article-author-popups-node-4240610--2780488982" style="display: none;"><div class="author-tooltip-0"><div class="author-tooltip-name">Dalma Bilbao </div><div class="author-tooltip-affiliation"><span class="author-tooltip-text"><div class='author-affiliation'><span class='nlm-sup'>1</span><span class='nlm-institution'>Instituto de Matemática Aplicada del Litoral (IMAL-CONICET-UNL)</span>, CCT CONICET, Santa Fé, <span class='nlm-country'>Argentina</span></div></span></div><ul class="author-tooltip-find-more"><li class="author-tooltip-gs-link first"><a href="/lookup/google-scholar?link_type=googlescholar&gs_type=author&author%5B0%5D=Dalma%2BBilbao%2B" target="_blank" class="" data-icon-position="" data-hide-link-title="0">Find this author on Google Scholar</a></li><li class="author-tooltip-pubmed-link"><a href="/lookup/external-ref?access_num=Bilbao%20D&link_type=AUTHORSEARCH" target="_blank" class="" data-icon-position="" data-hide-link-title="0">Find this author on PubMed</a></li><li class="author-site-search-link last"><a href="/search/author1%3ADalma%2BBilbao%2B" rel="nofollow" class="" data-icon-position="" data-hide-link-title="0">Search for this author on this site</a></li></ul></div><div class="author-tooltip-1"><div class="author-tooltip-name">Hugo Aimar </div><div class="author-tooltip-affiliation"><span class="author-tooltip-text"><div class='author-affiliation'><span class='nlm-sup'>1</span><span class='nlm-institution'>Instituto de Matemática Aplicada del Litoral (IMAL-CONICET-UNL)</span>, CCT CONICET, Santa Fé, <span class='nlm-country'>Argentina</span></div></span></div><ul class="author-tooltip-find-more"><li class="author-tooltip-gs-link first"><a href="/lookup/google-scholar?link_type=googlescholar&gs_type=author&author%5B0%5D=Hugo%2BAimar%2B" target="_blank" class="" data-icon-position="" data-hide-link-title="0">Find this author on Google Scholar</a></li><li class="author-tooltip-pubmed-link"><a href="/lookup/external-ref?access_num=Aimar%20H&link_type=AUTHORSEARCH" target="_blank" class="" data-icon-position="" data-hide-link-title="0">Find this author on PubMed</a></li><li class="author-site-search-link last"><a href="/search/author1%3AHugo%2BAimar%2B" rel="nofollow" class="" data-icon-position="" data-hide-link-title="0">Search for this author on this site</a></li></ul></div><div class="author-tooltip-2"><div class="author-tooltip-name">Pablo Torterolo </div><div class="author-tooltip-affiliation"><span class="author-tooltip-text"><div class='author-affiliation'><span class='nlm-sup'>2</span><span class='nlm-institution'>Laboratorio de Neurobiología del Sueño, Departamento de Fisiología, Facultad de Medicina</span>, Universidad de la República, Montevideo, <span class='nlm-country'>Uruguay</span></div></span></div><ul class="author-tooltip-find-more"><li class="author-tooltip-gs-link first"><a href="/lookup/google-scholar?link_type=googlescholar&gs_type=author&author%5B0%5D=Pablo%2BTorterolo%2B" target="_blank" class="" data-icon-position="" data-hide-link-title="0">Find this author on Google Scholar</a></li><li class="author-tooltip-pubmed-link"><a href="/lookup/external-ref?access_num=Torterolo%20P&link_type=AUTHORSEARCH" target="_blank" class="" data-icon-position="" data-hide-link-title="0">Find this author on PubMed</a></li><li class="author-site-search-link"><a href="/search/author1%3APablo%2BTorterolo%2B" rel="nofollow" class="" data-icon-position="" data-hide-link-title="0">Search for this author on this site</a></li><li class="author-orcid-link last"><a href="https://orcid.org/0000-0002-3531-5008" target="_blank" class="" data-icon-position="" data-hide-link-title="0">ORCID record for Pablo Torterolo</a></li></ul></div><div class="author-tooltip-3"><div class="author-tooltip-name">Diego M. Mateos </div><div class="author-tooltip-affiliation"><span class="author-tooltip-text"><div class='author-affiliation'><span class='nlm-sup'>1</span><span class='nlm-institution'>Instituto de Matemática Aplicada del Litoral (IMAL-CONICET-UNL)</span>, CCT CONICET, Santa Fé, <span class='nlm-country'>Argentina</span></div><div class='author-affiliation'><span class='nlm-sup'>3</span><span class='nlm-institution'>Facultad de Ciencia y Tecnología. Universidad Autónoma de Entre Ríos (UADER). Oro Verde</span>, Entre Ríos, <span class='nlm-country'>Argentina</span></div><div class='author-affiliation'><span class='nlm-sup'>4</span><span class='nlm-institution'>Achucarro Basque Center For Neuroscience</span>. Leioa, Vizcaya, <span class='nlm-country'>Spain</span></div></span></div><ul class="author-tooltip-find-more"><li class="author-tooltip-gs-link first"><a href="/lookup/google-scholar?link_type=googlescholar&gs_type=author&author%5B0%5D=Diego%2BM.%2BMateos%2B" target="_blank" class="" data-icon-position="" data-hide-link-title="0">Find this author on Google Scholar</a></li><li class="author-tooltip-pubmed-link"><a href="/lookup/external-ref?access_num=Mateos%20DM&link_type=AUTHORSEARCH" target="_blank" class="" data-icon-position="" data-hide-link-title="0">Find this author on PubMed</a></li><li class="author-site-search-link"><a href="/search/author1%3ADiego%2BM.%2BMateos%2B" rel="nofollow" class="" data-icon-position="" data-hide-link-title="0">Search for this author on this site</a></li><li class="author-orcid-link"><a href="https://orcid.org/0000-0002-1953-0875" target="_blank" class="" data-icon-position="" data-hide-link-title="0">ORCID record for Diego M. Mateos</a></li><li class="author-corresp-email-link last"><span>For correspondence: <a href="mailto:mateosdiego@gmail.com" class="" data-icon-position="" data-hide-link-title="0">mateosdiego@gmail.com</a></span></li></ul></div></div></div> </div> </div> <div class="panel-separator"></div><div class="panel-pane pane-highwire-panel-tabs pane-panels-ajax-tab-tabs" > <div class="pane-content"> <div class="item-list"><ul class="tabs inline panels-ajax-tab"><li class="first"><a href="/content/10.1101/2024.11.22.624800v1" class="panels-ajax-tab-tab" data-panel-name="biorxiv_tab_art" data-target-id="highwire_article_tabs" data-entity-context="node:4240610" data-trigger="" data-url-enabled="1">Abstract</a><a href="/panels_ajax_tab/biorxiv_tab_art/node:4240610/1" rel="nofollow" style="display:none" class="js-crawler-link"></a></li><li><a href="/content/10.1101/2024.11.22.624800v1.full-text" class="panels-ajax-tab-tab" data-panel-name="article_tab_full_text" data-target-id="highwire_article_tabs" data-entity-context="node:4240610" data-trigger="full-text" data-url-enabled="1">Full Text</a><a href="/panels_ajax_tab/article_tab_full_text/node:4240610/1" rel="nofollow" style="display:none" class="js-crawler-link"></a></li><li><a href="/content/10.1101/2024.11.22.624800v1.article-info" class="panels-ajax-tab-tab" data-panel-name="biorxiv_tab_info" data-target-id="highwire_article_tabs" data-entity-context="node:4240610" data-trigger="article-info" data-url-enabled="1">Info/History</a><a href="/panels_ajax_tab/biorxiv_tab_info/node:4240610/1" rel="nofollow" style="display:none" class="js-crawler-link"></a></li><li><a href="/content/10.1101/2024.11.22.624800v1.article-metrics" class="panels-ajax-tab-tab" data-panel-name="article_tab_metrics" data-target-id="highwire_article_tabs" data-entity-context="node:4240610" data-trigger="article-metrics" data-url-enabled="1">Metrics</a><a href="/panels_ajax_tab/article_tab_metrics/node:4240610/1" rel="nofollow" style="display:none" class="js-crawler-link"></a></li><li class="last"><a href="/content/10.1101/2024.11.22.624800v1.full.pdf+html" class="panels-ajax-tab-tab" data-panel-name="biorxiv_tab_pdf" data-target-id="highwire_article_tabs" data-entity-context="node:4240610" data-trigger="full.pdf+html" data-url-enabled="1"><i class="icon-file-alt"></i> Preview PDF</a><a href="/panels_ajax_tab/biorxiv_tab_pdf/node:4240610/1" rel="nofollow" style="display:none" class="js-crawler-link"></a></li></ul></div> </div> </div> <div class="panel-separator"></div><div class="panel-pane pane-highwire-panel-tabs-container" > <div class="pane-content"> <div data-panels-ajax-tab-preloaded="biorxiv_tab_art" id="panels-ajax-tab-container-highwire_article_tabs" class="panels-ajax-tab-container"><div class="panels-ajax-tab-loading" style ="display:none"><img class="loading" src="https://www.biorxiv.org/sites/all/modules/contrib/panels_ajax_tab/images/loading.gif" alt="Loading" title="Loading" /></div><div class="panels-ajax-tab-wrap-biorxiv_tab_art"><div class="panel-display panel-1col clearfix" > <div class="panel-panel panel-col"> <div><div class="panel-pane pane-highwire-markup" > <div class="pane-content"> <div class="highwire-markup"><div xmlns="http://www.w3.org/1999/xhtml" data-highwire-cite-ref-tooltip-instance="highwire_reflinks_tooltip" class="content-block-markup" xmlns:xhtml="http://www.w3.org/1999/xhtml"><div class="article abstract-view "><span class="highwire-journal-article-marker-start"></span><div class="section abstract" id="abstract-1"><h2 class="">Abstract</h2><p id="p-2">Higher-Order Interaction (HOI) theory offers a powerful framework for capturing complex, non-linear relationships within multidimensional systems, moving beyond traditional pairwise graph methods to encompass multi-way interactions. This study applies HOI analysis, specifically using hypergraph theory, to explore intricate connectivity patterns in electrophysiological signals from neuroscience. Hypergraphs were constructed from connectivity data across various frequency bands, characterized through metrics such as spectral entropy, hyperedge centrality, and vertex centrality, and compared using spectral and centrality distance measures. Three distinct neurophysiological datasets were analyzed: intracranial EEG signals from rats during different sleep stages, scalp EEG data to distinguish between epilepsy types, and MEG recordings of seizure dynamics. The findings highlight the effectiveness of hypergraph-based HOI analysis in mapping neural dynamics across normal and pathological brain states. In sleep studies, it reveals distinct connectivity patterns between REM and NREM stages, while in epilepsy, it differentiates seizure types and stages, identifying spectral entropy as a potential marker for seizure onset. Notably, HOI analysis captures differences between primary and secondary generalized epilepsy, suggesting enhanced diagnostic accuracy. This approach provides a powerful tool for understanding complex neural interactions in high-dimensional data.</p></div><h3>Competing Interest Statement</h3><p id="p-3">The authors have declared no competing interest.</p><span class="highwire-journal-article-marker-end"></span></div><span class="related-urls"></span></div></div> </div> </div> <div class="panel-separator"></div><div class="panel-pane pane-biorxiv-copyright" > <div class="pane-content"> <div class="field field-name-field-highwire-copyright field-type-text field-label-inline clearfix"><div class="field-label">Copyright </div><div class="field-items"><div class="field-item even">The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.<span class="license-type"> It is made available under a <a href="http://creativecommons.org/licenses/by-nc-nd/4.0/" class="" data-icon-position="" data-hide-link-title="0">CC-BY-NC-ND 4.0 International 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