{"id":20886,"date":"2025-01-15T11:08:09","date_gmt":"2025-01-15T10:08:09","guid":{"rendered":"https:\/\/sano.science\/?post_type=research&p=20886"},"modified":"2025-02-21T18:04:25","modified_gmt":"2025-02-21T17:04:25","slug":"end-to-end-stroke-imaging-analysis-using-effective-connectivity-and-interpretable-artificial-intelligence","status":"publish","type":"research","link":"https:\/\/sano.science\/research\/end-to-end-stroke-imaging-analysis-using-effective-connectivity-and-interpretable-artificial-intelligence\/","title":{"rendered":"End-to-end Stroke Imaging Analysis using Effective Connectivity and Interpretable Artificial intelligence"},"content":{"rendered":"\n

Wojciech Ciezobka<\/a>; Joan Falc\u00f3-Roget<\/a>; Cemal Koba; Alessandro Crimi<\/h2>\n\n\n\n
<\/div>\n\n\n\n

This study proposes a framework to construct directed graph representations of brain networks. By further integrating explainable AI techniques, the method reveals disruptions in brain connectivity associated with stroke. The pipeline also compares the performance of reservoir computing-based causality with Granger causality and transfer entropy, offering a comprehensive assessment of effective connectivity estimation methods. Explainable AI tools allowed insights into critical network alterations, clarifying the role of effective connectivity biomarkers in stroke. This transparent analytical approach highlights the potential of directed graph models for both improved diagnostic precision and understanding stroke mechanisms, with broader implications for brain disorder analysis.<\/p>\n\n\n\n

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DOI: <\/strong>10.1109\/ACCESS.2025.3529179<\/a><\/p>\n\n\n\n

Autors<\/strong>: Wojciech Ciezobka<\/a>; Joan Falc\u00f3-Roget<\/a>; Cemal Koba; Alessandro Crimi<\/p>\n\n\n\n

Keywords and subjects<\/strong><\/strong>:\u00a0Reservoir Computing, Brain Connectivity, Explainable Artificial Intelligence (XAI) Effective Connectivity, Neuroimaging Biomarkers, Magnetic Resonance Imaging (MRI), Causality Analysis, Machine Learning in Healthcare, Brain Network Disruption,\u00a0 Stroke Diagnosis<\/p>\n\n\n\n

<\/div>\n\n\n\n\t\n \n \n\t\t\t\n\n\t\t\t\t\n\t\t\t\t\tREAD HERE\n\t\t\t\t<\/span>\n\n\t\t\t<\/a>\n\n \n \n\n\n\n
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Source: https:\/\/ieeexplore.ieee.org\/document\/10839398<\/a><\/figcaption><\/figure>\n","protected":false},"excerpt":{"rendered":"

Conference abstract in: https:\/\/ieeexplore.ieee.org , 2025 <\/p>\n","protected":false},"featured_media":0,"template":"","research_type":[8],"research_team":[15],"class_list":["post-20886","research","type-research","status-publish","hentry","research_type-publications","research_team-computational-neuroscience"],"yoast_head":"\nEnd-to-end Stroke Imaging Analysis using Effective Connectivity and Interpretable Artificial intelligence - Centre for Computational Personalized Medicine<\/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:\/\/sano.science\/research\/end-to-end-stroke-imaging-analysis-using-effective-connectivity-and-interpretable-artificial-intelligence\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"End-to-end Stroke Imaging Analysis using Effective Connectivity 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Crimi<\/h2>\n","innerContent":["\n<h2 class=\"wp-block-heading eplus-wrapper\" id=\"h-wojciech-ciezobka-nbsp-joan-falco-roget-nbsp-cemal-koba-nbsp-alessandro-crimi\">Wojciech Ciezobka<a href=\"https:\/\/orcid.org\/0000-0003-2972-710X\" target=\"_blank\" rel=\"noreferrer noopener\"><\/a>; Joan Falc\u00f3-Roget<a href=\"https:\/\/orcid.org\/0000-0002-9410-6361\" target=\"_blank\" rel=\"noreferrer noopener\"><\/a>; Cemal Koba; Alessandro Crimi<\/h2>\n"]},{"blockName":"core\/spacer","attrs":{"height":"40px","epAnimationGeneratedClass":"edplus_anim-R8xTUk","epGeneratedClass":"eplus-wrapper"},"innerBlocks":[],"innerHTML":"\n<div style=\"height:40px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n","innerContent":["\n<div style=\"height:40px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n"]},{"blockName":"core\/paragraph","attrs":{"epAnimationGeneratedClass":"edplus_anim-cyWlSl","epGeneratedClass":"eplus-wrapper"},"innerBlocks":[],"innerHTML":"\n<p class=\" eplus-wrapper\">This study proposes a framework to construct directed graph representations of brain networks. By further integrating explainable AI techniques, the method reveals disruptions in brain connectivity associated with stroke. The pipeline also compares the performance of reservoir computing-based causality with Granger causality and transfer entropy, offering a comprehensive assessment of effective connectivity estimation methods. Explainable AI tools allowed insights into critical network alterations, clarifying the role of effective connectivity biomarkers in stroke. This transparent analytical approach highlights the potential of directed graph models for both improved diagnostic precision and understanding stroke mechanisms, with broader implications for brain disorder analysis.<\/p>\n","innerContent":["\n<p class=\" eplus-wrapper\">This study proposes a framework to construct directed graph representations of brain networks. By further integrating explainable AI techniques, the method reveals disruptions in brain connectivity associated with stroke. The pipeline also compares the performance of reservoir computing-based causality with Granger causality and transfer entropy, offering a comprehensive assessment of effective connectivity estimation methods. Explainable AI tools allowed insights into critical network alterations, clarifying the role of effective connectivity biomarkers in stroke. This transparent analytical approach highlights the potential of directed graph models for both improved diagnostic precision and understanding stroke mechanisms, with broader implications for brain disorder analysis.<\/p>\n"]},{"blockName":"core\/spacer","attrs":{"height":"40px","epAnimationGeneratedClass":"edplus_anim-R8xTUk","epGeneratedClass":"eplus-wrapper"},"innerBlocks":[],"innerHTML":"\n<div style=\"height:40px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n","innerContent":["\n<div style=\"height:40px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n"]},{"blockName":"core\/paragraph","attrs":{"epAnimationGeneratedClass":"edplus_anim-ljrUO7","epGeneratedClass":"eplus-wrapper"},"innerBlocks":[],"innerHTML":"\n<p class=\" eplus-wrapper\"><strong>DOI: <\/strong><a href=\"https:\/\/doi.org\/10.1109\/ACCESS.2025.3529179\" target=\"_blank\" rel=\"noreferrer noopener\">10.1109\/ACCESS.2025.3529179<\/a><\/p>\n","innerContent":["\n<p class=\" eplus-wrapper\"><strong>DOI: <\/strong><a href=\"https:\/\/doi.org\/10.1109\/ACCESS.2025.3529179\" target=\"_blank\" rel=\"noreferrer noopener\">10.1109\/ACCESS.2025.3529179<\/a><\/p>\n"]},{"blockName":"core\/paragraph","attrs":{"epAnimationGeneratedClass":"edplus_anim-CWvrRp","epGeneratedClass":"eplus-wrapper"},"innerBlocks":[],"innerHTML":"\n<p class=\" eplus-wrapper\"><strong>Autors<\/strong>: Wojciech Ciezobka<a href=\"https:\/\/orcid.org\/0000-0003-2972-710X\" target=\"_blank\" rel=\"noreferrer noopener\"><\/a>; Joan Falc\u00f3-Roget<a href=\"https:\/\/orcid.org\/0000-0002-9410-6361\" target=\"_blank\" rel=\"noreferrer noopener\"><\/a>; Cemal Koba; Alessandro Crimi<\/p>\n","innerContent":["\n<p class=\" eplus-wrapper\"><strong>Autors<\/strong>: Wojciech Ciezobka<a href=\"https:\/\/orcid.org\/0000-0003-2972-710X\" target=\"_blank\" rel=\"noreferrer noopener\"><\/a>; Joan Falc\u00f3-Roget<a href=\"https:\/\/orcid.org\/0000-0002-9410-6361\" target=\"_blank\" rel=\"noreferrer noopener\"><\/a>; Cemal Koba; Alessandro Crimi<\/p>\n"]},{"blockName":"core\/paragraph","attrs":{"epAnimationGeneratedClass":"edplus_anim-CiACgV","epGeneratedClass":"eplus-wrapper"},"innerBlocks":[],"innerHTML":"\n<p class=\" eplus-wrapper\"><strong><strong>Keywords and subjects<\/strong><\/strong>:\u00a0Reservoir Computing, Brain Connectivity, Explainable Artificial Intelligence (XAI) Effective Connectivity, Neuroimaging Biomarkers, Magnetic Resonance Imaging (MRI), Causality Analysis, Machine Learning in Healthcare, Brain Network Disruption,\u00a0 Stroke Diagnosis<\/p>\n","innerContent":["\n<p class=\" eplus-wrapper\"><strong><strong>Keywords and subjects<\/strong><\/strong>:\u00a0Reservoir Computing, Brain Connectivity, Explainable Artificial Intelligence (XAI) Effective Connectivity, Neuroimaging Biomarkers, Magnetic Resonance Imaging (MRI), Causality Analysis, Machine Learning in Healthcare, Brain Network Disruption,\u00a0 Stroke Diagnosis<\/p>\n"]},{"blockName":"core\/spacer","attrs":{"height":"40px","epAnimationGeneratedClass":"edplus_anim-R8xTUk","epGeneratedClass":"eplus-wrapper"},"innerBlocks":[],"innerHTML":"\n<div style=\"height:40px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n","innerContent":["\n<div style=\"height:40px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n"]},{"blockName":"acf\/button","attrs":{"title":"READ HERE","button_type":"link","url":"https:\/\/ieeexplore.ieee.org\/document\/10839398","button_style":"primary","target":"_blank","button_extra_classes":""},"innerBlocks":[],"innerHTML":"","innerContent":[]},{"blockName":"core\/spacer","attrs":{"height":"40px","epAnimationGeneratedClass":"edplus_anim-R8xTUk","epGeneratedClass":"eplus-wrapper"},"innerBlocks":[],"innerHTML":"\n<div style=\"height:40px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n","innerContent":["\n<div style=\"height:40px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n"]},{"blockName":"core\/image","attrs":{"id":20902,"sizeSlug":"large","linkDestination":"none","epAnimationGeneratedClass":"edplus_anim-VNKh1f","epGeneratedClass":"eplus-wrapper"},"innerBlocks":[],"innerHTML":"\n<figure class=\"wp-block-image size-large eplus-wrapper\"><img src=\"https:\/\/sano.science\/wp-content\/uploads\/2025\/01\/pipeline_End-to-end-Stroke-Imaging-Analysis-using-Effective-Connectivity-and-Interpretable-Artificial-intelligence.svg\" alt=\"\" class=\"wp-image-20902\"\/><figcaption class=\"wp-element-caption\">Source: <a href=\"https:\/\/ieeexplore.ieee.org\/document\/10839398\">https:\/\/ieeexplore.ieee.org\/document\/10839398<\/a><\/figcaption><\/figure>\n","innerContent":["\n<figure class=\"wp-block-image size-large eplus-wrapper\"><img src=\"https:\/\/sano.science\/wp-content\/uploads\/2025\/01\/pipeline_End-to-end-Stroke-Imaging-Analysis-using-Effective-Connectivity-and-Interpretable-Artificial-intelligence.svg\" alt=\"\" class=\"wp-image-20902\"\/><figcaption class=\"wp-element-caption\">Source: <a href=\"https:\/\/ieeexplore.ieee.org\/document\/10839398\">https:\/\/ieeexplore.ieee.org\/document\/10839398<\/a><\/figcaption><\/figure>\n"]}],"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\/20886","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":11,"href":"https:\/\/sano.science\/index.php\/wp-json\/wp\/v2\/research\/20886\/revisions"}],"predecessor-version":[{"id":21584,"href":"https:\/\/sano.science\/index.php\/wp-json\/wp\/v2\/research\/20886\/revisions\/21584"}],"wp:attachment":[{"href":"https:\/\/sano.science\/index.php\/wp-json\/wp\/v2\/media?parent=20886"}],"wp:term":[{"taxonomy":"research_type","embeddable":true,"href":"https:\/\/sano.science\/index.php\/wp-json\/wp\/v2\/research_type?post=20886"},{"taxonomy":"research_team","embeddable":true,"href":"https:\/\/sano.science\/index.php\/wp-json\/wp\/v2\/research_team?post=20886"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}