{"id":15016,"date":"2024-01-18T09:42:19","date_gmt":"2024-01-18T08:42:19","guid":{"rendered":"https:\/\/sano.science\/?post_type=research&#038;p=15016"},"modified":"2024-01-18T09:42:19","modified_gmt":"2024-01-18T08:42:19","slug":"tractography-passes-the-test-results-from-the-diffusion-simulated-connectivity-disco-challenge","status":"publish","type":"research","link":"https:\/\/sano.science\/research\/tractography-passes-the-test-results-from-the-diffusion-simulated-connectivity-disco-challenge\/","title":{"rendered":"Tractography passes the test: Results from the diffusion-simulated connectivity (disco) challenge"},"content":{"rendered":"\n<h2 class=\"wp-block-heading eplus-wrapper\">Gabriel Girard, Jonathan Rafael-Pati\u00f1o, Rapha\u00ebl Truffet, Dogu Baran Aydogan, Nagesh Adluru, Veena A. Nair, Vivek Prabhakaran, Barbara B. Bendlin, Andrew L. Alexander, Sara Bosticardo, Ilaria Gabusi, Mario Ocampo-Pineda, Matteo Battocchio, Zuzana Piskorova, Pietro Bontempi, Simona Schiavi, Alessandro Daducci, Aleksandra Stafiej, Dominika Ciupek, Fabian Bogusz, Tomasz Pieciak et al.<\/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\">Estimating structural connectivity from diffusion-weighted magnetic resonance imaging is a challenging task, partly due to the presence of false-positive connections and the misestimation of connection weights. Building on previous efforts, the MICCAI-CDMRI Diffusion-Simulated Connectivity (DiSCo) challenge was carried out to evaluate state-of-the-art connectivity methods using novel large-scale numerical phantoms. The diffusion signal for the phantoms was obtained from\u00a0Monte Carlo simulations. The results of the challenge suggest that methods selected by the 14 teams participating in the challenge can provide high correlations between estimated and ground-truth connectivity weights, in complex numerical environments. Additionally, the methods used by the participating teams were able to accurately identify the binary connectivity of the numerical dataset. However, specific false positive and\u00a0false negative\u00a0connections were consistently estimated across all methods. Although the challenge dataset doesn\u2019t capture the complexity of a real brain, it provided unique data with known macrostructure and microstructure ground-truth properties to facilitate the development of connectivity estimation methods.<\/p>\n\n\n\n<div style=\"height:50px\" 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.sciencedirect.com\/science\/article\/pii\/S1053811923003828\" 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>In: NeuroImage, 2023.<\/p>\n","protected":false},"featured_media":0,"template":"","research_type":[8],"research_team":[16],"class_list":["post-15016","research","type-research","status-publish","hentry","research_type-publications","research_team-extreme-scale-data-and-computing"],"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v27.4 (Yoast SEO v27.4) - 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Nair, Vivek Prabhakaran, Barbara B. Bendlin, Andrew L. Alexander, Sara Bosticardo, Ilaria Gabusi, Mario Ocampo-Pineda, Matteo Battocchio, Zuzana Piskorova, Pietro Bontempi, Simona Schiavi, Alessandro Daducci, Aleksandra Stafiej, Dominika Ciupek, Fabian Bogusz, Tomasz Pieciak et al.<\/h2>\n","innerContent":["\n<h2 class=\"wp-block-heading eplus-wrapper\">Gabriel Girard, Jonathan Rafael-Pati\u00f1o, Rapha\u00ebl Truffet, Dogu Baran Aydogan, Nagesh Adluru, Veena A. Nair, Vivek Prabhakaran, Barbara B. Bendlin, Andrew L. Alexander, Sara Bosticardo, Ilaria Gabusi, Mario Ocampo-Pineda, Matteo Battocchio, Zuzana Piskorova, Pietro Bontempi, Simona Schiavi, Alessandro Daducci, Aleksandra Stafiej, Dominika Ciupek, Fabian Bogusz, Tomasz Pieciak et al.<\/h2>\n"]},{"blockName":"core\/spacer","attrs":{"height":"50px","epAnimationGeneratedClass":"edplus_anim-nnlFdY","epGeneratedClass":"eplus-wrapper"},"innerBlocks":[],"innerHTML":"\n<div style=\"height:50px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n","innerContent":["\n<div style=\"height:50px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n"]},{"blockName":"core\/paragraph","attrs":{"epAnimationGeneratedClass":"edplus_anim-XFZwcA","epGeneratedClass":"eplus-wrapper"},"innerBlocks":[],"innerHTML":"\n<p class=\" eplus-wrapper\">Estimating structural connectivity from diffusion-weighted magnetic resonance imaging is a challenging task, partly due to the presence of false-positive connections and the misestimation of connection weights. 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Although the challenge dataset doesn\u2019t capture the complexity of a real brain, it provided unique data with known macrostructure and microstructure ground-truth properties to facilitate the development of connectivity estimation methods.<\/p>\n","innerContent":["\n<p class=\" eplus-wrapper\">Estimating structural connectivity from diffusion-weighted magnetic resonance imaging is a challenging task, partly due to the presence of false-positive connections and the misestimation of connection weights. Building on previous efforts, the MICCAI-CDMRI Diffusion-Simulated Connectivity (DiSCo) challenge was carried out to evaluate state-of-the-art connectivity methods using novel large-scale numerical phantoms. The diffusion signal for the phantoms was obtained from\u00a0Monte Carlo simulations. The results of the challenge suggest that methods selected by the 14 teams participating in the challenge can provide high correlations between estimated and ground-truth connectivity weights, in complex numerical environments. Additionally, the methods used by the participating teams were able to accurately identify the binary connectivity of the numerical dataset. However, specific false positive and\u00a0false negative\u00a0connections were consistently estimated across all methods. Although the challenge dataset doesn\u2019t capture the complexity of a real brain, it provided unique data with known macrostructure and microstructure ground-truth properties to facilitate the development of connectivity estimation methods.<\/p>\n"]},{"blockName":"core\/spacer","attrs":{"height":"50px","epAnimationGeneratedClass":"edplus_anim-nnlFdY","epGeneratedClass":"eplus-wrapper"},"innerBlocks":[],"innerHTML":"\n<div style=\"height:50px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n","innerContent":["\n<div style=\"height:50px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n"]},{"blockName":"acf\/button","attrs":{"title":"READ HERE","button_type":"link","url":"https:\/\/www.sciencedirect.com\/science\/article\/pii\/S1053811923003828","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\/15016","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":2,"href":"https:\/\/sano.science\/index.php\/wp-json\/wp\/v2\/research\/15016\/revisions"}],"predecessor-version":[{"id":15018,"href":"https:\/\/sano.science\/index.php\/wp-json\/wp\/v2\/research\/15016\/revisions\/15018"}],"wp:attachment":[{"href":"https:\/\/sano.science\/index.php\/wp-json\/wp\/v2\/media?parent=15016"}],"wp:term":[{"taxonomy":"research_type","embeddable":true,"href":"https:\/\/sano.science\/index.php\/wp-json\/wp\/v2\/research_type?post=15016"},{"taxonomy":"research_team","embeddable":true,"href":"https:\/\/sano.science\/index.php\/wp-json\/wp\/v2\/research_team?post=15016"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}