{"id":1570,"date":"2023-06-09T13:54:05","date_gmt":"2023-06-09T11:54:05","guid":{"rendered":"https:\/\/sano.empressia.dev\/?post_type=people&p=1570"},"modified":"2026-03-24T11:45:53","modified_gmt":"2026-03-24T10:45:53","slug":"joan-falco-roget","status":"publish","type":"people","link":"https:\/\/sano.science\/people\/joan-falco-roget\/","title":{"rendered":"Joan Falco Roget"},"excerpt":{"rendered":"

PhD Student in Computational Neuroscience<\/p>\n","protected":false},"featured_media":18504,"template":"","people_teams":[19,33],"class_list":["post-1570","people","type-people","status-publish","has-post-thumbnail","hentry","people_teams-research","people_teams-computational-neuroscience"],"yoast_head":"\nJoan Falco Roget - 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\/people\/joan-falco-roget\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Joan Falco Roget\" \/>\n<meta property=\"og:description\" content=\"PhD Student in Computational Neuroscience\" \/>\n<meta property=\"og:url\" content=\"https:\/\/sano.science\/people\/joan-falco-roget\/\" \/>\n<meta property=\"og:site_name\" content=\"Centre for Computational Personalized Medicine\" \/>\n<meta property=\"article:publisher\" content=\"https:\/\/www.facebook.com\/sano.science\/\" \/>\n<meta property=\"article:modified_time\" content=\"2026-03-24T10:45:53+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/sano.science\/wp-content\/uploads\/2023\/06\/Sano-Joan-Falco-Roget.png\" \/>\n\t<meta property=\"og:image:width\" content=\"1000\" \/>\n\t<meta property=\"og:image:height\" content=\"1000\" \/>\n\t<meta property=\"og:image:type\" content=\"image\/png\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:site\" content=\"@sanoscience\" \/>\n<meta name=\"twitter:label1\" content=\"Est. reading time\" \/>\n\t<meta name=\"twitter:data1\" content=\"1 minute\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\\\/\\\/schema.org\",\"@graph\":[{\"@type\":\"WebPage\",\"@id\":\"https:\\\/\\\/sano.science\\\/people\\\/joan-falco-roget\\\/\",\"url\":\"https:\\\/\\\/sano.science\\\/people\\\/joan-falco-roget\\\/\",\"name\":\"Joan Falco Roget - 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Then he moved to Madrid to study a MSc in systems biology at the Universidad Autonoma de Madrid (UAM). His thesis was about computational neuroscience, specifically in “the learning role of dopamine.”\u00a0 After that Joan spent one year collaborating (as a research assistant) with the Theoretical Physics Department in Madrid studying the same things. In Sano he will be working on neuroplasticity after tumor and stroke, and surgical planning. From his personal side, he plays the piano, he loves any kind of sports (specially tennis and football) and he falls in love with mountains.<\/p>\n<p> <\/p>\n<p><iframe loading=\"lazy\" title=\"Meet Your Scientist - Joan Falco Roget\" width=\"500\" height=\"281\" src=\"https:\/\/www.youtube.com\/embed\/RQgEsc-srD8?feature=oembed\" frameborder=\"0\" allow=\"accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share\" referrerpolicy=\"strict-origin-when-cross-origin\" allowfullscreen><\/iframe><\/p>\n","social_media":[{"icon":{"ID":155,"id":155,"title":"linkedin","filename":"linkedin.svg","filesize":904,"url":"https:\/\/sano.science\/wp-content\/uploads\/2023\/04\/linkedin.svg","link":"https:\/\/sano.science\/people\/alessandro-crimi\/linkedin\/","alt":"LinkedIn logotype","author":"7","description":"","caption":"","name":"linkedin","status":"inherit","uploaded_to":539,"date":"2023-04-21 10:45:20","modified":"2023-05-30 13:15:05","menu_order":0,"mime_type":"image\/svg+xml","type":"image","subtype":"svg+xml","icon":"https:\/\/sano.science\/wp-includes\/images\/media\/default.png","width":16,"height":15,"sizes":{"thumbnail":"https:\/\/sano.science\/wp-content\/uploads\/2023\/04\/linkedin.svg","thumbnail-width":150,"thumbnail-height":140,"medium":"https:\/\/sano.science\/wp-content\/uploads\/2023\/04\/linkedin.svg","medium-width":300,"medium-height":280,"medium_large":"https:\/\/sano.science\/wp-content\/uploads\/2023\/04\/linkedin.svg","medium_large-width":768,"medium_large-height":718,"large":"https:\/\/sano.science\/wp-content\/uploads\/2023\/04\/linkedin.svg","large-width":1024,"large-height":957,"1536x1536":"https:\/\/sano.science\/wp-content\/uploads\/2023\/04\/linkedin.svg","1536x1536-width":16,"1536x1536-height":15,"2048x2048":"https:\/\/sano.science\/wp-content\/uploads\/2023\/04\/linkedin.svg","2048x2048-width":16,"2048x2048-height":15}},"link":"https:\/\/www.linkedin.com\/in\/joan-falc%C3%B3-roget-8080a219a\/?locale=en_US","name":"LinkedIn"}],"tabs":false,"quote":"","email":"","position_with_team":{"text_before_link":"PhD Student in","link_text":"Computational Neuroscience","text_after_link":"","link":"https:\/\/sano.science\/research-teams\/computer-vision-brain-and-more-lab\/"},"publications":[{"ID":30042,"post_author":"8","post_date":"2026-03-23 16:17:49","post_date_gmt":"2026-03-23 15:17:49","post_content":"<!-- wp:paragraph {\"epAnimationGeneratedClass\":\"edplus_anim-Cin69o\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<p class=\" eplus-wrapper\">A new paper by members of the Computational Neuroscience team at Sano, published in<br>Neuro-Oncology, introduces a novel neuroimaging biomarker for glioblastoma (GBM) called<br>the Lesion-Tract Density Index (L-TDI). Moving beyond viewing GBM as a focal lesion, this<br>study treats it as a network disease that interacts with the brain\u2019s white matter scaffold. By<br>analyzing large-scale white matter pathways in two independent patient cohorts, the<br>researchers found that L-TDI robustly stratifies survival rates and predicts outcomes more<br>accurately than traditional measures like tumor volume. This work marks a significant step<br>toward connectomics-guided neuro-oncology and improved individualized patient care.<\/p>\n<!-- \/wp:paragraph -->\n\n<!-- wp:spacer {\"height\":\"30px\",\"epAnimationGeneratedClass\":\"edplus_anim-IwNK8h\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<div style=\"height:30px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n<!-- \/wp:spacer -->\n\n<!-- wp:image {\"id\":30035,\"sizeSlug\":\"large\",\"linkDestination\":\"none\",\"epAnimationGeneratedClass\":\"edplus_anim-U0lq6Y\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<figure class=\"wp-block-image size-large eplus-wrapper\"><img src=\"https:\/\/sano.science\/wp-content\/uploads\/2026\/03\/A-non-local-diffusion-magnetic-resonance-imaging-tract-density-biomarker-to-stratify-predict-and-interpret-survival-rates-in-human-glioblastoma-1024x821.jpeg\" alt=\"The lesion-tract density index (L\u2009-TDI): a survival marker in human GBM. (A) For every subject, the data were normalized to a common template. The T2-weighted image was chosen as the reference modality, and the GBM was inversely masked to discard distorted tissue during the optimization. (B) The normalized tumor mask is used in combination with the whole brain normative tractogram to extract the set of streamlines that intersect the tumor in the same template space as in (A). (C) The lesion-tract density map (L\u2009-TDM) provides a unique white matter density map describing the tracts in (B) that interact with the GBM. (D) From the L\u2009-TDM, we can define the L\u2009-TDI marker by averaging the tract density within the L\u2009-TDM binary mask in (C). For a given cohort, the sample can be stratified according to a given percentile. (E) Once a stratification threshold has been set, we conduct thorough tests to assess the discovery of the biomarker. Additional survival analyses on multiple stratification thresholds help determine whether the potential biomarker holds prognostic value. The L\u2009-TDI can also be used to characterize both morphological and anatomical landscapes in GBMs, which can be used to understand differences in the survival rates. Once a robust effect has been identified, the L\u2009-TDI can be incorporated into standard risk analysis and predictive frameworks to improve patient care.\" class=\"wp-image-30035\"\/><figcaption class=\"wp-element-caption\">Image source: Figure 1 from \u201cA non-local diffusion magnetic resonance imaging tract density index to capture network-level tumor\u2013brain interactions\u201d, Neuro-Oncology, Oxford University Press <a href=\"https:\/\/academic.oup.com\/view-large\/figure\/556903333\/noaf234f1.tif\" target=\"_blank\" rel=\"noreferrer noopener nofollow\">academic.oup.com\/view-large\/figure\/556903333\/noaf234f1.tif<\/a><\/figcaption><\/figure>\n<!-- \/wp:image -->\n\n<!-- wp:paragraph {\"epAnimationGeneratedClass\":\"edplus_anim-Cin69o\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<p class=\" eplus-wrapper\"><br>See the full text:<\/p>\n<!-- \/wp:paragraph -->\n\n<!-- wp:spacer {\"height\":\"50px\",\"epAnimationGeneratedClass\":\"edplus_anim-SkBTix\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<div style=\"height:50px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n<!-- \/wp:spacer -->\n\n<!-- wp:acf\/button {\"id\":\"block_69c1598464b0b\",\"name\":\"acf\/button\",\"data\":{\"title\":\"Read the article\",\"_title\":\"field_61d40397c2f0a\",\"button_type\":\"link\",\"_button_type\":\"field_63bbde3b8f0d0\",\"url\":\"https:\/\/academic.oup.com\/neuro-oncology\/article\/28\/2\/564\/8287534\",\"_url\":\"field_61d4039bc2f0b\",\"button_style\":\"primary\",\"_button_style\":\"field_63872d045d0f0\",\"target\":\"_self\",\"_target\":\"field_63872c705d0ef\",\"button_extra_classes\":\"\",\"_button_extra_classes\":\"field_642beab6a97de\"},\"align\":\"\",\"mode\":\"edit\"} \/-->\n\n<!-- wp:spacer {\"height\":\"50px\",\"epAnimationGeneratedClass\":\"edplus_anim-SkBTix\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<div style=\"height:50px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n<!-- \/wp:spacer -->\n\n<!-- wp:paragraph {\"epAnimationGeneratedClass\":\"edplus_anim-Plfl6U\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<p class=\" eplus-wrapper\">Autors: <a href=\"https:\/\/sano.science\/wp-content\/uploads\/2023\/06\/Sano-Joan-Falco-Roget.png\" type=\"attachment\" id=\"18504\">Joan Falc\u00f3-Roget<\/a>, Gianpaolo Antonio Basile, <a href=\"https:\/\/sano.science\/people\/anna-janus\/\" type=\"people\" id=\"22468\">Anna Janus<\/a>, Sara Lillo, Letterio S Politi, <a href=\"https:\/\/sano.science\/wp-content\/uploads\/2023\/08\/Jan_Argasinski.webp\" type=\"attachment\" id=\"21360\">Jan K Argasinski<\/a>, Alberto Cacciola<\/p>\n<!-- \/wp:paragraph -->\n\n<!-- wp:paragraph {\"epAnimationGeneratedClass\":\"edplus_anim-l9wZem\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<p class=\" eplus-wrapper\">Keywords: glioblastoma, brain connectome, diffusion tractography, glioblastoma, survival prediction<\/p>\n<!-- \/wp:paragraph -->","post_title":"A non-local diffusion magnetic resonance imaging tract density biomarker to stratify, predict, and interpret survival rates in human glioblastoma","post_excerpt":"Published in Neuro-Oncology Journals, 2026","post_status":"publish","comment_status":"closed","ping_status":"closed","post_password":"","post_name":"a-non-local-diffusion-magnetic-resonance-imaging-tract-density-biomarker-to-stratify-predict-and-interpret-survival-rates-in-human-glioblastoma","to_ping":"","pinged":"","post_modified":"2026-03-24 11:34:47","post_modified_gmt":"2026-03-24 10:34:47","post_content_filtered":"","post_parent":0,"guid":"https:\/\/sano.science\/?post_type=research&p=30042","menu_order":0,"post_type":"research","post_mime_type":"","comment_count":"0","filter":"raw"},{"ID":19058,"post_author":"5","post_date":"2024-10-03 13:32:55","post_date_gmt":"2024-10-03 11:32:55","post_content":"<!-- wp:heading {\"level\":3,\"epAnimationGeneratedClass\":\"edplus_anim-FyogrK\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<h3 class=\"wp-block-heading eplus-wrapper\" id=\"h-jan-k-nbsp-argasinski-nbsp-katarzyna-nbsp-baliga-nicholson-nbsp-anna-nbsp-partyka-nbsp-kamil-nbsp-pilch-nbsp-joan-nbsp-falco-roget-nbsp\">Jan K. Argasinski, Katarzyna Baliga-Nicholson, Anna Partyka, Kamil Pilch, Joan Falco-Roget <\/h3>\n<!-- \/wp:heading -->\n\n<!-- wp:spacer {\"height\":\"30px\",\"epAnimationGeneratedClass\":\"edplus_anim-xgdCqo\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<div style=\"height:30px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n<!-- \/wp:spacer -->\n\n<!-- wp:paragraph {\"epAnimationGeneratedClass\":\"edplus_anim-bxXdXJ\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<p class=\" eplus-wrapper\">Departing from the confines of conventional linear podcasts, our groundbreaking method introduces a dynamic, interactive framework designed to enrich user involvement and deepen educational insights. Envision a podcast that allows you to navigate through a network of video clips, each seamlessly connected to pertinent scientific publications, interactive charts, and learning materials. This format fosters a customized, investigative experience in computational neuroscience, enhancing both comprehension and intrigue. Utilizing principles from game design and dynamic hyperlinking, our goal is to construct an engaging, immersive educational atmosphere that captivates and educates listeners.<\/p>\n<!-- \/wp:paragraph -->\n\n<!-- wp:spacer {\"height\":\"30px\",\"epAnimationGeneratedClass\":\"edplus_anim-xgdCqo\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<div style=\"height:30px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n<!-- \/wp:spacer -->\n\n<!-- wp:paragraph {\"epAnimationGeneratedClass\":\"edplus_anim-bxXdXJ\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<p class=\" eplus-wrapper\"><strong>Authors<\/strong>: Jan K. Argasinski, Katarzyna Baliga-Nicholson, Anna Partyka, Kamil Pilch, Joan Falco-Roget <\/p>\n<!-- \/wp:paragraph -->\n\n<!-- wp:paragraph {\"epAnimationGeneratedClass\":\"edplus_anim-9XJw5w\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<p class=\" eplus-wrapper\"><strong>DOI<\/strong>: <a href=\"https:\/\/dl.acm.org\/doi\/10.1145\/3648188.3678217\" target=\"_blank\" rel=\"noreferrer noopener nofollow\">https:\/\/dl.acm.org\/doi\/10.1145\/3648188.3678217<\/a>\u00a0<\/p>\n<!-- \/wp:paragraph -->\n\n<!-- wp:paragraph {\"epAnimationGeneratedClass\":\"edplus_anim-oizXwr\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<p class=\" eplus-wrapper\"><strong>Link to article<\/strong>: <a href=\"https:\/\/dl.acm.org\/doi\/10.1145\/3648188.3678217\" target=\"_blank\" rel=\"noreferrer noopener nofollow\">https:\/\/dl.acm.org\/doi\/10.1145\/3648188.3678217<\/a><\/p>\n<!-- \/wp:paragraph -->\n\n<!-- wp:paragraph {\"epAnimationGeneratedClass\":\"edplus_anim-7myS8u\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<p class=\" eplus-wrapper\"><strong>Link to article<\/strong>: <a href=\"https:\/\/www.growkudos.com\/publications\/10.1145%25252F3648188.3678217\/reader\" target=\"_blank\" rel=\"noreferrer noopener nofollow\">https:\/\/www.growkudos.com\/publications\/10.1145%25252F3648188.3678217\/reader<\/a><\/p>\n<!-- \/wp:paragraph -->\n\n<!-- wp:paragraph {\"epAnimationGeneratedClass\":\"edplus_anim-suS7dT\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<p class=\" eplus-wrapper\"><strong>Keywords<\/strong>: scientific podcasts<\/p>\n<!-- \/wp:paragraph -->\n\n<!-- wp:spacer {\"height\":\"30px\",\"epAnimationGeneratedClass\":\"edplus_anim-xgdCqo\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<div style=\"height:30px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n<!-- \/wp:spacer -->\n\n<!-- wp:paragraph {\"epAnimationGeneratedClass\":\"edplus_anim-OnUHbe\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<p class=\" eplus-wrapper\"><\/p>\n<!-- \/wp:paragraph -->","post_title":"Disrupting scientific podcasts. Prototype and blueprints for an ergodic neuroscientific talk","post_excerpt":"In: dl.acm.org 2024 and: www.growkudos.com 2024","post_status":"publish","comment_status":"closed","ping_status":"closed","post_password":"","post_name":"disrupting-scientific-podcasts-prototype-and-blueprints-for-an-ergodic-neuroscientific-talk","to_ping":"","pinged":"","post_modified":"2024-10-04 15:18:38","post_modified_gmt":"2024-10-04 13:18:38","post_content_filtered":"","post_parent":0,"guid":"https:\/\/sano.science\/?post_type=research&p=19058","menu_order":0,"post_type":"research","post_mime_type":"","comment_count":"0","filter":"raw"},{"ID":21108,"post_author":"8","post_date":"2025-02-04 11:14:41","post_date_gmt":"2025-02-04 10:14:41","post_content":"<!-- wp:heading {\"epAnimationGeneratedClass\":\"edplus_anim-h4bvYG\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<h2 class=\"wp-block-heading eplus-wrapper\" id=\"h-szymon-mazurek-nbsp-rosmary-blanco-nbsp-joan-falco-roget-nbsp-alessandro-crimi\">Szymon Mazurek, Rosmary Blanco, Joan Falc\u00f3-Roget, Alessandro Crimi<\/h2>\n<!-- \/wp:heading -->\n\n<!-- wp:spacer {\"height\":\"50px\",\"epAnimationGeneratedClass\":\"edplus_anim-moezvg\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<div style=\"height:50px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n<!-- \/wp:spacer -->\n\n<!-- wp:paragraph {\"epAnimationGeneratedClass\":\"edplus_anim-NnUHpg\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<p class=\" eplus-wrapper\">Electroencephalography (EEG) is currently the most used way to accurately diagnose epilepsy given its ability to measure hypersinchronized periods of brain activity known as seizures. However, EEG recordings are noisy and require trained practitioners for meaningful information to be extracted. Most importantly, further post hoc analyses are inherently time-consuming and subjective. Recent advances in artificial intelligence have paved the way to develop automated workflows easing the task of preprocessing and detecting epileptic activity from EEG. Yet, these models are ubiquitously difficult to interpret thus posing a challenge for its wide acceptance in clinical scenarios. Here, we propose a graph neural network enhanced with attention layers able to accurately and robustly identify pathological brain activity. We provide both feature and graph explanations for each prediction of the trained model. Crucially, we show how graph neural networks capture non-trivial dependencies between cortical regions that agree with the current clinical consensus. Altogether, these results highlight the fact that explainable artificial intelligence need not compromise its performance and represent an improvement in the applicability of artificial intelligence networks in clinical practice<\/p>\n<!-- \/wp:paragraph -->\n\n<!-- wp:spacer {\"height\":\"50px\",\"epAnimationGeneratedClass\":\"edplus_anim-moezvg\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<div style=\"height:50px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n<!-- \/wp:spacer -->\n\n<!-- wp:paragraph {\"epAnimationGeneratedClass\":\"edplus_anim-5FMDft\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<p class=\" eplus-wrapper\"><strong>Authors<\/strong>: Szymon Mazurek, Rosmary Blanco, Joan Falc\u00f3-Roget, Alessandro Crimi<\/p>\n<!-- \/wp:paragraph -->\n\n<!-- wp:paragraph {\"epAnimationGeneratedClass\":\"edplus_anim-5FMDft\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<p class=\" eplus-wrapper\"><strong>DOI: <\/strong><a href=\"https:\/\/doi.org\/10.1109\/ISBI56570.2024.10635821\" target=\"_blank\" rel=\"noreferrer noopener\">10.1109\/ISBI56570.2024.10635821<\/a><\/p>\n<!-- \/wp:paragraph -->\n\n<!-- wp:paragraph {\"epAnimationGeneratedClass\":\"edplus_anim-bK0bPv\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<p class=\" eplus-wrapper\"><strong>Keywords<\/strong>: EEG, epilepsy<\/p>\n<!-- \/wp:paragraph -->\n\n<!-- wp:spacer {\"height\":\"50px\",\"epAnimationGeneratedClass\":\"edplus_anim-moezvg\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<div style=\"height:50px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n<!-- \/wp:spacer -->\n\n<!-- wp:acf\/button {\"id\":\"block_67a1db75a8da7\",\"name\":\"acf\/button\",\"data\":{\"title\":\"READ HERE\",\"_title\":\"field_61d40397c2f0a\",\"button_type\":\"link\",\"_button_type\":\"field_63bbde3b8f0d0\",\"url\":\"https:\/\/ieeexplore.ieee.org\/abstract\/document\/10635821\",\"_url\":\"field_61d4039bc2f0b\",\"button_style\":\"primary\",\"_button_style\":\"field_63872d045d0f0\",\"target\":\"_self\",\"_target\":\"field_63872c705d0ef\",\"button_extra_classes\":\"\",\"_button_extra_classes\":\"field_642beab6a97de\"},\"align\":\"\",\"mode\":\"edit\"} \/-->","post_title":"Explainable Graph Neural Networks for EEG Classification and Seizure Detection in Epileptic Patients","post_excerpt":"Conference manuscript in 2024 IEEE International Symposium on Biomedical Imaging (ISBI), 2024","post_status":"publish","comment_status":"closed","ping_status":"closed","post_password":"","post_name":"explainable-graph-neural-networks-for-eeg-classification-and-seizure-detection-in-epileptic-patients","to_ping":"","pinged":"","post_modified":"2026-01-31 19:21:33","post_modified_gmt":"2026-01-31 18:21:33","post_content_filtered":"","post_parent":0,"guid":"https:\/\/sano.science\/?post_type=research&p=21108","menu_order":0,"post_type":"research","post_mime_type":"","comment_count":"0","filter":"raw"},{"ID":14883,"post_author":"5","post_date":"2024-01-12 17:19:18","post_date_gmt":"2024-01-12 16:19:18","post_content":"<!-- wp:heading {\"epAnimationGeneratedClass\":\"edplus_anim-4jLCcv\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<h2 class=\"wp-block-heading eplus-wrapper\">M.Wierzbinski, J.Falco-Roget, A.Crimi<\/h2>\n<!-- \/wp:heading -->\n\n<!-- wp:spacer {\"height\":\"50px\",\"epAnimationGeneratedClass\":\"edplus_anim-4kxIfc\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<div style=\"height:50px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n<!-- \/wp:spacer -->\n\n<!-- wp:paragraph {\"epAnimationGeneratedClass\":\"edplus_anim-mhoDyp\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<p class=\" eplus-wrapper\">Recent advancements in network neuroscience are pointing in the direction of considering the brain as a small-world system with an efficient integration-segregation balance that facilitates different cognitive tasks and functions. In this context, community detection is a pivotal issue in computational neuroscience. In this paper we explored community detection within brain connectomes using the power of quantum annealers, and in particular the Leap\u2019s Hybrid Solver in D-Wave. By reframing the modularity optimization problem into a Discrete Quadratic Model, we show that quantum annealers achieved higher modularity indices compared to the Louvain Community Detection Algorithm without the need to overcomplicate the mathematical formulation. We also found that the number of communities detected in brain connectomes slightly differed while still being biologically interpretable. These promising preliminary results, together with recent findings, strengthen the claim that quantum optimization methods might be a suitable alternative against classical approaches when dealing with community assignment in networks.<\/p>\n<!-- \/wp:paragraph -->\n\n<!-- wp:spacer {\"height\":\"50px\",\"epAnimationGeneratedClass\":\"edplus_anim-4kxIfc\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<div style=\"height:50px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n<!-- \/wp:spacer -->\n\n<!-- wp:acf\/button {\"id\":\"block_65a161a9101de\",\"name\":\"acf\/button\",\"data\":{\"title\":\"READ HERE\",\"_title\":\"field_61d40397c2f0a\",\"button_type\":\"link\",\"_button_type\":\"field_63bbde3b8f0d0\",\"url\":\"https:\/\/www.nature.com\/articles\/s41598-023-30579-y\",\"_url\":\"field_61d4039bc2f0b\",\"button_style\":\"primary\",\"_button_style\":\"field_63872d045d0f0\",\"target\":\"_blank\",\"_target\":\"field_63872c705d0ef\",\"button_extra_classes\":\"\",\"_button_extra_classes\":\"field_642beab6a97de\"},\"align\":\"\",\"mode\":\"edit\"} \/-->","post_title":"Community Detection in Brain Connectome using Quantum Annealer Devices","post_excerpt":"In: Scientific Reports, 2023.","post_status":"publish","comment_status":"closed","ping_status":"closed","post_password":"","post_name":"community-detection-in-brain-connectome-using-quantum-annealer-devices","to_ping":"","pinged":"","post_modified":"2024-01-12 17:19:18","post_modified_gmt":"2024-01-12 16:19:18","post_content_filtered":"","post_parent":0,"guid":"https:\/\/sano.science\/?post_type=research&p=14883","menu_order":10,"post_type":"research","post_mime_type":"","comment_count":"0","filter":"raw"},{"ID":14886,"post_author":"5","post_date":"2024-01-12 17:21:31","post_date_gmt":"2024-01-12 16:21:31","post_content":"<!-- wp:heading {\"epAnimationGeneratedClass\":\"edplus_anim-6ynx4A\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<h2 class=\"wp-block-heading eplus-wrapper\">N\u00e9stor Parga, Luis Serrano-Fern\u00e1ndez, and Joan Falc\u00f3-Roget<\/h2>\n<!-- \/wp:heading -->\n\n<!-- wp:spacer {\"height\":\"50px\",\"epAnimationGeneratedClass\":\"edplus_anim-Ap4kBb\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<div style=\"height:50px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n<!-- \/wp:spacer -->\n\n<!-- wp:paragraph {\"epAnimationGeneratedClass\":\"edplus_anim-QNnYzC\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<p class=\" eplus-wrapper\">Synaptic plasticity allows cortical circuits to learn new tasks and to adapt to changing environments. How do cortical circuits use plasticity to acquire functions such as decision-making or working memory? Neurons are connected in complex ways, forming recurrent neural networks, and learning modifies the strength of their connections. Moreover, neurons communicate emitting brief discrete electric signals. Here we describe how to train recurrent neural networks in tasks like those used to train animals in neuroscience laboratories and how computations emerge in the trained networks. Surprisingly, artificial networks and real brains can use similar computational strategies.<\/p>\n<!-- \/wp:paragraph -->\n\n<!-- wp:spacer {\"height\":\"50px\",\"epAnimationGeneratedClass\":\"edplus_anim-Ap4kBb\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<div style=\"height:50px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n<!-- \/wp:spacer -->\n\n<!-- wp:acf\/button {\"id\":\"block_65a166f58c433\",\"name\":\"acf\/button\",\"data\":{\"title\":\"READ HERE\",\"_title\":\"field_61d40397c2f0a\",\"button_type\":\"link\",\"_button_type\":\"field_63bbde3b8f0d0\",\"url\":\"https:\/\/iopscience.iop.org\/article\/10.1088\/1748-0221\/18\/02\/C02060\",\"_url\":\"field_61d4039bc2f0b\",\"button_style\":\"primary\",\"_button_style\":\"field_63872d045d0f0\",\"target\":\"_blank\",\"_target\":\"field_63872c705d0ef\",\"button_extra_classes\":\"\",\"_button_extra_classes\":\"field_642beab6a97de\"},\"align\":\"\",\"mode\":\"edit\"} \/-->","post_title":"Emergent Computations in Trained Neural Networks and Real Brains","post_excerpt":"In: Journal of Instrumentation, 2023.","post_status":"publish","comment_status":"closed","ping_status":"closed","post_password":"","post_name":"emergent-computations-in-trained-neural-networks-and-real-brains","to_ping":"","pinged":"","post_modified":"2024-01-12 17:21:31","post_modified_gmt":"2024-01-12 16:21:31","post_content_filtered":"","post_parent":0,"guid":"https:\/\/sano.science\/?post_type=research&p=14886","menu_order":18,"post_type":"research","post_mime_type":"","comment_count":"0","filter":"raw"},{"ID":24899,"post_author":"8","post_date":"2025-07-08 16:32:49","post_date_gmt":"2025-07-08 14:32:49","post_content":"<!-- wp:heading {\"epAnimationGeneratedClass\":\"edplus_anim-nvUGAS\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<h2 class=\"wp-block-heading eplus-wrapper\" id=\"h-cemal-koba-joan-falco-roget-alessandro-crimi\">Cemal Koba,\u00a0Joan Falc\u00f3-Roget,\u00a0Alessandro Crimi<\/h2>\n<!-- \/wp:heading -->\n\n<!-- wp:paragraph {\"epAnimationGeneratedClass\":\"edplus_anim-pk2OOI\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<p class=\" eplus-wrapper\">Ischemic stroke disrupts cerebral blood flow, triggering both structural and functional brain alterations that often lead to behavioral impairments. Although the primary damage typically affects specific regions, the resulting changes in brain organization extend widely across the cortex and are high-dimensional in nature. The underlying mechanisms driving these widespread functional disturbances and their connection to behavioral symptoms remain largely underexplored.<\/p>\n<!-- \/wp:paragraph -->\n\n<!-- wp:paragraph {\"epAnimationGeneratedClass\":\"edplus_anim-aGILm8\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<p class=\" eplus-wrapper\">Functional connectivity gradients offer a reproducible and robust way to represent brain organization in a low-dimensional space, simplifying complex functional variation into a few interpretable axes. In this study, we examined how stroke affects this canonical gradient space by aligning individual patients\u2019 connectivity profiles to a normative template derived from healthy controls. We then measured how far each region deviated from its expected position, using these deviations to assess their relevance for behavioral outcomes. Crucially, we accounted for stroke-induced hemodynamic delays to better understand their influence.<\/p>\n<!-- \/wp:paragraph -->\n\n<!-- wp:paragraph {\"epAnimationGeneratedClass\":\"edplus_anim-O8rBys\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<p class=\" eplus-wrapper\">Our analysis showed that correcting for hemodynamic lags significantly improved gradient accuracy, particularly in the second gradient, which encompasses visual and somatomotor functions. Notable functional deviations were observed within the somatomotor, visual, and ventral attention networks\u2014areas linked to behavioral impairments following stroke.<\/p>\n<!-- \/wp:paragraph -->\n\n<!-- wp:paragraph {\"epAnimationGeneratedClass\":\"edplus_anim-hF8Ff2\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<p class=\" eplus-wrapper\">We also investigated hemispheric asymmetries in these deviations. Interestingly, unaffected hemispheres retained typical asymmetry patterns, whereas stroke-affected hemispheres showed marked disruptions. Moreover, right-hemisphere lesions resulted in more localized functional alterations compared to left-sided damage.<\/p>\n<!-- \/wp:paragraph -->\n\n<!-- wp:paragraph {\"epAnimationGeneratedClass\":\"edplus_anim-pk2OOI\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<p class=\" eplus-wrapper\">Overall, our findings highlight two key insights: (1) adjusting for hemodynamic delays increases the explanatory power of connectivity gradients, and (2) behavioral deficits and altered hemispheric asymmetries can be traced to shifts in region-specific connectivity patterns within a low-dimensional, interpretable framework. This supports the idea that post-stroke brain reorganization follows predictable trajectories along fundamental axes of brain function\u2014and that these shifts are not entirely independent of underlying white matter damage.<\/p>\n<!-- \/wp:paragraph -->\n\n<!-- wp:spacer {\"height\":\"30px\",\"epAnimationGeneratedClass\":\"edplus_anim-bYpygR\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<div style=\"height:30px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n<!-- \/wp:spacer -->\n\n<!-- wp:paragraph {\"epAnimationGeneratedClass\":\"edplus_anim-1g73Ls\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<p class=\" eplus-wrapper\"><strong>Authors<\/strong>: <a href=\"https:\/\/sano.science\/people\/cemal-koba\/\">Cemal Koba<\/a>, <a href=\"https:\/\/sano.science\/wp-content\/uploads\/2023\/06\/Sano-Joan-Falco-Roget.png\">Joan Falc\u00f3-Roget<\/a>, <a href=\"https:\/\/sano.science\/people\/alessandro-crimi\/\">Alessandro Crimi<\/a><\/p>\n<!-- \/wp:paragraph -->\n\n<!-- wp:paragraph {\"epAnimationGeneratedClass\":\"edplus_anim-gcfXou\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<p class=\" eplus-wrapper\"><strong>DOI<\/strong>: 10.1016\/j.nicl.2025.103755<\/p>\n<!-- \/wp:paragraph -->\n\n<!-- wp:paragraph {\"epAnimationGeneratedClass\":\"edplus_anim-YLydAq\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<p class=\" eplus-wrapper\"><strong>Keywords<\/strong>: Stroke, fMRI, Gradients, Temporal lag<\/p>\n<!-- \/wp:paragraph -->\n\n<!-- wp:spacer {\"height\":\"30px\",\"epAnimationGeneratedClass\":\"edplus_anim-bYpygR\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<div style=\"height:30px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n<!-- \/wp:spacer -->\n\n<!-- wp:acf\/button {\"id\":\"block_686d2ad64bda9\",\"name\":\"acf\/button\",\"data\":{\"title\":\"READ HERE\",\"_title\":\"field_61d40397c2f0a\",\"button_type\":\"link\",\"_button_type\":\"field_63bbde3b8f0d0\",\"url\":\"https:\/\/www.sciencedirect.com\/science\/article\/pii\/S2213158225000257#d1e1612\",\"_url\":\"field_61d4039bc2f0b\",\"button_style\":\"primary\",\"_button_style\":\"field_63872d045d0f0\",\"target\":\"_self\",\"_target\":\"field_63872c705d0ef\",\"button_extra_classes\":\"\",\"_button_extra_classes\":\"field_642beab6a97de\"},\"align\":\"\",\"mode\":\"edit\"} \/-->\n\n<!-- wp:paragraph {\"epAnimationGeneratedClass\":\"edplus_anim-eoFlhH\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<p class=\" eplus-wrapper\"><\/p>\n<!-- \/wp:paragraph -->","post_title":"Reshaped functional connectivity gradients in acute ischemic stroke","post_excerpt":"article in journal: Elsevier - NeuroImage: Clinical, 2025","post_status":"publish","comment_status":"closed","ping_status":"closed","post_password":"","post_name":"reshaped-functional-connectivity-gradients-in-acute-ischemic-stroke","to_ping":"","pinged":"","post_modified":"2025-07-22 14:36:57","post_modified_gmt":"2025-07-22 12:36:57","post_content_filtered":"","post_parent":0,"guid":"https:\/\/sano.science\/?post_type=research&p=24899","menu_order":0,"post_type":"research","post_mime_type":"","comment_count":"0","filter":"raw"},{"ID":14855,"post_author":"5","post_date":"2024-01-10 20:54:44","post_date_gmt":"2024-01-10 19:54:44","post_content":"<!-- wp:heading {\"epAnimationGeneratedClass\":\"edplus_anim-gM7nny\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<h2 class=\"wp-block-heading eplus-wrapper\">J. Falco-Roget, A.Crimi<\/h2>\n<!-- \/wp:heading -->\n\n<!-- wp:spacer {\"height\":\"50px\",\"epAnimationGeneratedClass\":\"edplus_anim-dBhALR\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<div style=\"height:50px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n<!-- \/wp:spacer -->\n\n<!-- wp:paragraph {\"epAnimationGeneratedClass\":\"edplus_anim-RpOUjn\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<p class=\" eplus-wrapper\">Graph representation learning methods have recently been applied to predict how brain functional and structural networks will evolve in time. However, to obtain minimally coherent predictions, these methods require large datasets that are rarely available in sensitive settings such as brain tumors. Because of this, the problem of plasticity reorganization after tumor resection has been largely neglected in the machine learning community despite having an enormous potential for surgical planning. We present a machine learning model able to predict brain graphs following brain surgery, which can provide valuable information to surgeons planning better surgery. We rely on the idea that surgical outcomes share network similarities with healthy subjects and combine them in a Bayesian approach. We show how our method significantly outperforms simpler models even when taking advantage of the same prior. Furthermore, generated brain graphs share topological features with the real brain graphs. Overall, we present the problem of plasticity reorganization after brain surgery in a normative manner while still achieving competitive results.<\/p>\n<!-- \/wp:paragraph -->\n\n<!-- wp:spacer {\"height\":\"50px\",\"epAnimationGeneratedClass\":\"edplus_anim-dBhALR\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<div style=\"height:50px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n<!-- \/wp:spacer -->\n\n<!-- wp:acf\/button {\"id\":\"block_659ef5f3fc537\",\"name\":\"acf\/button\",\"data\":{\"title\":\"\",\"_title\":\"field_61d40397c2f0a\",\"button_type\":\"link\",\"_button_type\":\"field_63bbde3b8f0d0\",\"url\":\"\",\"_url\":\"field_61d4039bc2f0b\",\"button_style\":\"primary\",\"_button_style\":\"field_63872d045d0f0\",\"target\":\"_self\",\"_target\":\"field_63872c705d0ef\",\"button_extra_classes\":\"\",\"_button_extra_classes\":\"field_642beab6a97de\"},\"align\":\"\",\"mode\":\"edit\"} \/-->","post_title":"Bayesian Filtered Generation of Post-surgical Brain Connectomes on Tumor Patients","post_excerpt":"In: MICCAI-Grail, 2022.","post_status":"publish","comment_status":"closed","ping_status":"closed","post_password":"","post_name":"bayesian-filtered-generation-of-post-surgical-brain-connectomes-on-tumor-patients","to_ping":"","pinged":"","post_modified":"2024-01-10 20:54:45","post_modified_gmt":"2024-01-10 19:54:45","post_content_filtered":"","post_parent":0,"guid":"https:\/\/sano.science\/?post_type=research&p=14855","menu_order":50,"post_type":"research","post_mime_type":"","comment_count":"0","filter":"raw"},{"ID":26041,"post_author":"8","post_date":"2025-09-15 15:58:23","post_date_gmt":"2025-09-15 13:58:23","post_content":"<!-- wp:paragraph {\"epAnimationGeneratedClass\":\"edplus_anim-y7LPzV\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<p class=\" eplus-wrapper\">Advances in neuroimaging have enabled non-invasive mapping of brain networks in patients with brain tumors. While conventional MRI reliably detects the tumor core and surrounding edema, signals and functions within these regions are often overlooked. As a result, the role of functional and diffusion signals, as well as their contribution to global connectivity reorganization, remains insufficiently understood. In this work, we investigate functional activity and white matter architecture while explicitly accounting for the entire tumor in a surgical context. Our findings reveal intertwined alterations in both local and spatially distributed resting-state functional signals, detectable in the frequency domain and potentially originating from within the tumor. We further introduce a fiber-tracking framework that integrates anatomical priors yet remains capable of reconstructing pathways in tumoral and peritumoral tissue. Finally, by applying machine learning and normative anatomical data, we predict post-surgical structural rearrangements from preoperative networks. This generative approach also disentangles tumor-type\u2013specific patterns of connectivity reorganization. Taken together, our results highlight the necessity of incorporating MR signals from damaged brain regions, as they reflect complex and non-trivial interactions between structural and functional (dis)connectivity.<\/p>\n<!-- \/wp:paragraph -->\n\n<!-- wp:spacer {\"height\":\"30px\",\"epAnimationGeneratedClass\":\"edplus_anim-Oz8kXv\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<div style=\"height:30px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n<!-- \/wp:spacer -->\n\n<!-- wp:paragraph {\"epAnimationGeneratedClass\":\"edplus_anim-ykcXR9\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<p class=\" eplus-wrapper\">Functional and structural reorganization in brain tumors: a machine learning approach using desynchronized functional oscillations<\/p>\n<!-- \/wp:paragraph -->\n\n<!-- wp:paragraph {\"epAnimationGeneratedClass\":\"edplus_anim-ykcXR9\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<p class=\" eplus-wrapper\"><strong>Autors<\/strong>: Joan Falc\u00f3-Roget, Alberto Cacciola, Fabio Sambataro, Alessandro Crimi<\/p>\n<!-- \/wp:paragraph -->\n\n<!-- wp:paragraph {\"epAnimationGeneratedClass\":\"edplus_anim-qU0UGg\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<p class=\" eplus-wrapper\"><strong>DOI<\/strong>: 10.1038\/s42003-024-06119-3<\/p>\n<!-- \/wp:paragraph -->\n\n<!-- wp:spacer {\"height\":\"30px\",\"epAnimationGeneratedClass\":\"edplus_anim-Oz8kXv\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<div style=\"height:30px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n<!-- \/wp:spacer -->\n\n<!-- wp:acf\/button {\"id\":\"block_68c81b6e857b8\",\"name\":\"acf\/button\",\"data\":{\"title\":\"READ HERE\",\"_title\":\"field_61d40397c2f0a\",\"button_type\":\"link\",\"_button_type\":\"field_63bbde3b8f0d0\",\"url\":\"https:\/\/www.nature.com\/articles\/s42003-024-06119-3#author-information\",\"_url\":\"field_61d4039bc2f0b\",\"button_style\":\"primary\",\"_button_style\":\"field_63872d045d0f0\",\"target\":\"_self\",\"_target\":\"field_63872c705d0ef\",\"button_extra_classes\":\"\",\"_button_extra_classes\":\"field_642beab6a97de\"},\"align\":\"\",\"mode\":\"edit\"} \/-->","post_title":"Functional and structural reorganization in brain tumors: a machine learning approach using desynchronized functional oscillations","post_excerpt":"","post_status":"publish","comment_status":"closed","ping_status":"closed","post_password":"","post_name":"functional-and-structural-reorganization-in-brain-tumors-a-machine-learning-approach-using-desynchronized-functional-oscillations","to_ping":"","pinged":"","post_modified":"2025-09-15 15:58:35","post_modified_gmt":"2025-09-15 13:58:35","post_content_filtered":"","post_parent":0,"guid":"https:\/\/sano.science\/?post_type=research&p=26041","menu_order":0,"post_type":"research","post_mime_type":"","comment_count":"0","filter":"raw"},{"ID":30090,"post_author":"8","post_date":"2026-03-24 11:41:02","post_date_gmt":"2026-03-24 10:41:02","post_content":"<!-- wp:paragraph {\"epAnimationGeneratedClass\":\"edplus_anim-bDi8Oq\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<p class=\" eplus-wrapper\">A new paper co-authored by Joan Falc\u00f3-Roget from Sano presents a fully quantum approach to community detection in complex networks using D-Wave Advantage systems. By recursively decomposing the modularity maximization problem into binary instances, the method avoids one-hot encoding and costly penalty tuning, while achieving performance comparable to state-of-the-art classical algorithms. Importantly, it produces interpretable dendrograms that can reveal normal and pathological hierarchies in brain networks, bringing quantum annealing closer to practical clinical applications. <\/p>\n<!-- \/wp:paragraph -->\n\n<!-- wp:paragraph {\"epAnimationGeneratedClass\":\"edplus_anim-bDi8Oq\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<p class=\" eplus-wrapper\"><em>See how quantum annealing helps find hidden structure in the brain and complex networks<\/em><\/p>\n<!-- \/wp:paragraph -->\n\n<!-- wp:spacer {\"height\":\"30px\",\"epAnimationGeneratedClass\":\"edplus_anim-TFmkrs\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<div style=\"height:30px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n<!-- \/wp:spacer -->\n\n<!-- wp:acf\/button {\"id\":\"block_69c26a1c2400c\",\"name\":\"acf\/button\",\"data\":{\"title\":\"Read the article\",\"_title\":\"field_61d40397c2f0a\",\"button_type\":\"link\",\"_button_type\":\"field_63bbde3b8f0d0\",\"url\":\"See\u00a0how\u00a0quantum\u00a0annealing\u00a0helps\u00a0find\u00a0hidden\u00a0structure\u00a0in\u00a0the\u00a0brain\u00a0and\u00a0complex\u00a0networks:\u00a0https:\/\/ieeexplore.ieee.org\/document\/11417306\",\"_url\":\"field_61d4039bc2f0b\",\"button_style\":\"primary\",\"_button_style\":\"field_63872d045d0f0\",\"target\":\"_self\",\"_target\":\"field_63872c705d0ef\",\"button_extra_classes\":\"\",\"_button_extra_classes\":\"field_642beab6a97de\"},\"align\":\"\",\"mode\":\"edit\"} \/-->\n\n<!-- wp:spacer {\"height\":\"30px\",\"epAnimationGeneratedClass\":\"edplus_anim-TFmkrs\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<div style=\"height:30px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n<!-- \/wp:spacer -->\n\n<!-- wp:paragraph {\"epAnimationGeneratedClass\":\"edplus_anim-bDi8Oq\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<p class=\" eplus-wrapper\">Autors: <a href=\"https:\/\/sano.science\/people\/joan-falco-roget\/\" type=\"people\" id=\"1570\">Joan Falc\u00f3-Roget<\/a>; Kacper Jurek; Barbara Wojtarowicz; <a href=\"https:\/\/sano.science\/people\/karol-capala\/\" type=\"people\" id=\"690\">Karol Capa\u0142a<\/a>; Katarzyna Rycerz <\/p>\n<!-- \/wp:paragraph -->","post_title":"Modularity Maximization and Community Detection in Complex Networks Through Recursive and Hierarchical Annealing in the DWAVE Advantage Quantum Processing Units","post_excerpt":"Published in IEEE Transactions on Network Science and Engineering, 2026","post_status":"publish","comment_status":"closed","ping_status":"closed","post_password":"","post_name":"modularity-maximization-and-community-detection-in-complex-networks-through-recursive-and-hierarchical-annealing-in-the-dwave-advantage-quan","to_ping":"","pinged":"","post_modified":"2026-03-24 11:45:17","post_modified_gmt":"2026-03-24 10:45:17","post_content_filtered":"","post_parent":0,"guid":"https:\/\/sano.science\/?post_type=research&p=30090","menu_order":0,"post_type":"research","post_mime_type":"","comment_count":"0","filter":"raw"}]},"_links":{"self":[{"href":"https:\/\/sano.science\/index.php\/wp-json\/wp\/v2\/people\/1570","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/sano.science\/index.php\/wp-json\/wp\/v2\/people"}],"about":[{"href":"https:\/\/sano.science\/index.php\/wp-json\/wp\/v2\/types\/people"}],"version-history":[{"count":19,"href":"https:\/\/sano.science\/index.php\/wp-json\/wp\/v2\/people\/1570\/revisions"}],"predecessor-version":[{"id":30103,"href":"https:\/\/sano.science\/index.php\/wp-json\/wp\/v2\/people\/1570\/revisions\/30103"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/sano.science\/index.php\/wp-json\/wp\/v2\/media\/18504"}],"wp:attachment":[{"href":"https:\/\/sano.science\/index.php\/wp-json\/wp\/v2\/media?parent=1570"}],"wp:term":[{"taxonomy":"people_teams","embeddable":true,"href":"https:\/\/sano.science\/index.php\/wp-json\/wp\/v2\/people_teams?post=1570"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}