{"id":539,"date":"2023-05-14T12:40:56","date_gmt":"2023-05-14T10:40:56","guid":{"rendered":"https:\/\/sano.empressia.dev\/?post_type=people&p=539"},"modified":"2025-07-14T15:56:13","modified_gmt":"2025-07-14T13:56:13","slug":"alessandro-crimi","status":"publish","type":"people","link":"https:\/\/sano.science\/people\/alessandro-crimi\/","title":{"rendered":"Alessandro Crimi"},"excerpt":{"rendered":"","protected":false},"featured_media":1634,"template":"","people_teams":[24],"class_list":["post-539","people","type-people","status-publish","has-post-thumbnail","hentry","people_teams-alumni"],"yoast_head":"\nAlessandro Crimi - 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\/alessandro-crimi\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Alessandro Crimi\" \/>\n<meta property=\"og:url\" content=\"https:\/\/sano.science\/people\/alessandro-crimi\/\" \/>\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=\"2025-07-14T13:56:13+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/sano.science\/wp-content\/uploads\/2023\/05\/alex_crimi.png\" \/>\n\t<meta property=\"og:image:width\" content=\"350\" \/>\n\t<meta property=\"og:image:height\" content=\"350\" \/>\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\\\/alessandro-crimi\\\/\",\"url\":\"https:\\\/\\\/sano.science\\\/people\\\/alessandro-crimi\\\/\",\"name\":\"Alessandro Crimi - 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He worked as a postdoctoral researcher in different countries including Italy, France and Switzerland, mostly on clinical neuroimaging projects about multiple sclerosis, Alzheimer’s and Parkinson’s disease and glioma. In the past, he also was a visiting lecturer at the African Institute for Mathematical Sciences in Ghana and South Africa, where he did several projects related to global health and prenatal care.<\/p>\n","social_media":[{"icon":{"ID":11990,"id":11990,"title":"research gate","filename":"research-gate.svg","filesize":14281,"url":"https:\/\/sano.science\/wp-content\/uploads\/2023\/05\/research-gate.svg","link":"https:\/\/sano.science\/people\/maciej-malawski\/research-gate\/","alt":"","author":"5","description":"","caption":"","name":"research-gate","status":"inherit","uploaded_to":531,"date":"2023-07-06 11:18:50","modified":"2023-07-06 11:18:50","menu_order":0,"mime_type":"image\/svg+xml","type":"image","subtype":"svg+xml","icon":"https:\/\/sano.science\/wp-includes\/images\/media\/default.png","width":1,"height":1,"sizes":{"thumbnail":"https:\/\/sano.science\/wp-content\/uploads\/2023\/05\/research-gate.svg","thumbnail-width":150,"thumbnail-height":150,"medium":"https:\/\/sano.science\/wp-content\/uploads\/2023\/05\/research-gate.svg","medium-width":300,"medium-height":300,"medium_large":"https:\/\/sano.science\/wp-content\/uploads\/2023\/05\/research-gate.svg","medium_large-width":768,"medium_large-height":1,"large":"https:\/\/sano.science\/wp-content\/uploads\/2023\/05\/research-gate.svg","large-width":1024,"large-height":1024,"1536x1536":"https:\/\/sano.science\/wp-content\/uploads\/2023\/05\/research-gate.svg","1536x1536-width":1,"1536x1536-height":1,"2048x2048":"https:\/\/sano.science\/wp-content\/uploads\/2023\/05\/research-gate.svg","2048x2048-width":1,"2048x2048-height":1}},"link":"https:\/\/www.researchgate.net\/profile\/Alessandro-Crimi","name":"ResearchGate"},{"icon":{"ID":11986,"id":11986,"title":"google","filename":"google.svg","filesize":14070,"url":"https:\/\/sano.science\/wp-content\/uploads\/2023\/05\/google.svg","link":"https:\/\/sano.science\/people\/maciej-malawski\/google\/","alt":"","author":"5","description":"","caption":"","name":"google","status":"inherit","uploaded_to":531,"date":"2023-07-06 10:59:21","modified":"2023-07-06 10:59:21","menu_order":0,"mime_type":"image\/svg+xml","type":"image","subtype":"svg+xml","icon":"https:\/\/sano.science\/wp-includes\/images\/media\/default.png","width":1,"height":1,"sizes":{"thumbnail":"https:\/\/sano.science\/wp-content\/uploads\/2023\/05\/google.svg","thumbnail-width":150,"thumbnail-height":150,"medium":"https:\/\/sano.science\/wp-content\/uploads\/2023\/05\/google.svg","medium-width":300,"medium-height":300,"medium_large":"https:\/\/sano.science\/wp-content\/uploads\/2023\/05\/google.svg","medium_large-width":768,"medium_large-height":1,"large":"https:\/\/sano.science\/wp-content\/uploads\/2023\/05\/google.svg","large-width":1024,"large-height":1024,"1536x1536":"https:\/\/sano.science\/wp-content\/uploads\/2023\/05\/google.svg","1536x1536-width":1,"1536x1536-height":1,"2048x2048":"https:\/\/sano.science\/wp-content\/uploads\/2023\/05\/google.svg","2048x2048-width":1,"2048x2048-height":1}},"link":"https:\/\/scholar.google.com\/citations?user=ciOVKiQAAAAJ&hl=en&oi=ao","name":"Google Scholar"}],"tabs":[{"title":"Scientific expertise","content":"<p>Mostly focused on neuroimaging and related topic to neuroscience. Particularly fascinated by diffusion MRI, functional MRI, connectivity and microscopy. The focus of the use of these technologies mostly aiming at finding biomarkers for neurodegenerative diseases, but also at fundamental level of neural circuitry.<\/p>\n<p>Extensive knowledge and experience in image analysis and machine learning.<\/p>\n<p>Strong supporter of neurotech devices (as EEG, EMG…), as well as translation of research into companies, and implementation in global health and development by education.<\/p>\n"},{"title":"Accomplishments","content":"<p>Author of several peer-reviewed pubblications on neuroimaging, medical imaging and global health.<\/p>\n<p>Editor and organizer of the MICCAI (Medical Image Computing and Computer Assisted Interventions) BrainLesion Workshop, published by Springer Verlag.<\/p>\n<p>Supervised several healthcare projects in low- and middle-income countries in Africa, related to diabetes, HIV and prenatal care.<\/p>\n<p>Visiting lecturer at the African Institute for Mathematical Sciences in Ghana and South Africa, mentor for the NextEinstein initiative.<\/p>\n<p>Serial entrepreneur and science communicator.<\/p>\n"},{"title":"Experience","content":"<ul>\n<li><strong>2013-ongoing<\/strong> – Visiting Lecturer, African Institute for Mathematical Sciences, Ghana & South Africa<\/li>\n<li><strong>2020-2021<\/strong> – Founder of Yawlab, Zurich, Switzerland<\/li>\n<li><strong>2017-2019<\/strong> – Postdoc\/Research Associate, University hospital of Zurich, Zurich, Switzerland<\/li>\n<li><strong>2015-2016<\/strong> – Postdoc\/Research Associate, Italian Institute for Technology, Genoa, Italy doc\/Research Associate, ETH-Zurich, Zurich, Switzerland<\/li>\n<li><strong>2011-2012<\/strong> – Postdoc\/Research Associate, INRIA-Atlantic & University hospital of Rennes, Rennes France<\/li>\n<\/ul>\n"},{"title":"Education","content":"<ul>\n<li>2016 \u2013 MBA in international health management, University of Basel, Switzerland<\/li>\n<li>2011 \u2013 Ph.D in Medical imaging, University of Copenhagen, Denmark<\/li>\n<li>2007 \u2013 M.Sc in Engineering for intelligent systems, University of Palermo, Italy<\/li>\n<\/ul>\n"},{"title":"Contact","content":"<p><strong>Sano Centre for Computational Medicine<\/strong><\/p>\n<p>Czarnowiejska 36, 33-332, Cracow, Poland<\/p>\n<p> <\/p>\n<p><strong>Email:<\/strong> <a href=\"mailto:a.crimi@sanoscience.org\">a.crimi@sanoscience.org<\/a><\/p>\n<p><strong>Tel:<\/strong> <a href=\"tel:+48 12 307 27 37\">+48 12 307 27 37<\/a><\/p>\n"}],"email":"","position_with_team":{"text_before_link":"","link_text":"Computer Vision Data Science","text_after_link":"","link":""},"publications":[{"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":21526,"post_author":"8","post_date":"2025-02-17 18:25:57","post_date_gmt":"2025-02-17 17:25:57","post_content":"<!-- wp:heading {\"epAnimationGeneratedClass\":\"edplus_anim-aKHf29\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<h2 class=\"wp-block-heading eplus-wrapper\" id=\"h-szymon-mazurek-nbsp-monika-pytlarz-nbsp-sylwia-malec-alessandro-crimi-nbsp\">Szymon Mazurek,  Monika Pytlarz,  Sylwia Malec, Alessandro Crimi <\/h2>\n<!-- \/wp:heading -->\n\n<!-- wp:spacer {\"height\":\"30px\",\"epAnimationGeneratedClass\":\"edplus_anim-vfGiFQ\",\"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-kaIHoN\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<p class=\" eplus-wrapper\">Advancements across various industries have been significantly propelled by artificial intelligence. However, the rapid proliferation of these technologies also raises environmental concerns, particularly due to the substantial carbon footprints associated with training computational models. Segmenting the fetal brain in medical imaging presents a challenge due to its small size and the limited quality of fast 2D sequences. Deep neural networks emerge as a promising solution to this issue. The development of larger models in this context requires significant data and computing resources, leading to increased energy consumption. Our research focuses on exploring model architectures and compression techniques that enhance energy efficiency. We aim to optimize the balance between accuracy and energy usage through strategies such as designing lightweight networks, conducting architecture searches, and utilizing optimized distributed training tools. We have identified several effective strategies, including optimizing data loading, employing modern optimizers, implementing distributed training strategies, and reducing the precision of floating-point operations in light model architectures while adjusting parameters to match available computing resources. Our findings confirm that these methods ensure satisfactory model performance with minimal energy consumption during the training of deep neural networks for medical image segmentation.<\/p>\n<!-- \/wp:paragraph -->\n\n<!-- wp:spacer {\"height\":\"30px\",\"epAnimationGeneratedClass\":\"edplus_anim-vfGiFQ\",\"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-aRLcXy\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<p class=\" eplus-wrapper\"><strong>DOI<\/strong>: 10.1007\/978-3-031-63772-8_5<\/p>\n<!-- \/wp:paragraph -->\n\n<!-- wp:spacer {\"height\":\"30px\",\"epAnimationGeneratedClass\":\"edplus_anim-vfGiFQ\",\"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_67b36f118fcac\",\"name\":\"acf\/button\",\"data\":{\"title\":\"READ HERE\",\"_title\":\"field_61d40397c2f0a\",\"button_type\":\"link\",\"_button_type\":\"field_63bbde3b8f0d0\",\"url\":\"https:\/\/link.springer.com\/chapter\/10.1007\/978-3-031-63772-8_5\",\"_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":"Investigation of Energy-Efficient AI Model Architectures and Compression Techniques for \u201cGreen\u201d Fetal Brain Segmentation","post_excerpt":"2024","post_status":"publish","comment_status":"closed","ping_status":"closed","post_password":"","post_name":"investigation-of-energy-efficient-ai-model-architectures-and-compression-techniques-for-green-fetal-brain-segmentation","to_ping":"","pinged":"","post_modified":"2025-02-17 18:30:06","post_modified_gmt":"2025-02-17 17:30:06","post_content_filtered":"","post_parent":0,"guid":"https:\/\/sano.science\/?post_type=research&p=21526","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":18951,"post_author":"8","post_date":"2024-09-23 12:32:44","post_date_gmt":"2024-09-23 10:32:44","post_content":"<!-- wp:heading {\"epAnimationGeneratedClass\":\"edplus_anim-o5lc6Y\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<h2 class=\"wp-block-heading eplus-wrapper\" id=\"h-rosmary-nbsp-blanco-nbsp-cemal-nbsp-koba-nbsp-alessandro-nbsp-crimi-nbsp\">Rosmary Blanco, Cemal Koba, Alessandro Crimi <\/h2>\n<!-- \/wp:heading -->\n\n<!-- wp:spacer {\"height\":\"50px\",\"epAnimationGeneratedClass\":\"edplus_anim-m7Y65m\",\"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-KiJf3F\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<p class=\" eplus-wrapper\">Rosmary Blanco, Cemal Koba, Alessandro Crimi Exploring the brain's complex networks requires multiple neuroimaging techniques, each offering unique insights. Combining electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) has gained attention for its potential to deepen our understanding of brain functioning. However, how these modalities relate is still an open question. Understanding how the electrical and hemodynamic activities relate is crucial for effectively integrating these modalities, potentially enhancing the spatio-temporal resolution of neuroimaging and revealing information about brain function that might be missed when each modality is used in isolation. In this study, we compared brain networks captured by EEG (electrical activity) and fNIRS (hemodynamic activity) in both resting and task-related conditions. Complementarity between modalities was observed, particularly during tasks, as well as a certain level of redundancy when comparing the multimodal and the unimodal approach, which depends on the modality and the specific brain state. Overall, the results highlight differences in how EEG and fNIRS capture brain network topology in different brain states and emphasize the value of integrating multiple modalities for a comprehensive view of brain functioning.<\/p>\n<!-- \/wp:paragraph -->\n\n<!-- wp:spacer {\"height\":\"50px\",\"epAnimationGeneratedClass\":\"edplus_anim-m7Y65m\",\"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-zeiYMr\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<p class=\" eplus-wrapper\"><strong>Authors<\/strong>:  Rosmary Blanco, Cemal Koba, Alessandro Crimi <\/p>\n<!-- \/wp:paragraph -->\n\n<!-- wp:paragraph {\"epAnimationGeneratedClass\":\"edplus_anim-KyXlgX\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<p class=\" eplus-wrapper\"><strong>DOI<\/strong>: <a href=\"https:\/\/doi.org\/10.1016\/j.jocs.2024.102416\" target=\"_blank\" rel=\"noreferrer noopener\">https:\/\/doi.org\/10.1016\/j.jocs.2024.102416<\/a> <\/p>\n<!-- \/wp:paragraph -->\n\n<!-- wp:paragraph {\"epAnimationGeneratedClass\":\"edplus_anim-zeiYMr\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<p class=\" eplus-wrapper\"><strong>Link to article<\/strong>: <a href=\"https:\/\/www.sciencedirect.com\/science\/article\/pii\/S1877750324002096\" target=\"_blank\" rel=\"noreferrer noopener nofollow\">www.sciencedirect.com<\/a><\/p>\n<!-- \/wp:paragraph -->\n\n<!-- wp:paragraph {\"epAnimationGeneratedClass\":\"edplus_anim-zeiYMr\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<p class=\" eplus-wrapper\"><strong>Keywords<\/strong>: multimodal neuroimaging, EEG, fNIRS, Multilayer Networks<\/p>\n<!-- \/wp:paragraph -->\n\n<!-- wp:spacer {\"height\":\"50px\",\"epAnimationGeneratedClass\":\"edplus_anim-m7Y65m\",\"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_6780e9ef083b5\",\"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\/S1877750324002096\",\"_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-m7Y65m\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<div style=\"height:50px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n<!-- \/wp:spacer -->\n\n<!-- wp:image {\"id\":18960,\"sizeSlug\":\"large\",\"linkDestination\":\"none\",\"epAnimationGeneratedClass\":\"edplus_anim-G005GI\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<figure class=\"wp-block-image size-large eplus-wrapper\"><img src=\"https:\/\/sano.science\/wp-content\/uploads\/2024\/09\/image-1024x609.png\" alt=\"\" class=\"wp-image-18960\"\/><figcaption class=\"wp-element-caption\"><strong>Method workflow:<\/strong> <strong>1.<\/strong> The EEG and fNIRS data collection. <strong>2.<\/strong> Data pre-processing. <strong>3.<\/strong> Source reconstruction. <strong>4.<\/strong> Mapping of the source signals onto the same brain space. <strong>5.<\/strong> Functional connectivity computation. <strong>6.<\/strong> Graph analysis for comparing the topology of brain networks. <strong>7.<\/strong> Multilayer network analysis for modalities integration and multimodal vs unimodal network comparison. <em>Source: https:\/\/www.sciencedirect.com\/science\/article\/pii\/S1877750324002096<\/em><\/figcaption><\/figure>\n<!-- \/wp:image -->","post_title":"Investigating the interaction between EEG and fNIRS: A multimodal network analysis of brain connectivity","post_excerpt":"Journal paper in: www.sciencedirect.com, 2024.","post_status":"publish","comment_status":"closed","ping_status":"closed","post_password":"","post_name":"investigating-the-interaction-between-eeg-and-fnirs-a-multimodal-network-analysis-of-brain-connectivity","to_ping":"","pinged":"","post_modified":"2025-01-10 13:46:48","post_modified_gmt":"2025-01-10 12:46:48","post_content_filtered":"","post_parent":0,"guid":"https:\/\/sano.science\/?post_type=research&p=18951","menu_order":0,"post_type":"research","post_mime_type":"","comment_count":"0","filter":"raw"},{"ID":14858,"post_author":"5","post_date":"2024-01-10 20:57:30","post_date_gmt":"2024-01-10 19:57:30","post_content":"<!-- wp:heading {\"epAnimationGeneratedClass\":\"edplus_anim-iacbEG\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<h2 class=\"wp-block-heading eplus-wrapper\">R.Blanco, C. Koba, A. Crimi<\/h2>\n<!-- \/wp:heading -->\n\n<!-- wp:spacer {\"height\":\"50px\",\"epAnimationGeneratedClass\":\"edplus_anim-UfjgwT\",\"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-paTfuw\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<p class=\" eplus-wrapper\">Contemporary neuroscience is highly focused on the synergistic use of machine learning and network analysis. Indeed, network neuroscience analysis intensively capitalizes on clustering metrics and statistical tools. In this context, the integrated analysis of functional near-infrared spectroscopy (fNIRS) and electroencephalography (EEG) provides complementary information about the electrical and hemodynamic activity of the brain. Evidence supports the mechanism of the neurovascular coupling mediates brain processing. However, it is not well understood how the specific patterns of neuronal activity are represented by these techniques. Here we have investigated the topological properties of functional networks of the resting-state brain between synchronous EEG and fNIRS connectomes, across frequency bands, using source space analysis, and through graph theoretical approaches. We observed that at global-level analysis small-world topology network features for both modalities. The edge-wise analysis pointed out increased inter-hemispheric connectivity for oxy-hemoglobin compared to EEG, with no differences across the frequency bands. Our results show that graph features extracted from fNIRS can reflect both short- and long-range organization of neural activity, and that is able to characterize the large-scale network in the resting state. Further development of integrated analyses of the two modalities is required to fully benefit from the added value of each modality. However, the present study highlights that multimodal source space analysis approaches can be adopted to study brain functioning in healthy resting states, thus serving as a foundation for future work during tasks and in pathology, with the possibility of obtaining novel comprehensive biomarkers for neurological diseases.<\/p>\n<!-- \/wp:paragraph -->\n\n<!-- wp:spacer {\"height\":\"50px\",\"epAnimationGeneratedClass\":\"edplus_anim-UfjgwT\",\"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_659ef67932183\",\"name\":\"acf\/button\",\"data\":{\"title\":\"READ HERE\",\"_title\":\"field_61d40397c2f0a\",\"button_type\":\"link\",\"_button_type\":\"field_63bbde3b8f0d0\",\"url\":\"https:\/\/link.springer.com\/chapter\/10.1007\/978-3-031-36021-3_58\",\"_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":"Resting State Brain Connectivity analysis from EEG and FNIRS signals","post_excerpt":"In: ICCS 2023 (23rd International Conference on Computational Science), 2022.","post_status":"publish","comment_status":"closed","ping_status":"closed","post_password":"","post_name":"resting-state-brain-connectivity-analysis-from-eeg-and-fnirs-signals","to_ping":"","pinged":"","post_modified":"2024-01-10 20:57:30","post_modified_gmt":"2024-01-10 19:57:30","post_content_filtered":"","post_parent":0,"guid":"https:\/\/sano.science\/?post_type=research&p=14858","menu_order":49,"post_type":"research","post_mime_type":"","comment_count":"0","filter":"raw"},{"ID":14861,"post_author":"5","post_date":"2024-01-10 21:00:10","post_date_gmt":"2024-01-10 20:00:10","post_content":"<!-- wp:heading {\"epAnimationGeneratedClass\":\"edplus_anim-wrhmZQ\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<h2 class=\"wp-block-heading eplus-wrapper\">A. Crimi, S. Bakas<\/h2>\n<!-- \/wp:heading -->\n\n<!-- wp:spacer {\"height\":\"50px\",\"epAnimationGeneratedClass\":\"edplus_anim-ZYx3Z6\",\"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_659ef73c562c6\",\"name\":\"acf\/button\",\"data\":{\"title\":\"READ HERE\",\"_title\":\"field_61d40397c2f0a\",\"button_type\":\"link\",\"_button_type\":\"field_63bbde3b8f0d0\",\"url\":\"https:\/\/link.springer.com\/book\/10.1007\/978-3-031-08999-2\",\"_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":"Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries","post_excerpt":"In: Lecture Notes in Computer Science (LNCS, volume 12963), BrainLes: International MICCAI Brainlesion Workshop, 2022.","post_status":"publish","comment_status":"closed","ping_status":"closed","post_password":"","post_name":"brainlesion-glioma-multiple-sclerosis-stroke-and-traumatic-brain-injuries","to_ping":"","pinged":"","post_modified":"2024-01-10 21:00:10","post_modified_gmt":"2024-01-10 20:00:10","post_content_filtered":"","post_parent":0,"guid":"https:\/\/sano.science\/?post_type=research&p=14861","menu_order":48,"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":14919,"post_author":"5","post_date":"2024-01-16 12:54:37","post_date_gmt":"2024-01-16 11:54:37","post_content":"<!-- wp:heading {\"epAnimationGeneratedClass\":\"edplus_anim-air8wi\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<h2 class=\"wp-block-heading eplus-wrapper\">Marcin WIERZBI\u0143SKI, Karolina L. TKACZUK, Alessandro CRIMI<\/h2>\n<!-- \/wp:heading -->\n\n<!-- wp:spacer {\"height\":\"50px\",\"epAnimationGeneratedClass\":\"edplus_anim-MXj9RZ\",\"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_65a66e6f0a179\",\"name\":\"acf\/button\",\"data\":{\"title\":\"READ HERE\",\"_title\":\"field_61d40397c2f0a\",\"button_type\":\"link\",\"_button_type\":\"field_63bbde3b8f0d0\",\"url\":\"https:\/\/www.deltami.edu.pl\/2023a\/01\/2023-01-delta-art-04-crimi-etal.pdf\",\"_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":"O pewnym modelu zawijania bia\u0142ek","post_excerpt":"In: deltami, 2023.","post_status":"publish","comment_status":"closed","ping_status":"closed","post_password":"","post_name":"o-pewnym-modelu-zawijania-bialek","to_ping":"","pinged":"","post_modified":"2024-01-16 12:54:37","post_modified_gmt":"2024-01-16 11:54:37","post_content_filtered":"","post_parent":0,"guid":"https:\/\/sano.science\/?post_type=research&p=14919","menu_order":16,"post_type":"research","post_mime_type":"","comment_count":"0","filter":"raw"},{"ID":15010,"post_author":"5","post_date":"2024-01-18 09:31:55","post_date_gmt":"2024-01-18 08:31:55","post_content":"<!-- wp:heading {\"epAnimationGeneratedClass\":\"edplus_anim-KEeM7k\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<h2 class=\"wp-block-heading eplus-wrapper\">Monika Pytlarz, Kamil Wojnicki, Paulina Pilanc, Bo\u017cena Kami\u0144ska, Alessandro Crimi<\/h2>\n<!-- \/wp:heading -->\n\n<!-- wp:spacer {\"height\":\"50px\",\"epAnimationGeneratedClass\":\"edplus_anim-GXNp11\",\"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-zCE5mp\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<p class=\" eplus-wrapper\">Gliomas are primary brain tumors that arise from neural stem cells or glial precursors. Diagnosis of glioma is based on histological evaluation of pathological cell features and some molecular markers. Gliomas are infiltrated by myeloid cells that accumulate preferentially in malignant tumors and their abundance inversely correlates with survival, which is of interest for cancer immunotherapies. To avoid time-consuming and laborious manual examination of the images, a deep learning approach for automatic multiclass classification of tumor grades was proposed. Moreover, the challenge of the study was given by small sample size of human leukocyte antigen tissue microarrays dataset (HLA-TMA)-total 204 images from 5 classes-and imbalanced data distribution. This has been addressed by images augmentation of the underrepresented classes, as already shown in a similar study about predicting mutations from glioma biopsies. 1 For this glioma multiclass classification task, the architecture of residual neural network has been adapted. The best model produced an accuracy score of 0.7727, and the mean accuracy of the cross-validation iterations was the value of 0.7248 on the validation set. This promising approach can be used as an additional diagnostic tool to improve assessment during intra-operative examination or sub-typing tissues for treatment selection, despite the challenges presented by the difficult dataset.<\/p>\n<!-- \/wp:paragraph -->\n\n<!-- wp:spacer {\"height\":\"50px\",\"epAnimationGeneratedClass\":\"edplus_anim-GXNp11\",\"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_65a8e1e78d376\",\"name\":\"acf\/button\",\"data\":{\"title\":\"READ HERE\",\"_title\":\"field_61d40397c2f0a\",\"button_type\":\"link\",\"_button_type\":\"field_63bbde3b8f0d0\",\"url\":\"https:\/\/www.researchgate.net\/publication\/369547664_Automated_Glioma_Multiclass_Tumor_Classification\",\"_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":"Automated glioma multiclass tumor classification","post_excerpt":"In: SPIE Medical Imaging 2023: Digital and Computational Pathology, 2023.","post_status":"publish","comment_status":"closed","ping_status":"closed","post_password":"","post_name":"automated-glioma-multiclass-tumor-classification","to_ping":"","pinged":"","post_modified":"2024-01-18 09:31:55","post_modified_gmt":"2024-01-18 08:31:55","post_content_filtered":"","post_parent":0,"guid":"https:\/\/sano.science\/?post_type=research&p=15010","menu_order":9,"post_type":"research","post_mime_type":"","comment_count":"0","filter":"raw"},{"ID":15033,"post_author":"5","post_date":"2024-01-18 10:19:04","post_date_gmt":"2024-01-18 09:19:04","post_content":"<!-- wp:heading {\"epAnimationGeneratedClass\":\"edplus_anim-7d2931\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<h2 class=\"wp-block-heading eplus-wrapper\">Monika Pytlarz, Adrian Onicas, Alessandro Crimi<\/h2>\n<!-- \/wp:heading -->\n\n<!-- wp:spacer {\"height\":\"50px\",\"epAnimationGeneratedClass\":\"edplus_anim-3TibNG\",\"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-AvMVpe\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<p class=\" eplus-wrapper\">Cross-modal augmentation of Magnetic Resonance Imaging (MRI) and microscopic imaging based on the same tissue samples is promising because it can allow histopathological analysis in the absence of an underlying invasive biopsy procedure. Here, we tested a method for generating microscopic histological images from MRI scans of the corpus callosum using conditional generative adversarial network (cGAN) architecture. To our knowledge, this is the first multimodal translation of the brain MRI to histological volumetric representation of the same sample. The technique was assessed by training paired image translation models taking sets of images from MRI scans and microscopy. The use of cGAN for this purpose is challenging because microscopy images are large in size and typically have low sample availability. The current work demonstrates that the framework reliably synthesizes histology images from MRI scans of corpus callosum, emphasizing the network's ability to train on high resolution histologies paired with relatively lower-resolution MRI scans. With the ultimate goal of avoiding biopsies, the proposed tool can be used for educational purposes.<\/p>\n<!-- \/wp:paragraph -->\n\n<!-- wp:spacer {\"height\":\"50px\",\"epAnimationGeneratedClass\":\"edplus_anim-Hqcac4\",\"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_65df5d5ef1033\",\"name\":\"acf\/button\",\"data\":{\"title\":\"READ MORE\",\"_title\":\"field_61d40397c2f0a\",\"button_type\":\"link\",\"_button_type\":\"field_63bbde3b8f0d0\",\"url\":\"https:\/\/arxiv.org\/abs\/2310.10414\",\"_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":"Style transfer between microscopy and magnetic resonance imaging via generative adversarial network in small sample size settings","post_excerpt":"In: IEEE International Conference on Image Processing (ICIP), Kuala Lumpur, Malaysia, 2023.","post_status":"publish","comment_status":"closed","ping_status":"closed","post_password":"","post_name":"style-transfer-between-microscopy-and-magnetic-resonance-imaging-via-generative-adversarial-network-in-small-sample-size-settings","to_ping":"","pinged":"","post_modified":"2024-02-28 17:23:04","post_modified_gmt":"2024-02-28 16:23:04","post_content_filtered":"","post_parent":0,"guid":"https:\/\/sano.science\/?post_type=research&p=15033","menu_order":13,"post_type":"research","post_mime_type":"","comment_count":"0","filter":"raw"},{"ID":15074,"post_author":"5","post_date":"2024-01-18 20:40:02","post_date_gmt":"2024-01-18 19:40:02","post_content":"<!-- wp:heading {\"epAnimationGeneratedClass\":\"edplus_anim-i03vtW\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<h2 class=\"wp-block-heading eplus-wrapper\">Luca Gherardini, Aleksandra Pestka, Lorenzo Pini, Alessandro Crimi<\/h2>\n<!-- \/wp:heading -->\n\n<!-- wp:spacer {\"height\":\"50px\",\"epAnimationGeneratedClass\":\"edplus_anim-NSRzPr\",\"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-a2eeGr\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<p class=\" eplus-wrapper\">The pervasive impact of Alzheimer\u2019s disease on aging society represents one of the main challenges at this time. Current investigations highlight 2 specific misfolded proteins in its development: Amyloid-B\u00a0and tau. Previous studies focused on spreading for misfolded proteins exploited simulations, which required several parameters to be empirically estimated. Here, we provide an alternative view based on 2 machine learning approaches which we compare with known simulation models. The first approach applies an autoregressive model constrained by structural connectivity, while the second is based on graph convolutional networks. The aim is to predict concentrations of Amyloid-B\u00a02 yr after a provided baseline. We also evaluate its real-world effectiveness and suitability by providing a web service for physicians and researchers. In experiments, the autoregressive model generally outperformed state-of-the-art models resulting in lower prediction errors. While it is important to note that a comprehensive prognostic plan cannot solely rely on amyloid beta concentrations, their prediction, achieved by the discussed approaches, can be valuable for planning therapies and other cures, especially when dealing with asymptomatic patients for whom novel therapies could prove effective.<\/p>\n<!-- \/wp:paragraph -->\n\n<!-- wp:spacer {\"height\":\"50px\",\"epAnimationGeneratedClass\":\"edplus_anim-NSRzPr\",\"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_65a97e4d6a5ef\",\"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":"Prediction of misfolded proteins spreading in Alzheimer\u2019s disease using machine learning and spreading models","post_excerpt":"In: CEREBRAL CORTEX, 2023.","post_status":"publish","comment_status":"closed","ping_status":"closed","post_password":"","post_name":"prediction-of-misfolded-proteins-spreading-in-alzheimers-disease-using-machine-learning-and-spreading-models","to_ping":"","pinged":"","post_modified":"2024-01-18 20:40:02","post_modified_gmt":"2024-01-18 19:40:02","post_content_filtered":"","post_parent":0,"guid":"https:\/\/sano.science\/?post_type=research&p=15074","menu_order":12,"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":12678,"post_author":"5","post_date":"2023-07-13 13:18:18","post_date_gmt":"2023-07-13 11:18:18","post_content":"<!-- wp:heading {\"epAnimationGeneratedClass\":\"edplus_anim-0xWdRB\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<h2 class=\"wp-block-heading eplus-wrapper\"><strong>Grzeszczyk M<\/strong>K, Sat\u0142awa T, Lungu A, Swift A, Narracott A, Hose R, Trzcinski T, <strong>Sitek A<\/strong><\/h2>\n<!-- \/wp:heading -->\n\n<!-- wp:spacer {\"height\":\"50px\",\"epAnimationGeneratedClass\":\"edplus_anim-yHjDTu\",\"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-YoNxvk\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<p class=\" eplus-wrapper\">Pulmonary Hypertension (PH) is a severe disease characterized by an elevated pulmonary artery pressure. The gold standard for PH diagnosis is measurement of mean Pulmonary Artery Pressure (mPAP) during an invasive Right Heart Catheterization. In this paper, we investigate noninvasive approach to PH detection utilizing Magnetic Resonance Imaging, Computer Models and Machine Learning. We show using the ablation study, that physics-informed feature engineering based on models of blood circulation increases the performance of Gradient Boosting Decision Trees-based algorithms for classification of PH and regression of values of mPAP. We compare results of regression (with thresholding of estimated mPAP) and classification and demonstrate that metrics achieved in both experiments are comparable. The predicted mPAP values are more informative to the physicians than the probability of PH returned by classification models. They provide the intuitive explanation of the outcome of the machine learning model (clinicians are accustomed to the mPAP metric, contrary to the PH probability).<\/p>\n<!-- \/wp:paragraph -->\n\n<!-- wp:spacer {\"height\":\"50px\",\"epAnimationGeneratedClass\":\"edplus_anim-yHjDTu\",\"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_64c02976587d4\",\"name\":\"acf\/button\",\"data\":{\"title\":\"READ HERE\",\"_title\":\"field_61d40397c2f0a\",\"button_type\":\"link\",\"_button_type\":\"field_63bbde3b8f0d0\",\"url\":\"https:\/\/link.springer.com\/chapter\/10.1007\/978-3-031-08757-8_2\",\"_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":"Noninvasive Estimation of\u00a0Mean Pulmonary Artery Pressure Using MRI, Computer Models, and\u00a0Machine Learning\u00a0","post_excerpt":"In: 22nd International Conference on Computational Science, 2022.","post_status":"publish","comment_status":"closed","ping_status":"closed","post_password":"","post_name":"noninvasive-estimation-of-mean-pulmonary-artery-pressure-using-mri-computer-models-and-machine-learning","to_ping":"","pinged":"","post_modified":"2024-01-10 19:45:00","post_modified_gmt":"2024-01-10 18:45:00","post_content_filtered":"","post_parent":0,"guid":"https:\/\/new.sano.science\/?post_type=research&p=12678","menu_order":62,"post_type":"research","post_mime_type":"","comment_count":"0","filter":"raw"},{"ID":12542,"post_author":"5","post_date":"2023-07-12 13:38:23","post_date_gmt":"2023-07-12 11:38:23","post_content":"<!-- wp:heading {\"epAnimationGeneratedClass\":\"edplus_anim-QEVsOI\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<h2 class=\"wp-block-heading eplus-wrapper\">Crimi, Alessandro; Mulder, Nicola J.; Chimusa, Emile R.; Elsheikh, Samar S. M.<\/h2>\n<!-- \/wp:heading -->\n\n<!-- wp:spacer {\"height\":\"50px\",\"epAnimationGeneratedClass\":\"edplus_anim-STNu3R\",\"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-FDcstW\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<p class=\" eplus-wrapper\">Variations in the human genome have been found to be an essential factor that affects susceptibility to Alzheimer\u2019s disease. Genome-wide association studies (GWAS) have identified genetic loci that significantly contribute to the risk of Alzheimers. The availability of genetic data, coupled with brain imaging technologies have opened the door for further discoveries, by using data integration methodologies and new study designs. Although methods have been proposed for integrating image characteristics and genetic information for studying Alzheimers, the measurement of disease is often taken at a single time point, therefore, not allowing the disease progression to be taken into consideration.<\/p>\n<!-- \/wp:paragraph -->\n\n<!-- wp:spacer {\"height\":\"20px\",\"epAnimationGeneratedClass\":\"edplus_anim-bYmqlc\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<div style=\"height:20px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n<!-- \/wp:spacer -->\n\n<!-- wp:paragraph {\"epAnimationGeneratedClass\":\"edplus_anim-FDcstW\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<p class=\" eplus-wrapper\">In longitudinal settings, we analyzed neuroimaging and single nucleotide polymorphism datasets obtained from the Alzheimer\u2019s Disease Neuroimaging Initiative for three clinical stages of the disease, including healthy control, early mild cognitive impairment and Alzheimer\u2019s disease subjects. We conducted a GWAS regressing the absolute change of global connectivity metrics on the genetic variants, and used the GWAS summary statistics to compute the gene and pathway scores. We observed significant associations between the change in structural brain connectivity defined by tractography and genes, which have previously been reported to biologically manipulate the risk and progression of certain neurodegenerative disorders, including Alzheimer\u2019s disease.<\/p>\n<!-- \/wp:paragraph -->\n\n<!-- wp:spacer {\"height\":\"50px\",\"epAnimationGeneratedClass\":\"edplus_anim-T8PvVL\",\"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_64ae909e33426\",\"name\":\"acf\/button\",\"data\":{\"title\":\"READ HERE\",\"_title\":\"field_61d40397c2f0a\",\"button_type\":\"link\",\"_button_type\":\"field_63bbde3b8f0d0\",\"url\":\"https:\/\/www.nature.com\/articles\/s41598-020-58291-1\",\"_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":"Genome-Wide Association Study of Brain Connectivity Changes for Alzheimer\u2019s Disease\u00a0","post_excerpt":"In: 2020.","post_status":"publish","comment_status":"closed","ping_status":"closed","post_password":"","post_name":"genome-wide-association-study-of-brain-connectivity-changes-for-alzheimers-disease","to_ping":"","pinged":"","post_modified":"2024-01-05 14:06:28","post_modified_gmt":"2024-01-05 13:06:28","post_content_filtered":"","post_parent":0,"guid":"https:\/\/new.sano.science\/?post_type=research&p=12542","menu_order":91,"post_type":"research","post_mime_type":"","comment_count":"0","filter":"raw"},{"ID":12484,"post_author":"5","post_date":"2023-07-12 11:35:22","post_date_gmt":"2023-07-12 09:35:22","post_content":"<!-- wp:heading {\"epAnimationGeneratedClass\":\"edplus_anim-47l2sI\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<h2 class=\"wp-block-heading eplus-wrapper\">Elsheikh, Samar S. M.; Chimusa, Emile R.; Initiative, Alzheimer's Disease Neuroimaging; Mulder, Nicola J.; Crimi, Alessandro<\/h2>\n<!-- \/wp:heading -->\n\n<!-- wp:spacer {\"height\":\"50px\",\"epAnimationGeneratedClass\":\"edplus_anim-uFlgSI\",\"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-JQAI7W\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<p class=\" eplus-wrapper\">Networks are present in many aspects of our lives, and networks in neuroscience have recently gained much attention leading to novel representations of brain connectivity. The integration of neuroimaging characteristics and genetics data allows a better understanding of the effects of the gene expression on brain structural and functional connections. The current work uses whole-brain tractography in a longitudinal setting, and by measuring the brain structural connectivity changes studies the neurodegeneration of Alzheimer's disease. This is accomplished by examining the effect of targeted genetic risk factors on the most common local and global brain connectivity measures. Furthermore, we examined the extent to which Clinical Dementia Rating relates to brain connections longitudinally, as well as to gene expression. For instance, here we show that the expression of PLAU gene increases the change over time in betweenness centrality related to the fusiform gyrus. We also show that the betweenness centrality metric impact dementia-related changes in distinct brain regions. Our findings provide insights into the complex longitudinal interplay between genetics and brain characteristics and highlight the role of Alzheimer's genetic risk factors in the estimation of regional brain connectivity alterations.<\/p>\n<!-- \/wp:paragraph -->\n\n<!-- wp:spacer {\"height\":\"50px\",\"epAnimationGeneratedClass\":\"edplus_anim-cF6mQE\",\"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_64ae74debc299\",\"name\":\"acf\/button\",\"data\":{\"title\":\"READ HERE\",\"_title\":\"field_61d40397c2f0a\",\"button_type\":\"link\",\"_button_type\":\"field_63bbde3b8f0d0\",\"url\":\"https:\/\/www.frontiersin.org\/articles\/10.3389\/fnhum.2021.761424\/full\",\"_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":"Relating Global and Local Connectome Changes to Dementia and Targeted Gene Expression in Alzheimer's Disease\u00a0","post_excerpt":"In: Front Hum Neurosci, 2021.","post_status":"publish","comment_status":"closed","ping_status":"closed","post_password":"","post_name":"relating-global-and-local-connectome-changes-to-dementia-and-targeted-gene-expression-in-alzheimers-disease","to_ping":"","pinged":"","post_modified":"2024-01-05 14:08:25","post_modified_gmt":"2024-01-05 13:08:25","post_content_filtered":"","post_parent":0,"guid":"https:\/\/new.sano.science\/?post_type=research&p=12484","menu_order":78,"post_type":"research","post_mime_type":"","comment_count":"0","filter":"raw"},{"ID":12497,"post_author":"5","post_date":"2023-07-12 11:45:54","post_date_gmt":"2023-07-12 09:45:54","post_content":"<!-- wp:heading {\"epAnimationGeneratedClass\":\"edplus_anim-a1ULpb\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<h2 class=\"wp-block-heading eplus-wrapper\">Kara, Eleanna; Crimi, Alessandro; Wiedmer, Anne; Hardy, John; Hyman, Bradley T.; Aguzzi, Adriano<\/h2>\n<!-- \/wp:heading -->\n\n<!-- wp:spacer {\"height\":\"50px\",\"epAnimationGeneratedClass\":\"edplus_anim-AWfBoC\",\"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-iQTqY9\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<p class=\" eplus-wrapper\">Neuropathological and experimental evidence suggests that the cell-to-cell transfer of \u03b1-synuclein has an important role in the pathogenesis of Parkinson\u2019s disease (PD). However, the mechanism underlying this phenomenon is not fully understood. We undertook a small interfering RNA (siRNA), genome-wide screen to identify genes regulating the cell-to-cell transfer of \u03b1-synuclein. A genetically encoded reporter, GFP-2A-\u03b1Synuclein-RFP, suitable for separating donor and recipient cells, was transiently transfected into HEK cells stably overexpressing \u03b1-synuclein. We find that 38 genes regulate the transfer of \u03b1-synuclein-RFP, one of which is ITGA8, a candidate gene identified through a recent PD genome-wide association study (GWAS). Weighted gene co-expression network analysis (WGCNA) and weighted protein-protein network interaction analysis (WPPNIA) show that those hits cluster in networks that include known PD genes more frequently than expected by random chance. The findings expand our understanding of the mechanism of \u03b1-synuclein spread.<\/p>\n<!-- \/wp:paragraph -->\n\n<!-- wp:spacer {\"height\":\"50px\",\"epAnimationGeneratedClass\":\"edplus_anim-me7JZO\",\"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_64ae763dca841\",\"name\":\"acf\/button\",\"data\":{\"title\":\"READ HERE\",\"_title\":\"field_61d40397c2f0a\",\"button_type\":\"link\",\"_button_type\":\"field_63bbde3b8f0d0\",\"url\":\"https:\/\/dx.doi.org\/https:\/\/doi.org\/10.1016\/j.celrep.2021.109189\",\"_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":"An integrated genomic approach to dissect the genetic landscape regulating the cell-to-cell transfer of \u03b1-synuclein\u00a0","post_excerpt":"In: 2021.","post_status":"publish","comment_status":"closed","ping_status":"closed","post_password":"","post_name":"an-integrated-genomic-approach-to-dissect-the-genetic-landscape-regulating-the-cell-to-cell-transfer-of-%ce%b1-synuclein","to_ping":"","pinged":"","post_modified":"2024-01-05 14:08:01","post_modified_gmt":"2024-01-05 13:08:01","post_content_filtered":"","post_parent":0,"guid":"https:\/\/new.sano.science\/?post_type=research&p=12497","menu_order":77,"post_type":"research","post_mime_type":"","comment_count":"0","filter":"raw"},{"ID":17163,"post_author":"8","post_date":"2024-06-12 18:14:22","post_date_gmt":"2024-06-12 16:14:22","post_content":"<!-- wp:heading {\"epAnimationGeneratedClass\":\"edplus_anim-tNFDKI\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<h2 class=\"wp-block-heading eplus-wrapper\">Authors: Agus Hartoyo, Jan K. Argasi\u0144ski, Aleksandra Trenk, Kinga Przybylska, Anna B\u0142asiak, Alessandro Crimi<\/h2>\n<!-- \/wp:heading -->\n\n<!-- wp:spacer {\"height\":\"20px\",\"epAnimationGeneratedClass\":\"edplus_anim-5WDO6d\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<div style=\"height:20px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n<!-- \/wp:spacer -->\n\n<!-- wp:paragraph {\"epAnimationGeneratedClass\":\"edplus_anim-Vsv85h\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<p class=\" eplus-wrapper\">Covariance and Hessian matrices have been analyzed separately in the literature for classification problems. However, integrating these matrices has the potential to enhance their combined power in improving classification performance. We present a novel approach that combines the eigenanalysis of a covariance matrix evaluated on a training set with a Hessian matrix evaluated on a deep learning model to achieve optimal class separability in binary classification tasks. Our approach is substantiated by formal proofs that establish its capability to maximize between-class mean distance and minimize within-class variances. By projecting data into the combined space of the most relevant eigendirections from both matrices, we achieve optimal class separability as per the linear discriminant analysis (LDA) criteria. Empirical validation across neural and health datasets consistently supports our theoretical framework and demonstrates that our method outperforms established methods. Our method stands out by addressing both LDA criteria, unlike PCA and the Hessian method, which predominantly emphasize one criterion each. This comprehensive approach captures intricate patterns and relationships, enhancing classification performance. Furthermore, through the utilization of both LDA criteria, our method outperforms LDA itself by leveraging higher-dimensional feature spaces, in accordance with Cover's theorem, which favors linear separability in higher dimensions. Our method also surpasses kernel-based methods and manifold learning techniques in performance. Additionally, our approach sheds light on complex DNN decision-making, rendering them comprehensible within a 2D space.<\/p>\n<!-- \/wp:paragraph -->","post_title":"Synergistic eigenanalysis of covariance and Hessian matrices for enhanced binary classification on health datasets","post_excerpt":"In: https:\/\/arxiv.org\/abs\/2402.09281, 2024","post_status":"publish","comment_status":"closed","ping_status":"closed","post_password":"","post_name":"synergistic-eigenanalysis-of-covariance-and-hessian-matrices-for-enhanced-binary-classification-on-health-datasets","to_ping":"","pinged":"","post_modified":"2024-09-23 12:57:38","post_modified_gmt":"2024-09-23 10:57:38","post_content_filtered":"","post_parent":0,"guid":"https:\/\/sano.science\/?post_type=research&p=17163","menu_order":0,"post_type":"research","post_mime_type":"","comment_count":"0","filter":"raw"},{"ID":24941,"post_author":"8","post_date":"2025-07-14 15:53:44","post_date_gmt":"2025-07-14 13:53:44","post_content":"<!-- wp:heading {\"epAnimationGeneratedClass\":\"edplus_anim-2swaxD\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\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<!-- \/wp:heading -->\n\n<!-- wp:spacer {\"height\":\"50px\",\"epAnimationGeneratedClass\":\"edplus_anim-zZh90O\",\"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-93Pv2T\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\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<!-- \/wp:paragraph -->\n\n<!-- wp:spacer {\"height\":\"105px\",\"epAnimationGeneratedClass\":\"edplus_anim-kBMl6x\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<div style=\"height:105px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n<!-- \/wp:spacer -->\n\n<!-- wp:paragraph {\"epAnimationGeneratedClass\":\"edplus_anim-f2GF6j\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\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<!-- \/wp:paragraph -->\n\n<!-- wp:paragraph {\"epAnimationGeneratedClass\":\"edplus_anim-f2GF6j\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<p class=\" eplus-wrapper\"><strong>DOI<\/strong>: 10.1038\/s41598-024-79817-x\u00a0<\/p>\n<!-- \/wp:paragraph -->\n\n<!-- wp:spacer {\"height\":\"105px\",\"epAnimationGeneratedClass\":\"edplus_anim-kBMl6x\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<div style=\"height:105px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n<!-- \/wp:spacer -->\n\n<!-- wp:acf\/button {\"id\":\"block_68750ba306ca9\",\"name\":\"acf\/button\",\"data\":{\"title\":\"READ HERE\",\"_title\":\"field_61d40397c2f0a\",\"button_type\":\"link\",\"_button_type\":\"field_63bbde3b8f0d0\",\"url\":\"https:\/\/www.nature.com\/articles\/s41598-024-79817-x\",\"_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":"Comparing structure\u2013function relationships in brain networks using EEG and fNIRS","post_excerpt":"article in journal: Scientific Reports, 2024","post_status":"publish","comment_status":"closed","ping_status":"closed","post_password":"","post_name":"comparing-structure-function-relationships-in-brain-networks-using-eeg-and-fnirs","to_ping":"","pinged":"","post_modified":"2025-07-14 15:54:18","post_modified_gmt":"2025-07-14 13:54:18","post_content_filtered":"","post_parent":0,"guid":"https:\/\/sano.science\/?post_type=research&p=24941","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\/539","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":17,"href":"https:\/\/sano.science\/index.php\/wp-json\/wp\/v2\/people\/539\/revisions"}],"predecessor-version":[{"id":24951,"href":"https:\/\/sano.science\/index.php\/wp-json\/wp\/v2\/people\/539\/revisions\/24951"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/sano.science\/index.php\/wp-json\/wp\/v2\/media\/1634"}],"wp:attachment":[{"href":"https:\/\/sano.science\/index.php\/wp-json\/wp\/v2\/media?parent=539"}],"wp:term":[{"taxonomy":"people_teams","embeddable":true,"href":"https:\/\/sano.science\/index.php\/wp-json\/wp\/v2\/people_teams?post=539"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}