{"id":557,"date":"2023-05-14T12:45:36","date_gmt":"2023-05-14T10:45:36","guid":{"rendered":"https:\/\/sano.empressia.dev\/?post_type=people&p=557"},"modified":"2025-07-14T15:55:16","modified_gmt":"2025-07-14T13:55:16","slug":"rosmary-blanco","status":"publish","type":"people","link":"https:\/\/sano.science\/people\/rosmary-blanco\/","title":{"rendered":"Rosmary Blanco"},"excerpt":{"rendered":"

PhD Student in Computational Neuroscience<\/p>\n","protected":false},"featured_media":20159,"template":"","people_teams":[19,33],"class_list":["post-557","people","type-people","status-publish","has-post-thumbnail","hentry","people_teams-research","people_teams-computational-neuroscience"],"yoast_head":"\nRosmary Blanco - 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\/rosmary-blanco\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Rosmary Blanco\" \/>\n<meta property=\"og:description\" content=\"PhD Student in Computational Neuroscience\" \/>\n<meta property=\"og:url\" content=\"https:\/\/sano.science\/people\/rosmary-blanco\/\" \/>\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=\"2025-07-14T13:55:16+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/sano.science\/wp-content\/uploads\/2023\/05\/Rosmary-Blanco-1-1024x1024.png\" \/>\n\t<meta property=\"og:image:width\" content=\"1024\" \/>\n\t<meta property=\"og:image:height\" content=\"1024\" \/>\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\\\/rosmary-blanco\\\/\",\"url\":\"https:\\\/\\\/sano.science\\\/people\\\/rosmary-blanco\\\/\",\"name\":\"Rosmary Blanco - 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She specializes in clinical network neuroscience and has skills in complex electrophysiological signal processing, particularly EEG source functional connectivity and network analysis. Her main interest is studying the large-scale network principles governing neuronal communication, cognition, and human behavior through computational approaches, personalized medicine, and clinical decision support.<\/p>\n","social_media":[{"icon":{"ID":11994,"id":11994,"title":"linkedin","filename":"linkedin.svg","filesize":914,"url":"https:\/\/sano.science\/wp-content\/uploads\/2023\/05\/linkedin.svg","link":"https:\/\/sano.science\/people\/maciej-malawski\/linkedin-2\/","alt":"","author":"5","description":"","caption":"","name":"linkedin-2","status":"inherit","uploaded_to":531,"date":"2023-07-06 11:24:13","modified":"2023-07-06 11:24:13","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\/linkedin.svg","thumbnail-width":150,"thumbnail-height":150,"medium":"https:\/\/sano.science\/wp-content\/uploads\/2023\/05\/linkedin.svg","medium-width":300,"medium-height":300,"medium_large":"https:\/\/sano.science\/wp-content\/uploads\/2023\/05\/linkedin.svg","medium_large-width":768,"medium_large-height":1,"large":"https:\/\/sano.science\/wp-content\/uploads\/2023\/05\/linkedin.svg","large-width":1024,"large-height":1024,"1536x1536":"https:\/\/sano.science\/wp-content\/uploads\/2023\/05\/linkedin.svg","1536x1536-width":1,"1536x1536-height":1,"2048x2048":"https:\/\/sano.science\/wp-content\/uploads\/2023\/05\/linkedin.svg","2048x2048-width":1,"2048x2048-height":1}},"link":"http:\/\/www.linkedin.com\/in\/rosmary-blanco-a6634b45","name":"LinkedIn"}],"tabs":false,"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":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":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"},{"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":15288,"post_author":"5","post_date":"2024-02-01 21:09:23","post_date_gmt":"2024-02-01 20:09:23","post_content":"<!-- wp:heading {\"epAnimationGeneratedClass\":\"edplus_anim-Hz4pcT\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<h2 class=\"wp-block-heading eplus-wrapper\">Michal K. Grzeszczyk, Paulina Adamczyk, Sylwia Marek, Ryszard Pr\u0119cikowski, Maciej Ku\u015b, M. Patrycja Lelujko, Rosmary Blanco, Tomasz Trzci\u0144ski, Arkadiusz Sitek, Maciej Malawski, Aneta Lisowska<\/h2>\n<!-- \/wp:heading -->\n\n<!-- wp:spacer {\"height\":\"50px\",\"epAnimationGeneratedClass\":\"edplus_anim-Uo3zsR\",\"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-L4VCME\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<p class=\" eplus-wrapper\">The effectiveness of digital treatments can be measured by requiring patients to self-report their state through applications, however, it can be overwhelming and causes disengagement. We conduct a study to explore the impact of gamification on self-reporting. Our approach involves the creation of a system to assess cognitive load (CL) through the analysis of photoplethysmography (PPG) signals. The data from 11 participants is utilized to train a machine learning model to detect CL. Subsequently, we create two versions of surveys: a gamified and a traditional one. We estimate the CL experienced by other participants (13) while completing surveys. We find that CL detector performance can be enhanced via pre-training on stress detection tasks. For 10 out of 13 participants, a personalized CL detector can achieve an F1 score above 0.7. We find no difference between the gamified and non-gamified surveys in terms of CL but participants prefer the gamified version.<\/p>\n<!-- \/wp:paragraph -->\n\n<!-- wp:spacer {\"height\":\"50px\",\"epAnimationGeneratedClass\":\"edplus_anim-Uo3zsR\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<div style=\"height:50px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n<!-- \/wp:spacer -->\n\n<!-- wp:group {\"layout\":{\"type\":\"constrained\"},\"epAnimationGeneratedClass\":\"edplus_anim-14y02Y\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<div class=\"wp-block-group eplus-wrapper\"><!-- wp:acf\/button {\"id\":\"block_65bbfa5f62c5d\",\"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\"} \/--><\/div>\n<!-- \/wp:group -->","post_title":"Can gamification reduce the burden of self-reporting in mHealth applications? A feasibility study using machine learning from smartwatch data to estimate cognitive load","post_excerpt":"In: AMIA 2023.","post_status":"publish","comment_status":"closed","ping_status":"closed","post_password":"","post_name":"can-gamification-reduce-the-burden-of-self-reporting-in-mhealth-applications-a-feasibility-study-using-machine-learning-from-smartwatch-data-to-estimate-cognitive-load","to_ping":"","pinged":"","post_modified":"2024-02-01 21:09:23","post_modified_gmt":"2024-02-01 20:09:23","post_content_filtered":"","post_parent":0,"guid":"https:\/\/sano.science\/?post_type=research&p=15288","menu_order":19,"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"}]},"_links":{"self":[{"href":"https:\/\/sano.science\/index.php\/wp-json\/wp\/v2\/people\/557","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":16,"href":"https:\/\/sano.science\/index.php\/wp-json\/wp\/v2\/people\/557\/revisions"}],"predecessor-version":[{"id":24949,"href":"https:\/\/sano.science\/index.php\/wp-json\/wp\/v2\/people\/557\/revisions\/24949"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/sano.science\/index.php\/wp-json\/wp\/v2\/media\/20159"}],"wp:attachment":[{"href":"https:\/\/sano.science\/index.php\/wp-json\/wp\/v2\/media?parent=557"}],"wp:term":[{"taxonomy":"people_teams","embeddable":true,"href":"https:\/\/sano.science\/index.php\/wp-json\/wp\/v2\/people_teams?post=557"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}