{"id":11961,"date":"2023-07-06T12:39:00","date_gmt":"2023-07-06T10:39:00","guid":{"rendered":"https:\/\/new.sano.science\/?post_type=people&#038;p=11961"},"modified":"2025-11-17T13:18:37","modified_gmt":"2025-11-17T12:18:37","slug":"cemal-koba","status":"publish","type":"people","link":"https:\/\/sano.science\/people\/cemal-koba\/","title":{"rendered":"Cemal Koba"},"excerpt":{"rendered":"<p>PostDoc in Computational Neuroscience<\/p>\n","protected":false},"featured_media":18370,"template":"","people_teams":[19,33],"class_list":["post-11961","people","type-people","status-publish","has-post-thumbnail","hentry","people_teams-research","people_teams-computational-neuroscience"],"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v27.4 (Yoast SEO v27.4) - https:\/\/yoast.com\/product\/yoast-seo-premium-wordpress\/ -->\n<title>Cemal Koba - 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\/cemal-koba\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Cemal Koba\" \/>\n<meta property=\"og:description\" content=\"PostDoc in Computational Neuroscience\" \/>\n<meta property=\"og:url\" content=\"https:\/\/sano.science\/people\/cemal-koba\/\" \/>\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=\"2025-11-17T12:18:37+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/sano.science\/wp-content\/uploads\/2023\/07\/Cemal-Koba-Sano-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\\\/cemal-koba\\\/\",\"url\":\"https:\\\/\\\/sano.science\\\/people\\\/cemal-koba\\\/\",\"name\":\"Cemal Koba - 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For his thesis, he examined structural brain plasticity in the case of early and acquired blindness. He is interested in methods for processing and analysing neuroimaging data. In Sano, he works on structural and functional brain changes in stroke patients using structural and functional MRI. His work aims to develop tools for early stroke prognosis and recovery prediction, particularly through the analysis of eye movements and the alterations in functional brain connectivity.<\/p>\n<p>&nbsp;<\/p>\n<p>Cemal Koba is carrying out the SONATA research grant titled \u201c<a href=\"https:\/\/sano.science\/projects\/national-grants\/#Sonata-Cemal\" target=\"_blank\" rel=\"noopener\">Linking Eye Movements and Brain Function to Monitor Stroke-induced Alterations with fMRI<\/a>.\u201d<\/p>\n","email":"","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 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in ","link_text":"Computational Neuroscience","text_after_link":"","link":"https:\/\/sano.science\/research-teams\/computer-vision-brain-and-more-lab\/"},"publications":[{"ID":23099,"post_author":"8","post_date":"2025-04-16 11:09:42","post_date_gmt":"2025-04-16 09:09:42","post_content":"<!-- wp:heading {\"epAnimationGeneratedClass\":\"edplus_anim-jlHxqX\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<h2 class=\"wp-block-heading eplus-wrapper\" id=\"h-ricardo-felix-nbsp-morais-nbsp-jose-maria-sousa-nbsp-cemal-nbsp-koba-nbsp-leon-nbsp-andres-tiago-jesus-nbsp-ines-nbsp-baldeiras-nbsp-tiago-gil-nbsp-oliveira-nbsp-isabel-nbsp-santana\">Ricardo F\u00e9lix&nbsp;Morais,&nbsp;Jos\u00e9 Maria Sousa,&nbsp; Cemal&nbsp; Koba,&nbsp; Leon&nbsp; Andres, Tiago Jesus,  &nbsp;In\u00eas&nbsp; Baldeiras,&nbsp;Tiago Gil&nbsp; Oliveira,&nbsp; Isabel &nbsp;Santana<\/h2>\n<!-- \/wp:heading -->\n\n<!-- wp:spacer {\"height\":\"50px\",\"epAnimationGeneratedClass\":\"edplus_anim-GVGpom\",\"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-52WlCB\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<p class=\" eplus-wrapper\">&nbsp;In the publication <strong>Differential involvement of neurotransmitter pathways in AD, bvFTD and MCI: Whole-brain MRI analysis<\/strong>, the authors explore how neurodegenerative conditions such as Alzheimer\u2019s disease (AD), mild cognitive impairment (MCI), and behavioral variant frontotemporal dementia (bvFTD) affect neurotransmitter systems differently. As these disorders grow more prevalent, understanding their neurochemical underpinnings becomes increasingly important. While structural brain changes have been widely studied, the role of neurotransmitter pathways has received less attention.<\/p>\n<!-- \/wp:paragraph -->\n\n<!-- wp:paragraph {\"epAnimationGeneratedClass\":\"edplus_anim-QjyljR\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<p class=\" eplus-wrapper\">This study analyzed MRI data from 214 participants (89 AD, 74 bvFTD, 51 MCI) using FreeSurfer segmentation and JuSpace neurotransmitter maps. Volumetric and correlation analyses identified disease-specific patterns: AD-related atrophy was linked to dopaminergic and GABAergic systems; bvFTD showed associations with mu-opioid receptor loss; MCI exhibited early serotonergic disruptions before structural changes were evident.<\/p>\n<!-- \/wp:paragraph -->\n\n<!-- wp:paragraph {\"epAnimationGeneratedClass\":\"edplus_anim-52WlCB\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<p class=\" eplus-wrapper\">The findings reveal distinct neurochemical profiles for each condition, suggesting that neurotransmitter mapping may provide valuable insights for diagnosis and therapeutic strategies beyond conventional imaging approaches.<\/p>\n<!-- \/wp:paragraph -->\n\n<!-- wp:spacer {\"height\":\"50px\",\"epAnimationGeneratedClass\":\"edplus_anim-GVGpom\",\"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-30HfSq\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<p class=\" eplus-wrapper\"><strong><strong>Autors<\/strong>:<\/strong> Ricardo F\u00e9lix\u00a0Morais,\u00a0<a href=\"https:\/\/sano.science\/people\/jose-sousa\/\">Jos\u00e9 Maria Sousa<\/a>,\u00a0 <a href=\"https:\/\/sano.science\/people\/cemal-koba\/\">Cemal\u00a0 Koba<\/a>,\u00a0 Leon\u00a0 Andres, Tiago Jesus, In\u00eas\u00a0 Baldeiras,\u00a0Tiago Gil\u00a0 Oliveira,\u00a0 Isabel \u00a0Santana<\/p>\n<!-- \/wp:paragraph -->\n\n<!-- wp:paragraph {\"epAnimationGeneratedClass\":\"edplus_anim-30HfSq\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<p class=\" eplus-wrapper\"><strong>DOI<\/strong>: 10.1016\/j.nbd.2025.106897<\/p>\n<!-- \/wp:paragraph -->\n\n<!-- wp:paragraph {\"epAnimationGeneratedClass\":\"edplus_anim-u0oM2M\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<p class=\" eplus-wrapper\"><strong>Keywords<\/strong>: JuSpace, Brain volumetry, Receptor density, Neuroimaging 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-->\n<div class=\"wp-block-button eplus-wrapper\"><a class=\"wp-block-button__link wp-element-button\"><\/a><\/div>\n<!-- \/wp:button --><\/div>\n<!-- \/wp:buttons -->\n\n<!-- wp:acf\/button {\"id\":\"block_67ff7295ff727\",\"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\/S0969996125001135?via%3Dihub#ks0005\",\"_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":"Differential involvement of neurotransmitter pathways in AD, bvFTD and MCI: Whole-brain MRI analysis","post_excerpt":"Article in journal: Neurobiology of Disease,  2025","post_status":"publish","comment_status":"closed","ping_status":"closed","post_password":"","post_name":"differential-involvement-of-neurotransmitter-pathways-in-ad-bvftd-and-mci-whole-brain-mri-analysis","to_ping":"","pinged":"","post_modified":"2025-05-08 11:23:08","post_modified_gmt":"2025-05-08 09:23:08","post_content_filtered":"","post_parent":0,"guid":"https:\/\/sano.science\/?post_type=research&#038;p=23099","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&nbsp;Blanco,&nbsp;Cemal&nbsp;Koba,&nbsp;Alessandro&nbsp;Crimi&nbsp;<\/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&nbsp;Blanco,&nbsp;Cemal&nbsp;Koba,&nbsp;Alessandro&nbsp;Crimi&nbsp;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>: &nbsp;Rosmary&nbsp;Blanco,&nbsp;Cemal&nbsp;Koba,&nbsp;Alessandro&nbsp;Crimi&nbsp;<\/p>\n<!-- \/wp:paragraph -->\n\n<!-- wp:paragraph {\"epAnimationGeneratedClass\":\"edplus_anim-KyXlgX\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<p class=\" eplus-wrapper\"><strong>DOI<\/strong>:&nbsp;<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>&nbsp;<\/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>:&nbsp;<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>&nbsp;<strong>1.<\/strong>&nbsp;The EEG and fNIRS data collection.&nbsp;<strong>2.<\/strong>&nbsp;Data pre-processing.&nbsp;<strong>3.<\/strong>&nbsp;Source reconstruction.&nbsp;<strong>4.<\/strong>&nbsp;Mapping of the source signals onto the same brain space.&nbsp;<strong>5.<\/strong>&nbsp;Functional connectivity computation.&nbsp;<strong>6.<\/strong>&nbsp;Graph analysis for comparing the topology of brain networks.&nbsp;<strong>7.<\/strong>&nbsp;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&#038;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&#038;p=14858","menu_order":49,"post_type":"research","post_mime_type":"","comment_count":"0","filter":"raw"},{"ID":24899,"post_author":"8","post_date":"2025-07-08 16:32:49","post_date_gmt":"2025-07-08 14:32:49","post_content":"<!-- wp:heading {\"epAnimationGeneratedClass\":\"edplus_anim-nvUGAS\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<h2 class=\"wp-block-heading eplus-wrapper\" id=\"h-cemal-koba-joan-falco-roget-alessandro-crimi\">Cemal Koba,\u00a0Joan Falc\u00f3-Roget,\u00a0Alessandro Crimi<\/h2>\n<!-- \/wp:heading -->\n\n<!-- wp:paragraph {\"epAnimationGeneratedClass\":\"edplus_anim-pk2OOI\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<p class=\" eplus-wrapper\">Ischemic stroke disrupts cerebral blood flow, triggering both structural and functional brain alterations that often lead to behavioral impairments. Although the primary damage typically affects specific regions, the resulting changes in brain organization extend widely across the cortex and are high-dimensional in nature. The underlying mechanisms driving these widespread functional disturbances and their connection to behavioral symptoms remain largely underexplored.<\/p>\n<!-- \/wp:paragraph -->\n\n<!-- wp:paragraph {\"epAnimationGeneratedClass\":\"edplus_anim-aGILm8\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<p class=\" eplus-wrapper\">Functional connectivity gradients offer a reproducible and robust way to represent brain organization in a low-dimensional space, simplifying complex functional variation into a few interpretable axes. In this study, we examined how stroke affects this canonical gradient space by aligning individual patients\u2019 connectivity profiles to a normative template derived from healthy controls. We then measured how far each region deviated from its expected position, using these deviations to assess their relevance for behavioral outcomes. Crucially, we accounted for stroke-induced hemodynamic delays to better understand their influence.<\/p>\n<!-- \/wp:paragraph -->\n\n<!-- wp:paragraph {\"epAnimationGeneratedClass\":\"edplus_anim-O8rBys\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<p class=\" eplus-wrapper\">Our analysis showed that correcting for hemodynamic lags significantly improved gradient accuracy, particularly in the second gradient, which encompasses visual and somatomotor functions. Notable functional deviations were observed within the somatomotor, visual, and ventral attention networks\u2014areas linked to behavioral impairments following stroke.<\/p>\n<!-- \/wp:paragraph -->\n\n<!-- wp:paragraph {\"epAnimationGeneratedClass\":\"edplus_anim-hF8Ff2\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<p class=\" eplus-wrapper\">We also investigated hemispheric asymmetries in these deviations. Interestingly, unaffected hemispheres retained typical asymmetry patterns, whereas stroke-affected hemispheres showed marked disruptions. Moreover, right-hemisphere lesions resulted in more localized functional alterations compared to left-sided damage.<\/p>\n<!-- \/wp:paragraph -->\n\n<!-- wp:paragraph {\"epAnimationGeneratedClass\":\"edplus_anim-pk2OOI\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<p class=\" eplus-wrapper\">Overall, our findings highlight two key insights: (1) adjusting for hemodynamic delays increases the explanatory power of connectivity gradients, and (2) behavioral deficits and altered hemispheric asymmetries can be traced to shifts in region-specific connectivity patterns within a low-dimensional, interpretable framework. This supports the idea that post-stroke brain reorganization follows predictable trajectories along fundamental axes of brain function\u2014and that these shifts are not entirely independent of underlying white matter damage.<\/p>\n<!-- \/wp:paragraph -->\n\n<!-- wp:spacer {\"height\":\"30px\",\"epAnimationGeneratedClass\":\"edplus_anim-bYpygR\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<div style=\"height:30px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n<!-- \/wp:spacer -->\n\n<!-- wp:paragraph {\"epAnimationGeneratedClass\":\"edplus_anim-1g73Ls\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<p class=\" eplus-wrapper\"><strong>Authors<\/strong>: <a href=\"https:\/\/sano.science\/people\/cemal-koba\/\">Cemal Koba<\/a>, <a href=\"https:\/\/sano.science\/wp-content\/uploads\/2023\/06\/Sano-Joan-Falco-Roget.png\">Joan Falc\u00f3-Roget<\/a>, <a href=\"https:\/\/sano.science\/people\/alessandro-crimi\/\">Alessandro Crimi<\/a><\/p>\n<!-- \/wp:paragraph -->\n\n<!-- wp:paragraph {\"epAnimationGeneratedClass\":\"edplus_anim-gcfXou\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<p class=\" eplus-wrapper\"><strong>DOI<\/strong>: 10.1016\/j.nicl.2025.103755<\/p>\n<!-- \/wp:paragraph -->\n\n<!-- wp:paragraph {\"epAnimationGeneratedClass\":\"edplus_anim-YLydAq\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<p class=\" eplus-wrapper\"><strong>Keywords<\/strong>: Stroke, fMRI, Gradients, Temporal lag<\/p>\n<!-- \/wp:paragraph -->\n\n<!-- wp:spacer {\"height\":\"30px\",\"epAnimationGeneratedClass\":\"edplus_anim-bYpygR\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<div style=\"height:30px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n<!-- \/wp:spacer -->\n\n<!-- wp:acf\/button {\"id\":\"block_686d2ad64bda9\",\"name\":\"acf\/button\",\"data\":{\"title\":\"READ HERE\",\"_title\":\"field_61d40397c2f0a\",\"button_type\":\"link\",\"_button_type\":\"field_63bbde3b8f0d0\",\"url\":\"https:\/\/www.sciencedirect.com\/science\/article\/pii\/S2213158225000257#d1e1612\",\"_url\":\"field_61d4039bc2f0b\",\"button_style\":\"primary\",\"_button_style\":\"field_63872d045d0f0\",\"target\":\"_self\",\"_target\":\"field_63872c705d0ef\",\"button_extra_classes\":\"\",\"_button_extra_classes\":\"field_642beab6a97de\"},\"align\":\"\",\"mode\":\"edit\"} \/-->\n\n<!-- wp:paragraph {\"epAnimationGeneratedClass\":\"edplus_anim-eoFlhH\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<p class=\" eplus-wrapper\"><\/p>\n<!-- \/wp:paragraph -->","post_title":"Reshaped functional connectivity gradients in acute ischemic stroke","post_excerpt":"article in journal: Elsevier - NeuroImage: Clinical, 2025","post_status":"publish","comment_status":"closed","ping_status":"closed","post_password":"","post_name":"reshaped-functional-connectivity-gradients-in-acute-ischemic-stroke","to_ping":"","pinged":"","post_modified":"2025-07-22 14:36:57","post_modified_gmt":"2025-07-22 12:36:57","post_content_filtered":"","post_parent":0,"guid":"https:\/\/sano.science\/?post_type=research&#038;p=24899","menu_order":0,"post_type":"research","post_mime_type":"","comment_count":"0","filter":"raw"},{"ID":20886,"post_author":"8","post_date":"2025-01-15 11:08:09","post_date_gmt":"2025-01-15 10:08:09","post_content":"<!-- wp:heading {\"epAnimationGeneratedClass\":\"edplus_anim-4rzloK\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<h2 class=\"wp-block-heading eplus-wrapper\" id=\"h-wojciech-ciezobka-nbsp-joan-falco-roget-nbsp-cemal-koba-nbsp-alessandro-crimi\">Wojciech Ciezobka<a href=\"https:\/\/orcid.org\/0000-0003-2972-710X\" target=\"_blank\" rel=\"noreferrer noopener\"><\/a>;&nbsp;Joan Falc\u00f3-Roget<a href=\"https:\/\/orcid.org\/0000-0002-9410-6361\" target=\"_blank\" rel=\"noreferrer noopener\"><\/a>;&nbsp;Cemal Koba;&nbsp;Alessandro Crimi<\/h2>\n<!-- \/wp:heading -->\n\n<!-- wp:spacer {\"height\":\"40px\",\"epAnimationGeneratedClass\":\"edplus_anim-R8xTUk\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<div style=\"height:40px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n<!-- \/wp:spacer -->\n\n<!-- wp:paragraph {\"epAnimationGeneratedClass\":\"edplus_anim-cyWlSl\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<p class=\" eplus-wrapper\">This study proposes a framework to construct directed graph representations of brain networks. By further integrating explainable AI techniques, the method reveals disruptions in brain connectivity associated with stroke. The pipeline also compares the performance of reservoir computing-based causality with Granger causality and transfer entropy, offering a comprehensive assessment of effective connectivity estimation methods. Explainable AI tools allowed insights into critical network alterations, clarifying the role of effective connectivity biomarkers in stroke. This transparent analytical approach highlights the potential of directed graph models for both improved diagnostic precision and understanding stroke mechanisms, with broader implications for brain disorder analysis.<\/p>\n<!-- \/wp:paragraph -->\n\n<!-- wp:spacer {\"height\":\"40px\",\"epAnimationGeneratedClass\":\"edplus_anim-R8xTUk\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<div style=\"height:40px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n<!-- \/wp:spacer -->\n\n<!-- wp:paragraph {\"epAnimationGeneratedClass\":\"edplus_anim-ljrUO7\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<p class=\" eplus-wrapper\"><strong>DOI:&nbsp;<\/strong><a href=\"https:\/\/doi.org\/10.1109\/ACCESS.2025.3529179\" target=\"_blank\" rel=\"noreferrer noopener\">10.1109\/ACCESS.2025.3529179<\/a><\/p>\n<!-- \/wp:paragraph -->\n\n<!-- wp:paragraph {\"epAnimationGeneratedClass\":\"edplus_anim-CWvrRp\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<p class=\" eplus-wrapper\"><strong>Autors<\/strong>: Wojciech Ciezobka<a href=\"https:\/\/orcid.org\/0000-0003-2972-710X\" target=\"_blank\" rel=\"noreferrer noopener\"><\/a>;&nbsp;Joan Falc\u00f3-Roget<a href=\"https:\/\/orcid.org\/0000-0002-9410-6361\" target=\"_blank\" rel=\"noreferrer noopener\"><\/a>;&nbsp;Cemal Koba;&nbsp;Alessandro Crimi<\/p>\n<!-- \/wp:paragraph -->\n\n<!-- wp:paragraph {\"epAnimationGeneratedClass\":\"edplus_anim-CiACgV\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<p class=\" eplus-wrapper\"><strong><strong>Keywords and subjects<\/strong><\/strong>:\u00a0Reservoir Computing, Brain Connectivity, Explainable Artificial Intelligence (XAI) Effective Connectivity, Neuroimaging Biomarkers, Magnetic Resonance Imaging (MRI), Causality Analysis, Machine Learning in Healthcare, Brain Network Disruption,\u00a0 Stroke Diagnosis<\/p>\n<!-- \/wp:paragraph -->\n\n<!-- wp:spacer {\"height\":\"40px\",\"epAnimationGeneratedClass\":\"edplus_anim-R8xTUk\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<div style=\"height:40px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n<!-- \/wp:spacer -->\n\n<!-- wp:acf\/button {\"id\":\"block_67867264d1a72\",\"name\":\"acf\/button\",\"data\":{\"title\":\"READ HERE\",\"_title\":\"field_61d40397c2f0a\",\"button_type\":\"link\",\"_button_type\":\"field_63bbde3b8f0d0\",\"url\":\"https:\/\/ieeexplore.ieee.org\/document\/10839398\",\"_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\"} \/-->\n\n<!-- wp:spacer {\"height\":\"40px\",\"epAnimationGeneratedClass\":\"edplus_anim-R8xTUk\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<div style=\"height:40px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n<!-- \/wp:spacer -->\n\n<!-- wp:image {\"id\":20902,\"sizeSlug\":\"large\",\"linkDestination\":\"none\",\"epAnimationGeneratedClass\":\"edplus_anim-VNKh1f\",\"epGeneratedClass\":\"eplus-wrapper\"} -->\n<figure class=\"wp-block-image size-large eplus-wrapper\"><img src=\"https:\/\/sano.science\/wp-content\/uploads\/2025\/01\/pipeline_End-to-end-Stroke-Imaging-Analysis-using-Effective-Connectivity-and-Interpretable-Artificial-intelligence.svg\" alt=\"\" class=\"wp-image-20902\"\/><figcaption class=\"wp-element-caption\">Source: <a href=\"https:\/\/ieeexplore.ieee.org\/document\/10839398\">https:\/\/ieeexplore.ieee.org\/document\/10839398<\/a><\/figcaption><\/figure>\n<!-- \/wp:image -->","post_title":"End-to-end Stroke Imaging Analysis using Effective Connectivity and Interpretable Artificial intelligence","post_excerpt":"Conference abstract in: https:\/\/ieeexplore.ieee.org , 2025 ","post_status":"publish","comment_status":"closed","ping_status":"closed","post_password":"","post_name":"end-to-end-stroke-imaging-analysis-using-effective-connectivity-and-interpretable-artificial-intelligence","to_ping":"","pinged":"","post_modified":"2025-02-21 18:04:25","post_modified_gmt":"2025-02-21 17:04:25","post_content_filtered":"","post_parent":0,"guid":"https:\/\/sano.science\/?post_type=research&#038;p=20886","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&#038;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\/11961","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\/11961\/revisions"}],"predecessor-version":[{"id":26728,"href":"https:\/\/sano.science\/index.php\/wp-json\/wp\/v2\/people\/11961\/revisions\/26728"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/sano.science\/index.php\/wp-json\/wp\/v2\/media\/18370"}],"wp:attachment":[{"href":"https:\/\/sano.science\/index.php\/wp-json\/wp\/v2\/media?parent=11961"}],"wp:term":[{"taxonomy":"people_teams","embeddable":true,"href":"https:\/\/sano.science\/index.php\/wp-json\/wp\/v2\/people_teams?post=11961"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}