{"id":14883,"date":"2024-01-12T17:19:18","date_gmt":"2024-01-12T16:19:18","guid":{"rendered":"https:\/\/sano.science\/?post_type=research&#038;p=14883"},"modified":"2024-01-12T17:19:18","modified_gmt":"2024-01-12T16:19:18","slug":"community-detection-in-brain-connectome-using-quantum-annealer-devices","status":"publish","type":"research","link":"https:\/\/sano.science\/research\/community-detection-in-brain-connectome-using-quantum-annealer-devices\/","title":{"rendered":"Community Detection in Brain Connectome using Quantum Annealer Devices"},"content":{"rendered":"\n<h2 class=\"wp-block-heading eplus-wrapper\">M.Wierzbinski, J.Falco-Roget, A.Crimi<\/h2>\n\n\n\n<div style=\"height:50px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n\n\n\n<p class=\" eplus-wrapper\">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\n\n\n<div style=\"height:50px\" aria-hidden=\"true\" class=\"wp-block-spacer eplus-wrapper\"><\/div>\n\n\n\n\t\n    \n        \n\t\t\t<a href=\"https:\/\/www.nature.com\/articles\/s41598-023-30579-y\" target=\"_blank\" rel= \"noopener noreferrer nofollow\" class=\"button primary \">\n\n\t\t\t\t<span>\n\t\t\t\t\tREAD HERE\n\t\t\t\t<\/span>\n\n\t\t\t<\/a>\n\n        \n    \n","protected":false},"excerpt":{"rendered":"<p>In: Scientific Reports, 2023.<\/p>\n","protected":false},"featured_media":0,"template":"","research_type":[8],"research_team":[15],"class_list":["post-14883","research","type-research","status-publish","hentry","research_type-publications","research_team-computational-neuroscience"],"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v27.3 (Yoast SEO v27.3) - 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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","innerContent":["\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. 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