{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,22]],"date-time":"2025-10-22T10:48:19Z","timestamp":1761130099537,"version":"3.41.2"},"reference-count":37,"publisher":"Wiley","issue":"12","license":[{"start":{"date-parts":[[2023,11,8]],"date-time":"2023-11-08T00:00:00Z","timestamp":1699401600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Brain and Behavior"],"published-print":{"date-parts":[[2023,12]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec><jats:title>Introduction<\/jats:title><jats:p>To investigate changes in the multilayer network in patients with migraine compared to healthy controls.<\/jats:p><jats:p><jats:bold>Methods<\/jats:bold>: This study enrolled 82 patients with newly diagnosed migraine without aura and 53 healthy controls. Brain magnetic resonance imaging (MRI) was conducted using a 3\u2010tesla MRI scanner, including three\u2010dimensional T1\u2010weighted and diffusion tensor imaging (DTI). A gray matter layer matrix was created with a morphometric similarity network using T1\u2010weighted imaging and the FreeSurfer program. A white matter layer matrix was also created with structural connectivity using the DTI studio (DSI) program. A multilayer network analysis was then performed by applying graph theory using the BRAPH program.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>Significant changes were observed in the multilayer network at the global level in patients with migraines compared to the healthy controls. The multilayer modularity (0.177\u00a0vs. 0.160, <jats:italic>p<\/jats:italic>\u00a0=\u00a0.0005) and average multiplex participation (0.934\u00a0vs. 0.924, <jats:italic>p<\/jats:italic>\u00a0=\u00a0.002) were higher in patients with migraines than in the healthy controls. In contrast, the average multilayer clustering coefficient (0.406\u00a0vs. 0.461, <jats:italic>p<\/jats:italic>\u00a0=\u00a0.0005), average overlapping strength (56.061\u00a0vs. 61.676, <jats:italic>p<\/jats:italic>\u00a0=\u00a0.0005), and average weighted multiplex participation (0.847\u00a0vs. 0.878, <jats:italic>p<\/jats:italic>\u00a0=\u00a0.0005) were lower in patients with migraine than in the healthy controls. In addition, several regions showed significant changes in the multilayer network at the nodal level, including multiplex participation, multilayer clustering coefficients, overlapping strengths, and weighted multiplex participation.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>This study demonstrated significant changes in the multilayer network in patients with migraines compared to healthy controls. This could aid an understanding of the complex brain network in patients with migraine and may be associated with the pathophysiology of migraines. Patients with migraine show multilayer network changes in widespreading brain regions compared to healthy controls, and specific brain areas seem to play a hub role for pathophysiology of the migraine.<\/jats:p><\/jats:sec>","DOI":"10.1002\/brb3.3316","type":"journal-article","created":{"date-parts":[[2023,11,9]],"date-time":"2023-11-09T08:33:33Z","timestamp":1699518813000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Multilayer network changes in patients with migraine"],"prefix":"10.1002","volume":"13","author":[{"given":"Jinseung","family":"Kim","sequence":"first","affiliation":[{"name":"Department of Family Medicine Busan Paik Hospital, Inje University College of Medicine  Busan South Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dong Ah","family":"Lee","sequence":"additional","affiliation":[{"name":"Departments of Neurology Haeundae Paik Hospital, Inje University College of Medicine 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