{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,13]],"date-time":"2026-10-13T15:49:39Z","timestamp":1791906579569,"version":"4.3.4"},"reference-count":72,"publisher":"Oxford University Press (OUP)","license":[{"start":{"date-parts":[[2026,9,25]],"date-time":"2026-09-25T00:00:00Z","timestamp":1790294400000},"content-version":"am","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/pages\/standard-publication-reuse-rights"}],"funder":[{"DOI":"10.13039\/501100003725","name":"National Research Foundation of Korea","doi-asserted-by":"publisher","award":["RS-2024-00350834"],"award-info":[{"award-number":["RS-2024-00350834"]}],"id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100024838","name":"Samsung Medical Center","doi-asserted-by":"publisher","award":["OTC1190671"],"award-info":[{"award-number":["OTC1190671"]}],"id":[{"id":"10.13039\/501100024838","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Study Objectives<\/jats:title>\n                    <jats:p>Comorbid insomnia and sleep apnea (COMISA) is a prevalent and clinically challenging phenotype, yet the neural substrates distinguishing it from isolated obstructive sleep apnea (OSA) remain elusive. We employed a multimodal graph theoretical approach to identify the structural and functional network patterns associated with the COMISA phenotype.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Materials and Methods<\/jats:title>\n                    <jats:p>We analyzed 124 male participants (45 controls, 58 OSA, and 21 COMISA). Structural covariance networks were derived from regional gray matter volume data, and functional networks from resting-state functional MRI. Graph metrics\u2014including strength, clustering coefficient (CC), efficiency, and closeness centrality (CloC)\u2014were quantified for weighted, undirected networks to assess global and nodal topological properties.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Functional network analysis revealed significant global hyper-connectivity in both patient groups compared to controls. Crucially, COMISA demonstrated a distinct functional pattern compared to OSA, characterized by significantly elevated nodal metrics (CC and CloC) within thalamostriatal circuits, specifically involving the bilateral thalamus and putamen. Exploratory analyses across the cohort suggested possible positive associations between these nodal metrics and the severity of insomnia and depressive symptoms. In contrast, structural covariance networks showed no significant group differences, indicating a dissociation between the two modalities.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>Our findings suggest that COMISA presents a distinct functional pattern, which is consistent with circuit-level dysregulation, specifically within the thalamostriatal axis. This subcortical pattern within arousal-modulating circuits offers a potential pathophysiological explanation for the clinical complexity and treatment resistance frequently observed in COMISA.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1093\/sleep\/zsag252","type":"journal-article","created":{"date-parts":[[2026,9,25]],"date-time":"2026-09-25T11:12:17Z","timestamp":1790334737000},"source":"Crossref","is-referenced-by-count":0,"title":["Distinct thalamostriatal functional network dysregulation in comorbid insomnia and sleep apnea"],"prefix":"10.1093","author":[{"given":"Dongyeop","family":"Kim","sequence":"first","affiliation":[{"name":"Department of Neurology, Ewha Womans University Seoul Hospital, Ewha Womans University College of Medicine , Seoul ,","place":["Republic of Korea"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Seunghu","family":"Kim","sequence":"additional","affiliation":[{"name":"Department of Applied Artificial Intelligence, Hanyang University , Ansan ,","place":["Republic of Korea"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Euijin","family":"Kim","sequence":"additional","affiliation":[{"name":"Department of Human-Computer Interaction, Hanyang University , Ansan ,","place":["Republic of Korea"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sungkean","family":"Kim","sequence":"additional","affiliation":[{"name":"Department of Applied Artificial Intelligence, Hanyang University , Ansan ,","place":["Republic of Korea"]},{"name":"Department of Human-Computer Interaction, Hanyang University , Ansan ,","place":["Republic of Korea"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Eun Yeon","family":"Joo","sequence":"additional","affiliation":[{"name":"Department of Neurology, Neuroscience Center, Samsung Medical Center, Sungkyunkwan University School of Medicine , Seoul ,","place":["Republic of 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