{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T04:30:12Z","timestamp":1788237012549,"version":"build-2803163510"},"reference-count":28,"publisher":"Wiley","issue":"9","license":[{"start":{"date-parts":[[2025,9,16]],"date-time":"2025-09-16T00:00:00Z","timestamp":1757980800000},"content-version":"vor","delay-in-days":15,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"},{"start":{"date-parts":[[2025,9,1]],"date-time":"2025-09-01T00:00:00Z","timestamp":1756684800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Health Science Reports"],"published-print":{"date-parts":[[2025,9]]},"abstract":"ABSTRACT<\/jats:title>\n \n Background and Aim<\/jats:title>\n There is compelling evidence that gray matter changes associated with migraine, which in turn may alter morphological network topology. The aim of this study is to characterize morphological network topology of the brains of migraineurs and non\u2010migraine subjects using wavelet\u2010based morphometry.<\/jats:p>\n <\/jats:sec>\n \n Methods<\/jats:title>\n \n 3D, T1W brain images were obtained from 45 patients with migraine and 46 non\u2010migraine subjects. Then, gray matter volume images were developed, and they were decomposed and reconstructed at a level of\n n<\/jats:italic>\n \u2009=\u20093 using wavelet\u2010based morphometry. 4D gray matter volumes were constructed for each subject and they were parcellated into 625 anatomical regions, and structural covariance matrices were developed. Each matrix was binarized by applying a series of sparsity thresholds, and global network topological metrics were computed. Finally, two sample\n t<\/jats:italic>\n \u2010tests were performed using area under curves of each metric for group\u2010level comparisons of network topology.\n <\/jats:p>\n <\/jats:sec>\n \n Results<\/jats:title>\n \n Patients with migraine showed increased small worldness (\n p<\/jats:italic>\n \u2009=\u20090.003) and global efficiency (\n p<\/jats:italic>\n \u2009=\u20090.002) compared to non\u2010migraine subjects. Local efficiency (\n p<\/jats:italic>\n \u2009=\u20090.49) and assortativity (\n p<\/jats:italic>\n \u2009=\u20090.70) have shown similar characteristics for both groups against network sparsity with no significant differences. Hierarchy (\n p<\/jats:italic>\n \u2009=\u20090.41) was largely dispersed in the middle sparsity thresholds (0.15\u20130.35). The characteristics of synchronization (\n p<\/jats:italic>\n \u2009=\u20090.32) between groups were almost the same from 0.05 to 0.4 of network sparsities.\n <\/jats:p>\n <\/jats:sec>\n \n Conclusion<\/jats:title>\n Patients with migraine exhibit better integration of information processing and wavelet\u2010based morphometry in combining with graph theory provides valuable information on altered gray matter network topologies in migraineurs.<\/jats:p>\n <\/jats:sec>","DOI":"10.1002\/hsr2.71093","type":"journal-article","created":{"date-parts":[[2025,9,17]],"date-time":"2025-09-17T05:36:31Z","timestamp":1758087391000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Characterizing the Morphological Brain Network Topology in Patients With Migraine Using Wavelet Based Morphometry: A Descriptive Cross\u2010Sectional Retrospective Study"],"prefix":"10.1002","volume":"8","author":[{"given":"G.D.C.J.","family":"Prabhashana","sequence":"first","affiliation":[{"name":"Department of Radiography and Radiotherapy, Faculty of Allied Health Sciences General Sir John Kotelawala Defense University Sri Lanka"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"R.L.T.","family":"Sirimanne","sequence":"additional","affiliation":[{"name":"Department of Radiography and Radiotherapy, Faculty of Allied Health Sciences General Sir John Kotelawala Defense University Sri Lanka"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"A.D.I.","family":"Amarasinghe","sequence":"additional","affiliation":[{"name":"Department of Radiography and Radiotherapy, Faculty of Allied Health Sciences General Sir John Kotelawala Defense University Sri Lanka"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"W.K.C.","family":"Sampath","sequence":"additional","affiliation":[{"name":"Department of Radiology University Hospital KDU Sri Lanka"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"P.P.C.R.","family":"Karunasekara","sequence":"additional","affiliation":[{"name":"Department of Electronic, Electrical and Telecommunication Engineering, Faculty of Engineering General Sir John Kotelawala Defense University Sri Lanka"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3750-8070","authenticated-orcid":false,"given":"W.M.","family":"Ediri Arachchi","sequence":"additional","affiliation":[{"name":"Department of Radiography and Radiotherapy, Faculty of Allied Health Sciences General Sir John Kotelawala Defense University Sri Lanka"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2025,9,16]]},"reference":[{"key":"e_1_2_10_2_1","doi-asserted-by":"publisher","DOI":"10.1212\/WNL.0b013e3182a6cb32"},{"key":"e_1_2_10_3_1","first-page":"283","volume-title":"Handbook of Clinical Neurology, Vol. 138","author":"Peck K. 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