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As the headache days\/month is often measured by patient self\u2010report and tends to be overestimated than actually experienced, the possibility of using neuroimaging data to predict migraine attack frequency is of great interest. To identify neuroimaging features that could objectively evaluate patients' headache days, a total of 179 migraineurs were recruited from two data center with one dataset used as the training\/test cohort and the other used as the validating cohort. The guidelines for controlled trials of prophylactic treatment of chronic migraine in adults were used to identify the frequency of attacks and migraineurs were divided into low (MOl) and high (MOh) subgroups. Whole\u2010brain functional connectivity was used to build multivariate logistic regression models with model iteration optimization to identify MOl and MOh. The best model accurately discriminated MOh from MOl with AUC of 0.91 (95%CI [0.86, 0.95]) in the training\/test cohort and 0.79 in the validating cohort. The discriminative features were mainly located within the limbic lobe, frontal lobe, and temporal lobe. Permutation tests analysis demonstrated that the classification performance of these features was significantly better than chance. Furthermore, the indicator of functional connectivity had a higher odds ratio than behavioral variables with implementing a holistic regression analysis. The current findings suggested that the migraine attack frequency could be distinguished by using machine\u2010learning algorithms, and highlighted the role of brain functional connectivity in revealing underlying migraine\u2010related neurobiology.<\/jats:p>","DOI":"10.1002\/hbm.24854","type":"journal-article","created":{"date-parts":[[2019,11,4]],"date-time":"2019-11-04T08:29:31Z","timestamp":1572856171000},"page":"984-993","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":25,"title":["Neuroimaging features of whole\u2010brain functional connectivity predict attack frequency of migraine"],"prefix":"10.1002","volume":"41","author":[{"given":"Junya","family":"Mu","sequence":"first","affiliation":[{"name":"Center for Brain Imaging, School of Life Science and Technology Xidian University  Xi'an China"},{"name":"Engineering Research Center of Molecular and Neuro Imaging Ministry of Education  Xi'an 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