{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,1]],"date-time":"2025-11-01T05:37:48Z","timestamp":1761975468407,"version":"3.41.0"},"reference-count":53,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2015,7,20]],"date-time":"2015-07-20T00:00:00Z","timestamp":1437350400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"funder":[{"name":"State Scholarship Fund of China Scholarship Council"},{"name":"Vice Chancellor Research Grant in University of Wollongong"},{"DOI":"10.13039\/501100012246","name":"Priority Academic Program Development of Jiangsu Higher Education Institutions","doi-asserted-by":"crossref","id":[{"id":"10.13039\/501100012246","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Magnetic Resonance in Med"],"published-print":{"date-parts":[[2016,7]]},"abstract":"<jats:sec><jats:title>Purpose<\/jats:title><jats:p>Multifractal applications to resting state functional MRI (rs\u2010fMRI) time series for diagnosing Alzheimer's disease (AD) are still limited. We aim to address two issues: (I) if and what multifractal features are sufficiently discriminative to detect AD from the healthy; (II) if AD classification could be further improved by combining multifractal features with traditional features in this field.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>Rs\u2010fMRI data of 25 AD patients and 38 normal controls were analyzed. A set of multifractal features were systematically investigated. Traditional features in monofractal, linear, and network\u2010based categories were also extracted for comparison and combination. Both support vector machines and multiple kernel learning (MKL) were used to perform classification with individual and combined features.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>We identified a multifractal feature,<jats:inline-graphic xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" xlink:href=\"graphic\/mrm25853-math-0008.png\" xlink:title=\"urn:x-wiley:07403194:media:mrm25853:mrm25853-math-0008\"\/>, which has the strongest discriminative power among all the features in our study. Moreover, we found that the classification accuracy could be significantly improved from 69% (by<jats:inline-graphic xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" xlink:href=\"graphic\/mrm25853-math-0009.png\" xlink:title=\"urn:x-wiley:07403194:media:mrm25853:mrm25853-math-0009\"\/>only) to up to 76%, when nonsparse MKL is used to combine<jats:inline-graphic xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" xlink:href=\"graphic\/mrm25853-math-0010.png\" xlink:title=\"urn:x-wiley:07403194:media:mrm25853:mrm25853-math-0010\"\/>with the monofractal feature, Hurst. Finally, we showed that incorporating other multifractal features,<jats:inline-graphic xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" xlink:href=\"graphic\/mrm25853-math-0011.png\" xlink:title=\"urn:x-wiley:07403194:media:mrm25853:mrm25853-math-0011\"\/>,<jats:inline-graphic xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" xlink:href=\"graphic\/mrm25853-math-0012.png\" xlink:title=\"urn:x-wiley:07403194:media:mrm25853:mrm25853-math-0012\"\/>and<jats:inline-graphic xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" xlink:href=\"graphic\/mrm25853-math-0013.png\" xlink:title=\"urn:x-wiley:07403194:media:mrm25853:mrm25853-math-0013\"\/>, could also improve traditional\u2010feature\u2010based AD classification.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>Our work demonstrated the potential usefulness of multifractal analysis for AD research, especially when combining with the traditional rs\u2010fMRI features. It contributes to distinguishing AD from NC subjects. Magn Reson Med 76:259\u2013269, 2016. \u00a9 2015 Wiley Periodicals, Inc.<\/jats:p><\/jats:sec>","DOI":"10.1002\/mrm.25853","type":"journal-article","created":{"date-parts":[[2015,7,20]],"date-time":"2015-07-20T12:59:21Z","timestamp":1437397161000},"page":"259-269","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":22,"title":["Exploring multifractal\u2010based features for mild Alzheimer's disease classification"],"prefix":"10.1002","volume":"76","author":[{"given":"Huangjing","family":"Ni","sequence":"first","affiliation":[{"name":"School of Electronic Science and Engineering, Nanjing University Nanjing China"},{"name":"School of Computer Science and Software Engineering, University of Wollongong Wollongong NSW Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Luping","family":"Zhou","sequence":"additional","affiliation":[{"name":"School of Computer Science and Software Engineering, University of 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