{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,18]],"date-time":"2026-01-18T08:06:35Z","timestamp":1768723595372,"version":"3.49.0"},"reference-count":48,"publisher":"Wiley","issue":"7","license":[{"start":{"date-parts":[[2020,5,13]],"date-time":"2020-05-13T00:00:00Z","timestamp":1589328000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["U1736219"],"award-info":[{"award-number":["U1736219"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61571327"],"award-info":[{"award-number":["61571327"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["aapm.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Medical Physics"],"published-print":{"date-parts":[[2020,7]]},"abstract":"<jats:sec><jats:title>Purpose<\/jats:title><jats:p>This work aims to establish a classification framework for the diagnosis of mild cognitive impairment (MCI) at different stages (early MCI and late MCI) through direct analysis of resting\u2010state functional magnetic resonance imaging (rs\u2010fMRI) signals and using the accuracy (total correct rate), specificity (correct rate of late MCI) and sensitivity (correct rate of early MCI) to validate its classification performance.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>All fMR images of subjects were parcellated into 116 regions of interest (ROIs) by applying the Anatomical Automatic Labeling (AAL) template, and the average rs\u2010fMRI signals of each ROI were extracted. The Hilbert\u2010Huang transform (HHT) was introduced into the framework to decompose each rs\u2010fMRI signal into a series of intrinsic mode functions (IMFs) and to analyze these nonstationary and nonlinear time\u2010series from the perspective of multiresolution. After obtaining the instantaneous frequencies and amplitudes of all IMFs of a signal, the Hilbert weighted frequencies (HWFs) were calculated and combined into a vector as the feature of the corresponding ROI. Support Vector Machine (SVM) was implemented to classify MCI at different stages. We used the independent two\u2010sample <jats:italic>t<\/jats:italic>\u2010test as the feature selection method and measured the classification performance through the leave\u2010one\u2010out cross\u2010validation (LOOCV) method.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>Results on 77 early MCI (eMCI) and 64 late MCI (lMCI) with baseline rs\u2010fMRI data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) yielded 87.94% classification accuracy. Some of the brain regions with significant differences found by previous studies have been confirmed in this work. We found that HWF characteristics exhibited a significant downward trend in all cerebellar regions. The rs\u2010fMRI signals in differential brain regions have not changed completely, but only altered in some narrow frequency bands. The analysis results showed that during the progress of MCI, the main changes of rs\u2010fMRI were concentrated in IMF3, while IMFs with other indexes also contained HWF features with high SVM weights, such as Orbitofrontal superior frontal gyrus in IMF2, Insula in IMF4, and Lobule \u2162 of vermis in IMF5, indicating that other IMFs provide important information for the diagnosis of MCI as well.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusions<\/jats:title><jats:p>This work confirmed the classification ability of HHT\u2010based classification framework in classification of at different stages of MCI. Through the analysis, we found that during the progress of MCI the main changes of rs\u2010fMRI were concentrated in IMF3, and HWF characteristics showed a significant downward trend in all cerebellar regions.<\/jats:p><\/jats:sec>","DOI":"10.1002\/mp.14183","type":"journal-article","created":{"date-parts":[[2020,4,17]],"date-time":"2020-04-17T19:24:40Z","timestamp":1587151480000},"page":"2902-2915","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":17,"title":["Stage detection of mild cognitive impairment via fMRI using Hilbert Huang transform based classification framework"],"prefix":"10.1002","volume":"47","author":[{"given":"Jiahao","family":"Shi","sequence":"first","affiliation":[{"name":"College of Intelligence and Computing Tianjin Key Laboratory of Cognitive Computing and Application Tianjin University Tianjin 300350 P. R. 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