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However, the brain functional mechanism of T2DM\u2010related dementia is still less understood. Recent resting\u2010state functional magnetic resonance imaging functional connectivity (FC) studies have proved its potential value in the study of T2DM with cognitive impairment (T2DM\u2010CI). However, they mainly used a mass\u2010univariate statistical analysis that was not suitable to reveal the altered FC \u201cpattern\u201d in T2DM\u2010CI, due to lower sensitivity. In this study, we proposed to use high\u2010order FC to reveal the abnormal connectomics pattern in T2DM\u2010CI with a multivariate, machine learning\u2010based strategy. We also investigated whether such patterns were different between T2DM\u2010CI and T2DM without cognitive impairment (T2DM\u2010noCI) to better understand T2DM\u2010induced cognitive impairment, on 23 T2DM\u2010CI and 27 T2DM\u2010noCI patients, as well as 50 healthy controls (HC). We first built the large\u2010scale high\u2010order brain networks based on temporal synchronization of the dynamic FC time series among multiple brain region pairs and then used this information to classify the T2DM\u2010CI (as well as T2DM\u2010noCI) from the matched HC based on support vector machine. Our model achieved an accuracy of 79.17% in T2DM\u2010CI versus HC differentiation, but only 59.62% in T2DM\u2010noCI versus HC classification. We found abnormal high\u2010order FC patterns in T2DM\u2010CI compared to HC, which was different from that in T2DM\u2010noCI. Our study indicates that there could be widespread connectivity alterations underlying the T2DM\u2010induced cognitive impairment. 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