PMID- 36216169 OWN - NLM STAT- MEDLINE DCOM- 20221107 LR - 20221110 IS - 1872-7972 (Electronic) IS - 0304-3940 (Linking) VI - 791 DP - 2022 Nov 20 TI - Detection of mild cognitive impairment in type 2 diabetes mellitus based on machine learning using privileged information. PG - 136908 LID - S0304-3940(22)00469-4 [pii] LID - 10.1016/j.neulet.2022.136908 [doi] AB - Type 2 diabetes mellitus (T2DM) patients may develop into mild cognitive impairment (MCI) or even dementia. However, there is lack of reliable machine learning model for detection MCI in T2DM patients based on machine learning method. In addition, the brain network changes associated with MCI have not been studied. The aim of this study is to develop a machine learning based algorithm to help detect MCI in T2DM. There are 164 participants were included in this study. They were divided into T2DM-MCI (n = 56), T2DM-nonMCI (n = 49), and normal controls (n = 59) according to the neuropsychological evaluation. Functional connectivity of each participant was constructed based on resting-state magnetic resonance imaging (rs-fMRI). Feature selection was used to reduce the feature dimension. Then the selected features were set into the cascaded multi-column random vector functional link network (RVFL) classifier model using privileged information. Finally, the optimal model was trained and the classification performance was obtained using the testing data. The results show that the proposed algorithm has outstanding performance compared with classic methods. The classification accuracy of 73.18 % (T2DM-MCI vs NC) and 79.42 % (T2DM-MCI vs T2DM-nonMCI) were achieved. The functional connectivity related to T2DM-MCI mainly distribute in the frontal lobe, temporal lobe, and central region (motor cortex), which could be used as neuroimaging biomarkers to recognize MCI in T2DM patients. This study provides a machine learning model for diagnosis of MCI in T2DM patients and has potential clinical significance for timely intervention and treatment to delay the development of MCI. CI - Copyright (c) 2022 Elsevier B.V. All rights reserved. FAU - Xia, Shuiwei AU - Xia S AD - Key Laboratory of Imaging Diagnosis and Minimally Invasive Intervention Research, Affiliated Lishui Hospital of Zhejiang University, The Fifth Affiliated Hospital of Wenzhou Medical University, Lishui Central Hospital, Lishui 323000, China. FAU - Zhang, Yu AU - Zhang Y AD - School of Life Science and Technology, Changchun University of Science and Technology, Changchun 130000, China. FAU - Peng, Bo AU - Peng B AD - Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou 215163, China; Jinan Guoke Medical Engineering Technology Development Co., LTD, Jinan 25000, China. FAU - Hu, Xianghua AU - Hu X AD - Key Laboratory of Imaging Diagnosis and Minimally Invasive Intervention Research, Affiliated Lishui Hospital of Zhejiang University, The Fifth Affiliated Hospital of Wenzhou Medical University, Lishui Central Hospital, Lishui 323000, China. FAU - Zhou, Limin AU - Zhou L AD - Key Laboratory of Imaging Diagnosis and Minimally Invasive Intervention Research, Affiliated Lishui Hospital of Zhejiang University, The Fifth Affiliated Hospital of Wenzhou Medical University, Lishui Central Hospital, Lishui 323000, China. FAU - Chen, Chunmiao AU - Chen C AD - Key Laboratory of Imaging Diagnosis and Minimally Invasive Intervention Research, Affiliated Lishui Hospital of Zhejiang University, The Fifth Affiliated Hospital of Wenzhou Medical University, Lishui Central Hospital, Lishui 323000, China. FAU - Lu, Chenying AU - Lu C AD - Key Laboratory of Imaging Diagnosis and Minimally Invasive Intervention Research, Affiliated Lishui Hospital of Zhejiang University, The Fifth Affiliated Hospital of Wenzhou Medical University, Lishui Central Hospital, Lishui 323000, China. FAU - Chen, Minjiang AU - Chen M AD - Key Laboratory of Imaging Diagnosis and Minimally Invasive Intervention Research, Affiliated Lishui Hospital of Zhejiang University, The Fifth Affiliated Hospital of Wenzhou Medical University, Lishui Central Hospital, Lishui 323000, China. FAU - Pang, Chunying AU - Pang C AD - School of Life Science and Technology, Changchun University of Science and Technology, Changchun 130000, China. FAU - Dai, Yakang AU - Dai Y AD - Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou 215163, China; Jinan Guoke Medical Engineering Technology Development Co., LTD, Jinan 25000, China. Electronic address: daiyk@sibet.ac.cn. FAU - Ji, Jiansong AU - Ji J AD - Key Laboratory of Imaging Diagnosis and Minimally Invasive Intervention Research, Affiliated Lishui Hospital of Zhejiang University, The Fifth Affiliated Hospital of Wenzhou Medical University, Lishui Central Hospital, Lishui 323000, China. Electronic address: jijiansong@zju.edu.cn. LA - eng PT - Journal Article PT - Research Support, Non-U.S. Gov't DEP - 20221007 PL - Ireland TA - Neurosci Lett JT - Neuroscience letters JID - 7600130 SB - IM MH - Humans MH - *Alzheimer Disease/diagnostic imaging/complications MH - *Diabetes Mellitus, Type 2/complications MH - *Cognitive Dysfunction/complications MH - Machine Learning MH - Magnetic Resonance Imaging/methods MH - Brain OTO - NOTNLM OT - Cascaded multi=column RVFL+ OT - Functional connectivity OT - Mild cognitive impairment OT - Privilege information OT - T2DM COIS- Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. EDAT- 2022/10/11 06:00 MHDA- 2022/11/08 06:00 CRDT- 2022/10/10 19:24 PHST- 2022/08/08 00:00 [received] PHST- 2022/09/28 00:00 [revised] PHST- 2022/10/04 00:00 [accepted] PHST- 2022/10/11 06:00 [pubmed] PHST- 2022/11/08 06:00 [medline] PHST- 2022/10/10 19:24 [entrez] AID - S0304-3940(22)00469-4 [pii] AID - 10.1016/j.neulet.2022.136908 [doi] PST - ppublish SO - Neurosci Lett. 2022 Nov 20;791:136908. doi: 10.1016/j.neulet.2022.136908. Epub 2022 Oct 7.