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.
School of Life Science and Technology, Changchun University of Science and Technology, Changchun 130000, China.
Neurosci Lett. 2022 Nov 20;791:136908. doi: 10.1016/j.neulet.2022.136908. Epub 2022 Oct 7.
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.
2 型糖尿病（T2DM）患者可能会发展为轻度认知障碍（MCI）甚至痴呆。然而，基于机器学习方法，缺乏用于检测 T2DM 患者 MCI 的可靠机器学习模型。此外，与 MCI 相关的脑网络变化尚未得到研究。本研究旨在开发一种基于机器学习的算法来帮助检测 T2DM 中的 MCI。本研究共纳入 164 名参与者。根据神经心理学评估，将他们分为 T2DM-MCI（n=56）、T2DM-nonMCI（n=49）和正常对照组（n=59）。基于静息态磁共振成像（rs-fMRI）构建每个参与者的功能连接。使用特征选择来减少特征维度。然后，将选定的特征设置为具有特权信息的级联多列随机向量功能链接网络（RVFL）分类器模型。最后，使用测试数据训练最佳模型并获得分类性能。结果表明，与经典方法相比，所提出的算法具有出色的性能。与 NC 相比，分类精度为 73.18%（T2DM-MCI 与 NC）和 79.42%（T2DM-MCI 与 T2DM-nonMCI）。与 T2DM-MCI 相关的功能连接主要分布在前额叶、颞叶和中央区域（运动皮层），可作为神经影像学生物标志物用于识别 T2DM 患者的 MCI。本研究为 T2DM 患者 MCI 的诊断提供了一种机器学习模型，对及时干预和治疗以延缓 MCI 的发展具有潜在的临床意义。
