Majority of type 2 diabetes mellitus (T2DM) patients are highly susceptible to several forms of cognitive impairments, particularly dementia. However, the underlying neural mechanism of these cognitive impairments remains unclear. We aimed to investigate the correlation between whole brain resting state functional connections (RSFCs) and the cognitive status in 95 patients with T2DM. We constructed an elastic net model to estimate the Montreal Cognitive Assessment (MoCA) scores, which served as an index of the cognitive status of the patients, and to select the RSFCs for further prediction. Subsequently, we utilized a machine learning technique to evaluate the discriminative ability of the connectivity pattern associated with the selected RSFCs. The estimated and chronological MoCA scores were significantly correlated with R?=?0.81 and the mean absolute error (MAE)?=?1.20. Additionally, cognitive impairments of patients with T2DM can be identified using the RSFC pattern with classification accuracy of 90.54% and the area under the receiver operating characteristic (ROC) curve (AUC) of 0.9737. This connectivity pattern not only included the connections between regions within the default mode network (DMN), but also the functional connectivity between the task-positive networks and the DMN, as well as those within the task-positive networks. The results suggest that an RSFC pattern could be regarded as a potential biomarker to identify the cognitive status of patients with T2DM.
二型糖尿病会导致患者出现认知功能损伤，甚至导致患者痴呆，但其背后的神经机制尚不明确。我们采用全脑功能连接度分析和机器学习的方法，探索二型糖尿病患者在脑功能连接上与正常对照组之间的差异，并尝试利用全脑功能连接度分类来对存在认知功能障碍的患者进行诊断。我们利用弹性网回归的方法建立了脑功能连接度与认知功能评分之间的关联模型，并选择出与认知功能损伤存在显著相关性的关键功能连接因子，利用这些关键因子对患者的认知功能进行预测。我们建立的关联模型能够准确估计患者的认知功能评分，并且利用选择的关键特征可以建立精准的认知功能障碍预测模型，达到精准预测患者认知功能障碍的效果。此外我们还发现，选择出的关键特征不仅包括了默认脑网络内部脑区之间的连接，还包括默认脑网络与功能网络之间的连接以及功能网络内部的连接。通过这个工作，证实了全脑功能连接有可能作为揭示二型糖尿病患者认知功能障碍的一个潜在生物学标志物。





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