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.

中文翻译：

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使用全脑功能连接来识别T2DM患者的认知功能障碍。

大多数2型糖尿病（T2DM）患者高度易受多种形式的认知障碍，尤其是痴呆症。但是，这些认知障碍的潜在神经机制仍不清楚。我们旨在调查95名T2DM患者的全脑静息状态功能连接（RSFC）与认知状态之间的相关性。我们构建了一个弹性网模型来估计蒙特利尔认知评估（MoCA）得分，该得分可作为患者认知状态的指标，并选择RSFC进行进一步预测。随后，我们利用机器学习技术来评估与所选RSFC相关的连接模式的判别能力。MoCA的估算值和时间顺序与R = 0显着相关。81，而平均绝对误差（MAE）= 1.20。此外，可以使用RSFC模式识别T2DM患者的认知障碍，其分类精度为90.54％，接收器工作特征（ROC）曲线（AUC）下面积为0.9737。这种连接模式不仅包括默认模式网络（DMN）内的区域之间的连接，还包括任务阳性网络与DMN之间以及任务阳性网络内部之间的功能连接。结果表明，RSFC模式可以被视为识别T2DM患者认知状态的潜在生物标记。54％，接收器工作特性（ROC）曲线（AUC）下的面积为0.9737。这种连接模式不仅包括默认模式网络（DMN）内的区域之间的连接，还包括任务阳性网络与DMN之间以及任务阳性网络内部之间的功能连接。结果表明，RSFC模式可以被视为识别T2DM患者认知状态的潜在生物标记。54％，接收器工作特性（ROC）曲线（AUC）下的面积为0.9737。这种连接模式不仅包括默认模式网络（DMN）内的区域之间的连接，还包括任务阳性网络与DMN之间以及任务阳性网络内部之间的功能连接。结果表明，RSFC模式可以被视为识别T2DM患者认知状态的潜在生物标记。