Identification of T2DM Patients Associated Cognitive Dysfunction Using Whole Brain Functional Connectivity

Publication date: Available online 28 November 2019Source: Genomics, Proteomics & BioinformaticsAuthor(s): Zhenyu Liu, Jiangang Liu, Huijuan Yuan, Taiyuan Liu, Xingwei Cui, Zhenchao Tang, Yang Du, Meiyun Wang, Yusong Lin, Jie TianAbstractMajority 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 ...
Source: Genomics, Proteomics and Bioinformatics - Category: Bioinformatics Source Type: research