Identification of Cognitive Dysfunction in Patients with T2DM Using Whole Brain Functional Connectivity |
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| Institution: | 1. CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China;2. School of Computer and Information Technology, Beijing Jiaotong University, Beijing 100044, China;3. Department of Endocrinology and Metabolism, Henan Provincial People’s Hospital & the People’s Hospital of Zhengzhou University, Zhengzhou 450003, China;4. Department of Radiology, Henan Provincial People’s Hospital & the People’s Hospital of Zhengzhou University, Zhengzhou 450003, China;5. Cooperative Innovation Center for Internet Healthcare & School of Software, Zhengzhou University, Zhengzhou 450003, China;6. School of Mechanical, Electrical & Information Engineering, Shandong University (Weihai), Weihai 264209, China;7. University of Chinese Academy of Sciences, Beijing 100080, China;8. Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, School of Medicine, Beihang University, Beijing 100191, China;9. Engineering Research Center of Molecular and Neuro Imaging of Ministry of Education, School of Life Science and Technology, Xidian University, Xi’an 710126, China |
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| Abstract: | 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. |
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| Keywords: | Type 2 diabetes mellitus Resting state functional connectivity Elastic net Support vector machines MoCA |
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