Characteristics of Papez Loop Neural Network in T2DM （Type 2 Diabetes Mellitus）

This is a cross-sectional and longitudinal study to investigate the characteristic changes in Papez's circuit neural network activity and connectivity based on multimodal MRI, and through follow-up study of the interaction between the internal brain regions of Papez circuit and the function of the external neural network, a prediction model of the characteristic changes of Papez circuit neural network was constructed based on machine learning technology.

T2DM patients may have multidimensional cognitive impairment, which is related to the damage of key brain regions in Papez's circuit. The purpose of this study is to establish a prediction model for the occurrence, development, and severity of cognitive impairment by using machine learning of Papez circuit neural network in T2DM patients. This will allow for early intelligent assessment with high accuracy and efficiency, and assist in clinical personalized treatment and early intervention. The research center has 1 principal investigator, 4 sub-investigators, and 1 nurse. Participants will include 200 patients with type 2 diabetes recruited from outpatient and inpatient departments. Additionally, 200 healthy controls will be recruited from the community. Each subject will undergo clinical information collection, biochemical measurements including fasting blood glucose, C-peptide, HbA1c, blood lipid, postprandial blood glucose, and postprandial C-peptide, multimodal MRI scans, and cognitive assessments at baseline and each follow-up visit. The study duration is 6 years, with a follow-up every 36 months. At the end of the study, all assessments will be performed again for all recruited subjects.