Altered individual-based morphological brain network in type 2 diabetes mellitus

This is the authors' abstract. We don't add key points for this paper.

Type 2 diabetes mellitus (T2DM) is recognized as a risk factor for cognitive decline, potentially linked to disrupted network connectivity. However, few previous studies have examined individual-based morphological brain networks in T2DM and their association with clinical characteristics. In our study, we enrolled 123 patients with T2DM and 91 healthy controls (HC). We constructed the networks using symmetric Kullback–Leibler (KL) divergence-based similarity (KLS) and calculated various global and nodal metrics based on graph theory to describe the topological properties of the networks. Firstly, T2DM exhibited increased nodal degree in the left para-hippocampus, left amygdala, left precuneus, bilateral putamen, and right inferior temporal gyrus, and the concentrations of glycosylated hemoglobin (HbA1c) were positively correlated with the nodal degree of the left precuneus. Secondly, we identified hypo-connected and hyper-connected subnetworks, primarily involved with reward circuits and attention network, respectively. Lastly, altered morphological connectivity (MC) was linked to cognitive performance, and the aforementioned subnetworks may serve as predictors of cognitive performance. In conclusion, this study provided neuroimaging evidence for understanding cognitive changes by analyzing the properties and connections of individual-based morphological brain networks (MBNs) in T2DM patients. • Dementia was related to disrupted network connectivity. • Individual based morphological network was constructed. • Altered nodal degree was related to the levels of HbA1c. • Altered morphological connections was tied to cognitive performance. • Morphological connectivity patterns could serve as predictors of cognitive performance.