Topological Alterations of the Brain Functional Network in Type 2 Diabetes Mellitus Patients With and Without Mild Cognitive Impairment

Abstract

The aim of this study was to explore the topological alterations of the brain functional network in type 2 diabetes mellitus (T2DM) patients with and without mild cognitive impairment (MCI) using resting-state functional magnetic resonance imaging (rs-fMRI) and graph theory approaches. In total, 27 T2DM patients with MCI, 27 T2DM patients without MCI, and 27 healthy controls (HCs) underwent rs-fMRI scanning. The whole-brain functional network was constructed by thresholding the Pearson’s correlation matrices of 90 brain regions. The topological organization of the constructed networks was analyzed by using graph theory approaches. The global and nodal properties of the participants in the three groups were compared by using one-way ANOVA as well as post hoc Tukey’s t-tests. The relationships between the altered topological properties and clinical features or scores of neuropsychological tests were analyzed in T2DM patients with MCI. At the global level, the global and local efficiency of the patients in the T2DM with MCI group were significantly higher than that of participants in the HCs group, and the length of the characteristic path was significantly lower than that of the participants in the HCs group (p < 0.05). No significant difference was found among the other groups. At the nodal level, when compared with T2DM patients without MCI, T2DM patients with MCI showed significantly increased nodal centrality in four brain regions, which were mainly located in the orbitofrontal lobe and anterior cingulate gyrus (ACG) (p < 0.05). No significant difference was found between the T2DM patients without MCI and HCs. Moreover, nodal degree related coefficient (r = −0381, p = 0.050) and nodal efficiency (r = −0.405, P = 0.036) of the ACG showed a significant closed correlation with the scores of the digit span backward test in the T2DM patients with MCI. Our results suggested that the increased nodal properties in brain regions of the orbitofrontal lobe and ACG were biomarkers of cognitive impairment in T2DM patients and could be used for its early diagnosis. The global topological alterations may be related to the combination of MCI and T2DM, rather than any of them.

Functional connectivity density aberrance in type 2 diabetes mellitus with and without mild cognitive impairment

“…Previous studies on topological alterations of the brain functional network showed that DMCI patients had considerably higher global and local efficiency, as well as multiple nodal centralities, compared to HCs. However, the topological properties of the entire brain network did not display any significant abnormalities among DMCN patients ( 34 ). The difference may be attributed to methodological and/or clinical factors.…”

Aberrant brain functional networks in type 2 diabetes mellitus: A graph theoretical and support-vector machine approach

“… Xiong et al (2020) showed that compared to T2DM with normal cognition function, T2DM with cognitive impairment group had a higher clustering coefficient and local efficiency, as well as increased nodal properties (efficiency, degree, and betweenness) in the parietal, temporal, and occipital lobes, but decreased in the right inferior temporal gyrus. On the other hands, according to Zhou et al (2022) , there were no significant differences in the global and nodal properties between the T2DM patients with and without cognitive impairment. Briefly, the complex alterations in the topological properties of functional brain networks in patients with T2DM are uncertain, and their correlation to cognitive function is unclear.…”

Effects of transcutaneous auricular vagus nerve stimulation and exploration of brain network mechanisms in children with high−functioning autism spectrum disorder: study protocol for a randomized controlled trial

“…The small-world properties, global properties, and local properties of brain networks will be analyzed using graph theory. There are five small-world properties: clustering coefficient (Cp), characteristic path length (Lp), normalized clustering coefficient (Gamma, γ), normalized characteristic path length (Lambda, λ), and small-worldness (Sigma, σ) ( 50 , 51 ). A network is regarded to have small-world features when γ >> 1, λ ≈1, and σ > 1 ( 48 , 52 ).…”