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

Area of Science:

• Neuroimaging
• Medical Neuroscience
• Systems Neuroscience

Background:

• Type 2 diabetes mellitus (T2DM) is a known risk factor for cognitive decline.
• Disrupted brain network connectivity is a potential mechanism linking T2DM to cognitive impairment.
• Limited research exists on individual-based morphological brain networks in T2DM and their clinical correlations.

Purpose of the Study:

• To investigate topological properties of individual-based morphological brain networks (MBNs) in T2DM patients.
• To explore the association between MBN alterations, clinical characteristics (HbA1c), and cognitive performance in T2DM.
• To identify specific brain subnetworks affected in T2DM and their predictive value for cognitive function.

Main Methods:

• Utilized graph theory to analyze morphological brain networks (MBNs) in 123 T2DM patients and 91 healthy controls (HC).
• Constructed networks using symmetric Kullback-Leibler (KL) divergence-based similarity (KLS).
• Calculated global and nodal network metrics to assess topological properties and identify altered connectivity patterns.

Main Results:

• T2DM patients showed increased nodal degree in specific brain regions including the left para-hippocampus, left amygdala, left precuneus, bilateral putamen, and right inferior temporal gyrus.
• Glycosylated hemoglobin (HbA1c) concentrations positively correlated with the nodal degree of the left precuneus.
• Identified hypo-connected subnetworks (reward circuits) and hyper-connected subnetworks (attention network) in T2DM, which were linked to cognitive performance.

Conclusions:

• This study provides neuroimaging evidence of altered morphological brain network topology in T2DM.
• Specific brain regions and subnetworks are implicated in T2DM-related cognitive changes.
• Morphological brain network properties may serve as potential biomarkers for cognitive performance in T2DM.