Investigation of the topological properties of brain structural and functional networks in patients with mild cognitive impairment.

Background: Mild cognitive impairment (MCI) is a transitional stage between subjective cognitive decline and Alzheimer's dementia, representing a critical window for intervention. We characterize the small-world properties of brain networks in MCI to identify sensitive biomarkers for early detection and assessment. Methods: Thirty-one patients diagnosed with MCI were recruited as the experimental group, while 30 healthy elderly individuals served as the normal control (NC) group. Based on diffusion tensor imaging (DTI) and resting-state functional magnetic resonance imaging (rs-fMRI), small-world properties of the brain networks were observed using graph theory analysis. Global and nodal properties were computed to assess differences in brain network topology. Results: Both structural and functional brain networks in the MCI and NC groups exhibited small-world properties (σ>1), and significant differences were noted in nodal properties such as nodal efficiency, nodal degree centrality, and nodal shortest path length (P<0.05). Importantly, these nodal properties in brain regions were significantly correlated with Montreal Cognitive Assessment (MoCA) scores in patients with MCI (P<0.05). Conclusions: Patients with MCI exhibit small-world properties in their brain networks, suggesting preserved efficiency of information transfer. Node property metrics in regions such as the posterior cingulate cortex, prefrontal cortex, and occipital lobe are promising biomarkers for early detection of MCI.