Motivation: Grey matter changes are thought to be closely related to cognitive decline in mild cognitive impairment (MCI) patients but no consensus has yet emerged. Goal(s): We investigated alterations of cortical morphology, subcortical nuclei volume and morphology in MCI using multiple morphological analysis methods. Additionally, we explored the relationship between these imaging features and cognitive performances, along with their efficiency in classifying MCI using support vector machine (SVM). Approach: Voxel-based morphometry (VBM) analysis, vertex-based shape analysis and surface-based morphometry (SBM) were used to the analysis. Results: Integrating volumetric and morphological analysis methods outperformed single analysis method in identifying MCI. Impact: These findings highlight the significance of structural alterations in brain regions associated with memory impairment in individuals with MCI, and could be helpful for the early identification and clinical diagnosis of MCI.
Wang et al. (2025) studied this question.