Use of magnetic resonance structural imaging to identify disease progression in patients with mild cognitive...

Area of Science:

• Neuroimaging
• Neurology
• Biomarkers

Background:

• Mild cognitive impairment (MCI) is a transitional stage to Alzheimer's disease.
• Accurate identification of MCI progression is crucial for timely intervention.
• Current diagnostic tools may not fully capture the structural changes associated with MCI progression.

Purpose of the Study:

• To investigate the utility of Voxel-based morphometry (VBM) and Surface-based morphometry (SBM) for detecting structural differences in MCI patients.
• To develop and validate a diagnostic model for predicting MCI progression using neuroimaging data.
• To compare the diagnostic performance of structural imaging models with cognitive assessment-based models.

Main Methods:

• Retrospective analysis of 154 MCI patients from the ADNI database (62 progressive MCI, 92 stable MCI).
• Application of VBM and SBM to identify structural differences between progressive and stable MCI groups.
• Development of logistic regression models using structural indices and cognitive scores (MMSE, MOCA), with external validation using NACC data.

Main Results:

• Significant structural differences were found between progressive and stable MCI patients.
• Volume reductions in frontal and temporal lobes, cortical thinning in parietal regions, and reduced gyrification in the insular gyrus were observed in progressive MCI.
• A structural joint model combining VBM and SBM indices showed superior diagnostic accuracy compared to a model based on MMSE and MOCA scores.

Conclusions:

• Combined VBM and SBM analysis provides sensitive and noninvasive detection of structural biomarkers for MCI progression.
• The developed structural joint model demonstrates high diagnostic performance for identifying progressive MCI.
• These findings support the use of integrated neuroimaging approaches for early risk stratification and personalized management of MCI.