Cortical Thickness and Complexity in aMCI Patients: Altered Pattern Analysis and Early Diagnosis.

Abstract

Amnestic Mild Cognitive Impairment (aMCI) is a prodromal phase of Alzheimer's disease. Although recent studies have focused on cortical thickness as a key indicator, cortical complexity has not been exhaustively investigated. To investigate the altered patterns of cortical features in aMCI patients and their correlation with memory function for early identification. 25 aMCI patients and 54 normal controls underwent neuropsychological assessments and 3D-T1 MRI scans. Cortical thickness and complexity measures were calculated using CAT12 software. Differences between groups were analyzed using two-sample t-tests, and multiple linear regression was employed to identify features associated with memory function. A support vector machine (SVM) model was constructed using multidimensional structural indicators to evaluate diagnostic performance. aMCI patients exhibited extensive reductions in cortical thickness (pFDR-corrected <0.05), with complexity reduction predominantly in the left parahippocampal, entorhinal, rostral anterior cingulate, fusiform, and orbitofrontal (pFWE-corrected<0.05). Cortical indicators exhibited robust correlations with auditory verbal learning test (AVLT) scores. Specifically, the fractal dimension of the left medial orbitofrontal region was independently and positively associated with AVLT-short delayed score (r=0.348, p=0.002), while the gyrification index of the left rostral anterior cingulate region showed independent positive correlations with AVLT-long delayed and recognition scores (r=0.408, p=0.000; r=0.332, p=0.003). Finally, the SVM model integrating these cortical features achieved an AUC of 0.91, with 82.28% accuracy, 76% sensitivity, and 85.19% specificity. Cortical morphological indicators provide important neuroimaging evidence for the early diagnosis of aMCI. Integrating multiple structural indicators significantly improves diagnostic accuracy.