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

Mengling Tao, Zhongfeng Xie, Peiying Chen, Xiaowen Xu, Peijun Wang
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Abstract

Background: 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.

Objectives: To investigate the altered patterns of cortical features in aMCI patients and their correlation with memory function for early identification.

Methods: 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.

Results: 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.

Conclusion: Cortical morphological indicators provide important neuroimaging evidence for the early diagnosis of aMCI. Integrating multiple structural indicators significantly improves diagnostic accuracy.

aMCI 患者的皮质厚度和复杂性:改变的模式分析与早期诊断
背景:失忆性轻度认知功能障碍(aMCI)是阿尔茨海默病的前驱期。尽管最近的研究将皮质厚度作为一个关键指标,但皮质复杂性尚未得到详尽研究:方法:25 名阿尔茨海默病患者和 54 名正常对照者接受了神经心理学评估和 3D-T1 MRI 扫描。使用 CAT12 软件计算皮质厚度和复杂度。使用双样本 t 检验分析组间差异,并采用多元线性回归确定与记忆功能相关的特征。使用多维结构指标构建了一个支持向量机(SVM)模型,以评估诊断性能:皮质形态学指标为早期诊断 aMCI 提供了重要的神经影像学证据。整合多种结构指标可显著提高诊断准确性。
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