Zarei Soheil, Shalbaf Reza, Shalbaf Ahmad
Institute for Cognitive Science Studies, Tehran, Iran.
Department of Biomedical Engineering and Medical Physics, School of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
Basic Clin Neurosci. 2025;16(Spec Issue):233-250. doi: 10.32598/bcn.2024.2034.7. Epub 2025 Mar 18.
Predicting the progression from mild cognitive impairment (MCI) to Alzheimer's disease (AD) is crucial for early intervention. Identifying reliable predictive markers can enhance diagnostic accuracy and improve clinical decision-making. This study aimed to explore multimodal predictive markers to distinguish stable MCI (sMCI) from progressive MCI (pMCI) to AD using statistical analysis.
We analyzed data from the Alzheimer's disease neuroimaging initiative (ADNI), categorizing 487 individuals as sMCI and 348 as pMCI. The study incorporated multiple assessment modalities, including demographics, positron emission tomography (PET), genotyping, magnetic resonance imaging (MRI), and neurocognitive tests. A rigorous data preprocessing approach was applied, including cleaning and feature selection. The area under the curve (AUC) and the Wilcoxon test were used to evaluate the discriminative power of predictive markers.
Our findings showed the strong predictive potential of PET, particularly florbetaben (FBB), which achieved an AUC of 0.84. Neurocognitive tests, including the Alzheimer's disease assessment scale (ADAS13), ADNI-modified preclinical Alzheimer cognitive composite (mPACCtrailsB and mPACCdigit), logical memory delayed recall total (LDELTOTAL), and ADAS cognitive subscale question 4 (ADASQ4), also demonstrated high discriminatory power with AUC values ranging from 0.82 to 0.83. These results indicated that a combination of neuroimaging and cognitive assessments can significantly differentiate between sMCI and pMCI.
The results emphasize the importance of multimodal assessments, particularly PET imaging and neurocognitive tests, in distinguishing sMCI from pMCI. These findings contribute to early AD diagnosis strategies and personalized intervention planning..
预测从轻度认知障碍（MCI）进展为阿尔茨海默病（AD）对于早期干预至关重要。识别可靠的预测标志物可提高诊断准确性并改善临床决策。本研究旨在使用统计分析探索多模态预测标志物，以区分稳定型MCI（sMCI）和进展型MCI（pMCI）至AD。
我们分析了来自阿尔茨海默病神经影像倡议（ADNI）的数据，将487人分类为sMCI，348人分类为pMCI。该研究纳入了多种评估方式，包括人口统计学、正电子发射断层扫描（PET）、基因分型、磁共振成像（MRI）和神经认知测试。应用了严格的数据预处理方法，包括清理和特征选择。曲线下面积（AUC）和威尔科克森检验用于评估预测标志物的判别力。
我们的研究结果显示PET具有很强的预测潜力，尤其是氟代贝他班（FBB），其AUC为0.84。神经认知测试，包括阿尔茨海默病评估量表（ADAS13）、ADNI修改的临床前阿尔茨海默病认知综合量表（mPACCtrailsB和mPACCdigit）、逻辑记忆延迟回忆总分（LDELTOTAL）和ADAS认知子量表问题4（ADASQ4），也显示出高判别力，AUC值范围为0.82至0.83。这些结果表明，神经影像学和认知评估相结合可以显著区分sMCI和pMCI。
结果强调了多模态评估，特别是PET成像和神经认知测试，在区分sMCI和pMCI中的重要性。这些发现有助于早期AD诊断策略和个性化干预计划。
