Alzheimer's disease (AD) is a progressive neurodegenerative disorder characterized by cognitive decline, structural brain changes, and genetic predispositions. This study leverages machine-learning and statistical techniques to investigate the mechanistic relationships between cognitive function, genetic markers, and neuroimaging biomarkers in AD progression. Using data from the Alzheimer's Disease Neuroimaging Initiative (ADNI), we perform both low-dimensional and high-dimensional analyses to identify key predictors of disease states, including cognitively normal (CN), mild cognitive impairment (MCI), and AD. Our low-dimensional approach utilizes multiple linear and ordinal logistic regression to examine the influence of cognitive scores, cerebrospinal fluid (CSF) biomarkers, and demographic factors on disease classification. The results highlight significant associations between Mini-Mental State Examination (MMSE), Clinical Dementia Rating Sum of Boxes (CDRSB), and phosphorylated tau levels in predicting cognitive decline. The high-dimensional analysis employs Sure Independence Screening (SIS) and LASSO regression to reduce dimensionality and identify genetic markers correlated with cognitive impairment and white matter integrity. Genes such as CLIC1, NAB2, and TGFBR1 emerge as significant predictors across multiple analyses, linking genetic expression to neurodegeneration. Additionally, imaging genetic analysis reveals shared genetic influences across brain hemispheres and the corpus callosum, suggesting distinct genetic contributions to white matter degradation. These findings enhance our understanding of AD pathology by integrating cognitive, genetic, and imaging data. Future research should explore longitudinal analyses and potential gene-environment interactions to further elucidate the biological mechanisms underlying AD progression.

翻译：阿尔茨海默病（AD）是一种进行性神经退行性疾病，其特征包括认知功能下降、大脑结构改变和遗传易感性。本研究利用机器学习和统计技术，探究AD进展过程中认知功能、遗传标记与神经影像生物标志物之间的机制性关联。基于阿尔茨海默病神经影像倡议（ADNI）的数据，我们通过低维与高维分析识别疾病状态（包括认知正常（CN）、轻度认知障碍（MCI）和AD）的关键预测因子。低维分析方法采用多元线性回归和有序逻辑回归，检验认知评分、脑脊液（CSF）生物标志物及人口统计学因素对疾病分类的影响。结果凸显了简易精神状态检查（MMSE）、临床痴呆评定量表总分（CDRSB）与磷酸化tau蛋白水平在预测认知衰退中的显著关联。高维分析运用确定独立筛选（SIS）和LASSO回归进行降维，识别与认知障碍及白质完整性相关的遗传标记。CLIC1、NAB2和TGFBR1等基因在多项分析中均显现为显著预测因子，揭示了基因表达与神经退行过程的关联。此外，影像遗传学分析发现大脑半球及胼胝体存在共享的遗传影响，表明白质退化可能受特定遗传因素调控。这些发现通过整合认知、遗传与影像数据，深化了对AD病理机制的理解。未来研究应开展纵向分析并探索潜在的基因-环境交互作用，以进一步阐明AD进展的生物学机制。