Imaging Genetics Analysis of Alzheimer's Disease

2025-10-26

AI Summary

This study investigates the causal mechanisms linking cognitive decline, genetic variation, and white matter (WM) integrity in Alzheimer’s disease (AD). Leveraging multimodal data from the ADNI cohort—including cognitive assessments (MMSE, CDRSB), genome-wide SNPs, diffusion tensor imaging (DTI), and cerebrospinal fluid biomarkers (e.g., p-tau181)—we employed multivariate/ordinal logistic regression, Sure Independence Screening (SIS), and LASSO for integrated low- and high-dimensional modeling. We identified CLIC1, NAB2, and TGFBR1 as novel genetic regulators of WM integrity in bilateral brain regions and the corpus callosum, mediating cognitive deterioration. MMSE, CDRSB, and p-tau181 emerged as robust predictors of cognitive decline. Crucially, the study disentangles the specific contribution of genetic factors to WM degeneration, establishing an interpretable, multiscale biomarker model that bridges genomics, neuroimaging, and clinical phenotypes—thereby enabling improved early risk stratification and informing genetically informed, targeted interventions in AD.

Technology Category

• Cognitive Modeling & Cognitive Systems: Other Foundations of Cognitive Modeling & Systems
• Machine Learning: Hardware-aware ML
• Humans and AI: Brain-Sensing and Analysis

Application Category

• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Web Mining and Content Analysis: Web data integration and cleaning
• Economics, Online Markets and Human Computation: Economic ramifications for generative AI infrastructure and applications

Abstract

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.

Problem

• Investigating genetic and neuroimaging biomarkers for Alzheimer's disease progression
• Identifying key predictors linking cognitive decline to genetic markers
• Analyzing shared genetic influences on white matter degradation in AD

Innovation

• Machine learning analyzes cognitive-genetic-imaging relationships
• LASSO regression identifies key genetic markers for impairment
• Imaging genetics reveals shared genetic influences across hemispheres