Neuroanatomical-based machine learning prediction of Alzheimer's Disease across sex and age.

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Abstract

Alzheimer's Disease (AD) is a progressive neurodegenerative disorder characterized by cognitive decline and memory loss. In 2024 it affected approximately 1 in 9 people aged 65 and older in the U.S., 6.9 million individuals. Early detection and accurate AD diagnosis are crucial for improving patient outcomes. Magnetic resonance imaging (MRI) has emerged as a valuable tool for examining brain structure and identifying potential AD biomarkers. This study performs predictive analyses by employing machine learning techniques to identify key brain regions associated with AD using numerical data derived from anatomical MRI scans, going beyond standard statistical methods. Using the Random Forest Algorithm, we achieved 92.87 % accuracy in detecting AD from Mild Cognitive Impairment and Cognitive Normals. Subgroup analyses across nine sex- and age-based cohorts (69-76 years, 77-84 years, and unified 69-84 years) revealed the hippocampus, amygdala, and entorhinal cortex as con- sistent top-rank predictors. These regions showed distinct volume reductions across age and sex groups, reflecting distinct age- and sex-related neuroanatomical patterns. Younger males and females (aged 69-76) exhibited volume decreases in the right hippocampus, suggesting its importance in the early stages of AD. Older males (77-84) showed substantial volume decreases in the left inferior temporal cortex. The left middle temporal cortex showed decreased volume in females, suggesting a potential female-specific influence, while the right entorhinal cortex may have a male-specific impact. These age-specific sex differences could inform clinical research and treatment strategies, aiding in identifying neuroanatomical markers and therapeutic targets for future clinical interventions.

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