A Multi-Agent AI Framework for Predicting Alzheimer’s Disease Trajectory Through Sleep-Dependent Memory Consolidation Biomarkers: A Conceptual Architecture

Background : Alzheimer’s disease (AD) affects over 55 million people worldwide and is projected to nearly triple by 2050, yet clinical diagnosis typically occurs after substantial irreversible neuronal damage. Emerging evidence demonstrates that disruptions in sleep-dependent memory consolidation—including alterations in sleep spindle–slow oscillation coupling, rapid eye movement (REM) architecture, and circadian integrity— serve as early biomarkers detectable years before clinical onset. Current AI approaches predominantly rely on single-modality analyses and binary classification schemes, failing to capture the temporal dynamics of cognitive decline. Methods : Following design science research methodology, we developed a multiagent AI framework integrating four data modalities through specialized agents: a Sleep Electrophysiology Agent (EEG/polysomnography), aWearable Data Agent (actigraphy, heart rate variability), a Cognitive Assessment Agent (neuropsychological and digital biomarkers), and a Neuroimaging Agent (structural MRI volumetrics). A Central Orchestrator Agent performs attention-based multimodal fusion and generates individualized decline trajectory predictions using neural ordinary differential equations (Neural ODEs). A proof-of-concept (POC) implementation was evaluated on synthetic data (N = 500 subjects, 10 timepoints over 5 years) calibrated to published biomarker effect sizes. Results : Comparative analysis against existing approaches demonstrates that no published framework combines all four modalities with sleep biomarkers as the central feature set within a trajectory prediction paradigm. The POC achieved 89.3% risk staging accuracy across four decline categories, with RMSE of 0.037 and trajectorylevel Pearson correlation of 0.56. Misclassifications were predominantly adjacent-tier. Cross-modal attention analysis revealed clinically interpretable modality weighting: the Cognitive Assessment Agent received highest attention (0.46–0.72), while the Neuroimaging Agent contribution increased progressively from cognitively normal to AD groups, consistent with advancing structural neurodegeneration. Conclusions : The proposed framework establishes an architecturally validated foundation for sleep-based AD trajectory prediction. The POC demonstrates technical feasibility, clinically meaningful modality prioritization, and actionable risk staging. Validation on real clinical datasets following the proposed phased roadmap is essential to establish clinical utility.