Templated from COGINTEL: A CLOSED-LOOP MECHANISM FOR ENHANCING NEUROPLASTICITY IN EARLY STAGE ALZHEIMER'S DISEASES

CogIntel is a closed-loop cognitive mechanism designed to enhance neuroplasticity and potentially mitigate cognitive decline in early-stage Alzheimer’s disease. Integrating computational neuroscience, cognitive psychology, artificial intelligence, and social-behavioral frameworks, the system provides a multifaceted, adaptive intervention platform. Grounded in the Free Energy Principle and Active Inference, Adaptive Predictive Theory, and neural network models, CogIntel employs non-invasive brain-computer interfaces, decision-acuity metrics, and socially-informed interventions to personalize therapeutic strategies. The framework leverages AI to decode multimodal data including neuroimaging, genetic, immunological, and social metrics enabling predictive modeling of cognitive trajectories. This paper presents the theoretical foundation, computational architecture, social-behavioral integration, first-phase empirical testing design, and ethical considerations for CogIntel. By uniting neuroplastic mechanisms, decision-making processes, and social-contextual dynamics, CogIntel offers a novel, integrative approach for personalized interventions in cognitive neuroscience, with potential applications in other neurodegenerative disorders