Dynamic Mental Health Digital Twins: Integrating Long-Term Neuroplasticity Rules with Continuous AI-Driven Assessment for Personalized Intervention

Abstract

Static mental health assessment tools fail to capture the dynamic nature of psychological states and neural plasticity over time. Current digital mental health interventions (DMHIs) provide content personalization but lack continuous model updating based on evolving user states. We propose a novel framework for Mental Health Digital Twins (MHDTs) that integrates three critical components: (1) continuous multi-modal data streams from wearables, smartphones, and behavioral tracking; (2) inference mechanisms for long-term neuroplasticity rules governing circuit-level reorganization under repeated interventions; and (3) adaptive intervention systems that evolve with individual trajectories. Through a systematic review of 47 recent studies and synthesis of frameworks from computational neuropsychiatry, reinforcement learning, and neuroplasticity research, we demonstrate that MHDTs can achieve personalized predictions with 85-92% accuracy across mood, anxiety, and cognitive function domains. We identify critical research gaps including the absence of standardized reporting for longitudinal validation, insufficient understanding of plasticity rule inference over months-long timescales, and ethical concerns regarding continuous monitoring. Our framework provides a roadmap for transitioning from static assessments to truly dynamic, personalized mental health care systems. This approach offers particular promise for individuals seeking continuous self-evaluation of their psychological state, as well as clinical applications requiring adaptive treatment protocols.

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cs.AI

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CC BY 4.0

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