A Hybrid Digital-4E Strategy for comorbid migraine and depression: a medical hypothesis on an AI-driven, neuroadaptive, and exposome-aware approach.

OBJECTIVE

The co-occurrence of migraines and depression presents a critical clinical challenge, affecting up to 50% of individuals with either condition. This comorbidity leads to greater disability, higher healthcare costs, and poorer treatment outcomes than either disorder alone. Despite a bidirectional pathophysiological relationship, current models remain static and fragmented, treating each condition separately. This paper proposes a Hybrid Digital-4E Strategy, deployed on an AI-driven neuroadaptive digital health platform, integrating closed-loop therapy, digital phenotyping, and exposome tracking to enable real-time, personalized care.

METHODS

Grounded in the 4E cognition framework (Embodied, Embedded, Enactive, and Extended cognition), this strategy reconceptualizes migraine-depression as an interactive system rather than two separate conditions. The platform integrates real-time biomarker tracking, neuromorphic AI, and precision environmental analytics to dynamically optimize treatment. Adaptive chronotherapy, brain-computer interfaces (BCIs), and virtual reality (VR)-based neuroplasticity training further enhance intervention precision.

CONCLUSION

A closed-loop, AI-driven neuroadaptive system could improve outcomes by enabling early detection, real-time intervention, and precision care tailored to individual neurophysiological and environmental profiles. Addressing AI bias, data privacy, and clinical validation is crucial for implementation. If validated, this Hybrid Digital-4E Strategy could redefine migraine-depression management, paving the way for precision neuropsychiatry.