Artificial intelligence for adaptive neuromodulation in drug-resistant epilepsy

Area of Science:

• Neurology
• Biomedical Engineering
• Artificial Intelligence

Background:

• Drug-resistant epilepsy (DRE) affects a significant portion of epilepsy patients, leading to severe cognitive, psychiatric, and mortality issues.
• Neuromodulation therapies offer an alternative for patients unsuitable for surgery, but clinical responses vary and require extensive optimization.
• Epilepsy is increasingly viewed as a network disorder, necessitating personalized approaches for effective neuromodulation.

Purpose of the Study:

• To reframe neuromodulation for DRE as a control problem, addressing key clinical questions: patient selection, stimulation targeting, timing, and parameter adaptation.
• To explore the potential of artificial intelligence (AI) as a unifying framework for personalized, data-driven neuromodulation in DRE.
• To outline a roadmap for AI-guided neuromodulation by synthesizing current advances.

Main Methods:

• Conceptual framework: Reframing neuromodulation as a control problem with four key questions.
• AI applications discussed: response prediction, network analysis for target identification, state estimation for seizure risk forecasting, and adaptive control for parameter optimization.
• Literature synthesis: Integrating biological, clinical, and computational findings.

Main Results:

• AI can guide patient selection through response prediction.
• AI can identify optimal stimulation targets based on network influence.
• AI can enable adaptive, state-dependent stimulation for improved efficacy and safety.

Conclusions:

• AI offers a promising framework for personalized and data-driven neuromodulation in DRE.
• AI-guided strategies can address critical aspects of the neuromodulation workflow, from patient selection to adaptive control.
• The proposed roadmap facilitates the development of safe, interpretable, and individualized AI-guided neuromodulation for DRE.