An Integrated Multi-Scale Computational Framework for the Precise Localization of Infection-Induced Epileptic Foci

This paper presents a multi-scale computational framework for the precise localization of epileptic foci induced by peripheral infections. The framework integrates principles from neurobiology, biochemistry, clinical medicine, neuroengineering, and pharmacology into a unified diagnostic system. It combines data-driven artificial intelligence models, including hybrid CNN-LSTM for EEG and U-Net for MRI, with mechanistic models grounded in reaction-diffusion kinetics and wave propagation, supported by rigorous mathematical derivations. A Bayesian inference structure enables probabilistic estimation of focal parameters from multi-modal data, with explicit posterior and likelihood formulations. Designed for scientific rigor and reproducibility, the framework includes a pharmacological sub-model for therapeutic simulation, validated through an in silico proof-of-concept with verifiable Python code. Sensitivity analyses quantify parameter impacts, while preprocessing, robustness, and regulatory considerations ensure clinical translatability. The framework aims to enhance diagnostic precision and guide patient-specific interventions for infection-related epileptogenesis, advancing precision medicine.