Abstract / Summary
Post-traumatic epilepsy (PTE) affects up to 50% of severe traumatic brain injury survivors, yet antiseizure medication selection remains empirical- chosen by seizure type and EEG morphology, with no integration of the neurochemical mechanisms driving seizure activity at the lesion site. This paper proposes NeuroMap Precision, a conceptual AI-powered framework that integrates three data layers - structural neuroimaging (MRI/CT with lesion network mapping), functional neurophysiology (EEG, MR spectroscopy-derived Glu/GABA ratios), and continuous patient-reported outcomes (seizure diary, sleep, triggers) - to classify individual neurochemical phenotypes and generate mechanism-matched antiseizure medication recommendations in PTE. The framework identifies four neurochemical phenotype classes (glutamatergic excess, GABAergic deficit, channelopathy-dominant, mixed) and maps each to a specific drug mechanism class. It extends the SeLECT post-stroke epilepsy risk stratification framework (Galovic et al., Lancet Neurology 2018) to post-traumatic aetiology, and operationalises the lesion network mapping paradigm (Fox et al., PNAS 2014) into a clinical decision-support layer. This is a framework paper. No datasets were generated or analysed. Clinical and research collaborators with access to post-TBI populations and multimodal neuroimaging infrastructure are invited to engage.