Predicting levetiracetam neuropsychiatric adverse effects using graph‐theoretical electroencephalographic network metrics in drug‐naive focal epilepsy: A multicenter longitudinal study

Abstract Objective Levetiracetam (LEV) is an antiseizure medication approved for focal and generalized epilepsy. Despite its efficacy, neuropsychiatric adverse effects (NP‐AEs) may lead to treatment discontinuation. Quantitative electroencephalographic (qEEG) graph‐theoretical analysis has emerged as a valuable tool for identifying neurophysiological markers of treatment response. We investigated whether baseline network metrics could identify drug‐naive people with focal epilepsy (PwE) at risk of developing clinically significant LEV‐related NP‐AEs. Methods We conducted a multicenter retrospective longitudinal study across three Italian centers. From an original LEV monotherapy cohort of 134 PwE, we identified all 31 eligible patients who developed clinically significant LEV‐related NP‐AEs within the first month after LEV initiation (AE+) and matched them by age and sex with 31 patients without NP‐AEs (AE−). All participants underwent resting‐state EEG within 30 days before LEV. We compared relative power spectral density (PSD) and weighted phase lag index (wPLI) connectivity‐derived graph‐theoretical metrics (clustering coefficient, global efficiency, path length, modularity, node strength) using linear mixed‐effects models. Baseline variables showing significant between‐group differences were included in an elastic‐net penalized logistic regression model. Model performance was evaluated using nested cross‐validation and permutation testing (1000 permutations). Results Relative PSD showed no differences between groups. The AE+ group showed higher baseline theta‐band wPLI‐derived clustering coefficient ( p = .032), characteristic path length ( p = .033), and node strength ( p = .03) than AE−. In the elastic net model, theta‐band graph‐theoretical metrics predicted NP‐AE occurrence with an accuracy of .71 (95% confidence interval [CI] = .60–.82) and an area under the curve of .71 (95% CI = .57–.83). Permutation testing confirmed that model discrimination exceeded chance level ( p = .003). Significance Baseline theta‐band qEEG functional network organization emerged as a significant neurophysiological predictor of LEV‐related NP‐AEs. These findings suggest that pretreatment EEG may capture a neurophysiological vulnerability phenotype associated with LEV tolerability, supporting the potential role of network‐level biomarkers in guiding individualized treatment decisions.