Biagio Maria Sancetta

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.

Objective: This study was undertaken to assess the relationship between the severity of depression and anxiety symptoms and epilepsy‐related variables and cognitive burden in people with epilepsy (PwE), as assessed using EpiTrack. Methods: We prospectively enrolled a cohort of PwE who underwent EpiTrack and evaluation by Generalized Anxiety Disorder‐7 (GAD‐7) and Beck Depression Inventory‐II (BDI‐II) scales. We assessed the correlation strength between EpiTrack, GAD‐7, BDI‐II, and the other clinical variables. Analysis of variance and covariance assessed the existence of GAD‐7/BDI‐II differences between PwE with and without epilepsy‐related cognitive impairment. Hierarchical regression analysis (HRA) and logistic regression were performed to characterize the association between BDI‐II and GAD‐7 and the presence/severity of epilepsy‐related cognitive burden. Mediation analysis was performed to evaluate the relationship between EpiTrack and the other variables applied. Results: We enrolled 100 PwE (42 ± 18 years old). EpiTrack inversely correlated with BDI‐II (−.33, p = .003, not with GAD‐7), seizures (−.34, p < .001), epileptiform abnormalities (34, p < .001), and pharmacological burden (.21, p = .002). BDI‐II correlated with seizure frequency (.27, p = .02). PwE with cognitive impairment had significantly higher BDI‐II scores, independently of age, seizure frequency, epileptiform abnormalities, and pharmacological load ( p = .02–.04). BDI‐II effect on EpiTrack was independent from seizures. The addition of BDI‐II in HRA and logistic regression provided a significant increase of the R ² value ( p = .004) and of area under the curve ( p = .02). Significance: More severe depressive symptoms are strongly associated with worse cognitive performance in PwE, independently of the other epilepsy‐related variables. Depressive symptoms could either forecast the occurrence of epilepsy‐related cognitive impairment or arise as a consequence of cognitive dysfunction in PwE. We confirmed the association between epilepsy severity and epilepsy‐related cognitive impairment.

Background: EEG alpha rhythm is well‐proven to correlate with cognitive performance in healthy subjects. We aimed to evaluate the potential of EEG background activity, quantified as individual alpha frequency (IAF), as a biomarker of cognitive impairment induced by antiseizure medications (ASMs) in people with epilepsy (PwE). Methods: We conducted a cross‐sectional study (S1) and a prospective longitudinal study (S2). In S1, PwE underwent EEG recording and concurrent EpiTrack assessment. In S2, EEG and EpiTrack were performed before and 6 months after initiating cenobamate. Correlation analyses assessed the association between EpiTrack scores and IAF (S1) and their changes post‐cenobamate (S2). ANOVA and ANCOVA were used to compare IAF between cognitively impaired and unimpaired PwE. Logistic regression evaluated the incremental value of IAF combined with clinical variables to identify cognitive improvement following ASM modification. Results: We enrolled 100 PwE (42 ± 18 years) in S1 and 33 PwE (45 ± 15 years) in S2. IAF correlated significantly with EpiTrack scores in S1 ( R = 0.21, p = 0.03), and IAF changes mirrored EpiTrack improvements post‐cenobamate in S2 ( R = 0.34, p = 0.04). Cognitively impaired PwE displayed significantly lower IAF independent of age, seizure frequency, epileptiform activity, and pharmacological load ( p = 0.01–0.005). Adding IAF to clinical variables significantly improved the logistic model's accuracy in predicting cognitive improvement post‐treatment (AUC: 0.83 ± 0.04 vs. 0.71 ± 0.04, p = 0.005). Conclusions: EEG background activity, assessed via IAF, is sensitive to cognitive impairment related to ASMs in PwE, demonstrated in both cross‐sectional and longitudinal contexts. IAF is thus a promising, simple, cost‐effective biomarker to monitor cognitive burden in clinical epilepsy management.

Background Carbamazepine (CBZ) is a first-choice anti-seizure medication (ASM) whose efficacy is often invalidated by adverse effects (AEs). Eslicarbazepine (ESL) is a structural derivative of CBZ with better pharmacokinetic/tolerability profiles. We describe our experience of the overnight CBZ to ESL switch in people with epilepsy (PwE) to improve seizure control, AEs, and ASMs adherence. Methods We retrospectively included 19 PwE (12 females, 53 ± 21 years old) who underwent CBZ to ESL overnight switch due to single/multiple issues: poor efficacy (pEff, N = 8, 42%), tolerability (pToll, N = 11, 58%), adherence (pAdh, N = 2, 10%). 9/19 (47%) had psychiatric comorbidities. Clinical variables, seizure frequency, and AEs were recorded at switch time (T0) after 3.5 ± 3 (T1) and 6.5 ± 1.5 months (T2). Results At T1, in pEff group, 1/8 (13%) was seizure free, 2/8 (25%) were responders (> 50% seizure reduction), 2/8 (25%) had no seizure changes, 3/8 (37%) had seizure worsening; the latter were those with the most severe epilepsy and encephalopathy. In pToll group, all PwE experienced AEs disappearance/amelioration. In pAdh group, all PwE reported adherence amelioration. Four dropouts. At T2, no changes were recorded within groups, while in the whole sample, 6/15 (40%) were responders, and 4/15 (27%) were seizure-free. No one complained of Powered by Editorial Manager® and ProduXion Manager® from Aries Systems Corporation psychiatric worsening, while 6/19 (32%) experienced mood/behavior benefits. Conclusions CBZ to ESL overnight switch offers an opportunity to improve efficacy, tolerability, adherence, and psychiatric symptoms.

Transient epileptic amnesia (TEA) is a rare cause of acute amnestic syndromes (AAS), often misdiagnosed as transient global amnesia (TGA). We proposed a scoring system—the EPIlepsy AMNEsia (EPIAMNE) score—using quantitative EEG (qEEG) analysis to obtain a tool for differentiating TEA from TGA. We retrospectively reviewed clinical information and standard EEGs (stEEG) of 19 patients with TEA and 21 with TGA. We computed and compared Power Spectral Density, demonstrating an increased relative theta power in TGA. We subsequently incorporated qEEG features in EPIAMNE score, together with clinical and stEEG features. ROC curve models and pairwise ROC curve comparison were used to evaluate and compare the diagnostic accuracy for TEA detection of EPIAMNE score, presence of symptoms atypical for TGA (pSymAT) and identification of anomalies (interictal epileptiform or temporal focal spiky transients) at stEEG (PosEEG). Area Under the Curve (AUC) of EPIAMNE score revealed to be higher than PosEEG and pSymAT (AUC_EPIAMNE = 0.95, AUC_pSymAT = 0.85, AUC_PosEEG = 0.67) and this superiority proved to be statistically significant (p-value_{EPIAMNE-PosEEG} and p-value_{EPIAMNE-pSymAT} < 0.05). In conclusion, EPIAMNE score classified TEA with higher accuracy than PosEEG and pSymAT. This approach could become a promising tool for the differential diagnosis of AAS, especially for early TEA detection.