The most critical burden for People with Epilepsy (PwE) is represented by seizures, the unpredictability of which severely impacts quality of life. The design of real-time warning systems that can detect or even predict ictal events would enhance seizure management, leading to high benefits for PwE and their caregivers. In the past, various research works highlighted that seizure onset is anticipated by significant changes in autonomic cardiac control, which can be assessed through heart rate variability (HRV). This manuscript conducted a scoping review of the literature analyzing HRV-based methods for detecting or predicting ictal events. An initial search on the PubMed database returned 402 papers, 72 of which met the inclusion criteria and were included in the review. These results suggest that seizure detection is more accurate in neonatal and pediatric patients due to more significant autonomic modifications during the ictal transitions. In addition, conventional metrics are often incapable of capturing cardiac autonomic variations and should be replaced with more advanced methodologies, considering non-linear HRV features and machine learning tools for processing them. Finally, studies investigating wearable systems for heart monitoring denoted how HRV constitutes an efficient biomarker for seizure detection in patients presenting significant alterations in autonomic cardiac control during ictal events.

The m.3243A > G/MT-TL1 mutation underlies a broad spectrum of clinical manifestations, varying from the mitochondrial encephalopathy, lactic acidosis, and stroke-like episodes (MELAS) syndrome to incomplete phenotypes without stroke-like episodes (SLE), termed MELAS-Spectrum Syndrome (MSS). We systematically analyzed the longitudinal EEG changes in individuals carrying the m.3243A > G/MT-TL1 mutation through visual inspection of paroxysmal abnormalities and quantitative analysis of background activity. Adults with the m.3243A > G/MT-TL1 mutation and at least two EEG recordings during their follow-up were included. For each EEG, we obtained: 1) delta-theta/alpha energy ratio (ER); 2) Higuchi fractal dimension (HD); 3) paroxysmal activities (AR), quantified by two experienced clinicians. A linear mixed model assessed the effect of early SLE phases as a time-dependent covariate. Sixteen patients (9 females) were recruited. ER significantly increased over time in temporal-parieto-occipital regions. EEG traces recorded shortly after SLEs showed significantly higher ER, lower HD, and an increasing AR trend. Quantitative EEG metrics revealed a gradual disruption of posterior background rhythms in both MSS and MELAS. Early consequences of SLEs include background activity slowing, reduction of brain states complexity, and recrudescence of paroxysmal abnormalities. Neurophysiological signs of progressive brain dysfunction emerged in all individuals with m.3243A > G/MT-TL1 mutation, independently of SLEs.

Purpose: To evaluate the long-term efficacy of vagus nerve stimulation (VNS) in reducing tonic-clonic seizures (TCS), drop attacks, and seizure clusters in adults with drug-resistant epilepsy (DRE). Methods: This retrospective, single-center study included adults with DRE who received VNS and had ≥ 12 months of follow-up. Data were collected pre-implantation (T0), at 12 months (T1), and last follow-up (T2). Outcomes included reduction in total seizure frequency and severity, frequency of TCS and drop attacks, and frequency/duration of seizure clusters. Battery replacement and tolerability were also assessed. Results: Eighty-seven subjects (51 males, median age 33 at T0) with a mean follow-up of 8 years were analyzed. At T2, 54% showed reduced total seizure frequency and 71% reported decreased severity/duration. Among those with baseline TCS, 75% (T1)–80.6% (T2) had frequency reduction, with 20.6% seizure-free at T2. Drop attacks improved in > 70% of cases, with resolution in 21.8% at T2. Over 80% with seizure clusters reported decreased frequency/duration at both T1 and T2. Response rates for specific seizure types significantly exceeded non-response ( p < 0.001). Battery replacement was required in 85.7% near depletion. Side effects occurred in 49.4%, mostly mild/transient; major complications were rare. Conclusion: VNS exerts a robust effect on the most disabling seizures—TCS, drop attacks, and seizure clusters—surpassing its impact on overall seizure burden. Benefits appear within the first year and persist long term. These findings support a more tailored approach to VNS candidate selection, advocating earlier use in patients with refractory, harmful seizures not eligible for curative surgery.

Background: Complete resection of the epileptogenic zone (EZ) is the strongest predictor of seizure freedom in drug-resistant epilepsy (DRE). However, even in MRI-positive cases with anatomo-electro-clinical concordance, the EZ may not be clearly delineated, complicating intraoperative decision-making. Intraoperative ultrasound (ioUS) provides real-time anatomical feedback, while depth-electrode intraoperative electrocorticography (iECoG) enables electrophysiological delineation of epileptogenic tissue beyond the cortical surface, sampling deep regions not accessible to subdural electrodes. Their integration may improve intraoperative precision in defining resection limits and optimizing resective surgery. Methods: This study describes the workflow and feasibility of combining ioUS and depth-electrode iECoG for intraoperative guidance in MRI-positive focal DRE with an ill-defined EZ. In all cases, concordant anatomo-electro-clinical data identified a single EZ for which SEEG was not required, yet the suspected EZ remained poorly delineated. ioUS was used for real-time lesion visualization, verification of electrode trajectories, and guidance of resection depth and extent. Pre- and post-resective depth-electrode iECoG and ioUS were used in combination to delineate the resection margins, by identifying interictal epileptiform discharges (IEDs) and confirming lesion boundaries and resection completeness. Results: Six patients underwent resective surgery using the combined ioUS–iECoG workflow. The technique was feasible and safe in all cases, with no intraoperative or postoperative complications (37 depth-electrode insertions). iECoG revealed IEDs in four patients (66%), prompting resection extension in two. MRI confirmed complete resection in all cases. At last follow-up (6–40 months), 5/6 patients were seizure-free (Engel I). Histopathology revealed FCD IIb in three cases, a gliotic lesion related to an encephalocele in one, a gliotic scar post–arachnoid cyst marsupialization in another, and a tuberous sclerosis–related lesion in a case of tuberous-sclerosis-complex. Conclusion: The integration of ioUS and depth-electrode iECoG offers real-time anatomical and electrophysiological data, refining EZ delineation and resection assessment in complex MRI-positive epilepsy cases where SEEG is not clinically indicated.