The value of linear and non-linear quantitative EEG analysis in paediatric epilepsy surgery: a machine learning approach.

Epilepsy surgery is effective for patients with medication-resistant seizures, however 20-40% of them are not seizure free after surgery. Aim of this study is to evaluate the role of linear and non-linear EEG features to predict post-surgical outcome. We included 123 paediatric patients who underwent epilepsy surgery at Bambino Gesù Children Hospital (January 2009-April 2020). All patients had long term video-EEG monitoring. We analysed 1-min scalp interictal EEG (wakefulness and sleep) and extracted 13 linear and non-linear EEG features (power spectral density (PSD), Hjorth, approximate entropy, permutation entropy, Lyapunov and Hurst value). We used a logistic regression (LR) as feature selection process. To quantify the correlation between EEG features and surgical outcome we used an artificial neural network (ANN) model with 18 architectures. LR revealed a significant correlation between PSD of alpha band (sleep), Mobility index (sleep) and the Hurst value (sleep and awake) with outcome. The fifty-four ANN models gave a range of accuracy (46-65%) in predicting outcome. Within the fifty-four ANN models, we found a higher accuracy (64.8% ± 7.6%) in seizure outcome prediction, using features selected by LR. The combination of PSD of alpha band, mobility and the Hurst value positively correlate with good surgical outcome.

Post-Traumatic Stress-Disorder in Epilepsy: Meta-analysis of current evidence.

Epilepsy, a chronic neurological condition characterized by unpredictable seizures, poses considerable challenges, including disability, stigma, and increased mortality. Psychiatric comorbidities are prevalent in 20-30% of epilepsy patients, notably mood or anxiety disorders, psychotic symptoms, and personality disorders. Trauma and childhood adversities are pivotal risk factors for psychopathology, yet the link between Post-Traumatic Stress Disorder (PTSD) and epilepsy remains underexplored. This meta-analysis is aimed to establish updated estimates of PTSD prevalence among individuals with epilepsy. Fifteen studies, comprising 996 epilepsy patients, were included. The overall pooled prevalence of PTSD in epilepsy patients was 18%. Notably, patients with epilepsy exhibited a three-fold increased risk of developing PTSD compared to the general population. Subgroup analysis revealed a higher PTSD prevalence in uncontrolled studies (25%) compared to controlled studies (14%). Additionally, patients with Psychogenic Non-Epileptic Seizures (PNES) demonstrated higher PTSD prevalence than epilepsy patients, with a threefold higher risk in healthy controls compared to PNES controls. While gender prevalence did not significantly affect PTSD occurrence, drug resistant epilepsy did not correlate with PTSD prevalence. Moreover, age of epilepsy onset showed no significant correlation with PTSD prevalence. This meta-analysis underscores the substantial burden of PTSD among epilepsy patients, potentially attributable to the traumatic nature of seizures and the chronic stigma associated with epilepsy. Improved recognition and management of psychiatric conditions, particularly PTSD, are crucial in epilepsy care pathways to enhance patients' quality of life. Further research and comprehensive diagnostic tools are imperative to better understand and address the prevalence of PTSD in epilepsy patients.

Hypothalamic Hamartoma related epilepsy: A systematic review exploring clinical, neuropsychological, and psychiatric outcome after surgery.

The post-surgical outcome for Hypothalamic Hamartoma (HH) related epilepsy in terms of seizure freedom (SF) has been extensively studied, while cognitive and psychiatric outcome has been less frequently reported and defined. This is a systematic review of English language papers, analyzing the post-surgical outcome in series of patients with HH-related epilepsy (≥5 patients, at least 6 months follow-up), published within January 2002-December 2022. SF was measured using Engel scale/equivalent scales. We looked at the outcome related to different surgical techniques, and HH types according to Delalande classification. We evaluated the neuropsychological and neuropsychiatric status after surgery, and the occurrence of post-surgical complications. Forty-six articles reporting 1318 patients were included, of which ten pediatric series. SF was reported in 686/1222 patients (56,1%). Delalande classification was reported in 663 patients from 24 studies, of which 70 were type I HH (10%), 320 were type II HH (48%), 189 were type III HH (29%) and 84 were type IV HH (13%). The outcome in term of SF was reported in 243 out of 663 patients. SF was reported in 12 of 24 type I HH (50%), 80 of 132 type II HH (60,6%), 32 of 59 type III HH (54,2%) and 12 of 28 type IV HH (42,9%). SF was reached in 129/262 (49,2%) after microsurgery, 102/199 (51,3%) after endoscopic surgery, 46/114 (40,6%) after gamma knife surgery, 245/353 (69,4%) after radiofrequency thermocoagulation, and 107/152 (70,4%) after MRI-guided laser interstitial thermal therapy. Hyperphagia/weight gain were the most reported surgical complications. Others were electrolyte alterations, diabetes insipidus, hypotiroidism, transient hyperthermia/poikilothermia. The highest percentage of memory deficits was reported after microsurgery, while hemiparesis and cranial nerves palsy were reported after microsurgery or endoscopic surgery. Thirty studies reported developmental delay/intellectual disability in 424/819 (51,7%) patients. 248/346 patients obtained a global improvement (72%), 70/346 were stable (20%), 28/346 got worse (8%). 22 studies reported psychiatric disorders in 257/465 patients (55,3%). 78/98 patients improved (80%), 13/98 remained stable (13%), 7/98 got worse (7%). Most of the patients had non-structured cognitive/psychiatric assessments. Based on the available data, the surgical management in patients with HH related epilepsy should be individualized, aiming to reach not only the best epilepsy result, but also the optimal cognitive and psychiatric outcome.

Phase-amplitude coupling between low- and high-frequency activities as preoperative biomarker of focal cortical dysplasia subtypes.

OBJECTIVE: To evaluate whether ictal phase-amplitude coupling (PAC) between high-frequency activity and low-frequency activity could be used as a preoperative biomarker of Focal Cortical Dysplasia (FCD) subtypes. We hypothesize that FCD seizures present unique PAC characteristics that may be linked to their specific histopathological features. METHODS: We retrospectively examined 12 children with FCD and refractory epilepsy who underwent successful epilepsy surgery. We identified ictal onsets recorded with stereo-EEG. We estimated the strength of PAC between low-frequencies and high-frequencies for each seizure by means of modulation index. Generalized mixed effect models and receiver operating characteristic (ROC) curve analysis were used to test the association between ictal PAC and FCD subtypes. RESULTS: Ictal PAC was significantly higher in patients with FCD type II compared to type I, only on SOZ-electrodes (p < 0.005). No differences in ictal PAC were found on non-SOZ electrodes. Pre-ictal PAC registered on SOZ electrodes predicted FCD histopathology with a classification accuracy > 0.9 (p < 0.05). CONCLUSIONS: The correlations between histopathology and neurophysiology provide evidence for the contribution of ictal PAC as a preoperative biomarker of FCD subtypes. SIGNIFICANCE: Developed into a proper clinical application, such a technique may help improve clinical management and facilitate the prediction of surgical outcome in patients with FCD undergoing stereo-EEG monitoring.

Can Presurgical Interhemispheric EEG Connectivity Predict Outcome in Hemispheric Surgery? A Brain Machine Learning Approach.

OBJECTIVES: Hemispherotomy (HT) is a surgical option for treatment of drug-resistant seizures due to hemispheric structural lesions. Factors affecting seizure outcome have not been fully clarified. In our study, we used a brain Machine Learning (ML) approach to evaluate the possible role of Inter-hemispheric EEG Connectivity (IC) in predicting post-surgical seizure outcome. METHODS: We collected 21 pediatric patients with drug-resistant epilepsy; who underwent HT in our center from 2009 to 2020; with a follow-up of at least two years. We selected 5-s windows of wakefulness and sleep pre-surgical EEG and we trained Artificial Neuronal Network (ANN) to estimate epilepsy outcome. We extracted EEG features as input data and selected the ANN with best accuracy. RESULTS: Among 21 patients, 15 (71%) were seizure and drug-free at last follow-up. ANN showed 73.3% of accuracy, with 85% of seizure free and 40% of non-seizure free patients appropriately classified. CONCLUSIONS: The accuracy level that we reached supports the hypothesis that pre-surgical EEG features may have the potential to predict epilepsy outcome after HT. SIGNIFICANCE: The role of pre-surgical EEG data in influencing seizure outcome after HT is still debated. We proposed a computational predictive model, with an ML approach, with a high accuracy level.