SUMMARYObjective: Seizures may occur in close temporal association with a stroke or after a variable interval. Moreover, epilepsy is often encountered in patients with leukoaraiosis. Although early post-stroke seizures have been studied extensively, less attention has been paid to post-stroke epilepsy (PSE) and to epilepsy associated with leukoaraiosis (EAL). The aim of this paper is to review data concerning pathophysiology, prognosis, and treatment of PSE and EAL. Methods: We performed an extensive literature search to identify experimental and clinical articles on PSE and EAL. We also conducted a systematic review of risk factors for PSE and EAL among eligible studies. Results: PSE is caused by enhanced neuronal excitability within and near the scar. The role played by white matter changes in EAL remains to be elucidated. Meta-analysis showed that cortical involvement (odds ratio [OR]

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Background Epilepsy and hypertension are common chronic conditions, both showing high prevalence in older age groups. This review outlines current experimental and clinical evidence on both direct and indirect role of hypertension in epileptogenesis and discusses the principles of drug treatment in patients with hypertension and epilepsy. Methods We selected English-written articles on epilepsy, hypertension, stroke, and cerebrovascular disease until December, 2018. Results Renin-angiotensin system might play a central role in the direct interaction between hypertension and epilepsy, but other mechanisms may be contemplated. Large-artery stroke, small vessel disease and posterior reversible leukoencephalopathy syndrome are hypertension-related brain lesions able to determine epilepsy by indirect mechanisms. The role of hypertension as an independent risk factor for post-stroke epilepsy has not been demonstrated. The role of hypertension-related small vessel disease in adult-onset epilepsy has been demonstrated. Posterior reversible encephalopathy syndrome is an acute condition, often

The International League against Epilepsy (ILAE) proposed a diagnostic scheme for psychogenic non‐epileptic seizure (PNES). The debate on ethical aspects of the diagnostic procedures is ongoing, the treatment is not standardized and management might differ according to age group. The objective was to reach an expert and stakeholder consensus on PNES management. A board comprising adult and child neurologists, neuropsychologists, psychiatrists, pharmacologists, experts in forensic medicine and bioethics as well as patients’ representatives was formed. The board chose five main topics regarding PNES: diagnosis; ethical issues; psychiatric comorbidities; psychological treatment; and pharmacological treatment. After a systematic review of the literature, the board met in a consensus conference in Catanzaro (Italy). Further consultations using a model of Delphi panel were held. The global level of evidence for all topics was low. Even though most questions were formulated separately for children/adolescents and adults, no major age‐related differences emerged. The board established that the approach to PNES diagnosis should comply with ILAE recommendations. Seizure induction was considered ethical, preferring the least invasive techniques. The board recommended looking carefully for mood disturbances, personality disorders and psychic trauma in persons with PNES and considering cognitive‐behavioural therapy as a first‐line psychological approach and pharmacological treatment to manage comorbid conditions, namely anxiety and depression. Psychogenic non‐epileptic seizure management should be multidisciplinary. High‐quality long‐term studies are needed to standardize PNES management.

A novel technique of quantitative EEG for differentiating patients with early-stage Creutzfeldt-Jakob disease (CJD) from other forms of rapidly progressive dementia (RPD) is proposed. The discrimination is based on the extraction of suitable features from the time-frequency representation of the EEG signals through continuous wavelet transform (CWT). An average measure of complexity of the EEG signal obtained by permutation entropy (PE) is also included. The dimensionality of the feature space is reduced through a multilayer processing system based on the recently emerged deep learning (DL) concept. The DL processor includes a stacked auto-encoder, trained by unsupervised learning techniques, and a classifier whose parameters are determined in a supervised way by associating the known category labels to the reduced vector of high-level features generated by the previous processing blocks. The supervised learning step is carried out by using either support vector machines (SVM) or multilayer neural networks (MLP-NN). A subset of EEG from patients suffering from Alzheimer's Disease (AD) and healthy controls (HC) is considered for differentiating CJD patients. When fine-tuning the parameters of the global processing system by a supervised learning procedure, the proposed system is able to achieve an average accuracy of 89%, an average sensitivity of 92%, and an average specificity of 89% in differentiating CJD from RPD. Similar results are obtained for CJD versus AD and CJD versus HC.