No abstract

Although epilepsy as a comorbidity in neurodegenerative disorders is increasingly recognized, its incidence is still underestimated and the features of epilepsy in the different neurodegenerative conditions are still poorly defined. Improved health care, resulting in increased longevity, will unavoidably lead to an increment of epilepsy cases in the elderly. Thus, it is conceivable to expect that neurologists will have to deal with these comorbid conditions to a growing extent in the future. In this seminar, we provide an updated overview of the clinical features, pathophysiological mechanisms and diagnostic and treatment approaches of epilepsy in the most common neurodegenerative disorders (such as Alzheimer disease and other types of dementia, Parkinson disease, Down syndrome, prion diseases, and progressive myoclonus epilepsies), aiming to provide a tool that can help epileptologists and neurologists in the diagnosis and management of this increasingly reported comorbidity.

Until now, clinicians are not able to evaluate the Psychogenic Non-Epileptic Seizures (PNES) from the rest-electroencephalography (EEG) readout. No EEG marker can help differentiate PNES cases from healthy subjects. In this paper, we have investigated the power spectrum density (PSD), in resting-state EEGs, to evaluate the abnormalities in PNES affected brains. Additionally, we have used functional connectivity tools, such as phase lag index (PLI), and graph-derived metrics to better observe the integration of distributed information of regular and synchronized multi-scale communication within and across inter-regional brain areas. We proved the utility of our method after enrolling a cohort study of 20 age- and gender-matched PNES and 19 healthy control (HC) subjects. In this work, three classification models, namely support vector machine (SVM), linear discriminant analysis (LDA), and Multilayer perceptron (MLP), have been employed to model the relationship between the functional connectivity features (rest-HC versus rest-PNES). The best performance for the discrimination of participants was obtained using the MLP classifier, reporting a precision of 85.73%, a recall of 86.57%, an F1-score of 78.98%, and, finally, an accuracy of 91.02%. In conclusion, our results hypothesized two main aspects. The first is an intrinsic organization of functional brain networks that reflects a dysfunctional level of integration across brain regions, which can provide new insights into the pathophysiological mechanisms of PNES. The second is that functional connectivity features and MLP could be a promising method to classify rest-EEG data of PNES form healthy controls subjects.