https://www.selleckchem.com/products/ficz.html For people with idiopathic generalized epilepsy, functional networks derived from their resting-state scalp electrophysiological recordings have shown an inherent higher propensity to generate seizures than those from healthy controls when assessed using the concept of brain network ictogenicity (BNI). Herein we tested whether the BNI framework is applicable to resting-state magnetoencephalography (MEG) from people with juvenile myoclonic epilepsy (JME). The BNI framework consists in deriving a functional network from apparently normal brain activity, placing a mathematical model of ictogenicity into the network and then computing how often such network generates seizures in silico. We considered data from 26 people with JME and 26 healthy controls. We found that resting-state MEG functional networks from people with JME are characterized by a higher propensity to generate seizures (i.e., higher BNI) than those from healthy controls. We found a classification accuracy of 73%. The BNI framework is applicable to MEG and was capable of differentiating people with epilepsy from healthy controls. The BNI framework may be applied to resting-state MEG to aid in epilepsy diagnosis. The BNI framework may be applied to resting-state MEG to aid in epilepsy diagnosis. How Parkinson's disease (PD) affects an individual's empathic capacity remains poorly understood. By using the event-related potential (ERP) technique, we sought to (1) study the temporal dynamics of empathic responses in patients with PD; (2) explore whether dopaminergic medication modulates empathic processing. Twenty-six patients with early-to-moderate PD (13 on- and 13 off-medication) and 14 healthy controls performed an empathy-for-pain paradigm test while we recorded their electroencephalography. The participants responded to neutral or painful pictures during an active empathic condition (pain judgment task) and a control condition that was manipulated by task