Vagus nerve stimulation (VNS) has been widely used to treat different neurological disorders, especially epilepsy. Accumulating evidence also suggests its potential application in antidepressive therapy, given that VNS has been confirmed by several clinical trials to exert long-term effects on mitigating depression and reducing the risk of relapse in depressed patients. Likewise, VNS has also proven to ameliorate the behavioral deficits in a rat model of depression. While the influences of VNS on monoamine metabolism and mood improvement are well-recognized, the underlying mechanisms mediating its antidepressive action remain poorly understood. Recent findings suggest that VNS-enhanced proliferation of hippocampal neural progenitor cells (NPCs) and synaptic transmission might serve as a monoamine-independent pathway contributive to the beneficial effects of VNS on depression. Here we briefly reviewed the recent progress in this field, based on which we propose that there might be, at least, two little-overlapped, and yet interactive pathways mediating the antidepressive action of VNS.
Seizures in patients with medically refractory epilepsy (MRE) epilepsy cannot be controlled with drugs. For focal MRE, seizures originate in the epileptogenic zone (EZ), which is the minimum amount of cortex that must be treated to be seizure free. Localizing the EZ is often a laborious process wherein clinicians first inspect scalp EEG recordings during several seizure events, and then formulate an implantation plan for subsequent invasive monitoring. The goal of implantation is to place electrodes into the brain region covering the EZ. Then, during invasive monitoring, clinicians visually inspect intracranial EEG recordings to more precisely localize the EZ. The EZ is then surgically removed. Unfortunately surgical success rates average at 50%. Such grim outcomes call for analytical assistance in creating more accurate implantation plans from scalp EEG. In this paper, we introduce a method that combines imaging data (CT and MRI scans) with scalp EEG to derive an implantation distribution. Specifically, scalp EEG data recorded over a seizure event is converted into a time-gamma frequency map, which is then processed to derive a spectrally annotated implantation distribution (SAID). The SAID represents a distribution of gamma power in each of the eight cortical lobe/hemisphere partitions. We applied this method to 4 MRE patients who underwent treatment, and found that the SAID distribution overlapped more with clinical implantations in success cases than in failed cases. These preliminary findings suggest that the SAID may help in improving EZ localization accuracy and surgical outcomes.
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