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Background: We previously found that vagus nerve stimulation (VNS) strengthened stimulus-evoked activity in the superficial layer of the sensory cortex but not in the deep layer, suggesting that VNS altered the balance between the feedforward (FF) and feedback (FB) pathways. Band-specific oscillatory activities in the cortex could serve as an index of the FF-FB balance, but whether VNS affects cortical oscillations along sensory pathways through neuromodulators remains unclear. Hypothesis: VNS modulates the FF-FB balance through the cholinergic and noradrenergic systems, which modulate stimulus gain in the cortex. Methods: We investigated the effects of VNS using electrocorticography in the auditory cortex of 34 Wistar rats under general anesthesia while presenting click stimuli. In the time-frequency analyses, the putative modulation of the FF and FB pathways was estimated using high- and low-frequency power. We assessed, using analysis of variance, how VNS modulates auditory-evoked activities and how the modulation changes with cholinergic and noradrenergic antagonists. Results: VNS increased auditory cortical evoked potentials, consistent with results of our previous work. Furthermore, VNS increased auditory-evoked gamma and beta powers and decreased theta power. Local administration of cholinergic antagonists in the auditory cortex selectively disrupted the VNS-induced increase in gamma and beta power, while noradrenergic antagonists disrupted the decrease in theta power. Conclusions: VNS might strengthen the FF pathway through the cholinergic system and attenuate the FB pathway through the noradrenergic system in the auditory cortex. Cortical gain modulation through the VNS-induced neuromodulatory system provides new mechanistic insights into the effect of VNS on auditory processing.

Summary: The action observation network (AON) has been extensively studied using short, isolated motor acts. How activity in the network is altered when these isolated acts are embedded in meaningful sequences of actions remains poorly understood. Here we utilized intracranial electrocorticography to characterize how the exchange of information across key nodes of the AON—the precentral, supramarginal, and visual cortices—is affected by such embedding and the resulting predictability. We found more top-down beta oscillation from precentral to supramarginal contacts during the observation of predictable actions in meaningful sequences compared to the same actions in randomized, and hence less predictable, order. In addition, we find that expectations enabled by the embedding lead to a suppression of bottom-up visual responses in the high-gamma range in visual areas. These results, in line with predictive coding, inform how nodes of the AON integrate information to process the actions of others.

Objective: This study aimed to assess the efficacy and safety of quadripulse transcranial magnetic stimulation-50 (QPS-50) in patients with intractable epilepsy. Methods: Four patients were included in the study. QPS-50, which induces long-term depression in healthy subjects, was administered for 30 min on a weekly basis for 12 weeks. Patients’ clinical symptoms and physiological parameters were evaluated before, during, and after the repeated QPS-50 period. We performed two control experiments: the effect in MEP (Motor evoked potential) size after a single QPS-50 session with a round coil in nine healthy volunteers, and a follow-up study of physiological parameters by repeated QPS-50 sessions in four other healthy participants. Results: Motor threshold (MT) decreased during the repeated QPS-50 sessions in all patients. Epileptic symptoms worsened in two patients, whereas no clinical worsening was observed in the other two patients. In contrast, MT remained unaffected for 12 weeks in all healthy volunteers. Conclusions: QPS-50 may not be effective as a treatment for intractable epilepsy. Significance: In intractable epilepsy patients, administering repeated QPS-50 may paradoxically render the motor cortex more excitable, probably because of abnormal inhibitory control within the epileptic cortex. The possibility of clinical aggravation should be seriously considered when treating intractable epilepsy patients with non-invasive stimulation methods.

Gene therapy has been employed as a therapeutic approach for intractable focal epilepsies. Considering the potential of focal GABAergic neuromodulation in regulating epileptogenesis, the GABA-producing enzyme, γ-aminobutyric acid decarboxylase 67 (GAD67), is highly suitable for epilepsy therapy. The EL/Suz (EL) mouse is a model of multifactorial temporal lobe epilepsy. In the present study, we examined focal gene transduction in epileptic EL mice using recombinant adeno-associated virus serotype 8 (rAAV8) expressing human GAD67 to enhance GABA-mediated neural inhibition. Eight-week-old mice were bilaterally injected with rAAV8-GFP or rAAV8-GAD67 in the hippocampal CA3 region. After four weeks, the GAD67-transduced EL mice, but not the rAAV-GFP-treated EL mice, exhibited a significant reduction in seizure generation. The GAD67-mediated depression became stable after 14 weeks. The excitability of the CA3 region was markedly reduced in the GAD67-transduced EL mice, consistent with the results of the Ca2+ imaging using hippocampal slices. In addition, downregulation of c-Fos expression was observed in GAD67-transduced hippocampi. Our findings showed that rAAV8-GAD67 induced significant changes in the GABAergic system in the EL hippocampus. Thus, rAAV8-mediated GAD67 gene transfer is a promising therapeutic strategy for the treatment of epilepsies. Keywords: gene therapy, EL mouse, epilepsy, GAD67, CA3, AAV8

An epileptic seizure during the course of driving can result in a serious car accident. However, basic data on how epileptic seizures actually affect driving performance is significantly lacking. To understand the relationship, it is crucial to conduct not only behavioral but also electroencephalogram (EEG) analysis during epileptic seizures. Therefore, we developed a mobile driving simulator which makes it possible to record driving-related parameters time-lined with video-EEG. We report a case in which behavioral and EEG changes were successfully recorded during ictal periods of focal impaired awareness seizure in a patient engaged with the system. With the current lack of objective data describing how seizures impair driving performance, such an accumulation of information could improve personalized medical management, influence legal adjudication and assist in the development of driving support systems for people with epilepsy. Keywords: Driving simulator, Epilepsy, Focal impaired awareness seizure

Data presented in this article are related to our article entitled “Unilateral posterior reversible encephalopathy syndrome: A case report” [1]. Cases of Posterior Reversible Encephalopathy Syndrome (PRES) involving unilateral lesions are very rare. We searched the PubMed database using keywords such as PRES, unilateral, and asymmetric and found a small number of cases to include in our review. We summarized the characteristics of these reported cases of unilateral PRES, including our case. Keywords: Posterior reversible encephalopathy syndrome, Unilateral distribution, Brain, Vasogenic edema

We hypothesized that expert epileptologists can detect seizures directly by visually analyzing EEG plot images, unlike automated methods that analyze spectro-temporal features or complex, non-stationary features of EEG signals. If so, seizure detection could benefit from convolutional neural networks because their visual recognition ability is comparable to that of humans. We explored image-based seizure detection by applying convolutional neural networks to long-term EEG that included epileptic seizures. After filtering, EEG data were divided into short segments based on a given time window and converted into plot EEG images, each of which was classified by convolutional neural networks as ‘seizure’ or ‘non-seizure’. These resultant labels were then used to design a clinically practical index for seizure detection. The best true positive rate was obtained using a 1-s time window. The median true positive rate of convolutional neural networks labelling by seconds was 74%, which was higher than that of commercially available seizure detection software (20% by BESA and 31% by Persyst). For practical use, the median of detected seizure rate by minutes was 100% by convolutional neural networks, which was higher than the 73.3% by BESA and 81.7% by Persyst. The false alarm of convolutional neural networks' seizure detection was issued at 0.2 per hour, which appears acceptable for clinical practice. Moreover, we demonstrated that seizure detection improved when training was performed using EEG patterns similar to those of testing data, suggesting that adding a variety of seizure patterns to the training dataset will improve our method. Thus, artificial visual recognition by convolutional neural networks allows for seizure detection, which otherwise currently relies on skillful visual inspection by expert epileptologists during clinical diagnosis. Keywords: Convolutional neural networks, Seizure detection, Deep learning, Scalp electroencephalogram, Epileptic seizure