Transcutaneous auricular vagus nerve stimulation (taVNS) is a newer delivery system using a non-invasive stimulation device placed at the ear. taVNS research is focused on clinical trials showing potential therapeutic benefits, however the neurophysiological effects of this stimulation on brain activity are still unclear. We propose a systematic review that aims to describe the effects of taVNS on EEG measures and identify taVNS parameters that can potentially lead to consistent EEG-mediated biomarkers for this therapy. A systematic literature review was carried out following the Preferred Reporting Items for Systematic Reviews and Meta-Analyzes (PRISMA) and the Cochrane handbook for systematic reviews. Clinical trials examining EEG parameters were considered, including absolute and relative power, coherence, degree of symmetry, evoked potentials, and peak frequency of all bands. According to our criteria, 18 studies (from 122 articles) were included. Our findings show a general trend towards increased EEG power spectrum activity in lower frequencies, and changes on early components of the ERP related to inhibitory tasks. This review suggests that quantitative electroencephalography can be used to assess the effects of taVNS on brain activity, however more studies are needed to systematically establish the specific effects and metrics that would reflect the non-invasive stimulation through the auricular branch of the vagus nerve.

BACKGROUND: Early identification of large vessel occlusion (LVO) in patients with ischemic stroke is crucial for timely interventions. We propose a machine learning–based algorithm (JLK-CTL) that uses handcrafted features from noncontrast computed tomography to predict LVO. METHODS: We included patients with ischemic stroke who underwent concurrent noncontrast computed tomography and computed tomography angiography in seven hospitals. Patients from 5 of these hospitals, admitted between May 2011 and March 2015, were randomly divided into training and internal validation (9:1 ratio). Those from the remaining 2 hospitals, admitted between March 2021 and September 2021, were designated for external validation. From each noncontrast computed tomography scan, we extracted differences in volume, tissue density, and Hounsfield unit distribution between bihemispheric regions (striatocapsular, insula, M1–M3, and M4–M6, modified from the Alberta Stroke Program Early Computed Tomography Score). A deep learning algorithm was used to incorporate clot signs as an additional feature. Machine learning models, including ExtraTrees, random forest, extreme gradient boosting, support vector machine, and multilayer perceptron, as well as a deep learning model, were trained and evaluated. Additionally, we assessed the models’ performance after incorporating the National Institutes of Health Stroke Scale scores as an additional feature. RESULTS: Among 2919 patients, 83 were excluded. Across the training (n=2463), internal validation (n=275), and external validation (n=95) datasets, the mean ages were 68.5±12.4, 67.6±13.8, and 67.9±13.6 years, respectively. The proportions of men were 57%, 53%, and 59%, with LVO prevalences of 17.0%, 16.4%, and 26.3%, respectively. In the external validation, the ExtraTrees model achieved a robust area under the curve of 0.888 (95% CI, 0.850–0.925), with a sensitivity of 80.1% (95% CI, 72.0–88.1) and a specificity of 88.6% (95% CI, 84.7–92.5). Adding the National Institutes of Health Stroke Scale score to the ExtraTrees model increased sensitivity (from 80.1% to 92.1%) while maintaining specificity. CONCLUSIONS: Our algorithm provides reliable predictions of LVO using noncontrast computed tomography. By enabling early LVO identification, our algorithm has the potential to expedite the stroke workflow.

Objective To evaluate the relationship between fluid-attenuated inversion recovery (FLAIR) vascular hyperintensity (FVH) and early ischemic lesion recurrence (follow-up diffusion-weighted imaging [FU-DWI] [+]) in patients with lesion-negative TIA. Methods We recruited consecutive patients with lesion-negative TIA within 24 hours of symptom onset, who underwent follow-up MRI during the acute period. FVH was defined as a focal or serpentine high signal intensity on FLAIR images. Other potential confounders were adjusted to evaluate the relationship between FVH and FU-DWI (+). Furthermore, to compare clinical outcomes between the FU-DWI (+) and FU-DWI (−) groups, we assessed 1-year recurrent ischemic stroke or TIA. Results Among 392 patients with lesion-negative TIA, 82 patients had FU-DWI (+) on the follow-up MRI. In the multivariate analysis, FVH remained an independent predictor of FU-DWI (+) (adjusted odds ratio [aOR] = 4.77, 95% confidence interval [CI] 2.45–9.29, p < 0.001). The time to initial MRI (aOR = 0.49, 95% CI = 0.33–0.70, p < 0.001) and intracranial atherosclerosis (aOR = 2.07, 95% CI = 1.10–3.92, p = 0.025) were also associated with FU-DWI (+), independent of FVH. In clinical outcomes, the FU-DWI (+) group showed more frequent 1-year recurrent ischemic stroke events than the FU-DWI (−) group (10.7% vs 3.1%, respectively, p = 0.007). Conclusions FVH is associated with FU-DWI (+) in patients with lesion-negative TIA. As FU-DWI (+) frequently occurs during the acute period and has a subsequent worse outcome after discharge, additional radiologic or clinical markers for it are necessary.

Background: Among the patients with established coronary artery diseases, obese patients tend to have a more favorable prognosis, which is called as obesity paradox. Interestingly, mildly obese patients who underwent coronary revascularization had a lower risk of bleeding. In this context, we have investigated the association between obesity and hemorrhagic transformation (HTf) after acute ischemic stroke. Methods: A total of 365 patients with first-ever acute ischemic stroke were included in this study. Demographic, clinical and radiological information was collected and HTf was evaluated through follow-up T2*-weighted gradient-recalled echo MRI performed usually within 1 week after occurrence of stroke. Body mass index was calculated, and obesity was defined using the World Health Organization Western Pacific Regional Office criteria. Results: The HTf was identified in 59 patients (16.2%). As the severity of obesity increased, the occurrence of HTf decreased. Compared with the normal weight group and after controlling possible confounders including acute and previous treatment, stroke severity and subtype, the risk of HTf decreased significantly in the obese group (odds ratio, 0.39; 95% confidence interval, 0.17-0.87). Conclusions: The better outcome for HTf seen in obese patients suggests the existence of a “bleeding-obesity paradox” in acute ischemic stroke.