Abstract Non-invasive brain stimulation techniques, such as transcutaneous auricular vagus nerve stimulation (taVNS), have considerable potential for clinical use. Beneficial effects of taVNS have been demonstrated on symptoms in patients with mental or neurological disorders as well as transdiagnostic dimensions, including mood and motivation. However, since taVNS research is still an emerging field, the underlying neurophysiological processes are not yet fully understood, and the replicability of findings on biomarkers of taVNS effects has been questioned. Here, we perform a living Bayesian random effects meta-analysis to synthesize the current evidence concerning the effects of taVNS on heart rate variability (HRV), a candidate biomarker that has, so far, received most attention in the field. To keep the synthesis of evidence transparent and up to date as new studies are being published, we developed a Shiny web app that regularly incorporates new results and enables users to modify study selection criteria to evaluate the robustness of the inference across potential confounds. Our analysis focuses on 17 single-blind studies comparing taVNS versus sham in healthy participants. These newly synthesized results provide strong evidence for the null hypothesis ( g = 0.011, CI shortest = [−0.103, 0.125], BF 01 = 25.587), indicating that acute taVNS does not alter HRV compared to sham. To conclude, based on a synthesis of the available evidence to date, there is no support for the hypothesis that HRV is a robust biomarker for acute taVNS. By increasing transparency and timeliness, we believe that the concept of living meta-analyses can lead to transformational benefits in emerging fields such as non-invasive brain stimulation.

Does non-invasive vagus nerve stimulation affect heart rate variability? A living and interactive Bayesian meta-analysis