This is an open access article under the terms of the Creat ive Commo ns Attri butio n-NonCo mmerc ial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.
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, CIshortest = [−0.103, 0.125], BF01 = 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.
Background: To evaluate how prominent the currently hot issue of “epilepsy and dementia” is in the daily practice of a large level 4 epilepsy university clinic. Methods: In this retrospective monocentric cohort study, a total of 145,501 letters of all 40,360 adult patients seen between 2003 and 2021 were screened for dementia related terms. Files with at least on hit were extracted and analyzed in regard to diagnoses, age, age at epilepsy onset, and the question of whether epilepsy preceded or followed the diagnosis of dementia. Results: This resulted in 513 patients who had at least one hit. Of those, 12.7% respectively 6.6% definitively had or were suspected to have dementia, 4.9% had Mild Cognitive Impairment, and 6.6% other neurodegenerative conditions. Referring to all patients, a diagnosed or suspected dementia prevalence of 0.25% is indicated. An older age (>age 60), late-onset epilepsy (>age 60), but not a longer duration of epilepsy increased the odds by 6.1 and 3.1. Additionally, vascular, metabolic, inflammatory, and behavioral mood related comorbidities were common. Epilepsy preceded rather than followed the dementia diagnosis. Conclusions: Considering an eventual selection bias and under-diagnosis because not all patients were explicitly screened for dementia, the results put the dementia issue from an epileptologist’s point of view into perspective. The prevalence of dementia in epilepsy is low. However, physicians should be aware that the risk for dementia is increased in the elderly, in late onset epilepsies, and with comorbid risk factors, and that seizures can be the early sign of a neurodegenerative disease. Future research needs to screen for dementia in epilepsy more explicitly while stratifying the patients according to the underlying pathologies and comorbidities.
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