Measuring brain response to transcutaneous vagus nerve stimulation (tVNS) using simultaneous magnetoencephalography (MEG)

Keatch, Charlotte; Lambert, Elisabeth; Woods, Will; Kameneva, Tatiana

doi:10.1088/1741-2552/ac620c

Cited by 10 publications

(9 citation statements)

References 84 publications

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“…3). Similar artifact durations have been reported in MEG as well (Keatch et al, 2023). In the power spectrum, the artifacts are visible as peaks at the stimulation frequency and its higher order harmonics (see fig.…”

Section: Introductionsupporting

confidence: 82%

“…A variety of methods have been proposed for the removal of artifacts in taVNS-EEG/MEG recordings. These include notch filtering (Chen et al, 2023;Lloyd et al, 2023;Sharon et al, 2021), independent component analysis (ICA) (Gurtubay et al, 2023), the Cleanline algorithm (Poppa et al, 2022), linear interpolation (Schuerman et al, 2021), and autoregressive modelling (Keatch et al, 2023). A lack of thoroughly evaluated artifact correction methods is recognized as a current challenge for concurrent taVNS-M/EEG studies (Wienke et al, 2023).…”

Section: Introductionmentioning

confidence: 99%

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EEG denoising during transcutaneous auricular vagus nerve stimulation across simulated, phantom and human data

Woller,

Menrath,

Gharabaghi

2024

Preprint

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Objective: The acquisition of electroencephalogram (EEG) data during neurostimulation, particularly concurrent transcutaneous electrical stimulation of the auricular vagus nerve, introduces unique challenges for data preprocessing and analysis due to the presence of significant stimulation artifacts. This study evaluates various denoising techniques to address these challenges effectively. Methods: A variety of denoising techniques were investigated, including interpolation methods, spectral filtering, and spatial filtering techniques. The techniques evaluated included low-pass and notch filtering, spectrum interpolation, average artifact subtraction, the Zapline algorithm, and advanced methods such as independent component analysis (ICA), signal-space projection (SSP), and generalized eigendecomposition with stimulation artifact source separation (GED/SASS). The efficacy of these algorithms was evaluated across three distinct datasets: simulated data, data from a gelatin phantom model, and real human subject data. Results: Our findings indicate that GED (SASS) and SSP significantly outperformed other methods in reducing artifacts while preserving the integrity of the EEG signal. ICA and Zapline were effective too, but came with important limitations. These methods demonstrated robustness across different data types and conditions, providing effective artifact mitigation with minimal disruption to other essential signal components. Conclusion: This comprehensive analysis demonstrates the efficacy of advanced spatial filtering techniques in the preprocessing of EEG data during auricular vagus nerve stimulation. These methods offer promising avenues for enhancing the quality and reliability of neurostimulation-associated EEG data, facilitating a deeper understanding and wider applications in clinical and research settings.

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Section: Introductionsupporting

confidence: 82%

Section: Introductionmentioning

confidence: 99%

EEG denoising during transcutaneous auricular vagus nerve stimulation across simulated, phantom and human data

Woller,

Menrath,

Gharabaghi

2024

Preprint

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“…To account for the influence of disease state on response to stimulation, future studies should consider a systematic approach to identify optimal parameters in PD participants. Target engagement studies using neuroimaging and neurophysiological measures can be used to determine stimulation parameters as well as ear target (left vs. right vs. bilateral) which optimally engage afferent targets ( 26 , 52 ). For example, iterative testing of these various parameters can be evaluated in the context of their ability to elicit changes in markers of vagal tone (i.e., pupil dilation) or blood oxygen level-dependent response within specific brain regions or networks ( 53 ).…”

Section: Discussionmentioning

confidence: 99%

Multi-session transcutaneous auricular vagus nerve stimulation for Parkinson's disease: evaluating feasibility, safety, and preliminary efficacy

Lench,

Turner,

McLeod

et al. 2023

Front. Neurol.

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BackgroundIn pre-clinical animal models of Parkinson's disease (PD), vagus nerve stimulation (VNS) can rescue motor deficits and protect susceptible neuronal populations. Transcutaneous auricular vagus nerve stimulation (taVNS) has emerged as a non-invasive alternative to traditional invasive cervical VNS. This is the first report summarizing the safety, feasibility, and preliminary efficacy of repeated sessions of taVNS in participants with PD.ObjectivesTo evaluate the feasibility, safety, and possible efficacy of taVNS for motor and non-motor symptoms in mild to moderate PD.MethodsThis is a double-blind, sham controlled RCT (NCT04157621) of taVNS in 30 subjects with mild to moderate PD without cognitive impairment. Participants received 10, 1-h taVNS sessions (25 Hz, 200% of sensory threshold, 500 μs pulse width, 60 s on and 30 s off) over a 2-week period. Primary outcome measures were feasibility and safety of the intervention; secondary outcomes included the MDS-UPDRS, cognitive function and self-reported symptom improvement.ResultstaVNS treatment was feasible, however, daily in-office visits were reported as being burdensome for participants. While five participants in the taVNS group and three in the sham group self-reported one or more minor adverse events, no major adverse events occurred. There were no group differences on blood pressure and heart rate throughout the intervention. There were no group differences in MDS-UPDRS scores or self-reported measures. Although global cognitive scores remained stable across groups, there was a reduction in verbal fluency within the taVNS group.ConclusionstaVNS was safe, and well-tolerated in PD participants. Future studies of taVNS for PD should explore at-home stimulation devices and optimize stimulation parameters to reduce variability and maximize engagement of neural targets.

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“…Since the challenges of personalization exist universally, our findings can be extended to other stimulation modalities and observables. The general method of meta-learning can be extended to other contexts, such as the opposite direction (fiberto-physio) in VNS or other types of neural activity in general, like the responses from specific brain regions discussed in [5]. Stimulation parameters with constant scalings are also compatible, as what we did for charge-per-pulse in section 3.2.…”

Section: Significancementioning

confidence: 90%

“…Neurostimulation is growing in popularity as a potential treatment for several ailments, for example, spinal cord stimulation (SCS) for chronic pain [1], deep brain stimulation (DBS) for Parkinson's disease [2] and vagus nerve stimulation (VNS) for epilepsy [3]. It is administered via either invasive or non-invasive approaches, such as transcutaneous VNS [4,5], to deliver stimulation to nervous system [6]. This can be done either in the central or peripheral nervous systems for varied targeted outcomes.…”

Section: Introductionmentioning

confidence: 99%

Personalized inference for neurostimulation with meta-learning: a case study of vagus nerve stimulation

Mao,

Chang,

Zanos

et al. 2024

J. Neural Eng.

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Objective. Neurostimulation is emerging as treatment for several diseases of the brain and peripheral organs. Due to variability arising from placement of stimulation devices, underlying neuroanatomy and physiological responses to stimulation, it is essential that neurostimulation protocols are personalized to maximize efficacy and safety. Building such personalized protocols would benefit from accumulated information in increasingly large datasets of other individuals’ responses. Approach. To address that need, we propose a meta-learning family of algorithms to conduct few-shot optimization of key fitting parameters of physiological and neural responses in new individuals. While our method is agnostic to neurostimulation setting, here we demonstrate its effectiveness on the problem of physiological modeling of fiber recruitment during vagus nerve stimulation (VNS). Using data from acute VNS experiments, the mapping between amplitudes of stimulus-evoked compound action potentials (eCAPs) and physiological responses, such as heart rate and breathing interval modulation, is inferred. Main results. Using additional synthetic data sets to complement experimental results, we demonstrate that our meta-learning framework is capable of directly modeling the physiology-eCAP relationship for individual subjects with much fewer individually queried data points than standard methods. Significance. Our meta-learning framework is general and can be adapted to many input-response neurostimulation mapping problems. Moreover, this method leverages information from growing data sets of past patients, as a treatment is deployed. It can also be combined with several model types, including regression, Gaussian processes with Bayesian optimization, and beyond.

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Measuring brain response to transcutaneous vagus nerve stimulation (tVNS) using simultaneous magnetoencephalography (MEG)

Cited by 10 publications

References 84 publications

EEG denoising during transcutaneous auricular vagus nerve stimulation across simulated, phantom and human data

EEG denoising during transcutaneous auricular vagus nerve stimulation across simulated, phantom and human data

Multi-session transcutaneous auricular vagus nerve stimulation for Parkinson's disease: evaluating feasibility, safety, and preliminary efficacy

Personalized inference for neurostimulation with meta-learning: a case study of vagus nerve stimulation

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