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Five weeks of intermittent transcutaneous vagus nerve stimulation shape neural networks: a machine learning approach

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Title: Five weeks of intermittent transcutaneous vagus nerve stimulation shape neural networks: a machine learning approach
Authors: Martina. A. Obst, Arkan Al-Zubaidi, Marcus Heldmann, Janis Marc Nolde, Nick Blümel, Swantje Kannenberg, Thomas F. Münte
Source: Brain Imaging Behav
Publisher Information: Springer Science and Business Media LLC, 2021.
Publication Year: 2021
Subject Terms: Machine Learning, Male, 0301 basic medicine, 03 medical and health sciences, 0302 clinical medicine, Vagus Nerve Stimulation, Obesity, Humans [MeSH], fALFF, Reward, Body Weight [MeSH], Vagus Nerve Stimulation/methods [MeSH], Neural Networks, Computer [MeSH], Human, Magnetic Resonance Imaging [MeSH], Male [MeSH], Interoception, rs- fMRI, Machine Learning [MeSH], Machine learning classification, Original Research, Saliency, tVNS, Body Weight, Humans, Neural Networks, Computer, Magnetic Resonance Imaging
Description: Invasive and transcutaneous vagus nerve stimulation [(t)-VNS] have been used to treat epilepsy, depression and migraine and has also shown effects on metabolism and body weight. To what extent this treatment shapes neural networks and how such network changes might be related to treatment effects is currently unclear. Using a pre-post mixed study design, we applied either a tVNS or sham stimulation (5 h/week) in 34 overweight male participants in the context of a study designed to assess effects of tVNS on body weight and metabolic and cognitive parameters resting state (rs) fMRI was measured about 12 h after the last stimulation period. Support vector machine (SVM) classification was applied to fractional amplitude low-frequency fluctuations (fALFF) on established rs-networks. All classification results were controlled for random effects and overfitting. Finally, we calculated multiple regressions between the classification results and reported food craving. We found a classification accuracy (CA) of 79 % in a subset of four brainstem regions suggesting that tVNS leads to lasting changes in brain networks. Five of eight salience network regions yielded 76,5 % CA. Our study shows tVNS’ post-stimulation effects on fALFF in the salience rs-network. More detailed investigations of this effect and their relationship with food intake seem reasonable for future studies.
Document Type: Article
Other literature type
Language: English
ISSN: 1931-7565
1931-7557
DOI: 10.1007/s11682-021-00572-y
Access URL: https://link.springer.com/content/pdf/10.1007/s11682-021-00572-y.pdf
https://pubmed.ncbi.nlm.nih.gov/34966977
https://repository.publisso.de/resource/frl:6445686
Rights: CC BY
URL: http://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (http://creativecommons.org/licenses/by/4.0/) .
Accession Number: edsair.doi.dedup.....d32f300d244f2cb78ed577ea1d7c84d5
Database: OpenAIRE
Description
Abstract:Invasive and transcutaneous vagus nerve stimulation [(t)-VNS] have been used to treat epilepsy, depression and migraine and has also shown effects on metabolism and body weight. To what extent this treatment shapes neural networks and how such network changes might be related to treatment effects is currently unclear. Using a pre-post mixed study design, we applied either a tVNS or sham stimulation (5 h/week) in 34 overweight male participants in the context of a study designed to assess effects of tVNS on body weight and metabolic and cognitive parameters resting state (rs) fMRI was measured about 12 h after the last stimulation period. Support vector machine (SVM) classification was applied to fractional amplitude low-frequency fluctuations (fALFF) on established rs-networks. All classification results were controlled for random effects and overfitting. Finally, we calculated multiple regressions between the classification results and reported food craving. We found a classification accuracy (CA) of 79 % in a subset of four brainstem regions suggesting that tVNS leads to lasting changes in brain networks. Five of eight salience network regions yielded 76,5 % CA. Our study shows tVNS’ post-stimulation effects on fALFF in the salience rs-network. More detailed investigations of this effect and their relationship with food intake seem reasonable for future studies.
ISSN:19317565
19317557
DOI:10.1007/s11682-021-00572-y