Five weeks of intermittent transcutaneous vagus nerve stimulation shape neural networks: a machine learning approach
Author: Martina A Obst1, Arkan Al-Zubaidi2, Marcus Heldmann1, Janis Marc Nolde3, Nick Blümel4, Swantje Kannenberg4, Thomas F Münte5,6
Affiliation:
1 Department of Neurology, University of Lübeck, Lübeck, Germany.
2 Applied Neurocognitive Psychology Lab, University of Oldenburg, Oldenburg, Germany.
3 University of Western Australia, Perth, Australia.
4 Department of Internal Medicine 1, University of Lübeck, Lübeck, Germany.
5 Department of Neurology, University of Lübeck, Lübeck, Germany. thomas.muente@neuro.uni-luebeck.de.
6 Centre of Brain, Behavior and Metabolism (CBBM), Universität of Lübeck, Building 66 Ratzeburger Allee 160, 23562, Lübeck, Germany. thomas.muente@neuro.uni-luebeck.de.
Conference/Journal: Brain Imaging Behav
Date published: 2021 Dec 29
Other:
Special Notes: doi: 10.1007/s11682-021-00572-y. , Word Count: 221
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.
Keywords: Human; Interoception; Machine learning classification; Obesity; Reward; Saliency; fALFF; rs- fMRI; tVNS.
PMID: 34966977 DOI: 10.1007/s11682-021-00572-y
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