Brain anatomy predicts the efficacy of noninvasive vagus nerve stimulation on pain learning
Poster No:
44
Submission Type:
Abstract Submission
Authors:
Hui-Ling Chan1,2, Cheng-Yang Ko1, Alan Fermin3,4, Atsuo Yoshino3,5, Shigeto Yamawaki3
Institutions:
1Department of Computer Science and Information Engineering, National Cheng Kung University, Tainan, Taiwan, 2Institute of Medical Informatics, National Cheng Kung University, Tainan, Taiwan, 3Center for Brain, Mind, and KANSEI Sciences Research, Hiroshima University, Hiroshima, Japan, 4Department of Psychiatry and Neurosciences, Hiroshima University, Hiroshima, Japan, 5Health Service Center, Hiroshima University, Hiroshima, Japan
First Author:
Hui-Ling Chan
Department of Computer Science and Information Engineering, National Cheng Kung University|Institute of Medical Informatics, National Cheng Kung University
Tainan, Taiwan|Tainan, Taiwan
Department of Computer Science and Information Engineering, National Cheng Kung University|Institute of Medical Informatics, National Cheng Kung University
Tainan, Taiwan|Tainan, Taiwan
Co-Author(s):
Cheng-Yang Ko
Department of Computer Science and Information Engineering, National Cheng Kung University
Tainan, Taiwan
Department of Computer Science and Information Engineering, National Cheng Kung University
Tainan, Taiwan
Alan Fermin
Center for Brain, Mind, and KANSEI Sciences Research, Hiroshima University|Department of Psychiatry and Neurosciences, Hiroshima University
Hiroshima, Japan|Hiroshima, Japan
Center for Brain, Mind, and KANSEI Sciences Research, Hiroshima University|Department of Psychiatry and Neurosciences, Hiroshima University
Hiroshima, Japan|Hiroshima, Japan
Atsuo Yoshino
Center for Brain, Mind, and KANSEI Sciences Research, Hiroshima University|Health Service Center, Hiroshima University
Hiroshima, Japan|Hiroshima, Japan
Center for Brain, Mind, and KANSEI Sciences Research, Hiroshima University|Health Service Center, Hiroshima University
Hiroshima, Japan|Hiroshima, Japan
Shigeto Yamawaki
Center for Brain, Mind, and KANSEI Sciences Research, Hiroshima University
Hiroshima, Japan
Center for Brain, Mind, and KANSEI Sciences Research, Hiroshima University
Hiroshima, Japan
Introduction:
Transcutaneous auricular vagus nerve stimulation (taVNS) is a non-invasive neuromodulation technique targeting the vagus nerve that connects the body to the brain. TaVNS modulates visceral functions (Ventura-Bort & Weymar, 2024), brain activity (Badran et al., 2018), and cognition (Ridgewell et al., 2021). However, the neural substrates underlying the effects of taVNS remain poorly understood. Here, we leveraged magnetic resonance imaging (MRI) to uncover brain structures serving as predictive biomarkers of taVNS efficacy in enhancing cognitive performance.
Methods:
Forty-seven healthy participants underwent a pain prediction experiment using Pavlovian conditioning with three visual cues: high-risk cues were followed by strong pain in 75% of trials and mild pain in 25%, while low-risk cues led to mild pain in 75% and strong pain in 25%. No-risk cues were not associated with pain stimulation. In each trial, cues were displayed from 0 s to the end of pain stimulation, taVNS was applied from -3 to 6 s, and pain started ~2 s after taVNS ended. Participants then reported their pain type prediction based on the cue. TaVNS (25 Hz, 500 μs pulse width) was applied at the left cymba conchae (active) or earlobe (sham). Participants completed two active and two sham runs in random order. Based on differences in prediction accuracy (ΔAccact-sham), participants were grouped as taVNS responders (ΔAccact-sham>0) or nonresponders. Brain structure data collected from 3T MRI were used for voxel-based morphometry and structural covariance network (SCN) analysis to examine group differences in gray matter volume (GMV) and anatomical connectivity, with correlations calculated between GMV or SCN and ΔAccact-sham to assess their predictability on taVNS efficacy.
Results:
Twenty-nine (62%) participants were classified as responders. Responders showed increased GMV in the left medial superior frontal gyrus (mSFG, T=5.09, pFWE<.000) and supplementary motor area (SMA, T=4.07, pFWE<.066) (Fig. 1A). Nonresponders showed increased GMV in the cerebellum (T=5.05, pFWE<.000) (Fig. 1B). SMA SCN revealed clusters in the SMA, mSFG, and anterior cingulate cortex (ACC) (T=94.23, pFWE<.000) and in the anterior insula (T=4.03, pFWE<.005) (Fig. 2A). mSFG SCN showed clusters in the precuneus (T=5.52, pFWE<.000) and SMA/mSFG (T=92.66, pFWE<.000) (Fig. 2B). The left cerebellum SCN was linked with the right cerebellum (T=60.55, pFWE<.000, Fig. 2C). Furthermore, both GMV in the SMA (r=.38, pFDR=.009), mSFG (r=.50, pFDR=.002), cerebellum (r=─.44, pFDR=.006), and SCN between SMA and mSFG (r=.45, pFDR=.027) and intra-cerebellum (r=─.41, pFDR=.006), predicted taVNS efficacy.
·Figure 1 Regions with significantly (A) larger or (B) smaller gray matter volume in responders vs. nonresponders.
·Figure 2 Structural covariance networks of (A) left supplementary motor area, (B) left medial superior frontal gyrus and (C) left cerebellum.
Conclusions:
The current study identified key neuroanatomical substrates predicting taVNS efficacy in enhancing cognitive performance among healthy individuals. Responders and nonresponders differed in GMV and SCNs across prefrontal, insula, and cerebellar regions, which also predicted taVNS outcomes. These findings highlight the role of brain structures integrating interoception and cognition, including learning and decision-making (Fermin et al., 2022), in determining taVNS efficacy. The implications extend beyond cognitive enhancement, as taVNS shows therapeutic potential for interoception-related diseases, including depression (Sun et al., 2024), insomnia (Wu et al., 2021), and migraine (Feng et al., 2022). However, the limited efficacy observed in approximately 25–33% of patients after weeks of treatment underscores the need for predictive markers of taVNS responsiveness. Our findings suggest that the integrity and connectivity of the brain may serve as biomarkers for identifying individuals likely to benefit from taVNS.
Brain Stimulation:
Non-Invasive Stimulation Methods Other 1
Higher Cognitive Functions:
Decision Making
Learning and Memory:
Learning and Memory Other 2
Neuroanatomy, Physiology, Metabolism and Neurotransmission:
Anatomy and Functional Systems
Perception, Attention and Motor Behavior:
Perception: Pain and Visceral
Keywords:
Cognition
Learning
MRI
Pain
STRUCTURAL MRI
Other - Vagus nerve stimulation
1|2Indicates the priority used for review
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Provide references using APA citation style.
Feng, M. et al. (2022). Early Fractional Amplitude of Low Frequency Fluctuation Can Predict the Efficacy of Transcutaneous Auricular Vagus Nerve Stimulation Treatment for Migraine Without Aura. Frontiers in Molecular Neuroscience, 15.
Fermin, A. S. R. et al. (2022). An insula hierarchical network architecture for active interoceptive inference. Royal Society Open Science, 9(6), 220226.
Ridgewell, C. et al. (2021). The effects of transcutaneous auricular vagal nerve stimulation on cognition in healthy individuals: A meta-analysis. Neuropsychology, 35(4), 352-365.
Sun, J. et al. (2024). A predictive study of the efficacy of transcutaneous auricular vagus nerve stimulation in the treatment of major depressive disorder: An fMRI-based machine learning analysis. Asian Journal of Psychiatry, 98, 104079.
Ventura-Bort, C. et al. (2024). Transcutaneous auricular vagus nerve stimulation modulates the processing of interoceptive prediction error signals and their role in allostatic regulation. Human Brain Mapping, 45(3), e26613.
Wu, X. et al. (2021). Brain Functional Mechanisms Determining the Efficacy of Transcutaneous Auricular Vagus Nerve Stimulation in Primary Insomnia. Frontiers in Neuroscience, 15.