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      Transcutaneous auricular vagus nerve stimulation (taVNS) for migraine: an fMRI study

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          Abstract

          Background

          Dysfunction of the thalamocortical connectivity network is thought to underlie the pathophysiology of the migraine. This current study aimed to explore the thalamocortical connectivity changes during 4 weeks of continuous transcutaneous vagus nerve stimulation (taVNS) treatment on migraine patients.

          Methods

          70 migraine patients were recruited and randomized in an equal ratio to receive real taVNS or sham taVNS treatments for 4 weeks. Resting-state functional MRI was collected before and after treatment. The thalamus was parceled into functional regions of interest (ROIs) on the basis of six priori-defined cortical ROIs covering the entire cortex. Seed-based functional connectivity analysis between each thalamic subregion and the whole brain was further compared across groups after treatment.

          Results

          Of the 59 patients that finished the study, those in the taVNS group had significantly reduced number of migraine days, pain intensity and migraine attack times after 4 weeks of treatment compared with the sham taVNS. Functional connectivity analysis revealed that taVNS can increase the connectivity between the motor-related thalamus subregion and anterior cingulate cortex/medial prefrontal cortex, and decrease the connectivity between occipital cortex-related thalamus subregion and postcentral gyrus/precuneus.

          Conclusion

          Our findings suggest that taVNS can relieve the symptoms of headache as well as modulate the thalamocortical circuits in migraine patients. The results provide insights into the neural mechanism of taVNS and reveal potential therapeutic targets for migraine patients.

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          Most cited references51

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          Fitting Linear Mixed-Effects Models Usinglme4

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            lmerTest Package: Tests in Linear Mixed Effects Models

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              Conn: a functional connectivity toolbox for correlated and anticorrelated brain networks.

              Resting state functional connectivity reveals intrinsic, spontaneous networks that elucidate the functional architecture of the human brain. However, valid statistical analysis used to identify such networks must address sources of noise in order to avoid possible confounds such as spurious correlations based on non-neuronal sources. We have developed a functional connectivity toolbox Conn ( www.nitrc.org/projects/conn ) that implements the component-based noise correction method (CompCor) strategy for physiological and other noise source reduction, additional removal of movement, and temporal covariates, temporal filtering and windowing of the residual blood oxygen level-dependent (BOLD) contrast signal, first-level estimation of multiple standard functional connectivity magnetic resonance imaging (fcMRI) measures, and second-level random-effect analysis for resting state as well as task-related data. Compared to methods that rely on global signal regression, the CompCor noise reduction method allows for interpretation of anticorrelations as there is no regression of the global signal. The toolbox implements fcMRI measures, such as estimation of seed-to-voxel and region of interest (ROI)-to-ROI functional correlations, as well as semipartial correlation and bivariate/multivariate regression analysis for multiple ROI sources, graph theoretical analysis, and novel voxel-to-voxel analysis of functional connectivity. We describe the methods implemented in the Conn toolbox for the analysis of fcMRI data, together with examples of use and interscan reliability estimates of all the implemented fcMRI measures. The results indicate that the CompCor method increases the sensitivity and selectivity of fcMRI analysis, and show a high degree of interscan reliability for many fcMRI measures.
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                Author and article information

                Journal
                Regional Anesthesia & Pain Medicine
                Reg Anesth Pain Med
                BMJ
                1098-7339
                1532-8651
                January 19 2021
                February 2021
                February 2021
                December 01 2020
                : 46
                : 2
                : 145-150
                Article
                10.1136/rapm-2020-102088
                33262253
                0319ea01-89e9-45e3-af99-df22b650d1f2
                © 2020
                History

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