Gamma-band enhancement of functional brain connectivity following transcutaneous electrical nerve stimulation

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Abstract

Objective. Transcutaneous electrical nerve stimulation (TENS) has been suggested as a possible non-invasive pain treatment. However, the underlying mechanism of the analgesic effect of TENS and how brain network functional connectivity (FC) is affected following the use of TENS is not yet fully understood. The purpose of this study was to investigate the effect of high-frequency TENS on the alteration of functional brain network connectivity and the corresponding topographical changes, besides perceived sensations. Approach. Forty healthy subjects participated in this study. Electroencephalography (EEG) data and sensory profiles were recorded before and up to an hour following high-frequency TENS (100 Hz) in sham and intervention groups. Brain source activity from EEG data was estimated using the LORETA algorithm. In order to generate the functional brain connectivity network, the Phase Lag Index was calculated for all pair-wise connections of eight selected brain areas over six different frequency bands (i.e. δ, θ, α, β, 3, and 0.5-90 Hz). Main results. The results suggested that the FC between the primary somatosensory cortex (SI) and the anterior cingulate cortex, in addition to FC between SI and the medial prefrontal cortex, were significantly increased in the gamma-band, following the TENS intervention. Additionally, using graph theory, several significant changes were observed in global and local characteristics of functional brain connectivity in gamma-band. Significance. Our observations in this paper open a neuropsychological window of understanding the underlying mechanism of TENS and the corresponding changes in functional brain connectivity, simultaneously with alteration in sensory perception.

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Zarei, A. A., Jensen, W., Faghani Jadidi, A., Lontis, E. R., & Atashzar, S. F. (2022). Gamma-band enhancement of functional brain connectivity following transcutaneous electrical nerve stimulation. Journal of Neural Engineering, 19(2). https://doi.org/10.1088/1741-2552/ac59a1

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