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• W2917841330 abstract "Background High-frequency transcranial random noise stimulation (hf-tRNS) is a neuromodulatory technique consisting of the application of alternating current at random intensities and frequencies. hf-tRNS induces random neural activity in the system that may boost the sensitivity of neurons to weak inputs. Stochastic resonance is a nonlinear phenomenon whereby the addition of an optimal amount of noise results in performance enhancement, whereas further noise increments impair signal detection or discrimination. Objective The aim of the study was to assess whether modulatory effects of hf-tRNS rely on the stochastic resonance phenomenon, and what is the specific neural mechanism producing stochastic resonance. Method Observers performed a two-interval forced choice motion direction discrimination task in which they had to report whether two moving patches presented in two temporal intervals had the same or different motion directions. hf-tRNS was administered at five intensity levels (0.5, 0.75, 1.0, 1.5, and 2.25 mA). Results The results showed a significant improvement in performance when hf-tRNS was applied at 1.5 mA, representing the optimal level of external noise. However, stimulation intensity at 2.25 mA significantly impaired direction discrimination performance. An equivalent noise (EN) analysis, used to assess how hf-tRNS modulates the mechanisms underlying global motion processing, showed an increment in motion signal integration with the optimal current intensity, but reduced motion signal integration at 2.25 mA. Conclusion These results indicate that hf-tRNS-induced noise modulates neural signal-to-noise ratio in a way that is compatible with the stochastic resonance phenomenon." @default.
• W2917841330 created "2019-03-02" @default.
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• W2917841330 date "2019-07-01" @default.
• W2917841330 modified "2023-10-11" @default.
• W2917841330 title "Modulatory mechanisms underlying high-frequency transcranial random noise stimulation (hf-tRNS): A combined stochastic resonance and equivalent noise approach" @default.
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• W2917841330 doi "https://doi.org/10.1016/j.brs.2019.02.018" @default.
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