bioRxivNeurosciencePreprint
Characterising modulatory effects of transcranial random noise stimulation using the perceptual template model.
Efstathia Stephanie GotsisiD, Jeroen J. A. van BoxteliD, Christoph TeufeliD, Mark EdwardsiD, Bruce K. ChristenseniD
Abstract
Neural noise is an inherent property of all nervous systems. However, the mechanisms by which such random and fluctuating neural activity influences perception are still unclear. To elucidate the relationship between neural noise and perceptual performance we require techniques that can safely manipulate neural noise in humans. Transcranial random noise stimulation (tRNS), a form of electrical brain stimulation, has been proposed to increase neural noise according to principles of stochastic resonance (SR); where small to moderate intensities of tRNS improve performance, while higher intensities are detrimental. To date, high intensity (i.e., >2mA) tRNS effects on neural noise levels have not been directly quantified, nor have the detrimental effects proposed by SR been demonstrated in early visual processing. For this purpose, we applied a maximum current intensity of 3mA high-frequency tRNS to the visual cortex (V1) during an orientation discrimination task across increasing external visual noise levels, and fit the perceptual template model to contrast thresholds to quantify intrinsic mechanisms related to noise underlying changes in perceptual performance. We found that tRNS generally worsened perceptual performance by increasing observers internal noise and reducing the ability to filter external noise compared to sham. While most observers experienced detrimental effects, others demonstrated improved perceptual performance (i.e., reduced internal noise and better noise filtering). Preliminary evidence suggests that individual baseline internal noise levels may drive the observed beneficial or detrimental observer responses to tRNS. These findings have important implications for the application of tRNS to investigate the impact of internal noise and noise filtering processes on perception.
- organism: human
- assay: electronic health records
- research focus: differential expression
- approach: statistical model
- Citations
- 0
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