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tRNS effects on visual contrast detection
Neuroscience Letters 717 (2020) 134696
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Research article
tRNS effects on visual contrast detection Luca Battaglini
a,b
, Giulio Contemori
a,b,c
T a
d,
, Sofia Penzo , Marcello Maniglia *
a
Department of General Psychology, University of Padova, Padova, Italy Neuro.Vis.U.S. Laboratory, University of Padova, Padova, Italy Université de Toulouse-UPS, Centre de Recherche Cerveau et Cognition, Toulouse, France d Department of Neurobiology, University of Alabama at Birmingham, Birmingham, AL, USA b c
A R T I C LE I N FO
A B S T R A C T
Keywords: tRNS Brain stimulation Contrast detection Spatial frequency Gabor
In recent years, transcranial electrical stimulation (tES) has been used to improve cognitive and perceptual abilities and to boost learning. In the visual domain, transcranial random noise stimulation (tRNS), a type of tES in which electric current is randomly alternating in between two electrodes at high frequency, has shown potential in inducing long lasting perceptual improvements when coupled with tasks such as contrast detection. However, its cortical mechanisms and online effects have not been fully understood yet, and it is still unclear whether these long-term improvements are due to early-stage perceptual enhancements of contrast sensitivity or later stage mechanisms such as learning consolidation. Here we tested tRNS effects on multiple spatial frequencies and orientation, showing that tRNS enhances detection of a low contrast Gabor, but only for oblique orientation and high spatial frequency (12 cycles per degree of visual angle). No improvement was observed for low contrast and vertical stimuli. These results indicate that tRNS can enhance contrast sensitivity already after one training session, however this early onset is dependent on characteristics of the stimulus such as spatial frequency and orientation. In particular, the shallow depth of tRNS is likely to affect superficial layers of the visual cortex where neurons have higher preferred spatial frequencies than cells in further layers, while the lack of effect on vertical stimuli might reflect the optimization of the visual system to see cardinally oriented low contrast stimuli, leaving little room for short-term improvement. Taken together, these results suggest that online tRNS effects on visual perception are the result of a complex interaction between stimulus intensity and cortical anatomy, consistent with previous literature on brain stimulation.
1. Introduction Transcranial electric stimulation (tES) has become in recent years a valuable tool for the study of brain mechanisms [1], modulating behavioral performance or improving perceptual learning effects [2,3]. tES refers to a number of techniques based on the use of a weak (< 2.0 mA) electrical current delivered through electrodes placed on the scalp targeting specific brain regions affecting in turn the activity of neuronal populations in those areas [4]. A common type of tES is transcranial direct current stimulation (tDCS), in which a homogenous electric field is induced in between the electrodes to elicit polarity-specific modulation of cortical excitability, in particular several studies showed an increase of cortical excitability by means of sub-threshold depolarization for anodal stimulation and a decrease of cortical excitability for cathodal stimulation as a consequence of hyperpolarization, with effects lasting beyond the stimulation period [4–7]. However, more
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recent studies suggest that these effects are not straightforward and that the modulation of neuronal excitability by tES results from the interaction of several factors such as the morphology of the brain and neurons, and the duration and intensity of the stimulation [8–10]. Recently, transcranial random noise stimulation (tRNS), a tES protocol in which an alternating current is passed between the electrodes with a random frequency switch in polarity (usually between 100 and 640 Hz), has been successfully used to improve visual performances in both normal sighted participants [3,11] and clinical population [2]. Despite the multiple applications both in human basic and clinical research, we are only now beginning to understand the underlying physiological mechanisms [12]. tRNS seems to have similar effects to the more common anodal tDCS, for example it has been shown that 10 min of tRNS applied over M1 can enhance the cortical excitability up to 1–1.5 h [13], similarly to tDCS, however recent studies showed that its effect might exceed in duration that of anodal tDCS [3,14]. Studies using
Corresponding author at:Department of Neurobiology, University of Alabama at Birmingham, Birmingham, AL, USA E-mail addresses: [email protected], [email protected] (L. Battaglini), [email protected] (M. Maniglia).
https://doi.org/10.1016/j.neulet.2019.134696 Received 8 August 2019; Received in revised form 21 November 2019; Accepted 13 December 2019 Available online 14 December 2019 0304-3940/ © 2019 Elsevier B.V. All rights reserved.
Neuroscience Letters 717 (2020) 134696
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polarity of the stimulation is not univocal but in rapid fluctuation. Thus a possible mechanism of action might be the temporal summation of weak depolarizing currents that increase the frequency of opening of the ions gate [33–35]. Furthermore, it has been shown that tRNS has specific effects for different neural subpopulations within the stimulated region, thus producing a globally non-linear effect [36]. Concerning the late and after-effects of the stimulation, tDCS seems to be NMDA receptor dependent [31] while tRNS effect are resistant to the administration of NMDA antagonist but can be suppressed by benzodiazepines. The difference in terms of neurophysiological mechanisms and time courses between anodal tDCS and tRNS [26,27] might also be reflected in the behavioral outcome. For example, Fertonani and colleagues [3] showed that online hf-tRNS improved between-blocks performance in a orientation discrimination task significantly more than online anodal-tDCS. Moreover, Inukai and colleagues [37] demonstrated a further difference in the extent of the late effect of the two types of stimulation, with tRNS exhibiting stronger after-effect than tDCS. All things considered, tRNS effects on contrast sensitivity at different spatial frequency might be different then those found by Richard et al. [26] with tDCS. Moreover, while a previous study investigated the effect of tRNS on a contrast sensitivity task [38], no study so far has systematically tested tRNS effects over multiple dimensions of the testing stimuli. Therefore, in the present study we investigated, in a within subject design, the effect of tRNS in a low contrast detection task with Gabor patches at different spatial frequencies and orientation. Following evidence from training studies, we predicted that, in case tRNS affects stimulus processing, active stimulation would improve participants’ performance. On the contrary, in case tRNS acts only by increasing later-stage learning [11,15,23,39] or has no effect, no improvement in the task would be observed.
tRNS in the visual domain tend to focus on its efficacy in boosting learning, both between blocks [3] and between days [2,11], and inducing larger transfer of learning [15,16] with respect to behavioral training alone or coupled with tDCS [17]. The majority of these studies uses low-level training tasks such as orientation discrimination and contrast detection, and relies on the idea that in the hyerarchical structure of visual processing, improving the perception of basic features would provide better input to higher-level stages, in turn improving visual functions such as visual acuity, reading or figure-ground segmentation [18,19]. Considering the specific occipital locus of stimulation in the aforementioned studies, roughly corresponding to early visual cortex (V1/V2), it is plausible to assume that tRNS affects the units responsible for processing contrast. Therefore, the contrast sensitivity function (CSF), which represents one of the basic responses of the visual system and has its neural basis in the orientation-selective units in the early visual cortex, constitutes an ideal probe to explore the effect of different types of brain stimulation on basic visual functions. However, most of tRNS studies conducted so far on contrast sensitivity relied on longitudinal studies involving multiple sessions/days, rather than on the effect of ‘online’ tRNS, and although some studies using different tasks seem to suggest that tRNS might be effective already after few blocks [3] or a single daily session [11], a systematic study of its early effect on the CSF has not been conducted yet. Following tRNS stimulation, the visual cortex might undergo short- or long-term neuroplastic changes. Usually, we refer to neuroplasticity as an experiencedependent adaptive process that alters synaptic efficiency. Short-term neuroplasticity is accomplished when the synaptic efficacy changes due to a short task repetition (in a range of minutes) [20,21], whereas longterm neuroplasticity reflects a long-term potientation (or depression) of neural circuits that may require several days [22]. Previous studies have shown that the modulatory effect of the stimulation interacts effectively with both the short and the long-term neuroplastic processis at several stages [2,3,15,23]. Thus, the question remains open whether the training and transfer effects reported in previous studies coupling tRNS and contrast sensitivity are due to perceptual improvements early on in the training (short-term plasticity) or to later stage (long-term neural plasticity- or learning-related processes, e.g. consolidation). A number of recent studies aimed at testing the effects of online tDCS on contrast sensitivity: for example, it was shown that anodal tDCS transiently increases contrast sensitivity in individuals with amblyopia [24,25]. Richard et al. [26] tested the effect of tDCS on the CSF for vertical and oblique orientations in healthy participants, observing polarity-dependent effects only for high spatial frequencies (8–12, cycle per degree of visual angle [cpd]) and oblique orientation. Interestingly, authors reported an opposite effect of tDCS with respect to previous studies in the visual domain: cathodal stimulation increased contrast sensitivity, while anodal decreased it, further suggesting that these effects are optimized by stimuli that elicit a weak contrast sensitivity response, such as a high spatial frequency and oblique gratings. Richard et al. [26] suggested that the modulation effect was observed only at high-spatial frequencies because of the anatomical structure of the visual system, where units responding to high spatial frequency stimuli are located closer to the scalp, thus closer to the surface and the occipital electrode. A possible explanation for the apparent inconsistencies in the polarity of the effect among previous studies posits that the tES effect might be different in (sub) clinical population compared to healthy participants [27], but also that the task-induced activity is more important than the polarization in predicting the stimulation effect on a participant’s behavior [28]. hf-tRNS stimulation is often associated with a-tDCS stimulation as both have the main effect of increasing cortical excitability by increasing the inflow of sodium in the stimulated neurons [29,30]. Despite this similarity, the mechanism of action at the cellular level differs substantially. While the online effect of a-tDCS relies on current-induced changes in resting membrane potentials [31,32], the same mechanism cannot take place during tRNS since the
2. Methods 2.1. Participants Twenty participants (13 females), age 21–31 (25 ± 3.4), took part in this study. All participants had normal or corrected-to-normal visual acuity. They sat in a dark room at a distance of 57 cm from the screen. Viewing was binocular. Participants were instructed to fixate in the center of the screen. All participants took part voluntarily and written and oral informed consent was obtained from all the subjects before the study was initiated. The study and protocol conformed to the tenets of the Declaration of Helsinki. Furthermore, the study was approved by the Ethics Committee of the School of Psychology of the University of Padova. 2.2. Apparatus Stimuli were generated using Matlab Psychtoolbox [40,41] and displayed on a 22-in. Philips 202P4 CRT monitor with a refresh rate of 85 Hz. The resolution of the screen was 1600 × 1200 pixels. The minimum and maximum luminance of the screen were 0.60 and 110 cd/m2, respectively, and the mean luminance was 55 cd/m2. Luminance was measured with a CRS Optical photometer (OP200-E; Cambridge Research System Ltd., Rochester, Kent, UK). A digital-to-analog converter (Bits#, Cambridge Research Systems, Cambridge, UK) was used to increase the dynamic contrast range (12-bit luminance resolution). A 12-bit gamma-corrected lookup table (LUT) was applied so that luminance was a linear function of the digital representation of the image. 2.3. Stimuli The stimulus target was a Gabor patch consisting of a cosinusoidal carrier enveloped by a stationary Gaussian. Each Gabor patch (Eq. (1)) 2
Neuroscience Letters 717 (2020) 134696
L. Battaglini, et al.
Fig. 1. Figure represents: Electrode positioning, modeled electrical field strength (V/m) at the moment when the current reach the maximum anodal intensity and illustration of a trial with a diagonal gabor patch. The modeled electrical field shows that the highest current density corresponded to the early visual cortices. Fig. 2. Experimental paradigm. Participants completed four sessions over two days. Each session consisted of 5 blocks of about 3 min of a contrast detection task. Each session was a combination of stimulus orientation (vertical vs diagonal) and stimulation condition (tRNS vs Sham). Session order was counterbalanced across participants. Between two consecutive sessions there was a break of thirty minutes.
was characterized by its wavelength (λ), its phase (φ), and the standard deviation (σ) of the luminance Gaussian envelope in the space of the image (σ = 1.5 and φ = 0). In Experiment 1, the Gabor patch was always presented vertically, while in Experiment 2 it was tilted 45 degrees to the left. −(x 2 + y 2 )
G (x , y ) = e
2σ 2
× cos[
2π x + φ] λ
(vertical vs. diagonal) was counterbalanced across participants. Each session was composed by five blocks. The spatial frequency of the target was varied between blocks (1, 3, 5, 7, 12 cpd). The order of the blocks was randomized. Each block lasted about 3 min.
(1)
2.4. tRNS procedure 3. Results tRNS was delivered using a battery-driven stimulator (BrainSTIM, EMS) through a pair of saline-soaked sponge electrodes. The size of the stimulating electrode placed over the occipital cortex (Oz) was 72 × 60 mm and the size of the reference electrode placed over the vertex (Cz) was 115 × 95 mm, rendering the large reference electrode almost inert due to low current density (Fig. 1). The current was initially ramped up over 15 s to an intensity of 1.5 mA and then kept constant. The tRNS frequency ranged from 100 to 600 hz. The stimulation was concomitant to the task and lasted for about 15 min (single blind design). In the sham condition, the current was delivered only during the first and last 30 s (rumped up + stimulation: 15 s + 15 s). Less than half of the participants reported mild skin sensation at the onset of the stimulation, but it disappeared quickly (within 30 s) [42]. The guessing rate of real/ placebo stimulation was at chance level.
3.1. Data analysis To explore the main effects of Stimulation and Spatial frequency, we conducted two separate repeated measure ANOVAs for vertical and diagonal stimuli. To test spatial frequency-specific effect, in case of significant interaction, we used Bonferroni-corrected paired t-tests. Following previous findings, showing cathodal tDCS effect for high spatial frequencies [25,26], we planned two main comparisons, namely 7cpd Sham vs 7cpd tRNS and 12cpd Sham vs 12cpd tRNS. All the analyses were run separately for each Experiment. Finally, we conducted an omnibus Anova with Orientation, Stimulation and Spatial frequency as main factors. 3.2. Experiment 1
2.5. Contrast detection procedure Results for Experiment 1 (Gabor patches presented vertically) are shown in Fig. 3. The Anova with factors Stimulation (tRNS vs Sham) and Spatial Frequency (1cpd vs 3cpd vs 5cpd vs 7cpd vs 12 cpd) showed a main effect of the latter (F(4,76) = 69.55, p < .0001, η2 = .785) but no effect of Stimulation (F(1,19) = .47, p = .83, η2 = .02) or interaction (F(4,76) = 1.34, p < .26, η2 = .066).
Participants were asked to perform a contrast detection task. The contrast of the Gabor patch varied according to a one-up three-down staircase procedure, estimating a threshold of 79% [43]. The staircase ended after 100 trials or 18 reversals. In the first trial the contrast of the target was set to 0.2 Michelson contrast. A trial started with a white fixation dot (0.12 deg) for 0.5 s, then the first interval was presented for 80 ms and after an ISI of 0.5 s the second temporal interval was presented for 80 ms. The target was presented in the center of the screen either in the first or in the second temporal interval. Participants were asked to indicate which of the two temporal intervals contained the target (temporal-2 alternative forced choices, 2AFC) by pressing a button on the computer keyboard. The fixation dot was not presented during the temporal intervals. The next trial started 1 s after the button press. No feedback was given. Each participant performed four sessions, two stimulation conditions (tRNS vs. Sham) × two Gabor patch orientations (vertical vs. diagonal) in two separate days (Fig. 2). Between two consecutive sessions there was a break of thirty minutes. The order of stimulation condition (tRNS vs. Sham) and Gabor patch orientation
3.3. Experiment 2 Following Richard et al. [26], we tested the same participants as in Experiment 1 with diagonally oriented (45°) stimuli. Results for Experiment 2 are shown in Fig. 4. The Anova conducted on Stimulation (tRNS vs Sham) and Spatial Frequency (1cpd vs 3cpd vs 5cpd vs 7cpd vs 12 cpd) showed a significant main effect for both (Stimulation: F(1,19) = 10.23, p = .005 η2 = .35; Spatial frequency: F(4,76) = 58.21, p < .0001, η2 = .758). Additionaly, the interaction Stimulation × Spatial frequency was significant (F(4,76) = 3.81, p = .007, η2 = .167). Bonferroni corrected paired t-tests showed a significant difference between tRNS and Sham at 12 cpd (t(19) = 3.03, p = .012) but not at 7cpd 3
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number of perceptual learning studies using oriented gratings as training stimuli and contrast detection as task [2,15,23,44]. These studies showed that coupling low-level perceptual training and tRNS increases learning rate and transfer to untrained visual functions such as visual acuity. However, differently from other visual tasks such as orientation discrimination [3] and crowding [11], no study so far investigated directly the effect of online tRNS1 on contrast detection testing multiple spatial frequencies and different orientations. Here we report an online (single-session) improvement in contrast detection with concomitant tRNS that was specific for the stimulus orientation (45°) and spatial frequency (12 cpd). No significant changes in contrast detection were observed for vertically oriented stimuli and/or lower spatial frequencies. A possible interpretation of these results is in line with what suggested by Richard et al. [26] and consistent with the anatomy of the visual cortex and the mechanisms of tRNS, in particular within the stochastic resonance famework2 [45]. The lack of effect on vertical stimuli can be attributed to the visual system’s perceptual optimization for sensitivity along cardinal directions, so that perceptual perfomances for stimuli aligned with the horizontal and vertical axes are already at or close to their peak (thus reflecting a sort of ceiling effect in which performance cannot be further improved by training), especially above the peak of the CSF [46]. Indeed, threshold along the diagonal axes were on average almost twice as high with respect to the vertical axis, and at 12 cpd they were five times higher. It is possible then that in healthy participants tES cannot further improve the performance for this specific combination of conditions within a single session. Alternatively, if the effect of the active stimulation was present, it could have been so small that was hidden by the error of measurement. Concerning the spatial frequency-selectivity of the effect, this could be explained by the proximity of visual units with higher preferred spatial frequencies to the scalp [26,47], where the electric concentration might be appropriate to became a good noise for the system. As demonstrated in previous studies [38,48] the addition of noise produces an improvement in terms of behavior only if the intensity of the stimulation is optimal. In fact, stimulations that are too intense or too weak would end up sampling the two extremes of the inverted U curve that characterizes the phenomenon of stochastic resonance. It is possible that the innermost layers of the cortex receiving a decreased intensity of stimulation were outside the sweet spot for improved performance. As per the mechanisms of the tRNS, several hypotheses have been proposed: For example, the improvement in performance might be due to temporal summation of the depolarizing currents induced by the random sub-threshold stimulation [13] that might facilitate the activation of targeted units. Alternatively, the rapid changes of direction of the current might prevent these units from homeostasis [3]. This is consistent with evidence that tRNS induces greater online improvements than anodal tDCS, a protocol of stimulation that induces cortical excitability through a constant flow of current in the same direction. Finally, and most relevant to the results presented here, the introduction of external noise in the system from the electric stimulation might alter the overall level of cortical excitability and the probability of discharge of the neurons, thus altering the signal-to-noise ratio during stimulus processing [3]. The stochastic resonance framework of brain stimulation [45]
Fig. 3. Contrast thresholds (Michelson Contrast: (max luminance – min luminance) / max (luminance + min luminance). as function of spatial frequency for the sham and tRNS session in the condition with vertical target. Error bars represents standard error of the mean (s.e.m.).
Fig. 4. Contrast thresholds (Michelson Contrast) as function of spatial frequency for the sham and tRNS session in the condition with diagonal (45°) target. Error bars represent s.e.m.
(t(19) = 1.9, p = .14). Finally, since the same participants took part in the two Experiments, we ran an omnibus Anova with factors Orientation (vertical vs diagonal), Stimulation (Sham vs tRNS) and Spatial Frequency (1cpd vs 3cpd vs 5cpd vs 7cpd vs 12 cpd). Results showed a main effect of Orientation (F(1,19) = 40.08, p < .001, η2 = .678), Stimulation (F(1,19) = 7.23, p = .015, η2 = .276) and Spatial Frequency (F(1,19) = 71.13, p < .001, η2 = .789). Significant interactions were observed for Orientation x Stimulation (F(1,19) = 8.01, p = .01, η2 = .279), Orientation × Spatial Frequency (F(4,76) = 44.14, p < .001, η2 = .699) and the three way interaction Orientation × Stimulation × Spatial Frequency (F(4,76) = 4.72, p = .002, η2 = .199). Bonferroni-corrected paired t-tests showed significant difference between diagonal sham and diagonal tRNS (p = .005) and confirmed the significant difference at 12cpd for the tRNS group over the Sham for the diagonal condition (p = .022). Finally, we ran an additional Anova with Block Order and Spatial Frequency as factors to inverstigate any possible learning effect. We found no effect of Block Order (F(1.88,35.7) = .82, p = .44, η2 = .042) or interaction Block Order × Spatial Frequency (F(2.03,35.5) = .72, p = .58, η2 = .028).
Note that with “online tRNS” we meant that the stimulation was concomitant with the task. With 15 minutes of stimulation we cannot exclude a build-up effect over time rather than a genuine “online effect” of the online tRNS. 2 Stochastic resonance is a phenomenon where a signal that is normally too weak to be detected by a sensor, can be boosted by adding a certain amount of noise to the signal. When the visual input signal is too weak, it produces subthreshold neural response, however tRNS can introduce good noise directly to the visual system that resonate with the signal input of the stimulus producing a supra-threshold response. 1
4. Discussion The aim of the present study was to systematically test the effects of online tRNS on contrast detection at different spatial frequencies and orientations. Results showed that tRNS improved contrast detection but only when the stimuli were presented tilted 45° from the vertical axis and had high spatial frequency (12 cpd). Electrical brain stimulation, in particular tRNS, has been used to improve learning and transfer in a 4
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combines the interaction between this externally induced noise, internal activity and stimulus-driven activity, providing predictions on the perceptual outcome: in particular, in case of a low intensity stimulus, external noise would enhance the signal above the threshold. On the other hand, in case of a higher intensity stimulus, the external noise might boost the noise of the spontaneous internal activity, thus reducing the signal-to-noise ratio [38,48]. At a neurophysiological level the tRNS has been shown to modulate the activation and inactivation of the Na+ channels exerting the maximum excitability increase at an intermediate level of noise [36]. This short-term molecular activity due to the tRNS, that increases the neuronal activation, might be the neural bases for the behavioural effect observed. Morevoer, the repetitive activation and inactivation of the NA+ channels might activate a biological cascade responsible for a long lasting plastic mechanism that is NMDA-receptor independent but sodium channel blocker and benzodiazepines sensitive that might explain the offline after effect [33] and the long-lasting effect that was found in perceptual learning studies. In conclusion, contrast detection can be improved in healthy participants not only by online tDCS [24–26], but also by tRNS. Furthermore, this study showed that this improvement could occur not only at a later stage of learning [2,3,15,23], but also that tRNS can affect the processing of the stimulus. Similarly, to previous studies using tDCS, these improvements seem specific for high spatial frequencies and noncardinal orientations [26]. The mechanism underlining the tRNS effect are certainly different from those of the tDCS, but are not still completely clear in vision. A good way to explain the tRNS effect is via stochastic resonance mechanism in which the ‘good’ noise introduced in the visual system by tRNS resonates with the weak signal input producing a signal that surpasses the threshold of a neural population. Authors contribution statement L.B., G.C. and M.M. designed and implemented the experiments. S.P. collected and preprocessed the data. M.M. analyzed the data. All authors interpreted the results, L.B. and M.M. wrote the main manuscript. Disclosure of funding sources The study was supported by a grant form MIUR (Dipartimenti di Eccellenza DM 11/05/2017 n.262) to the Department of General Psychology Declaration of Competing Interest The authors declare no competing interests. References [1] C.J. Stagg, M.A. Nitsche, Physiological basis of transcranial direct current stimulation, Neuroscience 17 (2011) 37–53, https://doi.org/10.1177/ 1073858410386614. [2] R. Camilleri, A. Pavan, F. Ghin, L. Battaglini, G. Campana, Improvement of uncorrected visual acuity (UCVA) and contrast sensitivity (UCCS) with perceptual learning and transcranial random noise stimulation (tRNS) in individuals with mild myopia, Front. Psychol. 5 (2014) 1234, https://doi.org/10.3389/fpsyg.2014. 01234. [3] A. Fertonani, C. Pirulli, C. Miniussi, Random noise stimul ation improves neuropl asticity in Perceptu al le arning, J. Neurosci. 31 (2011) 15416–15423, https://doi. org/10.1523/JNEUROSCI.2002-11.2011. [4] M.A. Nitsche, W. Paulus, Excitability changes induced in the human motor cortex by weak transcranial direct current stimulation, J. Physiol. 527 (2000) 633–639, https://doi.org/10.1111/j.1469-7793.2000.t01-1-00633.x. [5] T. Radman, R.L. Ramos, J.C. Brumberg, M. Bikson, Role of cortical cell type and morphology in subthreshold and suprathreshold uniform electric field stimulation in vitro, Brain Stimul. 2 (2009), https://doi.org/10.1016/J.BRS.2009.03.007 215–228.e3. [6] D. Reato, A. Rahman, M. Bikson, L.C. Parra, Low-intensity electrical stimulation affects network dynamics by modulating population rate and spike timing, J. Neurosci. 30 (2010) 15067–15079, https://doi.org/10.1523/JNEUROSCI.2059-10. 2010.
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[41] D.G. Pelli, The VideoToolbox software for visual psychophysics: transforming numbers into movies, Spat. Vis. 10 (1997) 437–442, https://doi.org/10.1163/ 156856897X00366. [42] A. Fertonani, C. Ferrari, C. Miniussi, What do you feel if I apply transcranial electric stimulation? Safety, sensations and secondary induced effects, Clin. Neurophysiol. 126 (2015) 2181–2188, https://doi.org/10.1016/J.CLINPH.2015.03.015. [43] H. Levitt, Transformed up‐down methods in psychoacoustics, J. Acoust. Soc. Am. 49 (1971) 467–477, https://doi.org/10.1121/1.1912375. [44] B. Moret, R. Camilleri, A. Pavan, G. Lo Giudice, A. Veronese, R. Rizzo, G. Campana, Differential effects of high-frequency transcranial random noise stimulation (hftRNS) on contrast sensitivity and visual acuity when combined with a short perceptual training in adults with amblyopia, Neuropsychologia 114 (2018) 125–133, https://doi.org/10.1016/J.NEUROPSYCHOLOGIA.2018.04.017. [45] C. Miniussi, J.A. Harris, M. Ruzzoli, Modelling non-invasive brain stimulation in cognitive neuroscience, Neurosci. Biobehav. Rev. 37 (2013) 1702–1712, https:// doi.org/10.1016/J.NEUBIOREV.2013.06.014. [46] F.W. Campbell, J.J. Kulikowski, J. Levinson, The effect of orientation on the visual resolution of gratings, J. Physiol. 187 (1966) 427–436, https://doi.org/10.1113/ jphysiol.1966.sp008100. [47] R. Tootell, M. Silverman, R.- De Valois, Spatial Frequency Columns in Primary Visual Cortex, Science.Sciencemag.ORg, (1981) https://science.sciencemag.org/ content/214/4522/813.short. [48] O. van der Groen, N. Wenderoth, Transcranial random noise stimulation of visual cortex: stochastic resonance enhances central mechanisms of perception, J. Neurosci. 36 (2016) 5289–5298, https://doi.org/10.1523/JNEUROSCI.4519-15. 2016.
benzodiazepines sensitive, Front. Neurosci. 9 (2015) 125, https://doi.org/10.3389/ fnins.2015.00125. B.J. Gluckman, T.I. Netoff, E.J. Neel, W.L. Spano, M.L. Spano, S.J. Schiff, Stochastic resonance in a neuronal network from mammalian brain, Phys. Rev. Lett. 77 (1996) 4098–4101, https://doi.org/10.1103/PhysRevLett.77.4098. I. Onorato, G. D’Alessandro, M.A. Di Castro, M. Renzi, G. Dobrowolny, A. Musarò, M. Salvetti, C. Limatola, A. Crisanti, F. Grassi, Noise enhances action potential generation in mouse sensory neurons via stochastic resonance, PLoS One 11 (2016) e0160950, , https://doi.org/10.1371/journal.pone.0160950. L. Remedios, P. Mabil, J. Flores-Hernández, O. Torres-Ramírez, N. Huidobro, G. Castro, L. Cervantes, J.A. Tapia, B. De la Torre Valdovinos, E. Manjarrez, Effects of short-term random noise electrical stimulation on dissociated pyramidal neurons from the cerebral cortex, Neuroscience 404 (2019) 371–386, https://doi.org/10. 1016/j.neuroscience.2019.01.035. Y. Inukai, K. Saito, R. Sasaki, S. Tsuiki, S. Miyaguchi, S. Kojima, M. Masaki, N. Otsuru, H. Onishi, Comparison of three non-invasive transcranial electrical stimulation methods for increasing cortical excitability, Front. Hum. Neurosci. 10 (2016), https://doi.org/10.3389/fnhum.2016.00668. O. van der Groen, N. Wenderoth, Random noise stimulation of the cortex: stochastic resonance enhances central mechanisms of perception, Brain Stimul. 10 (2017) e4, https://doi.org/10.1016/j.brs.2016.11.030. R. Camilleri, A. Pavan, F. Ghin, L. Battaglini, G. Campana, Improvement of uncorrected visual acuity and contrast sensitivity with perceptual learning and transcranial random noise stimulation in individuals with mild myopia, Front. Psychol. 5 (2014) 1234, https://doi.org/10.3389/fpsyg.2014.01234. D.H. Brainard, The psychophysics toolbox, Spat. Vis. 10 (1997) 433–436, https:// doi.org/10.1163/156856897X00357.
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Neuroscience Letters journal homepage: www.elsevier.com/locate/neulet
Research article
tRNS effects on visual contrast detection Luca Battaglini
a,b
, Giulio Contemori
a,b,c
T a
d,
, Sofia Penzo , Marcello Maniglia *
a
Department of General Psychology, University of Padova, Padova, Italy Neuro.Vis.U.S. Laboratory, University of Padova, Padova, Italy Université de Toulouse-UPS, Centre de Recherche Cerveau et Cognition, Toulouse, France d Department of Neurobiology, University of Alabama at Birmingham, Birmingham, AL, USA b c
A R T I C LE I N FO
A B S T R A C T
Keywords: tRNS Brain stimulation Contrast detection Spatial frequency Gabor
In recent years, transcranial electrical stimulation (tES) has been used to improve cognitive and perceptual abilities and to boost learning. In the visual domain, transcranial random noise stimulation (tRNS), a type of tES in which electric current is randomly alternating in between two electrodes at high frequency, has shown potential in inducing long lasting perceptual improvements when coupled with tasks such as contrast detection. However, its cortical mechanisms and online effects have not been fully understood yet, and it is still unclear whether these long-term improvements are due to early-stage perceptual enhancements of contrast sensitivity or later stage mechanisms such as learning consolidation. Here we tested tRNS effects on multiple spatial frequencies and orientation, showing that tRNS enhances detection of a low contrast Gabor, but only for oblique orientation and high spatial frequency (12 cycles per degree of visual angle). No improvement was observed for low contrast and vertical stimuli. These results indicate that tRNS can enhance contrast sensitivity already after one training session, however this early onset is dependent on characteristics of the stimulus such as spatial frequency and orientation. In particular, the shallow depth of tRNS is likely to affect superficial layers of the visual cortex where neurons have higher preferred spatial frequencies than cells in further layers, while the lack of effect on vertical stimuli might reflect the optimization of the visual system to see cardinally oriented low contrast stimuli, leaving little room for short-term improvement. Taken together, these results suggest that online tRNS effects on visual perception are the result of a complex interaction between stimulus intensity and cortical anatomy, consistent with previous literature on brain stimulation.
1. Introduction Transcranial electric stimulation (tES) has become in recent years a valuable tool for the study of brain mechanisms [1], modulating behavioral performance or improving perceptual learning effects [2,3]. tES refers to a number of techniques based on the use of a weak (< 2.0 mA) electrical current delivered through electrodes placed on the scalp targeting specific brain regions affecting in turn the activity of neuronal populations in those areas [4]. A common type of tES is transcranial direct current stimulation (tDCS), in which a homogenous electric field is induced in between the electrodes to elicit polarity-specific modulation of cortical excitability, in particular several studies showed an increase of cortical excitability by means of sub-threshold depolarization for anodal stimulation and a decrease of cortical excitability for cathodal stimulation as a consequence of hyperpolarization, with effects lasting beyond the stimulation period [4–7]. However, more
⁎
recent studies suggest that these effects are not straightforward and that the modulation of neuronal excitability by tES results from the interaction of several factors such as the morphology of the brain and neurons, and the duration and intensity of the stimulation [8–10]. Recently, transcranial random noise stimulation (tRNS), a tES protocol in which an alternating current is passed between the electrodes with a random frequency switch in polarity (usually between 100 and 640 Hz), has been successfully used to improve visual performances in both normal sighted participants [3,11] and clinical population [2]. Despite the multiple applications both in human basic and clinical research, we are only now beginning to understand the underlying physiological mechanisms [12]. tRNS seems to have similar effects to the more common anodal tDCS, for example it has been shown that 10 min of tRNS applied over M1 can enhance the cortical excitability up to 1–1.5 h [13], similarly to tDCS, however recent studies showed that its effect might exceed in duration that of anodal tDCS [3,14]. Studies using
Corresponding author at:Department of Neurobiology, University of Alabama at Birmingham, Birmingham, AL, USA E-mail addresses: [email protected], [email protected] (L. Battaglini), [email protected] (M. Maniglia).
https://doi.org/10.1016/j.neulet.2019.134696 Received 8 August 2019; Received in revised form 21 November 2019; Accepted 13 December 2019 Available online 14 December 2019 0304-3940/ © 2019 Elsevier B.V. All rights reserved.
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polarity of the stimulation is not univocal but in rapid fluctuation. Thus a possible mechanism of action might be the temporal summation of weak depolarizing currents that increase the frequency of opening of the ions gate [33–35]. Furthermore, it has been shown that tRNS has specific effects for different neural subpopulations within the stimulated region, thus producing a globally non-linear effect [36]. Concerning the late and after-effects of the stimulation, tDCS seems to be NMDA receptor dependent [31] while tRNS effect are resistant to the administration of NMDA antagonist but can be suppressed by benzodiazepines. The difference in terms of neurophysiological mechanisms and time courses between anodal tDCS and tRNS [26,27] might also be reflected in the behavioral outcome. For example, Fertonani and colleagues [3] showed that online hf-tRNS improved between-blocks performance in a orientation discrimination task significantly more than online anodal-tDCS. Moreover, Inukai and colleagues [37] demonstrated a further difference in the extent of the late effect of the two types of stimulation, with tRNS exhibiting stronger after-effect than tDCS. All things considered, tRNS effects on contrast sensitivity at different spatial frequency might be different then those found by Richard et al. [26] with tDCS. Moreover, while a previous study investigated the effect of tRNS on a contrast sensitivity task [38], no study so far has systematically tested tRNS effects over multiple dimensions of the testing stimuli. Therefore, in the present study we investigated, in a within subject design, the effect of tRNS in a low contrast detection task with Gabor patches at different spatial frequencies and orientation. Following evidence from training studies, we predicted that, in case tRNS affects stimulus processing, active stimulation would improve participants’ performance. On the contrary, in case tRNS acts only by increasing later-stage learning [11,15,23,39] or has no effect, no improvement in the task would be observed.
tRNS in the visual domain tend to focus on its efficacy in boosting learning, both between blocks [3] and between days [2,11], and inducing larger transfer of learning [15,16] with respect to behavioral training alone or coupled with tDCS [17]. The majority of these studies uses low-level training tasks such as orientation discrimination and contrast detection, and relies on the idea that in the hyerarchical structure of visual processing, improving the perception of basic features would provide better input to higher-level stages, in turn improving visual functions such as visual acuity, reading or figure-ground segmentation [18,19]. Considering the specific occipital locus of stimulation in the aforementioned studies, roughly corresponding to early visual cortex (V1/V2), it is plausible to assume that tRNS affects the units responsible for processing contrast. Therefore, the contrast sensitivity function (CSF), which represents one of the basic responses of the visual system and has its neural basis in the orientation-selective units in the early visual cortex, constitutes an ideal probe to explore the effect of different types of brain stimulation on basic visual functions. However, most of tRNS studies conducted so far on contrast sensitivity relied on longitudinal studies involving multiple sessions/days, rather than on the effect of ‘online’ tRNS, and although some studies using different tasks seem to suggest that tRNS might be effective already after few blocks [3] or a single daily session [11], a systematic study of its early effect on the CSF has not been conducted yet. Following tRNS stimulation, the visual cortex might undergo short- or long-term neuroplastic changes. Usually, we refer to neuroplasticity as an experiencedependent adaptive process that alters synaptic efficiency. Short-term neuroplasticity is accomplished when the synaptic efficacy changes due to a short task repetition (in a range of minutes) [20,21], whereas longterm neuroplasticity reflects a long-term potientation (or depression) of neural circuits that may require several days [22]. Previous studies have shown that the modulatory effect of the stimulation interacts effectively with both the short and the long-term neuroplastic processis at several stages [2,3,15,23]. Thus, the question remains open whether the training and transfer effects reported in previous studies coupling tRNS and contrast sensitivity are due to perceptual improvements early on in the training (short-term plasticity) or to later stage (long-term neural plasticity- or learning-related processes, e.g. consolidation). A number of recent studies aimed at testing the effects of online tDCS on contrast sensitivity: for example, it was shown that anodal tDCS transiently increases contrast sensitivity in individuals with amblyopia [24,25]. Richard et al. [26] tested the effect of tDCS on the CSF for vertical and oblique orientations in healthy participants, observing polarity-dependent effects only for high spatial frequencies (8–12, cycle per degree of visual angle [cpd]) and oblique orientation. Interestingly, authors reported an opposite effect of tDCS with respect to previous studies in the visual domain: cathodal stimulation increased contrast sensitivity, while anodal decreased it, further suggesting that these effects are optimized by stimuli that elicit a weak contrast sensitivity response, such as a high spatial frequency and oblique gratings. Richard et al. [26] suggested that the modulation effect was observed only at high-spatial frequencies because of the anatomical structure of the visual system, where units responding to high spatial frequency stimuli are located closer to the scalp, thus closer to the surface and the occipital electrode. A possible explanation for the apparent inconsistencies in the polarity of the effect among previous studies posits that the tES effect might be different in (sub) clinical population compared to healthy participants [27], but also that the task-induced activity is more important than the polarization in predicting the stimulation effect on a participant’s behavior [28]. hf-tRNS stimulation is often associated with a-tDCS stimulation as both have the main effect of increasing cortical excitability by increasing the inflow of sodium in the stimulated neurons [29,30]. Despite this similarity, the mechanism of action at the cellular level differs substantially. While the online effect of a-tDCS relies on current-induced changes in resting membrane potentials [31,32], the same mechanism cannot take place during tRNS since the
2. Methods 2.1. Participants Twenty participants (13 females), age 21–31 (25 ± 3.4), took part in this study. All participants had normal or corrected-to-normal visual acuity. They sat in a dark room at a distance of 57 cm from the screen. Viewing was binocular. Participants were instructed to fixate in the center of the screen. All participants took part voluntarily and written and oral informed consent was obtained from all the subjects before the study was initiated. The study and protocol conformed to the tenets of the Declaration of Helsinki. Furthermore, the study was approved by the Ethics Committee of the School of Psychology of the University of Padova. 2.2. Apparatus Stimuli were generated using Matlab Psychtoolbox [40,41] and displayed on a 22-in. Philips 202P4 CRT monitor with a refresh rate of 85 Hz. The resolution of the screen was 1600 × 1200 pixels. The minimum and maximum luminance of the screen were 0.60 and 110 cd/m2, respectively, and the mean luminance was 55 cd/m2. Luminance was measured with a CRS Optical photometer (OP200-E; Cambridge Research System Ltd., Rochester, Kent, UK). A digital-to-analog converter (Bits#, Cambridge Research Systems, Cambridge, UK) was used to increase the dynamic contrast range (12-bit luminance resolution). A 12-bit gamma-corrected lookup table (LUT) was applied so that luminance was a linear function of the digital representation of the image. 2.3. Stimuli The stimulus target was a Gabor patch consisting of a cosinusoidal carrier enveloped by a stationary Gaussian. Each Gabor patch (Eq. (1)) 2
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Fig. 1. Figure represents: Electrode positioning, modeled electrical field strength (V/m) at the moment when the current reach the maximum anodal intensity and illustration of a trial with a diagonal gabor patch. The modeled electrical field shows that the highest current density corresponded to the early visual cortices. Fig. 2. Experimental paradigm. Participants completed four sessions over two days. Each session consisted of 5 blocks of about 3 min of a contrast detection task. Each session was a combination of stimulus orientation (vertical vs diagonal) and stimulation condition (tRNS vs Sham). Session order was counterbalanced across participants. Between two consecutive sessions there was a break of thirty minutes.
was characterized by its wavelength (λ), its phase (φ), and the standard deviation (σ) of the luminance Gaussian envelope in the space of the image (σ = 1.5 and φ = 0). In Experiment 1, the Gabor patch was always presented vertically, while in Experiment 2 it was tilted 45 degrees to the left. −(x 2 + y 2 )
G (x , y ) = e
2σ 2
× cos[
2π x + φ] λ
(vertical vs. diagonal) was counterbalanced across participants. Each session was composed by five blocks. The spatial frequency of the target was varied between blocks (1, 3, 5, 7, 12 cpd). The order of the blocks was randomized. Each block lasted about 3 min.
(1)
2.4. tRNS procedure 3. Results tRNS was delivered using a battery-driven stimulator (BrainSTIM, EMS) through a pair of saline-soaked sponge electrodes. The size of the stimulating electrode placed over the occipital cortex (Oz) was 72 × 60 mm and the size of the reference electrode placed over the vertex (Cz) was 115 × 95 mm, rendering the large reference electrode almost inert due to low current density (Fig. 1). The current was initially ramped up over 15 s to an intensity of 1.5 mA and then kept constant. The tRNS frequency ranged from 100 to 600 hz. The stimulation was concomitant to the task and lasted for about 15 min (single blind design). In the sham condition, the current was delivered only during the first and last 30 s (rumped up + stimulation: 15 s + 15 s). Less than half of the participants reported mild skin sensation at the onset of the stimulation, but it disappeared quickly (within 30 s) [42]. The guessing rate of real/ placebo stimulation was at chance level.
3.1. Data analysis To explore the main effects of Stimulation and Spatial frequency, we conducted two separate repeated measure ANOVAs for vertical and diagonal stimuli. To test spatial frequency-specific effect, in case of significant interaction, we used Bonferroni-corrected paired t-tests. Following previous findings, showing cathodal tDCS effect for high spatial frequencies [25,26], we planned two main comparisons, namely 7cpd Sham vs 7cpd tRNS and 12cpd Sham vs 12cpd tRNS. All the analyses were run separately for each Experiment. Finally, we conducted an omnibus Anova with Orientation, Stimulation and Spatial frequency as main factors. 3.2. Experiment 1
2.5. Contrast detection procedure Results for Experiment 1 (Gabor patches presented vertically) are shown in Fig. 3. The Anova with factors Stimulation (tRNS vs Sham) and Spatial Frequency (1cpd vs 3cpd vs 5cpd vs 7cpd vs 12 cpd) showed a main effect of the latter (F(4,76) = 69.55, p < .0001, η2 = .785) but no effect of Stimulation (F(1,19) = .47, p = .83, η2 = .02) or interaction (F(4,76) = 1.34, p < .26, η2 = .066).
Participants were asked to perform a contrast detection task. The contrast of the Gabor patch varied according to a one-up three-down staircase procedure, estimating a threshold of 79% [43]. The staircase ended after 100 trials or 18 reversals. In the first trial the contrast of the target was set to 0.2 Michelson contrast. A trial started with a white fixation dot (0.12 deg) for 0.5 s, then the first interval was presented for 80 ms and after an ISI of 0.5 s the second temporal interval was presented for 80 ms. The target was presented in the center of the screen either in the first or in the second temporal interval. Participants were asked to indicate which of the two temporal intervals contained the target (temporal-2 alternative forced choices, 2AFC) by pressing a button on the computer keyboard. The fixation dot was not presented during the temporal intervals. The next trial started 1 s after the button press. No feedback was given. Each participant performed four sessions, two stimulation conditions (tRNS vs. Sham) × two Gabor patch orientations (vertical vs. diagonal) in two separate days (Fig. 2). Between two consecutive sessions there was a break of thirty minutes. The order of stimulation condition (tRNS vs. Sham) and Gabor patch orientation
3.3. Experiment 2 Following Richard et al. [26], we tested the same participants as in Experiment 1 with diagonally oriented (45°) stimuli. Results for Experiment 2 are shown in Fig. 4. The Anova conducted on Stimulation (tRNS vs Sham) and Spatial Frequency (1cpd vs 3cpd vs 5cpd vs 7cpd vs 12 cpd) showed a significant main effect for both (Stimulation: F(1,19) = 10.23, p = .005 η2 = .35; Spatial frequency: F(4,76) = 58.21, p < .0001, η2 = .758). Additionaly, the interaction Stimulation × Spatial frequency was significant (F(4,76) = 3.81, p = .007, η2 = .167). Bonferroni corrected paired t-tests showed a significant difference between tRNS and Sham at 12 cpd (t(19) = 3.03, p = .012) but not at 7cpd 3
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number of perceptual learning studies using oriented gratings as training stimuli and contrast detection as task [2,15,23,44]. These studies showed that coupling low-level perceptual training and tRNS increases learning rate and transfer to untrained visual functions such as visual acuity. However, differently from other visual tasks such as orientation discrimination [3] and crowding [11], no study so far investigated directly the effect of online tRNS1 on contrast detection testing multiple spatial frequencies and different orientations. Here we report an online (single-session) improvement in contrast detection with concomitant tRNS that was specific for the stimulus orientation (45°) and spatial frequency (12 cpd). No significant changes in contrast detection were observed for vertically oriented stimuli and/or lower spatial frequencies. A possible interpretation of these results is in line with what suggested by Richard et al. [26] and consistent with the anatomy of the visual cortex and the mechanisms of tRNS, in particular within the stochastic resonance famework2 [45]. The lack of effect on vertical stimuli can be attributed to the visual system’s perceptual optimization for sensitivity along cardinal directions, so that perceptual perfomances for stimuli aligned with the horizontal and vertical axes are already at or close to their peak (thus reflecting a sort of ceiling effect in which performance cannot be further improved by training), especially above the peak of the CSF [46]. Indeed, threshold along the diagonal axes were on average almost twice as high with respect to the vertical axis, and at 12 cpd they were five times higher. It is possible then that in healthy participants tES cannot further improve the performance for this specific combination of conditions within a single session. Alternatively, if the effect of the active stimulation was present, it could have been so small that was hidden by the error of measurement. Concerning the spatial frequency-selectivity of the effect, this could be explained by the proximity of visual units with higher preferred spatial frequencies to the scalp [26,47], where the electric concentration might be appropriate to became a good noise for the system. As demonstrated in previous studies [38,48] the addition of noise produces an improvement in terms of behavior only if the intensity of the stimulation is optimal. In fact, stimulations that are too intense or too weak would end up sampling the two extremes of the inverted U curve that characterizes the phenomenon of stochastic resonance. It is possible that the innermost layers of the cortex receiving a decreased intensity of stimulation were outside the sweet spot for improved performance. As per the mechanisms of the tRNS, several hypotheses have been proposed: For example, the improvement in performance might be due to temporal summation of the depolarizing currents induced by the random sub-threshold stimulation [13] that might facilitate the activation of targeted units. Alternatively, the rapid changes of direction of the current might prevent these units from homeostasis [3]. This is consistent with evidence that tRNS induces greater online improvements than anodal tDCS, a protocol of stimulation that induces cortical excitability through a constant flow of current in the same direction. Finally, and most relevant to the results presented here, the introduction of external noise in the system from the electric stimulation might alter the overall level of cortical excitability and the probability of discharge of the neurons, thus altering the signal-to-noise ratio during stimulus processing [3]. The stochastic resonance framework of brain stimulation [45]
Fig. 3. Contrast thresholds (Michelson Contrast: (max luminance – min luminance) / max (luminance + min luminance). as function of spatial frequency for the sham and tRNS session in the condition with vertical target. Error bars represents standard error of the mean (s.e.m.).
Fig. 4. Contrast thresholds (Michelson Contrast) as function of spatial frequency for the sham and tRNS session in the condition with diagonal (45°) target. Error bars represent s.e.m.
(t(19) = 1.9, p = .14). Finally, since the same participants took part in the two Experiments, we ran an omnibus Anova with factors Orientation (vertical vs diagonal), Stimulation (Sham vs tRNS) and Spatial Frequency (1cpd vs 3cpd vs 5cpd vs 7cpd vs 12 cpd). Results showed a main effect of Orientation (F(1,19) = 40.08, p < .001, η2 = .678), Stimulation (F(1,19) = 7.23, p = .015, η2 = .276) and Spatial Frequency (F(1,19) = 71.13, p < .001, η2 = .789). Significant interactions were observed for Orientation x Stimulation (F(1,19) = 8.01, p = .01, η2 = .279), Orientation × Spatial Frequency (F(4,76) = 44.14, p < .001, η2 = .699) and the three way interaction Orientation × Stimulation × Spatial Frequency (F(4,76) = 4.72, p = .002, η2 = .199). Bonferroni-corrected paired t-tests showed significant difference between diagonal sham and diagonal tRNS (p = .005) and confirmed the significant difference at 12cpd for the tRNS group over the Sham for the diagonal condition (p = .022). Finally, we ran an additional Anova with Block Order and Spatial Frequency as factors to inverstigate any possible learning effect. We found no effect of Block Order (F(1.88,35.7) = .82, p = .44, η2 = .042) or interaction Block Order × Spatial Frequency (F(2.03,35.5) = .72, p = .58, η2 = .028).
Note that with “online tRNS” we meant that the stimulation was concomitant with the task. With 15 minutes of stimulation we cannot exclude a build-up effect over time rather than a genuine “online effect” of the online tRNS. 2 Stochastic resonance is a phenomenon where a signal that is normally too weak to be detected by a sensor, can be boosted by adding a certain amount of noise to the signal. When the visual input signal is too weak, it produces subthreshold neural response, however tRNS can introduce good noise directly to the visual system that resonate with the signal input of the stimulus producing a supra-threshold response. 1
4. Discussion The aim of the present study was to systematically test the effects of online tRNS on contrast detection at different spatial frequencies and orientations. Results showed that tRNS improved contrast detection but only when the stimuli were presented tilted 45° from the vertical axis and had high spatial frequency (12 cpd). Electrical brain stimulation, in particular tRNS, has been used to improve learning and transfer in a 4
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combines the interaction between this externally induced noise, internal activity and stimulus-driven activity, providing predictions on the perceptual outcome: in particular, in case of a low intensity stimulus, external noise would enhance the signal above the threshold. On the other hand, in case of a higher intensity stimulus, the external noise might boost the noise of the spontaneous internal activity, thus reducing the signal-to-noise ratio [38,48]. At a neurophysiological level the tRNS has been shown to modulate the activation and inactivation of the Na+ channels exerting the maximum excitability increase at an intermediate level of noise [36]. This short-term molecular activity due to the tRNS, that increases the neuronal activation, might be the neural bases for the behavioural effect observed. Morevoer, the repetitive activation and inactivation of the NA+ channels might activate a biological cascade responsible for a long lasting plastic mechanism that is NMDA-receptor independent but sodium channel blocker and benzodiazepines sensitive that might explain the offline after effect [33] and the long-lasting effect that was found in perceptual learning studies. In conclusion, contrast detection can be improved in healthy participants not only by online tDCS [24–26], but also by tRNS. Furthermore, this study showed that this improvement could occur not only at a later stage of learning [2,3,15,23], but also that tRNS can affect the processing of the stimulus. Similarly, to previous studies using tDCS, these improvements seem specific for high spatial frequencies and noncardinal orientations [26]. The mechanism underlining the tRNS effect are certainly different from those of the tDCS, but are not still completely clear in vision. A good way to explain the tRNS effect is via stochastic resonance mechanism in which the ‘good’ noise introduced in the visual system by tRNS resonates with the weak signal input producing a signal that surpasses the threshold of a neural population. Authors contribution statement L.B., G.C. and M.M. designed and implemented the experiments. S.P. collected and preprocessed the data. M.M. analyzed the data. All authors interpreted the results, L.B. and M.M. wrote the main manuscript. Disclosure of funding sources The study was supported by a grant form MIUR (Dipartimenti di Eccellenza DM 11/05/2017 n.262) to the Department of General Psychology Declaration of Competing Interest The authors declare no competing interests. References [1] C.J. Stagg, M.A. Nitsche, Physiological basis of transcranial direct current stimulation, Neuroscience 17 (2011) 37–53, https://doi.org/10.1177/ 1073858410386614. [2] R. Camilleri, A. Pavan, F. Ghin, L. Battaglini, G. Campana, Improvement of uncorrected visual acuity (UCVA) and contrast sensitivity (UCCS) with perceptual learning and transcranial random noise stimulation (tRNS) in individuals with mild myopia, Front. Psychol. 5 (2014) 1234, https://doi.org/10.3389/fpsyg.2014. 01234. [3] A. Fertonani, C. Pirulli, C. Miniussi, Random noise stimul ation improves neuropl asticity in Perceptu al le arning, J. Neurosci. 31 (2011) 15416–15423, https://doi. org/10.1523/JNEUROSCI.2002-11.2011. [4] M.A. Nitsche, W. Paulus, Excitability changes induced in the human motor cortex by weak transcranial direct current stimulation, J. Physiol. 527 (2000) 633–639, https://doi.org/10.1111/j.1469-7793.2000.t01-1-00633.x. [5] T. Radman, R.L. Ramos, J.C. Brumberg, M. Bikson, Role of cortical cell type and morphology in subthreshold and suprathreshold uniform electric field stimulation in vitro, Brain Stimul. 2 (2009), https://doi.org/10.1016/J.BRS.2009.03.007 215–228.e3. [6] D. Reato, A. Rahman, M. Bikson, L.C. Parra, Low-intensity electrical stimulation affects network dynamics by modulating population rate and spike timing, J. Neurosci. 30 (2010) 15067–15079, https://doi.org/10.1523/JNEUROSCI.2059-10. 2010.
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