key: cord-0263421-zzsc6igi
authors: Potok, Weronika; Post, Alain; Bächinger, Marc; Kiper, Daniel; Wenderoth, Nicole
title: Transcranial random noise stimulation of the primary visual cortex but not retina modulates visual contrast sensitivity
date: 2022-03-02
journal: bioRxiv
DOI: 10.1101/2022.02.28.482316
sha: 54a12924449d31232a96331d1a0c08efa3396233
doc_id: 263421
cord_uid: zzsc6igi

Transcranial random noise stimulation (tRNS) has been shown to significantly improve visual perception. Previous studies demonstrated that tRNS delivered over cortical areas acutely enhances visual contrast detection of stimuli when tRNS intensity is optimized for the individual. However, it is currently unknown whether tRNS-induced signal enhancement could be achieved within different neural substrates along the retino-cortical pathway and whether the beneficial effect of optimal tRNS intensities can be reproduced across sessions. In 3 experimental sessions, we tested whether tRNS applied to the primary visual cortex (V1) and/or to the retina improves visual contrast detection. We first measured visual contrast detection threshold (VCT; N=24, 16 females) during tRNS delivery separately over V1 (no tRNS, 0.75, 1, 1.5mA) and over the retina (no tRNS, 0.1, 0.2, 0.3mA), determined the optimal tRNS intensities for each individual (ind-tRNS), and retested the effects of ind-tRNS within the sessions. We further investigated whether we could reproduce the ind-tRNS-induced modulation on a different session (N=19, 14 females). Finally, we tested whether the simultaneous application of ind-tRNS to the retina and V1 causes additive effects. We found that at the group level tRNS of 0.75mA decreases VCT compared to baseline when delivered to the V1. Beneficial effects of ind-tRNS could be replicated when retested within the same experimental session but not when retested in a separate session. Applying tRNS to the retina did not cause a systematic reduction of VCT, irrespective of whether the individually optimized intensity was considered or not. We also did not observe consistent additive effects of V1 and retina stimulation. Our findings demonstrate that V1 seems to be more sensitive than the retina to tRNS-induced modulation of visual contrast processing. Significance statement Our findings confirm previous evidence showing acute online benefits of tRNS of V1 on visual contrast detection in accordance with the stochastic resonance phenomenon. We further extend it, demonstrating that the optimal tRNS intensity varies among participants, but when individually tailored it can improve visual processing when re-tested within the experimental session. The tRNS-induced enhancement in visual sensitivity seems to be specific for cortical contrast processing as stimulation of the retina did not lead to systematic effects.

24.4 ± 4.1, age range: 21-38) with normal or corrected-to-normal vision (see Figure 1A total 132 number of 24 individuals participated in both 1 st (tRNS over V1) and 2 nd (tRNS over the retina) 133

sessions. Due to the COVID-19 pandemic, we were forced to stop data collection for several 134 months (Bikson et al., 2020) . After returning to the lab, 5 participants dropped-out from the 135 initial sample (2 had newly acquired contraindications for brain stimulation and 3 were not able 136 to participate). 19 healthy volunteers (14 females, 5 males; 25.5 ± 5.2, age range: 21-39) were 137 included into 3 rd session (tRNS over V1 and retina, see 

To evaluate the influence of tRNS on visual contrast detection, we performed a series of three 142 experimental sessions in which we delivered tRNS over different levels of the visual system, 143 namely: V1, retina, or simultaneously over both V1 and retina (V1+Retina), during visual task 144 performance (see Figure 2A) . In each experiment, tRNS at low, medium and high intensity 145 and a control no tRNS condition were interleaved in a random order (see tRNS characteristics 146 below). 147

The order of experimental sessions for V1 and retina stimulation were counterbalanced across 148 participants (13 participants started with V1 and 11 with retina stimulation). These experimental sessions took place on different days which were on average 2 weeks apart. 150

Due to COVID-19 restrictions, the third session had to be delayed by 5 months on average. 151

Our main outcome parameter in all experimental sessions was a threshold of visual contrast 152 detection (VCT) that was determined for each of the different tRNS conditions. VCT was 153 independently estimated twice, in two separate blocks within each session (see Figure 2B) . 154

During the first two sessions we determined the individual optimal tRNS intensity (defined as 155 the intensity causing the lowest VCT, i.e., biggest improvement in contrast sensitivity) for each 156 participant in the V1 session (ind-tRNSV1) and the retina session (ind-tRNSretina). In the third 157 session we then applied ind-tRNSV1 and ind-tRNSretina to investigate the effect on VCT when 158 

All experiments took place in a dark and quiet room, ensuring similar lighting conditions for all 168 participants. Participants sat comfortably, 0.85m away from a screen, with their head supported by a chinrest. Visual stimuli were generated with Matlab (Matlab 2019b, 170

MathWorks, Inc., Natick, USA) using the Psychophysics Toolbox extension (Brainard, 1997; 171 Kleiner et al., 2007; Pelli, 1997 ) and displayed on a CRT computer screen (Sony CPD-G420). 172

The screen was characterized by a resolution of 1280 x 1024 pixels, refresh rate of 85Hz, 173 linearized contrast, and a luminance of 35 cd/m 2 (measured with J17 LumaColor Photometer, 174

Tektronix TM ). The target visual stimuli were presented on a uniform gray background in the 175 form of a Gabor patch -a pattern of sinusoidal luminance grating displayed within a Gaussian 176 envelope (full width at half maximum of 2.8 cm, i.e., 1° 53' visual angle, with 7.3 cm, i.e., 4° 177 55' presentation radius from the fixation cross). The Gabor patch pattern consisted of 16 cycles 178 with one cycle made up of one white and one black bars (grating spatial frequency of 8 c/deg). 179

Stimuli were oriented at 45° tilted to the left from the vertical axis (see Figure 3B ), since it was 180

shown that tRNS enhances detection of low contrast Gabor patches especially for non-vertical 181 stimuli of high spatial frequency (Battaglini et al., 2020) . 182

In all three experiments participants performed a four-alternative forced choice (4-AFC) visual 184 task, designed to assess an individual VCT, separately for each tRNS condition. Such protocol 185 was shown to be more efficient for threshold estimation than commonly used 2-AFC (Jäkel 186 and Wichmann, 2006) . In the middle of each 2.04s trial, a Gabor patch was presented for 187 40ms in one of the 8 locations (see Figure 3A ). To account for potential differences in the 188 extent to which tRNS affects different retinotopic coordinates and to avoid a spatial detection 189 bias, the visual stimuli were presented pseudo-randomly and appeared the same number of 190 times (20) in each of the eight locations on the screen within each experimental block (van der 191

Groen and Wenderoth, 2016). The possible locations were set on noncardinal axes, as the 192 detection performance for stimuli presented in this way is less affected (i.e. less variable) than 193 when stimuli are positioned on the cardinal axes (Cameron et al., 2002; van der Groen and 194 Wenderoth, 2016). The trial was followed by 1s presentation of fixation cross after which the 195 'response screen' appeared. Participants' task was to decide in which quadrant of the screen 196 the visual stimulus appeared and indicate its location on a keyboard. The timing of the 197 response period was self-paced and not limited. Participants completed a short training (10 198 trials) at the beginning of each session, with the stimulus presented always at high contrast, 199 in order to ensure that they understand the task ( Figure 2B) . 200

During the main experiment, VCT was estimated using the QUEST staircase maximum 201 likelihood procedure (Watson and Pelli, 1983) implemented in the Psychophysics Toolbox in 202

Matlab (Brainard, 1997; Kleiner et al., 2007; Pelli, 1997) . The thresholding procedure starts 203 with a presentation of the visual stimulus displayed with 0.5 contrast intensity (for visual 204 contrast intensity range of minimum 0 and maximum 1). When participants answer correctly 205 QUEST lowers the presented contrast intensity, when participants answer incorrectly QUEST 206

increases the presented contrast. The estimated stimulus contrast is adjusted to yield 50% 207 detection accuracy (i.e., detection threshold criterion, see Figure 3C ). Note that for 4-AFC 208 task 25% accuracy corresponds to a chance level. The remaining parameters used in the 209 QUEST staircase procedure included: steepness of the psychometric function, beta = 3; 210 fraction of trials on which the observer presses blindly, delta = 0.01; chance level of response, 

In tRNS trials, high-frequency tRNS (hf-tRNS, 100-640Hz) with no offset was delivered. The 226

probability function of random current intensities followed a Gaussian distribution with 99% of 227 the values lying between the peak-to-peak amplitude (Potok et al., 2022) . Stimulation started 228 20ms after trial onset and was maintained for 2s ( Figure 3A) . Subsequently a fixation cross 229 was displayed for 1 s, followed by the self-paced response time. tRNS waveforms were In the V1 session, we asked whether tRNS over V1 modulates VCT. To target V1 we used an 256 electrode montage that was previously shown to be suitable for V1 stimulation (Herpich, 2019 ; 257 van der Groen and Wenderoth, 2016). One tRNS 5x5cm rubber electrode was placed over 258 the occipital region (3 cm above inion, Oz in the 10-20 EEG system) and one 5x7cm rubber electrode over the vertex (Cz in the 10-20 EEG system). Electroconductive gel was applied to 260 the contact side of the rubber electrodes (NeuroConn GmbH, Ilmenau, Germany) to reduce 261 skin impedance. 262 tRNS was delivered with 0.75mA (low), 1mA (medium), and 1.5mA (high) amplitude (peak-to-263 baseline), resulting in maximum current density of 60 The final experimental session aimed to investigate potential additive effects of delivering 292 electrical random noise simultaneously to V1 and the retina on visual contrast sensitivity. 293

In this session, we combined the electrodes montages over V1 and the retina and applied 294 tRNS with individual optimal intensities as determined in the first two experimental sessions 295 (i.e., ind-tRNSV1 and ind-tRNSretina). 296

In the V1+Retina session, we compared the VCT in four conditions: (i) tRNS over V1 at its 297 optimal intensity (ind-tRNSV1), (ii) tRNS over retina at its optimal intensity (ind-tRNSretina), (iii) 298 simultaneous tRNS over V1 and the retina at their respective optimal intensities (ind-299 tRNSV1+retina), and (iv) no tRNS. All conditions were interleaved and presented in a randomized 300 order. 301

All the statistical analyses were preregistered and did not deviate from the original plan. VCT data collected in the V1 session (tRNSV1) was analyzed with a rmANOVA with the factor 316 tRNS (no, 0.75, 1, and 1.5mA tRNS) and the factor block (1 st , 2 nd ). For each individual and 317 each block, we determined the maximal behavioral improvement, i.e., lowest VCT measured 318 when tRNS was applied, and the associated "optimal" tRNS intensity (ind-tRNSV1). The 319 maximal behavioral improvements in the 1 st and the 2 nd block were compared using a t-test 320

(2-tailed) for dependent measurements. We further tested whether ind-tRNSV1 of the 1 st and 321 2 nd block were correlated using Spearman's rank correlation coefficient (because of 322 categorical characteristics of ind-tRNSV1 variable). Importantly, we determined ind-tRNSV1 in 323 the 1 st block, and then used the VCT data of the separate 2 nd block to test whether the 324 associated VCT is lower compared to the no tRNS condition using t-tests for dependent 325 measures. Since we had the directional hypothesis that VCT is lower for the optimal tRNS 326 intensity compared to no tRNS this test was 1-tailed. Determining ind-tRNSV1 and testing its effect on VCT in two separate datasets is important to not overestimate the effect of tRNS on 328 visual detection behavior. 329

VCT data collected in the Retina session (tRNSretina) was analyzed with a rmANOVA with the 330 factor of tRNS (no, 0.1, 0.2, and 0.3mA tRNS) and the factor block (1 st , 2 nd ). Again, for each 331 individual and each block, we determined the maximal behavioral improvement and the 332 associated ind-tRNSretina. We compared results obtained in the first and second block using 333 the same statistical tests as for the V1 session. The maximal behavioral improvements were 334 compared using a t-test (2-tailed) for dependent measurements. Correlation of ind-tRNSretina 335 of the 1 st and 2 nd block was tested using Spearman's rank correlation coefficient. We examined 336 whether the ind-tRNSretina determined based on the best behavioral performance in 1 st block, 337 caused VCT to be lower compared to the no tRNS condition when retested on the independent 338 dataset (2 nd block) using t-tests (1-tailed) for dependent measures. 339

VCT data collected in the V1+Retina session (tRNSV1+retina) was analyzed with a rmANOVA 340 with the factor tRNS site (ind-tRNSV1, ind-tRNSretina, ind-tRNSV1+retina, and no tRNS) and the 341 factor block (1 st , 2 nd ). Moreover, we compared behavioral improvement for ind-tRNSV1 and ind-342 tRNSretina between sessions (tRNSV1 and tRNSV1+retina, tRNSretina and tRNSV1+retina, respectively) 343 using a Pearson correlation coefficient. 344

As a control analysis we repeated the main analyses of VCT (rmANOVA were we observed 345 tRNS-induced significant difference) with adding cutaneous sensation as covariate (see tRNS 346 characteristics). 347

We first tested whether VCT measured during the no tRNS condition differed between the 349 experimental sessions or blocks (i.e., six consecutive time points, see Figure 4 ). Bayesian 350 rmANOVA with the factor time (1-6) revealed that the baseline VCT measured in the no tRNS 351 condition did not differ over time (BF10 = 0.06, i.e., strong evidence for the H0) indicating that 352 detection performance was rather stable across sessions. 353 

In the V1 session, we investigated whether tRNS modulates the visual contrast detection when 358 applied to V1. We measured VCT during tRNSV1 at intensities of 0.75, 1, to 1.5mA versus no 359 tRNS control condition. We found a general decrease in VCT when tRNS was applied (tRNS 360 participants' optimal ind-tRNSV1 of block 1 and 2 (i.e., the tRNS intensity causing the largest 375 VCT reduction in each block) were not correlated (rho = 0.225, p = 0.290). 376

Finally, we determined ind-tRNSV1 in the 1 st block ( Figure 5B ) and tested whether it caused a 377 decrease in VCT compared to the no tRNS condition using the data of block 2. Indeed, VCT 378 decreased in 15 out of 24 individuals (MD = -4.45 ± 17.9%) and this effect reached statistical significance (t(23) = 1.72, p = 0.049, Figure 5C ). Note that the optimal ind-tRNSV1 intensity and 380 the associated VCT effect were determined on independent data sets to avoid circularity. 

The present study investigated the effects of tRNS on visual contrast sensitivity, when applied 435 to different neuronal substrates along the retino-cortical pathway. We measured VCT during 436 tRNSV1 and tRNSretina and tRNSV1+retina across 3 experimental sessions. We found consistent 437 tRNS-induced enhancement of visual contrast detection during V1 stimulation (Figure 5A-C) . 438 but not retina stimulation (Figure 5D-F) . We also did not observe any additive effects on 439 contrast detection when noise stimulation was simultaneously applied to V1 and retina ( Figure  440   6A, B) . The online modulation effects of individually optimized tRNSV1 intensities were 441 replicated within session (i.e., across two separate blocks) (Figure 5C ), but not between 442 experimental sessions ( Figure 6B ). Our findings likely reflect acute effects on contrast 443 processing rather than after-effects, as stimulation was only applied for short intervals (2 s) 444 and always interleaved with control (no tRNS) conditions. 445

Our findings confirm previous evidence that the detection of visual stimuli is enhanced when 447 tRNS is added centrally to V1 at optimal intensity ( Figure 5A ; van der Groen and Wenderoth, der Groen and Wenderoth (2016). Thus, the threshold tracking procedure (Brainard, 1997; 453 Kleiner et al., 2007; Pelli, 1997; Watson and Pelli, 1983 ) used in our experiments seems to

The present study did not demonstrate systematic noise benefits at the level of the retina. 490

Thus, suggesting that previously reported SR effects on contrast detection might derive mainly 491 from cortical rather than retinal processing. It also shows that SR effects might differ based on 492 the specific characteristic of the stimulated neural tissue. 493

In our study, we targeted the retina bilaterally with tRNS, to investigate its effects on contrast 494 sensitivity. Although increases in tRNSretina intensity resulted in decreases in VCT, reflecting 495 relative task performance improvements (Figure 5D) , the effects did not reach statistical 496 significance. Similar to tRNSV1, the effects of tRNSretina were variable across study participants. 497

However, even individually determined optimal intensities of tRNSretina did not result in 498 consistent visual processing improvements when retested in separate blocks, both within or 499 between sessions ( Figure 5F ). 500

Why did tRNS improve contrast detection when applied to V1 but not when applied to retina? 501

In contrast to V1, the retina is characterized by much larger temporal frequency bandwidth 502 toward which it is responsive. One study measured cat ganglion cell responsivity towards 503 temporal frequencies ranging from 0.1 to 100Hz (Frishman et al., 1987) together, stimulus processing at the level of the retina seems to cover a much wider range of 511 temporal frequencies than in V1 and to be more variable. Thus, it is possible that the range of 512 tRNS frequencies used in our experiments, i.e., 100-640 Hz might have been too close to the 513 intrinsic signaling frequencies in the retinal circuitry and in ganglion cells to induce the typical 514 SR effect. V1 neurons, by contrast, respond to frequencies which are one to two magnitudes 515 lower than the tRNS frequencies, and therefore, larger noise benefits could be observed. 516

Alternatively, the weak effects of tRNSretina might simply be due to filtering properties of retinal 517 neurons. A recent study utilized amplitude modulated tACS (AM-tACS) applied to the retina to 518 investigate the efficacy of different carrier frequencies to induce phosphenes. AM-tACS 519 waveforms comprised of different carrier (50Hz, 200Hz, 1000Hz) and modulation frequencies 520 (8Hz, 16Hz, 28Hz). They found that from the conditions using different carrier frequencies only 521 the lowest one was able to induce phosphenes (Thiele et al., 2021) . Thus, suggesting the low-pass nature of retinal neurons which greatly reduces the stimulation effectiveness of evoking 523 suprathreshold response (Deans et al., 2007; Thiele et al., 2021) . The researchers point out, 524 however, that their findings do not rule out potential sub-threshold modulations of neural 525 activity during AM-tACS with high carrier frequencies. 526

In the Retina session we observed gradual decrease of VCT with increasing tRNSretina intensity 527 on the group level ( Figure 5D ). Even though this effect was not significant, we cannot exclude 528 that VCT would decrease further when higher tRNSretina intensities were used. We have based 529 our stimulation intensities on studies utilizing repetitive transorbital alternating current 530 stimulation with similar intensities Gall et al., 2011 Gall et al., , 2010 and 531 demonstrated improved vision in patients with damaged optic nerve (see also Sabel et al., 532 2020). However, it is possible that the induced current is more strongly attenuated in our study 533 (which used much higher stimulation frequencies) due to the filter properties of retinal neurons. 534

Moreover, in these studies the alternating current was delivered using set of four electrodes 535 positioned above and below participants' eyes. Such electrodes placement results in different 536 direction of the current and related orientation of the induced electric field than bilateral 537 placement used in this study (Figure 2A) In summary, we found no evidence that tRNS affects contrast detection at the retinal level. 541 This is interesting from a methodological perspective since it rules out that applying tRNS over 542 V1 elicits confounding effects in the retina, as previously discussed for tACS experiments 543 (Schutter, 2016; Schutter and Hortensius, 2010) . 544

sensitivity 546

The influence of individually optimized tRNS on VCT, defined separately for both V1 and the 547 retina in experimental sessions 1 and 2, were retested in session 3. The effects of neither ind-548 tRNSV1, nor ind-tRNSretina were replicated (Figure 7A-B) . This indicates that optimal tRNS 549 intensity for maximum task performance improvement needs to be individually re-adjusted on 550 each experimental session. These results confirm the well-known variability in the 551 effectiveness of non-invasive brain stimulation (Polanía et al., 2018) intrinsic factors such as the participants' arousal levels or attentional states. Additionally, even 555 though we made sure that our procedure was well standardized, there might have been slight differences in the precise electrodes montage or amount of electroconductive gel, potentially 557 resulting in variability of the electric field induced by tRNS of selected intensity across sessions 558 (Polanía et al., 2018) . It is also worth noting that the substantial delay between V1/Retina 559 sessions, and V1+Retina session (5 months on average) because of the COVID-19 pandemic 560 (Bikson et al., 2020) could have also influenced this variability. As the modulation of VCT with 561 ind-tRNSV1 or ind-tRNSretina was not replicated in session 3, we also did not observe consistent 562 beneficial additive effects of ind-tRNS delivered simultaneously to V1 and the retina ( Figure  563   8A-B) . 564

Our study confirms previous findings that tRNS might enhance visual signal processing of 566 cortical networks via the SR mechanism (Potok et al., 2021; van der Groen and Wenderoth, 567 2016). When probing the effects of tRNS on contrast sensitivity along the retino-cortical 568 pathway, we demonstrated that V1 seems to be more sensitive than the retina to tRNS-569 induced modulation of visual processing. Moreover, we found that the individual optimal tRNS 570 intensity applied to V1 to enhance contract detection appears to vary across sessions. The 571 appropriate adjustment of optimal tRNS intensity is therefore important to consider when 572 designing tRNS protocols for perceptual enhancement.

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task protocol used in our study was shown to be more efficient for threshold estimation than 456 commonly used 2-AFC (Jäkel and Wichmann, 2006) . 457It has been argued previously that tRNS benefits visual detection via the stochastic resonance 458 (SR) mechanism, i.e. the detection probability of weak, subthreshold signals in nonlinear Our study contributes to the evidence for SR as a mechanism underlying online visual 484 processing modulation when tRNS is applied to neural networks in human cortex (Battaglini 485