Background ---------- Transcranial direct current stimulation (tDCS), popular among non-invasive brain stimulation techniques, offers the possibility to modulate cortical excitability, which can potentially modify the performance of a broad range of cognitive functions (Coffman et al., 2014). The application of tDCS to improve performance gains special relevance in contexts where the targeted function is central to a broad range of tasks and degrades quickly over time. This is the case of vigilance, which requires sustaining the focus of attention over long time periods remaining alert to detect specific yet unpredictable stimuli (Parasurman et al., 1987; Warm et al., 2008). Using tDCS to mitigate this inevitable decrement of vigilance over time, has shown to serve as a fruitful intervention (Luna et al., 2020), at least, for now, when considering a subdivision of the concept into two separate components: (i) *arousal vigilance*, understood as the ability to maintain a basic state of activation which allows responding to any stimuli of the environment in a fast and relatively automatic manner; and (ii) *executive vigilance*, understood as the ability of monitoring and executing a specific response to infrequent but relevant and pre-specified contextual stimuli. A recent study (n = 90) applied anodal high-definition tDCS (HD-tDCS) over the right posterior parietal cortex (rPPC) and the right dorsolateral prefrontal cortex (rDLPFC), while participants performed a complex task (ANTI-Vea, Luna et al., 2018), that is suitable to simultaneously measure the executive and arousal components of vigilance, together with indexes of the three attentional networks: alertness, orienting, and cognitive control. Participants must respond to a flanker task in 60% of the trials (ANTI trials, by pressing the corresponding key depending on the direction of a central arrow, surrounded by either congruent or incongruent flankers), in which the indexes of the three attentional networks are measured. In 20% of the trials (executive vigilance trials), where the executive component of vigilance is measured, participants must detect a large vertical displacement of the central arrow by pressing the space bar. Finally, in the remaining 20% if trials (arousal vigilance trials), instead of the arrow stimuli a millisecond countdown is presented for participants to stop it as soon as possible by pressing any key; measuring the arousal component of vigilance. Interestingly, the two stimulation protocols (rPPC and rDLPFC) led to a mitigated decrement of vigilance in its executive component; whilst, no effect of stimulation was found on the arousal component (Luna et al., 2020). Stimulation over the right parietal cortex showed more promising results in a further analysis of EEG activity; as the observed increase of alpha power with time-on-task―generally associated with the attentional disengagement from the task at hand―was smaller in the stimulated group, as compared to the sham one. These results have been recently replicated with a new sample (n = 60) comparing the rPPC and sham protocol (Hemmerich et al., *in preparation*; https://osf.io/bncth/wiki/home/); again, the beneficial effect over the vigilance decrement was only observed for the executive component, whereas arousal vigilance was not affected. Furthermore, regarding EEG recordings, the same reduced increment of alpha power (recorded over parietal electrodes) with time-on-task as reported by Luna et al. (2020) was found for the group that received active stimulation compared to the sham group. Additionally, analyzing other frequency bands, it was found that gamma power (especially in the frontal electrodes) also increased with time-on-task; however, in this case stimulation seemed to increase this increment further, compared to the sham group. Based on these results, an index with the ratio of parietal alpha power/frontal gamma power was calculated, where basically, lower values of the index reflect a higher specific effect of tDCS on neural oscillations. This parietal alpha/frontal gamma ratio was equal in both groups (sham and stimulation) in the pre-stimulation EEG recording; in contrast, in the post-stimulation recording the index increased for the sham group, whereas it considerably decreased for the stimulation group. Based on this, the groups (stimulation and sham) of participants were split in half regarding whether there was a pronounced change in de index from pre- to post-stimulation, leading to a low change group (participants below their group median), and a high change group (participants above their group median). With this division, a new analysis of behavioral data showed that participants with a low alpha/gamma ratio change showed almost no decrement across blocks. Whereas, for those with a high alpha/gamma ratio change and receiving sham stimulation, a steep decrement across blocks was observed, which seemed to be palliated for participants receiving real stimulation, which showed no decrement across time. Having established―with evidence from a combined total of 150 participants―that anodal HD-tDCS over the rPPC mitigates the vigilance decrement, exclusively in its executive but not arousal component, and that this behavioral effect is mediated by the effect of tDCS on neural oscillations, we can pose further and more detailed research questions. For example, one must consider that the vigilance decrement may occur due to a heterogeneous set of causes, as highlighted by the different models of vigilance (Esterman & Rothlein, 2019). There are conditions in which the vigilance decrement might occur due to the monotony of the task itself, leading to a gradual allocation of resources towards other mental tasks (e.g., mind-wandering); this could occur while, for example, driving down a long, straight and uneventful road. Whereas, in other conditions, the decrement may be due to resources being overpowered by the complexity of the demands, like when driving through a busy street filled with other vehicles, pedestrians, streetlights, and road signaling. Between these under- or overwhelming scenarios, we might find a middle ground, where task demands and resources are balanced, allowing for a more stable performance over time. In fact, this has been recently demonstrated in a study comparing these three types of task demands on attentional performance also using the ANTI-Vea task. Note that, following the prior description of the ANTI-Vea task, it can be considered as a triple task. Luna et al. (2022) used this interesting feature of the task to manipulate its cognitive load, creating three load conditions while maintaining constant temporal parameters: In a single task condition participants had to only respond to the executive vigilance (EV) trials. In a dual task condition, they had to respond to both arousal (AV) and EV trials, whereas in the triple task condition, i.e., the standard ANTI-Vea Task, the three tasks must be performed. The main finding of this study was a significant decrement in the percentage of hits in (EV) across task blocks, both in the single task and in the triple task groups, that was absent in the dual task group. This seems to suggest that performing either an unchallenging or over-demanding task, increases the likelihood of experiencing the executive vigilance decrement. Due to this notable effect of task demands or cognitive load on the executive vigilance decrement, we want to explore how task demands and neuromodulatory effects interact, as measured through behavioral and electrophysiological measures. Additionally, as part of a larger and ongoing research project, participants will also undergo an MRI scan prior to completing the task, where data will be collected to obtain quantifications of white matter trajectories for each participant. In the current study, we want to compare the effects of either anodal or sham HD-tDCS over the rPPC, combined with different versions of a vigilance task, manipulating cognitive load. Namely, participants will either perform a single task, measuring executive vigilance, or a dual task, where executive and arousal vigilance are measured. Further comparisons will also be made with data from a triple task (standard ANTI-Vea, of a previously collected sample; Hemmerich et al., *in preparation*), in line with the approach taken by Luna et al. (2022) in order to accumulate large sample sizes. Given the specificity of HD-tDCS effects and the effect of task load manipulations on EV; we expect to find neuromodulatory effects, specifically in those conditions that have proven to produce a steeper vigilance decrement, namely the single and triple conditions. Aims and Hypotheses ------------------- The present study aims to elucidate the effect of HD-tDCS over the rPPC on behavioral and neurophysiological measures of executive vigilance and its dependency on cognitive load. In that sense, we propose the following hypotheses: ****1.** Manipulating the cognitive load of a task measuring attention whilst administering online anodal HD-tDCS over the right posterior parietal cortex (rPPC) we expect:** **1.1.** Improved performance in the Stimulation vs. Sham group, specific to the EV component (i.e.: % of hits across blocks) for the single task group, as under normal circumstances they under-perform compared to the dual task group (Luna et al., 2022). **1.2.** No effect of HD-tDCS, i.e., no difference between Stimulation and Sham groups, will be observed in the dual task groups, as no decrement or only a small decrement is expected in this condition, even without stimulation (Luna et al., 2022). **1.3.** No modulation in AV by HD-tDCS. ---------- ****2.** Regarding electroencephalography (EEG) outcomes we expect:** **2.1.** A replication of the EEG findings of prior studies as described above (Luna et al, 2020; Hemmerich et al, in preparation): a general increment of alpha power (mainly in parietal electrodes) and gamma power (especially in frontal electrodes) with time-on-task. **2.2.** More specifically, we expect that, the stimulation protocol reduces the parietal alpha power increment, whilst increasing the frontal gamma power increment in the group performing the single task. In other words, in the single task groups we expect HD-tDCS to reduce the parietal alpha/frontal gamma ratio from the first to the last block of trials, as in the previous study with a triple task (Hemmerich et al, in preparation). **2.3.** Furthermore, we expect no modulation of HD-tDCS over the parietal alpha/frontal gamma ratio in the groups performing the dual task (i.e.; where no behavioral benefit of tDCS is also anticipated). Method ------ **Participants** A power analysis conducted with G*Power (Faul et al., 2007) considering the effect size (*η2p* = .061) observed in the critical interaction Block (1-6) x Group (rPPC HD-tDCS stimulation vs. sham HD-tDCS stimulation), performed on the hits rate, with *α* = .05 and *1-β* = .80, provided an estimation of a minimum sample of 22 participants per group. We will collect 30 participants per group, in order to match the groups from the prior study with the standard ANTI-Vea, i.e., triple task (Hemmerich et al., in preparation; https://osf.io/bncth/wiki/home/). Signed consent will be obtained from all 120 participants, which will have been screened for normal or corrected to normal vision, right-handedness, no known neurological or psychiatric conditions, no safety contraindications for receiving tES (Rossi et al., 2009; Rossini et al., 2015) or MRI screening, and ensuring that they are naïve as to the purpose of the experiment. A reward of 10€/hour will be offered in exchange for participation. This study is embedded in larger research projects (PID2020-114790GB-I00 and B-CTS-132-UGR20) approved by the Ethical Committee of the University of Granada (2442/CEIH/2021 and 1188/CEIH/2020), following ethical standards of the 1964 Declaration of Helsinki. **Apparatus and Stimuli** *Behavioral measures* Participants will perform either one of two versions of the ANTI-Vea Task (as shown in Figure 1), where all standard trials of the ANTI-Vea will be presented, but participants will have to respond only to some of the stimuli (for a more detailed explanation of the task itself, please refer to Luna et al., 2018); generating: - A **single task**, which prompts participants to respond only to EV trials, while not responding to ANTI and AV trials. - A **dual task**, in which participants must respond to both types of vigilance (EV and AV) trials, whilst not responding to ANTI trials. Additionally, these groups will be compared with participants’ performance (from a sample that is already collected, as mentioned above) in: - A **triple task**, which corresponds to the standard ANTI-Vea task, where participants respond to ANTI, EV, and AV trials. ![enter image description here][1] **Figure 1**. Depiction of the ANTI-Vea task environment, showing the timing for arousal vigilance (AV) trials, executive vigilance (EV) and ANTI trials. Correct responses for single, dual and triple task conditions are shown in the panel below. ---------- *Online stimulation with HD-tDCS and EEG data acquisition* HD-tDCS will be applied over the right posterior parietal cortex (rPPC), from the 2nd to the 6th task block in both groups (for ~28 minutes), with an intensity of either 1.5 mA (anodal stimulation group, n = 60) or 0 mA (sham group, n = 60) and a ramp-up and ramp-down of 30 seconds. The electrode setup (shown in Figure 2.a) will be comprised of a 4 × 1 ring, with a central anode over P4, and the four surrounding cathodes over CP2, CP6, PO4 and P08. EEG recordings will be collected during the 1st block serving as a baseline, and during the last and 7th block, serving as a post-stimulation measure. Electrodes used for acquiring EEG (shown in Figure 2.b.) will be positioned over two main regions: a set of frontal electrodes over AF4, F4 and FC2, and a set of parietal electrodes over CP2, P4 and PO8. From each EEG recording, a 210-second-long epoch will be extracted (in order to avoid contamination by ramp-up and ramp-down periods). Each dataset will be subjected to Independent Component Analysis (ICA) through the EEGLAB plugin in MATLAB (Delorme & Makeig, 2004). Further visual inspection will be used to remove additional artifacts, prior to computing the mean power in each frequency. ![enter image description here][2] **Figure. 2. a)** Anodal tDCS (1.5 mA) will be applied through P4, with current returning through CP2, CP6, PO4 and PO8. Electrode setup is shown on the left. On the right, the simulated voltage field obtained through this protocol is represented; **b)** shows the 6 electrodes used for EEG data acquisition (CP2, P4 and P08 in the parietal region, and AF4, F4, FC2 in the frontal region). ---------- *DTI data acquisition* Before completing the experiment, DTI measures of the participants will be obtained. Note that this data is being collected across different studies as part of a larger research project, in order to gain a sufficiently large sample size to assess these in vivo dissections of white matter tracts of each participant to explore whether, amongst others, the integrity of the branches of the superior longitudinal fasciculus (SLF) modulates the effect of HD-tDCS on the executive vigilance component. DTI will be reconstructed by using the spherical deconvolution approach, and the Hindrance Modulated Orientational Anisotropy index (Dell’Acqua et al., 2013) will be used as the proxy for the tract microstructural organization. ---------- *Subjective Fatigue Ratings* We will record subjective ratings of mental and physical fatigue from each participant at three moments throughout the experiment: at baseline (before instructions are provided), pre-task (after the practice block, before the first experimental block) and post-task (after completing the experimental blocks). As in Luna et al. (2022) the order in which mental and physical fatigue are assessed, will be counterbalanced across participants (Luna et al., 2022). Responses will be recorded through an analog scale (a line that represents perceived fatigue and ranges from a minimum located on the left side of the screen to a maximum located on the right). ---------- **Procedure** Security questionnaires scanning for tDCS inclusion/exclusion criteria will be mailed to participants. Participants fulfilling inclusion criteria for a safe application of tDCS will participate in the experiment consisting of a 30-minute-long MRI scan (for DTI data acquisition), an initial fatigue assessment (baseline), familiarization with general instructions for the ANTI-Vea task and completion of the task’s practice block. In between this practice block and the experimental block, electrode set-up will take place, after which participants complete the pre-task fatigue assessment. During the experimental task, baseline EEG recordings will be completed during the 1st experimental block (5:47 min.). Then anodal HD-tDCS over the rPPC will be applied from the 2nd to the 6th experimental block (~28 min.). During the 7th and last experimental block, the post-task EEG measures will be recorded (5:47 min.). Right after completion of the experimental block, the last fatigue assessment will be completed (post-task). Finally, participants will complete a tES Survey, in order to record their subjective experience during stimulation (Fertonani, Ferrari, & Miniussi, 2015). **Design & Analysis Plan** Data from EV and/or AV trials will be analyzed from baseline (1st block) to the final active or sham stimulation block (6th), as in Luna et al. (2020). For EV trials (from the single and dual task), four indices will be computed: hits (percentage of correct responses to trials where the target arrow is displaced), false alarms (FA, percentage of space bar presses in response to ANTI trials, i.e., trials where the target arrow is not vertically displaced), sensitivity (A’) and response bias (B’’). Each EV index will be included in an ANOVA as a dependent variable, with blocks (1st-6th) as a within-participant factor and Group (rPPC HD-tDCS or sham HD-tDCS) and Load (single, dual, or triple) as between-participant factors. For AV trials (from the dual task) two indices will be computed: mean RT to each appearing red countdown, and the standard deviation of each of these reaction times (SD of RT). Both of these indices will be included in separate ANOVAs as dependent variables, with blocks (1st-6th) as a within-participant factor and Group and Load as a between-participant factors. For each index of both vigilance components’ individual Block × Group × Load interaction, planned comparisons will be used to test whether there is a significant linear component in the performance decrement across blocks depending on the Group and Load. Regarding EEG data, mean power of each different frequency band (delta, theta, alpha, beta and gamma) will be included in mixed ANOVAs including Period (pre-stimulation and post-stimulation) and Region (parietal or frontal electrodes) as within-participant factors, and Group /rPPC or sham HD-tDCS) and Load (single, double, triple) as between-participant factors. Furthermore, the parietal alpha to frontal gamma ratio will also be explored to test whether there are any differences and/or interactions between Group and Load, including the alpha/gamma ratio in an ANOVA with Period (pre/post stimulation) as a within-participant factor, and Group and Load as between-participant factors. Based on the results we will evaluate whether to split data by the median of each Group/Load condition in order to inspect differences in the effect of HD-tDCS on EV hits in participants with low vs. high change in the alpha/gamma ratio from pre- to post-stimulation. **Note by the authors: ---------------------- Currently, the 60 participants of the anodal HD-tDCS group have already been collected, and behavioral data have been superficially inspected. These preliminary analyses show that the performance for single and dual tasks (all completed whilst receiving anodal HD-tDCS), reflected a higher general percentage of hits across all blocks when compared to data from a triple task (Hemmerich et al., in preparation); and, most importantly, whereas the slope of hits was not significantly different across both task groups, but seemed to differ significantly from the sham group of the sham group collected with a triple task. Thus, after registration of these hypotheses, the remaining 60 participants for the sham HD-tDCS group will be collected, before inspecting behavioral data again as a whole and with the proper analyses described here. DTI and EEG data will be analyzed at that point as well. References ---------- Coffman, B. A., Clark, V. P., & Parasuraman, R. (2014). Battery powered thought: Enhancement of attention, learning, and memory in healthy adults using transcranial direct current stimulation. NeuroImage, 85, 895-908. https://doi.org/10.1016/j.neuroimage.2013.07.083 Dell’Acqua, F., Simmons, A., Williams, S. C. R., & Catani, M. (2013). Can spherical deconvolution provide more information than fiber orientations? Hindrance modulated orientational anisotropy, a true-tract specific index to characterize white matter diffusion: Hindrance Modulated Orientational Anisotropy. Human Brain Mapping, 34(10), 2464-2483. https://doi.org/10.1002/hbm.22080 Delorme, A., & Makeig, S. (2004). 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