On the relevance of the alpha frequency oscillation’s small-world network architecture for cognitive flexibility

Cognitive flexibility is a major requirement for successful behavior. nNeural oscillations in the alpha frequency band were repeatedly associated with cognitive flexibility in task-switching paradigms. Alpha frequencies are modulated by working memory load and are used to process information during task switching, however we do not know how this oscillatory network communication is modulated. In order to understand the mechanisms that drive cognitive flexibility, ERPs, oscillatory power and how the communication within these networks is organized are of importance. The EEG data show that during phases reflecting preparatory processes to pre-activate task sets, alpha oscillatory power but not the small world properties of the alpha network architecture was modulated. During the switching only the N2 ERP component showed clear modulations. After the response, alpha oscillatory power reinstates and therefore seems to be important to deactivate or maintain the previous task set. For these reactive control processes the network architecture in terms of small-world properties is modulated. Effects of memory load on small-world aspects were seen in repetition trials, where small-world properties were higher when memory processes were relevant. These results suggest that the alpha oscillatory network becomes more small-world-like when reactive control processes during task switching are less complex.

Neurophysiological Results. Analysis of the P1 and N1 ERP-component can be found in the supplemental material.
Target-locked N2 and P3 ERP-components were analysed using a repeated measures ANOVAs with the within-subject factors "condition" and "block". The respective ERP components are shown in Fig. 2.
Alpha-band oscillations. The time-frequency plots showing alpha band power (ABP) are given in Fig. 3 for each experimental condition. The time-frequency plots show the alpha band activity averaged across electrodes P7, P8, O1, O2, PO1, PO2, because these electrodes were identified in the electrode selection step (refer methods section) and there was no main or interaction effect "electrode".
Post Target processes of alpha-band oscillations (800-1500 ms): For the interval from 800 to 1500 ms post target, the mixed model ANOVA on the post-target alpha band power with the within-subject factors "condition" (repetition, switching), "block" (cue-based, memory-based) and "electrode" revealed no significant effect (all p > 0.054), but a trend for "block" (F(1,57) = 3.871, p = 0.054, η 2 p = 0.064), indicating increased ABP during cuevs memory-based blocks. Importantly, due to the long and jittered response-cue interval (RCI) (~2000 ms), the analyzed time interval does not simply reflect preparatory processes of the upcoming cue-stimuli.
Analysis of the alpha band small world network architecture. Post-Cue processes of alpha-band oscillations (−1300-0 ms): The analysis of the small world value (SWV) in the cue-target interval revealed no main or interaction effects regarding SWV (all p > 0. 25).
Post-Target processes of alpha-band oscillations (800-1500 ms): The analysis of the SWV of alpha frequency activity in the time range from 800 ms to 1500 ms after target stimulus revealed effects. The connectivity pattern between electrodes for all experimental conditions is shown in Fig. 4(A and B). The analysis of the SWV revealed the following: The was a main effect "block" (F[1,54] = 4.08; p = 0.048; η 2 p = 0.068) showing that the SWV was lower in the memory-based block (0.67 ± 0.01) than in the cue-based block (0.70 ± 0.01). The main effect "condition" (F[1,54] = 26.13; p < 0.001; η 2 p = 0.318) showed that the SWV was smaller during repetition (0.66 ± 0.01) than switching trials (0.71 ± 0.009). There was an interaction "block x condition" (F[1,54] = 5.27; p = 0.025; η 2 p = 0.086), which is shown in Fig. 1C. Post-hoc tests showed that for switching trials no difference between the memory-based and the cue-based block was evident (t [54] = 0.20; p > 0.4). In the repetition trials, the SWV was larger in the cue-based condition (0.68 ± 0.01) than in the memory-based condition (0.64 ± 0.01) (t[56] = 2.98; p = 0.002).
It is important that within the spectrum of maximum smallworldness (ω = 0) and maximum randomness (ω = 1) the genereal SWV is on average (ω = 0.67) more random than smallworldish.

Discussion
In the current study we focused on how alpha oscillations are modulated by working memory load during task switching and how oscillatory network communication within thus frequency range is modulated in order to process information. Therefore, alpha frequency oscillations and the network architecture before and after task switching processes as well as ERP processes were examined via a memory-and cue-based task switching paradigm. Behavioral, ERP and alpha band power data. The behavioral data show common differential effects of the cue-and memory-based block during switching 7,9 . Residual switch costs in response times (RTs) were generally higher in the memory-based block than in cue-based block. This effect was due to strong effects of the block on switching trials, where RTs were longer in the memory-based block than in the cue-based block. This possibly reflects an effect of modulated updating processes of internal representations and task sets for response selection. Underlining this interpretation an analysis of ERPs showed that the N2 and P3 ERP components reflected the interaction observed at the behavioral level. The higher N2 on switching trials in the memory-based block well reflects increased processes, which are related to the resolution of conflict (or task-set inertia, reflected by the N2) [48][49][50][51] . The finding of no similar modulations on cue-based switching trials underlines that cuing reduced demands on task switching processes, which is well in line with the literature 2,22,23 . Opposed to the N2 component, the P3 component did not well reflect the behavioural data. The P3 revealed a parietal maximum, but showed larger amplitudes in switching than in repetition trials, opposed to smaller amplitudes as frequently found [48][49][50][51][52][53][54][55] . It has been shown that mechanisms of working memory processes are reflected by the P3 ERP-component [56][57][58][59] . This may have led to the unusual increase in P3 amplitudes on switching trials. It can also not be ruled that this odd effect reflects a volume conduction effect of processes found for the N2 component.
The time-frequency data show a general increase in alpha frequency activity before and after task switching, in the post-cue (cue-target) interval and in the time period immediately following the response (~700 and 800 ms); i.e. between 800 and 1500 ms after both target presentation and response (see Fig. 3). These modulations can be disentangled as follows: within the cue-target interval the observed alpha oscillations likely reflect preparatory processes, as described previously [24][25][26][27] . The data shows that during repetition trials alpha power was stronger than during switching trials and that this effect was further amplified in the memory-based block. This is well in line with findings showing that alpha oscillations increase with working memory load 60,61 . The results suggest that proactive control processes exerted are intensified in the memory-based block, possibly because more memory-load increases demands on working memory processes.
Interestingly, we observed no modulatory effects in the alpha oscillatory power in the time range from 800-1500 ms. Alpha oscillations have been suggested to reflect inhibitory control processes regulating access of information of a knowledge system and working memory 34,35 , which is important for task set activation and switching [30][31][32][33] . Therefore, the data shows that these reactive control mechanisms are not coded in the power of the signal. Yet, the analysis of the small-world data suggests that modulations were observable at the level of the alpha network in terms of small-world properties.
Alpha band small world network analysis. The analysis of the SWV providing information about the small-world property of the network revealed an interaction "condition x block" in the period from 800 to 1500 ms after target presentation (i.e., where no modulations in alpha power were evident). As stated above, this second interval of strong alpha frequency activity immediately follows the executed responses, since the mean RT was between ~700 and 800 ms. Therefore, this time interval likely reflects reactive control processes. Importantly, due to the long and jittered response-cue interval (RCI) (~2000 ms), the analyzed time interval does not simply reflect preparatory processes of the upcoming cue-stimuli.
In the time interval from 800 to 1500 ms, alpha network properties in general had more properties of transmitting information effectively (more random network properties) than being very smallworldish and they are even further away from being specialized (more regular/lattices). In particular, the SWV was higher in switching than repetition trials in both, the memory-based and the cue-based condition. This suggests that once the response has been executed and the response selection process has been finished, the degree of efficient separation and functional integration of information on a network level becomes important 41,42,47 . After responses in switching trials, the SWV was larger than after responses in repetition trials; which means that the networks becomes more complex (random) in switching than repetition trials. Alpha oscillations have been suggested to coordinate top-down control processes 44,45 and reflect inhibitory mechanisms to control access to task-relevant information 35 . During switching trials such control processes are useful to inhibit interferences arising from previous tasks [30][31][32][33] . Because differences in the alpha SWV were evident after the execution of the response, it is possible that this reflects inhibition processes of the previous task set. The more random network architecture may convey more degrees of freedom for such processes to unfold and may make it easier to coordinate the necessary neural processes. Notably, these processes are not different between the memory-based and the cue-based block, suggesting that memory load in conditions requiring cognitive flexibility is not important to consider.
However, opposed to switching trials, strong effects between the cue-based and the memory-based blocks were observed for alpha oscillations after responses in repetition trials. This suggests that mechanisms related to switching are more powerful to alter alpha oscillatory network properties than it is the case for working memory load. The SWV was smaller in the memory-than in the cue-based block; i.e. in the memory-based block the alpha frequency networks showed more small-world properties than in cue-based block. This may be explained as follows: Task sets are stored in working memory 62 and once a response has been executed, it increases task performance when this task set needs to be inhibited or deactivated 30 -a function of alpha frequency oscillations 35 . However, in the memory-based block access to the task sets stored in working memory may be easier and more direct than in the cue-based block, because the cognitive operations have already been triggered from working memory. This may make it easier to integrate information and to coordinate top-down processes regulating task set inhibition and may reduce demands on the network to flexibly integrate information. Therefore, the alpha oscillation network is less complex (random) and shows stronger small-world properties.
Of special interest is the comparison with the result pattern obtained in the N2 ERP-component data. As discussed above, the N2 ERP data well reflects the behavioral data and is likely to reflect processes of task set activation. Alpha frequency oscillations and differences in the small-world network architecture may be interpreted as reflecting processes related to the inhibition of a previous task set (see above). Notably, in the time period of the N2 ERP, the time frequency plots show that no strong alpha activity was evident. It is therefore possible that the N2 ERP and alpha frequency oscillations reflect complementary processes related to the processing of task sets. It seems that the alpha network architecture is important for task set inhibition processes (reactive control processes), but not for preparation and activation processes; i.e. proactive control processes during task switching. Underlining this interpretation, there were no differences in small-world network architecture in alpha oscillations in the cue-target interval, in which proactive control processes preparing the system for an upcoming need for goal-appropriate are evident [24][25][26][27] .

Conclusions
The study focused on the role of alpha frequency oscillations and its network architecture during memory-based and cue-based task switching processes. The results show that alpha oscillations are modulated during phases of proactive control processes, likely reflecting processes to pre-activate task sets, but these processes did not affect the network architecture in terms of small-world properties. Alpha oscillations are not modulated during the switching process per se, in which only the N2 and ERP-component showed clear modulations. Alpha oscillations reinstate after the response and therefore seem to be important to maintain or deactivate the previous task set. For these reactive control processes the network architecture in terms of small-world properties is important. Generally, after task switches the network showed less small-world properties. Effects of memory load on small-world aspects were seen in repetition trials, where the network showed higher small-world properties when memory processes were relevant. These results suggest that the involved alpha oscillation network, had more properties of transmitting information effectively than being very smallworldish or specialized. Moreover, the involved alpha oscillation network becomes more small-world-like when reactive control processes during task switching are less complex. It is possible that small-world like network properties (i.e. higher degrees of freedom to organize network entities) helps to accomplish reactive control processes during task switching.

Participants.
A total of n = 55 healthy young participants (24 females) between 18 and 30 years (M = 23.75 ± 0.49) took part in the experiment. All participants were right handed, had normal or corrected-to-normal vision, were free of medication and reported no psychiatric or neurological disorders. Written informed consent was obtained from all participants before the test protocol was conducted. The study and all experimental protocols were approved by the local ethics committee of the Medical Faculty of the TU Dresden and realized in accordance with the Declaration of Helsinki.
Task. The procedure used to examine memory-based and cue-based task switching was adapted from previous studies 7,10,63,64 . The task is shown in Fig. 5.
During task execution, participants were seated in a separate room (to prevent any disturbances or distractions) in front of a monitor and a regular keyboard. The paradigm consisted of two conditions: switching and repetition trials, which were separated into 12 experimental blocks (6 cue-and 6 memory-based blocks, with 198 trials per condition, resulting in a total of 396 trials). The cue-based block consisted of 99 switch and 99 repetition trials, the memory-based block consisted of 66 switch and 132 repetition trials. Participants were free to take a self-paced period of rest between experimental blocks. The order of blocks was counterbalanced. In all conditions, participants were instructed to respond as quickly and accurately as possible. Stimuli consisted of digits from 1-9, excluding number 5 and were presented in white font, centrally on a black computer screen (viewing distance = 56.5 cm). In the cue-based block, cues alternated randomly but with a balanced proportion of 33.3% each while the frequency of switching was set to 50%. A cue for one out of three rules ('NUM' , 'GER' or 'SG' , font size: 65) was displayed 1300 ms prior to the onset of the digits. When 'NUM' (short form for german word "numerisch" = engl. "numeric") was displayed, participants were supposed to assess whether the digit was smaller or greater than 5. When 'GER' (for "gerade" = engl. "even") was presented, participants had to decide whether the digit is even or odd. 'SG' (for "Schriftgröße", meaning "font size") indicated that participants had to determine whether the digit was shown in large or small font size (font size varied between 50 and 80). The cue-stimulus remained visible until the digit (target) was presented. The digit appeared at the position where the fixation point had been presented before. After target onset, the participants had to respond within 2500 ms. Otherwise, the trial was regarded as missed response. Participants had to respond using their index finger (which was either the right or the left one, based on response-hand mapping and respective experimental version), if the digit was smaller than five, had small font size or was uneven in addition they had to respond using their right index finger, if the digit was larger than five, had a large font size or was even. The responses were given on a computer keyboard using the "Ctrl" buttons. 500 ms after the response a feedback was displayed for 500 ms (" + " or "−" sign). After the feedback the next cue was shown. The response-cue interval (RCI) was set to ~2000 ms and included the response-feedback delay (500 ms), the feedback (500 ms) and the feedback-cue delay, which jittered.
In contrast to the cue-based block, there was a determined loop of rules which had to be kept in mind (always in the same order: {NUM, NUM, NUM, GER, GER, GER, SG, SG, SG, NUM, NUM, NUM, GER, …}). To make the two blocks more comparable, a dummy cue was presented prior to the appearance of the digits for a period of 1300 ms. This dummy cue was "XXX" (see Fig. 5). In case a participant lost count and failed to apply the correct rule in 3 consecutive trials, the regular cues (i.e., NUM, GER, or SG) replaced the dummy cue in the following 3 trials. The regular cue during the memory-based block was shown to 20 participants. In these 20 participants this happens on average once (M = 1.3). Just like for the cue-based block, there was a balanced proportion of each rule (33.33%), but the frequency of switching was reduced to 33.3% due to the fixed order of task rules in the memory block. It may be argued that the cue-based and the memory based differ in regularity, because there was sequence of trials in the memory-based block and random trial presentation in the cue-based block. However, in the memory-based block there needs to be a fixed trial order to invoke memory processes. If a fixed order would have been used in the cue-based block as well, this may also have induced memory-related processes that may then have confounded cue-based task switching. Before the paradigm began, participants run one practice block with 18 trials per condition.
EEG recording and analysis. The EEG was recorded with a sampling rate of 500 Hz using a 64-channel system (BrainAmp, Brain Products Inc.) and electrode impedances were kept under 5 kΩ. Passive Ag/ AgCl-electrodes (60 recording electrodes) were mounted in an elastic cap (EasyCap Inc.) and arranged in equidistant positions approximating the positions of the 10/20 system. The ground and reference electrodes were placed at coordinates theta = 58, phi = 78 and theta = 90, phi = 90, respectively. After recording, data were down-sampled to 256 Hz and filtered (band-pass filter from 0.5 to 25 Hz, with a slope of 48 dB/oct each) using the BrainVision Analyzer 2 software package (BrainProducts Inc.). Raw data was inspected manually to reject technical artifacts from the EEG. For artifacts identified in the first step we cut out as much data around an artifact as possible. Afterwards, an independent component analysis (ICA; infomax algorithm) was conducted on the un-epoched data sets to remove recurring artifacts. ICA components revealing horizontal and vertical eye movements, blinks and pulse artifacts were manually rejected. The ICA was run for all blocks combined. Prior to manual raw data inspection, noisy electrodes were deleted and topographically interpolated after all preprocessing steps.
Afterwards, the EEG data was segmented for switch and repetition trials, and for cue-and memory-based blocks separately. Only trials with correct responses were taken into account. The segments started 2000 ms before In the memory-based part, the subjects had to remember when to switch a rule. RCI reflects the jittered response-cue-interval. target presentation of the respective trial and ended 1500 ms after its onset. Subsequently, an automated artifact rejection procedure was conducted for all segments, with the following rejection criteria: activity below 0.5 μV in a 100 ms period and a maximal value difference of 200 μV in a 200 ms interval. If an artifact was detected in a trial at only one electrode, the entire trial was discarded. This resulted in the rejection of 23.97% of the trials during cue-based switching, 24.41% during cue-based repetition, 27.91% during memory-based switching and 29.44% during memory-based repetition. To eliminate the reference potential from the data and to re-reference the data, we applied a current source density (CSD) transformation (4 splines and 10 polynominals) 65 which works as a spatial filter 66,67 , suppresses volume conduction and accentuates electrode sites and makes it easier to identify electrode sites that best reflect relevant neuronal activity. A baseline correction from 200 ms prior to both, cue and target onset was applied for ERP-analysis only; i.e. it was also set before the presentation of the cue and dummy-cue stimuli. Through the application of a specific cue-and target-locked baseline, we meet the needs of possible shifts of ERPs, and ensure that neural activity which might be increased due to attentive cues has no impact on the analysis of target-locked data analysis All ERP amplitudes were quantified against this baseline period at the single-subject level. Electrodes and intervals are chosen based on theoretical background, arguing where and when components show off. Accroding to 68 the P1 wave is largest at lateral occipital electrode sites and typically onsets 60-90 ms post stimulus with a peak between 100-130 ms. The visual P1 and N1 amplitudes (which are presented in the supplementary materials) on the target stimuli, as well as the cue and dummy-cue stimuli were quantified at electrodes P7 and P8. The P1 was quantified in the time window between 90-105 ms after either cue or target stimulus presentation. According to 68 "the P1 wave is followed by the N1 wave. N1 components that typically peak 150-200 ms post-stimulus, the lateral occipital N1 subcomponent appears to be larger when subjects are performing discrimination tasks than when they are performing detection tasks". For this analysis, the N1 was quantified in the time window between 150-170 ms after either cue or target stimulus presentation (please refer to supplementary materials for P1 and N1 analysis). In addition to theoretical background, electrodes are also chosen by visual detection due to analysis of grand averages, separately for each condition and block.
The N2 and the P3 ERP-components on the target stimuli were quantified at electrode Cz (for the N2 ERP-component; 290-350 ms) and at electrode P4 (for the P3 ERP-component; 325-340 ms). This choice of electrode positions and time windows was validated using the methods proposed by 69 : Briefly, the above time intervals were taken and the mean amplitude within the defined search intervals was determined for each of the 60 electrode positions. This was performed only after CSD transformation of the data which emphasizes scalp topography 66 . Then, to compare each electrode against an average of all other electrodes, Bonferroni correction for multiple comparisons (critical threshold, p = 0.0007) was used. Only electrodes, which displayed significantly larger mean amplitudes (i.e., negative for the N-potentials and positive for the P-potentials) when compared to other electrodes were chosen. This procedure revealed the same electrodes as previously chosen by visual inspection.
After baseline correction, the time frequency decomposition was conducted using Morlet's wavelets (w) in the time domain to different frequencies (f): For analysis and TF-plots, a ratio of f 0 /σ f = 5 was used, where σ f is the width of the Gaussian shape in the frequency domain and f 0 is the central frequency. The TF decomposition was applied to the single-trial data. Therefore, the total (induced) wavelet power was calculated. The analysis was conducted in the frequency range from 0.5 to 25 Hz and a central frequency at 0.5 Hz intervals was employed. For different f 0 , time and frequency resolutions (or wavelet duration and spectral bandwidth 70 ; can be calculated as 2σ t and 2σ f respectively. σ t and σ f are related by the equation σ t = 1/(2πσ f ). For example, for f 0 = 1 Hz, 2σ t = 1770 ms and 2σ f = 0.36 Hz; for f 0 = 3 Hz, 2σ t = 580 ms and 2σ f = 1.09 Hz; for f 0 = 5 Hz, 2σ t = 350 ms and 2σ f = 1.82 Hz. After TF-decomposition, the data were averaged at the single subject level to analyze the power of the alpha frequency oscillations. For statistical analysis, TF power was log 10 -transformed to normalize the distributions for statistical analyses. The power of the alpha frequency oscillations was analyzed between 8 Hz and 12 Hz (10 Hz central frequency) and quantified at electrodes P7, P8, O1, O2, PO1, PO2. These electrodes and time windows were selected on the scalp topography plots and subsequent validation of these electrode sites using the same method as used for the ERP data confirmed these electrode sites. Mean alpha band power (pooled across electrodes) was analyzed from the entire cue-target (−1300 ms -0 ms) interval, as well as in an interval from 800 ms to 1500 ms after target presentation, in which strong alpha band activity was evident (refer Fig. 3). Importantly, due to the long and jittered response-cue interval (RCI) (~2000 ms), the analyzed time interval does not simply reflect preparatory processes of the upcoming cue-stimuli.
Network connectivity and small world analysis. We focused on the alpha frequency band for the network connectivity analysis; i.e. on oscillations between 8 Hz and 12 Hz. The communication between all electrodes in terms of a "network" was examined by analyzing the connectivity between electrodes as the strength of association quantified by their coherence. That is, all electrodes (nodes) reflect a network and connections (edges) between nodes are defined as coherence between all possible pairs of electrodes. Therefore, only the imaginary part of the coherence spectrum for all possible pairs of nodes was calculated to effectively suppress spurious coherence driven by volume conduction 71 For the coherence values, three different individual percentile thresholds of P = 85, P = 90, P = 95 was set for each subject, so only the 15, 10 or 5 percent of each individual's highest coherence values were included in the analysis. While there are several ways to determine the threshold, for instance based on some statistical parameterization or previous observation in the literature, all of them remain arbitrary. Letting only the 10 percent of the highest values "survive" is a criteria that is a compromise or tradeoff for two problems: On the one hand, it makes sure that only electrodes with high coherence are defined as being "connected" and are included in the analysis. On the other hand, it also makes sure that enough connections between the electrodes still form a connectome and thus can be considered as a network. We therefore only reported data, where a cutoff of 10 percent of the highest values had been applied (for the results of the other thresholds, please see supplementary material). A binary 60 × 60 adjacency network matrix (because of 60 electrodes being used) was then calculated with 1 representing an un-weighted and undirected connection between any pair of electrodes and 0 representing no connection.
In order to study small world networks, the method by 72 was used and applied to each single-subject: Starting from a one-dimensional network, where each node in the network is only connected to its k nearest neighbors on either side, representing a 'regular' network with randomness ϐ = 0, a ring lattice with N nodes of mean degree 2k is created. Next, with increasing randomness (ϐ > 0), more connections ('edges') are randomly chosen to another random node. So, when ϐ = 0, no edges are rewired and the model returns a ring lattice. In contrast, when ϐ = 1, all of the edges are rewired and the ring lattice is transformed into a random network with N nodes and mean node degree of 2k. According to the Watts and Strogatz model, a network has small-world network properties if it demonstrates properties from both lattice networks, with clustered interconnectivity within groups of nodes sharing many nearest neighbors in common (high clustering coefficient, 'C'), and properties from random networks, represented by a short geodetic distance (average path length, 'L') between any two nodes in the network. Thus, the balance of local segregation and global integration in neural networks ('small worldness') can be quantified by C and L, respectively 42 . Regular networks have a high C but also a very high L. In contrast, random networks have a low C and a low L. So, neither regular nor random networks alone can explain the small world phenomenon 72 . For each subject, average number of edges from one node to all other nodes (degree, 2k), average shortest path length (geodetic distance, L real ) and average clustering coefficient (C real ) were calculated. Corresponding to each individuals degree, completely random (ϐ = 0) and completely regular (ϐ = 1) Watts Strogatz models were created and L rand and C rand and C latt were computed. We analyzed all small world values (ω) according to 73 ), who proposed a quantitative categorical definition of a small-world network in line with the definitions of the original Watts-Strogatz model 72 . In this way, the assertion, whether a network has small world properties can be tested statistically. The small-world value (SWV) formula is expressed by: Small-world values of ω are restricted to the interval −1 to 1 regardless of network size. If ω is close to zero, it is considered as small world. Positive ω values represent more random properties, negative values indicates that a network has more regular or lattice-like properties.
Statistics. The data was analyzed using repeated measures ANOVAs where the factor "block" (cue-based vs. memory-based) and the factor "condition (repetition vs. switch)" was included. For the analysis of the ERP and alpha power data, an additional factor "electrode" was included in the models. Greenhouse-Geisser correction was applied for all tests. All post-hoc tests were Bonferroni-corrected. All variables included in the ANOVAs were normal distributed as indicated by Shapiro-Wilks tests (all W > 0.65; p > 0.3). For the descriptive statistics, the mean and standard error of the mean are given. Data availability. The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request. Ethical standards. The authors assert that all procedures contributing to this work have been conducted in accordance with the ethical standards of the relevant national and institutional committees on human experimentation and with the Helsinki Declaration of 1975, as revised in 2008. The study and all experimental protocols were approved by the local ethics committee of the Medical Faculty of the TU Dresden.