Targeting interhemispheric balance to modulate language processing: a transcranial direct current stimulation study in healthy volunteers

Figure 1. Electrode placement across experimental conditions (color-coding: anode - green, cathode - red)

2.3 Procedure and tasks

Participants performed a single-word-level task (lexical decision) and a sentence-level task (sentence comprehension). They filled in the nesessary forms and received instructions and short training before the stimulation start on their first day. Participants completed both tasks on a PC in a quiet room. On both sessions (real and sham stimulation), participants practiced both tasks online (during stimulation) and were tested on them offline immediately after the stimulation. Task order was counterbalanced within each experimental group. Online, tasks were alternated twice with short breaks to prevent fatigue (see Table 4). The design included online practice because tDCS appears to preferentially modulate active neuronal networks (Bikson Rahman, 2013), so training during stimulation may potentially boost tDCS-induced changes. Only online data were analyzed.

Table 4. Task timeline during stimulation. Times are indicated relative to the stimulation start

In the lexical decision task (see Figure 2), participants saw a string of letters in the center of the screen and pressed a button to respond whether it was a real Russian word or a nonword (senseless string of letters). Participants were instructed that this task is not aimed at determining whether their vocabulary is rich or not, so no tricks was meant in it. In the left and in the right corner of a screen two possible responses were always present. Each stimulus was presented for 1200 ms, followed by a 800 ms fixation cross.

In the sentence comprehension task (see Figure 3), participants read sentences in a self-paced mode; words appeared in the center of the screen one at a time and participants pressed the button to see the next word in a sentence. After each sentence, participants saw a question with two possible responses in the lower left and right corners of the screen and responded by button press. Then followed by a fixation cross (800 ms) and the start of the next sentence. All experimental paradigms were programmed with E-Prime 2.0 (Psychology Software Tools, Pittsburgh, PA). In 1 min and 19 min after stimulation start, participants completed a tolerability questionnaire: they rated tDCS-related pain and unpleasantness on a scale from 1 (no pain/unpleasantness) to 10 (strong pain/unpleasantness). After the end of the study, participants were informed that one session was sham stimulation and were asked to guess which session it was and to score their level of confidence in their decision in order to understand whether they can distinguish between real and sham stimulation.

Figure 2. An example of the sentence comprehension task

Figure 3. An example of the lexical decision task

2.4 Stimuli

No stimuli were repeated across any online or offline sessions. In lexical decision, each offline list included 60 Russian words (nouns) (e.g. батон, семья) and 60 pronounceable non-words (e.g. юбака, негов), matched for length in letters and syllables. The two offline lists were balanced for stimuli length in letters and syllables, lexical frequency (Lyashevskaya Sharov, 2009) and orthographic neighborhood (Alexeeva, Slioussar, Chernova, in press) of real words, and mean reaction time (RT) from a pilot study with 24 neurologically healthy young participants (none of whom participated in this tDCS study). Each online list included 75 words and 75 non-words, split into two blocks.

In sentence comprehension, each list included 60 Russian sentences: grammatically complex sentences (44 in each offline and 28 in each online list) and fillers with simpler syntactic structure (16 in each offline and 32 in each online list) (see Table 5). Grammatically complex sentences were designed to avoid ceiling effects and included syntactic structures shown to present a difficulty even for individuals without language deficits: a non-finite (participial) clause attached to one of the two nouns in the genitive noun phrase (Chernova Slioussar, 2016); sentences with semantically reversible subject and object (including those with non-canonical object-verb- subject word order; (Slobin, 1966); subject- and object-relative clauses (Wanner Maratsos, 1978); and object-relative clauses with reflexive pronouns (Laurinavichyute, Jдger, Akinina, RoЯ, Dragoy, 2017).

Proportions of sentence types in offline versus online lists were different to minimize practice and strategy effects. Every sentence was followed by a comprehension question with two response options. For grammatically complex sentences, the incorrect response was a noun also mentioned in the sentence (e.g., The judge, who the attorney waited for in the office, never came. - Who waited in the office? - Judge / Attorney), requiring grammatical rather than superficial lexical analysis of sentences. Questions to fillers could refer to any part of speech and the incorrect response option was not mentioned in the sentence (e.g., The graduating student was wearing a beautiful beige knee-long dress. - What color dress was the graduating student wearing? - Beige / Blue). The offline lists were balanced for sentence and question length in words and syllables, log-transformed frequency of responses and all content words in sentences (Lyashevskaya et al. 2009), and length in syllables and grammatical gender of responses.

Table 5. Examples of sentence comprehension stimuli

3. Data analysis

Only offline tasks were analyzed. In lexical decision, the outcome measure were reaction times (RTs) (only from correct-response trials); accuracy was not analyzed due to ceiling effects. In sentence comprehension, the outcome measures were RTs (only from correct-response trials), accuracy, and self-paced reading speed (mean speed per word in each sentence).

Linear mixed-effect models was applied (lme4 package, version 1.1-13 (Bates, Maechler, Bolker, Walker, 2015) in R (R Core Team, 2017). The fixed factors were stimulation (real vs. sham), stimulation group (left anodal vs. right cathodal vs. bilateral), linguistic condition (word/non-word or sentence type), session (participant's day 1 or 2). The models included by-participant and by-item random intercepts and by-participant random slopes. P-values were obtained via likelihood ratio tests.

To account for individual language lateralization of each participants, handedness scores obtained prior to the experiment were correlated with individual stimulation effect sizes for each outcome measure (log odds ratio for accuracy and Cohen's d for RT and reading time), within and across the three experimental groups.

4. Results

4.1 Safety and tolerability

No participants reported any side effects during or after stimulation. Pain and unpleasantness ratings were low (mean(SD) 2.07 (1.58) and 3.54 (1.89) for pain and unpleasantness in 1 min after stimulation start, 1.25 (0.79) and 1.85 (1.31) in 19 min after stimulation start for real stimulation; 1.76 (1.28) and 2.89 (1.67) for pain and unpleasantness in 1 min after stimulation start and 1.18 (0.76) and 1.77 (1.62) in 19 min after stimulation start for sham stimulation). Some participants reported somnolency or mild headache after sessions, however, they were not sure of whether they were caused by stimulation. 41 (57.75%) participants correctly guessed which session corresponded to sham, indicating valid blinding.

4.2 Lexical decision

Mean accuracy and RTs are presented in Table 6. In the linear mixed-effect model on RT data, there was neither a significant main effect of stimulation, ч²(2) = 0.09, p = 0.77, nor a Stimulation by Group interaction, ч²(2) = 1.30, p = 0.52, nor a Stimulation by Session interaction, ч²(1) = 2.09, p = 0.15. Responses were faster for words than non-words, ч²(1) = 82.04, p < 0.001, and in second than first session, ч²(1) = 22.28, p < 0.001; there was no significant main effect of group (left anodal, right cathodal, bilateral), ч²(2) = 0.18, p = 0.92.

4.3 Sentence comprehension

Mean self-paced reading time (mean time per word within each sentence) and question RT and accuracy are presented in Table 6. Linear mixed-effect models revealed the same pattern for all three outcome measures. Namely, there was neither a significant effect of Stimulation (reading time: ч²(2) = 0.27, p = 0.60, RT: ч²(2) = 0.67, p = 0.41, response accuracy: ч²(2) = 1.96, p = 0.16), nor a Stimulation by Group interaction (reading time: ч²(2) = 0.08, p = 0.96, RT: ч²(2) = 0.55, p = 0.76, response accuracy: ч²(2) = 3.36, p = 0.34), nor a Stimulation by Session interaction (reading time: ч²(2) = 3.03, p = 0.08, RT: ч²(1) = 1.07, p = 0.30, response accuracy: ч²(1) = 1.96, p = 0.38). Performance was affected by sentence structure (reading time: ч²(2) = 31.37, p < 0.001, RT: ч²(1) = 52.94, p < 0.001, response accuracy: ч²(1) = 40.92, p < 0.001), but not by session (reading time: ч²(2) = 0.02, p = 0.90, RT: ч²(1) = 0.56, p = 0.45, response accuracy: ч²(1) = 0.51, p = 0.47) or experimental group (reading time: ч²(2) = 1.57, p = 0.46, RT: ч²(2) = 0.91, p = 0.63, response accuracy: ч²(2) = 0.13, p = 0.94).

Table 6. Task performance across stimulation conditions

4.4 Correlation of stimulation effect with handedness score

Across experimental groups, handedness scores were not significantly correlated with any individual stimulation effect sizes (lexical decision RT: r(70) = .04, p = .72; sentence comprehension reading time: r(70) = -.03, p = .80, RT: r(70) = .07, p = .58, accuracy: r(70) = .002, p = .99). Within experimental groups, the only significant correlation with handedness scores was found for sentence comprehension accuracy in the right cathodal group, r(22) = .44, p = .031: a greater right-hand preference was associated with a higher increase in sentence comprehension accuracy in right cathodal compared to sham stimulation.

5. Discussion

This study tested whether language processing in healthy young adults can be modulated by altering the interhemispheric balance with tDCS. Bilateral tDCS combining anodal (supposedly excitatory) stimulation of the Broca's area and cathodal (supposedly inhibitory) stimulation of its right-hemisphere homologue was applied and compared to both necessary unihemispheric control conditions. That has not been done in the previous studies; moreover, there are only tho studies that tested interhemispheric competition hypothesis in healthy individuals. The results of the previous studies are rather controversial. No significant effects of tDCS on either speed or accuracy of either single-word or sentence-level processing (no significant effects of stimulation across the experimental groups) was found. The effects of bilateral tDCS were not superior to those of control conditions: there were no significant interactions between stimulation (real versus sham) and experimental group (bilateral versus left anodal versus right cathodal).

Previous studies testing the interhemispheric competition hypothesis in healthy population show that bihemispheric stimulation might enhance language processing (Fiori et al., 2017; Meinzer et al., 2014); however, they did not find a significant difference between unhemispheric and bihemispheric stimulation. Fiori et al. (2017) found a significant difference between unhemispheric and bihemispheric stimulation only for a group of elder individuals, suggesting that bihemispheric stimulation might cause significant improvements only in a dusturbed language network (e.g. in elder people and patients). In contrast, in the current study, no significant effect of stimulation in general was found. It might be hypothesized that the current study employed the linguistic tasks that may be too easy to process for the young participants due to ceiling effects. Another explanation could be that previous studies mostly analysed performance in online tasks (tasks completed during stimulation). In contrast, in this study only the results of offline tasks (after stimulation) were analysed; it could be that the effect of stimulation in healthy individuals is so weak that it is present only during stimulation, but it does not last longer.

To our knowledge, this was the first study where bilateral tDCS was tested against both necessary control conditions: separate anodal stimulation of the left Broca's area and cathodal stimulation of the Broca's area homologue. Previous studies compared bihemispheric tDCS to either separate anodal stimulation of the left hemisphere (Fiori et al. 2017) or cathodal stimulation or the right hemisphere (Giglia et al., 2011), or did not compare them at all (Marangolo et al. 2013). The effects were tested not only in a single-word task but also in a sentence-level task, previously reported only by Giustolisi et al. (2018).

Since this study includes a large sample size (n = 72; cf. typical sample sizes of ca. 20 participants, Klaus Schutter, 2017) and, therefore, provides better statistical power, the study contributes to the debate on whether tDCS can modulate language processing in healthy speakers. While some argue that it can (Gauvin, Meinzer, de Zubicaray, 2015; Price, McAdams, Grossman, Hamilton, 2015), others pinpoint that positive effects in the published literature can be due to regression-to- the-mean in low-powered studies and/or publication bias (Westwood Romani, 2017; Westwood et al., 2017).

The null results of the current study provide a contribution to future meta-analyses. Null results in lexical decision have already been demonstrated following tDCS over Broca's area in healthy young participants: Malyutina and Den Ouden (2015) applied high-definition transcranial direct current stimulation (HD-tDCS) to Broca's area or to the left angular gyrus in a group of 27 young healthy participants. They found that cathodal stimulation over both Broca's area and the left angular gyrus increased naming speed in naming task, but the effect did not extend to the lexical decision task. However, Brьckner and Kammer (2017) found different results stimulating Wernicke's area: they stimulated 20 healthy participants with either anodal, cathodal or sham tDCS followed by lexical decision task. Stimulation led to faster RT in lexical decision task.

In a sentence-level task, this study did not replicate the positive effect of tDCS over Broca's area on sentence comprehension in healthy individuals recently found by Giustolisi et al. (2018). They tested 44 young healthy participants; half of them received anodal stimulation and the rest - sham stimulation. Participants heard recorded sentences and two pictures after each sentence. They were instructed to press the button to decide which picture corresponded to the sentence they just heard. One possible reason is different stimulation intensity (0.75 mA for 30 min in their study and 1.5 mA for 20 min in a current study): possibly, longer and/or lower-intensity stimulation may have greater effects (Hoy et al., 2013). Another possible reason is experimental design: in this study, each participant received real and sham stimulation on different days, while Giustolisi et al. (2018) used a between-subject design (n=22 each in real and sham stimulation), so between-group differences could be due to random individual variability. Moreover, Giustolisi et al. (2018) analyzed results of online task whereas we analyzed only offline measures; however, online measures could be more sensitive to stimulation.

There is evidence that lack of significant tDCS effects in the language domain may be due to ceiling effects in healthy young speakers because they have a non-disturbed language network. Westwood et al. (2017) conducted four separate experiments with healthy individuals to examine the effects of frontal and temporal lobe anodal tDCS on reading and naming tasks. The authors analysed both individual variability across participants and group analysis. They did not find any difference between sham and real stimulation of any of the target areas. The authors hypothesize, among other possible explanation, that the brain of healthy individuals is already in almost its maximum capacity and therefore their performance can not be bettered. Meanwhile, older adults, patients and lower performance do demonstrate sensitivity to stimulation (Fiori et al., 2017). For example, Habich et al. (2017) tested 43 healthy young adults. They targeted dorsolateral prefrontal cortex with either anodal or sham stimulation. Participants completed verbal episodic memory task. The analysis showed no improvement after real stimulation in comparison with sham session at group level. However, the authors found that the number of stimuli significantly moderated the stimulation effect in such a way that initially low performers experienced the highest gain from real stimulation.

In lexical decision task, participants indeed performed at ceiling. But in sentence comprehension task, the mean accuracy was 88.6% and could have been still improved by tDCS. However, that was not the case. There is a possibility that seemingly not-at-ceiling language performance in healthy young adults is not likely to be boosted by brain stimulation methods because it is observed for different reasons than, for example, low language performance in older adults or in patients. Such low language performance is due to a shortage of resources such as working memory span, processing speed, attention span etc. On the other hand, not-at-ceiling language performance in healthy young adults may originate from incomplete (“good-enough”) processing strategies of normal language processing (Ferreira, Bailey, Ferraro, 2002).

Importantly, the current study does not claim that tDCS is inefficient in general. Rather, it appears that tDCS can modulate language processing under some conditions, which remain to be further studied and determined. Individual factors such as language lateralization, associated with handedness, may play a role. Here, participants in the right cathodal group had a greater stimulation-related improvement in sentence comprehension accuracy if they had a greater right-hand preference, likely associated with more left-lateralized language processing. It is possible that this group benefitted from suppression of the right hemisphere and thus their “natural” lateralization was more enhanced than in the other two groups. However, we found no other correlations between handedness scores and stimulation. In addition to individual factors, important factors are stimulation parameters and electrode montages, including the positioning of the “reference” electrode. Their careful consideration is crucial for further reports and meta-analyses of tDCS setup efficacy.

The purpose of the current study was to inform future tDCS applications in aphasia. Due to lack of effects in control sample, the study did not provide clinical implications. Thus, it remains to be further investigated in the population with aphasia whether bilateral stimulation, altering the interhemispheric balance, has a greater effect on language processing than separate application of left anodal stimulation or right cathodal stimulation. Based on previous neuroimaging and brain stimulation research, altering the interhemispheric balance promises a positive effect on language recovery in aphasia and thus needs to be carefully tested.

To test the interhemispheric competition hypothesis on patients with aphasia, it might be important to consider conducting several sessions instead of one, as effects induced by single-seccion tDCS effects seem to be short-term, and multiple session are more likely to provide longer-term maintenance. The data of online tasks (during stimulation) should also be collected and analysed. Finally, larger experimental groups (n >= 24) have better statistical power and therefore should be recruited to make a reliable comparison between stimulation setups, which, to the best of our knowledge, has not been done in previous studies in aphasia.

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