Targeting The Frontoparietal Network Using Bifocal Transcranial Alternating Current Stimulation During A Motor Sequence Learning Task in Healthy Older Adults Part 3

3.4. Reaction time

We were able to replicate the results of Violante and colleagues for the parameter reaction time with a significant effect of stimulation F(1, 47.10) ¼ 5.33, p ¼ .025, hp 2 ¼ 0.1 (medium effect), as well as an effect for difficulty level F(1, 35.77) ¼ 44.61, p < .001 hp 2 ¼ 0.55 (large effect), and an interaction effect F(1, 34.54) ¼ 4.83, p ¼ .035, hp 2 ¼ 0.12 (medium effect). Post-hoc analysis with Tukey correction showed a significant difference between sham and real stimulation during difficulty level 2, t(42.1) ¼ 3.22, p ¼ .013, but not for level 1 t(42.6) ¼ 0.20, p ¼ .997. 

The relationship between sham stimulation and memory is a very interesting area of research. We often hear some people say that they can improve their memory by applying fake stimulation, and there are some scientific studies to confirm this.

Sham stimulation refers to a visual or auditory signal that does not exist in the environment but is artificially created. Taking visual sham stimulation as an example, we can use a flash to produce a sudden light, or quickly display some pictures to affect people's attention and memory. Similarly, auditory sham stimulation can also be achieved by playing a certain sound or quickly editing different speech clips.

Some recent studies have shown that sham stimulation can activate our brains and improve our memory. This is because when we are stimulated by these signals, our brains will automatically perform some processing, such as adding this information to our working memory or mobilizing our long-term memory. Of course, this effect is not long-lasting and usually lasts only a few minutes. But if we use these signals properly, we can improve our learning and memory efficiency.

It should be noted that it is very important to make reasonable use of false stimulation. Overreliance on stimulation can lead to excessive brain fatigue, which affects our learning and memory. Furthermore, this approach is not suitable for everyone. Some people may experience adverse reactions due to sensitivity to light or sound and need to avoid this method.

In short, reasonable use of false stimulation can improve our memory, but we need to pay attention to moderation and avoid over-reliance on this method. At the same time, we also need to continue to actively look for other learning and memory techniques to achieve better results. It can be seen that we need to improve memory, and Cistanche deserticola can significantly improve memory because Cistanche deserticola is a traditional Chinese medicinal material that has many unique effects, one of which is to improve memory. The efficacy of minced meat comes from the various active ingredients it contains, including acid, polysaccharides, flavonoids, etc. These ingredients can promote brain health in various ways.

Click know 10 ways to improve memory

Both conditions showed a significant increase in reaction time between level 1 and level 2, which was more prominent in the sham condition t(36.4) ¼ 6.16, p < .001 than the real condition t(36.4) ¼ 3.25, p ¼ .013 Adding the 3-back difficulty level to the model resulted in no effect for stimulation F(1,78.24) ¼ 1.61, p ¼ .209, a significant effect for difficulty level F(2, 67.65) ¼ 34.99, p < .001 and no interaction effect F(2, 67.65) ¼ 0.75, p ¼ .478, see Fig. 5.

3.5. N-back performance parameters (hits, false alarms, accuracy)

The analyses did not show a main effect of stimulation on any of these parameters for the 2 and the 3 levels of difficulty models. There was a significant main effect of difficulty level. Indicating a significant decrease in performance with increasing n-back levels on all parameters. There were no stimulation difficulty interaction effects. Please see Table 1 for statistical results.

3.6. Peak frequency analysis

During the EEG measurements data from 9 electrodes were acquired during the performance of the pre-baseline measurement of the N-back task. The individual average peak frequency of the 3 N-back levels combined was used as the personalized theta stimulation frequency for the rest of the study. Results of the overall average peak frequency showed a group mean of 4.5 (sd ¼ 0.28) with a range between 4.1 and 5.4. For more details, please see Table 2.

3.7. Stimulation sensations & blinding

Based on the stimulation sensation interview, there were no adverse effects due to the tACS stimulation and only minor tACS sensations were reported. Most participants responded either with “none” or “mild”. Moreover, there was no significant difference between the stimulation and sham conditions for any of the perceived sensations, see Table 3.

Participants were not able to discriminate between real and sham stimulation. A binomial test indicated that the proportion of correct answers during session 1 was 0.4, which was not significantly different than the chance level (0.5), p ¼ .503. For session 2, the proportion of correct answers was 0.6, which was not significantly different than chance, p ¼ .503, see Table 4.

4. Discussion

The main outcome of this study is that personalized, bifocal, synchronized tACS to the right FPN can enhance the performance during an SFTT with high WM load in healthy older. In contrast, this interventional paradigm did not affect the performance during the SFTT, if the WM load was low. These findings indicate that the efficacy of bifocal theta tACS applied synchronously to DLPFC and PPC is dependent on the cognitive requirements and underlying cognitive state during the task. This aspect is further supported by the findings that tACS also improved N-back task performance specific to difficulty levels that were demanding enough.

4.1. Motor task

The present results support the view of a causal effect of synchronized bifocal theta frequency oscillations applied to the right FPN on the performance of a motor sequence learning task.

Correlative evidence of the activation of the FPN during finger-tapping tasks has been previously shown using neuroimaging [62,63]. The meta-analysis of Witt and colleagues (2008) has shown that visually or self-paced finger-tapping tasks induce concordant activity in the right DLPFC and the right inferior parietal cortex [63]. However, to the best of our knowledge, this was the first time the FPN was used as a target for a tACS paradigm with the intent to improve MSL.

Here, we were able to demonstrate an improvement in performance and a hint to a possible improvement of the training effects with this approach, but exclusively for the SFTT condition with high WM load (memorized condition). Therefore, the efficacy of the present orchestrated stimulation paradigm on motor behavior was dependent on the amount of WM load during the task. This is in line with the study of Violante and colleagues (2017) that showed that theta tACS to the right FPN improved performance on a WM task, but only for the task with higher WM load [24]. This might be explained by the fact that the FPN shows more coherence in the theta range during WM tasks with high WM load [64]. With the use of tACS, it is suggested to be able to exogenously enhance coherence by the entrainment of the cortical oscillation between distant regions [20]. Although we did not verify network coherence with the use of EEG or other neuroimaging measures, we hypothesize that exogenously induced theta oscillations might have amplified the ongoing physiological oscillatory activity engaged in WM processing, which in turn has supported the performance and acquisition process of the motor task with high WM load, but not with low WM load.

Another possible explanation is that the involvement of the FPN is related to a specific sub-process of WM. WM can be roughly divided into three sub-processes: encoding, maintenance, and retrieval [65]. The non-memorized SFTT condition required the participants to learn the sequence while performing the movements, which falls under the encoding phase. During the memorized SFTT condition, the participants needed to maintain and retrieve the previously learned sequence while performing the movements. A recent meta-analysis has shown that during the transition from encoding to maintenance and retrieval stages, the involvement of the FPN progressively increases. Therefore, it can be well hypothesized that the memorized SFTT condition benefits more from the FPN as a target while the non-memorized SFTT condition profits more from stimulation of other brain regions. For instance, the acquisition phase relies heavily on the dorsal attention network, which predominantly includes the frontal eye fields and the intraparietal sulci [65,66]. Moreover, studies have shown a high involvement of the M1 during the early stages of learning, with a reduction of activity to baseline when a sequence becomes explicitly known [67,68].

The involvement of the frontal and parietal areas during MSL has been well established, however, their precise functional role is less clear [67,69e71]. MSL can be divided into three different learning phases: stage 1 for acquisition, stage 2 for consolidation, and stage 3 for retention. The early learning phase relies more heavily on cognitive processes such as WM, showing an activation in the prefrontal cortex and parietal areas [72e74]. In this study, the efficacy of targeting the FPN to enhance performance on the MSL task is most likely specific to the WM load. Studies that focused on the WM processes found that the FPN is associated with the maintenance and manipulation of information when theta oscillations are in synchrony between the two brain areas [25,64]. This might explain why the performance on the motor sequence task only improved during the high WM load task, where the participants had to perform the sequence from memory. 

Moreover, both accuracy and speed improved significantly in the memorized condition due to the tACS stimulation. However, the real stimulation induced a sharp increase in accuracy, while the sham group improved more gradually. Similar results have been shown in a study comparing real vs. sham anodal transcranial direct current stimulation (DCS) applied to the M1 on an SFTT. Different age groups were compared and older adults showed a sharp increase in accuracy in the real stimulation group and a gradual increase in the sham group [75]. They argue that the active M1 stimulation facilitated the encoding and storage of the sequence in memory. In the current study, the stimulation target was the FPN and was effective in the memorized condition when the sequences were already learned. This result could be driven by an enhanced capacity to maintain and retrieve the previously learned sequence, due to the synchronization of theta oscillations in the FPN [65].

This study aimed to extend previous studies that have targeted the FPN with bifocal theta tACS to improve WM performance [24,25,76,77] by using a similar setup to study the effect on MSL. This has been the first time that both the DLPFC and the PPC have been targeted with the use of bifocal theta tACS during a motor sequence learning task. The main aim was to target the FPN as a network that has shown to be important for WM and has shown activation during MSL [18,24e29,76,77]. Although we were able to show that bifocal tACS to the FPN was effective when WM-load was high, we cannot exclude that this effect might have been generated by a mono-focal stimulation of either the DLPFC or the PPC. This study did not intend to compare the efficacy of mono-focal theta tACS to bifocal theta tACS on MSL. However, based on the positive effects of targeting these areas with bifocal theta tACS on the performance of an MSL task more research is necessary to define the exact working mechanisms and to determine the effects of mono-focal stimulation on either of the two areas separately. Due to the lack of comparative studies, no final conclusive statement can be made about the beneficial effects of bifocal FPN stimulation over targeting one single of the target brain areas. Further research in upcoming studies will have to address this open question in detail.

4.2. Personalized tACS

This study has used personalized tACS stimulation in the theta range on MSL and cognitive function. This approach was based on a study by Reinhart and Nguyen (2019) who showed the beneficial effects of personalized frontotemporal theta tACS compared to a standard theta tACS on a WM task in healthy older adults [57]. Individual peak frequencies were measured while participants performed the N-back task to determine the individual stimulation frequency, though a comparison between personalized and standard theta was not in the scope of the present study. We assume that individualizing stimulation paradigms might be important due to a more effective peak frequency as suggested by e.g., Reinhart and Nguyen, but also based on the differential functional effects of low theta frequencies (4e4.5 Hz) compared to high theta frequencies (7 Hz) on WM performance [78e80]. More specifically, 4 Hz tACS to the right parietal cortex improved WM capacity, while 7 Hz tACS reduced WM capacity in healthy young adults [78e80]. Jones et al. compared bifocal 7 Hz tACS to 4.5 Hz tACS applied to the FPN and found positive effects for 4.5 Hz, but not 7 Hz stimulation on WM performance [79]. However, there are also reports, that did not show the effects of personalization such as in a current TMS study [81].
The average stimulation frequency in the present study was 4.5 Hz, which fits with the abovementioned low theta frequencies relevant for WM. As the comparison between personalized and standard (non-personalized) theta tACS was not in the scope of the present study, we cannot draw any conclusion about whether personalization in the present study is more effective than non-personalized bifocal tACS in the theta range, an interesting question that has to be addressed in upcoming studies.

4.3. N-back task

The reason for the use of the N-back task was twofold. First and foremost, as a way to measure the individual theta frequency while performing a WM task. Second, it was used as an additional control experiment to verify that the stimulation was indeed directed to the FPN and modulates a key function processed by the FPN. The behavioral results of the WM task support the notion that theta tACS to the FPN enhances WM performance [24,25]. As there was no neuroimaging data to confirm that the FPN was indeed targeted, a behavioral difference in WM performance provides correlational evidence.

In this study, we could replicate the observations of Violante et al. showing that exogenous synchronization of cortical oscillations in the theta range improved WM performance when cognitive demands were moderately high (2-back level) [24]. We have extended the results by showing that this was only applicable to level 2-back and not the more difficult 3-back level. The efficacy of the stimulation paradigm seems to follow an inverted U-shape in relationship to the difficulty of the task. The present study cohort was healthy older adults. Although it is currently unclear whether young adults would still benefit from the oscillatory synchronization during the 3-back task, one could speculate that the inverted u-shape with the peak at the 2-back task is age-related.

Studies that have compared performance on WM tasks between young and healthy older adults have shown age-related reduction in performance, especially in tasks with high cognitive demand [82,83]. In response to high WM load, older adults show a relative hypoactivation in fronto-parietal regions compared to young adults [82,84]. The “Compensation-Related Utilization of Neural Circuits Hypothesis” provides a framework for this phenomenon; age-related hyperactivations are seen during tasks with low WM load due to reduced neural efficiency, with hypoactivation for tasks with high WM load due to reduced neural capacity [85]. This shows that older adults use compensatory mechanisms already with low WM load tasks (1-back) and are therefore not able to recruit the necessary neural resources during high WM load tasks (3-back) [82,83]. 

Heinzel and colleagues hypothesized that the change in neuronal activity is due to a decrease in FPN coupling; they showed that fronto-parietal connectivity decreased in older adults during 2-back and even more during 3-back tasks [73,86]. This could indicate that the difference in efficacy of stimulation between the 2-back and the 3-back tasks is related to the degree of deficient coupling of the FPN within these tasks and that the interventional approach with tACS could only sufficiently compensate these mechanisms for the 2-back task, but not anymore for the 3-back task. The lack of improvement during the 1-back condition could indicate that the natural compensatory mechanisms are not sensitive to the effects of this stimulation paradigm. This points towards a specific efficacy that is dependent on the brain state caused by the amount of WM load.

The results of the N-back task showed a specific effect on reaction times, and not on hit-rate, false alarms, and accuracy. These findings are similar to previous studies by Polania et al. (2012), Violante et al. (2017), and Alekseichuk et al. (2017) that used theta tACS to target the FPN. Synchronized tACS decreased reaction times [24,25], while desynchronized tACS increased the reaction time on a visual WM task [25,76]. The exact reason for the effect on reaction times but not on other parameters remains elusive. Violante et al. showed a relation between increased parietal BOLD activation and decreased reaction times [24]. Evidence suggests a critical role of the parietal area in WM maintenance [87]. Therefore, Violante et al. suggest that the increase in neural activation in the parietal areas might have interacted with the mechanisms related to reaction times [24]. However, Alekseichuk et al. argue that the improved reaction times are network-related, as they found increased reaction times after desynchronization of the prefrontal areas from the parietal areas [76]. They argue that this is due to a decline in information uptake, reflected in the outlasting theta rhythm desynchronization in the cortex [76]. Although the results seem to point towards specific effects of synchronized theta tACS on reaction times, the exact mechanisms remain unclear. Further analysis is necessary to disentangle the exact physiological mechanisms of responses during WM tasks.

4.4. Future steps

The present study was a proof-of-principle study to investigate the involvement of the FPN in motor sequence learning. This study has a few limitations, which are discussed using suggestions for future studies. Firstly, the present data suggest a clear effect on behavioral performance of the interventional approach during the task, however, whether it impacts learning is not clear. There was no clear statistical interaction between condition and blocks that would substantiate a strong learning effect, though there are probably hints towards a potential additional effect on learning using changes in the course of accuracy and the trend to a difference for total learning at the end of the training. This important open question has to be addressed in detail in upcoming studies with more intensive training (e.g., longer training sessions, multiple training sessions). Moreover, follow-up sessions will enhance our understanding of the consolidation and possible retention of behavioral improvement. Motor learning encompasses multiple processes such as online and offline learning. Online learning is the improvement during the training of the task; offline learning happens after training and is a vital part of the consolidation of learned behavior [67,88e90]. 

Multiple sessions will allow us to investigate whether improvement continues with multiple training sessions, impacts differentially on online and offline learning (or only on performance), and whether it retains during longer periods. Secondly, we currently cannot conclude whether personalizing the stimulation frequency is beneficial compared to a standardized frequency (e.g., 6 Hz) for stimulation in the present study [23e25]. This aspect was beyond the scope of the present study and has to be addressed in upcoming studies. Comparing the standardized to a personalized stimulation paradigm will then provide more conclusive results about the importance of personalization to endogenous oscillatory activity within the present task. Lastly, to further personalize the approach future studies should personalize the placement of electrodes in the individual brain based on simulations. 

In the current study, the electrode placement was defined by standardized locations using an EEG cap with the 10/20 system. We have used concentric electrodes and each montage consisted of a small circular center electrode surrounded by a larger return electrode. This setup has been shown to improve focality compared with other electrodes such as the 5 5 cm rectangular electrodes or ring electrode set-ups with the return electrode on a separate region [91]. For a simulation of the electric field distribution, please see Fig. 2B. This improved facility highlights the importance of precision of the electrode placement as the stimulation is most effective close to the center of the electrodes [92]. 

The currently used technique based on the 10-20-electrode system has been widely used in NIBS studies [93]. However, this is a standardized electrode placement system based on anatomical landmarks that can vary across participants [94]. A recent study by Scrivener and Reader compared the locations of the electrode placements with the use of an EEG cap with MRI images of the same participants. They found that the electrode placements deviated from the actual cortical locations with the smallest SD of 4.35 mm in frontal areas and the largest SD of 6.25 mm in the occipital and parietal areas [95]. These deviations are unlikely to result in any behavioral differences due to the focality of the stimulation. However, it does show that there is room for improvement in terms of precise definition of target locations and consistency in electrode placement. A way to improve precision is by using neuronavigation techniques guided by structural neuroimaging or with the use of functional MRI to pinpoint the exact target locations for stimulation [93].

5. Conclusion

In conclusion, in this study, we were able to show a causal relationship between stimulating the FPN and improvements in an MSL task. Moreover, we were able to show distinctive efficacy of FPN synchronization for motor tasks with low- and high WM load, resulting in improved performance on the motor task with high WM load, but no stimulation effects on the motor task with low WM load. Despite the clear effect on the performance level, there was no clear effect, probably a hint, towards enhancement of motor learning, an aspect that has to be addressed in detail in upcoming studies. The mechanisms of action point towards an effect of the stimulation on an improved capacity to maintain and perform the sequences. The current knowledge about using tACS to target frontal and parietal areas to improve MSL is limited. However, these results indicate that targeting the FPN as a network using personalized bifocal oscillatory stimulation is a promising approach. In addition, the present study showed that theta tACS applied to the FPN improved WM performance. This reveals an important interplay between the motor and cognitive domain pointing to it as a promising target for interventional strategies based on NIBS. However, to do this successfully, it is critically important that such an approach might only be effective when the cognitive load of a respective task is significantly high as demonstrated here by the WM load.

Taken together, personalized orchestrated bifocal tACS applied to the FPN improved performance on an MSL task. This might offer a promising strategy to enhance motor skills and motor learning in healthy older adults and neurological patients showing deficits in motor performance and/or motor learning.

Funding

The present project was supported by the Defitech Foundation (Morges, CH) and by #2017-205 ‘Personalized Health and Related Technologies (PHRT-205)’ of the ETH Domain.

Credit authorship contribution statement

L.R. Draaisma: Conceptualization, Design of experiment, Methodology, Validation, Data acquisition, Formal analysis, Writing e original draft, Writing e review & editing, Visualization, Project administration. M.J. Wessel: Conceptualization, Project administration, Writing e review & editing. M. Moyne: Validation, Randomization, Data acquisition. T. Morishita: Writing e-review &editing. F.C. Hummel: Conceptualization, Design of experiment, Interpretation of results, Writing e review & editing.

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgments

We thank Pablo Maceira for reading the manuscript and providing excellent comments, Elena Beanato for providing the script to pre-process the SFTT data, Roberto Salamanca-Giron for his contribution by providing and adapting his Matlab script for the EEG peak frequency analysis, and Giorgia Giulia Evangelista for her contribution to the set-up of SimNIBS and her help with the visualization of the ring electrodes and the electric field distribution. This study was supported by the EEG facility of the Human Neuroscience Platform, Fondation Campus Biotech Geneva, Geneva, Switzerland.

References

[1] Willingham DB. A neuropsychological theory of motor skill learning. Psychol Rev 1998;105:558e84. https://doi.org/10.1037/0033-295X.105.3.558. 

[2] Dupont-Hadwen J, Bestmann S, Stagg CJ. Motor training modulates intracortical inhibitory dynamics in the motor cortex during movement preparation. Brain Stimul 2019;12:300e8. https://doi.org/10.1016/j.brs.2018.11.002. 

[3] Karni A, Meyer G, Jezzard P, Adams MM, Turner R, Ungerleider LG. Functional MRI evidence for adult motor cortex plasticity during motor skill learning. Nature 1995;377:155e8. https://doi.org/10.1038/377155a0. 

[4] Seidler RD, Bo J, Anguera JA. Neurocognitive contributions to motor skill learning: the role of working memory. J Mot Behav 2012;44:445e53. https:// doi.org/10.1080/00222895.2012.672348. 

[5] Buch ER, Santarnecchi E, Antal A, Born J, Celnik PA, Classen J, et al. Effects of tDCS on motor learning and memory formation: a consensus and critical position paper. Clin Neurophysiol Off J Int Fed Clin Neurophysiol 2017;128: 589e603. https://doi.org/10.1016/j.clinph.2017.01.004. 

[6] Wessel MJ, Zimerman M, Hummel FC. Non-invasive brain stimulation: an interventional tool for enhancing behavioral training after stroke. Front Hum Neurosci 2015;9:265. https://doi.org/10.3389/fnhum.2015.00265. 

[7] Krause V, Meier A, Dinkelbach L, Pollok B. Beta band transcranial alternating (tACS) and direct current stimulation (tDCS) applied after initial learning facilitate retrieval of a motor sequence. Front Behav Neurosci 2016;10. https:// doi.org/10.3389/fnbeh.2016.00004. 

[8] Pollok B, Boysen A-C, Krause V. The effect of transcranial alternating current stimulation (tACS) at alpha and beta frequency on motor learning. Behav Brain Res 2015;293:234e40. https://doi.org/10.1016/j.bbr.2015.07.049. 

[9] Anguera JA, Reuter-Lorenz PA, Willingham DT, Seidler RD. Contributions of spatial working memory to visuomotor learning. J Cognit Neurosci 2010;22: 1917e30. https://doi.org/10.1162/jocn.2009.21351. 

[10] Maxwell JP, Masters RSW, Eves FF. The role of working memory in motor learning and performance. Conscious Cognit 2003;12:376e402. https:// doi.org/10.1016/s1053-8100(03)00005-9.

For more information:1950477648nn@gmail.com