Author response:
Public Reviews:
Reviewer #1 (Public review):
Summary:
The authors claim that they can use a combination of repetitive transcranial magnetic stimulation (intermittent theta burst-iTBS) and transcranial alternating current stimulation (gamma tACS) to cause slight improvements in memory in a face/name/profession task.
Strengths:
The idea of stimulating the human brain non-invasively is very attractive because, if it worked, it could lead to a host of interesting applications. The current study aims to evaluate one such exciting application.
Weaknesses:
(1) It is highly unclear what, if anything, transpires in the brain with non-invasive stimulation. To cite one example of many, a rigorous study in rats and human cadavers, compellingly showed that traditional parameters of transcranial electrical stimulation lead to no change in brain activity due to the attenuation by the soft tissue and skull (Mihály Vöröslakos et al Nature Communications 2018): https://www.nature.com/articles/s41467-018-02928-3. It would be very useful to demonstrate via invasive neurophysiological recordings that the parameters used in the current study do indeed lead to any kind of change in brain activity. Of course, this particular study uses a different non-invasive stimulation protocol.
Thank you for raising the important issue of the actual neurophysiological effects of non-invasive brain stimulation. Unfortunately, invasive neurophysiological recordings in humans for this type of study are not feasible due to ethical constraints, while studies on cadavers or rodents would not fully resolve our question. Indeed, the authors of the cited study (Mihály Vöröslakos et al., Nature Communications, 2018) highlight the impossibility of drawing definitive conclusions about the exact voltage required in the in-vivo human brain due to significant differences between rats and humans, as well as the in-vivo human brain and cadavers due to alterations in electrical conductivity that occur in postmortem tissue.
We acknowledge that further exploration of this aspect would be highly valuable, and we agree that it is worth discussing both as a technical limitation and as a potential direction for future research, we therefore modify the manuscript correspondingly. However, to address the challenge of in vivo recordings, we conducted Experiments 3 and 4, which respectively examined the neurophysiological and connectivity changes induced by the stimulation in a non-invasive manner. The observed changes in brain oscillatory activity (increased gamma oscillatory activity), cortical excitability (enhanced posteromedial parietal cortex reactivity), and brain connectivity (strengthened connections between the precuneus and hippocampi) provided evidence of the effects of our non-invasive brain stimulation protocol, further supporting the behavioral data.
Additionally, we carefully considered the issue of stimulation distribution and, in response, performed a biophysical modeling analysis and E-field calculation using the parameters employed in our study (see Supplementary Materials).
(2) If there is any brain activity triggered by the current stimulation parameters, then it is extremely difficult to understand how this activity can lead to enhancing memory. The brain is complex. There are hundreds of neuronal types. Each neuron receives precise input from about 10,000 other neurons with highly tuned synaptic strengths. Let us assume that the current protocol does lead to enhancing (or inhibiting) simultaneously the activity of millions of neurons. It is unclear whether there is any activity at all in the brain triggered by this protocol, it is also unclear whether such activity would be excitatory, or inhibitory. It is also unclear how many neurons, let alone what types of neurons would change their activity. How is it possible that this can lead to memory enhancement? This seems like using a hammer to knock on my laptop and hope that the laptop will output a new Mozart-like sonata.
Thank you for your musical observation. As you correctly point out, we still do not have precise knowledge of which neurons—and to what extent—are activated during non-invasive brain stimulation in humans. However, this challenge is not limited to brain stimulation but applies to many other therapeutic interventions, including psychiatric medications, without limiting their use.
Nevertheless, a substantial body of research has investigated the mechanisms underlying the efficacy of TMS in enhancing memory performance, primarily through its ability to induce long-term potentiation (Bliss & Collingridge, The Journal of Physiology, 1993a; Huang et al., Clinical Neurophysiology, 2007; Ridding & Rothwell, Nature Reviews Neuroscience, 2007; Koch et al., Neuroimage 2018; Koch et al., Brain 2022; Jannati et al., Neuropsychopharmacology, 2023).
We acknowledge that we took this important aspect for granted, and we will expand the discussion accordingly.
(3) Even if there is any kind of brain activation, it is unclear why the authors seem to be so sure that the precuneus is responsible. Are there neurophysiological data demonstrating that the current protocol only activates neurons in the precuneus? Of note, the non-invasive measurements shown in Figure 3 are very weak (Figure 3A top and bottom look very similar, and Figure 3C left and right look almost identical). Even if one were to accept the weak alleged differences in Figure 3, there is no indication in this figure that there is anything specific to the precuneus, rather a whole brain pattern. This would be the kind of minimally rigorous type of evidence required to make such claims. In a less convincing fashion, one could look at different positions of the stimulation apparatus. This would not be particularly compelling in terms of making a statement about the precuneus. But at least it would show that the position does matter, and over what range of distances it matters, if it matters.
Thank you for your feedback. We will improve the clarity of the manuscript to better address this important aspect. Our assumption that the precuneus plays a key role in the observed effects is based on several factors:
(1) The non-invasive stimulation protocol was applied to an individually identified precuneus for each participant. Given existing evidence on TMS propagation, we can reasonably assume that the precuneus was at least a mediator of the observed effects (Ridding & Rothwell, Nature Reviews Neuroscience 2007). For further details about target identification and TMS and tACS propagation, please refer to the MRI data acquisition section in the main text and Biophysical modeling and E-field calculation section in the supplementary materials.
(2) To investigate the effects of the neuromodulation protocol on cortical responses, we conducted a whole-brain analysis using multiple paired t-tests comparing each data point between different experimental conditions. To minimize the type I error rate, data were permuted with the Monte Carlo approach and significant p-values were corrected with the false discovery rate method (see the Methods section for details). The results identified the posterior-medial parietal areas as the only regions showing significant differences across conditions.
(3) To control for potential generalized effects, we included a control condition in which TMS-EEG recordings were performed over the left parietal cortex (adjacent to the precuneus). This condition did not yield any significant results, reinforcing the cortical specificity of the observed effects.
However, as stated in the Discussion, we do not claim that precuneus activity alone accounts for the observed effects. As shown in Experiment 4, stimulation led to connectivity changes between the precuneus and hippocampus, a network widely recognized as a key contributor to long-term memory formation (Bliss & Collingridge, Nature 1993). These connectivity changes suggest that precuneus stimulation triggered a ripple effect extending beyond the stimulation site, engaging the broader precuneus-hippocampus network.
Regarding Figure 3A, it represents the overall expression of oscillatory activity detected by TMS-EEG. Since each frequency band has a different optimal scaling, the figure reflects a graphical compromise. A more detailed representation of the significant results is provided in Figure 3B. The effect sizes for gamma oscillatory activity in the delta T1 and T2 conditions were 0.52 and 0.50, respectively, which correspond to a medium effect based on Cohen’s d interpretation.
(4) In the absence of any neurophysiological documentation of a direct impact on the brain, an argument in this type of study is that the behavioral results show that there must be some kind of effect. I agree with this argument. This is also the argument for placebo effects, which can be extremely powerful and useful even if the mechanism is unrelated to what is studied. Then let us dig into the behavioral results.
Hoping to have already addressed your concern regarding the neurophysiological impact of the stimulation on the brain, we would like to emphasize that the behavioral results were obtained controlling for placebo effects. This was achieved by having participants perform the task under different stimulation conditions, including a sham condition.
(4a) There does not seem to be any effect on the STMB task, therefore we can ignore this.
(4b) The FNAT task is minimally described in the supplementary material. There are no experimental details to understand what was done. What was the size of the images? How long were the images presented for? Were there any repetitions of the images? For how long did the participants study the images? Presumably, all the names and occupations are different? What were the genders of the faces? What is chance level performance? Presumably, the same participant saw different faces across the different stimulation conditions. If not, then there can be memory effects across different conditions that are even more complex to study. If yes, then it would be useful to show that the difficulty is the same across the different stimuli.
We thank you for signaling the lack in the description of FNAT task. We will add all the information required to the manuscript.
In the meantime, here we provide the answers to your questions. The size of the images 19x15cm. They were presented in the learning phase and the immediate recall for 8 seconds each, while in the delayed recall they were shown (after the face recognition phase) until the subject answered. The learning phase, where name and occupation were shown together with the faces, lasted around 2 minutes comprising the instructions. We used a different set of stimuli for each stimulation condition, for a total of 3 parallel task forms balanced across the condition and order of sessions. All the parallel forms were composed of 6 male and 6 female faces, for each sex there were 2 young adults (aged around 30 years old), 2 middle adults (aged around 50 years old), and 2 old adults (aged around 70 years old). Before the experiments, we ran a pilot study to ensure there were no differences between the parallel forms of the task. We can provide the task with its parallel form upon request. The chance level in the immediate and delayed recall is not quantifiable since the participants had to freely recall the name and the occupation without a multiple choice. In the recognition, the chance level was around 33% (since the possible answers were 3).
(4c) Although not stated clearly, if I understand FNAT correctly, the task is based on just 12 presentations. Each point in Figure 2A represents a different participant. Unfortunately, there is no way of linking the performance of individual participants across the conditions with the information provided. Lines joining performance for each participant would be useful in this regard. Because there are only 12 faces, the results are quantized in multiples of 100/12 % in Figure 3A. While I do not doubt that the authors did their homework in terms of the statistical analyses, it is difficult to get too excited about these 12 measurements. For example, take Figure 3A immediate condition TOTAL, arguably the largest effect in the whole paper. It seems that on average, the participants may remember one more face/name/occupation.
Thank you for the suggestion. We will add another graph to the manuscript with lines connecting each participant's performance. Unfortunately, we were not able to incorporate it in the box-and-whisker plot.
We apologize for the lack of clarity in the description of the FNAT. As you correctly pointed out, we used the percentage based on the single association between face, name and occupation (12 in total). However, each association consisted of three items, resulting in a total of 36 items to learn and associate – we will make it more explicit in the manuscript.
In the example you mentioned, participants were, on average, able to recall three more items compared to the other conditions. While this difference may not seem striking at first glance, it is important to consider that we assessed memory performance after a single, three-minute stimulation session. Similar effects are typically observed only after multiple stimulation sessions (Koch et al., NeuroImage, 2018; Grover et al., Nature Neuroscience, 2022).
(4d) Block effects. If I understand correctly, the experiments were conducted in blocks. This is always problematic. Here is one example study that articulated the big problems in block designs (Li et al TPAMI 2021): https://ieeexplore.ieee.org/document/9264220
Thank you for the interesting reference. According to this paper, in a block design, EEG or fMRI recordings are performed in response to different stimuli of a given class presented in succession. If this is the case, it does not correspond to our experimental design where both TMS-EEG and fMRI were conducted in a resting state on different days according to the different stimulation conditions.
(4e) Even if we ignore the lack of experimental descriptions, problems with lack of evidence of brain activity, the minimalistic study of 12 faces, problems with the block design, etc. at the end of the day, the results are extremely weak. In FNAT, some results are statistically significant, some are not. The interpretation of all of this is extremely complex. Continuing with Figure 3A, it seems that the author claims that iTBS+gtACS > iTBS+sham-tACS, but iTBS+gtACS ~ sham+sham. I am struggling to interpret such a result. When separating results by name and occupation, the results are even more perplexing. There is only one condition that is statistically significant in Figure 3A NAME and none in the occupation condition.
Thank you again for your feedback. We will work on making the large amount of data we reported easier to interpret.
Hoping to have thoroughly addressed your initial concerns in our previous responses, we now move on to your observations regarding the behavioral results, assuming you were referring to Figure 2A. The main finding of this study is the improvement in long-term memory performance, specifically the ability to correctly recall the association between face, name, and occupation (total FNAT), which was significantly enhanced in both Experiments 1 and 2. However, we also aimed to explore the individual contributions of name and occupation separately to gain a deeper understanding of the results. Our analysis revealed that the improvement in total FNAT was primarily driven by an increase in name recall rather than occupation recall. We understand that this may have caused some confusion. Therefore we will clarify this in the manuscript and consider presenting the name and occupation in a separate plot.
Regarding the stimulation conditions, your concerns about the performance pattern (iTBS+gtACS > iTBS+sham-tACS, but iTBS+gtACS ~ sham+sham) are understandable. However, this new protocol was developed precisely in response to the variability observed in behavioral outcomes following non-invasive brain stimulation, particularly when used to modulate memory functions (Corp et al., 2020; Pabst et al., 2022). As discussed in the manuscript, it is intended as a boost to conventional non-invasive brain stimulation protocols, leveraging the mechanisms outlined in the Discussion section.
(5) In sum, it would be amazing to be able to use non-invasive stimulation for any kind of therapeutic purpose as the authors imagine. More work needs to be done to convince ourselves that this kind of approach is viable. The evidence provided in this study is weak.
We hope our response will be carefully considered, fostering a constructive exchange and leading to a reassessment of your evaluation.
Reviewer #2 (Public review):
Summary:
The manuscript "Dual transcranial electromagnetic stimulation of the precuneus-hippocampus network boosts human long-term memory" by Borghi and colleagues provides evidence that the combination of intermittent theta burst TMS stimulation and gamma transcranial alternating current stimulation (γtACS) targeting the precuneus increases long-term associative memory in healthy subjects compared to iTBS alone and sham conditions. Using a rich dataset of TMS-EEG and resting-state functional connectivity (rs-FC) maps and structural MRI data, the authors also provide evidence that dual stimulation increased gamma oscillations and functional connectivity between the precuneus and hippocampus. Enhanced memory performance was linked to increased gamma oscillatory activity and connectivity through white matter tracts.
Strengths:
The combination of personalized repetitive TMS (iTBS) and gamma tACS is a novel approach to targeting the precuneus, and thereby, connected memory-related regions to enhance long-term associative memory. The authors leverage an existing neural mechanism engaged in memory binding, theta-gamma coupling, by applying TMS at theta burst patterns and tACS at gamma frequencies to enhance gamma oscillations. The authors conducted a thorough study that suggests that simultaneous iTBS and gamma tACS could be a powerful approach for enhancing long-term associative memory. The paper was well-written, clear, and concise.
Weaknesses:
(1) The study did not include a condition where γtACS was applied alone. This was likely because a previous work indicated that a single 3-minute γtACS did not produce significant effects, but this limits the ability to isolate the specific contribution of γtACS in the context of this target and memory function
Thank you for your comments. As you pointed out, we did not include a condition where γtACS was applied alone. This decision was based on the findings of Guerra et al. (Brain Stimulation 2018), who investigated the same protocol and reported no aftereffects. Given the substantial burden of the experimental design on patients and our primary goal of demonstrating an enhancement of effects compared to the standalone iTBS protocol, we decided to leave out this condition. However, we agree that investigating the effects of γtACS alone is an interesting and relevant aspect worthy of further exploration. In line with these observations, we will expand the discussion on this point in the study’s limitations section.
(2) The authors applied stimulation for 3 minutes, which seems to be based on prior tACS protocols. It would be helpful to present some rationale for both the duration and timing relative to the learning phase of the memory task. Would you expect additional stimulation prior to recall to benefit long-term associative memory?
Thank you for your comment and for raising this interesting point. As you correctly noted, the protocol we used has a duration of three minutes, a choice based on previous studies demonstrating its greater efficacy with respect to single stimulation from a neurophysiological point of view. Specifically, these studies have shown that the combined stimulation enhanced gamma-band oscillations and increased cortical plasticity (Guerra et al., Brain Stimulation 2018; Maiella et al., Scientific Reports 2022). Given that the precuneus (Brodt et al., Science 2018; Schott et al., Human Brain Mapping 2018), gamma oscillations (Osipova et al., Journal of Neuroscience 2006; Deprés et al., Neurobiology of Aging 2017; Griffiths et al., Trends in Neurosciences 2023), and cortical plasticity (Brodt et al., Science 2018) are all associated with encoding processes, we decided to apply the co-stimulation immediately before it to enhance the efficacy.
Regarding the question of whether stimulation could also benefit recall, the answer is yes. We can speculate that repeating the stimulation before recall might provide an additional boost. This is supported by evidence showing that both the precuneus and gamma oscillations are involved in recall processes (Flanagin et al., Cerebral Cortex 2023; Griffiths et al., Trends in Neurosciences 2023). Furthermore, previous research suggests that reinstating the same brain state as during encoding can enhance recall performance (Javadi et al., The Journal of Neuroscience 2017).
We will expand the study rationale and include these considerations in the future directions section.
(3) How was the burst frequency of theta iTBS and gamma frequency of tACS chosen? Were these also personalized to subjects' endogenous theta and gamma oscillations? If not, were increases in gamma oscillations specific to patients' endogenous gamma oscillation frequencies or the tACS frequency?
The stimulation protocol was chosen based on previous studies (Guerra et al., Brain Stimulation 2018; Maiella et al., Scientific Reports 2022). Gamma tACS sinusoid frequency wave was set at 70 Hz while iTBS consisted of ten bursts of three pulses at 50 Hz lasting 2 s, repeated every 10 s with an 8 s pause between consecutive trains, for a total of 600 pulses total lasting 190 s (see iTBS+γtACS neuromodulation protocol section). In particular, the theta iTBS has been inspired by protocols used in animal models to elicit LTP in the hippocampus (Huang et al., Neuron 2005). Consequently, neither Theta iTBS nor the gamma frequency of tACS were personalized. The increase in gamma oscillations was referred to the patient’s baseline and did not correspond to the administrated tACS frequency.
(4) The authors do a thorough job of analyzing the increase in gamma oscillations in the precuneus through TMS-EEG; however, the authors may also analyze whether theta oscillations were also enhanced through this protocol due to the iTBS potentially targeting theta oscillations. This may also be more robust than gamma oscillations increases since gamma oscillations detected on the scalp are very low amplitude and susceptible to noise and may reflect activity from multiple overlapping sources, making precise localization difficult without advanced techniques.
Thank you for the suggestion. We analyzed theta oscillations finding no changes.
(5) Figure 4: Why are connectivity values pre-stimulation for the iTBS and sham tACS stimulation condition so much higher than the dual stimulation? We would expect baseline values to be more similar.
We acknowledge that the pre-stimulation connectivity values for the iTBS and sham tACS conditions appear higher than those for the dual stimulation condition. However, as noted in our statistical analyses, there were no significant differences at baseline between conditions (p-FDR= 0.3514), suggesting that any apparent discrepancy is due to natural variability rather than systematic bias. One potential explanation for these differences is individual variability in baseline connectivity measures, which can fluctuate due to factors such as intrinsic neural dynamics, participant state, or measurement noise. Despite these variations, our statistical approach ensures that any observed post-stimulation effects are not confounded by pre-existing differences.
(6) Figure 2: How are total association scores significantly different between stimulation conditions, but individual name and occupation associations are not? Further clarification of how the total FNAT score is calculated would be helpful.
We apologize for any lack of clarity. The total FNAT score reflects the ability to correctly recall all the information associated with a person—specifically, the correct pairing of the face, name, and occupation. Participants received one point for each triplet they accurately recalled. The scores were then converted into percentages, as detailed in the Face-Name Associative Task Construction and Scoring section in the supplementary materials.
Total FNAT was the primary outcome measure. However, we also analyzed name and occupation recall separately to better understand their individual contributions. Our analysis revealed that the improvement in total FNAT was primarily driven by an increase in name recall rather than occupation recall.
We acknowledge that this distinction may have caused some confusion. To improve clarity, we will revise the manuscript accordingly and consider presenting name and occupation recall in separate plots.
Reviewer #3 (Public review):
Summary:
Borghi and colleagues present results from 4 experiments aimed at investigating the effects of dual γtACS and iTBS stimulation of the precuneus on behavioral and neural markers of memory formation. In their first experiment (n = 20), they found that a 3-minute offline (i.e., prior to task completion) stimulation that combines both techniques leads to superior memory recall performance in an associative memory task immediately after learning associations between pictures of faces, names, and occupation, as well as after a 15-minute delay, compared to iTBS alone (+ tACS sham) or no stimulation (sham for both iTBS and tACS). Performance in a second task probing short-term memory was unaffected by the stimulation condition. In a second experiment (n = 10), they show that these effects persist over 24 hours and up to a full week after initial stimulation. A third (n = 14) and fourth (n = 16) experiment were conducted to investigate the neural effects of the stimulation protocol. The authors report that, once again, only combined iTBS and γtACS increase gamma oscillatory activity and neural excitability (as measured by concurrent TMS-EEG) specific to the stimulated area at the precuneus compared to a control region, as well as precuneus-hippocampus functional connectivity (measured by resting-state MRI), which seemed to be associated with structural white matter integrity of the bilateral middle longitudinal fasciculus (measured by DTI).
Strengths:
Combining non-invasive brain stimulation techniques is a novel, potentially very powerful method to maximize the effects of these kinds of interventions that are usually well-tolerated and thus accepted by patients and healthy participants. It is also very impressive that the stimulation-induced improvements in memory performance resulted from a short (3 min) intervention protocol. If the effects reported here turn out to be as clinically meaningful and generalizable across populations as implied, this approach could represent a promising avenue for the treatment of impaired memory functions in many conditions.
Methodologically, this study is expertly done! I don't see any serious issues with the technical setup in any of the experiments (with the only caveat that I am not an expert in fMRI functional connectivity measures and DTI). It is also very commendable that the authors conceptually replicated the behavioral effects of experiment 1 in experiment 2 and then conducted two additional experiments to probe the neural mechanisms associated with these effects. This certainly increases the value of the study and the confidence in the results considerably.
The authors used a within-subject approach in their experiments, which increases statistical power and allows for stronger inferences about the tested effects. They are also used to individualize stimulation locations and intensities, which should further optimize the signal-to-noise ratio.
Weaknesses:
I want to state clearly that I think the strengths of this study far outweigh the concerns I have. I still list some points that I think should be clarified by the authors or taken into account by readers when interpreting the presented findings.
I think one of the major weaknesses of this study is the overall low sample size in all of the experiments (between n = 10 and n = 20). This is, as I mentioned when discussing the strengths of the study, partly mitigated by the within-subject design and individualized stimulation parameters. The authors mention that they performed a power analysis but this analysis seemed to be based on electrophysiological readouts similar to those obtained in experiment 3. It is thus unclear whether the other experiments were sufficiently powered to reliably detect the behavioral effects of interest. That being said, the authors do report significant effects, so they were per definition powered to find those. However, the effect sizes reported for their main findings are all relatively large and it is known that significant findings from small samples may represent inflated effect sizes, which may hamper the generalizability of the current results. Ideally, the authors would replicate their main findings in a larger sample. Alternatively, I think running a sensitivity analysis to estimate the smallest effect the authors could have detected with a power of 80% could be very informative for readers to contextualize the findings. At the very least, however, I think it would be necessary to address this point as a potential limitation in the discussion of the paper.
Thank you for the observation. As you mentioned, our power analysis was based on our previous study investigating the same neuromodulation protocol with a corresponding experimental design. The relatively small sample could be considered a possible limitation of the study which we will add to the discussion. A fundamental future step will be to replay these results on a larger population, however, to strengthen our results we performed the sensitivity analysis you suggested.
In detail, we performed a sensitivity analysis for repeated-measures ANOVA with α=0.05 and power(1-β)=0.80 with no sphericity correction. For experiment 1, a sensitivity analysis with 1 group and 3 measurements showed a minimal detectable effect size of f=0.524 with 20 participants. In our paper, the ANOVA on total FNAT immediate performance revealed an effect size of η2=0.274 corresponding to f=0.614; the ANOVA on FNAT delayed performance revealed an effect size of η2 =0.236 corresponding to f=0.556. For experiment 2, a sensitivity analysis for total FNAT immediate performance (1 group and 3 measurements) showed a minimal detectable effect size of f=0.797 with 10 participants. In our paper, the ANOVA on total FNAT immediate performance revealed an effect size of η2 =0.448 corresponding to f=0.901. The sensitivity analysis for total FNAT delayed performance (1 group and 6 measurements) showed a minimal detectable effect size of f=0.378 with 10 participants. In our paper, the ANOVA on total FNAT delayed performance revealed an effect size of η2 =0.484 corresponding to f=0.968. Thus, the sensitivity analysis showed that both experiments were powered enough to detect the minimum effect size computed in the power analysis. We have now added this information to the manuscript and we thank the reviewer for her/his suggestion.
It seems that the statistical analysis approach differed slightly between studies. In experiment 1, the authors followed up significant effects of their ANOVAs by Bonferroni-adjusted post-hoc tests whereas it seems that in experiment 2, those post-hoc tests where "exploratory", which may suggest those were uncorrected. In experiment 3, the authors use one-tailed t-tests to follow up their ANOVAs. Given some of the reported p-values, these choices suggest that some of the comparisons might have failed to reach significance if properly corrected. This is not a critical issue per se, as the important test in all these cases is the initial ANOVA but non-significant (corrected) post-hoc tests might be another indicator of an underpowered experiment. My assumptions here might be wrong, but even then, I would ask the authors to be more transparent about the reasons for their choices or provide additional justification. Finally, the authors sometimes report exact p-values whereas other times they simply say p < .05. I would ask them to be consistent and recommend using exact p-values for every result where p >= .001.
Thank you again for the suggestions. Your observations are correct, we used a slightly different statistical depending on our hypothesis. Here are the details:
In experiment 1, we used a repeated-measure ANOVA with one factor “stimulation condition” (iTBS+γtACS; iTBS+sham-tACS; sham-iTBS+sham-tACS). Following the significant effect of this factor we performed post-hoc analysis with Bonferroni correction.
In experiment 2, we used a repeated-measures with two factors “stimulation condition” and “time”. As expected, we observed a significant effect of condition, confirming the result of experiment 1, but not of time. Thus, this means that the neuromodulatory effect was present regardless of the time point. However, to explore whether the effects of stimulation condition were present in each time point we performed some explorative t-tests with no correction for multiple comparisons since this was just an explorative analysis.
In experiment 3, we used the same approach as experiment 1. However, since we had a specific hypothesis on the direction of the effect already observed in our previous study, i.e. increase in spectral power (Maiella et al., Scientific Report 2022), our tests were 1-tailed.
For the p-values, we will correct the manuscript reporting the exact values for every result.
While the authors went to great lengths trying to probe the neural changes likely associated with the memory improvement after stimulation, it is impossible from their data to causally relate the findings from experiments 3 and 4 to the behavioral effects in experiments 1 and 2. This is acknowledged by the authors and there are good methodological reasons for why TMS-EEG and fMRI had to be collected in sperate experiments, but it is still worth pointing out to readers that this limits inferences about how exactly dual iTBS and γtACS of the precuneus modulate learning and memory.
Thank you for your comment. We fully agree with your observation, which is why this aspect has been considered in the study's limitations. To address your concern, we will further emphasize the fact that our findings do not allow precise inferences regarding the specific mechanisms by which dual iTBS and γtACS of the precuneus modulate learning and memory.
There were no stimulation-related performance differences in the short-term memory task used in experiments 1 and 2. The authors argue that this demonstrates that the intervention specifically targeted long-term associative memory formation. While this is certainly possible, the STM task was a spatial memory task, whereas the LTM task relied (primarily) on verbal material. It is thus also possible that the stimulation effects were specific to a stimulus domain instead of memory type. In other words, could it be possible that the stimulation might have affected STM performance if the task taxed verbal STM instead? This is of course impossible to know without an additional experiment, but the authors could mention this possibility when discussing their findings regarding the lack of change in the STM task.
Thank you for your insightful observation. We argue that the intervention primarily targeted long-term associative memory formation, as our findings demonstrated effects only on FNAT. However, as you correctly pointed out, we cannot exclude the possibility that the stimulation may also influence short-term verbal associative memory. We will acknowledge this potential effect when discussing the absence of significant findings in the STM task.
While the authors discuss the potential neural mechanisms by which the combined stimulation conditions might have helped memory formation, the psychological processes are somewhat neglected. For example, do the authors think the stimulation primarily improves the encoding of new information or does it also improve consolidation processes? Interestingly, the beneficial effect of dual iTBS and γtACS on recall performance was very stable across all time points tested in experiments 1 and 2, as was the performance in the other conditions. Do the authors have any explanation as to why there seems to be no further forgetting of information over time in either condition when even at immediate recall, accuracy is below 50%? Further, participants started learning the associations of the FNAT immediately after the stimulation protocol was administered. What would happen if learning started with a delay? In other words, do the authors think there is an ideal time window post-stimulation in which memory formation is enhanced? If so, this might limit the usability of this procedure in real-life applications.
Thank you for your comment and for raising these important points.
We hypothesized that co-stimulation would enhance encoding processes. Previous studies have shown that co-stimulation can enhance gamma-band oscillations and increase cortical plasticity (Guerra et al., Brain Stimulation 2018; Maiella et al., Scientific Reports 2022). Given that the precuneus (Brodt et al., Science 2018; Schott et al., Human Brain Mapping 2018), gamma oscillations (Osipova et al., Journal of Neuroscience 2006; Deprés et al., Neurobiology of Aging 2017; Griffiths et al., Trends in Neurosciences 2023), and cortical plasticity (Brodt et al., Science 2018) have all been associated with encoding processes, we decided to apply co-stimulation before the encoding phase, to boost it.
We applied the co-stimulation immediately before the learning phase to maximize its potential effects. While we observed a significant increase in gamma oscillatory activity lasting up to 20 minutes, we cannot determine whether the behavioral effects we observed would have been the same with a co-stimulation applied 20 minutes before learning. Based on existing literature, a reduction in the efficacy of co-stimulation over time could be expected (Huang et al., Neuron 2005; Thut et al., Brain Topography 2009). However, we hypothesize that multiple stimulation sessions might provide an additional boost, helping to sustain the effects over time (Thut et al., Brain Topography 2009; Koch et al., Neuroimage 2018; Koch et al., Brain 2022).
Regarding the absence of further forgetting in both stimulation conditions, we think that the clinical and demographical characteristics of the sample (i.e. young and healthy subjects) explain the almost absence of forgetting after one week.
