Author response:
The following is the authors’ response to the original reviews.
We are deeply appreciative of the reviewers' insightful comments and constructive feedback on our manuscript. In response, we have implemented substantial revisions to enhance the clarity and impact of our work. Key changes include:
Reframing: We have shifted our focus from cognitive control to attention and memory processes, aligning more closely with our experimental design. This reframing is reflected throughout the manuscript, including additional citations highlighting the triple network model's involvement in memory processing. To reflect this change, we have updated the title to "Causal dynamics of salience, default mode, and frontoparietal networks during episodic memory formation and recall: A multi-experiment iEEG replication".
Control analyses using resting-state epochs: We have conducted new analyses comparing task periods to resting baseline epochs. These results demonstrate enhanced directed information flow from the anterior insula to both the default mode and frontoparietal networks during encoding and recall periods compared to resting state across all four experiments. This finding underscores the anterior insula's critical role in memory and attention processing.
Control analysis using the inferior frontal gyrus: To address specificity concerns, we performed control analyses using the inferior frontal gyrus as a comparison region. This analysis confirms that the observed directed information flow to the default mode and frontoparietal networks is specific to the anterior insula, rather than a general property of task-engaged brain regions.
These revisions, combined with our rigorous methodologies and comprehensive analyses, provide compelling support for the central claims of our manuscript. We believe these changes significantly enhance the scientific contribution of our work.
Our point-by-point responses to the reviewers' comments are provided below.
Reviewer 1:
- The authors present results from an impressively sized iEEG sample. For reader context, this type of invasive human data is difficult and time-consuming to collect and many similar studies in high-level journals include 5-20 participants, typically not all of whom have electrodes in all regions of interest. It is excellent that they have been able to leverage open-source data in this way.
- Preprocessing of iEEG data also seems sensible and appropriate based on field standards.
- The authors tackle the replication issues inherent in much of the literature by replicating findings across task contexts, demonstrating that the principles of network communication evidenced by their results generalize in multiple task memory contexts. Again, the number of iEEG patients who have multiple tasks' worth of data is impressive.
We thank the reviewer for the encouraging comments and appreciate the positive feedback.
(1.1) The motivation for investigating the tripartite network during memory tasks is not currently well-elaborated. Though the authors mention, for example, that "the formation of episodic memories relies on the intricate interplay between large-scale brain networks (p. 4)", there are no citations provided for this statement, and the reader is unable to evaluate whether the nodes and networks evidenced to support these processes are the same as networks measured here.
Recommendation: Detail with citations the motivation for assessing the tripartite network in these tasks. Include work referencing network-level and local effects during encoding and recall.
We appreciate the reviewer's feedback and suggestions for improving our framing. We have substantially expanded and revised the Introduction to elaborate on the motivation for investigating the tripartite network during memory tasks, supported by relevant citations.
We now provide a stronger rationale for examining these networks in the context of episodic memory, emphasizing that while the tripartite network has been extensively studied in cognitive control tasks, growing evidence suggests its relevance to episodic memory as a domain-general network. We cite several key studies that demonstrate the involvement of the salience, default mode, and frontoparietal networks in memory processes, including work by Sestieri et al. (2014) and Vatansever et al. (2021), which show the consistent engagement of these networks during memory tasks. We have also included references to studies examining network-level and local effects during encoding and recall, such as the work by Xie et al. (2012) on disrupted intrinsic connectivity in amnestic mild cognitive impairment, and Le Berre et al. (2017) on the role of insula connectivity in memory awareness (pages 4-5).
Furthermore, we have clarified how our study aims to address gaps in the current understanding by investigating the electrophysiological basis of these network interactions during memory formation and retrieval, which has not been explored in previous research. This expanded framing provides a clearer motivation for our investigation and places our study within the broader context of memory and network neuroscience research (pages 3-6).
(1.2) In addition, though the tripartite network has been proposed to support cognitive control processes, and the neural basis of cognitive control is the framed focus of this work, the authors do not demonstrate that they have measured cognitive control in addition to simple memory encoding and retrieval processes. Tasks that have investigated cognitive control over memory (such as those cited on p. 13 - Badre et al., 2005; Badre & Wagner, 2007; Wagner et al., 2001; Wagner et al., 2005) generally do not simply include encoding, delay, and recall (as the tasks used here), but tend to include some manipulation that requires participants to engage control processes over memory retrieval, such as task rules governing what choice should be made at recall (e.g., from Badre et al., 2005 Fig. 1: congruency of match, associative strength, number of choices, semantic similarity). Moreover, though there are task-responsive signatures in the nodes of the tripartite networks, concluding that cognitive control is present because cognitive control networks are active would be a reverse inference.
Recommendation: If present, highlight components of the tasks that are known to elicit cognitive control processes and cite relevant literature. If the tasks cannot be argued to elicit cognitive control, reframe the motivation to focus on task-related attention or memory processes. If the latter, reframe the motivation for investigating the tripartite network in this context absent control.
We appreciate the reviewer's insightful comment and recommendation. We acknowledge that our tasks do not include specific manipulations designed to elicit cognitive control processes over memory retrieval. In light of this, we have reframed our motivation and discussion to focus on the role of the tripartite network in attention and memory processes more broadly, rather than cognitive control specifically (pages 3-6).
As noted in Response 1.1, we have revised the Introduction to emphasize the domain-general nature of these networks and their involvement in various cognitive processes, including memory. We also highlight how the salience, default mode, and frontoparietal networks contribute to different aspects of memory formation and retrieval, drawing on relevant literature.
Our revised framing examines the salience network's role in detecting behaviorally relevant stimuli and orienting attention during encoding, the default mode network's involvement in internally-driven processes during recall, and the frontoparietal network's contribution to maintaining and manipulating information in working memory. We now present our study as an investigation into how these networks interact during different phases of memory processing, rather than focusing specifically on cognitive control. This approach aligns better with our experimental design and allows us to explore the broader applicability of the tripartite network model to memory processes.
This revised reframing provides a more accurate representation of our study's scope and contribution to understanding the role of large-scale brain networks in memory formation and retrieval (pages 3-6).
(1.3) It is currently unclear if the directed information flow from AI to DMN and FPN nodes truly arises from task-related processes such as cognitive control or if it is a function of static brain network characteristics constrained by anatomy (such as white matter connection patterns, etc.). This is a concern because the authors did not find that influences of AI on DMN or FPN are increased relative to a resting baseline (collected during the task) or that directed information flow differs in successful compared to unsuccessful retrieval. I doubt that this AI influence is 1) supporting a switch between the DMN and FPN via the SN or 2) relevant for behavior if it doesn't differ from baseline-active task or across accuracy conditions. An additional comparison that may help investigate whether this is reflective of static connectivity characteristics would be a baseline comparison during non-task rest or sleep periods.
Recommendation: As described in the task of the concern, analyze the PTE across the same contacts during sleep or task-free rest periods (if present in the dataset).
We thank the reviewer for this suggestion. We have now carried out additional analyses using resting-state baseline epochs. We found that directed information flow from the AI to both the DMN and FPN were enhanced during the encoding and recall periods compared to resting-state baseline in all four experiments. These new results have now been included in the revised Results (page 12):
“Enhanced information flow from the AI to the DMN and FPN during episodic memory processing, compared to resting-state baseline
We next examined whether directed information flow from the AI to the DMN and FPN nodes during the memory tasks differed from the resting-state baseline. Resting-state baselines were extracted immediately before the start of the task sessions and the duration of task and rest epochs were matched to ensure that differences in network dynamics could not be explained by differences in duration of the epochs. Directed information flow from the AI to both the DMN and FPN were higher during both the memory encoding and recall phases and across the four experiments, compared to baseline in all but two cases (Figures S6, S7). These findings provide strong evidence for enhanced role of AI directed information flow to the DMN and FPN during memory processing compared to the resting state.”
(1.4) Related to the above concern, it is also questionable how directed information flow from AI facilitates switching between FPN and DMN during both encoding and recall if high gamma activity does not significantly differ in AI versus PCC or mPFC during recall as it does during encoding. It seems erroneous to conclude that the network-level communication is happening or happening with the same effect during both task time points when these effects are decoupled in such a way from the power findings.
We appreciate the reviewer's insightful observation regarding the apparent discrepancy between our directed information flow findings and the high-gamma activity results. This comment highlights an important distinction in interpreting our results, and we thank the reviewer for the opportunity to address this point.
Our findings demonstrate that directed information flow from the AI to the DMN and FPN persists during both encoding and recall, despite differences in local high-gamma activity patterns. This dissociation suggests that the network-level communication facilitated by the AI may operate independently of local activation levels in individual nodes. It is important to note that our directed connectivity analysis (using phase transfer entropy) was conducted on broadband signals (0.5-80 Hz), while the power analysis focused specifically on the high-gamma band (80-160 Hz). These different frequency ranges may capture distinct aspects of neural processing. The broadband connectivity might reflect more general, sustained network interactions, while high-gamma activity may be more sensitive to specific task demands or cognitive processes.
The phase transfer entropy analysis captures directed interactions over extended time periods, while the high-gamma power analysis provides a more temporally precise measure of local neural activity. The persistent directed connectivity from AI during recall, despite changes in local activity, might reflect the AI's ongoing role in coordinating network interactions, even when its local activation is not significantly different from other regions.
Rather than facilitating "switching" between FPN and DMN, as we may have previously overstated, our results suggest that the AI maintains a consistent pattern of influence on both networks across task phases. This influence might serve different functions during encoding (e.g., orienting attention to external stimuli) and recall (e.g., monitoring and evaluating retrieved information), even if local activation patterns differ.
It is crucial to note that in the three verbal tasks, our analysis of memory recall is time-locked to word production onset. However, the precise timing of the internal recall process initiation is unknown. This limitation may affect our ability to capture the full dynamics of network interactions during recall, particularly in the early stages of memory retrieval. Interestingly, in the spatial memory task WMSM, the PCC/precuneus exhibited an earlier onset and enhanced activity compared to the AI. This task may provide a clearer window into recall processes:
findings align with the view that DMN nodes may play a crucial role in triggering internal recall processes. However, the precise timing of internal retrieval initiation remains a challenge in the three verbal tasks, potentially limiting our ability to capture the full dynamics of regional activity, and its replicability, during early stages of recall.
These observations highlight the need for more detailed investigation of the temporal dynamics of network interactions during memory recall. To further elucidate the relationship between directed connectivity and local activity, future studies could employ time-resolved connectivity analyses and investigate coupling between different frequency bands. This could provide a more precise understanding of how network-level communication relates to local neural dynamics across different task phases.
We have revised the manuscript to more accurately reflect these points and avoid overstating the implications of our findings (pages 15-19). We thank the reviewer for prompting this important clarification, which we believe strengthens the interpretation and discussion of our results.
(1.5) Missing information about the methods used for time-frequency conversion for power calculation and the power normalization/baseline-correction procedure bars a thorough evaluation of power calculation methods and results.
Recommendation: Include more information about how power was calculated. For example, how were time-series data converted to time-frequency (with complex wavelets, filter-hilbert, etc.)? What settings were used (frequency steps, wavelet length)? How were power values checked for outliers and normalized (decibels, Z-transform)? How was baseline correction applied (subtraction, division)?
We have now included detailed information related to our power calculation and normalization steps as we note on page 28: “We first filtered the signals in the high-gamma (80160 Hz) frequency band (Canolty et al., 2006; Helfrich & Knight, 2016; Kai J. Miller, Weaver, & Ojemann, 2009) using sequential band-pass filters in increments of 10 Hz (i.e., 80–90 Hz, 90– 100 Hz, etc.), using a fourth order two-way zero phase lag Butterworth filter. We used these narrowband filtering processing steps to correct for the 1/f decay of power. We then calculated the amplitude (envelope) of each narrow band signal by taking the absolute value of the analytic signal obtained from the Hilbert transform (Foster, Rangarajan, Shirer, & Parvizi, 2015). Each narrow band amplitude time series was then normalized to its own mean amplitude, expressed as a percentage of the mean. Finally, we calculated the mean of the normalized narrow band amplitude time series, producing a single amplitude time series. Signals were then smoothed using 0.2s windows with 90% overlap (Kwon et al., 2021) and normalized with respect to 0.2s pre-stimulus periods by subtracting the pre-stimulus baseline from the post-stimulus signal.”
(1.6) If revisions to the manuscript can address concerns about directed information flow possibly being due to anatomical constraints - such as by indicating that directed information flow is not present during non-task rest or sleep - this work may convey important information about the structure and order of communication between these networks during attention to tasks in general. However, the ability of the findings to address cognitive control-specific communication and the nature of neurophysiological mechanisms of this communication - as opposed to the temporal order and structure of recruited networks - may be limited.
We appreciate the reviewer's insightful feedback, which has led to significant improvements in our manuscript. In response, we have made the following key changes. We have shifted our focus from cognitive control to the broader roles of the tripartite network in attention and memory processes. This reframing aligns more closely with our experimental design and the nature of our tasks. We have revised the Introduction, Results, and Discussion sections to reflect this perspective, providing a more accurate representation of our study's scope and contribution. Additionally, to strengthen our findings, we have conducted new analyses comparing task periods to resting-state baselines. These analyses revealed that directed information flow from the anterior insula to both the DMN and FPN was significantly enhanced during memory encoding and recall periods compared to resting-state across all four experiments. This finding provides robust evidence for the specific involvement of these network interactions in memory processing. Please also see Response 1.2 above.
(1.7) Because phase-transfer entropy is presented as a "causal" analysis in this investigation (PTE), I also believe it is important to highlight for readers recent discussions surrounding the description of "causal mechanisms" in neuroscience (see "Confusion about causation" section from Ross and Bassett, 2024, Nature Neuroscience). A large proportion of neuroscientists (admittedly, myself included) use "causal" only to refer to a mechanism whose modulation or removal (with direct manipulation, such as by lesion or stimulation) is known to change or control a given outcome (such as a successful behavior). As Ross and Bassett highlight, it is debatable whether such mechanistic causality is captured by Granger "causality" (a.k.a. Granger prediction) or the parametric PTE, and the imprecise use of "causation" may be confusing. The authors could consider amending language regarding this analysis if they are concerned about bridging these definitions of causality across a wide audience.
We thank the reviewer for this suggestion. We would like to clarify here that we define causality in our manuscript as follows: a brain region has a causal influence on a target if knowing the past history of temporal signals in both regions improves the ability to predict the target's signal in comparison to knowing only the target's past, as defined in earlier studies (Granger, 1969; Lobier, Siebenhühner, Palva, & Matias, 2014). We have now included this clarification in the Introduction section (page 6).
We also agree with the reviewer that to more mechanistically establish a causal link between the neural dynamics and behavior, lesion or brain stimulation studies are necessary. We have now acknowledged this in the revised Discussion as we note: “Although our computational methods suggest causal influences, direct causal manipulations, such as targeted brain stimulation during memory tasks, are needed to establish definitive causal relationships between network nodes.” (page 19).
Minor additional information that would be helpful to the reader to include:
(1.8) How exactly was line noise (p. 24) removed? (For example, if notch filtered, how were slight offsets of the line noise from exactly 60.0Hz and harmonics identified and handled?).
We would like to clarify here that to filter line noise and its harmonics, we used bandstop filters at 57-63 Hz, 117-123 Hz, and 177-183 Hz. To create a band-stop filter, we used a fourth order two-way zero phase lag Butterworth filter. This information has now been included in the revised Methods (page 26).
(1.9) Why were the alpha and beta bands collapsed for narrowband filtering?
Please note that we did not combine the alpha (8-12 Hz) and beta (12-30 Hz) bands for narrowband filtering, rather these two frequency bands were analyzed separately. However, we combined the delta (0.5-4 Hz) and theta (4-8 Hz) frequency bands into a combined delta-theta (0.5-8 Hz) frequency band for our analysis since previous human electrophysiology studies have not settled on a specific band of frequency (delta or theta) for memory processing. Previous human iEEG (Ekstrom et al., 2005; Ekstrom & Watrous, 2014; Engel & Fries, 2010; Gonzalez et al., 2015; Watrous, Tandon, Conner, Pieters, & Ekstrom, 2013) as well as scalp EEG and MEG studies, have shown that both the delta and theta frequency band oscillations play a prominent role for human memory encoding as well as retrieval (Backus, Schoffelen, Szebényi, Hanslmayr, & Doeller, 2016; Clouter, Shapiro, & Hanslmayr, 2017; Griffiths, Martín-Buro, Staresina, & Hanslmayr, 2021; Guderian & Düzel, 2005; Guderian, Schott, Richardson-Klavehn, & Düzel, 2009).
Reviewer 2:
In this study, the authors leverage a large public dataset of intracranial EEG (the University of Pennsylvania RAM repository) to examine electrophysiologic network dynamics involving the participation of salience, frontoparietal, and default mode networks in the completion of several episodic memory tasks. They do this through a focus on the anterior insula (AI; salience network), which they hypothesize may help switch engagement between the DMN and FPN in concert with task demands. By analyzing high-gamma spectral power and phase transfer entropy (PTE; a putative measure of information "flow"), they show that the AI shows higher directed PTE towards nodes of both the DMN and FPN, during encoding and recall, across multiple tasks. They further demonstrate that high-gamma power in the PCC/precuneus is decreased relative to the AI during memory encoding. They interpret these results as evidence of "triple-network" control processes in memory tasks, governed by a key role of the AI.
I commend the authors on leveraging this large public dataset to help contextualize network models of brain function with electrophysiological mechanisms - a key problem in much of the fMRI literature. I also appreciate that the authors emphasized replicability across multiple memory tasks, in an effort to demonstrate conserved or fundamental mechanisms that support a diversity of cognitive processes. However, I believe that their strong claims regarding causal influences within circumscribed brain networks cannot be supported by the evidence as presented. In my efforts to clearly communicate these inadequacies, I will suggest several potential analyses for the authors to consider that might better link the data to their central hypotheses.
We thank the reviewer for the encouraging comments and suggestions for improving the manuscript. Please see our detailed responses and clarifications below.
(2.1) As a general principle, the effects that the authors show - both in regards to their highgamma power analysis and PTE analysis - do not offer sufficient specificity for a reader to understand whether these are general effects that may be repeated throughout the brain, or whether they reflect unique activity to the networks/regions that are laid out in the Introduction's hypothesis. This lack of specificity manifests in several ways, and is best communicated through examples of control analyses.
We appreciate the reviewer's insightful comment regarding the specificity of our findings. We agree that additional analyses could provide valuable context for interpreting our results. In response, we have conducted the following additional analyses and made corresponding revisions to the manuscript:
Following the reviewer's suggestion, we have selected the inferior frontal gyrus (IFG, BA 44) as a control region. The IFG serves as an ideal control region due to its anatomical adjacency to the AI, its involvement in a wide range of cognitive control functions including response inhibition (Cai, Ryali, Chen, Li, & Menon, 2014), and its frequent co-activation with the AI in fMRI studies. Furthermore, the IFG has been associated with controlled retrieval of memory (Badre et al., 2005; Badre & Wagner, 2007; Wagner et al., 2001), making it a compelling region for comparison. We repeated our PTE analysis using the IFG as the source region, comparing its directed influence on the DMN and FPN nodes to that of the AI.
Our analysis revealed a striking contrast between the AI and IFG in their patterns of directed information flow. While the AI exhibited strong causal influences on both the DMN and FPN, the IFG showed the opposite pattern. Specifically, both the DMN and FPN demonstrated higher influence on the IFG than the reverse, during both encoding and recall periods, and across all four memory experiments (Figures S4, S5).
These findings highlight the unique role of the AI in orchestrating large-scale network dynamics during memory processes. The AI's pattern of directed information flow stands in contrast to that of the IFG, despite their anatomical proximity and shared involvement in cognitive control processes. This dissociation underscores the specificity of the AI's function in coordinating network interactions during memory formation and retrieval. These results have now been included in our revised Results on page 11.
(2.2) First, the PTE analysis is focused solely on the AI's interactions with nodes of the DMN and FPN; while it makes sense to focus on this putative "switch" region, the fact that the authors report significant PTE from the AI to nodes of both networks, in encoding and retrieval, across all tasks and (crucially) also at baseline, raises questions about the meaningfulness of this statistic. One way to address this concern would be to select a control region that would be expected to have little/no directed causal influence on these networks and repeat the analysis. Alternatively (or additionally), the authors could examine the time course of PTE as it evolves throughout an encoding/retrieval interval, and relate that to the timing of behavioral events or changes in high-gamma power. This would directly address an important idea raised in their own Discussion, "the AI is wellpositioned to dynamically engage and disengage with other brain areas."
Please see Response 2.1 above for additional analyses related to control region.
We also appreciate the reviewer's suggestion regarding time-resolved PTE analysis. However, it's important to note that our current methodology does not allow for such fine-grained temporal analysis. This is due to the fact that PTE, which is an information theoretic measure and relies on constructing histograms of occurrences of singles, pairs, or triplets of instantaneous phase estimates from the phase time-series (Hillebrand et al., 2016) (Methods), requires sufficient number of cycles in the phase time-series for its reliable estimation (Lobier et al., 2014). PTE is based on estimating the time-delayed directed influences from one time-series to the other and its estimate is the most accurate when a large number of time-points (cycles) are available (Lobier et al., 2014). Since our encoding and recall epochs in the verbal recall tasks were only 1.6 seconds long, which corresponds to only 800 time-points with a 500 Hz sampling rate, we used the entire encoding and recall epochs for the most efficient estimate of PTE, rather than estimating PTE in a time-resolved manner. Please note that this is consistent with previous literature which have used ~ 225000 time-points (3 minutes of resting-state data with 1250 Hz sampling rate) for estimating PTE, for example, see (Hillebrand et al., 2016).
This limitation prevents us from examining how directed connectivity evolves throughout the encoding and retrieval intervals on a moment-to-moment basis. Future studies employing longer task epochs or alternative methods for time-resolved connectivity analysis could provide valuable insights into the dynamic engagement and disengagement of the AI with other brain areas based on task demands. Such analyses could potentially reveal task-specific temporal patterns in the AI's influence on DMN and FPN nodes during different phases of memory processing.
Finally, it is crucial to note that in the three verbal tasks, our analysis of memory recall is timelocked to word production onset. However, the precise timing of the internal retrieval process initiation is unknown. This limitation may affect our ability to capture the full dynamics of network interactions during recall, particularly in the early stages of memory retrieval. Interestingly, in the spatial memory task, where this timing issue is less problematic due to the nature of the task, we observe that the PCC/precuneus shows an earlier onset of activity compared to the AI. This process is aligned with the view that DMN nodes may trigger internal recall processes, the full extent and replication of which across verbal and spatial tasks could not be examined in this study.
We have added a discussion of these limitations and future directions to the manuscript to provide a more nuanced interpretation of our findings and to highlight important areas for further investigation (page 19).
(2.3) Second, the authors state that high-gamma suppression in the PCC/precuneus relative to the AI is an anatomically specific signature that is not present in the FPN. This claim does not seem to be supported by their own evidence as presented in the Supplemental Data (Figures S2 and S3), which to my eye show clear evidence of relative suppression in the MFG and dPPC (e.g. S2a and S3a, most notably) which are notated as "significant" with green bars. I appreciate that the magnitude of this effect may be greater in the PCC/precuneus, but if this is the claim it should be supported by appropriate statistics and interpretation.
We thank the reviewer for raising this point. We have now directly compared the high-gamma power of the PCC/precuneus with the dPPC and MFG nodes of the FPN and we note that the suppression effects of the PCC/precuneus are stronger compared to those of the dPPC and MFG during memory encoding (Figures S8, S9).
(2.4) I commend the authors on emphasizing replicability, but I found their Bayes Factor (BF) analysis to be difficult to interpret and qualitatively inconsistent with the results that they show. For example, the authors state that BF analysis demonstrates "high replicability" of the gamma suppression effect in Figure 3a with that of 3c and 3d. While it does appear that significant effects exist across all three tasks, the temporal structure of high gamma signals appears markedly different between the two in ways that may be biologically meaningful. Moreover, it appears that the BF analysis did not support replicability between VFR and CATVFR, which is very surprising; these are essentially the same tasks (merely differing in the presence of word categories) and would be expected to have the highest degree of concordance, not the lowest. I would suggest the authors try to analytically or conceptually reconcile this surprising finding.
We appreciate the reviewer's commendation on our emphasis on replicability and thank the reviewer for the opportunity to provide clarification.
First, we would like to clarify the nature of our BF analysis. Bayes factors are calculated as the ratio of the marginal likelihood of the replication data, given the posterior distribution estimated from the original data, and the marginal likelihood for the replication data under the null hypothesis of no effect (Ly, Etz, Marsman, & Wagenmakers, 2019). Specifically, BFs use the posterior distribution from the first experiment as a prior distribution for the replication test of the second experiment to constitute a joint multivariate distribution (i.e., the additional evidence for the alternative hypothesis given what was already observed in the original study) and this joint distribution is dependent on the similarity between the two experiments (Ly et al., 2019). This analysis revealed that PCC/precuneus suppression, in comparison to the AI during memory encoding, observed in the VFR during memory encoding was detected in two other tasks, PALVCR, and WMSM with high BFs. In the CATVFR task, although there were short time periods of PCC/precuneus suppression (Figure 3), the effects were not strong enough like the three other tasks.
Regarding the high-gamma suppression effect, our BF analysis indeed supports replicability across the VFR, PALVCR, and WMSM tasks. While we agree with the reviewer that the temporal structure of high-gamma signals appears different across tasks, the BF analysis focuses on the overall presence of the suppression effect rather than its precise temporal profile. The high BFs indicate that the core finding - PCC/precuneus suppression relative to the AI during memory encoding - is replicated across these tasks, despite differences in the timing of this suppression. Moreover, at no time point did responses in the PCC/precuneus exceed that of the AI in any of the four memory encoding tasks.
The reason for differences in temporal profiles is not clear. While VFR and CATVFR are similar, the addition of categorical structure in CATVFR may have introduced cognitive processes that alter the temporal dynamics of regional responses. Moreover, differences in electrode placements across participants in each experiment may also have contributed to variability in the observed effects. Further studies using within-subjects experimental designs are needed to address this.
We have updated our Results and Discussion sections to reflect these points and to provide a more nuanced interpretation of the replicability across tasks.
(2.5) To aid in interpretability, it would be extremely helpful for the authors to assess acrosstask similarity in high-gamma power on a within-subject basis, which they are wellpowered to do. For example, could they report the correlation coefficient between HGP timecourses in paired-associates versus free-recall tasks, to better establish whether these effects are consistent on a within-subject basis? This idea could similarly be extended to the PTE analysis. Across-subject correlations would also be a welcome analysis that may provide readers with better-contextualized effect sizes than the output of a Bayes Factor analysis.
We thank the reviewer for this suggestion. However, a within-subject analysis was not possible because very few participants participated in multiple memory tasks.
For example, for the AI-PCC/Pr analysis, only 1 individual participated in both the VFR and PALVCR tasks (Tables S2a, S2c). Similarly, for AI-mPFC analysis, only 3 subjects participated in both the VFR and PALVCR tasks (Tables S2a, S2c).
Due to these small sample sizes, it was not feasible for us to assess replicability across tasks on a within-subject basis in our dataset. Therefore, for all our analysis, we have pooled electrode pairs across subjects and then subjected these to a linear mixed effects modeling framework for assessing significance and then subsequently assessing replicability of these effects using the Bayes factor (BF) framework.
Recommendations For The Authors:
(2.6) I would emphasize manuscript organization in a potential rewrite; it was very difficult to follow which analyses were attempting to show a contrast between effects versus a similarity between effects. Results were grouped by the underlying experimental conditions (e.g. encoding/recall, network identity, etc.) but may be better grouped according to the actual effects that were found.
We thank the reviewer for this suggestion. We considered this possibility, but we feel that the Results section is best organized in the order of the hypotheses we set out to test, starting from analyzing local brain activity using high-gamma power analysis, and then results related to analyzing brain connectivity using PTE. All these results are systematically ordered by presenting results related to encoding first and then the recall periods as they appear sequentially in our task-design, presenting the results related to the VFR task first and then demonstrating replicability of the results in the three other experiments. Results are furthermore arranged by nodes, where we first discuss results related to the DMN nodes, and then the same for the FPN nodes. This is to ensure systematic, unbiased organization of all our results for the readers to clearly follow the hypotheses, statistical analyses, and the brain regions considered. Therefore, for transparency and ethical reasons, we would respectfully like to present our results as they appear in our current manuscript, rather than presenting the results based on effect sizes.
However, please note that we indeed have ordered our results in the Discussion section based on actual effects, as suggested by the reviewer.
(2.7) The absence of a PTE effect when analyzing through the lens of successful vs. unsuccessful memory is an important limitation of the current study and a significant departure from the wealth of subsequent memory effects reported in the literature (which the authors have already done a good job citing, e.g. Burke et al. 2014 Neuroimage). I'm glad that the authors raised this in their Discussion, but it is important that the results of such an analysis actually be shown in the manuscript.
We thank the reviewer for this suggestion. We have now included the results related to PTE dynamics for successful vs. unsuccessful memory trials in the revised Results section as we note on page 12:
“Differential information flow from the AI to the DMN and FPN for successfully recalled and forgotten memory trials
We examined memory effects by comparing PTE between successfully recalled and forgotten memory trials. However, this analysis did not reveal differences in directed influence from the AI on the DMN and FPN or the reverse, between successfully recalled and forgotten memory trials during the encoding as well as recall periods in any of the memory experiments (all ps>0.05).”
(2.8) I believe the claims of causality through the use of the PTE are overstated throughout the manuscript and may contribute to further confusion in the literature regarding how causality in the brain can actually be understood. See Mehler and Kording, 2018 arXiv for an excellent discussion on the topic (https://arxiv.org/abs/1812.03363). My recommendation would be to significantly tone down claims that PTE reflects causal interactions in the brain.
We thank the reviewer for this suggestion. We would like to clarify here that we define causality in our manuscript as follows: a brain region has a causal influence on a target if knowing the past history of temporal signals in both regions improves the ability to predict the target's signal in comparison to knowing only the target's past, as defined in earlier studies (Granger, 1969; Lobier et al., 2014). We have now included this clarification in the Introduction section (page 6).
We also agree with the reviewer that to more mechanistically establish a causal link between the neural dynamics and behavior, lesion or brain stimulation studies are necessary. We have now acknowledged this in the revised Discussion as we note: “Although our computational methods suggest causal influences, direct causal manipulations, such as targeted brain stimulation during memory tasks, are needed to establish definitive causal relationships between network nodes.” (page 19).
Finally, we have now significantly toned down our claims that PTE reflects causal interactions in the brain, in the Introduction, Results, and Discussion sections of our revised manuscript.
(2.9) Relatedly, it may be useful for the authors to consider a supplemental analysis that uses classic measures of inter-regional synchronization, e.g. the PLV, and compare to their PTE findings. They cite literature to suggest a metric like the PTE may be useful, but this hardly rules out the potential utility of investigating narrowband phase synchronization.
We thank the reviewer for this suggestion. We have now run new analyses based on PLV to examine phase synchronization between the AI and the DMN and FPN. However, we did not find a significant PLV for the interactions between the AI and DMN and FPN nodes for the different task periods compared to the resting baselines, as we note on page 13:
“Narrowband phase synchronization between the AI and the DMN and FPN during encoding and recall compared to resting baseline
We next directly compared the phase locking values (PLVs) (see Methods for details) between the AI and the PCC/precuneus and mPFC nodes of the DMN and also the dPPC and MFG nodes of the FPN for the encoding and the recall periods compared to resting baseline. However, narrowband PLV values did not significantly differ between the encoding/recall vs. rest periods, in any of the delta-theta (0.5-8 Hz), alpha (8-12 Hz), beta (12-30 Hz), gamma (30-80 Hz), and high-gamma (80-160 Hz) frequency bands. These results indicate that PTE, rather than phase synchronization, more robustly captures the AI dynamic interactions with the DMN and the FPN.”
Please note that phase locking measures such as the PLV or coherence do not probe directed causal influences and cannot address how one region drives another. Instead, our study examined the direction of information flow between the AI and the DMN and FPN using robust estimators of the direction of information flow. PTE assesses with the ability of one time-series to predict future values of other time-series, thus estimating the time-delayed causal influences between the two time-series, whereas PLV or coherence can only estimate “instantaneous” phase synchronization, but not predict the future time-series.
Additionally, please note that the directed information flow from the AI to both the DMN and FPN were enhanced during the encoding and recall periods compared to resting state across all four experiments, in a new set of analyses that we have carried out in our revised manuscript. Specifically, we have now carried out our task versus rest comparison by using resting baseline epochs before the start of the entire session of the task periods, rather than our previously used rest epochs which were in between the task periods. These new results have now been included in the revised Results as we note on page 12:
“Enhanced information flow from the AI to the DMN and FPN during episodic memory processing, compared to resting-state baseline
We next examined whether directed information flow from the AI to the DMN and FPN nodes during the memory tasks differed from the resting-state baseline. Resting-state baselines were extracted immediately before the start of the task sessions and the duration of task and rest epochs were matched to ensure that differences in network dynamics could not be explained by differences in duration of the epochs. Directed information flow from the AI to both the DMN and FPN were higher during both the memory encoding and recall phases and across the four experiments, compared to baseline in all but two cases (Figures S6, S7). These findings provide strong evidence for enhanced role of AI directed information flow to the DMN and FPN during memory processing compared to the resting state.”
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