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Authors
Jessica S. L. Vidmark, Estefania Hernandez-Martin, Terence D. Sanger
Abstract
Objective. Peripheral nerve stimulation has been proposed as a noninvasive treatment for patients with movement disorders such as essential tremor, Parkinson’s disease, and dystonia. While the outcomes have shown clinical effect, the mechanism behind the effect is not yet clear. The goal of this work was to study the brain’s responses to peripheral stimulation bursts and explain the therapeutic results. Approach. We performed peripheral stimulation of the median nerve(s) in 12 pediatric patients undergoing deep brain stimulation for dystonia. Stimulation was given in bursts (50-200 Hz stimulation in blocks of 100 ms, separated by 100 ms without stimulation) and intracranial activity was simultaneously recorded from deep brain stimulation leads implanted in thalamic nuclei. After using a novel method to remove stimulus artifacts, sequences of neural responses during and after the bursts were analyzed. Results. Peripheral burst stimulation induced increasing consistency of successive evoked responses in thalamic nuclei. Significance. We propose that this phenomenon is due to progressive synchronization of small populations of thalamic neurons, so that over time there is phase locking of the response in an increasing number of neurons in the population. Clinical efficacy could thus be due to “synchronization blockade”, in which the synchronized response of the population prevents transmission of intrinsic abnormal signals due to tremor or dystonia. Further studies are necessary to confirm this model of clinical effect. Enhanced understanding will increase the potential for use of peripheral stimulation as a noninvasive alternative or adjunct to deep brain stimulation.
Link to paper
DOI: https://doi.org/10.1007/978-3-030-87237-3_23
SharedIt: https://rdcu.be/cymae
Link to the code repository
N/A
Link to the dataset(s)
N/A
Reviews
Review #1
- Please describe the contribution of the paper
The paper investigates the use of a novel artifact removal technique in a clinical study of peripheral nerve stimulation to treat movement disorders. The results show increasing correlations between subsequent evoked potentials through the course of stimulation.
- Please list the main strengths of the paper; you should write about a novel formulation, an original way to use data, demonstration of clinical feasibility, a novel application, a particularly strong evaluation, or anything else that is a strong aspect of this work. Please provide details, for instance, if a method is novel, explain what aspect is novel and why this is interesting.
The clinical procedure is well explained and the paper is clearly written in general.
The paper represents an in vivo experiment into the potential use of a new treatment method for movement disorders.
The authors claim that the proposed method might allow for more of the evoked potential to be analyzed after the stimulation artifact is removed.
The authors observe increasing correlations between evoked potentials. While the mechanism is not explained by the experiment, the authors suggest that the phenomenon might be useful for using PNS for movement disorders.
- Please list the main weaknesses of the paper. Please provide details, for instance, if you think a method is not novel, explain why and provide a reference to prior work.
Some measure of the goodness of fit of the complex exponential should be evaluated. This should also be compared with other removal techniques such as ICA and exponential fits to establish that the complex exponential is indeed better.
In the results shown in the paper, the original recording is difficult to see below the complex exponential. The results shown in the supplemental appear to show some continued oscillatory behavior when the complex exponential is applied, indicating that trend artifacts may still exist in the signal.
- Please rate the clarity and organization of this paper
Excellent
- Please comment on the reproducibility of the paper. Note, that authors have filled out a reproducibility checklist upon submission. Please be aware that authors are not required to meet all criteria on the checklist - for instance, providing code and data is a plus, but not a requirement for acceptance
The methods seem described well enough to reproduce the method on a relevant dataset. Whether the data is public or if code will be provided is not mentioned.
- Please provide detailed and constructive comments for the authors. Please also refer to our Reviewer’s guide on what makes a good review: https://miccai2021.org/en/REVIEWER-GUIDELINES.html
The paper would benefit from a direct comparison to other artifact modeling and removal techniques, such as ICA or complex exponential fitting.
Some measure of goodness of fit should be provided to validate that the complex exponential model performs as indicated.
The validity artifact removal technique might also be reinforced by application to another dataset if available.
The signal level results are difficult to interpret visually as the amplitude of the artifact is much higher than the amplitude of the signal. Improved visualization strategies would make the results more interpretable.
- Please state your overall opinion of the paper
Probably accept (7)
- Please justify your recommendation. What were the major factors that led you to your overall score for this paper?
The methodology seems sounds and the observed phenomenon of increasing correlations between subsequent evoked potentials is interesting and seems to be the first demonstration in the literature. However the paper could benefit from expanded experimentation using other techniques, measures of fit, and more data if possible.
- What is the ranking of this paper in your review stack?
1
- Number of papers in your stack
5
- Reviewer confidence
Somewhat confident
Review #2
- Please describe the contribution of the paper
The authors suggest an explanation of the clinical effect of peripheral stimulation.
- Please list the main strengths of the paper; you should write about a novel formulation, an original way to use data, demonstration of clinical feasibility, a novel application, a particularly strong evaluation, or anything else that is a strong aspect of this work. Please provide details, for instance, if a method is novel, explain what aspect is novel and why this is interesting.
Recorded real patient data of evoked potentials.
- Please list the main weaknesses of the paper. Please provide details, for instance, if you think a method is not novel, explain why and provide a reference to prior work.
The evidence to support their claims is weak. Other claims can be made with same evidence. Also, this article is less medical oriented and more brain-science related. Not sure if it is in the interest of the usual MICCAI attendee.
- Please rate the clarity and organization of this paper
Very Good
- Please comment on the reproducibility of the paper. Note, that authors have filled out a reproducibility checklist upon submission. Please be aware that authors are not required to meet all criteria on the checklist - for instance, providing code and data is a plus, but not a requirement for acceptance
OK
- Please provide detailed and constructive comments for the authors. Please also refer to our Reviewer’s guide on what makes a good review: https://miccai2021.org/en/REVIEWER-GUIDELINES.html
I think that there is not enough evidance to make such a bold conclusion: We propose that this phenomenon is due to progressive synchronization of small populations of thalamic neurons, so that over time there is phase locking of the response in an increasing number of neurons in the population. Clinical efficacy could thus be due to “synchronization blockade”, in which the synchronized response of the population prevents transmission of intrinsic abnormal signals due to tremor or dystonia.
How can you claim this while we know that synchronized response is the hallmark of movement disorders activity?!
- Please state your overall opinion of the paper
out of scope (1)
- Please justify your recommendation. What were the major factors that led you to your overall score for this paper?
Scientific, not directly related to MICCAI, more an SFN type of paper. Also the claim is invalid to my opinion.
- What is the ranking of this paper in your review stack?
5
- Number of papers in your stack
5
- Reviewer confidence
Very confident
Review #3
- Please describe the contribution of the paper
The authors proposed a novel stimulation method to improve the consistency of evoked potential. However, the description of the method is not clear.
- Please list the main strengths of the paper; you should write about a novel formulation, an original way to use data, demonstration of clinical feasibility, a novel application, a particularly strong evaluation, or anything else that is a strong aspect of this work. Please provide details, for instance, if a method is novel, explain what aspect is novel and why this is interesting.
The motivation is well justified by clinical requirements.
- Please list the main weaknesses of the paper. Please provide details, for instance, if you think a method is not novel, explain why and provide a reference to prior work.
The topic is more related to neurophysiology and neural engineering, instead of the MICCA community.
The method description is unclear.
- Please rate the clarity and organization of this paper
Satisfactory
- Please comment on the reproducibility of the paper. Note, that authors have filled out a reproducibility checklist upon submission. Please be aware that authors are not required to meet all criteria on the checklist - for instance, providing code and data is a plus, but not a requirement for acceptance
reproducibility is good
- Please provide detailed and constructive comments for the authors. Please also refer to our Reviewer’s guide on what makes a good review: https://miccai2021.org/en/REVIEWER-GUIDELINES.html
N/A
- Please state your overall opinion of the paper
probably reject (4)
- Please justify your recommendation. What were the major factors that led you to your overall score for this paper?
The study is more towards a neuroscience or neural engineering study instead of a typical MICCAI paper.
- What is the ranking of this paper in your review stack?
4
- Number of papers in your stack
5
- Reviewer confidence
Confident but not absolutely certain
Review #4
- Please describe the contribution of the paper
This paper presents a novel artifact removal method, which uses the complex exponential function that can successfully modeled and removed the decay artifacts of both simple and complex exponential shapes allowing for clear analysis of the underlying within-burst evoked potentials. Using this new method for stimulus artifact removal, they have shown that there is a consistent pattern of evoked responses, with increasing consistency in subsequent responses within a burst.
- Please list the main strengths of the paper; you should write about a novel formulation, an original way to use data, demonstration of clinical feasibility, a novel application, a particularly strong evaluation, or anything else that is a strong aspect of this work. Please provide details, for instance, if a method is novel, explain what aspect is novel and why this is interesting.
This paper presents a novel artefact removal method, which uses the complex exponential function. This paper also highlights the importance of fitting to the decay artefact portion before the first evoked potential whenever possible, which ensures that the fit is only based on, and hence will only remove, artefactual recordings. The proposed novel decay artefact removal method has allowed the author to demonstrate a previously unreported phenomenon: the progressive increase in consistency of thalamic evoked potentials in response to peripheral stimulation.
- Please list the main weaknesses of the paper. Please provide details, for instance, if you think a method is not novel, explain why and provide a reference to prior work.
The Analysis Methods is not super clear and more detailed information is missing. Some of the results figures are not clearly visible.
- Please rate the clarity and organization of this paper
Very Good
- Please comment on the reproducibility of the paper. Note, that authors have filled out a reproducibility checklist upon submission. Please be aware that authors are not required to meet all criteria on the checklist - for instance, providing code and data is a plus, but not a requirement for acceptance
Given that some of the detailed information are not that clear and the SNR threshold was empirically selected by visual inspection, to some extend the reproducibility of the paper would be a concern.
- Please provide detailed and constructive comments for the authors. Please also refer to our Reviewer’s guide on what makes a good review: https://miccai2021.org/en/REVIEWER-GUIDELINES.html
In 2.7 Analysis Methods, more detailed information needed regarding how the cross-correlation was done. Such as what is your time window for calculating cross-correlation, is there a overlap? Why only calculating the cross-correlation between each adjacent EP? Not sure what is the rationale to add white noise to higher-amplitude EPs to make all EP SNRs in each burst equal (or equal to 1, whichever was greatest). Won’t this added white noise affect your cross-correlation coefficient? In Fig 2, since the original data in grey is hardly visible in the top left plot, can you add one zoom in example (just plot one recording maybe on the left of the 5 recordings) to illustrate the original recordings and the complex exponential fitting? Similarly, you can show one zoom in plot for the artifacts-free recording. The right-most plot shows these EPs overlayed to highlight their similarities. Why they are so similar, are you expecting the artifacts-free signals are all very similar? Are they from the adjacent regions? In Fig 3, what the unit on the x-axis time [ms]? Why they are all different range for different frequency band? How the frequency band divided, is there a conventional meaning for each frequency band or is there a reference for this?
- Please state your overall opinion of the paper
Probably accept (7)
- Please justify your recommendation. What were the major factors that led you to your overall score for this paper?
In this study, the authors show the reliability and accuracy of the presented decay artefact removal method, which allows us to detect EPs during high-frequency stimulation. Many studies display difficulties in removing decay artefacts, generating unusable data portions that the proposed novel methods could greatly reduce. Using this new method for stimulus artefact removal, the paper has shown that there is a consistent pattern of evoked responses, with increasing consistency in subsequent responses within a burst. This phenomenon has not previously been reported for centrally recorded EPs, and it suggests that the evoked response to short bursts of stimulation may be substantially different from the steady-state response to prolonged stimulation.
- What is the ranking of this paper in your review stack?
2
- Number of papers in your stack
3
- Reviewer confidence
Confident but not absolutely certain
Primary Meta-Review
- Please provide your assessment of this work, taking into account all reviews. Summarize the key strengths and weaknesses of the paper and justify your recommendation. In case you deviate from the reviewers’ recommendations, explain in detail the reasons why. In case of an invitation for rebuttal, clarify which points are important to address in the rebuttal.
The reviewers appreciated the strong clinical motivation for the method and the potential impact on treating movement disorders. However, two reviewers felt that the paper is not appropriate for MICCAI due to limited technical innovation. The authors should clearly discuss the following in their rebuttal:
(1) The technical contribution of their work, including why it would be of interest to the MICCAI audience.
(2) How their artifact modeling/removal techniques compare to standard approaches (e.g., ICA). Specifically, what are the advantages and disadvantages of the proposed technique over existing ones?
(3) Justification for why “goodness of fit” (or another quantitative metric) was not provided.
- What is the ranking of this paper in your stack? Use a number between 1 (best paper in your stack) and n (worst paper in your stack of n papers).
5
Author Feedback
First of all, we would like to thank the reviewers and meta reviewers for their valuable feedback.
(1) Two of the reviewers questioned the relevance of this paper in the MICCAI conference, so we would here like to address the technical contribution of our work, including its interest to the MICCAI audience.
The present work shows a new approach which allows for the removal of stimulation artefacts without loss of the neural information that can be masked by the artefact itself. This data processing is a prior step necessary to model the brain behavior and avoid false data interpretation. Our approach can be extrapolated to any stimulation study, studying either the human or animal brain. In our case, data used to develop the approach were recorded from externalized deep brain stimulation electrodes in the human brain, allowing us to corroborate the reliability and precision of the approach directly in deep structures through dystonic subjects; recordings that are inaccessible with healthy subjects.
The method presented is a new model of the artefact itself, suggesting more than one physical process (e.g., multiple capacitors) causing the artefact decays. This novel artefact removal method allows us to distinguish clear evoked potentials (EPs) within rapid stimulation bursts, supporting and permitting creation of a dynamic model of the brain. Moreover, by modelling the artefact based on the first stimulation pulse, decay artefacts could be removed in real-time from subsequent stimulations, for use in control and feedback applications.
Our paper is highly relevant under the “CAI” portion of MICCAI (Computer Assisted Innovation). In particular, it falls under the topics “Computational (Integrative) Pathology” and “Computational Anatomy and Physiology”, as listed on the MICCAI website.
(2) Another point of concern was the comparison of this technique to other standard approaches available, e.g., ICA.
The stimulation artefact and the evoked potential always correlate, and there is high mutual information because one causes the other. Hence, ICA is not an applicable approach for artefact removal – you must have an artefact model in order to accurately remove it. This has been shown in previous studies comparing artefact removal methods, including the one conducted by E. P. Casula et al. (referenced in our Introduction, ref. [18]: “TMS-evoked long-lasting artefacts: A new adaptive algorithm for EEG signal correction,” Clin. Neurophysiol., vol. 128, no. 9, pp. 1563–1574, 2017). In this study, a (non-complex) exponential decay removal method was tested in EEG recordings and proven advantageous over other methods, including two common ICA methods. Hence, rather than repeating this comparison, we deemed demonstrating a proof of concept through application on our datasets to be more valuable.
(3) We were also asked about the lack of reporting a goodness of fit (GoF) measure.
In our revised figures (e.g., Fig. 2 & Fig. S2), we could certainly provide the GoF to the segment used for fitting. However, the true outcome measure of a successful artefact removal is whether a meaningful EP is unveiled. Changes seen in EP amplitude, shape, and timing (e.g., Fig. S2; Fig. 3) show that we can detect these changes through a (constant) artefact. The similarities of the EPs to previous studies (e.g., references [13]-[16]), along with the electrical basis of the artefact model, differing from typical EP shapes and models, provide even stronger proof of success than GoF alone.
Finally, the suggestions for how to improve the visibility of figures and plots were greatly appreciated and can be implemented in our revision – including contrasting colors, more understandable axis units, improving thickness of lines, and providing zoom plots and/or double y-axes for easier comparisons.
Thank you again for the suggestions. We hope we have provided a strong case for why our paper would be of great interest and value to the MICCAI audience.
Post-rebuttal Meta-Reviews
Meta-review # 1 (Primary)
- Please provide your assessment of the paper taking all information into account, including rebuttal. Highlight the key strengths and weaknesses of the paper, clarify how you reconciled contrasting review comments and scores, indicate if concerns were successfully addressed in the rebuttal, and provide a clear justification of your decision. If you disagree with some of the (meta)reviewer statements, you can indicate so in your meta-review. Please make sure that the authors, program chairs, and the public can understand the reason for your decision.
This paper addresses an interesting problem (artifact removal to better detected evoked potentials), and the authors did a good job of addressing the concerns.
- After you have reviewed the rebuttal, please provide your final rating based on all reviews and the authors’ rebuttal.
Accept
- What is the rank of this paper among all your rebuttal papers? Use a number between 1 (best paper in your stack) and n (worst paper in your stack of n papers).
4
Meta-review #2
- Please provide your assessment of the paper taking all information into account, including rebuttal. Highlight the key strengths and weaknesses of the paper, clarify how you reconciled contrasting review comments and scores, indicate if concerns were successfully addressed in the rebuttal, and provide a clear justification of your decision. If you disagree with some of the (meta)reviewer statements, you can indicate so in your meta-review. Please make sure that the authors, program chairs, and the public can understand the reason for your decision.
This paper attracted very mixed reviews, pointing out both
- strenghts: the clinical motivation and the potential impact eg on treating disorders related to movement, and -weaknesses: mainly the lack of technical motivation (attracting even one score 1 = out of scope)
The authors address the major criticism (= the lack of comparison to other methods) and some minor criticisms in their rebuttal.
While I agree that the technical novelty is limited, I think this paper is of interest for the community. It addresses an important problem in an area attracting increasing interest.
The quality of the figures and a number of pointed out small areas of improvement should really be addressed in the final version! But they are all doable and I thus would recommend acceptance of this paper.-
- After you have reviewed the rebuttal, please provide your final rating based on all reviews and the authors’ rebuttal.
Accept
- What is the rank of this paper among all your rebuttal papers? Use a number between 1 (best paper in your stack) and n (worst paper in your stack of n papers).
9
Meta-review #3
- Please provide your assessment of the paper taking all information into account, including rebuttal. Highlight the key strengths and weaknesses of the paper, clarify how you reconciled contrasting review comments and scores, indicate if concerns were successfully addressed in the rebuttal, and provide a clear justification of your decision. If you disagree with some of the (meta)reviewer statements, you can indicate so in your meta-review. Please make sure that the authors, program chairs, and the public can understand the reason for your decision.
The authors responded effectively to some of the comments, however, other critical comments related to the clarity of the methods, compelling arguments for the R2.4 and R2.7, and comparative results are missing in their rebuttal. Also, ICA is not the only method for artefacts removal and the authors needed to confirm that they will compare against, at least one of, the existing artefact removals techniques, or provide argumentative response otherwise. By the way CAI stand for “Computer Assisted Interventions”.
- After you have reviewed the rebuttal, please provide your final rating based on all reviews and the authors’ rebuttal.
Reject
- What is the rank of this paper among all your rebuttal papers? Use a number between 1 (best paper in your stack) and n (worst paper in your stack of n papers).
18