Peer Review History
Original SubmissionJanuary 15, 2020 |
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Dear Mr. Hartoyo, Thank you very much for submitting your manuscript "Inferring a simple mechanism for alpha-blocking by fitting a neural population model to EEG spectra" for consideration at PLOS Computational Biology. As with all papers reviewed by the journal, your manuscript was reviewed by members of the editorial board and by several independent reviewers. The reviewers appreciated the attention to an important topic. Based on the reviews, we are likely to accept this manuscript for publication, providing that you modify the manuscript according to the review recommendations. Please prepare and submit your revised manuscript within 30 days. If you anticipate any delay, please let us know the expected resubmission date by replying to this email. When you are ready to resubmit, please upload the following: [1] A letter containing a detailed list of your responses to all review comments, and a description of the changes you have made in the manuscript. Please note while forming your response, if your article is accepted, you may have the opportunity to make the peer review history publicly available. The record will include editor decision letters (with reviews) and your responses to reviewer comments. If eligible, we will contact you to opt in or out [2] Two versions of the revised manuscript: one with either highlights or tracked changes denoting where the text has been changed; the other a clean version (uploaded as the manuscript file). Important additional instructions are given below your reviewer comments. Thank you again for your submission to our journal. We hope that our editorial process has been constructive so far, and we welcome your feedback at any time. Please don't hesitate to contact us if you have any questions or comments. Sincerely, Peter Neal Taylor Associate Editor PLOS Computational Biology Lyle Graham Deputy Editor PLOS Computational Biology *********************** A link appears below if there are any accompanying review attachments. If you believe any reviews to be missing, please contact ploscompbiol@plos.org immediately: [LINK] Reviewer's Responses to Questions Comments to the Authors: Please note here if the review is uploaded as an attachment. Reviewer #1: This is a well-written and clear study inferring an alpha-blocking mechanism estimated by fitting EEG data with a neural population model. Overall, the results help to shed light on the still debated alpha-blocking mechanism discussion through a clear, direct and data-driven approach. The authors analyzed several EEG data corresponding to multiple individuals using a neural population model and a fitting method with the addition of a regularization term. From that, they were able to justify and find their parameter choices by fixing the ones not mutable (based on biological motivation) and by isolating the most meaningful parameter affecting the measures in the EO and EC comparison. The mechanism found corroborate with their previous study and links the alpha generation with the alpha-blocking, where the activation of the inhibitory population has an important role. Moreover, the codes and methods are available online, which is a positive point. The paper is comprehensive and clearly written and I recommend its publication. A few specific comments: 1) The link to access the EEG data is not working. I believe the correct is: https://archive.physionet.org/pn4/eegmmidb/ . Please, check it. 2) Although is mentioned the neuronal population model used and well-referenced, I believe that it would be more clear and easier for the reader to understand if the authors show explicitly the equations. 3) The source of the extra-cortical input (pei) and its limitations could be better discussed. Extra-cortical inputs include other sources than thalamus and, depending on the source, the cortical layers and the interneurons receiving it might be different. Moreover, it also depends if you are looking at a primary or higher-order area of the cortex. In that way, still there is no specific answer about who is driving the alpha-blocking. Enriching this discussion will clarify the limitations and the possible ways to test it through experiments and more detailed models. Reviewer #2: The authors construct a firing rate/population model of multiple neural populations (E, I) and fit the spectra generated by the model to EEG data from 5 subjects with eyes open and closed conditions. They use a particle swarm optimization to fit the different conditions and find that in their model the excitatory inputs to the interneuron population is the major determinant of alpha reduction in the EO condition. Overall, the writing is clear, and the results will be of interest to the neuroscience community. In line with current practices, the authors have shared their source code. While compelling, the authors should more clearly explain a neuroanatomical rationale for why only inputs to the interneurons regulate alpha. Is there a thalamic source for this? Could it be provided via thalamic matrix inputs (Biological Psychiatry 87(8):770)? Authors should cite relevant literature. In addition, the authors should discuss whether the type of model they developed has enough biological detail to offer novel insights into mechanisms of brain rhythm generation and their modulation. Many detailed circuit models and modeling platforms are now available that have competing explanations for the origin of alpha (e.g. see eLife. 2020; 9: e51214.). Authors should compare their model against some of these other models/tools. As far as organization of the manuscript, the authors should move the figures in Discussion into the Results, along with the description of those figures. Detailed comments: field models - external input to inhibitory neurons in cortex responsible for attenuating alpha they fit EEG data with eyes open (alpha higher) and closed (alpha lower) using population model and found that one parameter - external input to inhibitory neurons in cortex was responsible for modulating alpha power. that's not so surprising - but what is the explanation? does it fit the neuroanatomical data? mechanistic models? why would opening eyes increase drive to cortical ihibitory neurons? which pathway is responsible? there are many models that can account for the data ... 105-108: "Local equations are linearized around a fixed point and the power spectral density (PSD) is derived assuming a stochastic driving signal of the excitatory population that represents thalamo-cortical and long range cortico-cortical inputs, assumed to be Gaussian white noise. The modelled PSD can then be written as a" Why is thalamocortical drive assumed to be white noise? Is that realistic given knowledge of thalamocortical dynamics? I would think that some peaks in frequency, e.g. in alpha range would be more realistic. OK, then later they mention that the inputs are not white noise, so that's a fittable parameter that influences the noise type provided (white, pink, brown, etc.). plos comp bio thalamic model - more realistic and offers more plausible insights into mechanisms of rhythm generation DJS - nice measure for quantifying differences in power spectra Fig.3 may have too much detail for the typical reader. Is there a way to summarize the fitted distributions for each patient rather than displaying 23 x 5 distributions?? Line 188-190: if most parameter responses are 0 or insignificant trend with degree of alpha blocking, why not instead show the parameter response that are significant or not 0?? The beginning of the Discussion and Figures 4 and 5 should be moved into the Results. Although the discussion around lines 221 address some of this, can the authors comment on the mechanism as to why the parameter p_ei (excitatory input to inhibitory neurons) is the major determinant of changes in alpha between the EO and EC conditions and why p_ee is not important? I would have thought both parameters should influence the magnitude of oscillations. In addition, which neuroanatomical pathway would set the p_ei value and how would that pathway influence only the interneurons? Is the model-predicted parameter influencing alpha consistent with experimental data? ********** Have all data underlying the figures and results presented in the manuscript been provided? Large-scale datasets should be made available via a public repository as described in the PLOS Computational Biology data availability policy, and numerical data that underlies graphs or summary statistics should be provided in spreadsheet form as supporting information. 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Revision 1 |
Dear Hartoyo, We are pleased to inform you that your manuscript 'Inferring a simple mechanism for alpha-blocking by fitting a neural population model to EEG spectra' has been provisionally accepted for publication in PLOS Computational Biology. Before your manuscript can be formally accepted you will need to complete some formatting changes, which you will receive in a follow up email. A member of our team will be in touch with a set of requests. Please note that your manuscript will not be scheduled for publication until you have made the required changes, so a swift response is appreciated. IMPORTANT: The editorial review process is now complete. PLOS will only permit corrections to spelling, formatting or significant scientific errors from this point onwards. Requests for major changes, or any which affect the scientific understanding of your work, will cause delays to the publication date of your manuscript. Should you, your institution's press office or the journal office choose to press release your paper, you will automatically be opted out of early publication. We ask that you notify us now if you or your institution is planning to press release the article. All press must be co-ordinated with PLOS. Thank you again for supporting Open Access publishing; we are looking forward to publishing your work in PLOS Computational Biology. Best regards, Peter Neal Taylor Associate Editor PLOS Computational Biology Lyle Graham Deputy Editor PLOS Computational Biology *********************************************************** |
Formally Accepted |
PCOMPBIOL-D-20-00063R1 Inferring a simple mechanism for alpha-blocking by fitting a neural population model to EEG spectra Dear Dr Hartoyo, I am pleased to inform you that your manuscript has been formally accepted for publication in PLOS Computational Biology. Your manuscript is now with our production department and you will be notified of the publication date in due course. The corresponding author will soon be receiving a typeset proof for review, to ensure errors have not been introduced during production. Please review the PDF proof of your manuscript carefully, as this is the last chance to correct any errors. Please note that major changes, or those which affect the scientific understanding of the work, will likely cause delays to the publication date of your manuscript. Soon after your final files are uploaded, unless you have opted out, the early version of your manuscript will be published online. The date of the early version will be your article's publication date. The final article will be published to the same URL, and all versions of the paper will be accessible to readers. Thank you again for supporting PLOS Computational Biology and open-access publishing. We are looking forward to publishing your work! With kind regards, Sarah Hammond PLOS Computational Biology | Carlyle House, Carlyle Road, Cambridge CB4 3DN | United Kingdom ploscompbiol@plos.org | Phone +44 (0) 1223-442824 | ploscompbiol.org | @PLOSCompBiol |
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