Dear Dr. Lepperød,

Thank you very much for submitting your manuscript "Inferring causal connectivity from pairwise recordings and optogenetics" 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. In light of the reviews (below this email), we would like to invite the resubmission of a significantly-revised version that takes into account the reviewers' comments.

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Thank you again for your submission. 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,

Marcus Kaiser, Ph.D.

Academic Editor

PLOS Computational Biology

Lyle Graham

Section Editor

PLOS Computational Biology

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Reviewer's Responses to Questions

Comments to the Authors:

Please note here if the review is uploaded as an attachment.

Reviewer #1: The authors suggest the use of the idea of Instrumental Variables from economics to facilitate the inference of synaptic connectivity (effective connectivity?) from neural data where optogenetics can be employed as a perturbation. The analysis of the proposed method is done on simulated neural data showing the positive effect it has on the inference process.

I find the general idea of this paper quite interesting and important. Using perturbation (optogenetics or otherwise) as a tool for facilitating inference of connectivity is a very promising tool. Also, the concept of instrumental variables is a welcome novel addition to efforts for inferring connectivity. However, I find the results themselves weak and the exposition of the results quite unclear.

Regarding the results, my criticism are as follows

-the authors perform their analysis on simulated data. Although the authors do study the effect of various features of the simulated network (such as type external field), important features are unexplored. For example the network size is rather small (200 neurons: 100 excitatory, 100 inhibitory), the connectivity is stated to be excitatory. I would have excepted the simulations to be done with larger networks, and in particular with more realistic model neurons, e.g. Hodgkin-Huxely or Integrate-and-Fire. There many biologically realistic neural network models, e.g. of the visual cortex, and the authors come from one of the major centres for building such models. So I am a bit surprised that such analyses are not performed.

- The results are section 2.4 are based on stimulating 5 neurons. How does this change if the analysis is performed on a larger fraction of neurons being stimulated? Is it important to know a priori what the fraction of activated neurons are? if so, how can one have a good estimate of that in a real data?

- The authors do not explore their approach on any real dataset. It is true that for real datasets, the ground truth is not known. Still one can compare the results of different methods and see if they indeed do give different results, and how big the difference is.

Regarding the presentation, although I really liked the introduction, I found the presentation of the Results right after the Introduction without having the author know the main methodological approaches made the paper difficult to read. The Methods section at the end is also very cluttered and jumps between definitions, proofs and material (e.g. 4.3.2) that can also be described in the results section. Some of the material (e.g. proofs) can be moved to a supplemental information/apprendices. I think the paper needs a major rewrite even if not further analyses are described.

Reviewer #2: This study is interesting and creative, but unlike the authors I would not discount the usefulness of two-photon optogenetic stimulation of arbitrary subgroups of cells in vivo (Packer et al. 2015 https://www.nature.com/articles/nmeth.3217) to infer functional connectivity – it creates far more favourable and realistic conditions than blanket 1-photon optogenetic stimulation, and can already be applied to hundreds of presynaptic and thousands of postsynaptic neurons across brain areas (Fisek et al. 2023 https://www.nature.com/articles/s41586-023-06007-6) and more in the future. It would be useful to compare blanket 1-photon and patterned 2-photon stimulation when inferring functional connectivity.

My main concern, however, is that the simulated model system (eq. 2) used by the authors to apply their statistical methods to may not exhibit sufficiently realistic types of noise that are found in cortical networks in vivo, leading to an underestimation of confounds. London et al. (2010) showed that cortical networks are chaotic because small perturbations in the spiking history grow. This is because the probability of a presynaptic spike evoking an extra postsynaptic spike (which is on the order df a percent) multiplied by the fan-out of the presynaptic neuron (the number of its postsynaptic targets, which in the cortex is on the order of thousands) is much larger than one. Two spiking histories of the same network in response to the same stimulus will therefore be massively different if looked at in small time bins, which may interfere with the statistical analysis methods presented in this manuscript. Are the networks used in this manuscript sufficiently chaotic, and do perturbations in these models grow as fast (e.g. with a gain of 28, London et al. 2010) as they do in vivo? The authors should ensure and demonstrate that their model system generates sufficiently realistic irreproducibility of spiking histories of the network.

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Have the authors made all data and (if applicable) computational code underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data and code underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data and code should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data or code —e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

Reviewer #2: Yes

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Reviewer #1: No

Reviewer #2: No

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Dear Dr. Lepperød,

We are pleased to inform you that your manuscript 'Inferring causal connectivity from pairwise recordings and optogenetics' 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,

Marcus Kaiser, Ph.D.

Academic Editor

PLOS Computational Biology

Lyle Graham

Section Editor

PLOS Computational Biology

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Reviewer's Responses to Questions

Comments to the Authors:

Please note here if the review is uploaded as an attachment.

Reviewer #2: The rebuttals of the reviewers' comments by the authors are well argued, and the changes they made to their manuscript are good. Yet I'm torn because the changes are relatively minor and the authors did not include tests with larger networks as suggested by Reviewer 1, in which neurons have more realistic connectivity and synaptic weights. Future studies should involve random (Brunel) networks of integrate-and-fire neurons with realistic distributions of synaptic weights, as well as structured networks in which connectivity and connection strength represent similarity of sensory coding (see Cossell et al., 2015 for example).

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Have the authors made all data and (if applicable) computational code underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data and code underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data and code should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data or code —e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #2: Yes

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PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #2: No