PONE-D-25-13192 Long-term neuron tracking reveals balance of stability and plasticity in functional properties PLOS ONE

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Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Yes

Reviewer #2: Partly

Reviewer #3: Yes

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2. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: No

Reviewer #3: Yes

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3. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data 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 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—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

Reviewer #2: No

Reviewer #3: No

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4. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: Yes

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5. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: This is an automated report for PONE-D-25-13192. This report was solicited by the PLOS One editorial team and provided by ScreenIT.

ScreenIT is an independent group of scientists developing automated tools that analyze academic papers. A set of automated tools screened your submitted manuscript and provided the report below. Each tool was created by your academic colleagues with the goal of helping authors. The tools look for factors that are important for transparency, rigor and reproducibility, and we hope that the report might help you to improve reporting in your manuscript. Within the report you will find links to more information about the items that the tools check. These links include helpful papers, websites, or videos that explain why the item is important. While our screening tools aim to improve and maintain quality standards they may, on occasion, miss nuances specific to your study type or flag something incorrectly. Each tool has limitations that are described on the ScreenIT website. The tools screen the main file for the paper; they are not able to screen supplements stored in separate files. Please note that the Academic Editor had access to these comments while making a decision on your manuscript. The Academic Editor may ask that issues flagged in this report be addressed. If you would like to provide feedback on the ScreenIT tool, please email the team at ScreenIt@bih-charite.de. If you have questions or concerns about the review process, please contact the PLOS One office at plosone@plos.org.

Reviewer #2: In this manuscript the authors report a method to track single-unit waveforms recorded from classic MEAs across sessions and days. They start with neurons that were ‘online’ sorted by their recording system within a session and use a waveform similarity metric to track these sorted neurons across days/sessions.

The underlying algorithm is relatively simple, it’s a pairwise comparison of mean neural waveforms. Specifically, it computes the euclidean distance and the pearson correlation, normalizes it, and takes the mean of these two metrics. The similarity is compared to a null-distribution and if it is significant the neural waveforms from different days are ‘merged’. Using this method, they can track ~40% of their total sorted neurons across multiple days. The median is close to ~20 days, but a subset were tracked over ~100 days.

For these tracked neurons, they analyze the mean firing-rates, inter-spike-intervals, tuning direction and field coupling and state that these properties are mostly stable (although see comments).

The major strengths of this paper are the long-term neural recordings, and the manuscript is largely straightforward and the methods reported here could be useful to researchers using classic MEA probes.

The main weakness of the paper is that the lack of comparisons to other methods make it hard to determine how well the method works, and the biological insight gained from these analyses is very limited, and although the results here are clear - they could use some more analyses, nuance and controls.

Major Comments:

1. How good is this method compared to other approaches? The lack of a ‘ground truth’ makes it difficult to evaluate how much better this method is or how effective it is. A meaningful comparison to other methods - i.e. ones that use other features or different metrics would make the effectiveness of this system more obvious and a clear validation step would be useful.

2. The system seems to heavily rely on online-sorted/pre-sorted neurons, which can have their own error rates etc - however, more modern tools combine sorting and tracking these steps into a single computation, which allows them to capture drift over the session - could the authors comment on why they chose their approach?

3. For the more biologically relevant results reported here, some more comparative analysis is warranted. The role of the task in the results reported here is largely ignored - There are two kinds of neurons in these recordings, neurons that are used for the kalman filter and presumably other unrelated neurons - which wouldn’t have a pd. A comparative analysis for these two kinds of neurons would have been a useful discovery - are the neurons that are task related the more stable ones? And the ones which are not task related are the ones that go silent or have larger changes? Are there any consistent trends about which neurons shift and which dont? Is it related to task performance, does stability depend on how informative that neural response was for the kalman filter?

4. I am having a hard time understanding how one can claim stability when the PD for a neuron changes over 200 degrees over time. This shows stability in the neural recording, not in the neural responses. For determining PDs did the authors use a threshold for finding neurons that had a good fit to the cosine model? The shifting PDs could also be a sign of assigning PDs to neurons that aren’t actually directionally tuned.

5. Was there any behavioral consequence for having stable neurons? Right now this is largely a phenomenological study, it could help if the authors had a biological hypothesis this allowed one to test.

Minor comments:

1. Its not clear to me why you would use the mean of the pearson correlation and the euclidean distance for similarity scores, instead of treating them as a 2D vector - as the the authors themselves point out, these capture two different aspects of similarity

2. The reasoning for comparison of mean firing rates across sessions was not clear to me, wouldn’t comparing only the first and last sessions, bias your estimates toward stability since many neurons are recorded for <20 days? Was there a measurement of session by session mean firing rate changes, maybe a regression analysis here would clarify how the number of sessions alters firing rates?

3. In general, readability would have been improved if the results section used more declarative titles that described the result instead of a generic title. For e.g. instead of titles like “Neuron tracking”, something more along the lines of “Neurons were tracked for xx days” etc.

4. The copy I received for review had the figure legends inserted at odd locations into the main text, please check that the submission is appropriately formatted before sending it out to review.

Specific text suggestions:

“Neural stability is essential for executing learned … “

While I understand the general sentiment behind this statement, this dichotomy is not true - Degeneracy in neural circuits enables skilled behavior even though neural firing patterns are not identical. E.g. EMG activity (and therefore motor neuron activity) can vary substantially, but the hand kinematics can be identical. For cortical studies, the Null-Space shown by kaufman and colleagues is another example of neural acitivity in individual neurons not being deterministic for behavior. There are more examples for other flexible behaviors such as the STG patterns of neural activity shown by Prinz and Marder and in other invertebrates. It would be prudent to dial back these dichotomies.

For stable/unstable representations since the authors cited the Liberti et al 2016 paper, they should also cite the Katlowitz et al 2018 neuron paper which challenges the observations in the Liberti paper.

Reviewer #3: In the present study, the authors implemented a novel methodology to track neural activity across recording sessions spread over many days (3 months), with the larger goal of assessing the long term stability of neural activity associated with the control of a robotic arm using a Brain-Machine Interface (BMI) which translated neural population spiking activity into cursor velocity, enabling macaques to perform a "center-out" task. Using their tracking approach, based in waveform similarity comparison across sessions, the authors were able to successfully track individual neurons across days and to assess the stability of their functional properties (direction tuning, Inter-Spike Interval distribution, and Spike-Field Coherence), and show that individual neurons exhibit a high degree of stability, while some plasticity is present, and hypothesize that the balance between stability and flexibility allow motor learning.

The manuscript is nicely and clearly written, and presents the information in a clear and concise manner. The data analysis is sound, and the authors utilized stringent criteria to select those neurons that would be considerer to be the same across different recording sessions, showing that their intrinsic functional properties were preserved.

The main weakness of the manuscript is the lack of some sort of "ground truth" test. I believe it would be relatively easy to generate synthetic data that could be analyzed using the proposed tracking method. This would allow to also determine what kind of variations in the detected spikes may lead the tracking algorithm to fail. Another option would be to apply the tracking method to subsets of data taking randomly a fraction of the trials on each session, and assessing the consistency of the results.

Other than that, I only have a few minor comments, which I list below:

1) Please specify what kind of tungsten electrode arrays were used (i.e. manufacturer) and their characteristics (tip length, spacing, impedance range, etc.)

2) How stable were the units sorted during a given recording session. It would be useful for the reader to have an idea of the waveform stability. The authors mention that only units with high SNR were used for the analysis, it would be nice to know how stable the SNR was during a session, in addition to across sessions.

3) It would be useful to indicate in Figure 1 that the log transformed Euclidean distance was used in the similarity matrix, to avoid confusion in the readers that may be thinking of correlation as "similarity" vs distance as "dissimilarity".

4) Line 440. "Monkey B exhibited significant changes in mean firing rates, whereas Monkey A did not." The distributions in Figure 2C look very similar. In fact, the distribution of relative changes in mean FR for monkey B peaks at zero, while the distributor monkey A peaks below 0. Therefore, this conclusion is hard to accept, at least without a statistic. Statistical test results are given for peak and through amplitude, but not for mean FR.

5) Lines 481-484. "An example tracked neuron (Fig 4C) exhibited a PD around 482 37.6°, gradually shifting clockwise to 246.8° over 91 days, yet its PD span (150.8°) 483 remained well below the null threshold (266.4° for 21 sessions), demonstrating 484 remarkable stability." This definition of PD stability is rather problematic, since it allows a neuron to shift its tuning to the opposite direction before being considered “unstable”. Circular statistic tests (perhaps the Rayleigh test) that measure the degree of directional concentration would be more appropriate to assess stability.

6) Related to comment 5. Lines 562-564 "We demonstrated that spiking dynamics, directional 563 tuning, and spike-field interactions remained stable over time a large proportion of 564 neurons, while the remaining neurons were more plastic". Sure, for the short term (neighboring sessions) this is true, but I'd argue it is debatable for the long term, given the authors’ definition of stability.

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Reviewer #1: No

Reviewer #2: No

Reviewer #3: No

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Submitted filename: report_10.1101+2025.03.14.643204.pdf

Long-term neuron tracking reveals balance of stability and plasticity in functional properties

PONE-D-25-13192R1

Dear Dr. Santacruz,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

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Kind regards,

Abdolvahed Narmashiri

Academic Editor

PLOS One

Additional Editor Comments (optional):

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #2: All comments have been addressed

Reviewer #3: All comments have been addressed

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2. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #2: Yes

Reviewer #3: Yes

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3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #2: Yes

Reviewer #3: Yes

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4. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data 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 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—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #2: No

Reviewer #3: Yes

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5. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #2: Yes

Reviewer #3: Yes

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6. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #2: (No Response)

Reviewer #3: The authors have addressed my comments.

I have no further concerns.

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Reviewer #2: No

Reviewer #3: No

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