PCSY-D-24-00167

Neural complexity in preterm infants is predicted by developmental variables

PLOS Complex Systems

Dear Dr. Semeia,

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Kind regards,

Morten Gram Pedersen, PhD

Section Editor

PLOS Complex Systems

Morten Pedersen

Section Editor

PLOS Complex Systems

Hocine Cherifi

Editor-in-Chief

PLOS Complex Systems

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Additional Editor Comments (if provided):

Dear Dr. Semeia,

thank you for submitting your work to PLOS Complex Systems.

Your manuscript has been carefully evaluated by three experts in the field. While their comments are generally positive, the reviewers point to several issues that would improve the paper. They also ask for data and/or code to be made available, in line with the policies of the journal.

I therefore invite you to submit a revised version of the manuscript that takes into consideration the issues raised, and prepare point-to-point responses to the reviewer comments.

Sincerely,

Morten G. Pedersen, PhD

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Reviewers' Comments:

Reviewer's Responses to Questions

Comments to the Author

1. Does this manuscript meet PLOS Complex Systems’s publication criteria ? Is the manuscript technically sound, and do the data support the conclusions? The manuscript must describe methodologically and ethically rigorous research with conclusions that are appropriately drawn based on the data presented.

Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: Yes

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

Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: Yes

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3. Have the authors made all data underlying the findings in their manuscript fully available (please refer to the Data Availability Statement at the start of the manuscript PDF file)?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception. 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: No

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 Complex Systems 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: Dear Authors,

Thank you for this submission which begins with a stellar introduction that both provides broader context and also sets the stage for some of the nuances in the results.  The study results strike me as sound and somewhat useful, but I think there are three dimensions along which this study could be expanded, while remaining within the current focus, to really make it a highly cited work.  These are 1) expansion to additional metrics  2) expansion to other cohorts 3) a few technical issues.

When reading related work on development cohorts (or even older ones, as you note various EEG metrics are sensitive to both development and late-life diseases) which deploy metrics that attempt to measure similar dynamic properties on the general topic of criticality and entropy, it's never clear if results differ due to the cohort or the metric.  If you were to deploy several of these all on this data, you would not only more comprehensively describe brain dynamics, but create a sort of rosetta stone for other studies.  As a side benefit, if you just wrote a wrapper around those several methods, I can see that being useful to other researchers and driving citations.

The gold standard for impactful results is replication, and I'm not asking you to go out and gather more data, but if you were to apply these metrics to some other equivalent cohort, or even a slightly older one like this one, then I think people will be more confident that these effects are robust and reliable, reflecting major developmental processes.  I noticed another cohort you might use here https://onlinelibrary.wiley.com/doi/full/10.1002/hbm.20524 and as experts in the area you likely know of more.

On the final topic - a collection of technical issues, while the number of electrodes are limited, it would be interesting to provide at least some spatial map for the results as it might at least suggest which areas are developed.  I know you took the whole frequency spectrum, but as it's fairly common to subset the analysis to the canonical bands, and it would be interesting to see  For the data sharing, typically I'll see some script that will reproduce the results, even if the raw data isn't publicly available.  Fine if it requires a DUA, but PLOS data sharing policy would like to see it in a repository with an updateable request system, so when people move on etc te data has a stable life.  Finally on a neurobiological note, could you tie these bursts more clearly to particular cell types, or the actions of some particular cell or multi-cell dynamics from simulation studies? Basically while they seem quite important to the study their neurobiological basis was not commented on.

Reviewer #2: The authors study factors that affect neural complexity in preterm infants using the EEG. They showed that linear combinations of 3 complexity measures per EEG channel were associated with age, sex and the percentage of bursts in the EEG.

Comments

A rationale on the use of PCA as a data reduction method here would be useful. Why would a method that estimates a linear combination of features in decreasing order of feature space variance be of any use here – particularly when the initial normalization/regularization of each individual feature due to the limited size of the dataset would have considerable uncertainty (which I suppose could be investigated with data selection methods)? How does such feature/spatial mixing effect the interpretation of results with respect to EEG activity?

The findings should be supported by initial analysis that is in-line with what is already known about complexity analysis and age in preterm infants (De Wel et al. Complexity analysis of neonatal EEG using multiscale entropy: applications in brain maturation and sleep stage classification. Entropy. 2017;19: 516 & Stevenson et al. Automated cot‐side tracking of functional brain age in preterm infants. Annals of clinical and translational neurology. 2020; 7: 891-902 - granted that the only useful information is the supporting information with this paper). The former looks at individual location and a combination therein and the later just averages across all EEG channels (a global assessment). These complexity measures are highly correlated with age (well at least MSE is) to the point where age would be the dominant explanatory variable and yet the findings that PCs aren’t really well correlated with age at all is interesting (in Table 2). Thus, a similar analysis (at least at the level of global average) would provide context to findings in the PCs and is highly recommended.

Minor Comment

A few extra details in the methods (I appreciated that details have been provided in another study but details directly relevant to this study would be useful), list the EEG electrode positions in the text (briefly, so I don’t have to look at the supplemental) and the reference, how long was the EEG recording duration used when calculating features (I see 1 minute epochs were used, but how many were used per recording to generate complexity summary statistics).

Was there any artefact rejection procedure implemented at the pre-processing stage? If not, why not?

What was the age distribution of the cohort (above and beyond the range) – a histogram would be useful? The age distribution is relatively small compared to other studies (and particularly sparse < 30 weeks), how does this affect the interpretation of results?

The authors use the term ‘trace alternans’ throughout but preterm EEG transitions from ‘trace discontinu’ to ‘trace alternans’ at around 32 weeks according to some (others would say TA is only a term pattern – c.f. Andre et al. Electroencephalography in premature and full-term infants. Developmental features and glossary. Neurophysiologie Clinique. 2010; 40: 59-124) suggesting the predominant activity in this work is trace discontinue depending on the age distribution of the data. Nomenclature is important to neonatal EEG folks. The safest bet may to rephrase without the French terminology, or focus on ‘trace discontinu’, or just use discontinuity instead?

Figure 1, the colour scheme could be improved – to my squinting eyes some of the feature and PC component colours overlap. There even appear to be colours present in the figure that are not present in the legends – e.g. I see green in (A)

Table 2 is a little confusing, what is the predictor bursts? Is this pSAT? Isn’t the whole signal bursts+non-bursts? Why repeat. What is difference, does this allude to mediation analysis?

A potential confounder of the relationship between pSAT and complexity measures may be technicalities of estimate complexity measures (variants of box counting) – not neural complexity. A brief simulation study could address this very quickly, a filtered 1/fa process with square wave inputs of various morphology would be an excellent model to test this). This also relates to 4.1 in the discussion.

Discussion, 4.1 – the authors state ‘Past studies’, but only reference 1 study.

Reviewer #3: In this study, Semeia et al., explore how the temporal complexity of EEG data in infants changes with gestational age, as well as between burst and inter-burst periods of brain activity. In general I think that this paper is strong and a worthwhile addition to the literature. Given the venue, however, I would like to see more engagement with the actual notions of “complexity”, as used here. As I discuss in more detail below, the authors use multiple different measures of complexity, but then aggregate them all into one big meta-measure using PCA. I question the conceptual utility of such a general “complexity” measure, and would encourage the author to read and consider this paper by Feldman and Crutchfield:

Feldman, D. P., & Crutchfield, J. P. (1998). Measures of statistical complexity: Why? Physics Letters A, 238(4), Article 4. https://doi.org/10.1016/S0375-9601(97)00855-4

It seems like the idea of a general measure of complexity is too vague to be particularly useful – instead, I think it is probably better to formally specify exactly what features of the signal you are interested in (entropy rate, multi-scale structure, frequency richness, etc), and then use specific measures tailed to those specific features. As it stands, I come away from this paper feeling a bit like I don’t know exactly what I’ve learned. Clearly “complexity” (defined as the principle component of an aggregate of semi-related measures) changes with age and with burstiness, but without really knowing what “complexity” means...why do I care?

General Comments:

- I would like to see a lot more formal detail in the Methods section describing the measures. For example, when computing Lempel-Ziv, were the scores normalized by the expected values of a shuffled null (as in Schartner et al., and subsequent studies)? For multi-scale entropy, how was the signal coarse-grained (averaging, downsampling, etc)? For MSE, why use the average as opposed to the area under the scale-by-entropy curve (which I believe is more common)? Similar questions can be asked about all the measures here. I think it is vitally important that detailed, formal descriptions of all measures (LaTeX equations) are included and discussed in detail. This is especially important when discussing choices for ad hoc free parameters (such as the number of discrete bins, coarse-graining procedures, etc), as these can introduce significant biases that should be acknowledged.

As it stands, it feels a little bit like the authors just reached for popular complexity measures “off the shelf” without sustained engagement with the underlying logic or mathematical structure of the measures.

- I don’t think it is appropriate to immediately start by doing a PCA on the different complexity measures. While it is true that there is significant correlation between complexity measures (you might consider citing: https://www.nature.com/articles/s41598-020-57695-3 here), these measures are also quite different in important ways. For example, Lempel-Ziv complexity increases monotonically with the entropy rate of the signal: a completely “random” signal (i.e. the present discloses no information about the future) will have maximal LZC. In contrast, multi-scale entropy can be non-monotonic: both periodic and random signals will generally have low MSE after sufficient coarse-graining, while a signal with either strong autocorrelation or fractal structure (which can be measured by the Hurst exponent) will generally retain entropy after serial coarse-graining. As such, the differences between LZ and MSE might be of interest, as well as correlations between the measures.

I don’t know much about the state-space entropy rate, but I imagine similar comparative analyses would be interesting.

- The authors say:

“When considering the EEG signal as a whole (i.e., without analyzing

sections with and without bursts), neural complexity, in particular when measured by MSE and CSER

(PC1), increases linearly as the proportion of bursts (pSAT) increases (Figure 2A1).”

I wonder if this is due to changes in the frequency spectrum of the signal. For example, a single, pure frequency will always have complexity of 0 (since it’s a periodic sine wave). As the signal gets “richer” (i.e. a broader frequency spectrum), interacting temporal frequencies will necessarily increase the entropy of the signal, at multiple scales. Is there some way you can test or control for this?

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Reviewer #1: No

Reviewer #2: No

Reviewer #3: No

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Neural complexity in preterm infants is predicted by developmental variables

PCSY-D-24-00167R1

Dear Dr. Frohlich,

We are pleased to inform you that your manuscript 'Neural complexity in preterm infants is predicted by developmental variables' has been provisionally accepted for publication in PLOS Complex Systems.

Before your manuscript can be formally accepted you will need to complete some formatting changes, which you will receive in a follow-up email from a member of our team. 

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Thank you again for supporting Open Access publishing; we are looking forward to publishing your work in PLOS Complex Systems.

Best regards,

Morten Gram Pedersen, PhD

Section Editor

PLOS Complex Systems

Hocine Cherifi

Editor-in-Chief

PLOS Complex Systems

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Reviewer Comments (if any, and for reference):

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

Reviewer #4: All comments have been addressed

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2. Does this manuscript meet PLOS Complex Systems's publication criteria ? Is the manuscript technically sound, and do the data support the conclusions? The manuscript must describe methodologically and ethically rigorous research with conclusions that are appropriately drawn based on the data presented.

Reviewer #2: Yes

Reviewer #3: Yes

Reviewer #4: Yes

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

Reviewer #2: Yes

Reviewer #3: Yes

Reviewer #4: Yes

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4. Have the authors made all data underlying the findings in their manuscript fully available (please refer to the Data Availability Statement at the start of the manuscript PDF file)?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception. 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: Yes

Reviewer #3: Yes

Reviewer #4: Yes

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

PLOS Complex Systems 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

Reviewer #4: 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: (No Response)

Reviewer #4: I have no further comments. Well done and congratulatoins.

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Reviewer #2: None

Reviewer #3: No

Reviewer #4: No

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