Authors

Hoyt Patrick Taylor IV, Pew-Thian Yap

Abstract

Functional connectivity (FC) gradients'' enable the investigation of connection topography of cognitive hierarchies and yield the primary axes along which FC is organized. In this work, we employ a variant of thegradient’’ approach wherein we solve for the normal modes of FC, yielding functional connectome harmonics. Until now, research in this vein has only considered static FC, neglecting the possibility that the principal axes of FC may depend on the timescale at which they are computed. Recent work suggests that momentary activation patterns, or brain states, mediate the dominant components of functional connectivity, suggesting that the principal axes may be invariant to change in timescale. In light of this, we compute functional connectome harmonics using time windows of varying lengths and demonstrate that they are stable across timescales. Our connectome harmonics correspond to meaningful brain states. The activation strength of the brain states and their inter-relationships are found to be reproducible for individuals. Further, we utilize our time-varying functional connectome harmonics to formulate a simple and elegant method for computing cortical flexibility at vertex resolution. Finally, we demonstrate qualitative similarity between flexibility maps from our method and a method standard in the literature.

Link to paper

DOI: https://doi.org/10.1007/978-3-031-43993-3_26

SharedIt: https://rdcu.be/dnwNr

Link to the code repository

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Link to the dataset(s)

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Reviews

Review #1

Please describe the contribution of the paper

The paper proposes a method to study functional modules of the connectome by determining harmonics of dynamic connectivity. Dynamic behaviour of functional modules provides ways to understand the flexibility or stability of the modules derived from resting-state fMRI data. Experiments were performed to study the flexibility of the harmonics derived.
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 method of determining harmonics and follow-up study are succinctly and clearly presented in the manuscript. The method is simple and seems novel. Sufficient experiments were done to demonstrate the potential and compare with the modules derived from static connectivity matrices.
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.

There is no apparent weakness of the paper. Only two known functional modules were visible with harmonic analysis. How can others be visible with this approach? It would be we compare DMN and TPN regions obtained from static connectivity matrices and by considering the dynamic connectivity. Others can take this research forward if the codes are released with the publication of the manuscript.
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

Good chance of reproducing the results.
Please provide detailed and constructive comments for the authors. Please also refer to our Reviewer’s guide on what makes a good review: https://conferences.miccai.org/2023/en/REVIEWER-GUIDELINES.html

The paper proposes a method to study functional modules of the connectome by determining harmonics of dynamic connectivity. Dynamic behaviour of functional modules provides ways to understand the flexibility or stability of the modules derived from resting-state fMRI data. Experiments were performed to study the flexibility of the harmonics derived. The method of determining harmonics and follow-up study are succinctly and clearly presented in the manuscript. The method is simple and seems novel. Sufficient experiments were done to demonstrate the potential and compare with the modules derived from static connectivity matrices.
There is no apparent weakness of the paper. Only two known functional modules were visible with harmonic analysis. How can others be visible with this approach? It would be we compare DMN and TPN regions obtained from static connectivity matrices and by considering the dynamic connectivity. Others can take this research forward if the codes are released with the publication of the manuscript.
Rate the paper on a scale of 1-8, 8 being the strongest (8-5: accept; 4-1: reject). Spreading the score helps create a distribution for decision-making

6
Please justify your recommendation. What were the major factors that led you to your overall score for this paper?

Sound method, clear presentation, and sufficient experiments
Reviewer confidence

Confident but not absolutely certain
[Post rebuttal] After reading the author’s rebuttal, state your overall opinion of the paper if it has been changed

N/A
[Post rebuttal] Please justify your decision

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Review #2

Please describe the contribution of the paper

This paper evaluated connectivity harmonics in a dynamic mannar using the sliding-window approach.
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.
- Noval idea to evaluate harmonics using different window length, although sliding window approach has been used on time series.
- Thorough evaluations: similarity within and subjects, between different window length, ICC between harmonics, etc.
- Thoughtful discussion
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.
- An experiment on reproducibility across independent groups could be an plus.
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

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://conferences.miccai.org/2023/en/REVIEWER-GUIDELINES.html
- It is very interesting to see how the harmonics vary over time and how reproducible the measures are within subjects, between test-retest sets as well as between different window length. And the results shown in Table 1 and 2 make sense and are what we expected.
- I particularly enjoy reading the discussion section of this paper, which raises several interesting questions and relates this work to the literature.
- There are several works that show the global signal (also referred as physiological signal/network) is present and dominant in fMRI data, particularly when minimally preprocessed fMRI data without global signal regression is applied. I’m wondering do the author also see this type of component in the dynamic harmonics?
- How reproducible the dynamic harmonics are between large but independent groups? e.g. random split 1000 HCP data into two 500-subject groups and perform the analysis on two groups independently.
Rate the paper on a scale of 1-8, 8 being the strongest (8-5: accept; 4-1: reject). Spreading the score helps create a distribution for decision-making

7
Please justify your recommendation. What were the major factors that led you to your overall score for this paper?

A solid paper with clear method description and result presentation and insightful discussion.
Reviewer confidence

Very confident
[Post rebuttal] After reading the author’s rebuttal, state your overall opinion of the paper if it has been changed

N/A
[Post rebuttal] Please justify your decision

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Review #3

Please describe the contribution of the paper

The contribution of this paper is to propose a new method for calculating brain flexibility using dynamic functional connectivity harmonics. Previous flexibility studies typically used community detection algorithms, which are affected by free parameters and computational limitations. With this method, the flexibility and stability of the domain can be calculated, leading to a better understanding of potential mechanisms for learning, development, and mental illness. In addition, this study also proposes a new method for quantitatively representing brain flexibility with a simpler, more reliable, and more interpretable quantitative graph than traditional methods.
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 main strengths of this paper are its novel formulation and its practical application in studying cognitive flexibility. The use of dynamic functional connectome harmonics is a new method and provides a simpler and more reliable way of computing flexibility than traditional methods. This allows for a more comprehensive understanding of the brain’s propensity for changing network allegiance, which has important implications for learning, development, and psychiatric disorders. Another strength of this paper is that the authors evaluated their method using multiple time windows, demonstrating the stability of their formulation across different timescales. This speaks to the robustness of their approach. Furthermore, the authors demonstrated the clinical relevance of their method by mapping cortical flexibility at a vertex resolution and comparing their results with those obtained using traditional methods. The good agreement between the two methods, with only slight differences in specific regions, indicates the clinical feasibility of this novel approach. Overall, this paper presents a significant contribution to the field of neuroscience by providing a new lens through which to examine cognitive flexibility, and by offering a simpler and more reliable method for computing flexibility that has potential clinical applications.
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.

One weakness of this paper is the limited sample size. The study only included 44 participants, which is relatively small for a neuroimaging study. This may affect the generalizability of the findings and the reliability of the results. Another weakness is the lack of validation against behavior or external criteria. The authors did not test the relationship between dynamic functional connectome harmonics metrics and external measures of cognitive flexibility or other cognitive domains. This could provide more evidence for the validity of the approach. Additionally, while the authors showed that their method can reveal meaningful patterns of brain flexibility, they did not demonstrate how this information can be utilized in a clinical context to improve diagnosis or treatment. Future studies should explore the clinical implications of this new method.
Please rate the clarity and organization of this paper

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

This paper only uses the method on one dataset with a small sample size, and the repeatability needs improvement.
Please provide detailed and constructive comments for the authors. Please also refer to our Reviewer’s guide on what makes a good review: https://conferences.miccai.org/2023/en/REVIEWER-GUIDELINES.html

Suggest adding one or two publicly available datasets for application, and use various tools such as Atlas to increase reproducibility.
Rate the paper on a scale of 1-8, 8 being the strongest (8-5: accept; 4-1: reject). Spreading the score helps create a distribution for decision-making

5
Please justify your recommendation. What were the major factors that led you to your overall score for this paper?

The research content is relatively novel and interesting, but the sample size is small, and the reproducibility needs to be improved.
Reviewer confidence

Somewhat confident
[Post rebuttal] After reading the author’s rebuttal, state your overall opinion of the paper if it has been changed

N/A
[Post rebuttal] Please justify your decision

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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.

This paper proposed a simple but effective framework to evaluate the functional connectome harmonics in a dynamic manner via the commonly-used sliding-window approach based on the HCP dataset. The key strength includes that it is well-organized and clear written, with sufficient experiments, interpretation and discussion, as recognized by all reviewers. Although there are several weaknesses such as limited sample size and lack of independent dataset, unclearness of potential future clinical applications, etc, the meta-reviewer agrees with all reviewers that this paper has provided a relatively novel and reliable study for brain mapping. The authors should consider integrating all reviewer comments into the final paper.

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Dynamic Functional Connectome Harmonics