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Abstract

Higher-order properties of functional magnetic resonance imaging (fMRI) induced connectivity have been shown to unravel many exclusive topological and dynamical insights beyond pairwise interactions. Nonetheless, whether these fMRI-induced higher-order properties play a role in disentangling other neuroimaging modalities’ insights remains largely unexplored and poorly understood. In this work, by analyzing fMRI data from the Human Connectome Project Young Adult dataset using persistent homology, we discovered that the volume-optimal persistence homological scaffolds of fMRI-based functional connectomes exhibited conservative topological reconfigurations from the resting state to attentional task-positive state. Specifically, while reflecting the extent to which each cortical region contributed to functional cycles following different cognitive demands, these reconfigurations were constrained such that the spatial distribution of cavities in the connectome is relatively conserved. Most importantly, such level of contributions covaried with powers of aperiodic activities mostly within the theta-alpha (4-12 Hz) band measured by magnetoencephalography (MEG). This comprehensive result suggests that fMRI-induced hemodynamics and MEG theta-alpha aperiodic activities are governed by the same functional constraints specific to each cortical morpho-structure. Methodologically, our work paves the way toward an innovative computing paradigm in multimodal neuroimaging topological learning. The code for our analyses is provided in https://github.com/ngcaonghi/scaffold_noise.

Links to Paper and Supplementary Materials

Main Paper (Open Access Version): https://papers.miccai.org/miccai-2024/paper/3894_paper.pdf

SharedIt Link: pending

SpringerLink (DOI): pending

Supplementary Material: https://papers.miccai.org/miccai-2024/supp/3894_supp.pdf

Link to the Code Repository

https://github.com/ngcaonghi/scaffold_noise

Link to the Dataset(s)

db.humanconnectome.org

BibTex

@InProceedings{Ngu_Volumeoptimal_MICCAI2024,
        author = { Nguyen, Nghi and Hou, Tao and Amico, Enrico and Zheng, Jingyi and Huang, Huajun and Kaplan, Alan D. and Petri, Giovanni and Goñi, Joaqúın and Kaufmann, Ralph and Zhao, Yize and Duong-Tran, Duy and Shen, Li},
        title = { { Volume-optimal persistence homological scaffolds of hemodynamic networks covary with MEG theta-alpha aperiodic dynamics } },
        booktitle = {proceedings of Medical Image Computing and Computer Assisted Intervention -- MICCAI 2024},
        year = {2024},
        publisher = {Springer Nature Switzerland},
        volume = {LNCS 15003},
        month = {October},
        page = {pending}
}

Reviews

Review #1

Please describe the contribution of the paper

This paper explores the role of higher-order properties of fMRI in understanding brain dynamics from a topological perspective. By analyzing fMRI data using persistent homology, the researchers found that the topological structures of functional connectomes showed conservative reconfigurations from resting state to attentional task-positive state. The study also found a correlation between the topological features and aperiodic activities within the theta-alpha band measured by magnetoencephalography (MEG), suggesting a link between these two aspects of brain function.
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.
1. This paper provides a novel perspective in investigating the relationship between fMRI and MEG data. The motivation is also clearly stated, with the potential of topological data analysis to uncover higher-order properties that haven’t been thoroughly explored before.
2. The experiment is well-designed with significant statistical evidence supporting the results, which adds to the reliability of the findings.
3. The clear and effective visualizations contribute to the overall accessibility and comprehensibility of the pape
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.

My major concern is that the paper presumes a significant amount of prior knowledge from the audience, and some crucial technical details are omitted, which could potentially make the study difficult to understand and make it less accessible to broader audience. Please see details in my constructive comments below.
Please rate the clarity and organization of this paper

Good
Please comment on the reproducibility of the paper. Please be aware that providing code and data is a plus, but not a requirement for acceptance.

The submission does not mention open access to source code or data but provides a clear and detailed description of the algorithm to ensure reproducibility.
Do you have any additional comments regarding the paper’s reproducibility?

The dataset is specified, but the source code is not mentioned.
Please provide detailed and constructive comments for the authors. Please also refer to our Reviewer’s guide on what makes a good review. Pay specific attention to the different assessment criteria for the different paper categories (MIC, CAI, Clinical Translation of Methodology, Health Equity): https://conferences.miccai.org/2024/en/REVIEWER-GUIDELINES.html
Please revise technical descriptions significantly in the paper.
1. Some of the important details are omitted. Important details are currently missing. For instance, the key term “scaffold” is never formally defined. Based on the method section, it appears to refer to a matrix where each entry represents a weighted edge, with weights derived from the persistence information extracted from the graph. Including such definitions would enhance clarity.
2. Additionally, the explanation of how persistent homology is computed in section 2.1 is quite abstract. Crucial details, such as the computation of the simplicial complex $\Delta(G)$ from the FC (G), and how $H_i(\Delta(G))$ becomes a function of $r$, are not provided. The meaning of the Betti number, another important term, is also not mentioned.
3. The paper refers to the use of barcodes to visualize persistent homology, but there is no figure to demonstrate what a real barcode looks like. Given all these omissions, it would be almost impossible for an audience without TDA knowledge to understand the framework. I recommend adding necessary details, omitting some technical terms that are used in the subsequent content (e.g., Betti number), and providing a simple example (perhaps a simple graph) with figures to help readers understand what persistent homology really is.
- Minor: in page 8, second para, “Figures 3.A and 4.B …” -> “Figures 3.A and 3.B …”, “Figures 3.C to 4.G … “ -> “Figures 3.C to 3.G … “
Rate the paper on a scale of 1-6, 6 being the strongest (6-4: accept; 3-1: reject). Please use the entire range of the distribution. Spreading the score helps create a distribution for decision-making

Weak Accept — could be accepted, dependent on rebuttal (4)
Please justify your recommendation. What were the major factors that led you to your overall score for this paper?

The strengths and weaknesses of this paper are both obvious. Overall, the strengths outweigh the weaknesses. I would recommend the paper to be accepted with a revision of technical descriptions.
Reviewer confidence

Confident but not absolutely certain (3)
[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

N/A

Review #2

Please describe the contribution of the paper

The paper presents a new perspective on the relationship between fMRI-induced higher-order properties and MEG signals, providing insights into the conservative topological reconfiguration of functional connectomes and its association with theta-alpha aperiodic power. These findings contribute to the understanding of brain function and the development of innovative computing paradigms in multimodal neuroimaging topological learning.
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 paper’s main strengths lie in its novel formulation using persistent homology, original use of multimodal neuroimaging data, strong evaluation of topological reconfiguration consistency, novel application in understanding brain function, and innovative computing paradigm in multimodal neuroimaging topological learning. These strengths make the paper a significant contribution to the field of medical image computing and brain network analysis.
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.

the main weaknesses of the paper include a limited discussion of the relationship between fMRI and MEG signals, the accessibility of persistent homology concepts, a limited exploration of alternative connectivity measures, a lack of task-specific effects and analysis of other frequency bands, and a limited discussion of biological mechanisms and clinical implications. Addressing these weaknesses by providing more context, improving accessibility, exploring alternative methods, and expanding the discussion would further strengthen the paper’s contributions and impact.
Please rate the clarity and organization of this paper

Good
Please comment on the reproducibility of the paper. Please be aware that providing code and data is a plus, but not a requirement for acceptance.

The submission does not mention open access to source code or data but provides a clear and detailed description of the algorithm to ensure reproducibility.
Do you have any additional comments regarding the paper’s reproducibility?

N/A
Please provide detailed and constructive comments for the authors. Please also refer to our Reviewer’s guide on what makes a good review. Pay specific attention to the different assessment criteria for the different paper categories (MIC, CAI, Clinical Translation of Methodology, Health Equity): https://conferences.miccai.org/2024/en/REVIEWER-GUIDELINES.html

The main strengths of the paper are:

The novel formulation using persistent homology to analyze fMRI functional connectomes and their topological reconfigurations between resting state and attentional task-positive states. The original use of multimodal neuroimaging data from the Human Connectome Project, combining fMRI and MEG to investigate the relationship between hemodynamic and electrophysiological signals in the brain.

However, there are a few areas that could be improved to strengthen the paper:

The introduction would benefit from a more detailed discussion of the current understanding of the relationship between fMRI and MEG signals, as well as a clearer explanation of how your approach differs from or complements existing methods. This would help contextualize your study’s contributions within the existing literature. The methods section could be made more accessible to readers less familiar with topological data analysis by providing a more intuitive explanation of persistent homology concepts and the computation of volume-optimal persistent cycles. This would increase the paper’s reach and impact.
Rate the paper on a scale of 1-6, 6 being the strongest (6-4: accept; 3-1: reject). Please use the entire range of the distribution. Spreading the score helps create a distribution for decision-making

Weak Accept — could be accepted, dependent on rebuttal (4)
Please justify your recommendation. What were the major factors that led you to your overall score for this paper?

Novelty and potential impact: The paper introduces a novel approach using persistent homology and volume-optimal persistent cycles to analyze fMRI functional connectomes and their topological reconfigurations. The findings provide new insights into the relationship between fMRI-induced higher-order properties and MEG aperiodic activities, which could have implications for understanding brain function and dysfunctions. The innovative computing paradigm in multimodal neuroimaging topological learning presented in this work has the potential to be applied to other datasets and research questions.

Methodological strengths:The authors utilize a comprehensive dataset from the Human Connectome Project, combining fMRI and MEG data to investigate the relationship between hemodynamic and electrophysiological signals in the brain.

The paper would benefit from exploring alternative functional connectivity measures and their potential impact on the results.
Reviewer confidence

Confident but not absolutely certain (3)
[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

N/A

Review #3

Please describe the contribution of the paper

A new manner of using and interpreting persistence homology in neuroimaging analysis and in particular to relate two neuroimaging modalities.

The authors do claim as the contribution the leveraging of persistence homology to associate fMRI-based connectivity with MEG-probed activity.
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 innovative way in which the persistence homology is used to bring together multimodal data without an explicit data fusion.
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.

Although the methods existed previously, but their use has not been validated the intended application and this paper does not make any effort to address such validation.
Please rate the clarity and organization of this paper

Good
Please comment on the reproducibility of the paper. Please be aware that providing code and data is a plus, but not a requirement for acceptance.

The submission does not mention open access to source code or data but provides a clear and detailed description of the algorithm to ensure reproducibility.
Do you have any additional comments regarding the paper’s reproducibility?

N/A
Please provide detailed and constructive comments for the authors. Please also refer to our Reviewer’s guide on what makes a good review. Pay specific attention to the different assessment criteria for the different paper categories (MIC, CAI, Clinical Translation of Methodology, Health Equity): https://conferences.miccai.org/2024/en/REVIEWER-GUIDELINES.html

• Validation about how this method can support this kind of interpretation is missing • Statistical analysis is missing. • Comparison with alternatives for concurrent validity is missing. • The 1-correlation pseudometric projects to the hypersphere shown in Fig 1. However, from previous literature e.g. [Friston, 1996], and given the very nature of how Pearson coefficient work, I would have expected that the poles of the sphere to be more densely populated with the equator almost empty (and indeed, from personal experience this is why I’ve seen in my projections). Here, however, the hypersphere manifold is populated more uniformly. Has any kind of regularization been applied (and not reported)? If not, can the authors comment about this intriguing outcome? • At the end of section 2.3 the authors move from the filtration manifold to the “snapshot” given by a graph where node centrality (in two fashions) is calculated. This I reckon helps to summarise the information but the price to pay is a big loss of it. Is there any future plans to interpreted the full filtration as a whole. • The explicit maths linking the MRI derived persistent cycles and the MEG retrieved spectra is not given. I understand this is simple loci based, but obviously MRI and MEG do not share the same voxel space. If I got it right the data from these two modalities are linked by beamprojecting in MEG and labelling in MRI to a common anatomical space. Is that correct? • Fig 2C and F is mention to represent hemisphere asymmetry but it is not clear to me what exactly is being computed here. • The specific claim that this high-order relationship “cannot be described …in terms of pairwise interactions” is mathematically incorrect (It is ALWAYS possible to decompose n-ary relations into binary ones without any loss), but I do agree that this high-order point of view is far more convenient (feasibility vs convenience). Just rephrase for precision.
Rate the paper on a scale of 1-6, 6 being the strongest (6-4: accept; 3-1: reject). Please use the entire range of the distribution. Spreading the score helps create a distribution for decision-making

Accept — should be accepted, independent of rebuttal (5)
Please justify your recommendation. What were the major factors that led you to your overall score for this paper?

In my opinion, the big selling point is not the method itself as all the pieces already existed and moreover, persistence homology has now been used many times for neuroimaging analysis, but the way it is applied to “fuse” fMRI and MEG data to gain holistic insight of how activity leads connectivity.
Reviewer confidence

Confident but not absolutely certain (3)
[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

N/A

Author Feedback

N/A

Meta-Review

Meta-review not available, early accepted paper.

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Volume-optimal persistence homological scaffolds of hemodynamic networks covary with MEG theta-alpha aperiodic dynamics

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