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Authors

Sukesh Kumar Das, Anil K. Sao, Bharat Biswal

Abstract

Blood Oxygen Level-Dependent (BOLD) signal changes in functional magnetic resonance imaging (fMRI) measures neuronal activities blurred by hemodynamic response function (HRF) and hence may not be reliable to estimate functional connectivity (FC). Several methods have been attempted to estimate the neuronal activity signal (NAS) from the observed BOLD signal. Using this as a blind source separation problem, these methods assume a parametric model of HRF. But it is not clear if these models accurately reflect the biophysical process. In this paper, we have proposed an approach based on a homomorphic filter (HMF) to deconvolve NAS from resting state fMRI (rs-fMRI) time course. It exploits the hypothesis that the HRF has predominantly low frequency energy in comparison to the NAS. Hence, by choosing an appropriate value of cutoff quefrency with the help of thresholded BOLD signal after HMF, HRF can be suppressed from observed BOLD signal to get an estimate of NAS. The estimated NAS, in the framework of dictionary learning (DL), is able to produce subtle resting state networks (RSNs) in comparison with the existing blind deconvolution (BD) method. The Jaccard similarity distance between RSNs by taking random samples and the entire subjects underpins the robustness of the estimated RSNs. Another quantitative comparison has also been drawn to show the efficacy of the HMF in the estimation of NAS using the maximum normalized cross-correlation coefficient (MNCC) distribution for different RSNs.

Link to paper

DOI: https://doi.org/10.1007/978-3-030-87234-2_58

SharedIt: https://rdcu.be/cyl84

Link to the code repository

N/A

Link to the dataset(s)

N/A

Reviews

Review #1

Please describe the contribution of the paper

This paper has adopted the homomorphic filtering to estimate the neural activity underlying the rs-fMRI. The resultant activity patterns can be used to reconstruct the resting-state functional networks.
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 homomorphic filtering is not the invention from this paper, however, this is the first time that this method was applied for rs-fMRI analysis. There is potential extension of the proposed method for multimodal analysis, like linking the EEG and fMRI signal.
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.

Becaused of no ground truth, it is hard to validate the estimated neural activity. On the other hand, more technical detail will be desirable.
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

The method has been tested on one single dataset of 40 subjects, however, I suspect that the result will be quite sensitive to scan parameters.
Please provide detailed and constructive comments for the authors. Please also refer to our Reviewer’s guide on what makes a good review: https://miccai2021.org/en/REVIEWER-GUIDELINES.html

The authors should clafiry the parameters for the band pass filtering in the fMRI preprocessing as it will be quite related to the data filtering in the proposed method.
Please state your overall opinion of the paper

accept (8)
Please justify your recommendation. What were the major factors that led you to your overall score for this paper?

The novelty of this paper was compromised because the homomorphic filtering was not a new method proposed in this paper. The but approach of the method is interesting.
What is the ranking of this paper in your review stack?

2
Number of papers in your stack

3
Reviewer confidence

Very confident

Review #2

Please describe the contribution of the paper

This paper proposes a homomorphic filtering based method to extract neuronal activity signals (NAS) from BOLD time series. It is essentially done by high pass liftering in the cepstrum domain. The method does not posit stringent parametric assumptions on the decomposition of BOLD signals, and avoids the issue of naive high pass filtering due to the overlap in frequency of NAS and the low-frequency hemodynamic response. Estimation of functional connectivity is then done by dictionary learning. The method is a generalization of the previous application in task fMRI data. Performance is evaluated in reproducibility scores of Jaccard similarity or MNCC.
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.

This paper is an interesting generalization of the previous application homomorphic filtering on task fMRI to voxel-level resting-state data. It is interesting to note that, despite that one loses the similarity in signal for task-related regions in task fMRI, the method outperforms state-of-the-art parametric methods. The proposed method can lead to potentially more reliable and reproducible estimation of functional connectivity.
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 direct demonstration of the potential clinical impact, although authors had showed promising robustness of the resulted estimations. For example, predictivenss of certain connectivity biomarkers might have been improved due to the better estimation of NAS.
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

The code is not available but the procedure of estimation is described with details for 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://miccai2021.org/en/REVIEWER-GUIDELINES.html

Publicly available datasets such as Human Connectome Project might help improve both the sample size and the potential applications of the method.
Please state your overall opinion of the paper

accept (8)
Please justify your recommendation. What were the major factors that led you to your overall score for this paper?

I recommend based on the potential significance of the work. Functional connectivity has been a topic of great interest, but current estimation strategies usually are sub-optimal in terms of the reliability. The proposed method provides a reliable and nonparametric alternative estimation method.
What is the ranking of this paper in your review stack?

2
Number of papers in your stack

5
Reviewer confidence

Confident but not absolutely certain

Review #3

Please describe the contribution of the paper

Based on homomorphic filtering, this paper designed a new method to estimate NAS from the BOLD rs-fMRI data without assuming any parametric model of HRF. In this method, the BOLD time courses can be represented as a linear sum of HRF and NAS in the cepstrum domain. By choosing a suitable Qc, NAS and HRF can be separated.
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.

This paper can estimate NAS from the BOLD rs-fMRI data without assuming any parametric model of HRF. By representing BOLD time courses in cepstrum domain, the multiplicative relationship between HRF and NAS is changed to be additive.
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.
1. The explanation of the results is not enough. For example, for figure 2, the computed MNCC=0.544, is the results good enough? It is better to give some comparison about the similar measures in existing work? The same problem is existing for section 4.2, the mean of the MNCC for BD and HMF methods for the given six RSNs.
2. In Section1, the paper mentioned “intra and inter-subject variability of HRF is also a serious concern in the deconvolution of NAS from rs-fMRI”, and in Section 4, ther is a sentence “The Qc, for the time course is 3 at DFT length of 158.”. It seems that to solve the problem of intra and inter-subject variability, the Qc seems to be calculated for each voxel of each subject. If Qc is calculated in this way, it should be computationally expensive. If the Qc is not calculated in this way, and all voxels share the same Qc, how to solve the problem of intra and inter-subject variability is not clear.
3. The format of the paper is chaos.
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 will not release data and code.
Please provide detailed and constructive comments for the authors. Please also refer to our Reviewer’s guide on what makes a good review: https://miccai2021.org/en/REVIEWER-GUIDELINES.html
1. The format of the paper is chaos. For example, the space between paragraphs in Section1 is larger than other parts. Some paragraphs are indented (Section1), some are not (Section 5). I suggested a thorough revision to address this aspect.
2. For the results part, it is better to give more explanation about the results.
Please state your overall opinion of the paper

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

Firstly, I pay attention to whether the topic that the paper aim to study is meaningful. Secondly, I pay attention to if this paper has a comprehensive understanding of the topic including the analysis of problems and advantages and disadvantages of the existing methods. Thirdly, I will pay attention to the method proposed by this paper, if the design logic is clear and if the evaluation methods are reasonable and comprehensive.

Specifically, for this paper, the topic of this paper is meaningful. However, the format of this paper is chaos and I also have some concerns about the results parts which are listed in block4 and block7.
What is the ranking of this paper in your review stack?

2
Number of papers in your stack

8
Reviewer confidence

Confident but not absolutely certain

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 presents an interesting and novel method that uses homomorphic filtering to estimate neuronal activity signals. All three reviewers gave high scores to this paper regarding the importance of the topic, novelty, and method. The authors need to discuss the sensitivity to scan parameters (raised by Reviewer#4) in the final version.
What is the ranking of this paper in your stack? Use a number between 1 (best paper in your stack) and n (worst paper in your stack of n papers).

1

Author Feedback

The only scanning parameter, repetition time (TR) can effect the HMF method. The smaller TR value is always a better option for HMF. However moderate TR values (2 - 3 sec) are still fine with the method. In our experiment, the scanning TR is 2sec.

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Estimation of spontaneous neuronal activity using homomorphic filtering