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Abstract
Delineating the normative developmental profile of functional connectome is important for both standardized assessment of individual growth and early detection of diseases. However, functional connectome has been mostly studied using functional connectivity (FC), where undirected connectivity strengths are estimated from statistical correlation of resting-state functional MRI (rs-fMRI) signals. To address this limitation, we applied regression dynamic causal modeling (rDCM) to delineate the developmental trajectories of effective connectivity (EC), the directed causal influence among neuronal populations, in whole-brain networks from infancy to adolescence (0-21 years old) based on high-quality rs-fMRI data from Baby Connectome Project (BCP) and Human Connectome Project Development (HCPD). Analysis with linear mixed model demonstrates significant age effect on the mean nodal EC which is best fit by a “U” shaped quadratic curve with minimal EC at around 2 years old. Further analysis indicates that five brain regions including the left and right cuneus, left precuneus, left supramarginal gyrus and right inferior temporal gyrus have the most significant age effect on nodal EC (p < 0.05, FDR corrected). Moreover, the frontoparietal control (FPC) network shows the fastest increase from early childhood (1-2 years) to adolescence (6-21 years) followed by the visual and salience networks. Our findings suggest complex nonlinear developmental profile of effective connectivity from infancy to adolescence, which may reflect dynamic structural and functional maturation during this critical growth period.
Links to Paper and Supplementary Materials
Main Paper (Open Access Version): https://papers.miccai.org/miccai-2024/paper/3736_paper.pdf
SharedIt Link: pending
SpringerLink (DOI): pending
Supplementary Material: N/A
Link to the Code Repository
N/A
Link to the Dataset(s)
N/A
BibTex
@InProceedings{Li_Development_MICCAI2024,
author = { Li, Guoshi and Thung, Kim-Han and Taylor, Hoyt and Wu, Zhengwang and Li, Gang and Wang, Li and Lin, Weili and Ahmad, Sahar and Yap, Pew-Thian},
title = { { Development of Effective Connectome from Infancy to Adolescence } },
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 applies regression dynamic causal modeling (rDCM) to map the developmental trajectories of effective connectivity (EC) in whole-brain networks from infancy to adolescence (0-22 years old) using fMRI data. The authors use the Baby Connectome Project (BCP) and Human Connectome Project datasets. They demonstrate significant age effect on the nodal EC and also identify brain regions that change from infancy to adolescence.
- 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 application of the regression dynamic causal modeling method to longitudinal data from infancy to adolescence is sound.
- 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.
This is an applications paper.
No new methods or novel ideas are proposed.
A global measure of connectivity (net nodal effective connectivity) is used. Since this is a global measure, and the dataset spans a large age range in childhood, one is expected to see significant changes when comparing brains for pairs of age ranges.
The trajectory of development is also plotted for the same global measure. While significant results are obtained, the scientific novelty is also missing.
It was interesting that no significant sex effects were found on the net nodal effective connectivity. This is not necessarily a major weakness, but other studies have observed sex effects in this population (but they have used static approaches).
- 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
This is a valuable dataset and the authors raise somewhat critical hypotheses. This will become a good paper for an application-oriented conference or a journal upon expanding on the results.
- 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
Reject — should be rejected, independent of rebuttal (2)
- Please justify your recommendation. What were the major factors that led you to your overall score for this paper?
There was a lack of novelty of methods as well as a convincing clinical application.
- Reviewer confidence
Very confident (4)
- [Post rebuttal] After reading the author’s rebuttal, state your overall opinion of the paper if it has been changed
Strong Reject — must be rejected due to major flaws (1)
- [Post rebuttal] Please justify your decision
After going through the author’s feedback, my concerns about novelty still stand. The authors claim that the study represents the first attempt to chart effective connectome from infancy to adolescence. There have been several sophisticated approaches for mapping effective connectivity. The use of EC as the authors do here is not a representative feature for looking at the connectome, especially as it’s a global measure, and thus does not take advantage of the rich dataset described here.
If this was a novel application, then a lot more rigor of technical methods (even if they are not novel) is required here. This was missing from the paper.
The authors comment that they intend to make the model source code and data accessible to the public if the paper is accepted for publication. However, all the methods used in the paper have been previously described elsewhere, thus there is no benefit for releasing the model source code.
Review #2
- Please describe the contribution of the paper
This paper applies an existing method, regression dynamic causal modeling (rDCM) to calculate effective connectivity (EC) metrics within a set of resting state fMRI scans from two large studies that collectively spanned the range from infancy to young adulthood (age 22). The main finding is that there is a U-shaped association between EC and age, with EC being higher in infants, lower in toddlers and young children, then higher again in adolescence and young adulthood.
- 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 strength is the relatively novel application. Classical functional connectivity (FC) metrics have been applied to this problem setting before, but apparently not EC metrics, which provide potentially different insights about the time course of brain development. Applying specifically a brain-wide EC method is a strength, as most other EC methods allow modeling of more complex causal relationships among regions, but at a cost of requiring a priori choices about which brain regions to analyze vs skip.
- 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 question— I’m not sure if it is a weakness— is whether a paper that applies a well-established method in a standard fashion to a well-established data set, and focuses on interesting neuroscience findings rather than novel computational methods, is of sufficient interest to the MICCAI community. It may well be of interest, but we should be clear that neither the method nor the data are the novel part— the neuroscience findings are the novel part.
One genuine limitation is that the younger-age scans were from one study, and the older-age scans were from a separate one. That leads us to wonder whether age differences in EC reflect inter-scanner differences or other inter-study differences in protocol between the young child study (BCP) and the older child/ young adult study (HCP-D).
While rDCM can handle a large number of inter-regional EC relationships, it is apparently cannot handle all 100% of such relationships, and therefore a priori culling of such relationships was required. Unfortunately, this culling was based on functional, not effective, connectivity values. There is no reason to believe that selecting high FC connections has a side-effect of selecting high EC connections. This culling could well have culled out many high-EC links that are of scientific interest. As an aside, I do not believe the manuscript stated how many of those links were actually culled, and how many remained in the final analysis.
- Please rate the clarity and organization of this paper
Excellent
- 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 data sets analyzed are in the public domain, which should ease reproducibility.
- 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
See above.
- 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 neuroscience findings are of interest to the neuroscience community. It is all very clearly presented. The only reason I pause is that this is not a novel method and not a novel data set or problem setting; it is a novel application of an established method to an established data set. I just wonder if that is of sufficient interest to the MICCAI community.
- Reviewer confidence
Very confident (4)
- [Post rebuttal] After reading the author’s rebuttal, state your overall opinion of the paper if it has been changed
Weak Accept — could be accepted, dependent on rebuttal (4)
- [Post rebuttal] Please justify your decision
I think the reviewers are largely convergent about our concerns. The main one, which authors do not dispute, is that neither the method nor data set are novel, and therefore it is unclear whether it is within the MICCAI scope. That question would seem to be the one critical one for the acceptance decision. Other concerns about possible biases involved in combining two disjoint data sets, and whether some connections are culled or not, remain regardless of author replies, but they aren’t critical problems that would warrant rejection.
Review #3
- Please describe the contribution of the paper
The study utilizes regression dynamic causal modeling to map the development of effective connectomes in a longitudinal dataset spanning from infancy to adolescence. This provides a more detailed understanding of directed causal influences in brain development.
- 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 approach allows for the exploration of directed connections across the brain, providing insights that are more mechanistic compared to traditional functional connectivity analyses.
- The findings have practical implications, potentially aiding in the early detection of developmental disorders.
- 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 study primarily uses two datasets, which might limit the generalizability of the findings.
- While the study provides valuable insights into some brain networks, it may not cover all potential networks of interest.
- Please rate the clarity and organization of this paper
Satisfactory
- 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 provide sufficient information for 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
please see weakness section of the review
- 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?
This work delineated the developmental trajectory of effective connectome from infancy to adolescence for the first time, which may be in the interests of the community.
- Reviewer confidence
Not confident (1)
- [Post rebuttal] After reading the author’s rebuttal, state your overall opinion of the paper if it has been changed
Weak Accept — could be accepted, dependent on rebuttal (4)
- [Post rebuttal] Please justify your decision
Thanks for explaination. I prone to maintain my original recommendation
Author Feedback
One major concern of Reviewer #3 and Reviewer #5 is that the study (method) lacks novelty. But as Reviewer #3 pointed out, the novelty of this study lies in the novel application of an established method to established datasets. Previously, no studies have attempted to chart the effective connectome from infancy to young adulthood. Our work bridges this gap and reveals important insights about the nonlinear growth trajectories of effective connectivity (EC) during these critical brain development stages, which could serve to benchmark individual brain development and aid in the early detection of developmental disorders. Therefore, our study represents a novel investigation that is of great interest to the human connectome and brain development communities.
Reviewer #3 and Reviewer #4 have concerns about the utilization of two separate datasets, whose inter-scanner and inter-study differences may lead to the observed EC differences across age. Though such concerns are reasonable, we believe that the inter-study differences may not explain the developmental changes in EC. The BCP dataset covers the age range from 0 to 5 years old while the HCP-Development dataset spans the age range from 5 to 22 years old. As shown in Fig. 1D and Fig. 2, there is a significant difference within the same dataset (e.g., from neonatal to late infancy) and there may not be significant differences between age groups of two different datasets (e.g., neonatal and late childhood). Also, we observe a graduate increase in EC from early childhood to adolescence, which more likely reflects the age difference than inter-study differences.
Reviewer #3 has concerns about the potential side effects of connection pruning of rDCM. We inferred EC using rDCM for resting-state fMRI (Frassle et al., Human Brain Mapp. 2021) and did not use sparsity constraints to remove connections (Frassle et al., NeuroImage, 2018). Though as classical DCM, rDCM regularizes the estimates of individual connection strengths by introducing shrinkage prior of variance that depends on the number of regions, it does not remove specific connections. Thus, an analysis of how priori culling affects EC estimation is not necessary.
Reviewer #5 has concerns about missing scientific novelty. As mentioned above, our study represents the first attempt to chart effective connectome from infancy to adolescence, which reveals novel insights into the nonlinear nature of early brain development. Specifically, we find that rather than following a linear trajectory, the EC decreases first from neonatal to late infancy and early childhood before increasing to adolescence. Such a normative EC reference chart could be used to quantify normal brain development and detect developmental disorders at an early stage. Therefore, we believe our study has sufficient scientific novelty to be published by MICCAI.
Reviewer #4 has concerns that the study may not cover all potential networks of interest. As shown in the paper, we have covered all the networks in the whole brain.
Reviewer #4 has additional concerns about reproducibility. As Reviewers #3 and #5 commented, the manuscript has provided a clear and detailed description of the algorithm to ensure reproducibility. We intend to make the model source code and data accessible to the public if the paper is accepted for publication.
Meta-Review
Meta-review #1
- After you have reviewed the rebuttal and updated reviews, please provide your recommendation based on all reviews and the authors’ rebuttal.
Accept
- Please justify your recommendation. You may optionally write justifications for ‘accepts’, but are expected to write a justification for ‘rejects’
This is an interesting application paper that tries to infer the trajectory of effective connectivity (EC) from infancy through young adulthood. One concern, which is acknowledged but insufficiently addressed in the rebuttal, is the use of two different cohorts across. Essentially, the results confound cohort and age differences, and without additional information, I do not believe it is possible to disambiguate the two. R5 also notes that the experimental rigor and validation is weak for an application paper. Overall, I would rate this as a borderline paper but would lean towards acceptance given the developmental application.
- What is the rank of this paper among all your rebuttal papers? Use a number between 1/n (best paper in your stack) and n/n (worst paper in your stack of n papers). If this paper is among the bottom 30% of your stack, feel free to use NR (not ranked).
This is an interesting application paper that tries to infer the trajectory of effective connectivity (EC) from infancy through young adulthood. One concern, which is acknowledged but insufficiently addressed in the rebuttal, is the use of two different cohorts across. Essentially, the results confound cohort and age differences, and without additional information, I do not believe it is possible to disambiguate the two. R5 also notes that the experimental rigor and validation is weak for an application paper. Overall, I would rate this as a borderline paper but would lean towards acceptance given the developmental application.
Meta-review #2
- After you have reviewed the rebuttal and updated reviews, please provide your recommendation based on all reviews and the authors’ rebuttal.
Reject
- Please justify your recommendation. You may optionally write justifications for ‘accepts’, but are expected to write a justification for ‘rejects’
The technical novelty is limited and this is mainly an applications paper. I don’t think this paper is sufficiently matches interests of MICCAI audience that tends to be more technically oriented. There are other criticisms such as confounding factors in the cohorts. The application itself is interesting and the paper can be revised and send to a suitable journal.
- What is the rank of this paper among all your rebuttal papers? Use a number between 1/n (best paper in your stack) and n/n (worst paper in your stack of n papers). If this paper is among the bottom 30% of your stack, feel free to use NR (not ranked).
The technical novelty is limited and this is mainly an applications paper. I don’t think this paper is sufficiently matches interests of MICCAI audience that tends to be more technically oriented. There are other criticisms such as confounding factors in the cohorts. The application itself is interesting and the paper can be revised and send to a suitable journal.
Meta-review #3
- After you have reviewed the rebuttal and updated reviews, please provide your recommendation based on all reviews and the authors’ rebuttal.
Accept
- Please justify your recommendation. You may optionally write justifications for ‘accepts’, but are expected to write a justification for ‘rejects’
The paper performs an analysis of effective connectome from infancy to adolescense. This is an application paper that does not bring technical contributions. Instead, the contribution is the analysis of the effective connectivity from infacny to adolescence. There are concerns regarding methodological rigor, especially as it pertains to the combination of the two datasets. However, most of the reviewers lean to towards accepting the paper. In line with these reviewers, I believe that this is an interesting imaging-related study, which may contribute to improving our understanding of the early development of functional connectome
- What is the rank of this paper among all your rebuttal papers? Use a number between 1/n (best paper in your stack) and n/n (worst paper in your stack of n papers). If this paper is among the bottom 30% of your stack, feel free to use NR (not ranked).
The paper performs an analysis of effective connectome from infancy to adolescense. This is an application paper that does not bring technical contributions. Instead, the contribution is the analysis of the effective connectivity from infacny to adolescence. There are concerns regarding methodological rigor, especially as it pertains to the combination of the two datasets. However, most of the reviewers lean to towards accepting the paper. In line with these reviewers, I believe that this is an interesting imaging-related study, which may contribute to improving our understanding of the early development of functional connectome