Venue: MIDL 2022 Conference

Reviews:

final_score: rating: 3: Borderline
review_content: summary: The authors evaluated the influence of adding an inductive bias based on the connections between ROIs in a GCN compared to a fully FCNN without connectivity information. To evaluate this, they predictied age using brain structural MRI data.
They used 2 different methods to generate the adjacency matrix that connected the different ROIs, and assessed if the inductive bias introduced in the GCN had influence at two levels of the GCN: message passing and global pooling.
- They found a mild non-significant improvement in the age prediction taks when using the global pooling compared to the FCNN baseline.
- Results are much worse when permutation invariant global pooling is used
- ROI-aware message passing seems irrelevant compared to permuation equivariant 
strengths: 
- The application of GCNs in brain parcellations is interesting, the results can be useful as a baseline for future network architectures. In particular, the fact that the authors show that "permutation invariance is not a desirable property for a global pooling layer when working with ROI-graphs".

- The experiments were performed on real data from UK Biobank.

- Intereseting ROI-aware implementation of message passing.

- Detailed explanation of the metrics and graph-based methods. 
weaknesses: - I think the papers falls a little short in the methods comparison. Specially, I think adding a more recent tools to generare similarity matrices like similarity network fusion (https://github.com/rmarkello/) would have been interesting. 
- Despite the detailed description of the graph-based metrics, the authors do not explain how the training strategy was designed. 
- The authors do not detail how the demographic information (sex, year of education...) is used.
detailed_comments: There are some minor errors:
- In Section 3.2 "Figure ?? (right)"
- In Section 3.3, the whole section describes results/draws conclusions without referring to the figures/tables that show them, for example:
"Divergences between node degree and node centrality distributions of both networks are near zero, suggesting that there are no substantial structural differences between them", which figure/table shows this?
and some sentences are not completely clear.

recommendation: Poster
review_title: null

final_score: rating: 2: Weak reject
review_content: summary: The paper performs a set of experiments comparing different methods of creating brain connectivity graphs from structural MRI data (correlation/graphical LASSO) to different types of graph convolution (GIN, PointNet++) and graph pooling. The task used to make the comparison is brain age prediction (regression) on UKBioBank data.
strengths: The paper attempts to compare and contrast different methods of constructing brain graphs and how effective each one is when used in different types of graph neural network (GNN) architecture. This type of study serves as a useful benchmark for the community especially when trying to develop new methods using the same type of data and/or task.
weaknesses: Unfortunately the paper is hard to follow and the experiments section is not clear. The authors claim that the papers main objective is to test how different graph construction methods perform in supervised downstream tasks using GNNs. However, in Section 3.2 label propagation community detection is introduced. Only one baseline is used and it is not explained.
detailed_comments: - In Section 2.1.1 the definition of correlation is only valid if the data has been mean centred which has not been mentioned anywhere previously. 

recommendation: []
review_title: null

final_score: rating: 3: Borderline
review_content: summary: The paper represents a graph-based approach for the analysis of brain structural networks by implementing techniques from GSP for the learning of graph topology. Then, a qualitative and quantitative comparison of the resulting networks was obtained with common graph topology learning strategies and finally, a GNN-based model was trained for brain age prediction.
strengths: 1) Increasing interest in the topic of GNN and relevant state-of-the-art work neuroimaging field.
2) Mathematical definitions are well articulated and well linked in the paper.
3) The experimental section (including the appendix) shows the good performance of the proposed method.
weaknesses: As follows:

1) a few English mistakes and referencing (Figure ??) should be removed before submission.
2) the authors claimed that "There is increasing literature using GNNs for fMRI analysis". I suggest first defining well the analysis of fMRI and second citing some papers that used GNN for this task and explaining the methods proposed. Here it is a bit confusing to analyze an image using a GNN while it is euclidean structured data.
3) in the introduction part it is mentioned: "rarely have GNNs been used at ROI level for sMRI analysis.". I suggest citing existing papers related to this task and performing a comparison between them and the proposed method in this manuscript.
4)   The experimental analysis is not convincing for the dataset, evaluation metrics and comparing algorithms are not enough. More description of the universality of the proposed method should be proved using more experiments.
5) The motivation in the introduction section is not well defined.
6) I cannot see a technical novelty. The authors should clarify the importance of such analysis and why GNN is a good choice for this.
recommendation: Poster
review_title: null

Meta-review: metareview: The paper examines the impact of graph construction, message passing, and global pooling options in graph neural networks applied to an age prediction task based on automated atlas-based brain MR parcellation. In particular, regularised covariance and graphical lasso are compared, and the impact of permutation equivariance/invariance (in an atlas-based context) is examined. Performance is compared to a baseline fully-connected network.

The work is performed correctly, and the pipeline description and evaluation set-up (data split) has been improved compared to the previous version of the paper. While there is no technical novelty per se, it is valuable to see that no version of the GNN outperforms the baseline, that GNN performance is largely the same for both graph construction approaches, and that global pooling is sensitive to ROI order in the graph.

Pros
* Very large-scale data used (UKB)
* Experiments are informative for other types of graph data whose vertices are in a fixed order

Cons
* Motivation for the choice of baseline, as well as description, is insufficient
* Unclear from the paper - the approach to graph construction from structural imaging is probably suboptimal compared to using multiple morphometric features (see e.g. Seidlitz et al Neuro 2018)

confidence: 4: The area chair is confident but not absolutely certain
Decision: Accept (Poster)