Venue: MIDL 2023 Conference

Reviews:

final_score: rating: 4: Weak accept
review_content: summary: This paper investigates a new method of analyzing resting-state fMRI images in experiments of graph generation and graph classification tasks. Compared to existing methods, it focuses on extracting the temporally dynamic information of brain connectivity in both node and community levels. The idea could be a valuable addition to existing work of deep-learning-based spatio-temporal analysis on fMRI data.  
strengths: This paper proposes a novel graph generative model that shows outstanding results in graph generative tasks. The method of jointly learning static graph embedding and dynamic node/community embeddings is innovative. Using two publicly available datasets improves the reliability of work. The explanation of general algorithm is sufficient. 

weaknesses: 1. As already stated in the paper, the performance of the proposed model is similar to several baseline approaches in the graph classification task. Some discussions of the model performance gap between generation and classification task would be good.

2. The presentation of materials lacks graphical content for ease of understanding, such as graphs for overall pipeline or network implementation details. 

detailed_comments: Typo
Page 6 line 1: ‘import’ should be ‘input’  

recommendation: Poster
review_title: null

final_score: rating: 4: Weak accept
review_content: summary: The authors propose the use of a deep generative model to learn from dynamic brain graphs. The generative model treats node embeddings as samples from a random process over community clusters that are able to change over time. The authors use the proposed model to learn from fMRI data to perform of graph reconstruction, dynamic link prediction and graph classification.
strengths: The paper is generally well written and easy to understand, the experimental results presented show that the proposed model is superior to many other baselines for the task of graph reconstruction and link prediction. Additionally, code is to be made publicly available. 
weaknesses: The paper could benefit from a more introduction in the fMRI data to better motivate the use of dynamic brain graphs over static brain graphs. It is not immediately clear to me that incorporating dynamic information improves graph classification (for the proposed model, VGRNN and ELSM)
recommendation: []
review_title: null

final_score: rating: 4: Weak accept
review_content: summary: The paper proposes a deep generative model for clustering brain regions into dynamically evolving communities in rs-fMRI data. Specifically, regions of the brain are represented as nodes in a graph whose embeddings evolve over time and are distributed across a small set of communities common to all subjects. The distribution and community memberships are parameterized using deep neural networks whose parameters can be estimated in a data-driven fashion.

Experimental validation is performed on the HCP dataset against a variety of dynamic connectivity baselines to evaluate tasks of graph generation, dynamic link prediction, and graph-based classification. A qualitative comparison of the learned communities is also performed to validate their consistency with neuroscientific findings in the literature
strengths: The neural network parameterization for the community detection problem and variational formulation for training and inference is an interesting and possibly novel methodological contribution to the field.  The main motivation is clearly articulated in the context of the shortcomings of existing frameworks.  Extensive comparisons have been performed against several dynamic and static connectivity modeling baselines on several graph prediction tasks, many of which the model performs well on.

weaknesses: 1. The description of the tasks used for evaluation is very hard to parse from the description. 

(a) For example, what is the difference in the setup of the graph reconstruction and dynamic link prediction task conceptually? Are positive and negative values in the FC correlation features (ground truth connectivity) treated the same for evaluation? 

(b)  The description of the graph classification task mentions "To predict graph labels, we average node embeddings per subject for the baselines and the community embeddings for DBGDGM before training a SVM using 10-fold cross-validation. Does this mean that unsupervised representation learning is first performed on the entire dataset or is the learning a part of the cross validation loop? Would the averaging of the node/community embeddings (before training the classifier) be prone to issues of over-smoothing, as in the case of graph convolutional networks?

2. "We split both datasets into 80/10/10% training/validation/test data along the time dimension." I am not sure I understand this statement entirely. Does this mean that the folds are *not* stratified across subjects? Is the cross validation set up differently for the graph labeling setup? Per random seed, does the split change or is the only change in the initialization of the models? Are the communities uncovered consistent across subsamples of the data?
detailed_comments: 1. The methods section introduces a lot of notation/indices, making it cumbersome to reference across pages. A small suggestion would be to include a table of notation in the appendix to improve readability.

2. It would be great if the authors could comment on the relative training time for this framework in comparison with the other unsupervised learning frameworks.
recommendation: []
review_title: null

Meta-review: metareview: The PC discussion concluded that the paper has enough technical novelty, combined with clinical application, that the paper could be moved to an Oral presentation. 
confidence: 5: The area chair is absolutely certain
Decision: Accept (Poster)