Completed on 4 Jul 2017 by Krzysztof Jacek Gorgolewski .
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In the manuscript “Beyond Consensus: Embracing Heterogeneity in Neuroimaging Meta-Analysis” Ngo et al. apply a previously published variant of the author-topic model to two new sets of labeled data: peak coordinates aggregated from three previously published meta-analyses somehow related to “self-generated thoughts” and a subset peak coordinates from studies overlapping with the IFG.
Even though I found the manuscript to be interesting and the presented application intriguing, the overall feeling it left me with was of an “identity crisis”.
On one hand, the reader might think the paper is proposing a new method. This would be suggested by the general nature of the title and the fact that the two example applications have very little to do with each other (cognitively or neuroanatomically). However, authors clearly state that all of the methods used in the paper (the vanilla author-topic model, the coordinate author-topic adaptation, and finally the variational Bayes estimation method) were already presented in previously published papers. What is more the paper lacks the usual parts present in a methods paper: null simulations/permutations, out of sample prediction, comparison with existing methods etc.
On the other hand, the paper might appear as reporting new cognitive finding. This perspective is also murky. There is no clear statement of hypotheses and combination of studying “self-generated thoughts” and IFG is not justified in the manuscript. Furthermore, details such as the inclusion criteria for the “self-generated through” analysis are not included.
To add to the confusion the manuscript includes 11 pages of mathematical derivations that authors themselves suggest should’ve been supplementary materials for their PRNI paper.
I propose two directions to improve the manuscript:
- Route 1: Turn the paper into a full-fledged methods paper. This will require investigating how the model performs when presented with realistic noise (null simulations or permutations), looking at out of sample predictions and evaluating the amount of variance explained. Other ideas include comparison with other factor decomposition methods (PCA, ICA) that do not take into account labels as well as comparing the maps obtained with a “meta-analysis” subset of coordinates to maps obtained from a model using the full BrainMap database in the previous paper. For this approach, it might be beneficial to pick a brain region that has been previously evaluated using similar methods (for example looking at insula and comparing with this paper https://academic.oup.com/cercor/article/23/3/739/317372/Decoding-the-Role-of-the-Insula-in-Human-Cognition). This would allow to contrast an compare different approaches and highlight the advantages of the author-topic mapping.
- Route 2: Focus on cognitive findings. This would require splitting the two analyses into two manuscripts and focus more on the cognitive implications of the findings. If hypotheses about the resulting maps exist they should be clearly stated – if not the exploratory nature should be noted. Interpretation (reverse inference) of the output maps can be improved by using the neurosynth cognitive decoder. Inclusion criteria for the meta-analysis need to be elucidated in more detail.
- I have performed a very simple reanalysis of the data used for the “self generated though” meta-analysis. Taking average activation maps from the 7 categories (navigation, autobiographic memory, ToM story, ToM non-story, narrative comprehension, and task deactivation) and running ICA on it gave me two components that were very similar spatially to the ones presented in the paper (one for navigation and one for everything else - see https://gist.github.com/chrisfilo/0722b520bc56da8c55aa6bba22eb85aa). This begs the question if the more complex author topic model was necessary? What advantages does it provide? Is it more interpretable? More “accurate”? Those issues should be discussed in the paper. This insight into the manuscript was only possible because authors decided to share the data (at least for half of their analyses) for which they should be applauded.
- When describing the author-topic model I would recommend putting “authors” in inverted quotes when referring to an entity in the original model rather than researchers authoring a paper. This should minimize confusion.
- Selection criteria for the meta-analyses and the individual studies have not been clearly defined for the “Self generated thought section”. For example why where studies labelled as “navigation" included? This needs to be justified since the selection of studies going into the model can greatly influence the end result.
- Not all studies used in the two example meta-analyses were cited in the paper. Citations are the important way of showing academic credit – all of the studies used in the paper should be appropriately credited. It is unusual for a paper to cite that many studies, but the work you are doing is cutting edge and require unusual means to accommodate appropriated credit dissemination.
- Only left Inferior Frontal gyrus is investigated – this should be a) justified b) made explicit each time IFG is mentioned in the abstract, methods and discussion.
- Please add L/R labels to all brain figures.
- “Reading” is listed twice in Table S1.
- The fact that perfoming the meta-analytic connectivity analysis requires a collaborative agreement with the BrainMap team (and thus the inclusion of a member of the brainmap project as a collaboration) should be explicitly mentioned in the discussion. Unfortunately, limited accessibility to this dataset is a limitation of the presented method. Alternatively, the authors might explore using other more open labelled coordinate datasets such as the neurosynth dataset.
- Please add a more thorough description of what code and data are available on GitHub.
- Sharing of the estimated spatial component maps. To improve transparency and reusability of the results presented in your paper please share the unthresholded spatial maps of the estimated components on ANIMA, BALSA, or NeuroVault (the last will make comparing them to other spatial maps such as Smith 2009 very easy).
Minor comments (aka pet peeves):
- Visualizations use cluster size and cluster-forming threshold. This might (or might not) be obscuring the true pattern. Presenting unthresholded pattern would be more accurate.
- The use of the jet color-map is unfortunate. It imposes unnatural contrast between some ranges of values thus introducing another level of perceptual thresholding. Using a luminescence calibrated colormap such as perula will improve interpretability of your figures.
I applaud the authors for sharing code and data. The only gripe I have is that I wish it was done before the manuscript was submitted for review. The same way one would never submit a manuscript with a missing figure the same way we should try not to submit papers with placeholder links to code and data.
Finally, I was not able to fully evaluate the mathematical derivations in the appendix. I hope another volunteer reviewer will be able to verify their accuracy.
I am looking forward to reviewing a revised version of the manuscript.