Preprint reviews by Krzysztof Jacek Gorgolewski

Privacy and anonymity in public sharing of high-dimensional datasets: legal and ethical restrictions

Jacob Jolij, Els Van Maeckelberghe, Rosalie Koolhoven, and Monicque Lorist

Review posted on 15th September 2017

Jolij and colleagues argue in their paper that it is unethical and soon it will be illegal in the EU to publicly share data describing human participants of academic research experiments. Their perspective is deliberately biased to “spark a debate”. The authors strongly urge researchers not to share data.


There are several issues with the paper:

• The title and the summary are misleadingly broad and suggest a thorough review of the legal status of data sharing around the world. However, the paper only analyzes data sharing under a new not yet implemented European Union regulation with strong emphasis on the legal system in the Netherlands.

• The authors purposefully take the strictest possible interpretation of ethical guidelines. I find this approach of very limited use. For example, the excerpt from the Declaration of Helsinki they quote: “Every precaution must be taken to protect the privacy of research subjects and the confidentiality of their personal information” in its strictest interpretation would make doing any research impossible. If taken literally (which the authors seem to encourage) all human derived data – whether anonymized or not – would have to be stored on encrypted temper proof computers. Passwords would have to be entered in prescreened empty rooms to ensure eavesdropping would not be possible. One could even say that displaying the data in a room with windows is a danger of eavesdropping so such situations should be eliminated – as a precaution. This is obviously impractical, but it shows how strictest possible interpretation can be manipulated into absurdity making any research unethical.

• Furthermore, some argue that there is another aspect of the ethics of data sharing – that researchers have the ethical obligation to maximize the contribution of their participants. See Brakewood B, Poldrack RA. The ethics of secondary data analysis: considering the application of Belmont principles to the sharing of neuroimaging data. Neuroimage [Internet]. 2013 Nov 15;82:671–6. Available from: http://dx.doi.org/10.1016/j.neuroimage.2013.02.040

• I am not a scholar of law to judge if the authors interpretation of ‘General Data Protection Regulation’ is correct. It is, however, unclear if it is also illegal to share data with other researchers within the same institution or institutions outside of the EU. Such analysis would be useful to the reader.

• I might be mistaken, but judging from the affiliations none of the authors is experienced in practicing law. If I am not mistaken, adding a collaborator with a law background would strengthen the paper.

• It’s not even clear why the topic of anonymity needs to be discussed since under “strictest possible interpretation” of the rules if one cannot control the purpose of data processing in context of public data sharing and thus making data sharing illegal whether they are properly anonymized.

• The section on anonymity is a mixed bag. The point of that one can re-identify anyone if equipped with the right information is not very revealing. It is also not clear what is the purpose of the example of the author identifying himself from a public database using information only available to himself. The argument that EEG recordings or fMRI scans greatly increase the chance of re-identification, because of their high dimensionality is mute, because acquiring matching data by a third party would be very hard. A date of birth or a zip code even though includes less information is much more useful for reidentification.

• It is not clear if the rulings of the Dutch Council of State are legally binding in all of the EU (I suspect they are not).

• The section about the risk posed by potential re-identification is purely hypothetical and lacks any analysis or example of actual harm that was inflicted due to reidentification of research participants.

• The consent form section is also confusing. Why is the claim that participants don’t always read consent forms a problem only in context of data sharing? Does GDPR enforce researchers to do mandatory consent form comprehension checks? Would the type of a consent form done by The Harvard Personal Genome Project make public data sharing legal under GDPR? Would it be ethical? Was Russ Poldrak’s MyConnectome study ethical?

• The reference cited in support of “anecdotal (…) sharp drop in willingness to participate in experiment of which data may be published openly” is incorrect. There is no such journal as “Belief, Perception, and Cognition Lab”. I did find this piece in Winnower - https://thewinnower.com/papers/the-open-data-pitfall-ii-now-with-data A reader that is not careful enough might miss the fact that this piece (never peer reviewed) describes the same first author as the reviewed manuscript asking his students if they would participate in a study which data is going to be publicly shared. I have a mixed feeling about using this reference. On one side, I appreciate that the author acknowledged the ad hoc nature of it and lack of scientific merit, but finding those comments required some effort and are not clear in the currently reviewed manuscript.

• Finally, authors failed to reference the following five analyses of GDPR in context of research data:
Chassang G. The impact of the EU general data protection regulation on scientific research. Ecancermedicalscience [Internet]. 2017 Jan 3;11:709. Available from: http://dx.doi.org/10.3332/ecancer.2017.709

Rumbold JMM, Pierscionek BK. A critique of the regulation of data science in healthcare research in the European Union. BMC Med Ethics [Internet]. 2017 Apr 8;18(1):27. Available from: http://dx.doi.org/10.1186/s12910-017-0184-y

Stevens L. The Proposed Data Protection Regulation and Its Potential Impact on Social Sciences Research in the UK. European Data Protection Law Review [Internet]. 2015;1(2):97–112. Available from: http://edpl.lexxion.eu/article/EDPL/2015/2/4

European Society of Radiology (ESR). The new EU General Data Protection Regulation: what the radiologist should know. Insights Imaging [Internet]. 2017 Jun;8(3):295–9. Available from: http://dx.doi.org/10.1007/s13244-017-0552-7

Rumbold JMM, Pierscionek B. The Effect of the General Data Protection Regulation on Medical Research. J Med Internet Res [Internet]. 2017 Feb 24;19(2):e47. Available from: http://dx.doi.org/10.2196/jmir.7108
• Big plus for sharing the analysis code (in the future I recommend putting it in Zenodo or similar archive for long term preservation).

Overall the manuscript ends on a recommendation not to share data and statement that it is coincidentally the best thing for one’s scientific career which implicitly suggest that the ethical and legal reasons (and strictest interpretation of guidelines) is merely an excuse not to share data and maintain competitive edge. I am not sure if this was the intention of the authors, but this is how the manuscript reads now. Independent of legal and ethical arguments I am not convinced those are the values we want to foster in science.

I really wish this paper was more constructive in its nature and explore how scientists who want to or are required to publicly share human data could use consents forms to inform their participants of the risks. In the past, we have recommended a ready to use text that could be included in consent forms to ethically enable public data sharing: http://open-brain-consent.readthedocs.io/en/latest/ultimate.html. Considering that the new EU law will take effect in May 2018 this is the right time for researchers around EU to start adding such clauses to their consent forms.

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Structural and functional MRI from a cross-sectional Southwest University Adult lifespan Dataset (SALD)

Dongtao Wei, Kaixiang Zhuang, Qunlin Chen, Wenjing Yang, Wei Liu, Kangcheng Wang, Jiang-Zhou Sun, and Jiang Qiu

Review posted on 25th August 2017

Only a small percentage of neuroimaging data is being shared openly. The number of datasets expanding beyond caucasian white population and spanning wide range of ages is even smaller. Therefore this dataset is a valuable contribution to the field and merits a publication conditional on certain improvements.


Major:
- I strongly recommend distributing the dataset in the Brain Imaging Data Structure (http://bids.neuroimaging.io) format instead of the current custom file organization. This will greatly increase the ease of reuse and validation of the dataset.

- The dataset should be validated using bids-validator (https://github.com/INCF/bids-validator) to check for missing scans and consistency of scanning parameters across all subject.

- It is not clear if the anatomical and resting state scans were acquired during one or two separate sessions.

- "Image acquisitions" section mentions task data, but no other details are provided and files are missing. Is task data suppose to be part of this release?

- Context of the resting state scan should be explained - was it performed after or before a particular task?

- Please share the code/scripts/config files used to perform the analyses

Minor:
- No "known issues" are reported in the paper. Is it really try that in such a large sample there were no scans that caused your concern?
- DPARSF is misspelled as DPARF
- Please provide which version of DPARSF was used
- The "Sex" column in the demographics Excel file appears twice
- I would advice against using Jet colormap in Figure 5 since it's perceptually inaccurate https://www.youtube.com/watch?v=xAoljeRJ3lU
- Labels on the axes of figures 2 and 3 are unreadable

Looking forward to reviewing a revised version of this paper.

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Beyond Consensus: Embracing Heterogeneity in Neuroimaging Meta-Analysis

Gia H. Ngo, Simon B. Eickhoff, Peter T. Fox, R. Nathan Spreng, B. T. Thomas Yeo

Review posted on 04th July 2017

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.

Other comments:

- 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.

Chris Gorgolewski

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Advances in studying brain morphology: The benefits of open-access data

Christopher R Madan

Review posted on 11th June 2017

This short commentary introduces the reader to recent advancements in publicly available neuroimaging datasets with a special focus on data useful for evaluating anatomical features. It includes a brief historical perspective and provides an overview of the advantages of using publicly shared data.


The paper provides some unique and important points – for example, the fact that polling data from multiple sources gives researchers an opportunity to access previously inaccessible populations thus extending conclusions beyond the typically studied groups of participants. Even though all of the statements in the manuscripts are to my knowledge factually accurate I do find it a bit one sided. Advantages of using shared data are presented extensively, but disadvantages are almost completely omitted. It would be worth discussing aspects such as – 1) scientific questions are limited, by what data and metadata are available; 2) when combining data from multiple sites special care needs to be taken to account for scanner/sequence effects etc. I feel that addition would make this community more leveled.

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The GridCAT: A toolbox for automated analysis of human grid cell codes in fMRI

Matthias Stangl , Jonathan Shine and Thomas Wolbers

Review posted on 07th February 2017

GridCAT is a much-appreciated attempt to provide computational tools for modeling grid-like patterns in fMRI data. I am by no means an expert in grid cells, but I can provide advice and recommendations with regards to brain imaging software:


- Please mention the license the software is distributed under.

- Please mention the license the data is distributed under. To maximize the impact of this example dataset (fostering future comparisons and benchmarks) I would recommend distributing this dataset under public domain license (CC0 or PDDL) and putting it on openfmri.org

- I was, unfortunately, unable to run your software because I do not possess a valid MATLAB license. This costly dependency will most likely be the biggest limitation of your tool. There are two way to deal with this problem: make it compatible with Octave (free MATLAB alternative) or provide a standalone MATLAB Runtime executable (see https://www.mathworks.com/products/compiler/mcr.html)

- I would encourage the authors to add support for input event text files formatted according to the Brain Imaging Data Structure standard (see http://bids.neuroimaging.io/bids_spec1.0.0.pdf section 8.5 and Gorgolewski et al. 2016)

- Please describe in the paper how other developers can contribute to your toolbox. I recommend putting it on GitHub and using the excellent Pull Request functionality.

- Please describe in the paper how users can report errors and feature requests. I again would recommend using GitHub or neurostars.org.

- Is there a programmatic API built in your toolbox? In other words a set of functions that would allow advanced users to script their analyses. If so please describe it and provide an example.

- Please describe how you approached testing when writing the code. Is there any form of automatic tests (unit, smoke or integration tests)? Are you using continuous integration service to monitor the integrity of your code?

- For the GLM1 modeling step: is it possible to provide nuisance regressors (for example motion)? If so are you reporting information about colinearity of the fitted model?

- For the ROI feature - it would be useful to show users the location of their ROI on top of the BOLD data. This would provide a sanity check that can avoid using masks that are not properly coregistered.

- It would be beneficial for the paper to include some figures of the GUI from the manual and maybe list the plethora of different analysis option available on different steps in a table.

- Please add error bars to figure 5.

Chris Gorgolewski

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