Preprint reviews by Russell Poldrack

What the Success of Brain Imaging Implies about the Neural Code

Olivia Guest, Bradley C Love

Review posted on 25th August 2016

This is a really interesting and thoughtful piece that will be important reading for anyone in the field of cognitive neuroscience. I have a few comments that I hope will help make it clearer and more accurate.

- "BOLD response may spillover 3 to 5 millimetres away from neural activity because the brain supplies blood to adjacent areas — it “water[s] the entire garden for the sake of one thirsty flower”" - This is mixing together a couple of issues. It's true that the hemodynamic response is broader than the neuronal activation, but not by 3-5 mm. The “flower-watering” effect is probably on the order of hundreds of microns. The substantial spread in standard (i.e. 3T gradient-echo BOLD) fMRI is due primarily to the fact that this imaging technique has substantial contributions from venous signals that can spread fairly far from the neuronal activation.

- "Extraneous to the actual imaging itself, most statical models require some spatial smoothing in addition to the smoothing that is intrinsic to fMRI data acquisition.” - misspelling of statistical. also, I would disagree with this claim - it is increasingly common to analyze data without any smoothing, especially when one is not relying upon Gaussian random field theory.

- "Neural similarity is not recoverable by fMRI under a burstiness coding scheme.” - this seems to rely on the strong assumption that burstiness is just like regular firing, only with a different temporal organization. this is far outside my knowledge base, but I can imagine that differences in the synaptic physiology of bursting vs. constant firing might be evident from BOLD. Also, see this regarding synchrony: http://www.mitpressjournals.or...

- The general conclusions seem to rest heavily on the specific deep networks used in this analysis, which are trained on the categorization problem. Thus, it’s not surprising that the high-level representations show less overlap between categories - the training has worked! However, it’s not clear to me how well categorization training approximates what mammals learn as they come to perceive the world. It would be useful to have additional discussion regarding the impact of this particuclar topic on the generality of the conclusions.

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Brainhack: a collaborative workshop for the open neuroscience community

Review posted on 09th February 2016

This paper describes the Brainhack meetings, which have been an important presence in the field

of neuroimaging. I think that this paper and the related series of publications will be a useful
addition to the literature. I have no problems with publication of the current manuscript as is.

Level of interest

Please indicate how interesting you found the manuscript:
An article of importance in its field

Quality of written English

Please indicate the quality of language in the manuscript:
Acceptable

Declaration of competing interests

Please complete a declaration of competing interests, considering the following questions:

1. Have you in the past five years received reimbursements, fees, funding, or salary from an
organisation that may in any way gain or lose financially from the publication of this
manuscript, either now or in the future?

2. Do you hold any stocks or shares in an organisation that may in any way gain or lose
financially from the publication of this manuscript, either now or in the future?

3. Do you hold or are you currently applying for any patents relating to the content of the
manuscript?

4. Have you received reimbursements, fees, funding, or salary from an organization that
holds or has applied for patents relating to the content of the manuscript?

5. Do you have any other financial competing interests?

6. Do you have any non-financial competing interests in relation to this paper?
If you can answer no to all of the above, write 'I declare that I have no competing interests'
below.

If your reply is yes to any, please give details below.
I declare that I have no competing interests

I agree to the open peer review policy of the journal. I understand that my name will be included
on my report to the authors and, if the manuscript is accepted for publication, my named report
including any attachments I upload will be posted on the website along with the authors'
responses. I agree for my report to be made available under an Open Access Creative Commons
CC-BY license (http://creativecommons.org/licenses/by/4.0/). I understand that any comments
which I do not wish to be included in my named report can be included as confidential comments
to the editors, which will not be published.

I agree to the open peer review policy of the journal.

Authors' response to reviews: (http://www.gigasciencejournal.com/imedia/1891746637201004_comment.pdf)


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A test-retest fMRI dataset for motor, language and spatial attention functions

Review posted on 07th February 2013

This data paper describes a dataset with 10 subjects’ worth of fMRI data across two sessions, for the purposes of assessment of test-retest reliability. This dataset could certainly be of use to researchers, and I applaud the investigators’ desire to share it.

Major compulsory revisions:

  1. My primary concern is that the current release of the dataset does not provide the information necessary for someone to actually use it. The task paradigms and timing are not described sufficiently for another researcher to easily create a statistical model. The behavioral data files in principle contain this information, but they are not described well enough for a third party to actually use them. I would suggest that the authors instead include a standard set of onset timing files, which will be necessary for the creation of a statistical model in almost any software package. The authors should consider using a metadata framework similar to the one used for shared data in the OpenFMRI project (www.openfmri.org), which is meant to explicitly address the issue raised above.

  2. The authors should also ensure that their description contains all of the necessary information regarding the dataset. This includes the minimum information outlined by Poldrack et al. (2008) as well as the MR-specific guidelines laid out more recently at http://practicalfmri.blogspot.com/2013/01/a-checklist-for-fmri-acquisition.html .

Level of interest: An article of limited interest

Quality of written English: Acceptable

Statistical review: No, the manuscript does not need to be seen by a statistician.

Declaration of competing interests: I declare that I have no competing interests

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