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Revision Request: Henriques, Rokem, Garyfallidis, St-Jean, Perterson, Correia 2/2 #26
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…s to process them
1) Add more information about methods motivation 2) Add paragraph with advantages of the fwDTI model compared to nulling free water in the acquisition 3) Explain why eq 6 was reordered
…acts in voxels containing only free water
…plementation dependencies
Response to Reviewer 1 Thanks, @delsuc for your comments. Bellow, you can find a description of the changes done to address your comments for each of your review section. 1.1 General We agreed that replicating the assessment of the optimal experimental set-up by varying the number of gradient and their intensities might be of interest. Therefore, the replication of figure 2 in the original article and the lower panels of figure 4 have been added to the manuscript. These new analyses evaluate both the optimal set of b-values and the number of b-value shells/directions. In our replication study, we decided to not incorporate Figure 3 of the original article, because this is only related to small adjustments to the two-shell acquisition scheme. We also didn’t incorporate the upper panels of Figure 4, since the larger errors associated to larger f-values are already shown in our article’s Figure 1. Instead, we decided to expand the analysis of the lower panels of Figure 4 to three diffusion measures rather than two, and for two FA levels. The relevant code can be found in the files run_simulations_2.py and run_simulations_3.py of code’s subfolder. Notebook version of these files can be found in the notebook subfolder (run_simulations_2.ipynb and run_simulations_3.ipynb). 1.2 Text
1.2 Code
1.3 misc remarks
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Reviewer 2 Thanks @soolijoo for your comments. Bellow, you can find a description of the changes done to address your comments for each of your review section. 2.1 General @soolijoo pointed out some concerns regarding the theoretical background of the free water DTI (e.g. initial motivation of the use of this technique), the drawback of the simulations based on the same model, and the use of specific parameters such as the number of simulations repetitions and tested SNR levels. We added text to address these concerns, but we stress that we made these choices based primarily on our intention to replicate the work done by Hoy et al., (2014). This motivates the choice of SNR, and other choices of the simulation parameters. Further expansions of the original article go beyond the scope of this replication study.
[8] C Metzler-Baddeley et al. “Frontotemporal connections in episodic memory and aging: A [9] L J O’Donnell and O Pasternak. “Does diffusion MRI tell us anything about the white [12] O Pasternak et al. “Hockey Concussion Education Project, Part 2. Microstructural white 2.2 Text
[11] O Pasternak et al. “Free water elimination and mapping from diffusion MRI.” In: Magn.
2.3. Code
2.4. Minor points
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This version is an important rewrite of the original manuscript, and the text is very much improved. thanks to there complete set-up, the authors have now been able to performs the simulation for the optimal acquisition conditions. Code is now easier to read, in particular thanks to the comment added, detailing the specific methods of the Dipy module. All details have been fixed. One remark, may be, for having the code available in the notebooks,
is probably better than
as the authors do. In conclusion I consider that this manuscript is ready to be published. |
Thanks @soolijoo for the clarification. |
Thank you all for your comments. @delsuc
(from https://github.com/RafaelNH/Free-water-elimination-DTI/blob/master/notebook/run_simulations_2.ipynb). This was documented in the repository code and notebooks, but we have added a sentence in the methods mentioning the use of this procedure as well as a reference to the Jones et al. (1999) paper describing the algorithm and another explaining the issues in more detail (Jones et al., 2004). For a visual inspection of the performance of the repulsion algorithm please follow the following link: (http://nipy.org/dipy/examples_built/gradients_spheres.html#example-gradients-spheres). |
@soolijoo reminder |
Responses are in bold below. The paragraph 2, 3, 4 and 5 of the article's introduction was further edited to clarify the motivation of the use of the free water DTI model. The main objective of this model is to suppress the partial volume effects of CSF which diffusion coefficient equal to the expected free water value of 3.0e-3 mm2/s due to the absence of tissue barriers. In addition to this, recent studies have shown that measures of free water volume fractions can be also an indication extracellular changes. The use of this model was already shown promising results in removing partial volume confounds in ageing (Metzler-Baddeley, 2012) and to infer extracellular changes in the context of schizophrenia (Pasternak et al., 2012) and brain concussions (Pasternak et al., 2014). Ok. I also edited the 2nd paragraph of the article to clarify that DTI is not specific to concrete microstructural changes. Although this was the term used in the original article, I decided to replace the term Monte-Carlos simulations to multi-tensor simulations to avoid further confusions. Please remove the word "simulation" and replace it with the word "model" throughout. Regarding the sampling of random directions: we agree with the reviewer that a uniform sampling of directions does result in a bias, with a large concentration around the poles of the coordinate system. Indeed, this issue is well-known in diffusion MRI, and is already addressed in our implementation of the software: our software includes a correction of the initial uniform theta and phi angles. To correct this bias, we use the electrostatic potential energy algorithm (disperse_charges; Jones et al. 1999). For example, see the line of code highlighted in the following image:  (from https://github.com/RafaelNH/Free-water-elimination-DTI/blob/master/notebook/run_simulations_2.ipynb). This was documented in the repository code and notebooks, but we have added a sentence in the methods mentioning the use of this procedure as well as a reference to the Jones et al. (1999) paper describing the algorithm and another explaining the issues in more detail (Jones et al., 2004). For a visual inspection of the performance of the repulsion algorithm please follow the following link: (http://nipy.org/dipy/examples_built/gradients_spheres.html#example-gradients-spheres). **This is a very indirect method of doing this -- my original comment included a mathworld link which allows evenly distributed points to be generated simply. Instead of sampling theta and phi unformaly, all that is required is to sample theta and cos(phi) uniformly. What you have done is fine, but dispersing points electrostatically is a more heavyweight operation. Jones' goal is to construct reproducible sets of points for use as gradient direction sets. For this an electrostatic repulsion method is appropriate since it will lead to approximately even spacing for small numbers of points. You are making much larger sumbers of samples in order to get at the directional statistics of a problem and so a method which is assymtotically even will do. There's no need to change this, the method is up to you.** Overall |
Hi @soolijoo thank you for this final review. |
Thanks everyone :-) @RafaelNH I will start the publication/archival process in the coming days. |
@RafaelNH can you provide a few keywords for the article? You can find the keywords for previous ReScience articles here: http://rescience.github.io/read/ Typically they include the main programming language of the reproduction, the general field of research and a few more specific keywords. |
Also, can you confirm that the following are the proper last names of all authors? We use this for naming the archived version.
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Keywords: Neuroscience, python, diffusion-weighted imaging, diffusion modeling, diffusion tensor imaging, DTI, free water, partial volume, Cerebrospinal fluid, CSF. I confirm that the last names are correct! Many thanks! |
Hi @RafaelNH sorry for the delay, I proposed an update to the template that includes an article number, it should make citation a bit easier. Your article is ready for publication, you can check the final pdf here: https://github.com/ReScience-Archives/Henriques-Rokem-Garyfallidis-St-Jean-Peterson-Correia-2017/tree/master/article We don't typically do "proofs" in ReScience but why not :-) I am just waiting for your approval of the pdf to publish the final version. Please note that the day of publication is missing on purpose, I'll fill it in. |
Hi @pdebuyl! Is the page numeration correct? |
Yes. We use since now an article number. Pages are thus |
Ok got it! |
So? |
Hi @pdebuyl! Many thanks for the correction! I approve the PDF! |
EDITOR This submission has been accepted for publication and will appear soon at http://rescience.github.io/read/ |
All editorial steps done @rougier @khinsen |
AUTHOR
Dear @ReScience/editors,
I request a review for the following replication:
Original article
Title: Optimization of a free water elimination two-compartment model for diffusion tensor imaging
Author(s): Andrew R. Hoy, Cheng G. Koay, Steven R. Kecskemeti, Andrew L. Alexander
Journal (or Conference): NeuroImage
Year: 2014
DOI: 10.1016/j.neuroimage.2014.09.053
PDF: http://ac.els-cdn.com/S1053811914007952/1-s2.0-S1053811914007952-main.pdf?_tid=7b211902-b7b7-11e6-be78-00000aab0f6b&acdnat=1480591115_87d58e852819d91f683039ddc22f47d3**
Replication
Author(s): Rafael Neto Henriques, Ariel Rokem, Eleftherios Garyfallidis, Samuel St-Jean, Eric Thomas Peterson, Marta Morgado Correia
Repository: https://github.com/RafaelNH/ReScience-submission/tree/RNH-AR-EG-SSTJ-ETP-MMC-2016
PDF: https://github.com/RafaelNH/ReScience-submission/blob/RNH-AR-EG-SSTJ-ETP-MMC-2016/article/RNH_AR_EG_SSTJ_ETP_MMC-2016.pdf
Keywords: Diffusion MRI; Diffusion modeling; Diffusion tensor imaging; Partial volume; cerebrospinal fluid; free water elimination; partial volume effect
Language: English
Domain: Life Science
Results
Potential reviewers
EDITOR
2 December 2016
5 December 2016
18 December 2016
15 March 2017
30 March 2017
30 March 2017