Completed on 13 Feb 2017 by Krzysztof Jacek Gorgolewski .
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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.