[DC61] Pipeline reproduction (SPM - deriv)

[mgaubert]

Softwares

Software: SPM

Input data

derivatives (fMRIprep)

Additional context

Test SPM batch of the team

List of tasks

Please tick the boxes below once the corresponding task is finished. :+1:

• [ ] :ok_hand: A maintainer of the project approved the issue, by assigning a :checkered_flag:status: ready for dev label to it.
• [ ] :deciduous_tree: Create a branch on your fork to start the reproduction.
• [ ] :sunrise: Create a file team_{team_id}.py inside the narps_open/pipelines/ directory. You can use a file inside narps_open/pipelines/templates as a template if needed.
• [ ] :inbox_tray: Create a pull request as soon as you completed the previous task.
• [ ] :brain: Write the code for the pipeline, using Nipype and the file architecture described in docs/pipelines.md.
• [ ] :blue_book: Make sure your code is documented enough.
• [ ] :snake: Make sure your code is explicit and conforms with PEP8.
• [ ] :microscope: Create tests for your pipeline. You can use files in tests/pipelines/test_team_* as examples.
• [ ] :microscope: Make sure your code passes all the tests you created (see docs/testing.md).

[cmaumet]

@mgaubert: I am adding you as assignee on this issue just for the time of the hackathon so that we can more easily see which pipelines are awaiting contributors in https://github.com/orgs/Inria-Empenn/projects/1/views/1

[bclenet]

Hi @cmaumet,

Here is a quick question about this analysis. In the inference_contrast_effect section, the description mentions:

Second level contrasts for the gain analysis :

• ‘gain_param_indiff’: T-contrast of parametric effect of gain in the equal indifference group
  • [1 0] i.e. 1gain_param_indiff
• ‘gain_param_range’: T-contrast of parametric effect of gain in the equal range group
  • [0 1] i.e. 1gain_param_range

Does this mean that we design a one sample t-test with two covariates, one for the equalRange, another for the equalIndifference group ? And we pass subject-level contrasts files from both groups as input ?

[cmaumet]

@bclenet: I confirm that I think you have the right answer to this!

[bclenet]

Hi @cmaumet,

Later in the description (also in the inference_contrast_effect section):

Second level contrasts for the loss analysis :

• 'loss_param_indiff': F-contrast of parametric effect of loss in the equal indifference group
  • [1 0] i.e. 1loss_param_indiff
• 'loss_param_range': F-contrast of parametric effect of loss in the equal range group
  • [0 1] i.e. 1loss_param_range

I assume they actually used F-contrasts here (probably not a typo), but could you please let me know how their setup in SPM is different from the one of a t-contrast ?

[cmaumet]

We looked at the collection on NV: https://neurovault.org/collections/4963/ and we confirmed that gain was a T-contrast and loss an F-contrast.