[UK24] Pipeline reproduction (SPM - raw)

[cmaumet]

Softwares

SPM

Input data

raw data

Additional context

None

List of tasks

Please tick the boxes below once the corresponding task is finished. 👍

• 👌 A maintainer of the project approved the issue, by assigning a 🏁status: ready for dev label to it.
• 🌳 Create a branch on your fork to start the reproduction.
• 🌅 Create a file team_{team_id}.py inside the narps_open/pipelines/ directory. You can use a file inside narps_open/pipelines/templates file as a template if needed.
• 🧠 Write the code for the pipeline, using Nipype and the file architecture described in docs/pipelines.md.
• 📘 Make sure your code is documented enough.
• 🐍 Make sure your code is explicit and conforms with PEP8.
• 🔬 Create tests for your pipeline. You can use files in tests/pipelines/test_team_* as examples.
• 🔬 Make sure your code passes all the tests you created (see docs/testing.md).
• 📥 create a pull request from your code.

[cmaumet]

I am continuing this pipeline during Empenn hackathon

[cmaumet]

This is now in a PR that is ready for you @bclenet - changing the assignee accordingly :)

[cmaumet]

Discussed with @bclenet on March 7.

The code defined in Matlab in #161 was successfully ported into nipype by @bclenet in #179. But we end up with a set of contrasts that cannot be estimated due to colinearity (grey regressors in the SPM design matrix --> "not uniquely specified").

After looking back at the description from the team and at the original data paper, it is unclear to us how the possible_gain regressor and possible_loss regressor (mentionned in "a block-task regressor was derived respectively for all trials where the gamble could result in a possible gain, in a possible loss, and in a no-loss-nor-gain situation (i.e. 3 block-task regressors)" differ. Indeed our understanding is that all trials in which the participant choose accept (weak or strong) may result both in a possible loss and possible gain.