[43FJ] Pipeline reproduction (FSL - raw)

[elodiegermani]

Softwares

Pipeline using FSL Feat 6.0.0

Input data

raw data

Additional context

Preprocessing was conducted using FEAT (FMRI Expert Analysis Tool) v 6.00, part of FSL version 5.0.10 (FMRIB Software Library, www.fmrib.ox.ac.uk/fsl). Preprocessing consisted of nonbrain removal using BET (Brain Extraction Tool for FSL), high-pass filtering (100-s cutoff), and spatial smoothing using a Gaussian kernel of FWHM 5 mm. Motion correction was performed with MCFLIRT (intra-modal motion correction tool based on optimization and registration techniques in FSL’s registration tool FLIRT) using 24 standard and extended regressors (six motion parameters, the derivatives of those parameters, and the squares of the derivatives and the original parameters). Additional spike regressors created using fsl_motion_outliers (frame displacement threshold=75th percentile plus 1.5 times the interquartile range) were also included. Each participant’s functional data were registered to their T1 weighted anatomical image using boundary based registration (BBR; Greve & Fischl, 2009) and then to MNI (Montreal Neurological Institute) stereotaxic space with 12 degrees of freedom via FLIRT (FMRIB’s Linear Image Registration Tool). Alignment was visually confirmed for all participants. FILM (FMRIB’s Improved Linear Model) prewhitening was performed to estimate voxelwise autocorrelation and improve estimation efficiency.

The following participants were excluded due to motion of over .9mm absolute mean displacement on at least one run (Siegel et al., 2014): sub-016, sub-018, sub-030, sub-088, sub-116

Each trial onsets, duration 4s, PM 1, Gain trial onsets, duration 4s, PM gain amount, Loss trial onsets, duration 4s, PM loss amount. Expected value onsets, duration 4s, PM expected value calculated in accordance with Canessa et al. 2013 J Neurosci. Design was based on Canessa et al. 2013 J NeurosciAll regressors were convolved with a double-gamma HRF. Temporal derivatives were included and temporal filtering was applied.Motion parameters included 24 standard and extended regressors (six motion parameters, the derivatives of those parameters, and the squares of the derivatives and the original parameters). Additional spike regressors created using fsl_motion_outliers (frame displacement threshold=75th percentile plus 1.5 times the interquartile range) were also included. Gain and loss onsets (PM gain and loss amount, respectively) were orthogonalized with respect to the trial onsets (PM 1) (Mumford et al., 2015)

Subject specific mean response across runs was estimated using FEAT fixed effects analysis as Level 2.
Group level effects were modeled separately for equal range and equal indifference groups except for ER > EI comparison with no subject effects or conditional effects.

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 as a template if needed.
• 📥 Create a pull request as soon as you completed the previous task.
• 🧠 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).

[bclenet]

For further work on this pipeline :

According to the decription,

Gain and loss onsets (PM gain and loss amount, respectively) were orthogonalized with respect to the trial onsets (PM 1) (Mumford et al., 2015)

This seems to be feasible with the orthogonalization parameter of nipype's Level1Design FSL interface.