I am using the FSL interface in Nipype for fMRI analysis and having a hard time creating a feat-specific file for preprocessing. Specifically, what I would like to do is:
- Create a
fsf_file
using custom parameters for each participant - Loop across all participants and run preprocessing for each using
fsl.FEAT()
and thefsf_file
from step 1
In a previous workflow I have created an fsf for a level one analysis using model.SpecifyModel()
and fsl.Level1Design()
. However, I can’t find any guidance for how to perform the same function as fsl.Level1Design()
but running only pre-stats.
Looking closely into the nipype directory, I can see in /nipype/interfaces/fsl/model_templates
that there is a model template, feat_header.tcl
, which would allow me to change which stages are run by FEAT by accessing $analysis_stages
:
# Which stages to run
# 0 : No first-level analysis (registration and/or group stats only)
# 7 : Full first-level analysis
# 1 : Pre-Stats
# 3 : Pre-Stats + Stats
# 2 : Stats
# 6 : Stats + Post-stats
# 4 : Post-stats
set fmri(analysis) $analysis_stages
However, there is no script in the fsl interface that can be used to access this template. Am I missing something crucial here? How can I create an fsf file for preprocessing and run this in FEAT without running preprocessing node-by-node in a workflow? Thanks, I appreciate any advice.