Comments (4)
This is a strange error. I'm curious if it's more connected to PyMVPA than nibabel
. Unfortunately, PyMVPA hasn't been improved since more than 4-years. At the time that I created fmriflows
it was a great tool, but today I would rather recommend to switch to nilearn
as it is still very actively developed and can do the same thing, plus much more.
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Thanks for your anwers, miykael. Yes, fmriflows is convenient and efficient to process data. I have run the previous process smoothly,I am preparing to introduce fmriflows to other students in the laboratory. But I also think that nilearn will be better for MVPA than pyMVPA.
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For some tutorials on how to use nilearn, well, there's the nilearn documentation and the nilearn user guide. Additionally, you might find this resource helpful as well - in particular the following notebooks:
- 02b_image_manipulation_nilearn
- 03a_functional_connectivity
- 03b_predicting_age_with_machine_learning
- 05a_machine_learning_preparation
- 05b_machine_learning_nilearn
- 05c_machine_learning_keras
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Thanks for your sharing and kindness!
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Related Issues (19)
- summary html [enh] HOT 4
- Should there be an option to shorten normalization time? HOT 3
- How to do group analysis with SpaceNet? HOT 1
- How to do group analysis with Searchlight?
- Filters in functional preprocessing should be orthogonal HOT 3
- High-Pass value in CompCor estimation HOT 1
- Report of functional preprocessing doesn't plot individual runs, only the first one HOT 1
- Anatomical Pipeline: Segmentation output should be compressed HOT 4
- Error running workflow in 01_preproc_anat HOT 4
- html reports dependency
- Creation of `00_parameter_specification.ipynb` notebook
- Descriptive text in notebooks needs to be updated to publication HOT 1
- Error after pulling HOT 2
- antsApplyTransforms error in 03_preproc_func part HOT 4
- Fail to use docker HOT 1
- proposals for fmriflows logo HOT 29
- Workflow and node sub-segmentation HOT 1
- Rewriting of Function Nodes, concerning file paths.
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