Comments (1)
eurunuela
commented on July 24, 2024
@leandrolecca and I have written a silly script that runs maPCA and its MATLAB implementation on OpenNeuro data in #52.
We have run it on a bunch of the Multi-echo Cambridge (ds000258) datasets and have found that the results are identical 94% of the time. Note that at some point we decided to normalize in time rather than in space, which is what GIFT does in MATLAB and what we have done in this comparison.
I'm attaching a screenshot of a quick summary table I have made 👇
Here's the CSV file with the results in case you're interested.
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Related Issues (20)
- Set up continuous integration HOT 7
- Codecov not working
- [BUG] Dimensions of masked data not considered for fit_transform HOT 3
- Replacing utils._kurtn with scipy.stats.kurtosis HOT 5
- maPCA in tedana does not currently z-score data before performing PCA HOT 3
- Cite original maPCA paper and GIFT package
- Make class accept images instead of arrays HOT 3
- Scaler parameters overridden within MovingAveragePCA HOT 2
- Operate on images in subsampling function HOT 3
- Use nilearn's masking/unmasking functions internally
- Zenodo release
- Remove commented code
- Documentation missing HOT 4
- Tests fail due to make command not being installed
- Remove DueCredit dependency
- Identified part of the issue with component misestimation
- `n_features_` from sklearn's PCA is deprecated
- Update package installation and minimum Python version
- Black and isort need to be rerun
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