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This stems from ME-ICA/tedana#849 and is related to #42. Basically, I think it would be awesome if we had some idea of how many components are "typical" for the different criteria, depending on a few factors, such as (1) number of volumes, (2) temporal resolution, and (3) spatial resolution. We could then plot and share those results in the MAPCA documentation, much like how the tedana documentation includes distributions of typical ME-EPI parameters in the literature.
The text was updated successfully, but these errors were encountered:
@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 👇
This stems from ME-ICA/tedana#849 and is related to #42. Basically, I think it would be awesome if we had some idea of how many components are "typical" for the different criteria, depending on a few factors, such as (1) number of volumes, (2) temporal resolution, and (3) spatial resolution. We could then plot and share those results in the MAPCA documentation, much like how the tedana documentation includes distributions of typical ME-EPI parameters in the literature.
The text was updated successfully, but these errors were encountered: