ME-ICA / ME-ICA/multi-echo-data-analysis
Simulate data with different parameters to show effects on ICA denoising
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Description
I was thinking that we could probably simulate data with specific parameters, using real data as a basis. If we have (1) TE-(in)dependence model fit maps, (2) component weight maps, (3) component time series, and (4) variance explained maps, we could probably predict the multi-echo data for a range of echo times, numbers of echoes, etc. We could then run tedana on the simulated data to see how the various parameters impact the results.
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- Read the whole issue, then the project's contributing guide.
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Research direction
No files, tests, or entry points are named. Start by defining how the TE-(in)dependence fit maps, component weight maps, component time series, and variance explained maps become simulated multi-echo data, then identify how to run tedana across echo-time and echo-count parameters. Done means the parameter effects on ICA denoising are evaluated and documented.
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Assessment
- Domain
- data
- Issue type
- Feature
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 20/100