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.
Do we expect low numbers of echoes to perform well? We certainly assume as much in Tedana.
Echo times from just covering the range of T2 values to 1.5x the max T2 value (see Logan's post here) to something far beyond that.
We could probably disable the adaptive mask to see if there's a problem with including bottomed-out echoes, and specifically to see what the nature of that problem is.
Lagged BOLD-based global signals (i.e., sLFOs).
Tedana, as it currently exists, shouldn't be able to do much with this. It should identify a temporally-blurred version of the global signal and accept it.
Localized task-related signals.
Spatially correlated BOLD and non-BOLD signals.
Tedana's spatial ICA shouldn't be able to handle this, so we might expect components that have both high Kappa and high Rho.
Temporally correlated BOLD and non-BOLD signals.
Tedana's spatial ICA should handle this successfully.
Motion-correlated non-BOLD signals.
Amount of thermal noise.
We can probably use the amount of variance removed by the PCA for this.
Patterns of thermal noise.
Does the scale of the thermal noise vary by echo? I assume so.
Does the time series of the thermal noise to vary by echo?
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.