FAQ¶
ICA has failed to converge.¶
The TEDICA step may fail to converge if TEDPCA is either too strict (i.e., there are too few components) or too lenient (there are too many).
In our experience, this may happen when preprocessing has not been applied to the data, or when improper steps have been applied to the data (e.g., distortion correction, rescaling, nuisance regression). If you are confident that your data have been preprocessed correctly prior to applying tedana, and you encounter this problem, please submit a question to NeuroStars.
I think that some BOLD ICA components have been misclassified as noise.¶
tedana
allows users to manually specify accepted components when calling the pipeline.
You can use the --manacc
argument to specify the indices of components to accept.
Why isn’t v3.2 of the component selection algorithm supported in tedana
?¶
There is a lot of solid logic behind the updated version of the TEDICA component
selection algorithm, first added to the original ME-ICA codebase here by Dr. Prantik Kundu.
However, we (the tedana
developers) have encountered certain difficulties
with this method (e.g., misclassified components) and the method itself has yet
to be validated in any papers, posters, etc., which is why we have chosen to archive
the v3.2 code, with the goal of revisiting it when tedana
is more stable.
Anyone interested in using v3.2 may compile and install an earlier release (<=0.0.4) of tedana
.