Dear researchers and developers
We are using a multi-echo fMRI sequence in combination with tedana denoising algorithm (https://tedana.readthedocs.io/en/latest/index.html) to obtain a better signal from temporal lobes in cognitive fMRI studies.
We collected pilot data from one subject, asking the participant to do a language task that we expect to activate the temporal lobes. We processed the data minimally, using only slice timing and motion correction. We estimated motion parameters based on echo 1 and then applied these to the other two echos as recommended. We then used tedana version 0.0.5 (most recent release) on the three echos, and also used the new unreleased version for comparison. We then applied smoothing and spatial normalisation to the data of each echo, to the combination of echos (ts_OC), and to the denoised combination (dn_ts_OC) of echos. Finally, we applied a General Linear Model separately to each 4D time series (echo1, echo 2, echo 3, ts_OC, dn_ts_OC) to see in which brain areas we detect activation. Note that participant’s head movement was minimal.
The brain activation detected with version 0.0.5 of tedana made sense: when using the denoised time series (dn_ts_OC), we detected more activity in temporal lobes than when using the uncorrected time series. However, when using the new unrelased version of tedana (at https://github.com/ME-ICA/tedana/archive/master.zip) we detected less activation. We also got convergence warnings with both versions of tedana (“ConvergenceWarning: FastICA did not converge” for v0.0.5 and “WARNING:tedana.decomposition.eigendecomp:ICA failed to converge”).
Based on these results, we would like to ask:
Should we trust the results of the older tedana rather than the new unreleased version, because the results of the older version looked sensible, and because maybe the newer version is not stable yet?
Are the convergence warnings something to be worried about or can they can be ignored, given that the brain activation we detected looked sensible?
In addition, we noticed that we get very different results in our final analysis (i.e. the neural activation we detect), depending on whether the echo times are inputted to tedana version 0.0.5 as integers (13 31 49) or with two decimal places (13.00 30.99 48.98). These differences in activation may arise because of a few differences in what components are selected: component #9 is accepted with integer echo times but rejected with decimal echo times, and component #15 is rejected with integer echo times but accepted with decimal echo times. Although the ICA component tables themselves look very similar. Thus we would also like to ask:
- Is this to be expected that tedana component selection is influenced by small differences in the rounding of echo times, and would you generally recommend to use echo times rounded to integer, or retain some number of decimal places?
The data and scripts I used in the analysis can be downloaded from: https://uoe-my.sharepoint.com/:f:/g/personal/atamm2_ed_ac_uk/EnH95ahY35pHpxblNaMeDw8BdOZwi8p0UNYFMrUr4ejFuA?e=YU2izM
Check the README.txt for a more detailed description of the folders and files. More information about what the scripts do can be found from the comments in the scripts. If anything is unclear, let me know.
Many thanks for your help!
Andres Tamm, MSc
Department of Psychology
The University of Edinburgh