Hello all,

I have been attempting to use Tedana on multi echo 7T resting state fMRI data from a Siemens Magnetom Terra. The only preprocessing applied to it was motion correction with FSL mcflirt (same correction parameters applied to all 3 echoes). The scans are whole brain, 2mm voxels, TR = 2, multiband acceleration = 7, iPat = on.

I tried the previous suggestion of changing the seed value multiple times but it hasn’t helped.

The command that I enter is formatted like this:

tedana -d e1.nii.gz e2.nii.gz e3.nii.gz -e 16.2 40.8 124.08 --seed 3000 --tedpca kundu --verbose --debug

Then, it shows the following:

DEBUG tedana:tedana_workflow:491 Resulting data shape: (645120, 3, 300)

INFO tedana:tedana_workflow:577 Computing EPI mask from first echo

WARNING utils:make_adaptive_mask:110 5346 voxels in user-defined mask do not have good signal. Removing voxels from mask.

DEBUG tedana:tedana_workflow:592 Retaining 198975/645120 samples for denoising

DEBUG tedana:tedana_workflow:606 Retaining 111495/645120 samples for classification

INFO tedana:tedana_workflow:609 Computing T2* map

DEBUG decay:fit_decay:404 Setting cap on T2* map at 2663.91248

DEBUG tedana:tedana_workflow:617 Setting cap on T2* map at 0.26881s

INFO combine:make_optcom:242 Optimally combining data with voxel-wise T2* estimates

INFO tedana:tedana_workflow:634 Writing optimally combined data set: /Users/erizor1/Gdrive/Documents/grafton_lab/asap/asap_pilot_7T/evan_multiecho/ptx_scans/grappa_epis_mc_155/desc-optcom_bold.nii.gz

INFO pca:tedpca:229 Computing PCA of optimally combined multi-echo data with selection criteria: kundu

INFO collect:generate_metrics:123 Calculating weight maps

INFO collect:generate_metrics:132 Calculating parameter estimate maps for optimally combined data

INFO collect:generate_metrics:145 Calculating z-statistic maps

INFO collect:generate_metrics:155 Calculating F-statistic maps

INFO collect:generate_metrics:165 Thresholding z-statistic maps

INFO collect:generate_metrics:172 Calculating T2* F-statistic maps

INFO collect:generate_metrics:179 Calculating S0 F-statistic maps

INFO collect:generate_metrics:187 Counting significant voxels in T2* F-statistic maps

INFO collect:generate_metrics:193 Counting significant voxels in S0 F-statistic maps

INFO collect:generate_metrics:200 Thresholding optimal combination beta maps to match T2* F-statistic maps

INFO collect:generate_metrics:206 Thresholding optimal combination beta maps to match S0 F-statistic maps

INFO collect:generate_metrics:213 Calculating kappa and rho

INFO collect:generate_metrics:222 Calculating variance explained

INFO collect:generate_metrics:228 Calculating normalized variance explained

INFO collect:generate_metrics:236 Calculating DSI between thresholded T2* F-statistic and optimal combination beta maps

INFO collect:generate_metrics:247 Calculating DSI between thresholded S0 F-statistic and optimal combination beta maps

/opt/anaconda3/lib/python3.7/site-packages/tedana/utils.py:211: RuntimeWarning: invalid value encountered in true_divide

dsi = (2.0 * intersection.sum(axis=axis)) / arr_sum

INFO collect:generate_metrics:257 Calculating signal-noise t-statistics

INFO collect:generate_metrics:295 Counting significant noise voxels from z-statistic maps

INFO collect:generate_metrics:306 Calculating decision table score

INFO tedpca:kundu_tedpca:50 Performing PCA component selection with Kundu decision tree

INFO tedpca:kundu_tedpca:128 Selected 261 components with Kappa threshold: 8.76, Rho threshold: 24.25

INFO ica:tedica:85 ICA with random seed 3000 converged in 135 iterations

INFO tedana:tedana_workflow:671 Making second component selection guess from ICA results

INFO collect:generate_metrics:123 Calculating weight maps

INFO collect:generate_metrics:132 Calculating parameter estimate maps for optimally combined data

INFO collect:generate_metrics:145 Calculating z-statistic maps

INFO collect:generate_metrics:155 Calculating F-statistic maps

INFO collect:generate_metrics:165 Thresholding z-statistic maps

INFO collect:generate_metrics:172 Calculating T2* F-statistic maps

INFO collect:generate_metrics:179 Calculating S0 F-statistic maps

INFO collect:generate_metrics:187 Counting significant voxels in T2* F-statistic maps

INFO collect:generate_metrics:193 Counting significant voxels in S0 F-statistic maps

INFO collect:generate_metrics:200 Thresholding optimal combination beta maps to match T2* F-statistic maps

INFO collect:generate_metrics:206 Thresholding optimal combination beta maps to match S0 F-statistic maps

INFO collect:generate_metrics:213 Calculating kappa and rho

INFO collect:generate_metrics:222 Calculating variance explained

INFO collect:generate_metrics:228 Calculating normalized variance explained

INFO collect:generate_metrics:236 Calculating DSI between thresholded T2* F-statistic and optimal combination beta maps

/opt/anaconda3/lib/python3.7/site-packages/tedana/utils.py:211: RuntimeWarning: invalid value encountered in true_divide

dsi = (2.0 * intersection.sum(axis=axis)) / arr_sum

INFO collect:generate_metrics:247 Calculating DSI between thresholded S0 F-statistic and optimal combination beta maps

INFO collect:generate_metrics:257 Calculating signal-noise t-statistics

INFO collect:generate_metrics:295 Counting significant noise voxels from z-statistic maps

INFO collect:generate_metrics:306 Calculating decision table score

INFO tedica:kundu_selection_v2:138 Performing ICA component selection with Kundu decision tree v2.5

/opt/anaconda3/lib/python3.7/site-packages/tedana/selection/_utils.py:112: RuntimeWarning: invalid value encountered in true_divide

b_hat = np.reshape(b / np.sqrt((b**2).sum()), (2, 1))

WARNING tedica:kundu_selection_v2:287 Too few BOLD-like components detected. Ignoring all remaining.

WARNING tedana:tedana_workflow:699 No BOLD components found. Re-attempting ICA.

After this, it re-attempts the ICA over and over again without resolving. Is it possible that the echo times are too long?

My data and report files are located at shared_with_neurostar – Google Drive for viewing and download. It never gets to the point of creating the html report.

Thanks so much for your help!