Multi-Echo Denoising with tedana#
In this analysis tutorial, we will use tedana [DuPre et al., 2021] to perform multi-echo denoising.
Specifically, we will use tedana.workflows.tedana_workflow().
import json
import os
from glob import glob
from pprint import pprint
import pandas as pd
from IPython.display import HTML, display
from repo2data.repo2data import Repo2Data
from tedana import workflows
# Install the data if running locally, or point to cached data if running on neurolibre
DATA_REQ_FILE = os.path.join("../binder/data_requirement.json")
# Download data
repo2data = Repo2Data(DATA_REQ_FILE)
data_path = repo2data.install()
data_path = os.path.abspath(data_path[0])
---- repo2data starting ----
/opt/hostedtoolcache/Python/3.10.14/x64/lib/python3.10/site-packages/repo2data
Config from file :
../binder/data_requirement.json
Destination:
./../data/multi-echo-data-analysis
Info : ./../data/multi-echo-data-analysis already downloaded
func_dir = os.path.join(data_path, "func/")
data_files = [
os.path.join(
func_dir,
"sub-04570_task-rest_echo-1_space-scanner_desc-partialPreproc_bold.nii.gz",
),
os.path.join(
func_dir,
"sub-04570_task-rest_echo-2_space-scanner_desc-partialPreproc_bold.nii.gz",
),
os.path.join(
func_dir,
"sub-04570_task-rest_echo-3_space-scanner_desc-partialPreproc_bold.nii.gz",
),
os.path.join(
func_dir,
"sub-04570_task-rest_echo-4_space-scanner_desc-partialPreproc_bold.nii.gz",
),
]
echo_times = [12.0, 28.0, 44.0, 60.0]
mask_file = os.path.join(
func_dir, "sub-04570_task-rest_space-scanner_desc-brain_mask.nii.gz"
)
confounds_file = os.path.join(
func_dir, "sub-04570_task-rest_desc-confounds_timeseries.tsv"
)
out_dir = os.path.join(data_path, "tedana")
workflows.tedana_workflow(
data_files,
echo_times,
out_dir=out_dir,
mask=mask_file,
prefix="sub-04570_task-rest_space-scanner",
fittype="curvefit",
tedpca="mdl",
verbose=True,
gscontrol=["mir"],
)
Show code cell output
INFO tedana:tedana_workflow:488 Using output directory: /home/runner/work/multi-echo-data-analysis/multi-echo-data-analysis/data/multi-echo-data-analysis/tedana
INFO tedana:tedana_workflow:506 Initializing and validating component selection tree
WARNING component_selector:validate_tree:127 Decision tree includes fields that are not used or logged {'_comment'}
INFO component_selector:__init__:250 Performing component selection with tedana_orig_decision_tree
INFO component_selector:__init__:251 Very similar to the decision tree designed by Prantik Kundu
INFO tedana:tedana_workflow:509 Loading input data: ['/home/runner/work/multi-echo-data-analysis/multi-echo-data-analysis/data/multi-echo-data-analysis/func/sub-04570_task-rest_echo-1_space-scanner_desc-partialPreproc_bold.nii.gz', '/home/runner/work/multi-echo-data-analysis/multi-echo-data-analysis/data/multi-echo-data-analysis/func/sub-04570_task-rest_echo-2_space-scanner_desc-partialPreproc_bold.nii.gz', '/home/runner/work/multi-echo-data-analysis/multi-echo-data-analysis/data/multi-echo-data-analysis/func/sub-04570_task-rest_echo-3_space-scanner_desc-partialPreproc_bold.nii.gz', '/home/runner/work/multi-echo-data-analysis/multi-echo-data-analysis/data/multi-echo-data-analysis/func/sub-04570_task-rest_echo-4_space-scanner_desc-partialPreproc_bold.nii.gz']
INFO io:__init__:155 Generating figures directory: /home/runner/work/multi-echo-data-analysis/multi-echo-data-analysis/data/multi-echo-data-analysis/tedana/figures
INFO tedana:tedana_workflow:572 Using user-defined mask
INFO tedana:tedana_workflow:620 Computing T2* map
INFO combine:make_optcom:192 Optimally combining data with voxel-wise T2* estimates
INFO tedana:tedana_workflow:645 Writing optimally combined data set: /home/runner/work/multi-echo-data-analysis/multi-echo-data-analysis/data/multi-echo-data-analysis/tedana/sub-04570_task-rest_space-scanner_desc-optcom_bold.nii.gz
INFO pca:tedpca:203 Computing PCA of optimally combined multi-echo data with selection criteria: mdl
INFO pca:tedpca:243 Optimal number of components based on different criteria:
INFO pca:tedpca:244 AIC: 81 | KIC: 71 | MDL: 46 | 90% varexp: 142 | 95% varexp: 187
INFO pca:tedpca:249 Explained variance based on different criteria:
INFO pca:tedpca:250 AIC: 0.821% | KIC: 0.806% | MDL: 0.762% | 90% varexp: 0.9% | 95% varexp: 0.95%
INFO pca:tedpca:267 Plotting maPCA optimization curves
INFO collect:generate_metrics:121 Calculating weight maps
INFO collect:generate_metrics:130 Calculating parameter estimate maps for optimally combined data
INFO collect:generate_metrics:143 Calculating z-statistic maps
INFO collect:generate_metrics:153 Calculating F-statistic maps
INFO collect:generate_metrics:173 Thresholding z-statistic maps
INFO collect:generate_metrics:180 Calculating T2* F-statistic maps
INFO collect:generate_metrics:187 Calculating S0 F-statistic maps
INFO collect:generate_metrics:195 Counting significant voxels in T2* F-statistic maps
INFO collect:generate_metrics:201 Counting significant voxels in S0 F-statistic maps
INFO collect:generate_metrics:208 Thresholding optimal combination beta maps to match T2* F-statistic maps
INFO collect:generate_metrics:214 Thresholding optimal combination beta maps to match S0 F-statistic maps
INFO collect:generate_metrics:221 Calculating kappa and rho
INFO collect:generate_metrics:230 Calculating variance explained
INFO collect:generate_metrics:236 Calculating normalized variance explained
INFO collect:generate_metrics:243 Calculating DSI between thresholded T2* F-statistic and optimal combination beta maps
INFO collect:generate_metrics:254 Calculating DSI between thresholded S0 F-statistic and optimal combination beta maps
INFO collect:generate_metrics:265 Calculating signal-noise t-statistics
INFO collect:generate_metrics:303 Counting significant noise voxels from z-statistic maps
INFO collect:generate_metrics:314 Calculating decision table score
INFO pca:tedpca:397 Selected 46 components with 76.18% normalized variance explained using mdl dimensionality estimate
/opt/hostedtoolcache/Python/3.10.14/x64/lib/python3.10/site-packages/tedana/io.py:346: FutureWarning: Downcasting behavior in `replace` is deprecated and will be removed in a future version. To retain the old behavior, explicitly call `result.infer_objects(copy=False)`. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`
deblanked = data.replace("", np.nan)
INFO ica:tedica:80 ICA with random seed 42 converged in 91 iterations
INFO collect:generate_metrics:121 Calculating weight maps
INFO collect:generate_metrics:130 Calculating parameter estimate maps for optimally combined data
INFO collect:generate_metrics:143 Calculating z-statistic maps
INFO collect:generate_metrics:153 Calculating F-statistic maps
INFO collect:generate_metrics:173 Thresholding z-statistic maps
INFO collect:generate_metrics:180 Calculating T2* F-statistic maps
INFO collect:generate_metrics:187 Calculating S0 F-statistic maps
INFO collect:generate_metrics:195 Counting significant voxels in T2* F-statistic maps
INFO collect:generate_metrics:201 Counting significant voxels in S0 F-statistic maps
INFO collect:generate_metrics:208 Thresholding optimal combination beta maps to match T2* F-statistic maps
INFO collect:generate_metrics:214 Thresholding optimal combination beta maps to match S0 F-statistic maps
INFO collect:generate_metrics:221 Calculating kappa and rho
INFO collect:generate_metrics:230 Calculating variance explained
INFO collect:generate_metrics:236 Calculating normalized variance explained
INFO collect:generate_metrics:243 Calculating DSI between thresholded T2* F-statistic and optimal combination beta maps
INFO collect:generate_metrics:254 Calculating DSI between thresholded S0 F-statistic and optimal combination beta maps
INFO collect:generate_metrics:265 Calculating signal-noise t-statistics
INFO collect:generate_metrics:303 Counting significant noise voxels from z-statistic maps
INFO collect:generate_metrics:314 Calculating decision table score
INFO tedana:tedana_workflow:694 Selecting components from ICA results
INFO tedica:automatic_selection:55 Performing ICA component selection
INFO selection_nodes:manual_classify:104 Step 0: manual_classify: Set all to unclassified
INFO selection_utils:log_decision_tree_step:406 Step 0: manual_classify applied to 46 components. 46 True -> unclassified. 0 False -> nochange.
INFO selection_nodes:manual_classify:136 Step 0: manual_classify component classification tags are cleared
INFO selection_utils:log_classification_counts:451 Step 0: Total component classifications: 46 unclassified
INFO selection_nodes:dec_left_op_right:389 Step 1: left_op_right: rejected if rho>kappa, else nochange
INFO selection_utils:log_decision_tree_step:406 Step 1: left_op_right applied to 46 components. 18 True -> rejected. 28 False -> nochange.
INFO selection_utils:log_classification_counts:451 Step 1: Total component classifications: 18 rejected, 28 unclassified
INFO selection_nodes:dec_left_op_right:389 Step 2: left_op_right: rejected if ['countsigFS0>countsigFT2 & countsigFT2>0'], else nochange
INFO selection_utils:log_decision_tree_step:406 Step 2: left_op_right applied to 46 components. 19 True -> rejected. 27 False -> nochange.
INFO selection_utils:log_classification_counts:451 Step 2: Total component classifications: 22 rejected, 24 unclassified
INFO selection_nodes:calc_median:653 Step 3: calc_median: Median(median_varex)
INFO selection_utils:log_decision_tree_step:418 Step 3: calc_median calculated: median_varex=0.5449334485072186
INFO selection_utils:log_classification_counts:451 Step 3: Total component classifications: 22 rejected, 24 unclassified
INFO selection_nodes:dec_left_op_right:389 Step 4: left_op_right: rejected if ['dice_FS0>dice_FT2 & variance explained>0.54'], else nochange
INFO selection_utils:log_decision_tree_step:406 Step 4: left_op_right applied to 46 components. 6 True -> rejected. 40 False -> nochange.
INFO selection_utils:log_classification_counts:451 Step 4: Total component classifications: 22 rejected, 24 unclassified
INFO selection_nodes:dec_left_op_right:389 Step 5: left_op_right: rejected if ['0>signal-noise_t & variance explained>0.54'], else nochange
INFO selection_utils:log_decision_tree_step:406 Step 5: left_op_right applied to 46 components. 9 True -> rejected. 37 False -> nochange.
INFO selection_utils:log_classification_counts:451 Step 5: Total component classifications: 26 rejected, 20 unclassified
INFO selection_nodes:calc_kappa_elbow:757 Step 6: calc_kappa_elbow: Calc Kappa Elbow
INFO selection_utils:kappa_elbow_kundu:621 Calculating kappa elbow based on min of all and nonsig components.
INFO selection_utils:log_decision_tree_step:418 Step 6: calc_kappa_elbow calculated: kappa_elbow_kundu=21.277964952355223, kappa_allcomps_elbow=40.1214389679082, kappa_nonsig_elbow=21.277964952355223, varex_upper_p=0.7802353000706743
INFO selection_utils:log_classification_counts:451 Step 6: Total component classifications: 26 rejected, 20 unclassified
INFO selection_nodes:dec_reclassify_high_var_comps:1120 Step 7: reclassify_high_var_comps: Change unclassified to unclass_highvar for the top couple of components with the highest jumps in variance
INFO selection_utils:log_decision_tree_step:406 Step 7: reclassify_high_var_comps applied to 20 components. 2 True -> unclass_highvar. 18 False -> nochange.
INFO selection_utils:log_classification_counts:451 Step 7: Total component classifications: 26 rejected, 2 unclass_highvar, 18 unclassified
INFO selection_nodes:calc_rho_elbow:882 Step 8: calc_rho_elbow: Calc Rho Elbow
INFO selection_utils:log_decision_tree_step:418 Step 8: calc_rho_elbow calculated: rho_elbow_kundu=17.93932546658279, rho_allcomps_elbow=27.779814570572192, rho_unclassified_elbow=15.910197343162249, elbow_f05=10.127964486013928
INFO selection_utils:log_classification_counts:451 Step 8: Total component classifications: 26 rejected, 2 unclass_highvar, 18 unclassified
INFO selection_nodes:dec_left_op_right:389 Step 9: left_op_right: provisionalaccept if kappa>=21.28, else nochange
INFO selection_utils:log_decision_tree_step:406 Step 9: left_op_right applied to 18 components. 12 True -> provisionalaccept. 6 False -> nochange.
INFO selection_utils:log_classification_counts:451 Step 9: Total component classifications: 12 provisionalaccept, 26 rejected, 2 unclass_highvar, 6 unclassified
INFO selection_nodes:dec_left_op_right:389 Step 10: left_op_right: unclassified if rho>17.94, else nochange
INFO selection_utils:log_decision_tree_step:406 Step 10: left_op_right applied to 12 components. 3 True -> unclassified. 9 False -> nochange.
INFO selection_utils:log_classification_counts:451 Step 10: Total component classifications: 9 provisionalaccept, 26 rejected, 2 unclass_highvar, 9 unclassified
INFO selection_nodes:dec_classification_doesnt_exist:1009 Step 11: classification_doesnt_exist: Change ['provisionalaccept', 'unclassified', 'unclass_highvar'] to accepted if less than 2 components with provisionalaccept exist
INFO selection_nodes:dec_classification_doesnt_exist:1011 Step 11: classification_doesnt_exist If nothing is provisionally accepted by this point, then rerun ICA & selection. If max iterations of rerunning done, then accept everything not already rejected
INFO selection_utils:log_decision_tree_step:406 Step 11: classification_doesnt_exist applied to 20 components. None True -> 0. None False -> 20.
INFO selection_utils:log_classification_counts:451 Step 11: Total component classifications: 9 provisionalaccept, 26 rejected, 2 unclass_highvar, 9 unclassified
INFO selection_nodes:calc_varex_thresh:1308 Step 12: calc_varex_thresh: Calc varex_upper_thresh, 90th percentile threshold
INFO selection_utils:log_decision_tree_step:418 Step 12: calc_varex_thresh calculated: varex_upper_thresh=0.8908095307208492, upper_perc=90
INFO selection_utils:log_classification_counts:451 Step 12: Total component classifications: 9 provisionalaccept, 26 rejected, 2 unclass_highvar, 9 unclassified
INFO selection_nodes:calc_varex_thresh:1308 Step 13: calc_varex_thresh: Calc varex_lower_thresh, 25th percentile threshold
INFO selection_utils:log_decision_tree_step:418 Step 13: calc_varex_thresh calculated: varex_lower_thresh=0.32109341064129754, lower_perc=25
INFO selection_utils:log_classification_counts:451 Step 13: Total component classifications: 9 provisionalaccept, 26 rejected, 2 unclass_highvar, 9 unclassified
INFO selection_utils:get_extend_factor:798 extend_factor=2.0, based on number of fMRI volumes
INFO selection_utils:log_decision_tree_step:418 Step 14: calc_extend_factor calculated: extend_factor=2.0
INFO selection_utils:log_classification_counts:451 Step 14: Total component classifications: 9 provisionalaccept, 26 rejected, 2 unclass_highvar, 9 unclassified
INFO selection_utils:log_decision_tree_step:418 Step 15: calc_max_good_meanmetricrank calculated: max_good_meanmetricrank=18.0
INFO selection_utils:log_classification_counts:451 Step 15: Total component classifications: 9 provisionalaccept, 26 rejected, 2 unclass_highvar, 9 unclassified
INFO selection_utils:log_decision_tree_step:418 Step 16: calc_varex_kappa_ratio calculated: kappa_rate=75.59581211806685
INFO selection_utils:log_classification_counts:451 Step 16: Total component classifications: 9 provisionalaccept, 26 rejected, 2 unclass_highvar, 9 unclassified
INFO selection_nodes:dec_left_op_right:389 Step 17: left_op_right: rejected if ['d_table_score>18.0 & variance explained>2.0*0.89'], else nochange
INFO selection_nodes:dec_left_op_right:391 Step 17: left_op_right If variance and d_table_scores are high, then reject
INFO selection_utils:comptable_classification_changer:277 Step 17: No components fit criterion True to change classification
INFO selection_utils:log_decision_tree_step:406 Step 17: left_op_right applied to 20 components. 0 True -> rejected. 20 False -> nochange.
INFO selection_utils:log_classification_counts:451 Step 17: Total component classifications: 9 provisionalaccept, 26 rejected, 2 unclass_highvar, 9 unclassified
INFO selection_nodes:dec_left_op_right:389 Step 18: left_op_right: accepted if ['d_table_score>18.0 & variance explained<=0.32 & kappa<=21.28'], else nochange
INFO selection_nodes:dec_left_op_right:391 Step 18: left_op_right If low variance, accept even if bad kappa & d_table_scores
INFO selection_utils:log_decision_tree_step:406 Step 18: left_op_right applied to 20 components. 5 True -> accepted. 15 False -> nochange.
INFO selection_utils:log_classification_counts:451 Step 18: Total component classifications: 5 accepted, 9 provisionalaccept, 26 rejected, 2 unclass_highvar, 4 unclassified
INFO selection_nodes:dec_classification_doesnt_exist:1009 Step 19: classification_doesnt_exist: Change ['provisionalaccept', 'unclassified', 'unclass_highvar'] to accepted if ['unclassified', 'unclass_highvar'] doesn't exist
INFO selection_nodes:dec_classification_doesnt_exist:1011 Step 19: classification_doesnt_exist If nothing left is unclassified, then accept all
INFO selection_utils:log_decision_tree_step:406 Step 19: classification_doesnt_exist applied to 15 components. None True -> 0. None False -> 15.
INFO selection_utils:log_classification_counts:451 Step 19: Total component classifications: 5 accepted, 9 provisionalaccept, 26 rejected, 2 unclass_highvar, 4 unclassified
INFO selection_nodes:calc_revised_meanmetricrank_guesses:1768 Step 20: calc_revised_meanmetricrank_guesses: Calc revised d_table_score & num accepted component guesses
INFO selection_utils:log_decision_tree_step:418 Step 20: calc_revised_meanmetricrank_guesses calculated: num_acc_guess=11, conservative_guess=5.5, restrict_factor=2
INFO selection_utils:log_classification_counts:451 Step 20: Total component classifications: 5 accepted, 9 provisionalaccept, 26 rejected, 2 unclass_highvar, 4 unclassified
INFO selection_nodes:dec_left_op_right:389 Step 21: left_op_right: rejected if ['d_table_score_node20>5.5 & varex kappa ratio>2*2.0 & variance explained>2.0*0.89'], else nochange
INFO selection_nodes:dec_left_op_right:391 Step 21: left_op_right Reject if a combination of kappa, variance, and other factors are ranked worse than others
INFO selection_utils:log_decision_tree_step:406 Step 21: left_op_right applied to 15 components. 2 True -> rejected. 13 False -> nochange.
INFO selection_utils:log_classification_counts:451 Step 21: Total component classifications: 5 accepted, 9 provisionalaccept, 28 rejected, 4 unclassified
INFO selection_nodes:dec_left_op_right:389 Step 22: left_op_right: rejected if ['d_table_score_node20>0.9*11 & variance explained>2.0*0.32'], else nochange
INFO selection_nodes:dec_left_op_right:391 Step 22: left_op_right Reject if a combination of variance and ranks of other metrics are worse than others
INFO selection_utils:log_decision_tree_step:406 Step 22: left_op_right applied to 13 components. 2 True -> rejected. 11 False -> nochange.
INFO selection_utils:log_classification_counts:451 Step 22: Total component classifications: 5 accepted, 9 provisionalaccept, 30 rejected, 2 unclassified
INFO selection_nodes:calc_varex_thresh:1308 Step 23: calc_varex_thresh: Calc varex_new_lower_thresh, 25th percentile threshold
INFO selection_utils:log_decision_tree_step:418 Step 23: calc_varex_thresh calculated: varex_new_lower_thresh=0.33750948831195976, new_lower_perc=25
INFO selection_utils:log_classification_counts:451 Step 23: Total component classifications: 5 accepted, 9 provisionalaccept, 30 rejected, 2 unclassified
INFO selection_nodes:dec_left_op_right:389 Step 24: left_op_right: accepted if ['d_table_score_node20>11 & variance explained>0.34'], else nochange
INFO selection_nodes:dec_left_op_right:391 Step 24: left_op_right Accept components with a bad d_table_score, but are at the higher end of the remaining variance so more cautious to not remove
INFO selection_utils:log_decision_tree_step:406 Step 24: left_op_right applied to 11 components. 1 True -> accepted. 10 False -> nochange.
INFO selection_utils:log_classification_counts:451 Step 24: Total component classifications: 6 accepted, 9 provisionalaccept, 30 rejected, 1 unclassified
INFO selection_nodes:dec_left_op_right:389 Step 25: left_op_right: accepted if ['kappa<=21.28 & variance explained>0.34'], else nochange
INFO selection_nodes:dec_left_op_right:391 Step 25: left_op_right For not already rejected components, accept ones below the kappa elbow, but at the higher end of the remaining variance so more cautious to not remove
INFO selection_utils:comptable_classification_changer:277 Step 25: No components fit criterion True to change classification
INFO selection_utils:log_decision_tree_step:406 Step 25: left_op_right applied to 10 components. 0 True -> accepted. 10 False -> nochange.
INFO selection_utils:log_classification_counts:451 Step 25: Total component classifications: 6 accepted, 9 provisionalaccept, 30 rejected, 1 unclassified
INFO selection_nodes:manual_classify:104 Step 26: manual_classify: Set ['provisionalaccept', 'unclassified', 'unclass_highvar'] to accepted
INFO selection_nodes:manual_classify:106 Step 26: manual_classify Anything still provisional (accepted or rejected) or unclassified should be accepted
INFO selection_utils:log_decision_tree_step:406 Step 26: manual_classify applied to 10 components. 10 True -> accepted. 0 False -> nochange.
INFO selection_utils:log_classification_counts:451 Step 26: Total component classifications: 16 accepted, 30 rejected
INFO io:denoise_ts:527 Variance explained by decomposition: 92.18%
INFO io:write_split_ts:599 Writing high-Kappa time series: /home/runner/work/multi-echo-data-analysis/multi-echo-data-analysis/data/multi-echo-data-analysis/tedana/sub-04570_task-rest_space-scanner_desc-optcomAccepted_bold.nii.gz
INFO io:write_split_ts:607 Writing low-Kappa time series: /home/runner/work/multi-echo-data-analysis/multi-echo-data-analysis/data/multi-echo-data-analysis/tedana/sub-04570_task-rest_space-scanner_desc-optcomRejected_bold.nii.gz
INFO io:write_split_ts:614 Writing denoised time series: /home/runner/work/multi-echo-data-analysis/multi-echo-data-analysis/data/multi-echo-data-analysis/tedana/sub-04570_task-rest_space-scanner_desc-denoised_bold.nii.gz
INFO io:writeresults:663 Writing full ICA coefficient feature set: /home/runner/work/multi-echo-data-analysis/multi-echo-data-analysis/data/multi-echo-data-analysis/tedana/sub-04570_task-rest_space-scanner_desc-ICA_components.nii.gz
INFO io:writeresults:667 Writing denoised ICA coefficient feature set: /home/runner/work/multi-echo-data-analysis/multi-echo-data-analysis/data/multi-echo-data-analysis/tedana/sub-04570_task-rest_space-scanner_desc-ICAAccepted_components.nii.gz
INFO io:writeresults:673 Writing Z-normalized spatial component maps: /home/runner/work/multi-echo-data-analysis/multi-echo-data-analysis/data/multi-echo-data-analysis/tedana/sub-04570_task-rest_space-scanner_desc-ICAAccepted_stat-z_components.nii.gz
INFO gscontrol:minimum_image_regression:167 Performing minimum image regression to remove spatially-diffuse noise
INFO io:writeresults_echoes:713 Writing Kappa-filtered echo #1 timeseries
INFO io:denoise_ts:527 Variance explained by decomposition: 84.08%
INFO io:write_split_ts:596 Writing high-Kappa time series: /home/runner/work/multi-echo-data-analysis/multi-echo-data-analysis/data/multi-echo-data-analysis/tedana/sub-04570_task-rest_space-scanner_echo-1_desc-Accepted_bold.nii.gz
INFO io:write_split_ts:604 Writing low-Kappa time series: /home/runner/work/multi-echo-data-analysis/multi-echo-data-analysis/data/multi-echo-data-analysis/tedana/sub-04570_task-rest_space-scanner_echo-1_desc-Rejected_bold.nii.gz
INFO io:write_split_ts:614 Writing denoised time series: /home/runner/work/multi-echo-data-analysis/multi-echo-data-analysis/data/multi-echo-data-analysis/tedana/sub-04570_task-rest_space-scanner_echo-1_desc-Denoised_bold.nii.gz
INFO io:writeresults_echoes:713 Writing Kappa-filtered echo #2 timeseries
INFO io:denoise_ts:527 Variance explained by decomposition: 85.17%
INFO io:write_split_ts:596 Writing high-Kappa time series: /home/runner/work/multi-echo-data-analysis/multi-echo-data-analysis/data/multi-echo-data-analysis/tedana/sub-04570_task-rest_space-scanner_echo-2_desc-Accepted_bold.nii.gz
INFO io:write_split_ts:604 Writing low-Kappa time series: /home/runner/work/multi-echo-data-analysis/multi-echo-data-analysis/data/multi-echo-data-analysis/tedana/sub-04570_task-rest_space-scanner_echo-2_desc-Rejected_bold.nii.gz
INFO io:write_split_ts:614 Writing denoised time series: /home/runner/work/multi-echo-data-analysis/multi-echo-data-analysis/data/multi-echo-data-analysis/tedana/sub-04570_task-rest_space-scanner_echo-2_desc-Denoised_bold.nii.gz
INFO io:writeresults_echoes:713 Writing Kappa-filtered echo #3 timeseries
INFO io:denoise_ts:527 Variance explained by decomposition: 85.59%
INFO io:write_split_ts:596 Writing high-Kappa time series: /home/runner/work/multi-echo-data-analysis/multi-echo-data-analysis/data/multi-echo-data-analysis/tedana/sub-04570_task-rest_space-scanner_echo-3_desc-Accepted_bold.nii.gz
INFO io:write_split_ts:604 Writing low-Kappa time series: /home/runner/work/multi-echo-data-analysis/multi-echo-data-analysis/data/multi-echo-data-analysis/tedana/sub-04570_task-rest_space-scanner_echo-3_desc-Rejected_bold.nii.gz
INFO io:write_split_ts:614 Writing denoised time series: /home/runner/work/multi-echo-data-analysis/multi-echo-data-analysis/data/multi-echo-data-analysis/tedana/sub-04570_task-rest_space-scanner_echo-3_desc-Denoised_bold.nii.gz
INFO io:writeresults_echoes:713 Writing Kappa-filtered echo #4 timeseries
INFO io:denoise_ts:527 Variance explained by decomposition: 85.47%
INFO io:write_split_ts:596 Writing high-Kappa time series: /home/runner/work/multi-echo-data-analysis/multi-echo-data-analysis/data/multi-echo-data-analysis/tedana/sub-04570_task-rest_space-scanner_echo-4_desc-Accepted_bold.nii.gz
INFO io:write_split_ts:604 Writing low-Kappa time series: /home/runner/work/multi-echo-data-analysis/multi-echo-data-analysis/data/multi-echo-data-analysis/tedana/sub-04570_task-rest_space-scanner_echo-4_desc-Rejected_bold.nii.gz
INFO io:write_split_ts:614 Writing denoised time series: /home/runner/work/multi-echo-data-analysis/multi-echo-data-analysis/data/multi-echo-data-analysis/tedana/sub-04570_task-rest_space-scanner_echo-4_desc-Denoised_bold.nii.gz
INFO tedana:tedana_workflow:885 Making figures folder with static component maps and timecourse plots.
INFO io:denoise_ts:527 Variance explained by decomposition: 92.18%
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
Cell In[3], line 1
----> 1 workflows.tedana_workflow(
2 data_files,
3 echo_times,
4 out_dir=out_dir,
5 mask=mask_file,
6 prefix="sub-04570_task-rest_space-scanner",
7 fittype="curvefit",
8 tedpca="mdl",
9 verbose=True,
10 gscontrol=["mir"],
11 )
File /opt/hostedtoolcache/Python/3.10.14/x64/lib/python3.10/site-packages/tedana/workflows/tedana.py:906, in tedana_workflow(data, tes, out_dir, mask, convention, prefix, fittype, combmode, tree, tedpca, fixed_seed, maxit, maxrestart, tedort, gscontrol, no_reports, png_cmap, verbose, low_mem, debug, quiet, overwrite, t2smap, mixm, tedana_command)
889 reporting.static_figures.carpet_plot(
890 optcom_ts=data_oc,
891 denoised_ts=dn_ts,
(...)
896 gscontrol=gscontrol,
897 )
898 reporting.static_figures.comp_figures(
899 data_oc,
900 mask=mask_denoise,
(...)
904 png_cmap=png_cmap,
905 )
--> 906 reporting.static_figures.plot_t2star_and_s0(io_generator=io_generator, mask=mask_denoise)
908 LGR.info("Generating dynamic report")
909 reporting.generate_report(io_generator)
File /opt/hostedtoolcache/Python/3.10.14/x64/lib/python3.10/site-packages/tedana/reporting/static_figures.py:593, in plot_t2star_and_s0(io_generator, mask)
591 # Plot T2* and S0 maps
592 t2star_plot = f"{io_generator.prefix}t2star_brain.svg"
--> 593 plotting.plot_stat_map(
594 t2star_img,
595 bg_img=None,
596 display_mode="mosaic",
597 symmetric_cbar=False,
598 black_bg=True,
599 cmap="gray",
600 vmin=t2s_p02,
601 vmax=t2s_p98,
602 annotate=False,
603 output_file=os.path.join(io_generator.out_dir, "figures", t2star_plot),
604 )
606 s0_plot = f"{io_generator.prefix}s0_brain.svg"
607 plotting.plot_stat_map(
608 s0_img,
609 bg_img=None,
(...)
617 output_file=os.path.join(io_generator.out_dir, "figures", s0_plot),
618 )
File /opt/hostedtoolcache/Python/3.10.14/x64/lib/python3.10/site-packages/nilearn/plotting/img_plotting.py:934, in plot_stat_map(stat_map_img, bg_img, cut_coords, output_file, display_mode, colorbar, cbar_tick_format, figure, axes, title, threshold, annotate, draw_cross, black_bg, cmap, symmetric_cbar, dim, vmax, resampling_interpolation, **kwargs)
929 bg_img, black_bg, bg_vmin, bg_vmax = _load_anat(bg_img, dim=dim,
930 black_bg=black_bg)
932 stat_map_img = _utils.check_niimg_3d(stat_map_img, dtype='auto')
--> 934 cbar_vmin, cbar_vmax, vmin, vmax = _get_colorbar_and_data_ranges(
935 _safe_get_data(stat_map_img, ensure_finite=True),
936 vmax,
937 symmetric_cbar,
938 kwargs)
940 display = _plot_img_with_bg(
941 img=stat_map_img, bg_img=bg_img, cut_coords=cut_coords,
942 output_file=output_file, display_mode=display_mode,
(...)
947 cbar_vmin=cbar_vmin, cbar_vmax=cbar_vmax,
948 resampling_interpolation=resampling_interpolation, **kwargs)
950 return display
File /opt/hostedtoolcache/Python/3.10.14/x64/lib/python3.10/site-packages/nilearn/plotting/img_plotting.py:76, in _get_colorbar_and_data_ranges(stat_map_data, vmax, symmetric_cbar, kwargs, force_min_stat_map_value)
66 """Internal function for setting colormap and colorbar limits.
67
68 Used by for plot_stat_map and plot_glass_brain.
(...)
73
74 """
75 if 'vmin' in kwargs:
---> 76 raise ValueError('this function does not accept a "vmin" '
77 'argument, as it uses a symmetrical range '
78 'defined via the vmax argument. To threshold '
79 'the plotted map, use the "threshold" argument')
81 # make sure that the color range is symmetrical
82 if vmax is None or symmetric_cbar in ['auto', False]:
83 # Avoid dealing with masked_array:
ValueError: this function does not accept a "vmin" argument, as it uses a symmetrical range defined via the vmax argument. To threshold the plotted map, use the "threshold" argument
The tedana workflow writes out a number of files.
out_files = sorted(glob(os.path.join(out_dir, "*")))
out_files = [os.path.basename(f) for f in out_files]
print("\n".join(out_files))
metrics = pd.read_table(
os.path.join(out_dir, "sub-04570_task-rest_space-scanner_desc-tedana_metrics.tsv")
)
Show code cell source
def color_rejected_red(series):
"""Color rejected components red."""
return [
"color: red" if series["classification"] == "rejected" else "" for v in series
]
metrics.style.apply(color_rejected_red, axis=1)
with open(
os.path.join(out_dir, "sub-04570_task-rest_space-scanner_desc-tedana_metrics.json"),
"r",
) as fo:
data = json.load(fo)
first_five_keys = list(data.keys())[:5]
reduced_data = {k: data[k] for k in first_five_keys}
pprint(reduced_data)
df = pd.DataFrame.from_dict(data, orient="index")
df = df.fillna("n/a")
display(HTML(df.to_html()))
report = os.path.join(out_dir, "tedana_report.html")
with open(report, "r") as fo:
report_data = fo.read()
figures_dir = os.path.relpath(os.path.join(out_dir, "figures"), os.getcwd())
report_data = report_data.replace("./figures", figures_dir)
display(HTML(report_data))