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])
/opt/hostedtoolcache/Python/3.10.18/x64/lib/python3.10/site-packages/pybtex/plugin/__init__.py:26: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81.
import pkg_resources
---- repo2data starting ----
/opt/hostedtoolcache/Python/3.10.18/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
/opt/hostedtoolcache/Python/3.10.18/x64/lib/python3.10/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html
from .autonotebook import tqdm as notebook_tqdm
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:608 Using output directory: /home/runner/work/multi-echo-data-analysis/multi-echo-data-analysis/data/multi-echo-data-analysis/tedana
INFO tedana:tedana_workflow:627 Initializing and validating component selection tree
WARNING component_selector:validate_tree:146 Decision tree includes fields that are not used or logged ['_comment']
INFO component_selector:__init__:333 Performing component selection with tedana_orig_decision_tree
INFO component_selector:__init__:334 Very similar to the decision tree designed by Prantik Kundu
INFO tedana:tedana_workflow:630 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__:156 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:718 Using user-defined mask
INFO utils:make_adaptive_mask:202 Echo-wise intensity thresholds for adaptive mask: [258.33994278 180.98638476 134.6796175 91.51006253]
WARNING utils:make_adaptive_mask:231 734 voxels in user-defined mask do not have good signal. Removing voxels from mask.
INFO tedana:tedana_workflow:774 Computing T2* map
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/opt/hostedtoolcache/Python/3.10.18/x64/lib/python3.10/site-packages/tedana/decay.py:541: RuntimeWarning: Mean of empty slice
rmse_map = np.nanmean(rmse, axis=1)
INFO combine:make_optcom:192 Optimally combining data with voxel-wise T2* estimates
INFO tedana:tedana_workflow:822 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:208 Computing PCA of optimally combined multi-echo data with selection criteria: mdl
/opt/hostedtoolcache/Python/3.10.18/x64/lib/python3.10/site-packages/tedana/io.py:904: UserWarning: Data array used to create a new image contains 64-bit ints. This is likely due to creating the array with numpy and passing `int` as the `dtype`. Many tools such as FSL and SPM cannot deal with int64 in Nifti images, so for compatibility the data has been converted to int32.
nii = new_img_like(ref_img, newdata, affine=affine, copy_header=copy_header)
INFO pca:tedpca:248 Optimal number of components based on different criteria:
INFO pca:tedpca:249 AIC: 77 | KIC: 67 | MDL: 43 | 90% varexp: 141 | 95% varexp: 186
INFO pca:tedpca:254 Explained variance based on different criteria:
INFO pca:tedpca:255 AIC: 0.816% | KIC: 0.801% | MDL: 0.757% | 90% varexp: 0.9% | 95% varexp: 0.95%
INFO pca:tedpca:272 Plotting maPCA optimization curves
INFO collect:generate_metrics:161 Calculating weight maps
INFO collect:generate_metrics:173 Calculating parameter estimate maps for optimally combined data
INFO collect:generate_metrics:193 Calculating z-statistic maps
INFO collect:generate_metrics:203 Calculating F-statistic maps
INFO collect:generate_metrics:228 Thresholding z-statistic maps
INFO collect:generate_metrics:238 Calculating T2* F-statistic maps
INFO collect:generate_metrics:248 Calculating S0 F-statistic maps
INFO collect:generate_metrics:259 Counting significant voxels in T2* F-statistic maps
INFO collect:generate_metrics:265 Counting significant voxels in S0 F-statistic maps
INFO collect:generate_metrics:272 Thresholding optimal combination beta maps to match T2* F-statistic maps
INFO collect:generate_metrics:281 Thresholding optimal combination beta maps to match S0 F-statistic maps
INFO collect:generate_metrics:291 Calculating kappa and rho
INFO collect:generate_metrics:300 Calculating variance explained
INFO collect:generate_metrics:306 Calculating normalized variance explained
INFO collect:generate_metrics:313 Calculating DSI between thresholded T2* F-statistic and optimal combination beta maps
INFO collect:generate_metrics:323 Calculating DSI between thresholded S0 F-statistic and optimal combination beta maps
INFO collect:generate_metrics:334 Calculating signal-noise t-statistics
/opt/hostedtoolcache/Python/3.10.18/x64/lib/python3.10/site-packages/scipy/_lib/deprecation.py:234: SmallSampleWarning: One or more sample arguments is too small; all returned values will be NaN. See documentation for sample size requirements.
return f(*args, **kwargs)
INFO collect:generate_metrics:368 Counting significant noise voxels from z-statistic maps
INFO collect:generate_metrics:380 Calculating decision table score
INFO pca:tedpca:412 Selected 43 components with 75.69% normalized variance explained using mdl dimensionality estimate
/opt/hostedtoolcache/Python/3.10.18/x64/lib/python3.10/site-packages/tedana/io.py:355: 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:f_ica:344 ICA with random seed 42 converged in 62 iterations
INFO collect:generate_metrics:161 Calculating weight maps
INFO collect:generate_metrics:173 Calculating parameter estimate maps for optimally combined data
INFO collect:generate_metrics:193 Calculating z-statistic maps
INFO collect:generate_metrics:203 Calculating F-statistic maps
INFO collect:generate_metrics:228 Thresholding z-statistic maps
INFO collect:generate_metrics:238 Calculating T2* F-statistic maps
INFO collect:generate_metrics:248 Calculating S0 F-statistic maps
INFO collect:generate_metrics:259 Counting significant voxels in T2* F-statistic maps
INFO collect:generate_metrics:265 Counting significant voxels in S0 F-statistic maps
INFO collect:generate_metrics:272 Thresholding optimal combination beta maps to match T2* F-statistic maps
INFO collect:generate_metrics:281 Thresholding optimal combination beta maps to match S0 F-statistic maps
INFO collect:generate_metrics:291 Calculating kappa and rho
INFO collect:generate_metrics:300 Calculating variance explained
INFO collect:generate_metrics:306 Calculating normalized variance explained
INFO collect:generate_metrics:313 Calculating DSI between thresholded T2* F-statistic and optimal combination beta maps
INFO collect:generate_metrics:323 Calculating DSI between thresholded S0 F-statistic and optimal combination beta maps
INFO collect:generate_metrics:334 Calculating signal-noise t-statistics
/opt/hostedtoolcache/Python/3.10.18/x64/lib/python3.10/site-packages/scipy/_lib/deprecation.py:234: SmallSampleWarning: One or more sample arguments is too small; all returned values will be NaN. See documentation for sample size requirements.
return f(*args, **kwargs)
INFO collect:generate_metrics:368 Counting significant noise voxels from z-statistic maps
INFO collect:generate_metrics:380 Calculating decision table score
INFO tedana:tedana_workflow:894 Selecting components from ICA results
INFO tedica:automatic_selection:54 Performing ICA component selection with tree: tedana_orig
INFO selection_nodes:manual_classify:104 Step 0: manual_classify: Set all to unclassified
INFO selection_utils:comptable_classification_changer:293 Step 0: No components fit criterion False to change classification
INFO selection_utils:log_decision_tree_step:447 Step 0: manual_classify applied to 43 components. 43 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:492 Step 0: Total component classifications: 43 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:447 Step 1: left_op_right applied to 43 components. 15 True -> rejected. 28 False -> nochange.
INFO selection_utils:log_classification_counts:492 Step 1: Total component classifications: 15 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:447 Step 2: left_op_right applied to 43 components. 15 True -> rejected. 28 False -> nochange.
INFO selection_utils:log_classification_counts:492 Step 2: Total component classifications: 16 rejected, 27 unclassified
INFO selection_nodes:calc_median:653 Step 3: calc_median: Median(median_varex)
INFO selection_utils:log_decision_tree_step:459 Step 3: calc_median calculated: median_varex=0.6369008306484385
INFO selection_utils:log_classification_counts:492 Step 3: Total component classifications: 16 rejected, 27 unclassified
INFO selection_nodes:dec_left_op_right:389 Step 4: left_op_right: rejected if ['dice_FS0>dice_FT2 & variance explained>0.64'], else nochange
INFO selection_utils:log_decision_tree_step:447 Step 4: left_op_right applied to 43 components. 4 True -> rejected. 39 False -> nochange.
INFO selection_utils:log_classification_counts:492 Step 4: Total component classifications: 16 rejected, 27 unclassified
INFO selection_nodes:dec_left_op_right:389 Step 5: left_op_right: rejected if ['0>signal-noise_t & variance explained>0.64'], else nochange
INFO selection_utils:log_decision_tree_step:447 Step 5: left_op_right applied to 43 components. 2 True -> rejected. 41 False -> nochange.
INFO selection_utils:log_classification_counts:492 Step 5: Total component classifications: 18 rejected, 25 unclassified
INFO selection_nodes:calc_kappa_elbow:767 Step 6: calc_kappa_elbow: Calc Kappa Elbow
INFO selection_utils:kappa_elbow_kundu:664 Calculating kappa elbow based on min of all and nonsig components.
INFO selection_utils:log_decision_tree_step:459 Step 6: calc_kappa_elbow calculated: kappa_elbow_kundu=24.006482976612357, kappa_allcomps_elbow=37.56289039370621, kappa_nonsig_elbow=24.006482976612357, varex_upper_p=0.8089089913339598
INFO selection_utils:log_classification_counts:492 Step 6: Total component classifications: 18 rejected, 25 unclassified
INFO selection_nodes:dec_reclassify_high_var_comps:1140 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:447 Step 7: reclassify_high_var_comps applied to 25 components. 3 True -> unclass_highvar. 22 False -> nochange.
INFO selection_utils:log_classification_counts:492 Step 7: Total component classifications: 18 rejected, 3 unclass_highvar, 22 unclassified
INFO selection_nodes:calc_rho_elbow:902 Step 8: calc_rho_elbow: Calc Rho Elbow
INFO selection_utils:log_decision_tree_step:459 Step 8: calc_rho_elbow calculated: rho_elbow_kundu=16.593474162929414, rho_allcomps_elbow=24.547042487268047, rho_unclassified_elbow=15.105415515506271, elbow_f05=10.127964486013928
INFO selection_utils:log_classification_counts:492 Step 8: Total component classifications: 18 rejected, 3 unclass_highvar, 22 unclassified
INFO selection_nodes:dec_left_op_right:389 Step 9: left_op_right: provisionalaccept if kappa>=24.01, else nochange
INFO selection_utils:log_decision_tree_step:447 Step 9: left_op_right applied to 22 components. 16 True -> provisionalaccept. 6 False -> nochange.
INFO selection_utils:log_classification_counts:492 Step 9: Total component classifications: 16 provisionalaccept, 18 rejected, 3 unclass_highvar, 6 unclassified
INFO selection_nodes:dec_left_op_right:389 Step 10: left_op_right: unclassified if rho>16.59, else nochange
INFO selection_utils:log_decision_tree_step:447 Step 10: left_op_right applied to 16 components. 6 True -> unclassified. 10 False -> nochange.
INFO selection_utils:log_classification_counts:492 Step 10: Total component classifications: 10 provisionalaccept, 18 rejected, 3 unclass_highvar, 12 unclassified
INFO selection_nodes:dec_classification_doesnt_exist:1029 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:1031 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:447 Step 11: classification_doesnt_exist applied to 25 components. None True -> 0. None False -> 25.
INFO selection_utils:log_classification_counts:492 Step 11: Total component classifications: 10 provisionalaccept, 18 rejected, 3 unclass_highvar, 12 unclassified
INFO selection_nodes:calc_varex_thresh:1328 Step 12: calc_varex_thresh: Calc varex_upper_thresh, 90th percentile threshold
INFO selection_utils:log_decision_tree_step:459 Step 12: calc_varex_thresh calculated: varex_upper_thresh=1.0599282255701312, upper_perc=90
INFO selection_utils:log_classification_counts:492 Step 12: Total component classifications: 10 provisionalaccept, 18 rejected, 3 unclass_highvar, 12 unclassified
INFO selection_nodes:calc_varex_thresh:1328 Step 13: calc_varex_thresh: Calc varex_lower_thresh, 25th percentile threshold
INFO selection_utils:log_decision_tree_step:459 Step 13: calc_varex_thresh calculated: varex_lower_thresh=0.40360285414716973, lower_perc=25
INFO selection_utils:log_classification_counts:492 Step 13: Total component classifications: 10 provisionalaccept, 18 rejected, 3 unclass_highvar, 12 unclassified
INFO selection_utils:get_extend_factor:846 extend_factor=2.0, based on number of fMRI volumes
INFO selection_utils:log_decision_tree_step:459 Step 14: calc_extend_factor calculated: extend_factor=2.0
INFO selection_utils:log_classification_counts:492 Step 14: Total component classifications: 10 provisionalaccept, 18 rejected, 3 unclass_highvar, 12 unclassified
INFO selection_utils:log_decision_tree_step:459 Step 15: calc_max_good_meanmetricrank calculated: max_good_meanmetricrank=20.0
INFO selection_utils:log_classification_counts:492 Step 15: Total component classifications: 10 provisionalaccept, 18 rejected, 3 unclass_highvar, 12 unclassified
INFO selection_utils:log_decision_tree_step:459 Step 16: calc_varex_kappa_ratio calculated: kappa_rate=76.84859750445794
INFO selection_utils:log_classification_counts:492 Step 16: Total component classifications: 10 provisionalaccept, 18 rejected, 3 unclass_highvar, 12 unclassified
INFO selection_nodes:dec_left_op_right:389 Step 17: left_op_right: rejected if ['d_table_score>20.0 & variance explained>2.0*1.06'], 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:293 Step 17: No components fit criterion True to change classification
INFO selection_utils:log_decision_tree_step:447 Step 17: left_op_right applied to 25 components. 0 True -> rejected. 25 False -> nochange.
INFO selection_utils:log_classification_counts:492 Step 17: Total component classifications: 10 provisionalaccept, 18 rejected, 3 unclass_highvar, 12 unclassified
INFO selection_nodes:dec_left_op_right:389 Step 18: left_op_right: accepted if ['d_table_score>20.0 & variance explained<=0.4 & kappa<=24.01'], 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:447 Step 18: left_op_right applied to 25 components. 6 True -> accepted. 19 False -> nochange.
INFO selection_utils:log_classification_counts:492 Step 18: Total component classifications: 6 accepted, 9 provisionalaccept, 18 rejected, 3 unclass_highvar, 7 unclassified
INFO selection_nodes:dec_classification_doesnt_exist:1029 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:1031 Step 19: classification_doesnt_exist If nothing left is unclassified, then accept all
INFO selection_utils:log_decision_tree_step:447 Step 19: classification_doesnt_exist applied to 19 components. None True -> 0. None False -> 19.
INFO selection_utils:log_classification_counts:492 Step 19: Total component classifications: 6 accepted, 9 provisionalaccept, 18 rejected, 3 unclass_highvar, 7 unclassified
INFO selection_nodes:calc_revised_meanmetricrank_guesses:1788 Step 20: calc_revised_meanmetricrank_guesses: Calc revised d_table_score & num accepted component guesses
INFO selection_utils:log_decision_tree_step:459 Step 20: calc_revised_meanmetricrank_guesses calculated: num_acc_guess=13, conservative_guess=6.5, restrict_factor=2
INFO selection_utils:log_classification_counts:492 Step 20: Total component classifications: 6 accepted, 9 provisionalaccept, 18 rejected, 3 unclass_highvar, 7 unclassified
INFO selection_nodes:dec_left_op_right:389 Step 21: left_op_right: rejected if ['d_table_score_node20>6.5 & varex kappa ratio>2*2.0 & variance explained>2.0*1.06'], 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:447 Step 21: left_op_right applied to 19 components. 2 True -> rejected. 17 False -> nochange.
INFO selection_utils:log_classification_counts:492 Step 21: Total component classifications: 6 accepted, 9 provisionalaccept, 20 rejected, 1 unclass_highvar, 7 unclassified
INFO selection_nodes:dec_left_op_right:389 Step 22: left_op_right: rejected if ['d_table_score_node20>0.9*13 & variance explained>2.0*0.4'], 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:447 Step 22: left_op_right applied to 17 components. 5 True -> rejected. 12 False -> nochange.
INFO selection_utils:log_classification_counts:492 Step 22: Total component classifications: 6 accepted, 8 provisionalaccept, 25 rejected, 1 unclass_highvar, 3 unclassified
INFO selection_nodes:calc_varex_thresh:1328 Step 23: calc_varex_thresh: Calc varex_new_lower_thresh, 25th percentile threshold
INFO selection_nodes:calc_varex_thresh:1352 Step 23: calc_varex_thresh: num_highest_var_comps (13) > len(comps2use) (12). Setting to equal len(comps2use) since selection should not use more components than exist
INFO selection_utils:log_decision_tree_step:459 Step 23: calc_varex_thresh calculated: varex_new_lower_thresh=0.5074016845951423, new_lower_perc=25
INFO selection_utils:log_classification_counts:492 Step 23: Total component classifications: 6 accepted, 8 provisionalaccept, 25 rejected, 1 unclass_highvar, 3 unclassified
INFO selection_nodes:dec_left_op_right:389 Step 24: left_op_right: accepted if ['d_table_score_node20>13 & variance explained>0.51'], 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:447 Step 24: left_op_right applied to 12 components. 1 True -> accepted. 11 False -> nochange.
INFO selection_utils:log_classification_counts:492 Step 24: Total component classifications: 7 accepted, 8 provisionalaccept, 25 rejected, 1 unclass_highvar, 2 unclassified
INFO selection_nodes:dec_left_op_right:389 Step 25: left_op_right: accepted if ['kappa<=24.01 & variance explained>0.51'], 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:293 Step 25: No components fit criterion True to change classification
INFO selection_utils:log_decision_tree_step:447 Step 25: left_op_right applied to 11 components. 0 True -> accepted. 11 False -> nochange.
INFO selection_utils:log_classification_counts:492 Step 25: Total component classifications: 7 accepted, 8 provisionalaccept, 25 rejected, 1 unclass_highvar, 2 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:comptable_classification_changer:293 Step 26: No components fit criterion False to change classification
INFO selection_utils:log_decision_tree_step:447 Step 26: manual_classify applied to 11 components. 11 True -> accepted. 0 False -> nochange.
INFO selection_utils:log_classification_counts:492 Step 26: Total component classifications: 18 accepted, 25 rejected
INFO io:denoise_ts:613 Variance explained by decomposition: 91.66%
INFO io:write_split_ts:685 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:693 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:700 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:749 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:753 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:759 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:213 Performing minimum image regression to remove spatially-diffuse noise
WARNING gscontrol:minimum_image_regression:225 Decision tree does not contain classification tags indicating low variance components (low variance, accept borderline).
INFO gscontrol:minimum_image_regression:271 Variance in optimally combined data explained by T1-like global signal: 12.87%
INFO io:writeresults_echoes:799 Writing Kappa-filtered echo #1 timeseries
INFO io:denoise_ts:613 Variance explained by decomposition: 83.21%
INFO io:write_split_ts:682 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:690 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:700 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:799 Writing Kappa-filtered echo #2 timeseries
INFO io:denoise_ts:613 Variance explained by decomposition: 84.82%
INFO io:write_split_ts:682 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:690 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:700 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:799 Writing Kappa-filtered echo #3 timeseries
INFO io:denoise_ts:613 Variance explained by decomposition: 85.41%
INFO io:write_split_ts:682 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:690 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:700 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:799 Writing Kappa-filtered echo #4 timeseries
INFO io:denoise_ts:613 Variance explained by decomposition: 85.34%
INFO io:write_split_ts:682 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:690 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:700 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:1116 Making figures folder with static component maps and timecourse plots.
INFO io:denoise_ts:613 Variance explained by decomposition: 91.66%
/opt/hostedtoolcache/Python/3.10.18/x64/lib/python3.10/site-packages/tedana/io.py:904: UserWarning: Data array used to create a new image contains 64-bit ints. This is likely due to creating the array with numpy and passing `int` as the `dtype`. Many tools such as FSL and SPM cannot deal with int64 in Nifti images, so for compatibility the data has been converted to int32.
nii = new_img_like(ref_img, newdata, affine=affine, copy_header=copy_header)
/opt/hostedtoolcache/Python/3.10.18/x64/lib/python3.10/site-packages/tedana/io.py:904: UserWarning: Data array used to create a new image contains 64-bit ints. This is likely due to creating the array with numpy and passing `int` as the `dtype`. Many tools such as FSL and SPM cannot deal with int64 in Nifti images, so for compatibility the data has been converted to int32.
nii = new_img_like(ref_img, newdata, affine=affine, copy_header=copy_header)
/opt/hostedtoolcache/Python/3.10.18/x64/lib/python3.10/site-packages/tedana/io.py:904: UserWarning: Data array used to create a new image contains 64-bit ints. This is likely due to creating the array with numpy and passing `int` as the `dtype`. Many tools such as FSL and SPM cannot deal with int64 in Nifti images, so for compatibility the data has been converted to int32.
nii = new_img_like(ref_img, newdata, affine=affine, copy_header=copy_header)
/opt/hostedtoolcache/Python/3.10.18/x64/lib/python3.10/site-packages/nilearn/plotting/img_plotting.py:1317: UserWarning: Non-finite values detected. These values will be replaced with zeros.
safe_get_data(stat_map_img, ensure_finite=True),
INFO tedana:tedana_workflow:1172 Generating dynamic report
INFO html_report:_update_template_bokeh:164 Checking for adaptive mask: sub-04570_task-rest_space-scanner_adaptive_mask.svg, exists: True
INFO html_report:_update_template_bokeh:204 T2* files exist: True
INFO html_report:_update_template_bokeh:205 S0 files exist: True
INFO html_report:_update_template_bokeh:206 RMSE files exist: True
INFO html_report:_update_template_bokeh:212 External regressors exist: False
INFO tedana:tedana_workflow:1175 Workflow completed
INFO utils:log_newsletter_info:705 Don't forget to subscribe to the tedana newsletter for updates! This is a very low volume email list.
INFO utils:log_newsletter_info:709 https://groups.google.com/g/tedana-newsletter
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))
figures
sub-04570_task-rest_space-scanner_S0map.nii.gz
sub-04570_task-rest_space-scanner_T2starmap.nii.gz
sub-04570_task-rest_space-scanner_dataset_description.json
sub-04570_task-rest_space-scanner_desc-ICAAcceptedMIRDenoised_components.nii.gz
sub-04570_task-rest_space-scanner_desc-ICAAccepted_components.nii.gz
sub-04570_task-rest_space-scanner_desc-ICAAccepted_stat-z_components.nii.gz
sub-04570_task-rest_space-scanner_desc-ICAAveragingWeights_components.nii.gz
sub-04570_task-rest_space-scanner_desc-ICACrossComponent_metrics.json
sub-04570_task-rest_space-scanner_desc-ICAMIRDenoised_mixing.tsv
sub-04570_task-rest_space-scanner_desc-ICAS0_stat-F_statmap.nii.gz
sub-04570_task-rest_space-scanner_desc-ICAT2_stat-F_statmap.nii.gz
sub-04570_task-rest_space-scanner_desc-ICA_components.nii.gz
sub-04570_task-rest_space-scanner_desc-ICA_decision_tree.json
sub-04570_task-rest_space-scanner_desc-ICA_decomposition.json
sub-04570_task-rest_space-scanner_desc-ICA_mixing.tsv
sub-04570_task-rest_space-scanner_desc-ICA_stat-z_components.nii.gz
sub-04570_task-rest_space-scanner_desc-ICA_status_table.tsv
sub-04570_task-rest_space-scanner_desc-PCAAveragingWeights_components.nii.gz
sub-04570_task-rest_space-scanner_desc-PCACrossComponent_metrics.json
sub-04570_task-rest_space-scanner_desc-PCAS0_stat-F_statmap.nii.gz
sub-04570_task-rest_space-scanner_desc-PCAT2_stat-F_statmap.nii.gz
sub-04570_task-rest_space-scanner_desc-PCA_decomposition.json
sub-04570_task-rest_space-scanner_desc-PCA_metrics.json
sub-04570_task-rest_space-scanner_desc-PCA_metrics.tsv
sub-04570_task-rest_space-scanner_desc-PCA_mixing.tsv
sub-04570_task-rest_space-scanner_desc-PCA_stat-z_components.nii.gz
sub-04570_task-rest_space-scanner_desc-T1likeEffect_min.nii.gz
sub-04570_task-rest_space-scanner_desc-adaptiveGoodSignal_mask.nii.gz
sub-04570_task-rest_space-scanner_desc-confounds_timeseries.tsv
sub-04570_task-rest_space-scanner_desc-denoised_bold.nii.gz
sub-04570_task-rest_space-scanner_desc-limited_S0map.nii.gz
sub-04570_task-rest_space-scanner_desc-limited_T2starmap.nii.gz
sub-04570_task-rest_space-scanner_desc-optcomAcceptedMIRDenoised_bold.nii.gz
sub-04570_task-rest_space-scanner_desc-optcomAccepted_bold.nii.gz
sub-04570_task-rest_space-scanner_desc-optcomMIRDenoised_bold.nii.gz
sub-04570_task-rest_space-scanner_desc-optcomRejected_bold.nii.gz
sub-04570_task-rest_space-scanner_desc-optcom_bold.nii.gz
sub-04570_task-rest_space-scanner_desc-optcom_whitened_bold.nii.gz
sub-04570_task-rest_space-scanner_desc-rmse_statmap.nii.gz
sub-04570_task-rest_space-scanner_desc-tedana_metrics.json
sub-04570_task-rest_space-scanner_desc-tedana_metrics.tsv
sub-04570_task-rest_space-scanner_desc-tedana_registry.json
sub-04570_task-rest_space-scanner_echo-1_desc-Accepted_bold.nii.gz
sub-04570_task-rest_space-scanner_echo-1_desc-Denoised_bold.nii.gz
sub-04570_task-rest_space-scanner_echo-1_desc-ICAS0ModelPredictions_components.nii.gz
sub-04570_task-rest_space-scanner_echo-1_desc-ICAT2ModelPredictions_components.nii.gz
sub-04570_task-rest_space-scanner_echo-1_desc-ICA_components.nii.gz
sub-04570_task-rest_space-scanner_echo-1_desc-PCAS0ModelPredictions_components.nii.gz
sub-04570_task-rest_space-scanner_echo-1_desc-PCAT2ModelPredictions_components.nii.gz
sub-04570_task-rest_space-scanner_echo-1_desc-PCA_components.nii.gz
sub-04570_task-rest_space-scanner_echo-1_desc-Rejected_bold.nii.gz
sub-04570_task-rest_space-scanner_echo-2_desc-Accepted_bold.nii.gz
sub-04570_task-rest_space-scanner_echo-2_desc-Denoised_bold.nii.gz
sub-04570_task-rest_space-scanner_echo-2_desc-ICAS0ModelPredictions_components.nii.gz
sub-04570_task-rest_space-scanner_echo-2_desc-ICAT2ModelPredictions_components.nii.gz
sub-04570_task-rest_space-scanner_echo-2_desc-ICA_components.nii.gz
sub-04570_task-rest_space-scanner_echo-2_desc-PCAS0ModelPredictions_components.nii.gz
sub-04570_task-rest_space-scanner_echo-2_desc-PCAT2ModelPredictions_components.nii.gz
sub-04570_task-rest_space-scanner_echo-2_desc-PCA_components.nii.gz
sub-04570_task-rest_space-scanner_echo-2_desc-Rejected_bold.nii.gz
sub-04570_task-rest_space-scanner_echo-3_desc-Accepted_bold.nii.gz
sub-04570_task-rest_space-scanner_echo-3_desc-Denoised_bold.nii.gz
sub-04570_task-rest_space-scanner_echo-3_desc-ICAS0ModelPredictions_components.nii.gz
sub-04570_task-rest_space-scanner_echo-3_desc-ICAT2ModelPredictions_components.nii.gz
sub-04570_task-rest_space-scanner_echo-3_desc-ICA_components.nii.gz
sub-04570_task-rest_space-scanner_echo-3_desc-PCAS0ModelPredictions_components.nii.gz
sub-04570_task-rest_space-scanner_echo-3_desc-PCAT2ModelPredictions_components.nii.gz
sub-04570_task-rest_space-scanner_echo-3_desc-PCA_components.nii.gz
sub-04570_task-rest_space-scanner_echo-3_desc-Rejected_bold.nii.gz
sub-04570_task-rest_space-scanner_echo-4_desc-Accepted_bold.nii.gz
sub-04570_task-rest_space-scanner_echo-4_desc-Denoised_bold.nii.gz
sub-04570_task-rest_space-scanner_echo-4_desc-ICAS0ModelPredictions_components.nii.gz
sub-04570_task-rest_space-scanner_echo-4_desc-ICAT2ModelPredictions_components.nii.gz
sub-04570_task-rest_space-scanner_echo-4_desc-ICA_components.nii.gz
sub-04570_task-rest_space-scanner_echo-4_desc-PCAS0ModelPredictions_components.nii.gz
sub-04570_task-rest_space-scanner_echo-4_desc-PCAT2ModelPredictions_components.nii.gz
sub-04570_task-rest_space-scanner_echo-4_desc-PCA_components.nii.gz
sub-04570_task-rest_space-scanner_echo-4_desc-Rejected_bold.nii.gz
sub-04570_task-rest_space-scanner_references.bib
sub-04570_task-rest_space-scanner_report.txt
sub-04570_task-rest_space-scanner_tedana_report.html
tedana_2025-06-10T003444.tsv
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)
| Component | kappa | rho | variance explained | normalized variance explained | countsigFT2 | countsigFS0 | dice_FT2 | dice_FS0 | countnoise | signal-noise_t | signal-noise_p | d_table_score | optimal sign | varex kappa ratio | d_table_score_node20 | Var Exp of rejected to accepted | classification | classification_tags | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | ICA_00 | 23.963910 | 18.845160 | 0.974387 | 0.014406 | 813 | 766 | 0.000000 | 0.000000 | 1223 | -2.264125 | 0.025652 | 31.200000 | 1 | 3.124711 | nan | nan | rejected | Unlikely BOLD |
| 1 | ICA_01 | 18.386390 | 15.105416 | 0.209236 | 0.002638 | 322 | 163 | 0.243200 | 0.000000 | 1228 | 0.000000 | 0.000000 | 31.200000 | 1 | 0.874533 | nan | 5.337717 | accepted | Low variance |
| 2 | ICA_02 | 53.904796 | 11.570936 | 0.365079 | 0.004694 | 1849 | 375 | 0.621872 | 0.252033 | 880 | 10.298052 | 0.000000 | 7.600000 | -1 | 0.520470 | 6.600000 | 11.642397 | accepted | Likely BOLD |
| 3 | ICA_03 | 22.443964 | 29.659039 | 0.394177 | 0.005023 | 840 | 1175 | 0.336829 | 0.364685 | 1183 | 0.000000 | 0.000000 | 25.800000 | 1 | 1.349670 | nan | nan | rejected | Unlikely BOLD |
| 4 | ICA_04 | 36.402447 | 36.417731 | 1.079447 | 0.010505 | 2030 | 1616 | 0.447545 | 0.424479 | 1136 | 7.114071 | 0.000000 | 14.400000 | -1 | 2.278803 | nan | nan | rejected | Unlikely BOLD |
| 5 | ICA_05 | 14.776330 | 53.185372 | 0.539564 | 0.012173 | 280 | 786 | 0.000000 | 0.362080 | 509 | -8.452758 | 0.000000 | 31.000000 | -1 | 2.806160 | nan | nan | rejected | Unlikely BOLD |
| 6 | ICA_06 | 35.682686 | 41.152975 | 1.411969 | 0.016422 | 722 | 729 | 0.268085 | 0.332160 | 1050 | 6.150607 | 0.000002 | 19.000000 | -1 | 3.040910 | nan | nan | rejected | Unlikely BOLD |
| 7 | ICA_07 | 102.876143 | 10.929019 | 0.754819 | 0.009029 | 3277 | 362 | 0.669988 | 0.166436 | 613 | 6.615788 | 0.000000 | 2.800000 | -1 | 0.563851 | 2.400000 | 14.470979 | accepted | Likely BOLD |
| 8 | ICA_08 | 14.768110 | 20.934533 | 0.273712 | 0.003667 | 226 | 300 | 0.059908 | 0.384615 | 997 | -6.963965 | 0.000000 | 34.200000 | -1 | 1.424309 | nan | nan | rejected | Unlikely BOLD |
| 9 | ICA_09 | 21.605372 | 17.761140 | 0.307787 | 0.004137 | 388 | 282 | 0.000000 | 0.000000 | 1225 | 0.000000 | 0.000000 | 32.000000 | 1 | 1.094775 | nan | 13.269860 | accepted | Low variance |
| 10 | ICA_10 | 49.297713 | 30.183255 | 0.940788 | 0.012705 | 3215 | 1499 | 0.515872 | 0.268544 | 1121 | -1.525198 | 0.131335 | 16.400000 | 1 | 1.466563 | nan | nan | rejected | Unlikely BOLD |
| 11 | ICA_11 | 22.601135 | 36.298328 | 0.453209 | 0.004387 | 470 | 538 | 0.341151 | 0.458527 | 1089 | 1.325181 | 0.197289 | 23.400000 | -1 | 1.541007 | nan | nan | rejected | Unlikely BOLD |
| 12 | ICA_12 | 23.968036 | 20.341244 | 0.472085 | 0.004739 | 570 | 377 | 0.255958 | 0.176471 | 1148 | -3.632142 | 0.000857 | 29.000000 | -1 | 1.513643 | 17.000000 | 26.592542 | accepted | Likely BOLD |
| 13 | ICA_13 | 24.006483 | 14.921336 | 0.218386 | 0.003102 | 1128 | 630 | 0.319091 | 0.296675 | 1231 | 0.000000 | 0.000000 | 26.000000 | -1 | 0.699088 | nan | 9.380691 | accepted | Low variance |
| 14 | ICA_14 | 52.863978 | 11.030685 | 0.808909 | 0.009880 | 2942 | 188 | 0.563806 | 0.000000 | 929 | 12.391319 | 0.000000 | 7.600000 | -1 | 1.175915 | 6.600000 | 11.451016 | accepted | Likely BOLD |
| 15 | ICA_15 | 28.881649 | 23.392165 | 1.202041 | 0.011165 | 1297 | 433 | 0.313320 | 0.163170 | 1233 | 0.000000 | 0.000000 | 25.200000 | 1 | 3.198404 | 15.200000 | nan | rejected | Less likely BOLD |
| 16 | ICA_16 | 25.912802 | 31.719562 | 0.420603 | 0.004614 | 392 | 463 | 0.304124 | 0.282916 | 1099 | 0.000000 | 0.000000 | 26.400000 | 1 | 1.247365 | nan | nan | rejected | Unlikely BOLD |
| 17 | ICA_17 | 16.999247 | 19.297453 | 0.278863 | 0.003151 | 420 | 655 | 0.203593 | 0.222566 | 1230 | 0.000000 | 0.000000 | 31.800000 | -1 | 1.260659 | nan | nan | rejected | Unlikely BOLD |
| 18 | ICA_18 | 66.799022 | 11.946827 | 0.683821 | 0.006818 | 2054 | 421 | 0.645953 | 0.255696 | 689 | 14.985958 | 0.000000 | 4.400000 | -1 | 0.786699 | 3.800000 | 19.209823 | accepted | Likely BOLD |
| 19 | ICA_19 | 37.562890 | 40.905321 | 1.464678 | 0.015207 | 547 | 710 | 0.389199 | 0.400868 | 1001 | 3.334854 | 0.002702 | 17.000000 | 1 | 2.996533 | nan | nan | rejected | Unlikely BOLD |
| 20 | ICA_20 | 15.305864 | 21.290880 | 0.407149 | 0.006202 | 45 | 192 | 0.000000 | 0.317708 | 831 | -2.145011 | 0.032462 | 33.200000 | -1 | 2.044238 | nan | nan | rejected | Unlikely BOLD |
| 21 | ICA_21 | 84.952283 | 11.250273 | 1.244685 | 0.008481 | 2580 | 288 | 0.658527 | 0.117647 | 628 | 10.338983 | 0.000000 | 3.400000 | 1 | 1.125953 | 3.000000 | 7.678323 | accepted | Likely BOLD |
| 22 | ICA_22 | 47.428357 | 22.884456 | 18.118683 | 0.147507 | 5043 | 2404 | 0.457943 | 0.179092 | 1041 | 0.000000 | 0.000000 | 13.600000 | -1 | 29.357867 | 9.400000 | nan | rejected | Less likely BOLD |
| 23 | ICA_23 | 12.120251 | 29.573528 | 0.326093 | 0.004685 | 94 | 214 | 0.000000 | 0.503529 | 650 | -8.669511 | 0.000000 | 33.600000 | 1 | 2.067595 | nan | nan | rejected | Unlikely BOLD |
| 24 | ICA_24 | 34.286932 | 24.547042 | 0.972365 | 0.010708 | 1262 | 419 | 0.400000 | 0.000000 | 1251 | 0.000000 | 0.000000 | 22.800000 | 1 | 2.179397 | 13.800000 | nan | rejected | Less likely BOLD |
| 25 | ICA_25 | 16.953422 | 16.732650 | 0.211095 | 0.002731 | 260 | 459 | 0.000000 | 0.317181 | 1270 | 0.000000 | 0.000000 | 36.400000 | 1 | 0.956880 | nan | nan | rejected | Unlikely BOLD |
| 26 | ICA_26 | 20.361392 | 14.432911 | 0.176224 | 0.002748 | 500 | 235 | 0.377510 | 0.186147 | 1203 | 3.117638 | 0.002406 | 24.200000 | 1 | 0.665110 | nan | 7.400702 | accepted | Low variance |
| 27 | ICA_27 | 33.321036 | 32.060287 | 0.865706 | 0.010818 | 492 | 365 | 0.357895 | 0.000000 | 1084 | 0.000000 | 0.000000 | 22.800000 | 1 | 1.996586 | 14.600000 | nan | rejected | Less likely BOLD |
| 28 | ICA_28 | 30.108351 | 12.927686 | 1.039400 | 0.007045 | 1158 | 121 | 0.388220 | 0.000000 | 1155 | 0.000000 | 0.000000 | 22.200000 | 1 | 2.652965 | 14.400000 | nan | rejected | Less likely BOLD |
| 29 | ICA_29 | 80.883760 | 12.626881 | 0.636901 | 0.010566 | 2945 | 465 | 0.581311 | 0.026287 | 601 | 8.423694 | 0.000000 | 4.000000 | -1 | 0.605127 | 3.600000 | 11.847878 | accepted | Likely BOLD |
| 30 | ICA_30 | 43.827473 | 11.578461 | 0.519174 | 0.005640 | 1534 | 313 | 0.566335 | 0.130081 | 819 | 14.342942 | 0.000000 | 9.000000 | 1 | 0.910338 | 8.000000 | 14.850684 | accepted | Likely BOLD |
| 31 | ICA_31 | 29.139108 | 23.873300 | 0.759698 | 0.011004 | 1720 | 1160 | 0.207729 | 0.188510 | 1255 | 0.000000 | 0.000000 | 26.600000 | -1 | 2.003553 | 15.400000 | 27.335733 | accepted | Accept borderline |
| 32 | ICA_32 | 14.755411 | 17.029743 | 0.269894 | 0.003557 | 401 | 440 | 0.000000 | 0.000000 | 1239 | 0.000000 | 0.000000 | 35.000000 | 1 | 1.405653 | nan | nan | rejected | Unlikely BOLD |
| 33 | ICA_33 | 44.086324 | 18.077004 | 1.513722 | 0.009673 | 2119 | 408 | 0.523480 | 0.037783 | 777 | 1.150975 | 0.250259 | 10.600000 | -1 | 2.638628 | 8.400000 | 12.408701 | accepted | Likely BOLD |
| 34 | ICA_34 | 56.429382 | 10.814910 | 0.247334 | 0.004278 | 1781 | 95 | 0.617319 | 0.000000 | 666 | 11.382307 | 0.000000 | 6.000000 | 1 | 0.336833 | 5.200000 | 6.951763 | accepted | Likely BOLD |
| 35 | ICA_35 | 17.790500 | 12.597793 | 0.232961 | 0.003241 | 285 | 51 | 0.352113 | 0.000000 | 1242 | -5.030514 | 0.000018 | 33.600000 | -1 | 1.006310 | nan | 13.233910 | accepted | Low variance |
| 36 | ICA_36 | 32.499106 | 34.934669 | 0.689722 | 0.007488 | 1095 | 1807 | 0.434334 | 0.462183 | 1082 | 0.153060 | 0.878607 | 18.200000 | 1 | 1.630942 | nan | nan | rejected | Unlikely BOLD |
| 37 | ICA_37 | 70.548540 | 17.969418 | 29.229082 | 0.233936 | 5731 | 1645 | 0.566419 | 0.110131 | 709 | 0.000000 | 0.000000 | 8.600000 | -1 | 31.839269 | 5.800000 | 79.586949 | accepted | Likely BOLD |
| 38 | ICA_38 | 21.288556 | 23.449890 | 1.429819 | 0.018098 | 1218 | 1285 | 0.240820 | 0.334156 | 1131 | 2.114198 | 0.037227 | 23.600000 | -1 | 5.161439 | nan | nan | rejected | Unlikely BOLD |
| 39 | ICA_39 | 32.808892 | 32.459510 | 1.024512 | 0.010649 | 683 | 544 | 0.362297 | 0.336725 | 1065 | 0.000000 | 0.000000 | 21.600000 | 1 | 2.399724 | 14.000000 | nan | rejected | Less likely BOLD |
| 40 | ICA_40 | 16.606237 | 18.724607 | 0.389232 | 0.005196 | 270 | 307 | 0.172084 | 0.164204 | 920 | -10.839641 | 0.000000 | 32.800000 | 1 | 1.801246 | nan | nan | rejected | Unlikely BOLD |
| 41 | ICA_41 | 48.017993 | 36.938387 | 26.082638 | 0.311829 | 6059 | 5930 | 0.515417 | 0.356028 | 719 | 0.000000 | 0.000000 | 10.800000 | 1 | 41.742980 | 7.600000 | nan | rejected | Less likely BOLD |
| 42 | ICA_42 | 17.346959 | 15.014701 | 0.360350 | 0.005460 | 268 | 68 | 0.434263 | 0.135338 | 956 | 5.441800 | 0.000000 | 22.600000 | -1 | 1.596382 | nan | 16.339245 | accepted | Low variance |
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)
{'Component': {'Description': 'The unique identifier of each component. This '
'identifier matches column names in the mixing '
'matrix TSV file.',
'LongName': 'Component identifier'},
'classification_tags': {'Description': 'A single tag or a comma separated '
'list of tags to describe why a '
'component received its classification',
'LongName': 'Component classification tags'},
'countnoise': {'Description': "Number of 'noise' voxels (voxels highly "
'weighted for component, but not from clusters) '
'from each component.',
'LongName': 'Noise voxel count',
'Units': 'voxel'},
'countsigFS0': {'Description': 'Number of significant voxels from the '
'cluster-extent thresholded S0 model '
'F-statistic map for each component.',
'LongName': 'S0 model F-statistic map significant voxel count',
'Units': 'voxel'},
'countsigFT2': {'Description': 'Number of significant voxels from the '
'cluster-extent thresholded T2 model '
'F-statistic map for each component.',
'LongName': 'T2 model F-statistic map significant voxel count',
'Units': 'voxel'}}
df = pd.DataFrame.from_dict(data, orient="index")
df = df.fillna("n/a")
display(HTML(df.to_html()))
| Description | LongName | Units | Levels | |
|---|---|---|---|---|
| Component | The unique identifier of each component. This identifier matches column names in the mixing matrix TSV file. | Component identifier | n/a | n/a |
| classification_tags | A single tag or a comma separated list of tags to describe why a component received its classification | Component classification tags | n/a | n/a |
| countnoise | Number of 'noise' voxels (voxels highly weighted for component, but not from clusters) from each component. | Noise voxel count | voxel | n/a |
| countsigFS0 | Number of significant voxels from the cluster-extent thresholded S0 model F-statistic map for each component. | S0 model F-statistic map significant voxel count | voxel | n/a |
| countsigFT2 | Number of significant voxels from the cluster-extent thresholded T2 model F-statistic map for each component. | T2 model F-statistic map significant voxel count | voxel | n/a |
| d_table_score | Summary score compiled from five metrics, with smaller values (i.e., higher ranks) indicating more BOLD dependence and less noise. | Decision table score | arbitrary | n/a |
| dice_FS0 | Dice value of cluster-extent thresholded maps of S0-model betas and F-statistics. | S0 model beta map-F-statistic map Dice similarity index | arbitrary | n/a |
| dice_FT2 | Dice value of cluster-extent thresholded maps of T2-model betas and F-statistics. | T2 model beta map-F-statistic map Dice similarity index | arbitrary | n/a |
| kappa | A pseudo-F-statistic indicating TE-dependence of the component. This metric is calculated by computing fit to the TE-dependence model at each voxel, and then performing a weighted average based on the voxel-wise weights of the component. | Kappa | arbitrary | n/a |
| normalized variance explained | Normalized variance explained in the optimally combined data of each component.On a scale from 0 to 1. | Normalized variance explained | arbitrary | n/a |
| optimal sign | Optimal sign determined based on skew direction of component parameter estimates across the brain. In cases where components were left-skewed (-1), the component time series and map weights are flipped prior to metric calculation. This sign applies to the original mixing matrix and map weights. The outputs produced by tedana are already flipped. | Optimal component sign | n/a | {'-1': 'Component is flipped prior to metric calculation.', '1': 'Component is not flipped prior to metric calculation.'} |
| rho | A pseudo-F-statistic indicating TE-independence of the component. This metric is calculated by computing fit to the TE-independence model at each voxel, and then performing a weighted average based on the voxel-wise weights of the component. | Rho | arbitrary | n/a |
| signal-noise_p | P-value for two-sample t-test of F-statistics from 'signal' voxels (voxels in clusters) against 'noise' voxels (voxels not in clusters) for T2 model. | Signal > noise p-value | arbitrary | n/a |
| signal-noise_t | T-statistic for two-sample t-test of F-statistics from 'signal' voxels (voxels in clusters) against 'noise' voxels (voxels not in clusters) for T2 model. | Signal > noise t-statistic | arbitrary | n/a |
| variance explained | Variance explained in the optimally combined data of each component. On a scale from 0 to 100. | Variance explained | arbitrary | n/a |
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))
---------------------------------------------------------------------------
FileNotFoundError Traceback (most recent call last)
Cell In[9], line 2
1 report = os.path.join(out_dir, "tedana_report.html")
----> 2 with open(report, "r") as fo:
3 report_data = fo.read()
5 figures_dir = os.path.relpath(os.path.join(out_dir, "figures"), os.getcwd())
File /opt/hostedtoolcache/Python/3.10.18/x64/lib/python3.10/site-packages/IPython/core/interactiveshell.py:324, in _modified_open(file, *args, **kwargs)
317 if file in {0, 1, 2}:
318 raise ValueError(
319 f"IPython won't let you open fd={file} by default "
320 "as it is likely to crash IPython. If you know what you are doing, "
321 "you can use builtins' open."
322 )
--> 324 return io_open(file, *args, **kwargs)
FileNotFoundError: [Errno 2] No such file or directory: '/home/runner/work/multi-echo-data-analysis/multi-echo-data-analysis/data/multi-echo-data-analysis/tedana/tedana_report.html'