tedana: TE Dependent ANAlysis

tedana: TE Dependent ANAlysis#

Process five-echo flashing checkerboard dataset for software demo#

Authors: Daniel Handwerker & Monika Doerig

Date: 23 June 2025

License:

Note: If this notebook uses neuroimaging tools from Neurocontainers, those tools retain their original licenses. Please see Neurodesk citation guidelines for details.

Citation:#

Tools included in this workflow#

tedana: The tedana Community, Ahmed, Z., Bandettini, P. A., Bottenhorn, K. L., Caballero-Gaudes, C., Dowdle, L. T., DuPre, E., Gonzalez-Castillo, J., Handwerker, D., Heunis, S., Kundu, P., Laird, A. R., Markello, R., Markiewicz, C. J., Maullin-Sapey, T., Moia, S., Molfese, P., Salo, T., Staden, I., … Whitaker, K. (2026). ME-ICA/tedana: 26.0.3 (26.0.3). Zenodo. https://doi.org/10.5281/zenodo.19410167

Publications#

DuPre, E. M., Salo, T., Ahmed, Z., Bandettini, P. A., Bottenhorn, K. L., Caballero-Gaudes, C., Dowdle, L. T., Gonzalez-Castillo, J., Heunis, S., Kundu, P., Laird, A. R., Markello, R., Markiewicz, C. J., Moia, S., Staden, I., Teves, J. B., Uruñuela, E., Vaziri-Pashkam, M., Whitaker, K., & Handwerker, D. A. (2021). TE-dependent analysis of multi-echo fMRI with tedana. Journal of Open Source Software, 6(66), 3669. doi:10.21105/joss.03669.
Kundu, P., Inati, S. J., Evans, J. W., Luh, W. M., & Bandettini, P. A. (2011). Differentiating BOLD and non-BOLD signals in fMRI time series using multi-echo EPI. NeuroImage, 60, 1759-1770.
Kundu, P., Brenowitz, N. D., Voon, V., Worbe, Y., Vértes, P. E., Inati, S. J., Saad, Z. S., Bandettini, P. A., & Bullmore, E. T. (2013). Integrated strategy for improving functional connectivity mapping using multiecho fMRI. Proceedings of the National Academy of Sciences, 110, 16187-16192.

Educational resources#

tedana documentation

Dataset#

DuPre, E., Salo, T., Whitaker, K. J., Teves, J., Dowdle, L., Reynolds, R. C., & Handwerker, D. A. (2024, February 21). tedana data. Retrieved from osf.io/bpe8h

Installations and Imports#

%%capture
! pip install tedana==26.0.3

import module
await module.load('afni/24.3.00')
await module.list()

['afni/24.3.00']

%matplotlib inline
import os
import os.path as op
from glob import glob
import webbrowser
from IPython.display import display, Markdown, Image
from IPython.core.display import SVG
from tedana.workflows import tedana_workflow

Download 5 echo data#

%%time
dset_dir5 = 'five-echo-dataset/'
wd = os.getcwd()

if not glob(op.join(dset_dir5, 'p06*.nii.gz')):
    os.makedirs(dset_dir5, exist_ok=True)
    !curl -L -o five_echo_NIH.tar.xz https://osf.io/ea5v3/download
    !tar xf five_echo_NIH.tar.xz -C five-echo-dataset
    os.remove('five_echo_NIH.tar.xz')

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68.5M    0  5551    0     0    166      0   5d 00h  0:00:33   5d 00h 12474
68.5M    0  134k    0     0   4023      0  4:57:35  0:00:34  4:57:01  102k
68.5M    2 1806k    0     0  52472      0  0:22:48  0:00:35  0:22:13  780k
68.5M   22 15.6M    0     0   441k      0  0:02:38  0:00:36  0:02:02 4830k
68.5M   46 31.8M    0     0   876k      0  0:01:20  0:00:37  0:00:43 7561k
68.5M   67 46.4M    0     0  1240k      0  0:00:56  0:00:38  0:00:18 9552k
68.5M   89 61.1M    0     0  1594k      0  0:00:43  0:00:39  0:00:04 12.1M
68.5M  100 68.5M    0     0  1765k      0  0:00:39  0:00:39 --:--:-- 14.9M
CPU times: user 456 ms, sys: 144 ms, total: 600 ms
Wall time: 40.3 s

# Clone GitHub repo and copy files
![ -d temp_repo ] || git clone https://github.com/ME-ICA/ohbm-2025-multiecho.git temp_repo
!cp -r temp_repo/five-echo-dataset/* five-echo-dataset/
!rm -rf temp_repo

Cloning into 'temp_repo'...
remote: Enumerating objects: 156, done.
remote: Counting objects: 100% (156/156), done.
remote: Compressing objects: 100% (124/124), done.
Receiving objects:  10% (16/156)
Receiving objects:  25% (39/156)
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Receiving objects:  28% (44/156)
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Receiving objects:  55% (86/156)
Receiving objects:  69% (108/156)
Receiving objects:  74% (116/156)
Receiving objects:  83% (130/156), 10.75 MiB | 21.49 MiB/s
Receiving objects:  87% (136/156), 10.75 MiB | 21.49 MiB/s
remote: Total 156 (delta 44), reused 141 (delta 32), pack-reused 0 (from 0)
Receiving objects: 100% (156/156), 16.37 MiB | 21.71 MiB/s, done.
Resolving deltas:  68% (30/44)
Resolving deltas:  88% (39/44)
Resolving deltas: 100% (44/44), done.

Run workflow on 5 echo data#

%%time
dset_dir5_out = f"{dset_dir5}tedana_processed"
files = sorted(glob(op.join(dset_dir5, 'p06*.nii.gz')))
tes = [15.4, 29.7, 44.0, 58.3, 72.6]
tedana_workflow(files, tes, 
    tree="minimal",
    fixed_seed=42,
    ica_method="robustica",
    n_robust_runs=30,
    tedpca=53,
    out_dir=dset_dir5_out,
    tedort=False
    )

Setting clustering defaults: {'min_samples': 15}
Running FastICA multiple times...
Inferring sign of components...
Clustering...
Computing centroids...
Computing Silhouettes...
Computing Iq...
CPU times: user 6h 15min 8s, sys: 27 s, total: 6h 15min 35s
Wall time: 44min 3s

INFO     tedana:tedana_workflow:636 Using output directory: /home/jovyan/workspace/books/examples/functional_imaging/five-echo-dataset/tedana_processed
WARNING  utils:check_te_values:796 TE values appear to be in milliseconds. Per BIDS convention, echo times should be provided in seconds. Support for millisecond TE values is deprecated and will be removed in a future version. Please provide TE values in seconds.
INFO     tedana:tedana_workflow:655 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__:345 Performing component selection with minimal_decision_tree
INFO     component_selector:__init__:346 first version of minimal decision tree
INFO     io:__init__:164 Generating figures directory: /home/jovyan/workspace/books/examples/functional_imaging/five-echo-dataset/tedana_processed/figures
WARNING  utils:load_mask:943 Computing EPI mask from first echo using nilearn's compute_epi_mask function. Most external pipelines include more reliable masking functions. It is strongly recommended to provide an external mask, and to visually confirm that mask accurately conforms to data boundaries.
INFO     tedana:tedana_workflow:690 Loading input data: ['five-echo-dataset/p06.SBJ01_S09_Task11_e1.sm.nii.gz', 'five-echo-dataset/p06.SBJ01_S09_Task11_e2.sm.nii.gz', 'five-echo-dataset/p06.SBJ01_S09_Task11_e3.sm.nii.gz', 'five-echo-dataset/p06.SBJ01_S09_Task11_e4.sm.nii.gz', 'five-echo-dataset/p06.SBJ01_S09_Task11_e5.sm.nii.gz']
INFO     utils:make_adaptive_mask:167 Echo-wise intensity thresholds for adaptive mask: [5853.639  4862.627  4073.2693 3377.1418 2800.739 ]
WARNING  utils:make_adaptive_mask:195 4 voxels in user-defined mask do not have good signal. Removing voxels from mask.
INFO     tedana:tedana_workflow:780 Computing T2* map
INFO     combine:make_optcom:202 Optimally combining data with voxel-wise T2* estimates
INFO     tedana:tedana_workflow:859 Writing optimally combined data set: /home/jovyan/workspace/books/examples/functional_imaging/five-echo-dataset/tedana_processed/desc-optcom_bold.nii.gz
INFO     pca:tedpca:212 Computing PCA of optimally combined multi-echo data with selection criteria: 53
INFO     collect:generate_metrics:153 Calculating standardized parameter estimate maps for optimally combined data
INFO     collect:generate_metrics:171 Calculating unstandardized parameter estimate maps for optimally combined data
INFO     collect:generate_metrics:203 Calculating F-statistic maps
INFO     collect:generate_metrics:229 Thresholding standardized parameter estimate maps
INFO     collect:generate_metrics:237 Thresholding T2* F-statistic maps
INFO     collect:generate_metrics:245 Thresholding S0 F-statistic maps
INFO     collect:generate_metrics:254 Counting significant voxels in T2* F-statistic maps
INFO     collect:generate_metrics:260 Counting significant voxels in S0 F-statistic maps
INFO     collect:generate_metrics:267 Thresholding optimal combination beta maps to match T2* F-statistic maps
INFO     collect:generate_metrics:275 Thresholding optimal combination beta maps to match S0 F-statistic maps
INFO     collect:generate_metrics:284 Calculating kappa and rho
INFO     collect:generate_metrics:293 Calculating variance explained
INFO     collect:generate_metrics:299 Calculating normalized variance explained
INFO     collect:generate_metrics:328 Calculating DSI between thresholded T2* F-statistic and optimal combination beta maps
INFO     collect:generate_metrics:338 Calculating DSI between thresholded S0 F-statistic and optimal combination beta maps
INFO     collect:generate_metrics:349 Calculating signal-noise t-statistics
/opt/conda/lib/python3.13/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:385 Counting significant noise voxels from z-statistic maps
INFO     collect:generate_metrics:398 Calculating decision table score
INFO     pca:tedpca:425 Selected 53 components with 88.73% normalized variance explained using a fixed number of components and no dimensionality estimate
/opt/conda/lib/python3.13/site-packages/tedana/io.py:394: 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).infer_objects(copy=False)
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INFO     ica:r_ica:213 For RobustICA, FastICA did not converge in 3 of 30 interations.
INFO     ica:r_ica:234 The DBSCAN clustering algorithm was used for clustering components across different runs
INFO     ica:r_ica:252 RobustICA with 30 robust runs and seed 42 was used. 40 components identified. The mean Index Quality is 0.9525780651557195.
INFO     ica:r_ica:260 The DBSCAN clustering algorithm detected outliers when clustering components for different runs. These outliers are excluded when calculating the index quality and the mixing matrix to maximise the robustness of the decomposition.
INFO     collect:generate_metrics:153 Calculating standardized parameter estimate maps for optimally combined data
INFO     collect:generate_metrics:171 Calculating unstandardized parameter estimate maps for optimally combined data
INFO     collect:generate_metrics:203 Calculating F-statistic maps
INFO     collect:generate_metrics:229 Thresholding standardized parameter estimate maps
INFO     collect:generate_metrics:237 Thresholding T2* F-statistic maps
INFO     collect:generate_metrics:245 Thresholding S0 F-statistic maps
INFO     collect:generate_metrics:254 Counting significant voxels in T2* F-statistic maps
INFO     collect:generate_metrics:260 Counting significant voxels in S0 F-statistic maps
INFO     collect:generate_metrics:267 Thresholding optimal combination beta maps to match T2* F-statistic maps
INFO     collect:generate_metrics:275 Thresholding optimal combination beta maps to match S0 F-statistic maps
INFO     collect:generate_metrics:284 Calculating kappa and rho
INFO     collect:generate_metrics:293 Calculating variance explained
INFO     collect:generate_metrics:299 Calculating normalized variance explained
INFO     collect:generate_metrics:328 Calculating DSI between thresholded T2* F-statistic and optimal combination beta maps
INFO     collect:generate_metrics:338 Calculating DSI between thresholded S0 F-statistic and optimal combination beta maps
INFO     collect:generate_metrics:349 Calculating signal-noise t-statistics
/opt/conda/lib/python3.13/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     tedana:tedana_workflow:933 Selecting components from ICA results
INFO     tedica:automatic_selection:54 Performing ICA component selection with tree: minimal
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 40 components. 40 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: 40 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 40 components. 5 True -> rejected. 35 False -> nochange.
INFO     selection_utils:log_classification_counts:492 Step 1: Total component classifications: 5 rejected, 35 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 40 components. 2 True -> rejected. 38 False -> nochange.
INFO     selection_utils:log_classification_counts:492 Step 2: Total component classifications: 5 rejected, 35 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=0.6119923398094267
INFO     selection_utils:log_classification_counts:492 Step 3: Total component classifications: 5 rejected, 35 unclassified
INFO     selection_nodes:dec_left_op_right:389 Step 4: left_op_right: rejected if ['dice_FS0>dice_FT2 & variance explained>0.61'], else nochange
INFO     selection_utils:comptable_classification_changer:293 Step 4: No components fit criterion True to change classification
INFO     selection_utils:log_decision_tree_step:447 Step 4: left_op_right applied to 40 components. 0 True -> rejected. 40 False -> nochange.
INFO     selection_utils:log_classification_counts:492 Step 4: Total component classifications: 5 rejected, 35 unclassified
INFO     selection_nodes:dec_left_op_right:389 Step 5: left_op_right: rejected if ['0>signal-noise_t & variance explained>0.61'], else nochange
INFO     selection_utils:log_decision_tree_step:447 Step 5: left_op_right applied to 40 components. 4 True -> rejected. 36 False -> nochange.
INFO     selection_utils:log_classification_counts:492 Step 5: Total component classifications: 7 rejected, 33 unclassified
INFO     selection_nodes:calc_kappa_elbow:767 Step 6: calc_kappa_elbow: Calc Kappa Elbow
INFO     selection_utils:kappa_elbow_kundu:668 Calculating kappa elbow based on all components.
INFO     selection_utils:log_decision_tree_step:459 Step 6: calc_kappa_elbow calculated: kappa_elbow_kundu=73.5238412168861, kappa_allcomps_elbow=73.5238412168861, kappa_nonsig_elbow=None, varex_upper_p=0.9381300999030646
INFO     selection_utils:log_classification_counts:492 Step 6: Total component classifications: 7 rejected, 33 unclassified
INFO     selection_nodes:calc_rho_elbow:902 Step 7: calc_rho_elbow: Calc Rho Elbow
INFO     selection_utils:log_decision_tree_step:459 Step 7: calc_rho_elbow calculated: rho_elbow_liberal=20.376565983429877, rho_allcomps_elbow=20.376565983429877, rho_unclassified_elbow=20.327098798245938, elbow_f05=7.708647422176786
INFO     selection_utils:log_classification_counts:492 Step 7: Total component classifications: 7 rejected, 33 unclassified
INFO     selection_nodes:dec_left_op_right:389 Step 8: left_op_right: provisionalaccept if kappa>=73.52, else provisionalreject
INFO     selection_utils:log_decision_tree_step:447 Step 8: left_op_right applied to 33 components. 11 True -> provisionalaccept. 22 False -> provisionalreject.
INFO     selection_utils:log_classification_counts:492 Step 8: Total component classifications: 11 provisionalaccept, 22 provisionalreject, 7 rejected
INFO     selection_nodes:dec_left_op_right:389 Step 9: left_op_right: accepted if kappa>2*rho, else nochange
INFO     selection_utils:comptable_classification_changer:293 Step 9: No components fit criterion False to change classification
INFO     selection_utils:log_decision_tree_step:447 Step 9: left_op_right applied to 11 components. 11 True -> accepted. 0 False -> nochange.
INFO     selection_utils:log_classification_counts:492 Step 9: Total component classifications: 11 accepted, 22 provisionalreject, 7 rejected
INFO     selection_nodes:dec_left_op_right:389 Step 10: left_op_right: provisionalreject if rho>20.38, else nochange
INFO     selection_utils:log_decision_tree_step:447 Step 10: left_op_right applied to 22 components. 3 True -> provisionalreject. 19 False -> nochange.
INFO     selection_utils:log_classification_counts:492 Step 10: Total component classifications: 11 accepted, 22 provisionalreject, 7 rejected
INFO     selection_nodes:dec_variance_lessthan_thresholds:533 Step 11: variance_lt_thresholds: accepted if variance explained<0.1. All variance<1.0, else nochange
INFO     selection_utils:comptable_classification_changer:293 Step 11: No components fit criterion True to change classification
INFO     selection_utils:log_decision_tree_step:447 Step 11: variance_lt_thresholds applied to 22 components. 0 True -> accepted. 22 False -> nochange.
INFO     selection_utils:log_classification_counts:492 Step 11: Total component classifications: 11 accepted, 22 provisionalreject, 7 rejected
INFO     selection_nodes:manual_classify:104 Step 12: manual_classify: Set provisionalaccept to accepted
INFO     selection_utils:log_decision_tree_step:441 Step 12: manual_classify not applied because no remaining components were classified as provisionalaccept
INFO     selection_utils:log_classification_counts:492 Step 12: Total component classifications: 11 accepted, 22 provisionalreject, 7 rejected
INFO     selection_nodes:manual_classify:104 Step 13: manual_classify: Set ['provisionalreject', 'unclassified'] to rejected
INFO     selection_utils:comptable_classification_changer:293 Step 13: No components fit criterion False to change classification
INFO     selection_utils:log_decision_tree_step:447 Step 13: manual_classify applied to 22 components. 22 True -> rejected. 0 False -> nochange.
INFO     selection_utils:log_classification_counts:492 Step 13: Total component classifications: 11 accepted, 29 rejected
INFO     io:denoise_ts:682 Variance explained by decomposition: 95.19%
INFO     io:write_split_ts:769 Writing denoised time series: /home/jovyan/workspace/books/examples/functional_imaging/five-echo-dataset/tedana_processed/desc-denoised_bold.nii.gz
INFO     io:writeresults:817 Writing full ICA coefficient feature set: /home/jovyan/workspace/books/examples/functional_imaging/five-echo-dataset/tedana_processed/desc-ICA_components.nii.gz
INFO     io:writeresults:824 Writing Z-normalized spatial component maps: /home/jovyan/workspace/books/examples/functional_imaging/five-echo-dataset/tedana_processed/desc-ICA_stat-z_components.nii.gz
INFO     io:writeresults:828 Writing denoised ICA coefficient feature set: /home/jovyan/workspace/books/examples/functional_imaging/five-echo-dataset/tedana_processed/desc-ICAAccepted_components.nii.gz
INFO     io:writeresults:835 Writing Z-normalized spatial component maps: /home/jovyan/workspace/books/examples/functional_imaging/five-echo-dataset/tedana_processed/desc-ICAAccepted_stat-z_components.nii.gz
INFO     tedana:tedana_workflow:1153 Making figures folder with static component maps and timecourse plots.
INFO     io:denoise_ts:682 Variance explained by decomposition: 95.19%
/opt/conda/lib/python3.13/site-packages/tedana/reporting/static_figures.py:734: UserWarning: Non-finite values detected. These values will be replaced with zeros.
  plotting.plot_stat_map(
INFO     tedana:tedana_workflow:1236 Generating dynamic report
INFO     html_report:_update_template_bokeh:164 Checking for adaptive mask: adaptive_mask.svg, exists: True
INFO     html_report:_update_template_bokeh:208 T2* files exist: True
INFO     html_report:_update_template_bokeh:209 S0 files exist: True
INFO     html_report:_update_template_bokeh:210 RMSE files exist: True
INFO     html_report:_update_template_bokeh:217 Variance files exist: False
INFO     html_report:_update_template_bokeh:223 External regressors exist: False
INFO     rica:setup_rica_report:787 Rica launcher created. Run 'python /home/jovyan/workspace/books/examples/functional_imaging/five-echo-dataset/tedana_processed/open_rica_report.py' to visualize results.
INFO     tedana:tedana_workflow:1242 Workflow completed
INFO     utils:log_newsletter_info:812 Don't forget to subscribe to the tedana newsletter for updates! This is a very low volume email list.
INFO     utils:log_newsletter_info:816 https://groups.google.com/g/tedana-newsletter

Tedana report of 5 echo data#

You can explore an example of an interactive tedana report here.

The interactive tedana report for the current dataset can be opened via the link below.

# this is the path of the tedana report
report_path = os.path.relpath(dset_dir5_out + '/tedana_report.html')
print(report_path)

five-echo-dataset/tedana_processed/tedana_report.html

display(Markdown(
    f'📊 [Open interactive tedana report]({report_path}) '
    f'*(local/JupyterLab only — not available on GitHub Pages)*'
))

📊 Open interactive tedana report (local/JupyterLab only — not available on GitHub Pages)

Below, selected components from the generated tedana report are visualized.

Carpet plot#

SVG(filename='five-echo-dataset/tedana_processed/figures/carpet_optcom.svg')

../../_images/0c624528449f9d3d7d498a94888983a380fa22a39b3080be5786cf08ab27e1de.svg

Adaptive Mask#

SVG(filename='five-echo-dataset/tedana_processed/figures/adaptive_mask.svg')

../../_images/65d02c62bb63b8ccdfc87942bbdf66a2714d0bdd754013382090a883cf58b746.svg

T2*#

t2star_brain = SVG(filename='five-echo-dataset/tedana_processed/figures/t2star_brain.svg')
t2star_histogram = SVG(filename='five-echo-dataset/tedana_processed/figures/t2star_histogram.svg')

display(t2star_brain, t2star_histogram)

../../_images/f4a6cedeed050034e2f4508d5d6072fd4beab771ae78bb74a66c276367a3172d.svg

../../_images/2c0abacc0d8e5d253983fd640d469376eb8923537f2b7e51c85d407c05c6d9b8.svg

S0#

s0_brain = SVG(filename='five-echo-dataset/tedana_processed/figures/s0_brain.svg')
s0_histogram = SVG(filename='five-echo-dataset/tedana_processed/figures/s0_histogram.svg')

display(s0_brain, s0_histogram)

../../_images/455b7a338a68689c5e797499749c4be75eac76698e22db1fdf19f702ffe3c0ed.svg

../../_images/926ab5ed721da807024e20f5a3c51f8e6142bd71a6e8bffbae45843b7b8a4455.svg

T2* and S0 model fit (RMSE). (Scaled between 2nd and 98th percentiles)#

rmse_brain = SVG(filename='five-echo-dataset/tedana_processed/figures/rmse_brain.svg')
rmse_timeseries = SVG(filename='five-echo-dataset/tedana_processed/figures/rmse_timeseries.svg')

display(rmse_brain, rmse_timeseries)

../../_images/d4c7c88fb42b3ff08afe901a9970b8f1018d561d0a6f23bfe274d89b584b4af3.svg

../../_images/d4a3f32080b3cd683b67ed19bb8fc477268729543113f45c4e6fc3af2fede353.svg

Time series generation using AFNI commands#

%%bash
# A rough CSF mask for demonstration purposes
# Segment the CSF, erode by 1 voxel, #
# and retain voxels containing 75% of the CSF mask when downsampled to EPI space
cd five-echo-dataset

3dSeg -anat SBJ01_Anatomy.nii.gz -mask AUTO \
    -classes 'CSF ; GM ; WM'  \
    -bias_classes 'GM ; WM' \
    -bias_fwhm 25 -mixfrac UNI -main_N 5 \
    -blur_meth BFT
3dcalc -a ./Segsy/Classes+tlrc -expr 'equals(a, 1)' -prefix CSF_anatresolution.nii.gz
3dmask_tool -input CSF_anatresolution.nii.gz \
    -prefix CSF_eroded.nii.gz \
    -dilate_result -1 -fill_holes 
3dfractionize -template p06.SBJ01_S09_Task11_e3.sm.nii.gz \
    -prefix CSF_mask.nii.gz \
    -input CSF_eroded.nii.gz \
    -clip 0.75

# make CSF principal components
3dpc -mask CSF_mask.nii.gz -pcsave 3  \
    -prefix CSF_timeseries \
    ./tedana_processed/desc-optcom_bold.nii.gz

# Combine all external regressors into a single file
external_regress_header="mot_x\tmot_y\tmot_z\tmot_pitch\tmot_roll\tmot_yaw\t"\
"mot_dx\tmot_dy\tmot_dz\tmot_dpitch\tmot_droll\tmot_dyaw\tcsf1\tcsf2\tcsf3\t"\
"signal_checkerboard"

1dcat -tsvout \
    SBJ01_S09_Task11_e2_Motion.demean.1D \
    SBJ01_S09_Task11_e2_Motion.demean.der.1D \
    CSF_timeseries0?.1D \
    block_task_response.1D \
    > tmp.tsv

# Convert spaces to tabs and skip the header line from 1dcat
tail -n +2 tmp.tsv | tr ' ' '\t' > tmp_clean.tsv

# Add header
(echo -e "$external_regress_header"; cat tmp_clean.tsv) > external_regressors.tsv

# Clean up
rm tmp.tsv tmp_clean.tsv

++ Compile date = Oct  1 2024 {AFNI_24.3.00:linux_ubuntu_24_64}

*+ WARNING: +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
*+ WARNING: ss.cp+tlrc[0] scale to shorts mean misfit error = 38.4% -- **** Red Alert ****
+ a) Numerical precision has been lost when truncating results
       from 32-bit floating point to
16-bit integers (shorts).
 + b) Consider writing datasets out in float format.
       In most AFNI p
rograms, use the '-float' option.
 + c) This warning is a new message, but is an old issue
       th
at arises when storing results in an integer format.
 + d) Don't panic! These messages likely origin
ate in peripheral
       or unimportant voxels. They mean that you must examine your output.
"Assess the situation and keep a calm head about you,
        because it doesn't do anybody any good
 to panic."
++ ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
*+ WARNING: GenPriorsOut.x+tlrc[0] scale to shorts mean misfit error = 40.0% -- ***** Purple
 Alert! *****
*+ WARNING: GenPriorsOut.x+tlrc[0] scale to shorts mean misfit error = 39.8% -- ***** Purple
 Alert! *****
*+ WARNING: GenPriorsOut.x+tlrc[0] scale to shorts mean misfit error = 39.3% -- ***** Purple
 Alert! *****
*+ WARNING: GenPriorsOut.x+tlrc[0] scale to shorts mean misfit error = 38.9% -- **** Red Ale
rt ****
*+ WARNING: GenPriorsOut.x+tlrc[0] scale to shorts mean misfit error = 38.4% -- **** Red Ale
rt ****
++ 3dcalc: AFNI version=AFNI_24.3.00 (Oct  1 2024) [64-bit]
++ Authored by: A cast of thousands
++ Output dataset ./CSF_anatresolution.nii.gz
++ no -frac option: defaulting to -union
++ processing 1 input dataset(s), NN=2...
++ padding all datasets by 0 (for dilations)
++ have 1 volumes of input to combine
++ frac 0 over 1 volumes gives min count 0
++ voxel limits: 0 clipped, 135363 survived, 11398973 were zero
++ filled 0 holes (0 voxels)
++ writing result CSF_eroded.nii.gz...
++ Output dataset ./CSF_eroded.nii.gz
++ 3dfractionize: AFNI version=AFNI_24.3.00 (Oct  1 2024) [64-bit]
++ Authored by: RW Cox
-- Writing 83 nonzero mask voxels to dataset ./CSF_mask.nii.gz
++ 3dpc: AFNI version=AFNI_24.3.00 (Oct  1 2024) [64-bit]

Run workflow on 5 echo data using existing mixing matrix and external regressors#

%%time
dset_dir5_extern_out = f"{dset_dir5}tedana_external_regress_processed"
files = sorted(glob(op.join(dset_dir5, 'p06*.nii.gz')))
tes = [15.4, 29.7, 44.0, 58.3, 72.6]
tedana_workflow(files, tes, 
    tree="demo_external_regressors_motion_task_models",
    external_regressors=op.join(dset_dir5,"external_regressors.tsv"),
    mixing_file=op.join(dset_dir5,"tedana_processed", "desc-ICA_mixing.tsv"),
    out_dir=dset_dir5_extern_out
    )

(160, 40)
(160, 40)
(160, 40)
(160, 40)
CPU times: user 9min 27s, sys: 2.22 s, total: 9min 29s
Wall time: 2min 43s

INFO     tedana:tedana_workflow:636 Using output directory: /home/jovyan/workspace/books/examples/functional_imaging/five-echo-dataset/tedana_external_regress_processed
WARNING  utils:check_te_values:796 TE values appear to be in milliseconds. Per BIDS convention, echo times should be provided in seconds. Support for millisecond TE values is deprecated and will be removed in a future version. Please provide TE values in seconds.
INFO     tedana:tedana_workflow:655 Initializing and validating component selection tree
INFO     component_selector:__init__:345 Performing component selection with demo_external_regressors_motion_task_models
INFO     component_selector:__init__:346 Demonstration based on the minimal decision tree that uses partial F stats on a model with multiple external regressors divided by category and task regressors to bias towards keeping.
INFO     io:__init__:164 Generating figures directory: /home/jovyan/workspace/books/examples/functional_imaging/five-echo-dataset/tedana_external_regress_processed/figures
WARNING  utils:load_mask:943 Computing EPI mask from first echo using nilearn's compute_epi_mask function. Most external pipelines include more reliable masking functions. It is strongly recommended to provide an external mask, and to visually confirm that mask accurately conforms to data boundaries.
INFO     tedana:tedana_workflow:690 Loading input data: ['five-echo-dataset/p06.SBJ01_S09_Task11_e1.sm.nii.gz', 'five-echo-dataset/p06.SBJ01_S09_Task11_e2.sm.nii.gz', 'five-echo-dataset/p06.SBJ01_S09_Task11_e3.sm.nii.gz', 'five-echo-dataset/p06.SBJ01_S09_Task11_e4.sm.nii.gz', 'five-echo-dataset/p06.SBJ01_S09_Task11_e5.sm.nii.gz']
INFO     utils:make_adaptive_mask:167 Echo-wise intensity thresholds for adaptive mask: [5853.639  4862.627  4073.2693 3377.1418 2800.739 ]
WARNING  utils:make_adaptive_mask:195 4 voxels in user-defined mask do not have good signal. Removing voxels from mask.
INFO     tedana:tedana_workflow:780 Computing T2* map
INFO     combine:make_optcom:202 Optimally combining data with voxel-wise T2* estimates
INFO     tedana:tedana_workflow:859 Writing optimally combined data set: /home/jovyan/workspace/books/examples/functional_imaging/five-echo-dataset/tedana_external_regress_processed/desc-optcom_bold.nii.gz
INFO     tedana:tedana_workflow:970 Using supplied mixing matrix from ICA
INFO     collect:generate_metrics:153 Calculating standardized parameter estimate maps for optimally combined data
INFO     collect:generate_metrics:171 Calculating unstandardized parameter estimate maps for optimally combined data
INFO     collect:generate_metrics:203 Calculating F-statistic maps
INFO     collect:generate_metrics:229 Thresholding standardized parameter estimate maps
INFO     collect:generate_metrics:237 Thresholding T2* F-statistic maps
INFO     collect:generate_metrics:245 Thresholding S0 F-statistic maps
INFO     collect:generate_metrics:254 Counting significant voxels in T2* F-statistic maps
INFO     collect:generate_metrics:260 Counting significant voxels in S0 F-statistic maps
INFO     collect:generate_metrics:267 Thresholding optimal combination beta maps to match T2* F-statistic maps
INFO     collect:generate_metrics:275 Thresholding optimal combination beta maps to match S0 F-statistic maps
INFO     collect:generate_metrics:284 Calculating kappa and rho
INFO     collect:generate_metrics:293 Calculating variance explained
INFO     collect:generate_metrics:299 Calculating normalized variance explained
INFO     collect:generate_metrics:328 Calculating DSI between thresholded T2* F-statistic and optimal combination beta maps
INFO     collect:generate_metrics:338 Calculating DSI between thresholded S0 F-statistic and optimal combination beta maps
INFO     collect:generate_metrics:349 Calculating signal-noise t-statistics
/opt/conda/lib/python3.13/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:422 Calculating external regressor fits. Fits all external nuisance regressors to a single model using an F statistic
INFO     collect:generate_metrics:422 Calculating external regressor fits. Fits all task regressors to a single model using an F statistic
INFO     external:fit_regressors:285 External regressors fit for nuisance includes detrending with 2 Legendre Polynomial regressors.
INFO     external:fit_mixing_to_regressors:379 Running fit_mixing_to_regressors for nuisance
INFO     external:fit_mixing_to_regressors:380 ICA matrix has 160 time points and 40 components
INFO     external:build_fstat_regressor_models:472 Size for base regressor model for nuisance: (160, 2)
INFO     external:build_fstat_regressor_models:487 Size for full regressor model for nuisance: (160, 17)
INFO     external:build_fstat_regressor_models:488 Regressors in full model for nuisance: ['baseline 0', 'baseline 1', 'csf1', 'csf2', 'csf3', 'mot_dpitch', 'mot_droll', 'mot_dx', 'mot_dy', 'mot_dyaw', 'mot_dz', 'mot_pitch', 'mot_roll', 'mot_x', 'mot_y', 'mot_yaw', 'mot_z']
INFO     external:build_fstat_regressor_models:510 Size of external regressor partial model 'no Motion': (160, 5)
INFO     external:build_fstat_regressor_models:514 Regressors in partial model (everything but regressors of interest) 'no Motion': ['baseline 0', 'baseline 1', 'csf1', 'csf2', 'csf3']
INFO     external:build_fstat_regressor_models:510 Size of external regressor partial model 'no CSF': (160, 14)
INFO     external:build_fstat_regressor_models:514 Regressors in partial model (everything but regressors of interest) 'no CSF': ['baseline 0', 'baseline 1', 'mot_dpitch', 'mot_droll', 'mot_dx', 'mot_dy', 'mot_dyaw', 'mot_dz', 'mot_pitch', 'mot_roll', 'mot_x', 'mot_y', 'mot_yaw', 'mot_z']
INFO     external:fit_regressors:285 External regressors fit for task includes detrending with 2 Legendre Polynomial regressors.
INFO     external:fit_mixing_to_regressors:379 Running fit_mixing_to_regressors for task
INFO     external:fit_mixing_to_regressors:380 ICA matrix has 160 time points and 40 components
INFO     external:build_fstat_regressor_models:472 Size for base regressor model for task: (160, 2)
INFO     external:build_fstat_regressor_models:487 Size for full regressor model for task: (160, 3)
INFO     external:build_fstat_regressor_models:488 Regressors in full model for task: ['baseline 0', 'baseline 1', 'signal_checkerboard']
INFO     tedica:automatic_selection:54 Performing ICA component selection with tree: demo_external_regressors_motion_task_models
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 40 components. 40 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: 40 unclassified
INFO     selection_nodes:dec_left_op_right:389 Step 1: left_op_right: provisionalreject if rho>kappa, else nochange
INFO     selection_utils:log_decision_tree_step:447 Step 1: left_op_right applied to 40 components. 5 True -> provisionalreject. 35 False -> nochange.
INFO     selection_utils:log_classification_counts:492 Step 1: Total component classifications: 5 provisionalreject, 35 unclassified
INFO     selection_nodes:dec_left_op_right:389 Step 2: left_op_right: provisionalreject if ['countsigFS0>countsigFT2 & countsigFT2>0'], else nochange
INFO     selection_utils:log_decision_tree_step:447 Step 2: left_op_right applied to 40 components. 2 True -> provisionalreject. 38 False -> nochange.
INFO     selection_utils:log_classification_counts:492 Step 2: Total component classifications: 5 provisionalreject, 35 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=0.6119923398094282
INFO     selection_utils:log_classification_counts:492 Step 3: Total component classifications: 5 provisionalreject, 35 unclassified
INFO     selection_nodes:dec_left_op_right:389 Step 4: left_op_right: provisionalreject if ['dice_FS0>dice_FT2 & variance explained>0.61'], else nochange
INFO     selection_utils:comptable_classification_changer:293 Step 4: No components fit criterion True to change classification
INFO     selection_utils:log_decision_tree_step:447 Step 4: left_op_right applied to 40 components. 0 True -> provisionalreject. 40 False -> nochange.
INFO     selection_utils:log_classification_counts:492 Step 4: Total component classifications: 5 provisionalreject, 35 unclassified
INFO     selection_nodes:dec_left_op_right:389 Step 5: left_op_right: provisionalreject if ['0>signal-noise_t & variance explained>0.61'], else nochange
INFO     selection_utils:log_decision_tree_step:447 Step 5: left_op_right applied to 40 components. 4 True -> provisionalreject. 36 False -> nochange.
INFO     selection_utils:log_classification_counts:492 Step 5: Total component classifications: 7 provisionalreject, 33 unclassified
INFO     selection_nodes:calc_kappa_elbow:767 Step 6: calc_kappa_elbow: Calc Kappa Elbow
INFO     selection_utils:kappa_elbow_kundu:668 Calculating kappa elbow based on all components.
INFO     selection_utils:log_decision_tree_step:459 Step 6: calc_kappa_elbow calculated: kappa_elbow_kundu=73.52384121688529, kappa_allcomps_elbow=73.52384121688529, kappa_nonsig_elbow=None, varex_upper_p=0.9381300999030658
INFO     selection_utils:log_classification_counts:492 Step 6: Total component classifications: 7 provisionalreject, 33 unclassified
INFO     selection_nodes:calc_rho_elbow:902 Step 7: calc_rho_elbow: Calc Rho Elbow
INFO     selection_utils:log_decision_tree_step:459 Step 7: calc_rho_elbow calculated: rho_elbow_liberal=20.376565983430464, rho_allcomps_elbow=20.376565983430464, rho_unclassified_elbow=20.32709879825412, elbow_f05=7.708647422176786
INFO     selection_utils:log_classification_counts:492 Step 7: Total component classifications: 7 provisionalreject, 33 unclassified
INFO     selection_nodes:dec_left_op_right:389 Step 8: left_op_right: provisionalaccept if ['kappa>=73.52 & rho<20.38'], else provisionalreject
INFO     selection_nodes:dec_left_op_right:391 Step 8: left_op_right If kappa> kappa elbow and rho<rho elbow, provisional accept. Otherwise provisional reject
INFO     selection_utils:log_decision_tree_step:447 Step 8: left_op_right applied to 33 components. 10 True -> provisionalaccept. 23 False -> provisionalreject.
INFO     selection_utils:log_classification_counts:492 Step 8: Total component classifications: 10 provisionalaccept, 30 provisionalreject
INFO     selection_nodes:dec_left_op_right:389 Step 9: left_op_right: provisionalaccept if ['kappa>2*rho & kappa>73.52'], else nochange
INFO     selection_nodes:dec_left_op_right:391 Step 9: left_op_right If kappa>elbow and kappa>2*rho accept even if rho>elbow
INFO     selection_utils:log_decision_tree_step:447 Step 9: left_op_right applied to 30 components. 1 True -> provisionalaccept. 29 False -> nochange.
INFO     selection_utils:log_classification_counts:492 Step 9: Total component classifications: 11 provisionalaccept, 29 provisionalreject
INFO     selection_nodes:dec_left_op_right:389 Step 10: left_op_right: provisionalreject if ['pval nuisance model<0.05 & R2stat nuisance model>0.5'], else nochange
INFO     selection_nodes:dec_left_op_right:391 Step 10: left_op_right If external regressors fit with p<0.05 and model R2>0.5 of the variance, then reject.
INFO     selection_utils:log_decision_tree_step:447 Step 10: left_op_right applied to 40 components. 10 True -> provisionalreject. 30 False -> nochange.
INFO     selection_utils:log_classification_counts:492 Step 10: Total component classifications: 9 provisionalaccept, 31 provisionalreject
INFO     selection_nodes:dec_left_op_right:389 Step 11: left_op_right: nochange if ['pval nuisance model<0.05 & R2stat nuisance model>0.5 & pval nuisance Motion partial model<0.05'], else nochange
INFO     selection_utils:log_decision_tree_step:447 Step 11: left_op_right applied to 31 components. 10 True -> nochange. 21 False -> nochange.
INFO     selection_utils:log_classification_counts:492 Step 11: Total component classifications: 9 provisionalaccept, 31 provisionalreject
INFO     selection_nodes:dec_left_op_right:389 Step 12: left_op_right: nochange if ['pval nuisance model<0.05 & R2stat nuisance model>0.5 & pval nuisance CSF partial model<0.05'], else nochange
INFO     selection_utils:log_decision_tree_step:447 Step 12: left_op_right applied to 31 components. 10 True -> nochange. 21 False -> nochange.
INFO     selection_utils:log_classification_counts:492 Step 12: Total component classifications: 9 provisionalaccept, 31 provisionalreject
INFO     selection_nodes:dec_left_op_right:389 Step 13: left_op_right: accepted if ['pval task model<0.05 & R2stat task model>0.5 & kappa>=73.52'], else nochange
INFO     selection_utils:comptable_classification_changer:293 Step 13: No components fit criterion True to change classification
INFO     selection_utils:log_decision_tree_step:447 Step 13: left_op_right applied to 31 components. 0 True -> accepted. 31 False -> nochange.
INFO     selection_utils:log_classification_counts:492 Step 13: Total component classifications: 9 provisionalaccept, 31 provisionalreject
INFO     selection_nodes:dec_variance_lessthan_thresholds:533 Step 14: variance_lt_thresholds: accepted if variance explained<0.1. All variance<1.0, else nochange
INFO     selection_utils:comptable_classification_changer:293 Step 14: No components fit criterion True to change classification
INFO     selection_utils:log_decision_tree_step:447 Step 14: variance_lt_thresholds applied to 31 components. 0 True -> accepted. 31 False -> nochange.
INFO     selection_utils:log_classification_counts:492 Step 14: Total component classifications: 9 provisionalaccept, 31 provisionalreject
INFO     selection_nodes:manual_classify:104 Step 15: manual_classify: Set provisionalaccept to accepted
INFO     selection_utils:comptable_classification_changer:293 Step 15: No components fit criterion False to change classification
INFO     selection_utils:log_decision_tree_step:447 Step 15: manual_classify applied to 9 components. 9 True -> accepted. 0 False -> nochange.
INFO     selection_utils:log_classification_counts:492 Step 15: Total component classifications: 9 accepted, 31 provisionalreject
INFO     selection_nodes:manual_classify:104 Step 16: manual_classify: Set ['provisionalreject', 'unclassified'] to rejected
INFO     selection_utils:comptable_classification_changer:293 Step 16: No components fit criterion False to change classification
INFO     selection_utils:log_decision_tree_step:447 Step 16: manual_classify applied to 31 components. 31 True -> rejected. 0 False -> nochange.
INFO     selection_utils:log_classification_counts:492 Step 16: Total component classifications: 9 accepted, 31 rejected
INFO     io:denoise_ts:682 Variance explained by decomposition: 95.19%
INFO     io:write_split_ts:769 Writing denoised time series: /home/jovyan/workspace/books/examples/functional_imaging/five-echo-dataset/tedana_external_regress_processed/desc-denoised_bold.nii.gz
INFO     io:writeresults:817 Writing full ICA coefficient feature set: /home/jovyan/workspace/books/examples/functional_imaging/five-echo-dataset/tedana_external_regress_processed/desc-ICA_components.nii.gz
INFO     io:writeresults:824 Writing Z-normalized spatial component maps: /home/jovyan/workspace/books/examples/functional_imaging/five-echo-dataset/tedana_external_regress_processed/desc-ICA_stat-z_components.nii.gz
INFO     io:writeresults:828 Writing denoised ICA coefficient feature set: /home/jovyan/workspace/books/examples/functional_imaging/five-echo-dataset/tedana_external_regress_processed/desc-ICAAccepted_components.nii.gz
INFO     io:writeresults:835 Writing Z-normalized spatial component maps: /home/jovyan/workspace/books/examples/functional_imaging/five-echo-dataset/tedana_external_regress_processed/desc-ICAAccepted_stat-z_components.nii.gz
INFO     tedana:tedana_workflow:1153 Making figures folder with static component maps and timecourse plots.
INFO     io:denoise_ts:682 Variance explained by decomposition: 95.19%
/opt/conda/lib/python3.13/site-packages/tedana/reporting/static_figures.py:734: UserWarning: Non-finite values detected. These values will be replaced with zeros.
  plotting.plot_stat_map(
INFO     tedana:tedana_workflow:1236 Generating dynamic report
INFO     html_report:_update_template_bokeh:164 Checking for adaptive mask: adaptive_mask.svg, exists: True
INFO     html_report:_update_template_bokeh:208 T2* files exist: True
INFO     html_report:_update_template_bokeh:209 S0 files exist: True
INFO     html_report:_update_template_bokeh:210 RMSE files exist: True
INFO     html_report:_update_template_bokeh:217 Variance files exist: False
INFO     html_report:_update_template_bokeh:223 External regressors exist: True
INFO     rica:setup_rica_report:787 Rica launcher created. Run 'python /home/jovyan/workspace/books/examples/functional_imaging/five-echo-dataset/tedana_external_regress_processed/open_rica_report.py' to visualize results.
INFO     tedana:tedana_workflow:1242 Workflow completed
INFO     utils:log_newsletter_info:812 Don't forget to subscribe to the tedana newsletter for updates! This is a very low volume email list.
INFO     utils:log_newsletter_info:816 https://groups.google.com/g/tedana-newsletter

Components of the tedana report of 5 echo data with external regressors#

# Here is the path to the second TEDANA report
report_path_ext = os.path.relpath(dset_dir5_extern_out + '/tedana_report.html')
print(report_path_ext)

five-echo-dataset/tedana_external_regress_processed/tedana_report.html

display(Markdown(
    f'📊 [Open interactive tedana report with external regressors]({report_path_ext}) '
    f'*(local/JupyterLab only — not available on GitHub Pages)*'
))

📊 Open interactive tedana report with external regressors (local/JupyterLab only — not available on GitHub Pages)

Carpet plot#

SVG(filename='five-echo-dataset/tedana_external_regress_processed/figures/carpet_optcom.svg')

../../_images/7047d17da685294e40e12c46e95f243a2dde6519bbeeded7b87dde9b834ef539.svg

Adaptive Mask#

SVG(filename='five-echo-dataset/tedana_external_regress_processed/figures/adaptive_mask.svg')

../../_images/f2414c10574904de42044eb71a76a18cb56b5f48646fd2d6f8f9430d4efb2e05.svg

T2*#

t2star_brain = SVG(filename='five-echo-dataset/tedana_external_regress_processed/figures/t2star_brain.svg')
t2star_histogram = SVG(filename='five-echo-dataset/tedana_external_regress_processed/figures/t2star_histogram.svg')

display(t2star_brain, t2star_histogram)

../../_images/4ebfcb8bc80d392da4661c27c17170f7318a11ffb1fefbbcb6850b732eded954.svg

../../_images/ebcab76c8c3252533c8c1518f1db3a27b643171298afabb3b56c565c9e61cdc4.svg

S0#

s0_brain = SVG(filename='five-echo-dataset/tedana_external_regress_processed/figures/s0_brain.svg')
s0_histogram = SVG(filename='five-echo-dataset/tedana_external_regress_processed/figures/s0_histogram.svg')

display(s0_brain, s0_histogram)

../../_images/7381e42e4beebe627f9600cec23b4b0cce6e59eca1ef1813594dc6c433dd5e68.svg

../../_images/bc4b41de58fe32b753bb328f5fceb5b68b1b084764437c4c5dcef348d00d4423.svg

T2* and S0 model fit (RMSE). (Scaled between 2nd and 98th percentiles)#

rmse_brain = SVG(filename='five-echo-dataset/tedana_external_regress_processed/figures/rmse_brain.svg')
rmse_timeseries = SVG(filename='five-echo-dataset/tedana_external_regress_processed/figures/rmse_timeseries.svg')

display(rmse_brain, rmse_timeseries)

../../_images/1fd0761a75a2e4a471458c0cb5dda78edc21757b11f166522ef10e81b8695202.svg

../../_images/935e2832cd674052cfbefd66b324d71edcb4e17dc1032dce6481129871ea3767.svg

External regressors#

SVG(filename='five-echo-dataset/tedana_external_regress_processed/figures/confound_correlations.svg')

../../_images/f4f4e2dd58d4817c38c5730e6a7df706adebe51983cdcb70f5c81241e7c35f5d.svg

Dependencies in Jupyter/Python#

Using the package watermark to document system environment and software versions used in this notebook, alongside the Neurodesktop version extracted from the JUPYTER_IMAGE or NEURODESKTOP_VERSION environment variables.

import os

%load_ext watermark

%watermark
%watermark --iversions

neurodesktop_version = (
    os.environ.get('JUPYTER_IMAGE', '').split(':')[-1] or
    os.environ.get('NEURODESKTOP_VERSION', 'unknown')
)

print(f"Neurodesktop version: {neurodesktop_version}")

Last updated: 2026-04-09T07:56:46.389931+00:00

Python implementation: CPython
Python version       : 3.13.9
IPython version      : 9.7.0

Compiler    : GCC 14.3.0
OS          : Linux
Release     : 5.15.0-171-generic
Machine     : x86_64
Processor   : x86_64
CPU cores   : 32
Architecture: 64bit

IPython: 9.7.0
tedana : 26.0.3

Neurodesktop version: 2025-12-20

tedana: TE Dependent ANAlysis

Contents

tedana: TE Dependent ANAlysis#

Process five-echo flashing checkerboard dataset for software demo#

Citation:#

Tools included in this workflow#

Publications#

Educational resources#

Dataset#

Installations and Imports#

Download 5 echo data#

Run workflow on 5 echo data#

Tedana report of 5 echo data#

Carpet plot#

Adaptive Mask#

T2*#

S0#

T2* and S0 model fit (RMSE). (Scaled between 2nd and 98th percentiles)#

Time series generation using AFNI commands#

Run workflow on 5 echo data using existing mixing matrix and external regressors#

Components of the tedana report of 5 echo data with external regressors#

Carpet plot#

Adaptive Mask#

T2*#

S0#

T2* and S0 model fit (RMSE). (Scaled between 2nd and 98th percentiles)#

External regressors#

Dependencies in Jupyter/Python#