Multi-Echo Denoising with tedana

Multi-Echo Denoising with `tedana`#

In this analysis tutorial, we will use tedana [DuPre et al., 2021] to perform multi-echo denoising.

Specifically, we will use tedana.workflows.tedana_workflow().

import json
import os
from glob import glob
from pprint import pprint

import pandas as pd
from IPython.display import HTML, display
from repo2data.repo2data import Repo2Data
from tedana import workflows

# Install the data if running locally, or point to cached data if running on neurolibre
DATA_REQ_FILE = os.path.join("../binder/data_requirement.json")

# Download data
repo2data = Repo2Data(DATA_REQ_FILE)
data_path = repo2data.install()
data_path = os.path.abspath(data_path[0])

---- repo2data starting ----
/opt/hostedtoolcache/Python/3.10.18/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

Hide 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

2-echo monoexponential:   0%|          | 0/2224 [00:00<?, ?it/s]

2-echo monoexponential:   3%|▎         | 64/2224 [00:00<00:03, 632.72it/s]

2-echo monoexponential:   7%|▋         | 146/2224 [00:00<00:02, 735.49it/s]

2-echo monoexponential:  10%|█         | 226/2224 [00:00<00:02, 758.86it/s]

2-echo monoexponential:  14%|█▍        | 310/2224 [00:00<00:02, 787.55it/s]

2-echo monoexponential:  17%|█▋        | 389/2224 [00:00<00:02, 788.06it/s]

2-echo monoexponential:  21%|██▏       | 474/2224 [00:00<00:02, 806.60it/s]

2-echo monoexponential:  25%|██▌       | 556/2224 [00:00<00:02, 809.17it/s]

2-echo monoexponential:  29%|██▊       | 637/2224 [00:00<00:01, 804.08it/s]

2-echo monoexponential:  32%|███▏      | 718/2224 [00:00<00:01, 798.03it/s]

2-echo monoexponential:  36%|███▌      | 802/2224 [00:01<00:01, 809.35it/s]

2-echo monoexponential:  40%|███▉      | 883/2224 [00:01<00:01, 804.35it/s]

2-echo monoexponential:  43%|████▎     | 964/2224 [00:01<00:01, 802.76it/s]

2-echo monoexponential:  47%|████▋     | 1045/2224 [00:01<00:01, 796.06it/s]

2-echo monoexponential:  51%|█████     | 1125/2224 [00:01<00:01, 790.37it/s]

2-echo monoexponential:  54%|█████▍    | 1205/2224 [00:01<00:01, 785.13it/s]

2-echo monoexponential:  58%|█████▊    | 1284/2224 [00:01<00:01, 778.21it/s]

2-echo monoexponential:  61%|██████▏   | 1364/2224 [00:01<00:01, 783.81it/s]

2-echo monoexponential:  65%|██████▍   | 1443/2224 [00:01<00:00, 783.76it/s]

2-echo monoexponential:  69%|██████▊   | 1525/2224 [00:01<00:00, 793.74it/s]

2-echo monoexponential:  72%|███████▏  | 1609/2224 [00:02<00:00, 806.07it/s]

2-echo monoexponential:  76%|███████▌  | 1690/2224 [00:02<00:00, 803.65it/s]

2-echo monoexponential:  80%|███████▉  | 1772/2224 [00:02<00:00, 805.57it/s]

2-echo monoexponential:  83%|████████▎ | 1855/2224 [00:02<00:00, 808.63it/s]

2-echo monoexponential:  87%|████████▋ | 1939/2224 [00:02<00:00, 816.52it/s]

2-echo monoexponential:  91%|█████████ | 2024/2224 [00:02<00:00, 822.72it/s]

2-echo monoexponential:  95%|█████████▍| 2107/2224 [00:02<00:00, 807.34it/s]

2-echo monoexponential:  98%|█████████▊| 2188/2224 [00:02<00:00, 797.16it/s]

2-echo monoexponential: 100%|██████████| 2224/2224 [00:02<00:00, 794.87it/s]

3-echo monoexponential:   0%|          | 0/1092 [00:00<?, ?it/s]

3-echo monoexponential:   5%|▍         | 50/1092 [00:00<00:02, 496.93it/s]

3-echo monoexponential:   9%|▉         | 103/1092 [00:00<00:01, 510.46it/s]

3-echo monoexponential:  14%|█▍        | 157/1092 [00:00<00:01, 520.01it/s]

3-echo monoexponential:  19%|█▉        | 211/1092 [00:00<00:01, 524.40it/s]

3-echo monoexponential:  24%|██▍       | 264/1092 [00:00<00:01, 524.31it/s]

3-echo monoexponential:  29%|██▉       | 317/1092 [00:00<00:01, 520.59it/s]

3-echo monoexponential:  34%|███▍      | 370/1092 [00:00<00:01, 512.82it/s]

3-echo monoexponential:  39%|███▊      | 422/1092 [00:00<00:01, 510.55it/s]

3-echo monoexponential:  44%|████▎     | 477/1092 [00:00<00:01, 519.87it/s]

3-echo monoexponential:  49%|████▊     | 530/1092 [00:01<00:01, 518.67it/s]

3-echo monoexponential:  53%|█████▎    | 582/1092 [00:01<00:00, 512.02it/s]

3-echo monoexponential:  58%|█████▊    | 635/1092 [00:01<00:00, 514.13it/s]

3-echo monoexponential:  63%|██████▎   | 687/1092 [00:01<00:00, 514.56it/s]

3-echo monoexponential:  68%|██████▊   | 739/1092 [00:01<00:00, 510.55it/s]

3-echo monoexponential:  72%|███████▏  | 791/1092 [00:01<00:00, 501.76it/s]

3-echo monoexponential:  77%|███████▋  | 844/1092 [00:01<00:00, 509.79it/s]

3-echo monoexponential:  82%|████████▏ | 896/1092 [00:01<00:00, 504.78it/s]

3-echo monoexponential:  87%|████████▋ | 950/1092 [00:01<00:00, 513.04it/s]

3-echo monoexponential:  92%|█████████▏| 1002/1092 [00:01<00:00, 513.16it/s]

3-echo monoexponential:  97%|█████████▋| 1056/1092 [00:02<00:00, 519.95it/s]

3-echo monoexponential: 100%|██████████| 1092/1092 [00:02<00:00, 514.25it/s]

4-echo monoexponential:   0%|          | 0/20176 [00:00<?, ?it/s]

4-echo monoexponential:   0%|          | 52/20176 [00:00<00:39, 514.60it/s]

4-echo monoexponential:   1%|          | 107/20176 [00:00<00:37, 533.93it/s]

4-echo monoexponential:   1%|          | 166/20176 [00:00<00:35, 557.53it/s]

4-echo monoexponential:   1%|          | 227/20176 [00:00<00:34, 575.57it/s]

4-echo monoexponential:   1%|▏         | 285/20176 [00:00<00:34, 571.28it/s]

4-echo monoexponential:   2%|▏         | 346/20176 [00:00<00:33, 584.04it/s]

4-echo monoexponential:   2%|▏         | 407/20176 [00:00<00:33, 590.75it/s]

4-echo monoexponential:   2%|▏         | 467/20176 [00:00<00:33, 586.10it/s]

4-echo monoexponential:   3%|▎         | 528/20176 [00:00<00:33, 591.27it/s]

4-echo monoexponential:   3%|▎         | 588/20176 [00:01<00:33, 588.02it/s]

4-echo monoexponential:   3%|▎         | 647/20176 [00:01<00:33, 580.04it/s]

4-echo monoexponential:   3%|▎         | 706/20176 [00:01<00:33, 580.66it/s]

4-echo monoexponential:   4%|▍         | 766/20176 [00:01<00:33, 583.30it/s]

4-echo monoexponential:   4%|▍         | 825/20176 [00:01<00:33, 582.17it/s]

4-echo monoexponential:   4%|▍         | 886/20176 [00:01<00:32, 590.31it/s]

4-echo monoexponential:   5%|▍         | 947/20176 [00:01<00:32, 593.00it/s]

4-echo monoexponential:   5%|▍         | 1007/20176 [00:01<00:34, 563.18it/s]

4-echo monoexponential:   5%|▌         | 1064/20176 [00:01<00:33, 564.42it/s]

4-echo monoexponential:   6%|▌         | 1124/20176 [00:01<00:33, 574.69it/s]

4-echo monoexponential:   6%|▌         | 1184/20176 [00:02<00:32, 581.26it/s]

4-echo monoexponential:   6%|▌         | 1243/20176 [00:02<00:32, 582.32it/s]

4-echo monoexponential:   6%|▋         | 1307/20176 [00:02<00:31, 596.17it/s]

4-echo monoexponential:   7%|▋         | 1370/20176 [00:02<00:31, 605.35it/s]

4-echo monoexponential:   7%|▋         | 1431/20176 [00:02<00:31, 598.58it/s]

4-echo monoexponential:   7%|▋         | 1491/20176 [00:02<00:32, 578.11it/s]

4-echo monoexponential:   8%|▊         | 1552/20176 [00:02<00:31, 585.39it/s]

4-echo monoexponential:   8%|▊         | 1613/20176 [00:02<00:31, 591.61it/s]

4-echo monoexponential:   8%|▊         | 1677/20176 [00:02<00:30, 603.04it/s]

4-echo monoexponential:   9%|▊         | 1740/20176 [00:02<00:30, 606.28it/s]

4-echo monoexponential:   9%|▉         | 1801/20176 [00:03<00:30, 603.90it/s]

4-echo monoexponential:   9%|▉         | 1864/20176 [00:03<00:29, 611.18it/s]

4-echo monoexponential:  10%|▉         | 1926/20176 [00:03<00:30, 602.13it/s]

4-echo monoexponential:  10%|▉         | 1987/20176 [00:03<00:31, 585.32it/s]

4-echo monoexponential:  10%|█         | 2046/20176 [00:03<00:30, 585.49it/s]

4-echo monoexponential:  10%|█         | 2107/20176 [00:03<00:30, 590.83it/s]

4-echo monoexponential:  11%|█         | 2168/20176 [00:03<00:30, 596.16it/s]

4-echo monoexponential:  11%|█         | 2233/20176 [00:03<00:29, 608.91it/s]

4-echo monoexponential:  11%|█▏        | 2298/20176 [00:03<00:28, 618.66it/s]

4-echo monoexponential:  12%|█▏        | 2361/20176 [00:03<00:28, 621.21it/s]

4-echo monoexponential:  12%|█▏        | 2425/20176 [00:04<00:28, 624.16it/s]

4-echo monoexponential:  12%|█▏        | 2489/20176 [00:04<00:28, 628.54it/s]

4-echo monoexponential:  13%|█▎        | 2552/20176 [00:04<00:28, 617.49it/s]

4-echo monoexponential:  13%|█▎        | 2614/20176 [00:04<00:29, 594.15it/s]

4-echo monoexponential:  13%|█▎        | 2674/20176 [00:04<00:29, 594.31it/s]

4-echo monoexponential:  14%|█▎        | 2736/20176 [00:04<00:28, 601.72it/s]

4-echo monoexponential:  14%|█▍        | 2799/20176 [00:04<00:28, 606.59it/s]

4-echo monoexponential:  14%|█▍        | 2865/20176 [00:04<00:27, 621.74it/s]

4-echo monoexponential:  15%|█▍        | 2930/20176 [00:04<00:27, 628.54it/s]

4-echo monoexponential:  15%|█▍        | 2994/20176 [00:05<00:27, 631.04it/s]

4-echo monoexponential:  15%|█▌        | 3059/20176 [00:05<00:26, 634.64it/s]

4-echo monoexponential:  15%|█▌        | 3124/20176 [00:05<00:26, 637.77it/s]

4-echo monoexponential:  16%|█▌        | 3188/20176 [00:05<00:26, 631.75it/s]

4-echo monoexponential:  16%|█▌        | 3252/20176 [00:05<00:27, 609.15it/s]

4-echo monoexponential:  16%|█▋        | 3315/20176 [00:05<00:27, 613.24it/s]

4-echo monoexponential:  17%|█▋        | 3377/20176 [00:05<00:27, 613.32it/s]

4-echo monoexponential:  17%|█▋        | 3439/20176 [00:05<00:27, 614.15it/s]

4-echo monoexponential:  17%|█▋        | 3504/20176 [00:05<00:26, 623.88it/s]

4-echo monoexponential:  18%|█▊        | 3570/20176 [00:05<00:26, 633.77it/s]

4-echo monoexponential:  18%|█▊        | 3634/20176 [00:06<00:26, 630.55it/s]

4-echo monoexponential:  18%|█▊        | 3698/20176 [00:06<00:26, 629.84it/s]

4-echo monoexponential:  19%|█▊        | 3762/20176 [00:06<00:25, 631.91it/s]

4-echo monoexponential:  19%|█▉        | 3826/20176 [00:06<00:26, 627.71it/s]

4-echo monoexponential:  19%|█▉        | 3889/20176 [00:06<00:26, 624.12it/s]

4-echo monoexponential:  20%|█▉        | 3952/20176 [00:06<00:26, 609.44it/s]

4-echo monoexponential:  20%|█▉        | 4014/20176 [00:06<00:26, 609.83it/s]

4-echo monoexponential:  20%|██        | 4076/20176 [00:06<00:26, 610.94it/s]

4-echo monoexponential:  21%|██        | 4138/20176 [00:06<00:26, 606.27it/s]

4-echo monoexponential:  21%|██        | 4200/20176 [00:06<00:26, 608.55it/s]

4-echo monoexponential:  21%|██        | 4265/20176 [00:07<00:25, 619.24it/s]

4-echo monoexponential:  21%|██▏       | 4331/20176 [00:07<00:25, 629.32it/s]

4-echo monoexponential:  22%|██▏       | 4394/20176 [00:07<00:25, 625.93it/s]

4-echo monoexponential:  22%|██▏       | 4459/20176 [00:07<00:24, 631.21it/s]

4-echo monoexponential:  22%|██▏       | 4523/20176 [00:07<00:24, 630.92it/s]

4-echo monoexponential:  23%|██▎       | 4587/20176 [00:07<00:24, 632.77it/s]

4-echo monoexponential:  23%|██▎       | 4651/20176 [00:07<00:24, 624.17it/s]

4-echo monoexponential:  23%|██▎       | 4714/20176 [00:07<00:25, 609.06it/s]

4-echo monoexponential:  24%|██▎       | 4778/20176 [00:07<00:25, 614.42it/s]

4-echo monoexponential:  24%|██▍       | 4841/20176 [00:08<00:24, 617.61it/s]

4-echo monoexponential:  24%|██▍       | 4903/20176 [00:08<00:24, 617.17it/s]

4-echo monoexponential:  25%|██▍       | 4966/20176 [00:08<00:24, 618.32it/s]

4-echo monoexponential:  25%|██▍       | 5032/20176 [00:08<00:24, 629.24it/s]

4-echo monoexponential:  25%|██▌       | 5097/20176 [00:08<00:23, 633.59it/s]

4-echo monoexponential:  26%|██▌       | 5164/20176 [00:08<00:23, 642.22it/s]

4-echo monoexponential:  26%|██▌       | 5229/20176 [00:08<00:23, 642.98it/s]

4-echo monoexponential:  26%|██▌       | 5294/20176 [00:08<00:23, 639.80it/s]

4-echo monoexponential:  27%|██▋       | 5359/20176 [00:08<00:23, 638.97it/s]

4-echo monoexponential:  27%|██▋       | 5423/20176 [00:08<00:23, 619.58it/s]

4-echo monoexponential:  27%|██▋       | 5486/20176 [00:09<00:23, 612.94it/s]

4-echo monoexponential:  28%|██▊       | 5549/20176 [00:09<00:23, 615.76it/s]

4-echo monoexponential:  28%|██▊       | 5611/20176 [00:09<00:23, 613.19it/s]

4-echo monoexponential:  28%|██▊       | 5673/20176 [00:09<00:23, 614.85it/s]

4-echo monoexponential:  28%|██▊       | 5740/20176 [00:09<00:22, 628.39it/s]

4-echo monoexponential:  29%|██▉       | 5807/20176 [00:09<00:22, 639.45it/s]

4-echo monoexponential:  29%|██▉       | 5872/20176 [00:09<00:22, 640.00it/s]

4-echo monoexponential:  29%|██▉       | 5937/20176 [00:09<00:22, 637.92it/s]

4-echo monoexponential:  30%|██▉       | 6001/20176 [00:09<00:22, 628.79it/s]

4-echo monoexponential:  30%|███       | 6064/20176 [00:09<00:22, 628.95it/s]

4-echo monoexponential:  30%|███       | 6129/20176 [00:10<00:22, 631.89it/s]

4-echo monoexponential:  31%|███       | 6193/20176 [00:10<00:22, 613.01it/s]

4-echo monoexponential:  31%|███       | 6255/20176 [00:10<00:23, 602.13it/s]

4-echo monoexponential:  31%|███▏      | 6316/20176 [00:10<00:23, 598.07it/s]

4-echo monoexponential:  32%|███▏      | 6377/20176 [00:10<00:22, 601.28it/s]

4-echo monoexponential:  32%|███▏      | 6439/20176 [00:10<00:22, 604.37it/s]

4-echo monoexponential:  32%|███▏      | 6505/20176 [00:10<00:22, 620.07it/s]

4-echo monoexponential:  33%|███▎      | 6569/20176 [00:10<00:21, 623.91it/s]

4-echo monoexponential:  33%|███▎      | 6634/20176 [00:10<00:21, 629.63it/s]

4-echo monoexponential:  33%|███▎      | 6698/20176 [00:10<00:21, 630.07it/s]

4-echo monoexponential:  34%|███▎      | 6762/20176 [00:11<00:21, 629.27it/s]

4-echo monoexponential:  34%|███▍      | 6825/20176 [00:11<00:21, 628.90it/s]

4-echo monoexponential:  34%|███▍      | 6888/20176 [00:11<00:21, 626.96it/s]

4-echo monoexponential:  34%|███▍      | 6951/20176 [00:11<00:21, 612.43it/s]

4-echo monoexponential:  35%|███▍      | 7013/20176 [00:11<00:22, 595.08it/s]

4-echo monoexponential:  35%|███▌      | 7075/20176 [00:11<00:21, 599.97it/s]

4-echo monoexponential:  35%|███▌      | 7136/20176 [00:11<00:21, 602.13it/s]

4-echo monoexponential:  36%|███▌      | 7197/20176 [00:11<00:21, 603.64it/s]

4-echo monoexponential:  36%|███▌      | 7260/20176 [00:11<00:21, 610.70it/s]

4-echo monoexponential:  36%|███▋      | 7327/20176 [00:11<00:20, 626.87it/s]

4-echo monoexponential:  37%|███▋      | 7392/20176 [00:12<00:20, 630.32it/s]

4-echo monoexponential:  37%|███▋      | 7456/20176 [00:12<00:20, 623.89it/s]

4-echo monoexponential:  37%|███▋      | 7519/20176 [00:12<00:20, 622.21it/s]

4-echo monoexponential:  38%|███▊      | 7582/20176 [00:12<00:20, 624.04it/s]

4-echo monoexponential:  38%|███▊      | 7647/20176 [00:12<00:19, 631.72it/s]

4-echo monoexponential:  38%|███▊      | 7711/20176 [00:12<00:19, 625.72it/s]

4-echo monoexponential:  39%|███▊      | 7774/20176 [00:12<00:20, 604.55it/s]

4-echo monoexponential:  39%|███▉      | 7835/20176 [00:12<00:20, 600.64it/s]

4-echo monoexponential:  39%|███▉      | 7896/20176 [00:12<00:20, 600.87it/s]

4-echo monoexponential:  39%|███▉      | 7958/20176 [00:13<00:20, 606.33it/s]

4-echo monoexponential:  40%|███▉      | 8019/20176 [00:13<00:20, 605.48it/s]

4-echo monoexponential:  40%|████      | 8083/20176 [00:13<00:19, 613.65it/s]

4-echo monoexponential:  40%|████      | 8148/20176 [00:13<00:19, 621.21it/s]

4-echo monoexponential:  41%|████      | 8212/20176 [00:13<00:19, 625.20it/s]

4-echo monoexponential:  41%|████      | 8275/20176 [00:13<00:19, 625.16it/s]

4-echo monoexponential:  41%|████▏     | 8340/20176 [00:13<00:18, 628.79it/s]

4-echo monoexponential:  42%|████▏     | 8403/20176 [00:13<00:18, 622.72it/s]

4-echo monoexponential:  42%|████▏     | 8467/20176 [00:13<00:18, 625.22it/s]

4-echo monoexponential:  42%|████▏     | 8530/20176 [00:13<00:18, 625.67it/s]

4-echo monoexponential:  43%|████▎     | 8593/20176 [00:14<00:19, 603.30it/s]

4-echo monoexponential:  43%|████▎     | 8654/20176 [00:14<00:19, 599.59it/s]

4-echo monoexponential:  43%|████▎     | 8715/20176 [00:14<00:19, 596.60it/s]

4-echo monoexponential:  44%|████▎     | 8777/20176 [00:14<00:18, 603.32it/s]

4-echo monoexponential:  44%|████▍     | 8842/20176 [00:14<00:18, 615.00it/s]

4-echo monoexponential:  44%|████▍     | 8904/20176 [00:14<00:18, 611.50it/s]

4-echo monoexponential:  44%|████▍     | 8966/20176 [00:14<00:18, 612.82it/s]

4-echo monoexponential:  45%|████▍     | 9031/20176 [00:14<00:17, 620.07it/s]

4-echo monoexponential:  45%|████▌     | 9094/20176 [00:14<00:17, 619.61it/s]

4-echo monoexponential:  45%|████▌     | 9160/20176 [00:14<00:17, 629.31it/s]

4-echo monoexponential:  46%|████▌     | 9226/20176 [00:15<00:17, 636.58it/s]

4-echo monoexponential:  46%|████▌     | 9290/20176 [00:15<00:17, 635.38it/s]

4-echo monoexponential:  46%|████▋     | 9354/20176 [00:15<00:17, 628.70it/s]

4-echo monoexponential:  47%|████▋     | 9417/20176 [00:15<00:17, 615.18it/s]

4-echo monoexponential:  47%|████▋     | 9479/20176 [00:15<00:17, 611.78it/s]

4-echo monoexponential:  47%|████▋     | 9541/20176 [00:15<00:17, 603.47it/s]

4-echo monoexponential:  48%|████▊     | 9602/20176 [00:15<00:17, 603.90it/s]

4-echo monoexponential:  48%|████▊     | 9664/20176 [00:15<00:17, 606.44it/s]

4-echo monoexponential:  48%|████▊     | 9727/20176 [00:15<00:17, 611.08it/s]

4-echo monoexponential:  49%|████▊     | 9791/20176 [00:15<00:16, 617.40it/s]

4-echo monoexponential:  49%|████▉     | 9853/20176 [00:16<00:16, 612.43it/s]

4-echo monoexponential:  49%|████▉     | 9919/20176 [00:16<00:16, 625.94it/s]

4-echo monoexponential:  49%|████▉     | 9985/20176 [00:16<00:16, 633.72it/s]

4-echo monoexponential:  50%|████▉     | 10050/20176 [00:16<00:15, 635.82it/s]

4-echo monoexponential:  50%|█████     | 10114/20176 [00:16<00:16, 627.30it/s]

4-echo monoexponential:  50%|█████     | 10177/20176 [00:16<00:16, 607.62it/s]

4-echo monoexponential:  51%|█████     | 10238/20176 [00:16<00:16, 599.61it/s]

4-echo monoexponential:  51%|█████     | 10299/20176 [00:16<00:16, 601.69it/s]

4-echo monoexponential:  51%|█████▏    | 10360/20176 [00:16<00:16, 604.02it/s]

4-echo monoexponential:  52%|█████▏    | 10421/20176 [00:17<00:16, 602.74it/s]

4-echo monoexponential:  52%|█████▏    | 10486/20176 [00:17<00:15, 616.19it/s]

4-echo monoexponential:  52%|█████▏    | 10550/20176 [00:17<00:15, 622.99it/s]

4-echo monoexponential:  53%|█████▎    | 10613/20176 [00:17<00:15, 622.23it/s]

4-echo monoexponential:  53%|█████▎    | 10676/20176 [00:17<00:15, 614.93it/s]

4-echo monoexponential:  53%|█████▎    | 10740/20176 [00:17<00:15, 621.68it/s]

4-echo monoexponential:  54%|█████▎    | 10805/20176 [00:17<00:14, 626.78it/s]

4-echo monoexponential:  54%|█████▍    | 10868/20176 [00:17<00:14, 625.88it/s]

4-echo monoexponential:  54%|█████▍    | 10931/20176 [00:17<00:14, 624.64it/s]

4-echo monoexponential:  54%|█████▍    | 10994/20176 [00:17<00:15, 605.34it/s]

4-echo monoexponential:  55%|█████▍    | 11055/20176 [00:18<00:15, 596.05it/s]

4-echo monoexponential:  55%|█████▌    | 11116/20176 [00:18<00:15, 598.44it/s]

4-echo monoexponential:  55%|█████▌    | 11177/20176 [00:18<00:15, 599.34it/s]

4-echo monoexponential:  56%|█████▌    | 11246/20176 [00:18<00:14, 624.82it/s]

4-echo monoexponential:  56%|█████▌    | 11310/20176 [00:18<00:14, 626.95it/s]

4-echo monoexponential:  56%|█████▋    | 11373/20176 [00:18<00:14, 626.94it/s]

4-echo monoexponential:  57%|█████▋    | 11436/20176 [00:18<00:13, 625.27it/s]

4-echo monoexponential:  57%|█████▋    | 11501/20176 [00:18<00:13, 631.54it/s]

4-echo monoexponential:  57%|█████▋    | 11565/20176 [00:18<00:13, 633.43it/s]

4-echo monoexponential:  58%|█████▊    | 11630/20176 [00:18<00:13, 636.05it/s]

4-echo monoexponential:  58%|█████▊    | 11696/20176 [00:19<00:13, 639.82it/s]

4-echo monoexponential:  58%|█████▊    | 11760/20176 [00:19<00:14, 590.75it/s]

4-echo monoexponential:  59%|█████▊    | 11820/20176 [00:19<00:14, 581.59it/s]

4-echo monoexponential:  59%|█████▉    | 11881/20176 [00:19<00:14, 587.62it/s]

4-echo monoexponential:  59%|█████▉    | 11944/20176 [00:19<00:13, 599.40it/s]

4-echo monoexponential:  60%|█████▉    | 12006/20176 [00:19<00:13, 602.72it/s]

4-echo monoexponential:  60%|█████▉    | 12067/20176 [00:19<00:13, 603.57it/s]

4-echo monoexponential:  60%|██████    | 12134/20176 [00:19<00:12, 618.83it/s]

4-echo monoexponential:  60%|██████    | 12199/20176 [00:19<00:12, 624.98it/s]

4-echo monoexponential:  61%|██████    | 12265/20176 [00:20<00:12, 633.65it/s]

4-echo monoexponential:  61%|██████    | 12330/20176 [00:20<00:12, 637.95it/s]

4-echo monoexponential:  61%|██████▏   | 12394/20176 [00:20<00:12, 635.52it/s]

4-echo monoexponential:  62%|██████▏   | 12458/20176 [00:20<00:12, 634.01it/s]

4-echo monoexponential:  62%|██████▏   | 12522/20176 [00:20<00:12, 630.01it/s]

4-echo monoexponential:  62%|██████▏   | 12586/20176 [00:20<00:12, 586.06it/s]

4-echo monoexponential:  63%|██████▎   | 12650/20176 [00:20<00:12, 598.70it/s]

4-echo monoexponential:  63%|██████▎   | 12714/20176 [00:20<00:12, 610.27it/s]

4-echo monoexponential:  63%|██████▎   | 12777/20176 [00:20<00:12, 615.93it/s]

4-echo monoexponential:  64%|██████▎   | 12844/20176 [00:20<00:11, 629.01it/s]

4-echo monoexponential:  64%|██████▍   | 12909/20176 [00:21<00:11, 635.11it/s]

4-echo monoexponential:  64%|██████▍   | 12973/20176 [00:21<00:11, 635.29it/s]

4-echo monoexponential:  65%|██████▍   | 13037/20176 [00:21<00:11, 636.67it/s]

4-echo monoexponential:  65%|██████▍   | 13101/20176 [00:21<00:11, 634.07it/s]

4-echo monoexponential:  65%|██████▌   | 13165/20176 [00:21<00:11, 629.31it/s]

4-echo monoexponential:  66%|██████▌   | 13229/20176 [00:21<00:11, 630.78it/s]

4-echo monoexponential:  66%|██████▌   | 13293/20176 [00:21<00:11, 620.29it/s]

4-echo monoexponential:  66%|██████▌   | 13356/20176 [00:21<00:11, 594.20it/s]

4-echo monoexponential:  67%|██████▋   | 13418/20176 [00:21<00:11, 599.29it/s]

4-echo monoexponential:  67%|██████▋   | 13479/20176 [00:21<00:11, 600.69it/s]

4-echo monoexponential:  67%|██████▋   | 13541/20176 [00:22<00:10, 605.84it/s]

4-echo monoexponential:  67%|██████▋   | 13605/20176 [00:22<00:10, 613.51it/s]

4-echo monoexponential:  68%|██████▊   | 13672/20176 [00:22<00:10, 627.03it/s]

4-echo monoexponential:  68%|██████▊   | 13738/20176 [00:22<00:10, 634.08it/s]

4-echo monoexponential:  68%|██████▊   | 13802/20176 [00:22<00:10, 633.80it/s]

4-echo monoexponential:  69%|██████▊   | 13866/20176 [00:22<00:09, 632.51it/s]

4-echo monoexponential:  69%|██████▉   | 13930/20176 [00:22<00:09, 629.72it/s]

4-echo monoexponential:  69%|██████▉   | 13995/20176 [00:22<00:09, 633.76it/s]

4-echo monoexponential:  70%|██████▉   | 14059/20176 [00:22<00:09, 630.60it/s]

4-echo monoexponential:  70%|██████▉   | 14123/20176 [00:23<00:09, 611.55it/s]

4-echo monoexponential:  70%|███████   | 14185/20176 [00:23<00:09, 599.89it/s]

4-echo monoexponential:  71%|███████   | 14246/20176 [00:23<00:09, 600.44it/s]

4-echo monoexponential:  71%|███████   | 14309/20176 [00:23<00:09, 606.31it/s]

4-echo monoexponential:  71%|███████   | 14373/20176 [00:23<00:09, 613.60it/s]

4-echo monoexponential:  72%|███████▏  | 14440/20176 [00:23<00:09, 629.74it/s]

4-echo monoexponential:  72%|███████▏  | 14507/20176 [00:23<00:08, 640.53it/s]

4-echo monoexponential:  72%|███████▏  | 14572/20176 [00:23<00:08, 641.55it/s]

4-echo monoexponential:  73%|███████▎  | 14637/20176 [00:23<00:08, 634.90it/s]

4-echo monoexponential:  73%|███████▎  | 14701/20176 [00:23<00:08, 632.26it/s]

4-echo monoexponential:  73%|███████▎  | 14765/20176 [00:24<00:08, 626.83it/s]

4-echo monoexponential:  73%|███████▎  | 14828/20176 [00:24<00:08, 623.47it/s]

4-echo monoexponential:  74%|███████▍  | 14891/20176 [00:24<00:08, 600.58it/s]

4-echo monoexponential:  74%|███████▍  | 14954/20176 [00:24<00:08, 606.00it/s]

4-echo monoexponential:  74%|███████▍  | 15018/20176 [00:24<00:08, 613.78it/s]

4-echo monoexponential:  75%|███████▍  | 15080/20176 [00:24<00:08, 605.85it/s]

4-echo monoexponential:  75%|███████▌  | 15144/20176 [00:24<00:08, 615.59it/s]

4-echo monoexponential:  75%|███████▌  | 15207/20176 [00:24<00:08, 619.11it/s]

4-echo monoexponential:  76%|███████▌  | 15272/20176 [00:24<00:07, 626.13it/s]

4-echo monoexponential:  76%|███████▌  | 15339/20176 [00:24<00:07, 636.56it/s]

4-echo monoexponential:  76%|███████▋  | 15403/20176 [00:25<00:07, 634.27it/s]

4-echo monoexponential:  77%|███████▋  | 15467/20176 [00:25<00:07, 634.87it/s]

4-echo monoexponential:  77%|███████▋  | 15531/20176 [00:25<00:07, 630.69it/s]

4-echo monoexponential:  77%|███████▋  | 15595/20176 [00:25<00:07, 609.82it/s]

4-echo monoexponential:  78%|███████▊  | 15657/20176 [00:25<00:07, 612.62it/s]

4-echo monoexponential:  78%|███████▊  | 15720/20176 [00:25<00:07, 614.84it/s]

4-echo monoexponential:  78%|███████▊  | 15782/20176 [00:25<00:07, 606.99it/s]

4-echo monoexponential:  79%|███████▊  | 15843/20176 [00:25<00:07, 600.93it/s]

4-echo monoexponential:  79%|███████▉  | 15910/20176 [00:25<00:06, 619.02it/s]

4-echo monoexponential:  79%|███████▉  | 15973/20176 [00:25<00:06, 622.22it/s]

4-echo monoexponential:  80%|███████▉  | 16041/20176 [00:26<00:06, 639.10it/s]

4-echo monoexponential:  80%|███████▉  | 16106/20176 [00:26<00:06, 638.83it/s]

4-echo monoexponential:  80%|████████  | 16170/20176 [00:26<00:06, 627.15it/s]

4-echo monoexponential:  80%|████████  | 16233/20176 [00:26<00:06, 627.19it/s]

4-echo monoexponential:  81%|████████  | 16296/20176 [00:26<00:06, 615.12it/s]

4-echo monoexponential:  81%|████████  | 16358/20176 [00:26<00:06, 598.99it/s]

4-echo monoexponential:  81%|████████▏ | 16420/20176 [00:26<00:06, 603.66it/s]

4-echo monoexponential:  82%|████████▏ | 16481/20176 [00:26<00:06, 597.28it/s]

4-echo monoexponential:  82%|████████▏ | 16542/20176 [00:26<00:06, 599.67it/s]

4-echo monoexponential:  82%|████████▏ | 16605/20176 [00:27<00:05, 606.55it/s]

4-echo monoexponential:  83%|████████▎ | 16669/20176 [00:27<00:05, 615.75it/s]

4-echo monoexponential:  83%|████████▎ | 16737/20176 [00:27<00:05, 634.30it/s]

4-echo monoexponential:  83%|████████▎ | 16801/20176 [00:27<00:05, 633.48it/s]

4-echo monoexponential:  84%|████████▎ | 16865/20176 [00:27<00:05, 632.94it/s]

4-echo monoexponential:  84%|████████▍ | 16929/20176 [00:27<00:05, 628.28it/s]

4-echo monoexponential:  84%|████████▍ | 16992/20176 [00:27<00:05, 608.53it/s]

4-echo monoexponential:  85%|████████▍ | 17053/20176 [00:27<00:05, 595.18it/s]

4-echo monoexponential:  85%|████████▍ | 17117/20176 [00:27<00:05, 605.44it/s]

4-echo monoexponential:  85%|████████▌ | 17178/20176 [00:27<00:05, 593.51it/s]

4-echo monoexponential:  85%|████████▌ | 17240/20176 [00:28<00:04, 599.07it/s]

4-echo monoexponential:  86%|████████▌ | 17301/20176 [00:28<00:04, 600.08it/s]

4-echo monoexponential:  86%|████████▌ | 17367/20176 [00:28<00:04, 615.26it/s]

4-echo monoexponential:  86%|████████▋ | 17431/20176 [00:28<00:04, 621.80it/s]

4-echo monoexponential:  87%|████████▋ | 17494/20176 [00:28<00:04, 621.27it/s]

4-echo monoexponential:  87%|████████▋ | 17557/20176 [00:28<00:04, 616.62it/s]

4-echo monoexponential:  87%|████████▋ | 17619/20176 [00:28<00:04, 608.57it/s]

4-echo monoexponential:  88%|████████▊ | 17680/20176 [00:28<00:04, 587.34it/s]

4-echo monoexponential:  88%|████████▊ | 17740/20176 [00:28<00:04, 589.36it/s]

4-echo monoexponential:  88%|████████▊ | 17800/20176 [00:28<00:04, 589.81it/s]

4-echo monoexponential:  89%|████████▊ | 17863/20176 [00:29<00:03, 598.52it/s]

4-echo monoexponential:  89%|████████▉ | 17926/20176 [00:29<00:03, 607.48it/s]

4-echo monoexponential:  89%|████████▉ | 17993/20176 [00:29<00:03, 625.04it/s]

4-echo monoexponential:  89%|████████▉ | 18056/20176 [00:29<00:03, 621.30it/s]

4-echo monoexponential:  90%|████████▉ | 18119/20176 [00:29<00:03, 606.87it/s]

4-echo monoexponential:  90%|█████████ | 18180/20176 [00:29<00:03, 596.55it/s]

4-echo monoexponential:  90%|█████████ | 18240/20176 [00:29<00:03, 581.62it/s]

4-echo monoexponential:  91%|█████████ | 18299/20176 [00:29<00:03, 576.98it/s]

4-echo monoexponential:  91%|█████████ | 18359/20176 [00:29<00:03, 582.42it/s]

4-echo monoexponential:  91%|█████████▏| 18418/20176 [00:30<00:03, 580.40it/s]

4-echo monoexponential:  92%|█████████▏| 18481/20176 [00:30<00:02, 592.35it/s]

4-echo monoexponential:  92%|█████████▏| 18544/20176 [00:30<00:02, 601.48it/s]

4-echo monoexponential:  92%|█████████▏| 18608/20176 [00:30<00:02, 611.35it/s]

4-echo monoexponential:  93%|█████████▎| 18670/20176 [00:30<00:02, 612.20it/s]

4-echo monoexponential:  93%|█████████▎| 18732/20176 [00:30<00:02, 599.00it/s]

4-echo monoexponential:  93%|█████████▎| 18792/20176 [00:30<00:02, 587.78it/s]

4-echo monoexponential:  93%|█████████▎| 18854/20176 [00:30<00:02, 595.07it/s]

4-echo monoexponential:  94%|█████████▎| 18914/20176 [00:30<00:02, 592.51it/s]

4-echo monoexponential:  94%|█████████▍| 18976/20176 [00:30<00:02, 599.75it/s]

4-echo monoexponential:  94%|█████████▍| 19042/20176 [00:31<00:01, 616.03it/s]

4-echo monoexponential:  95%|█████████▍| 19108/20176 [00:31<00:01, 627.66it/s]

4-echo monoexponential:  95%|█████████▌| 19171/20176 [00:31<00:01, 612.74it/s]

4-echo monoexponential:  95%|█████████▌| 19233/20176 [00:31<00:01, 595.12it/s]

4-echo monoexponential:  96%|█████████▌| 19293/20176 [00:31<00:01, 588.92it/s]

4-echo monoexponential:  96%|█████████▌| 19352/20176 [00:31<00:01, 583.14it/s]

4-echo monoexponential:  96%|█████████▌| 19411/20176 [00:31<00:01, 582.18it/s]

4-echo monoexponential:  97%|█████████▋| 19472/20176 [00:31<00:01, 588.23it/s]

4-echo monoexponential:  97%|█████████▋| 19535/20176 [00:31<00:01, 598.14it/s]

4-echo monoexponential:  97%|█████████▋| 19595/20176 [00:31<00:00, 594.59it/s]

4-echo monoexponential:  97%|█████████▋| 19656/20176 [00:32<00:00, 598.51it/s]

4-echo monoexponential:  98%|█████████▊| 19721/20176 [00:32<00:00, 612.87it/s]

4-echo monoexponential:  98%|█████████▊| 19788/20176 [00:32<00:00, 629.02it/s]

4-echo monoexponential:  98%|█████████▊| 19851/20176 [00:32<00:00, 615.47it/s]

4-echo monoexponential:  99%|█████████▊| 19913/20176 [00:32<00:00, 597.73it/s]

4-echo monoexponential:  99%|█████████▉| 19973/20176 [00:32<00:00, 593.57it/s]

4-echo monoexponential:  99%|█████████▉| 20033/20176 [00:32<00:00, 589.19it/s]

4-echo monoexponential: 100%|█████████▉| 20092/20176 [00:32<00:00, 588.03it/s]

4-echo monoexponential: 100%|█████████▉| 20151/20176 [00:32<00:00, 585.31it/s]

4-echo monoexponential: 100%|██████████| 20176/20176 [00:32<00:00, 612.06it/s]

/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_2025-08-21T151613.tsv

metrics = pd.read_table(
    os.path.join(out_dir, "sub-04570_task-rest_space-scanner_desc-tedana_metrics.tsv")
)

	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'