OSCR

A neuroimaging dataset combining movie-watching, eye-tracking, sensorimotor mapping, and cognitive tasks.

Code ↔ Paper

20 matches between paragraphs of the paper and lines of its authors' code, computed by the harvester (lexical-v1). Click a colored paragraph or line to see its counterpart.

The 20 matches · 5 of them tie a paragraph to a whole file, not to given lines: weak matches, whose lines are not tinted
  1. [1] § Methods › Session 2 › Retinotopic mapping ↔ retinotopy/SupportingScripts/fit_prf_V7.m, the whole file · a weak match · score 0.76 · coarse fitting, fine fitting, Gaussian, pRF, smoothed, polar
  2. [2] § Data Records › Eye tracker recordings ↔ 01_convert_to_bids.sh, lines 188–229 · score 0.76 · edf2asc, Developers Kit, ASCII, compressed, gzip, Eyelink
  3. [3] § Methods › Session 2 › Retinotopic mapping ↔ retinotopy/SupportingScripts/PlotpRFParams_Fit.m, lines 1–38 · score 0.75 · coarse fitting, fine fitting, predict, Gaussian, smoothed, polar
  4. [4] § Methods › Session 2 › Anatomical ↔ backtothefuture/01_preprocess_btf.sh, lines 83–169 · score 0.70 · SSwarper, eroded, AFNI, MNI152, skull, SUMA
  5. [5] § Data Records ↔ backtothefuture/01_preprocess_btf.sh, lines 1–45 · score 0.65 · backtothefuture_run, bold.nii.gz, SSwarper, FreeSurfer, ses, BIDS
  6. [6] § Methods › Session 2 › Anatomical ↔ somatotopy/01_preprocess_somatotopy.sh, lines 33–114 · score 0.64 · SSwarper, eroded, MNI152, skull, SUMA, regressors
  7. [7] § Data Records ↔ 01_convert_to_bids.sh, lines 103–142 · score 0.58 · raw eye tracker, Phase reverse encoding, sourcedata, BIDS, backtothefuture, sub
  8. [8] § Methods › Session 2 › Retinotopic mapping ↔ retinotopy/FS_SurfaceProjection_MGHtoMAT.m, the whole file · a weak match · score 0.58 · samsrf_mgh2srf, surface, Retinotopic
  9. [9] § Methods › Session 2 › Retinotopic mapping ↔ retinotopy/map_to_fsaverage.m, lines 21–73 · score 0.56 · Native2TemplateMap, fsaverage, mesh, surface, retinotopic, maps
  10. [10] § Technical Validation › Evaluation of head motion control ↔ somatotopy/02_plot_fd.py, lines 133–147 · score 0.56 · FD distributions, framewise displacement, somatotopic
  11. [11] § Usage Notes › Practical considerations ↔ tonotopy/01_preprocess_tonotopy.sh, lines 1–68 · score 0.55 · preprocessing scripts, FreeSurfer, AFNI, regression, motion
  12. [12] § Data Records › Freesurfer outputs ↔ 04_run_suma.sh, the whole file · a weak match · score 0.55 · spec fs, SUMA, FreeSurfer, derivatives, ID, sub
  13. [13] § Technical Validation › Evaluation of head motion control ↔ tonotopy/02_plot_fd.py, lines 60–84 · score 0.55 · FD distributions, framewise displacement, tonotopic
  14. [14] § Methods › Session 2 › MRI parameters: backtothefuture task ↔ retinotopy/SupportingScripts/spm_preprocessing.m, lines 77–121 · score 0.54 · phase encoding direction, bandwidth, echo, TE, sequence, scan
  15. [15] § Methods › Session 2 › MRI parameters: somatotopic mapping ↔ retinotopy/SupportingScripts/spm_preprocessing.m, lines 77–121 · score 0.54 · phase encoding direction, bandwidth, echo, TE, sequence, scan
  16. [16] § Data Records › Anatomical MRI ↔ 01_convert_to_bids.sh, lines 231–291 · score 0.54 · T1w.nii.gz, anatomical scan, defaced
  17. [17] § Methods › Session 2 › Retinotopic mapping ↔ retinotopy/03_reprocess_retinotopy.sh, the whole file · a weak match · score 0.53 · mri_vol2surf, FreeSurfer, FWHM, retinotopic
  18. [18] § Methods › Session 2 › Retinotopic mapping ↔ retinotopy/01_preprocess_retinotopy.sh, lines 42–93 · score 0.52 · mri_vol2surf, FreeSurfer, FWHM, retinotopic
  19. [19] § Methods › Session 2 › Retinotopic mapping ↔ retinotopy/generate_avgmap.m, the whole file · a weak match · score 0.51 · SamSrf, fsaverage, v7, template, surface, FreeSurfer
  20. [20] § Data Records › Eye tracker recordings ↔ 01_convert_to_bids.sh, lines 188–229 · score 0.50 · run_id, task_name, eyelinkraw, ses, sub

Paper

Loaded from Europe PMC by your browser, not stored by OSCR: doi.org · Europe PMC

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The authors' code

Shell · 291 lines · 9.4 KB · BSD-3-Clause · 4 matches

  1. #!/bin/bash
  4. # This script was developed and tested under bash 5.0.17(1)-release
  5. echo "The script was developed and tested under bash 5.0.17(1)-release"
  6. echo -e "Your version of bash is $BASH_VERSION\n"
  8. # Heudiconv
  9. echo "The script was developed and tested under heudiconv 1.3.0"
  10. heudiconv_version=$(docker run --rm nipy/heudiconv:latest heudiconv --version)
  12. # Parallelisation
  13. max_jobs=10
  15. ### Step 1
  16. ## Create heuristics.py and all dicominfo.tsv files for each subject with heudiconv tool
  17. ## Read more about it here: https://github.com/nipy/heudiconv
  18. ## Useful tutorial to understand heudiconv: https://reproducibility.stanford.edu/bids-tutorial-series-part-2a/
  19. ## Run from /MovieProject2/ folder
  21. # Define the path to the project
  22. project_path="/egor2/egor/MovieProject2"
  23. subjects="01 02 03 04 05 06 07 08 09 10 11 12 13 14 16 17 18 19 20 21 22 23 24 25 26 27 29 30 31 32 33 35 36 37 38 39 40 42 43 44"
  24. # Excluded:
  25. # 15 & 34 (didn't feel well inside the scanner)
  26. # 28 & 41 (never came)
  28. for subj_id in $subjects; do
  29. subj_dir="${project_path}/raw_data/sub-$subj_id/"
  31. (
  32. # Check if the subject directory exists
  33. if [ -d "$subj_dir" ]; then
  34. sess_ids=$(ls "$subj_dir")
  36. # Main loop for session processing
  37. for full_sess_id in $sess_ids; do
  38. sess_id="${full_sess_id:5}"
  39. echo "$sess_id"
  41. # main Docker run
  42. docker run --rm -v "${project_path}:/base" nipy/heudiconv:latest \
  43. -d "/base/raw_data/sub-{subject}/sess-{session}/*/*.dcm" \
  44. -o "/base/analysis/heudiconv_first_outputs/" \
  45. -f convertall -s "$subj_id" -ss "$sess_id" \
  46. -c none
  48. done
  49. else
  50. echo "Directory $subj_dir does not exist."
  51. fi
  52. ) &
  54. # Limit the number of parallel jobs
  55. while [ "$(ps -eo state= | grep -c 'R')" -ge "$max_jobs" ]; do
  56. sleep 1
  57. done
  59. done
  61. # Wait for all background jobs to finish
  62. wait
  65. ## The output gives you a bunch of files: filegroup.json, heuristic.py, {subject}.auto.txt, dicominfo.tsv, {subject}.edit.txt
  66. ## Review the dicominfo.tsv table and create/edit heuristic.py file. Heuristic file gonna convert all raw dicom files to BIDS-valid format.
  67. ## Take one heuristic.py and edit it
  70. ### Step 2
  71. ## Run actual conversion to BIDS
  73. for subj_id in $subjects; do
  74. subj_dir="${project_path}/raw_data/sub-$subj_id/"
  75. sess_i=1
  77. # Check if the subject directory exists
  78. if [ -d "$subj_dir" ]; then
  79. sess_ids=$(ls "$subj_dir")
  81. for full_sess_id in $sess_ids; do
  82. sess_id="${full_sess_id:5}"
  84. docker run --rm -v ${PWD}:/base nipy/heudiconv:latest \
  85. -d /base/raw_data/sub-{subject}/sess-${sess_id}/*/*.dcm \
  86. -o /base/bids_data/ \
  87. -f /base/analysis/heuristic_sess0"${sess_i}".py \
  88. -s "$subj_id" -ss "00${sess_i}" \
  89. -c dcm2niix -b
  91. sess_i=$((sess_i+1))
  92. done
  93. else
  94. echo "Directory $subj_dir does not exist."
  95. fi
  96. done
  98. echo "Changing the owner of bids_data folder from root to elevchenko and grant permissions to edit files..."
  99. sudo chown -R elevchenko:elevchenko "${project_path}/bids_data"
  100. sudo chmod 777 -R "${project_path}/bids_data"
  103. ### Step 3
  104. ## Add 'IntendedFor' field for phase reverse encoding files
  106. for subject in $subjects; do
  107. for session in 001 002; do
  108. python analysis/add_intendedfor_field.py "${project_path}/bids_data" "$subject" "$session"
  109. done
  110. done
  113. ### Step 4
  114. ## Put raw eye-tracker data (backtothefuture and retinotopy tasks) to sourcedata/ folder
  116. for subject in $subjects; do
  118. # Skip bad subject
  119. if [[ "$subject" == "29" ]]; then
  120. continue
  121. # Bad eye-tracker data, couldn't really calibrate
  122. fi
  124. # Get the list of session folders for the subject
  125. session_folders=($(ls -d "${project_path}"/raw_data/sub-"${subject}"/sess-* 2>/dev/null | sort))
  127. # Check the number of session folders found
  128. session_count=${#session_folders[@]}
  130. for session_num in 001 002; do
  132. if [[ "$session_num" == "001" || $session_count -eq 1 ]]; then
  133. # Use the first session folder if theres only one or if we're processing session 001
  134. session_folder="${session_folders[0]}"
  135. elif [[ "$session_num" == "002" && $session_count -eq 2 ]]; then
  136. # Use the second session folder if we're processing session 002 and it exists
  137. session_folder="${session_folders[1]}"
  138. else
  139. # If session 002 doesn't exist, skip
  140. echo "No session ${session_num} found for subject ${subject}. Skipping..."
  141. continue
  142. fi
  144. # Extract session name (e.g., sess-241123EL)
  145. session_name=$(basename "$session_folder")
  147. # Define the destination path in BIDS format
  148. dest_path="${project_path}/bids_data/sourcedata/sub-${subject}/ses-${session_num}/func"
  150. # Create the destination directory if it doesn't exist
  151. mkdir -p "$dest_path"
  153. # Find all .edf files in the session folder
  154. if [[ "$session_num" == "001" ]]; then
  155. edf_files=($(ls "$session_folder"/*_r*.edf | sort -V))
  156. else
  157. edf_files=($(ls "$session_folder"/*pRF*.edf | grep -v 'Calib' | sort -V))
  158. fi
  160. # Check the number of files found
  161. file_count=${#edf_files[@]}
  162. if [[ $file_count -ne 3 ]]; then
  163. echo "Warning: Found $file_count .edf files for sub-${subject}, ${session_name}. Expected 3. Please inspect the data!"
  164. fi
  166. # Find all .edf files in the earliest session folder and compress them
  167. for edf_file in "${edf_files[@]}"; do
  169. # Skip bad files
  170. if [[ "$edf_file" == *"06-Mar-2024_12_16_41"* || "$edf_file" == *"27-Mar-2024_13_27_44"* ]]; then
  171. continue
  172. fi
  174. # Extract the run number from the filename
  175. if [[ "$edf_file" == *"_pRF_"* ]]; then
  176. # For pRF cases, extract the second digit after _pRF_
  177. raw_run_number=$(echo "$edf_file" | grep -oP '_pRF_[0-9]+_[0-9]+' | cut -d'_' -f4)
  178. else
  179. # For run- or _r cases, extract the first digit after run- or _r
  180. raw_run_number=$(echo "$edf_file" | grep -oP '(?:run-|_r)\K[0-9]+')
  181. fi
  183. # Handle the exception with the typo
  184. if [[ $raw_run_number -eq 26 ]]; then
  185. raw_run_number=2
  186. fi
  188. # Ensure the raw run number is properly mapped to BIDS format run number (001, 002, 003)
  189. run_id=$(printf "%03d" "$raw_run_number")
  191. # Define task name based on session
  192. if [[ "$session_num" == "001" ]]; then
  193. task_name="backtothefuture"
  194. else
  195. task_name="retinotopy"
  196. fi
  198. # Define the output file path
  199. output_edf="${dest_path}/sub-${subject}_ses-${session_num}_run-${run_id}_task-${task_name}_eyelinkraw.edf"
  200. output_asc="${dest_path}/sub-${subject}_ses-${session_num}_run-${run_id}_task-${task_name}_eyelinkraw.asc"
  202. # Check if the file already exists
  203. if [[ -f "$output_edf" ]]; then
  204. echo "File already exists: ${output_edf}. Overwriting... (made on purpose!)"
  205. fi
  206. if [[ -f "$output_asc" ]]; then
  207. echo "File already exists: ${output_asc}. Overwriting... (made on purpose!)"
  208. fi
  210. # Rename and compress each .edf file with the appropriate run number
  211. echo "Copying and gziping raw eyetracker EDF data for sub-${subject}, ${session_name}, task-${task_name}, run-${run_id}..."
  212. cp "$edf_file" "$output_edf"
  213. gzip -f "$output_edf"
  215. # Convert to ASCII using eye-link developers kit and compress
  216. edf2asc "$edf_file" "$output_asc"
  217. gzip -f "$output_asc"
  219. done
  220. done
  221. done
  225. ### Step 5
  226. ## Save behavioural summary data to derivatives
  227. # Activate conda environment with all needed packages installed
  228. source "$project_path"/utils/miniconda3/bin/activate
  229. conda activate movieproject2
  231. # Tonotopy
  232. python "$project_path"/analysis/tonotopy_behaviour.py
  233. # Retinotopy
  234. python "$project_path"/analysis/retinotopy_behaviour.py
  237. ### Step 6
  238. ## Deface all anatomical scans
  240. # Setup FSL
  241. export FSLDIR=/tools/fsl
  242. . ${FSLDIR}/etc/fslconf/fsl.sh
  243. export PATH=${FSLDIR}/bin:${PATH}
  245. # Find and deface all T1w anatomical scans
  246. find "$project_path"/bids_data/ -type f -name "*_T1w.nii.gz" | sort | while read -r anat_file; do
  247. # Display the current file being processed
  248. echo "Processing: $anat_file"
  250. (
  251. # Run pydeface and overwrite the original file
  252. pydeface "$anat_file" --outfile "$anat_file" --force
  254. # Indicate completion of current file
  255. echo "Defacing complete for: $anat_file"
  256. ) &
  258. # Limit the number of parallel jobs
  259. while [ "$(ps -eo state= | grep -c 'R')" -ge "$max_jobs" ]; do
  260. sleep 1
  261. done
  262. done
  264. # Wait for all background jobs to finish
  265. wait
  266. echo "All anatomical scans have been defaced."
  269. ### Step 7
  270. # Physio data
  271. python "$project_path"/analysis/pulselog2sourcedata.py
  274. echo "Changing the owner of bids_data folder from root to elevchenko and grant permissions to edit files..."
  275. chown -R elevchenko:elevchenko "${project_path}/bids_data"
  276. chmod 777 -R "${project_path}/bids_data"
  279. ### Step 8
  280. # Compress all NIFTI files to save space
  282. find "$project_path"/bids_data -type f -name "*.nii" -exec sh -c 'echo "Processing: {}"; gzip -f "{}"' \;
  285. ### Step 9
  286. ## Check if the folder is BIDS valid
  288. # BIDS validator
  289. bidsvalidator_version=$(docker run -ti --rm bids/validator --version)
  291. docker run --rm -v "${project_path}/bids_data":/data:ro bids/validator /data

01_convert_to_bids.sh at commit 19a3dcf, under BSD-3-Clause · at the source

Overview

Authors: Egor Levchenko1,2, Hugo Chow-Wing-Bom2, Fred Dick2, Greg Cooper2, Adam Tierney1, Jeremy I Skipper2
ORCID iDs: Egor Levchenko
  1. Birkbeck, University of London, London, UK
  2. University College London, London, UK
Institutions: University College London (United Kingdom); Birkbeck, University of London (United Kingdom)
Journal: Scientific data, volume 13, issue 1, article 1184
Dates: received 23 October 2025; accepted 19 June 2026; published online 9 July 2026
Type: Data paper · Language: English
License: CC BY
Identifiers: DOI 10.1038/s41597-026-07676-4 · PMID 42426025 · PMCID PMC13469984 · OpenAlex W7167826746
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: structural MRI / diffusion (modality), human (organism), methods / tools (subfield)
Methods: Spectral & time-frequency, Connectivity, fMRI & imaging, Physiology & signal measures
MeSH: Brain*, Cognition*, Eye-Tracking Technology*, Neuroimaging*, Brain Mapping, Humans, Magnetic Resonance Imaging, Motion Pictures (* major topic)
Topic: Functional Brain Connectivity Studies (Cognitive Neuroscience, Neuroscience), according to OpenAlex
Funding: Biotechnology and Biological Sciences Research Council (BB/T008709/1); Wellcome Trust; Wellcome Leap
Citations: not cited yet (Europe PMC); 54 references in the paper

Abstract

We provide a multimodal naturalistic neuroimaging dataset (NNDb-3T+), designed to support the study of brain function under both naturalistic and controlled experimental conditions. The dataset includes high-quality 3 T fMRI data from 40 participants acquired during full-length movie-watching and somatotopic, retinotopic, and tonotopic sensory mapping tasks. Each participant also completed synchronised eye-tracking during movie-watching and retinotopic mapping tasks, physiological recordings, and a battery of behavioural and cognitive assessments. Data were collected across two MRI sessions and a remote testing session, with all data organised in a BIDS-compliant format. Technical validation confirms high data quality, with minimal head motion, accurate eye-tracker calibration, and robust task-evoked activation patterns. The dataset provides a unique resource for investigating individual differences, functional topographies, multimodal integration, and naturalistic cognition. All raw and preprocessed data, quality metrics, and preprocessing scripts are publicly available to support reproducible research.

Reproduced under the paper's license (CC BY), from the paper cited above.

Repositories

Its files are read in the Code ↔ Paper reader above, with 20 matches between paragraphs and lines of code.

Zenodo 19471544

License: BSD-3-Clause
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Size: 1 file
Software Heritage: not checked
Found in: the references
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: pandas (14 files), NumPy (13 files), AFNI (12 files), FreeSurfer (10 files), Matplotlib (8 files), SPM (5 files), FSL (4 files), HeuDiConv (3 files), NiBabel (3 files), Nilearn (2 files), BIDS Validator (1 file), dcm2niix (1 file), Parallel Computing Toolbox (1 file), Statistics and Machine Learning Toolbox (1 file), MNE-Python (1 file), SciPy (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers (HTTP 200)
  • 27 September 2026: the link answers (HTTP 200)
59 files
At the source:

levchenkoegor/movieproject2

License: BSD-3-Clause
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: 19a3dcff846f752afc50e183d7a3cd5f2a17a8e2, 17 August 2026
Languages: Python (22), Shell (18), MATLAB (17)
Size: 62 files, 57 scripts
Software Heritage: archived
Found in: the Zenodo archive record
Holds: README, license file, environment (conda-py-env.yml)
Not found: CITATION.cff, tests, continuous integration, documentation
Tools: pandas (14 files), NumPy (13 files), AFNI (12 files), FreeSurfer (10 files), Matplotlib (8 files), SPM (5 files), FSL (4 files), HeuDiConv (3 files), NiBabel (3 files), Nilearn (2 files), BIDS Validator (1 file), dcm2niix (1 file), Parallel Computing Toolbox (1 file), Statistics and Machine Learning Toolbox (1 file), MNE-Python (1 file), SciPy (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
59 files
At the source: github.com/levchenkoegor/movieproject2/tree/v1.0.1

Code availability

All analysis and preprocessing scripts for each task can be found in the GitHub repository16.

Reproduced under the paper's license (CC BY), from the paper cited above.

Tracing map

Proposed by the machine: these links were found in the paper and verified at the source, without human review. The map will receive a Zenodo DOI once one of the paper's authors has validated it with their ORCID.

What the map holds:

  • 2 repositories of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 114 scripts, each with its path and the digest of its content;
  • 20 matches between paragraphs of the paper and lines of the code (method lexical-v1);
  • neither the text of the paper nor the code itself.

Its JSON (tracing-map.json) is deposited on Zenodo with its DOI once the map is validated.

Data

Datasets cited

Data availability

The resulting dataset is available on the OpenNeuro.org platform.

Reproduced under the paper's license (CC BY), from the paper cited above.

Versions

The history of this record: each version stored by the harvester or made by a correction of its authors or of the maintainers of its code, and what changed in its facts. The texts of the paper (its abstract, its availability statements) are not part of it; versions that changed only those are not listed.

Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 6 authors, 8 MeSH terms, 3 funders, 48 references.

Cite

This paper

Levchenko, E., Chow-Wing-Bom, H., Dick, F., Cooper, G., Tierney, A., & Skipper, J. I. (2026). A neuroimaging dataset combining movie-watching, eye-tracking, sensorimotor mapping, and cognitive tasks. Scientific data, 13(1), 1184. https://doi.org/10.1038/s41597-026-07676-4

BibTeX

@article{levchenko2026neuroimaging,
author = {Levchenko, Egor and Chow-Wing-Bom, Hugo and Dick, Fred and Cooper, Greg and Tierney, Adam and Skipper, Jeremy I},
title = {{A neuroimaging dataset combining movie-watching, eye-tracking, sensorimotor mapping, and cognitive tasks}},
journal = {Scientific data},
year = {2026},
month = jul,
volume = {13},
number = {1},
pages = {1184},
publisher = {Nature Publishing Group},
issn = {2052-4463},
doi = {10.1038/s41597-026-07676-4},
url = {https://doi.org/10.1038/s41597-026-07676-4},
pmid = {42426025},
pmcid = {PMC13469984}
}

RIS

TY - JOUR
AU - Levchenko, Egor
AU - Chow-Wing-Bom, Hugo
AU - Dick, Fred
AU - Cooper, Greg
AU - Tierney, Adam
AU - Skipper, Jeremy I
TI - A neuroimaging dataset combining movie-watching, eye-tracking, sensorimotor mapping, and cognitive tasks
T2 - Scientific data
J2 - Sci Data
PY - 2026
DA - 2026/07/09
VL - 13
IS - 1
SP - 1184
SN - 2052-4463
PB - Nature Publishing Group
DO - 10.1038/s41597-026-07676-4
UR - https://doi.org/10.1038/s41597-026-07676-4
LA - en
ER -

CSL-JSON

{
"id": "10.1038/s41597-026-07676-4",
"type": "article-journal",
"title": "A neuroimaging dataset combining movie-watching, eye-tracking, sensorimotor mapping, and cognitive tasks",
"container-title": "Scientific data",
"author": [
{
"family": "Levchenko",
"given": "Egor"
},
{
"family": "Chow-Wing-Bom",
"given": "Hugo"
},
{
"family": "Dick",
"given": "Fred"
},
{
"family": "Cooper",
"given": "Greg"
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{
"family": "Tierney",
"given": "Adam"
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{
"family": "Skipper",
"given": "Jeremy I"
}
],
"container-title-short": "Sci Data",
"volume": "13",
"issue": "1",
"page": "1184",
"DOI": "10.1038/s41597-026-07676-4",
"PMID": "42426025",
"PMCID": "PMC13469984",
"ISSN": "2052-4463",
"publisher": "Nature Publishing Group",
"URL": "https://doi.org/10.1038/s41597-026-07676-4",
"language": "en",
"issued": {
"date-parts": [
[
2026,
7,
9
]
]
}
}

The tracing map gets a citation of its own once an author has validated it and it has a DOI.

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In common: AFNI, Parallel Computing Toolbox, FreeSurfer, 8 other tools, methods / tools, 1 reference
[6] doi:10.1038/s41597-026-07377-y [code]
An open-access multi-site fMRI dataset for investigating conscious visual perception.
Journal: Scientific data
In common: AFNI, FreeSurfer, MNE-Python, 9 other tools, 1 reference
[7] doi:10.1038/s41467-026-76452-0 [code]
Music evokes shared neural representations of imagined narratives across sensory modalities.
Journal: Nature communications
In common: AFNI, FreeSurfer, FSL, 8 other tools, 2 references
[8] doi:10.7554/elife.107933 [code]
Modality-agnostic decoding of vision and language from fMRI.
Journal: eLife
In common: FreeSurfer, Nilearn, SPM, 5 other tools, 5 references
[9] doi:10.1038/s41597-026-07350-9 [code]
An open multi-center MEG-EEG dataset for studying conscious visual perception.
Journal: Scientific data
In common: AFNI, FreeSurfer, MNE-Python, 9 other tools, structural MRI / diffusion
[10] doi:10.1093/nc/niag029 [code]
A data-driven approach to identifying and evaluating connectivity-based neural correlates of conscious visual perception.
Journal: Neuroscience of consciousness
In common: AFNI, FreeSurfer, MNE-Python, 9 other tools

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