Data, scripts and derivatives for the CNeuroMod-THINGS dataset, for which N=4 CNeuroMod participants underwent 33-36 fMRI sessions of a continuous recognition task based on images from the THINGS dataset.
Files related to the main task are found under THINGS:
THINGS/fmriprepincludes raw and preprocessed bold data, eye-tracking data,*events.tsvfiles with trialwise metrics, stimuli and annotations.THINGS/behaviourincludes analyses of the subjects' performance on the continuous recognition task and of fixation compliance.THINGS/glmsingleincludes fMRI analyses and derivatives, including trialwise and imagewise beta scores estimated with GLMsingle, voxelwise noise ceilings, and proof-of-principle analyses to showcase the quality of the data.
In addition, this repository includes data, scripts and derivatives from two complementary vision localizer tasks,
fLoc and retino (population receptive field), used to derive subject-specific ROIs. anatomical data
that include flat maps to visualize voxelwise statistics on a flattened cortical surface are also included.
datapaper includes jupyter notebooks with code to re-create figures from the upcoming data paper using data and results files saved in the current repository. TODO: add link to datapaper manuscript.
├── LICENSE
├── README.md <- The top-level README for this repository.
├── anatomical <- Anatomical datasets and scripts
│ ├── README.md <- Anatomical dataset overview, links to flat map instructions
│ ├── smriprep <- smriprep anatomical output
│ │ ├── sub-0*
│ │ │ └── anat
│ │ │ └── sub-0*_label-GM_probseg.nii.gz <- fmriprep grey matter mask
│ │ └── sourcedata
│ │ └── freesurfer <- freesurfer output
│ │ ├── doc
│ │ │ └── flatmaps.md <- Instructions to generate flat maps in pycortex
│ │ └── sub-0*
│ │ └── surf
│ │ ├── {lh, rh}.full.patch.3d <- patches to create flat maps
│ │ ├── {lh, rh}.full.flat.patch.3d
│ │ └── {lh, rh}.full.flat.patch.3d.out
│ └── pycortex
│ ├── README.md
│ ├── doc
│ │ └── using_flatmaps.md <- Instructions to generate flat maps in pycortex
│ └── db <- database of pycortex files with annotated surfaces that delineate visual ROIs
│ └── sub-0*
│ ├── anatomicals
│ ├── surfaces
│ ├── transforms
│ └── overlays.svg <- annotated with manually traced ROIs for sub-01, 02 and 03
│
├── fLoc <- fLoc visual localizer dataset and scripts
│ ├── README.md <- Overview of fLoc dataset and scripts
│ ├── fmriprep <- fmriprep output
│ │ ├── sub-0*
│ │ │ └── ses-*
│ │ │ └── func <- preprocessed fMRI files in T1w space
│ │ │ ├── sub-0*_ses-0*_task-fLoc_run-*_space-T1w_desc-preproc_part-mag_bold.nii.gz
│ │ │ └── sub-0*_ses-0*_task-fLoc_run-*_desc-confounds_part-mag_timeseries.tsv <- noise confounds
│ │ │
│ │ └── sourcedata <- raw files
│ │ └── floc <- bidsified raw fMRI data & output
│ │ ├── sub-0*
│ │ │ └── ses-*
│ │ │ └── func
│ │ │ └── sub-0*_ses-00*_task-fLoc_run-0*_events.tsv <- events.tsv files
│ │ │
│ │ └── stimuli <- stimulus images per category
│ │
│ └── rois <- fLoc derivative datasets and scripts
│ ├── code <- scripts to run glm and generate ROIs
│ │ ├── README.md
│ │ ├── requirements.txt
│ │ ├── fLoc_makedesign.py
│ │ ├── fLoc_firstLevel_nilearn.py
│ │ ├── fLoc_split_CVSparcels_perROI.py
│ │ ├── fLoc_reconcile_parcelMasks.py
│ │ └── fLoc_reconcile_ROImasks.py
│ │
│ ├── standard_masks
│ │ ├── kanwisher_parcels
│ │ │ ├── cvs <- download and unzip cvs_*_parcels.zip files from https://web.mit.edu/bcs/nklab/GSS.shtml#download
│ │ │ │ ├── cvs_body_parcels
│ │ │ │ ├── cvs_face_parcels
│ │ │ │ ├── cvs_object_parcels
│ │ │ │ ├── cvs_scene_parcels
│ │ │ │ └── readme.txt
│ │ │ └── mni
│ │ │ └── parcel-kanwisher_space-MNI152T1_res-2mm_contrast-{body, face, object, scene}_mask.nii
│ │ └── standard_rois <- unilateral and bilateral ROI masks in CVS and MNI space
│ │ ├── parcel-kanwisher_space-CVSavg35_contrast-face_roi-{FFA, OFA, pSTS}_desc-{L, R, bilat}_mask.nii
│ │ ├── parcel-kanwisher_space-MNI152T1_contrast-face_roi-{FFA, OFA, pSTS}_desc-{L, R, bilat}_mask.nii
│ │ ├── parcel-kanwisher_space-CVSavg35_contrast-scene_roi-{PPA, OPA, MPA}_desc-{L, R, bilat}_mask.nii
│ │ ├── parcel-kanwisher_space-MNI152T1_contrast-scene_roi-{PPA, OPA, MPA}_desc-{L, R, bilat}_mask.nii
│ │ ├── parcel-kanwisher_space-CVSavg35_contrast-body_roi-EBA_desc-{L, R, bilat}_mask.nii
│ │ └── parcel-kanwisher_space-MNI152T1_contrast-body_roi-EBA_desc-{L, R, bilat}_mask.nii
│ │
│ └── sub-0*
│ ├── glm <- first-level GLM contrast on fLoc BOLD data
│ │ ├── sub-*_task-floc_model-GLM_design.h5
│ │ ├── sub-*_task-floc_space-T1w_label-brain_desc-unionNonNaN_mask.nii
│ │ ├── sub-*_task-floc_space-T1w_label-brain_desc-unionNaN_mask.nii
│ │ ├── sub-*_task-floc_space-T1w_model-GLM_stats-{betas, tscores}_contrast-*_desc-smooth_statseries.nii.gz
│ │ └── sub-*_task-floc_space-T1w_model-GLM_stats-{betas, tscores}_contrast-*_desc-unsmooth_statseries.nii.gz
│ └── rois
│ ├── from_atlas <- Kanwisher parcels and ROI masks warped to subject space
│ │ ├── sub-*_parcel-kanwisher_space-T1w_contrast-{body, face, object, scene}_mask.nii
│ │ ├── sub-*_parcel-kanwisher_space-T1w_contrast-face_roi-{FFA, OFA, pSTS}_desc-{L, R, bilat}_mask.nii
│ │ ├── sub-*_parcel-kanwisher_space-T1w_contrast-scene_roi-{PPA, OPA, MPA}_desc-{L, R, bilat}_mask.nii
│ │ └── sub-*_parcel-kanwisher_space-T1w_contrast-body_roi-EBA_desc-{L, R, bilat}_mask.nii
│ └── task-derived <- parcels and ROI masks derived from the fLoc task
│ ├── sub-*_task-floc_space-T1w_stats-tscores_contrast-*_cutoff-*_desc-smooth_mask.nii.gz
│ ├── sub-*_task-floc_space-T1w_stats-tscores_contrast-*_cutoff-*_desc-unsmooth_mask.nii.gz
│ ├── sub-*_task-floc_space-T1w_stats-tscores_contrast-*_roi-*_cutoff-*_nvox-*_fwhm-*_ratio-*_desc-smooth_mask.nii.gz
│ └── sub-*_task-floc_space-T1w_stats-tscores_contrast-*_roi-*_cutoff-*_nvox-*_fwhm-*_ratio-*_desc-unsmooth_mask.nii.gz
│
├── retinotopy <- retinotopy (pRF) visual localizer datasets and scripts
│ ├── README.md <- Overview of retinotopy dataset and scripts
│ ├── fmriprep <- retinotopy fmriprep output
│ │ ├── sub-0*
│ │ │ └── ses-*
│ │ │ └── func <- preprocessed fMRI files in T1w space
│ │ │ ├── sub-0*_ses-0*_task-{bars, rings, wedges}_space-T1w_desc-preproc_part-mag_bold.nii.gz
│ │ │ └── sub-0*_ses-0*_task-{bars, rings, wedges}_desc-confounds_timeseries.tsv <- noise confounds
│ │ │
│ │ └── sourcedata <- raw files
│ │ └── retinotopy <- bidsified raw fMRI data & output
│ │ ├── sub-0*
│ │ │ └── ses-*
│ │ │ └── func
│ │ │ └── sub-0*_ses-*_task-{bars, rings, wedges}_events.tsv <- events.tsv files
│ │ └── stimuli
│ │ ├── {grid, images, scenes}.npz
│ │ └── apertures_{bars, ring, wedge_newtr}.npz
│ │
│ └── prf <- population receptive fiels scripts and derivatives (e.g., visual ROIs)
│ ├── code <- scripts to run glm single and process output
│ │ ├── README.md
│ │ ├── requirements.txt
│ │ ├── analyzePRF <- analyzePRF repo submodule (a3ac908)
│ │ ├── retino_make_apertureMasks.py
│ │ ├── retino_prepare_BOLD.py
│ │ ├── retino_run_analyzePRF.m
│ │ ├── retino_reassamble_voxels.py
│ │ └── retino_resample_npythy.py
│ │
│ ├── apertures <- aperture masks that delineate task field of view
│ │ ├── task-retinotopy_condition-bars_desc-perTR_apertures.mat
│ │ ├── task-retinotopy_condition-rings_desc-perTR_apertures.mat
│ │ └── task-retinotopy_condition-wedges_desc-perTR_apertures.mat
│ │
│ └── sub-0*
│ ├── prf <- population receptive fields input and output files
│ │ ├── input
│ │ │ ├── sub-0*_task-retinotopy_space-T1w_label-brain_desc-unionNaN_mask.nii
│ │ │ ├── sub-0*_task-retinotopy_space-T1w_label-brain_desc-unionNonNaN_mask.nii
│ │ │ └── chunks
│ │ │ ├── sub-0*_task-retinotopy_condition-bars_space-T1w_desc-chunk{chunk_num}_bold.mat
│ │ │ ├── sub-0*_task-retinotopy_condition-rings_space-T1w_desc-chunk{chunk_num}_bold.mat
│ │ │ └── sub-0*_task-retinotopy_condition-wedges_space-T1w_desc-chunk{chunk_num}_bold.mat
│ │ └── output
│ │ ├── sub-0*_task-retinotopy_space-T1w_model-analyzepRF_label-brain_stats-{stat}_statseries.nii.gz
│ │ ├── sub-0*_task-retinotopy_space-T1w_model-analyzePRF_label-brain_stats-{stat}_desc-npythy_statseries.nii.gz
│ │ └── chunks
│ │ ├── sub-*_task-retinotopy_space-T1w_model-analyzePRF_stats-ang_desc-chunk{chunk_num}_statseries.mat
│ │ ├── sub-*_task-retinotopy_space-T1w_model-analyzePRF_stats-ecc_desc-chunk{chunk_num}_statseries.mat
│ │ ├── sub-0*_task-retinotopy_space-T1w_model-analyzePRF_stats-rfsize_desc-chunk{chunk_num}_statseries.mat
│ │ └── sub-0*_task-retinotopy_space-T1w_model-analyzePRF_stats-R2_desc-chunk{chunk_num}_statseries.mat
│ ├── npythy <- NeuroPythy toolbox input and output files
│ │ ├── input
│ │ │ ├── lh.s*_prf_{ang, ecc, x, y, R2, rfsize}.mgz
│ │ │ └── rh.s*_prf_{ang, ecc, x, y, R2, rfsize}.mgz
│ │ └── output
│ │ ├── inferred_{angle, eccen, sigma, varea}.mgz
│ │ ├── {lh, rh}.inferred_{angle, eccen, sigma, varea}.mgz
│ │ ├── {lh, rh}.retinotopy.sphere.reg
│ │ ├── inferred_{angle, eccen, sigma, varea}_fsorient.nii.gz
│ │ ├── sub-*_task-retinotopy_space-T1w_res-anat_model-npythy_atlas-varea_dseg.nii.gz
│ │ ├── sub-*_task-retinotopy_space-T1w_res-anat_model-npythy_stats-{angle, eccen, sigma}_statseries.nii.gz
│ │ ├── sub-*_task-retinotopy_space-T1w_res-func_model-npythy_atlas-varea_dseg.nii.gz
│ │ └── sub-*_task-retinotopy_space-T1w_res-func_model-npythy_stats-{angle, eccen, sigma}_statseries.nii.gz
│ └── rois <- visual ROI masks
│ ├── sub-*_task-retinotopy_space-T1w_res-anat_model-npythy_label-{roi}_mask.nii.gz
│ ├── sub-*_task-retinotopy_space-T1w_res-func_model-npythy_label-{roi}_desc-nn_mask.nii.gz
│ └── sub-*_task-retinotopy_space-T1w_res-func_model-npythy_label-{roi}_desc-linear_mask.nii.gz
│
├── THINGS <- THINGS datasets, scripts and derivatives
│ ├── README.md <- Overview of THINGS datasets and scripts
│ ├── fmriprep <- fmriprep output
│ │ ├── sub-0*
│ │ │ └── ses-*
│ │ │ └── func <- preprocessed fMRI files in MNI and T1w space
│ │ │ ├── sub-0*_ses-*_task-things_run-*_space-MNI152NLin2009cAsym_desc-preproc_part-mag_bold.nii.gz
│ │ │ ├── sub-0*_ses-*_task-things_run-*_space-MNI152NLin2009cAsym_desc-brain_part-mag_mask.nii.gz
│ │ │ ├── sub-0*_ses-*_task-things_run-*_space-T1w_desc-preproc_part-mag_bold.nii.gz
│ │ │ ├── sub-0*_ses-*_task-things_run-*_space-T1w_desc-brain_part-mag_mask.nii.gz
│ │ │ └── sub-0*_ses-*_task-things_run-*_desc-confounds_part-mag_timeseries.tsv <- noise confounds
│ │ │
│ │ └── sourcedata <- raw files
│ │ └── things <- bidsified raw fMRI data & output
│ │ ├── sub-0*
│ │ │ └── ses-*
│ │ │ └── func
│ │ │ ├── sub-0*_ses-*_task-things_run-*_eyetrack.tsv.gz <- eye-tracking files
│ │ │ └── sub-0*_ses-*_task-things_run-*_events.tsv <- events.tsv files
│ │ ├── stimuli
│ │ │ ├── images_fmri <- stimulus images per category
│ │ │ └── annotations <- image annotations
│ │ │ ├── README.md <- annotation doc, links to download THINGS+ ratings
│ │ │ ├── THINGS+ <- download annotations directly from THINGS+ database
│ │ │ │ ├── arousal_meanRatings.tsv
│ │ │ │ ├── category53_wideFormat.tsv
│ │ │ │ ├── imageLabeling_imageWise.tsv
│ │ │ │ ├── imageLabeling_objectWise.tsv
│ │ │ │ ├── objectProperties_meanRatings.tsv
│ │ │ │ ├── size_meanRatings.tsv
│ │ │ │ └── things_concepts.tsv
│ │ │ ├── task-things_desc-manual_annotation.json
│ │ │ └── task-things_desc-manual_annotation.tsv
│ │ ├── code
│ │ │ ├── README.md
│ │ │ ├── cleanup <- scripts to validate events.tsv files
│ │ │ │ ├── requirements.txt
│ │ │ │ ├── qc_notes.md <- notes on QCing runs & sessions
│ │ │ │ └── clean_events.py <- script to relabel/clean *events.tsv files
│ │ │ └── eyetracking <- scripts to process eye-tracking data
│ │ │ ├── requirements.txt
│ │ │ ├── step1_eyetrack_prep.py <- exports raw gaze to numpy, plots qc charts
│ │ │ ├── step2_eyetrack_prep.py <- drift corrects, exports gaze and fixation metrics
│ │ │ ├── step3_reconcile_events.py <- add fixation metrics to events files
│ │ │ └── utils.py <- support functions
│ │ │
│ │ └── task-things_events.json <- defines columns in events.tsv files
│ │
│ ├── behaviour <- performance on the image recognition task & fixation compliance
│ │ ├── README.md
│ │ ├── code
│ │ │ ├── requirements.txt
│ │ │ ├── analyze_fixations.py <- processes trial-wise fixations
│ │ │ ├── behav_data_annotate.py <- builds trial-wise image annotations
│ │ │ └── behav_data_extract.py <- computes memory scores from events.tsv files
│ │ ├── sub-0*
│ │ │ ├── fix
│ │ │ │ ├── sub-0*_task-things_desc-fixCompliance_statseries.tsv
│ │ │ │ └── sub-0*_task-things_desc-driftCor_gaze.tsv
│ │ │ └── beh
│ │ │ ├── sub-0*_task-things_desc-perTrial_annotation.tsv
│ │ │ ├── sub-0*_task-things_catNum.tsv
│ │ │ ├── sub-0*_task-things_imgNum.tsv
│ │ │ ├── sub-0*_task-things_desc-perTrial_beh.tsv
│ │ │ ├── sub-0*_task-things_desc-perRun_beh.tsv
│ │ │ ├── sub-0*_task-things_desc-perSession_beh.tsv
│ │ │ └── sub-0*_task-things_desc-global_beh.tsv
│ │ ├── task-things_desc-perTrial_annotation.json
│ │ ├── task-things_desc-fixCompliance_statseries.json
│ │ └── task-things_beh.json
│ │
│ └── glmsingle <- GLMsingle derivatives (voxel-wise betas, noise ceilings)
│ ├── code <- scripts to run GLMsingle and process output
│ │ ├── requirements.txt
│ │ ├── qc
│ │ │ ├── README.md
│ │ │ └── compile_headmotion.py
│ │ ├── glmsingle
│ │ │ ├── GLMsingle <- GLMsingle repo submodule (c4e298e)
│ │ │ ├── README.md
│ │ │ ├── GLMsingle_makedesign.py
│ │ │ ├── GLMsingle_preprocBOLD.py
│ │ │ ├── GLMsingle_makerunlist.py
│ │ │ ├── GLMsingle_cleanmask.py
│ │ │ ├── GLMsingle_run.m
│ │ │ ├── GLMsingle_noiseceilings.py
│ │ │ ├── GLMsingle_betasPerTrial.py
│ │ │ └── GLMsingle_betasPerImg.py
│ │ └── descriptive
│ │ ├── README.md
│ │ ├── extract_annotations.py
│ │ ├── rank_img_perVox.py
│ │ └── beta_scaling.py
│ │
│ ├── task-things_runlist.h5 <- list of valid runs per subject
│ ├── task-things_imgAnnotations.json <- dictionary of compiled image annotations
│ │
│ └── sub-0*
│ ├── glmsingle <- GLMsingle input and output (voxelwise betas, noise ceilings)
│ │ ├── input
│ │ │ ├── sub-*_task-things_model-glmsingle_desc-sparse_design.h5
│ │ │ ├── sub-*_task-things_imgDesignNumbers.json
│ │ │ ├── sub-*_task-things_space-T1w_maskedBOLD.h5
│ │ │ ├── sub-*_task-things_space-T1w_label-brain_desc-union_mask.nii
│ │ │ ├── sub-*_task-things_space-T1w_label-brain_desc-unionNonNaN_mask.nii
│ │ │ ├── sub-*_task-things_space-T1w_label-brain_desc-unionNaN_mask.nii
│ │ │ └── ...
│ │ └── output
│ │ ├── T1w
│ │ │ ├── TYPEA_ONOFF.mat
│ │ │ ├── TYPEB_FITHRF.mat
│ │ │ ├── TYPEC_FITHRF_GLMDENOISE.mat
│ │ │ └── TYPED_FITHRF_GLMDENOISE_RR.mat
│ │ ├── sub-0*_task-things_space-T1w_model-fitHrfGLMdenoiseRR_stats-imageBetas_desc-zscore_statseries.h5
│ │ ├── sub-0*_task-things_space-T1w_model-fitHrfGLMdenoiseRR_stats-trialBetas_desc-zscore_statseries.h5
│ │ └── sub-0*_task-things_space-T1w_model-fitHrfGLMdenoiseRR_stats-noiseCeilings_statmap.nii.gz
│ │
│ ├── qc <- quality checks
│ │ └── sub-0*_task-things_headmotion.tsv
│ └── descriptive <- annotated beta rankings and t-SNE plots per visual ROIs
│ ├── sub-*_task-things_desc-{perImage, perTrial}_labels.npy
│ ├── sub-*_task-things_space-T1w_stats-{betas, ranks}_desc-{perImage, perTrial}_statseries.npy
│ ├── sub-*_task-things_space-T1w_contrast-*_roi-*_cutoff-*_nvox-*_stats-{ranks, betas, noiceCeilings}_desc-{perImage, perTrial}_statseries.npy
│ └── sub-*_task-things_space-T1w_stats-tSNE_label-visualROIs_desc-{perImage, perTrial}_statseries.npz
│
│
└── datapaper <- Report, figures, visualization notebooks
├── figures <- Graphics and figures from the report
├── notebooks <- Code to generate datapaper figures
│ ├── behav_analysis.ipynb <- behav figures
│ ├── beta_ranking.ipynb <- beta ranking figures
│ ├── beta_dimReduction.ipynb <- beta t-SNE plots
│ ├── fixation_compliance.ipynb <- gaze position distribution
│ ├── noiseCeil_flatmaps.ipynb <- noise ceilings projected on cortical flat maps
│ └── head_motion.ipynb <- framewise displacement figs
└── report <- Data paper manuscript <- TODO
Project based on the cookiecutter data science project template. #cookiecutterdatascience