studyforrest_movie_denoised

A manually denoised audio-visual movie watching fMRI dataset for the studyforrest project
====================
Note: This dataset is compatible with the BIDS v1.3.0-dev as a standalone derivative dataset.
Due to that the OpenNeuro.org does not support the BIDs v1.3.0 yet ('desc-<label>' in file names),
a made-up subject 'sub-phantom' was created only to pass the data stucture validation for uploading dataset.

Source data
===========
We here provide a denoised version of the studyforrest 3T movie-watching fMRI data.
Specifically, the studyforrest 3T movie-watching fMRI dataset was hosted as ‘ses-movie’
under OpenNeuro ds000113 (https://openneuro.org/datasets/ds000113). Historically, the studyforrest
3T movie-watching fMRI data was hosted as ds000113d on OpenfMRI (https://legacy.openfmri.org/dataset/ds000113d/).
It shall be noted that ‘ses-movie’ under OpenNeuro ds000113 and OpenfMRI ds000113d are the same data, just different in names.

For more information about the studyforrest project, please refer to the data papers of the studyforrest project
and visit http://studyforrest.org.
Hanke, M. et al. A high-resolution 7-Tesla fMRI dataset from complex natural stimulation with an audio movie.
Sci. Data 1, sdata20143 (2014).
Hanke, M. et al. A studyforrest extension, simultaneous fMRI and eye gaze recordings during prolonged natural stimulation.
Sci. Data 3, 160092 (2016).

Pipeline description
====================
A four-step procedure was used to denoise the movie-watching fMRI dataset from the studyforrest project,
producing a denoised version of the data.

1. The data were preprocessed using FEAT in FSL v6.00 (https://fsl.fmrib.ox.ac.uk/fsl/fslwiki/FEAT)
including motion correction, slice timing correction, brain extraction, high-pass temporal filtering (200s cutoff)
and with or without spatial smoothing (i.e., FWHM= 5 mm or FWHM=0 mm).
2. ICA was performed with MELODIC v3.15 (https://fsl.fmrib.ox.ac.uk/fsl/fslwiki/MELODIC).
3. IC classification was manually performed using melview (https://git.fmrib.ox.ac.uk/fsl/melview).
4. Artifact removal was performed using fsl_regfilt in FSL’s MELODIC v3.15.

All code for data denoising and technical validation can be found at https://github.com/xingyu-liu/studyforrest_denoise.

Dataset content overview
========================
After the four-step denoising procedure, 4 kinds of data were produced for each run of each participant.

1. the denoised fMRI data
./sub-xx/ses-movie/func/sub-xx_ses-movie_task-movie-run-x_space-T1w_desc-{sm5,unsm}Denoised_bold.nii.gz
2. spatial maps of decomposed ICs
./sub-xx/ses-movie/func/sub-xx_ses-movie_task-movie-run-x_space-T1w_desc-{sm5,unsm}MELODIC_components.nii.gz
3. timeseries of decomposed ICs
./sub-xx/ses-movie/func/sub-xx_ses-movie_task-movie-run-x_space-T1w_desc-{sm5,unsm}MELODIC_mixing.tsv
4. Labels of decomposed ICs
./sub-xx/ses-movie/func/sub-xx_ses-movie_task-movie-run-x_space-T1w_desc-{sm5,unsm}MELODIC_decomposition.json