This dataset contains T1 and T2 weighted MRI scans, susceptibility weighted images, and diffusion imaging scans. These data are suitable for analyses of brain structure. For more information about the project visit: http://studyforrest.org

Extension of the dataset published in Hanke et al. (2014; doi:10.1038/sdata.2014.3) with additional acquisitions for 15 of the original 20 particpants. These additions include: retinotopic mapping, a localizer paradigm for higher visual areas (FFA, EBA, PPA), and another 2h movie recording with 3T full-brain BOLD fMRI with simultaneous 1000 Hz eyetracking.

Extension of the dataset published in Hanke et al. (2014; doi:10.1038/sdata.2014.3) with additional acquisitions for 6 of the original 20 participants (and one additional participant). Participants performed a central fixation task while being stimulated with oriented gratings. The experiment was repeated in four different sessions with 7T BOLD fMRI acquisition, each with a different scan resolution (0.8, 1.4, 2.0, and 3.0 mm).

studyforrest.org: Multi-resolution 7T fMRI on visual orientation representation [BIDS]

Here we presentcempirical ultra high-field fMRI data recorded at four spatial resolutions (0.8 mm, 1.4 mm, 2 mm, and 3 mm isotropic voxel size) for orientation decoding in visual cortex — in order to test hypotheses on the strength and spatial scale of orientation discriminating signals. The entire dataset and the analysis codes presented in BIDS (Brain Imaging Data Structure).

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-

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

Support vector machine searchlight accuracy map. Radius of the spherical ROIs was two voxels (plus center). ROIs were spaced by two voxels. Accuracy scores mapped onto a voxels are the mean accuracy of all ROI analysis a voxel was included into.

Collection description

The twenty participants were repeatedly stimulated with a total of 25 music clips, with and without speech content, from five different genres using a slow event-related paradigm. The data release includes raw fMRI data, as well as precomputed structural alignments for within-subject and group analysis. In addition to fMRI, simultaneously recorded cardiac and respiratory traces, as well the complete implementation of the stimulation paradigm, including stimuli, are provided. An initial quality control analysis reveals distinguishable patterns of response to individual genres throughout a large expanse of areas known to be involved in auditory and speech processing.

Subject species

homo sapiens

Modality

fMRI-BOLD

Cognitive paradigm (task)

auditory scene perception

Map type

Other

Study Forrest. Extensive functional brain imaging data from natural stimulation, a rich set of auxiliary data (such as structural brain scans, measurements of physiological, and technical confounds), as well as stimulus annotations.

Collection description

Subject species

homo sapiens

Modality

fMRI-BOLD

Analysis level

group

Cognitive paradigm (task)

film viewing

Map type

Z

Extension of the dataset published in Hanke et al. (2014; doi:10.1038/sdata.2014.3) with additional acquisitions for 15 of the original 20 particpants. These additions include: retinotopic mapping, a localizer paradigm for higher visual areas (FFA, EBA, PPA), and another 2h movie recording with 3T full-brain BOLD fMRI with simultaneous 1000 Hz eyetracking.

Only open-source software was employed in this study. We thank their respective authors for making it publicly available.

Please follow good scientific practice by citing the most appropriate publication(s) describing the aspects of this datasets that were used in a study.

This repository contains the fMRI data, annotations, analysis scripts to generate the results, and results in Häusler C.O. & Hanke M. (submitted) as Datalad datasets (https://github.com/datalad).

Extension of a matching fMRI dataset (Sengupta, et al., 2017; OpenFMRI ds000113c) on participants performing a central fixation task while being stimulated with oriented visual gratings. This dataset extends the previous one with acquisitions for 3 matching spatial resolutions (1.4, 2.0, and 3.0 mm) at 3T (complementing the previous 7T acquisitions at 0.8, 1.4, 2.0, and 3.0 mm). Five of the total of seven participants are identical in both datasets. All participants are part of the studyforrest.org project.