DataLad dataset containing the pre-processed MRI data using fMRIPrep by Wittkuhn & Schuck, 2020, Nature Communications. For further details, see: https://wittkuhn.mpib.berlin/highspeed/

Lennart Wittkuhn 86c6bf1e2f update documentation 6 months ago
.datalad 3968fb6234 [DATALAD] new dataset 9 months ago
bids @ 6578b15b9b f93c947b56 [DATALAD] auto-saved changes 6 months ago
code 86c6bf1e2f update documentation 6 months ago
fmriprep 302dfe74d5 save fmriprep logs (including citation templates) 6 months ago
logs 96d0069b1d add empty /logs and /work folders 9 months ago
tools @ 726c854e07 f93c947b56 [DATALAD] auto-saved changes 6 months ago
work 96d0069b1d add empty /logs and /work folders 9 months ago
.gitattributes 495aad2eb7 add LICENSE, file to create DOI and update README.md 9 months ago
.gitignore 3a75ea5551 add fmriprep data of all participants 7 months ago
.gitmodules eeb57ba380 [DATALAD] Recorded changes 9 months ago
CHANGELOG.md d90296728b Apply YODA dataset setup 9 months ago
LICENSE 86143815c3 update LICENSE to CC BY-SA 4.0 6 months ago
README.md 86143815c3 update LICENSE to CC BY-SA 4.0 6 months ago
datacite.yml 86143815c3 update LICENSE to CC BY-SA 4.0 6 months ago
highspeed-fmriprep.Rproj ae4aee2b5f add highspeed-fmriprep project documentation 9 months ago

README.md

Highspeed fMRIPrep

Overview

This repository contains pre-processed MRI data based on defaced BIDS-data used in Wittkuhn & Schuck, 2020, Nature Communications. Pre-processing was performed using fMRIPrep, version 1.2.2.

Dataset structure

  • /code contains all project-specific code with sub-directories /docs for project-specific documentation and /fmriprep for the code relevant to run fMRIPrep on the input /bids dataset
  • /bids contains the defaced BIDS-converted MRI dataset as an input to fMRIPrep and is included as an independent sub-datatset
  • /tools contains the relevant fMRIPrep container and the necessary Freesurfer license file in the fmriprep sub-directory.
  • /logs and /work are empty directories (held in place by .gitkeep files) and contain log files and the (huge) working directory ouput that fMRIPrep produced. They are populated during the execution of highspeed-fmriprep-cluster.sh but not committed to this repo because they will not be used further downstream in the analyses.

Citation

Wittkuhn, L. and Schuck, N. W. (2020). Dynamics of fMRI patterns reflect sub-second activation sequences and reveal replay in human visual cortex. Nature Communications.

A preprint (old version) is available at:

Wittkuhn, L. and Schuck, N. W. (2020). Faster than thought: Detecting sub-second activation sequences with sequential fMRI pattern analysis. bioRxiv. doi:10.1101/2020.02.15.950667

Contact

Please create a new issue if you have questions about the code or data, if there is anything missing, not working or broken.

For all other general questions, you may also write an email to:

License

All of the data are licensed under Creative Commons Attribution-ShareAlike 4.0. Please see the LICENSE file and https://creativecommons.org/licenses/by-sa/4.0/ for details.

datacite.yml
Title Dynamics of fMRI patterns reflect sub-second activation sequences and reveal replay in human visual cortex - Preprocessed MRI data using fMRIPrep
Authors Wittkuhn,Lennart;Max Planck Institute for Human Development, Max Planck UCL Centre for Computational Psychiatry and Ageing Research, Berlin, Germany;ORCID:0000-0001-2345-6789
Schuck,Nicolas W.;Max Planck Institute for Human Development, Max Planck UCL Centre for Computational Psychiatry and Ageing Research, Berlin, Germany;ORCID:0000-0002-0150-8776
Description Neural computations are often fast and anatomically localized. Yet, investigating such computations in humans is challenging because non-invasive methods have either high temporal or spatial resolution, but not both. Of particular relevance, fast neural replay is known to occur throughout the brain in a coordinated fashion about which little is known. We develop a multivariate analysis method for functional magnetic resonance imaging that makes it possible to study sequentially activated neural patterns separated by less than 100 ms with precise spatial resolution. Human participants viewed images individually and sequentially with speeds up to 32 ms between items. Probabilistic pattern classifiers were trained on activation patterns in visual and ventrotemporal cortex during individual image trials. Applied to sequence trials, probabilistic classifier time courses allow the detection of neural representations and their order. Order detection remains possible at speeds up to 32 ms between items. The frequency spectrum of the sequentiality metric distinguishes between sub- versus supra-second sequences. Importantly, applied to resting-state data our method reveals fast replay of task-related stimuli in visual cortex. This indicates that non-hippocampal replay occurs even after tasks without memory requirements and shows that our method can be used to detect such spontaneously occurring replay.
License Creative Commons Attribution-ShareAlike 4.0 (https://creativecommons.org/licenses/by-sa/4.0/)
References Wittkuhn, L. and Schuck, N. W. (2020). Dynamics of fMRI patterns reflect sub-second activation sequences and reveal replay in human visual cortex. Nature Communications [] (IsSupplementTo)
Wittkuhn, L. and Schuck, N. W. (2020). Faster than thought: Detecting sub-second activation sequences with sequential fMRI pattern analysis. bioRxiv. doi:10.1101/2020.02.15.950667 [doi:10.1101/2020.02.15.950667] (IsSupplementTo)
Funding Max Planck Society, Independent Max Planck Research Group grant
European Union, ERC Starting Grant ERC-2019-StG REPLAY-852669
Max Planck Institute for Human Development
Keywords cognitive neuroscience
functional magnetic resonance imaging
hippocampal replay
Resource Type Dataset