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

12 Commits

4 Branches

1 Releases

Lennart Wittkuhn eeb57ba380 [DATALAD] Recorded changes		4 years ago
.datalad	3968fb6234 [DATALAD] new dataset	4 years ago
bids @ 6578b15b9b	8ef8077142 [DATALAD] Recorded changes	4 years ago
code	ae4aee2b5f add highspeed-fmriprep project documentation	4 years ago
logs	96d0069b1d add empty /logs and /work folders	4 years ago
tools @ 1f9b4bfbd4	eeb57ba380 [DATALAD] Recorded changes	4 years ago
work	96d0069b1d add empty /logs and /work folders	4 years ago
.gitattributes	495aad2eb7 add LICENSE, file to create DOI and update README.md	4 years ago
.gitignore	cd7f80f997 ignore /logs and /work folders	4 years ago
.gitmodules	eeb57ba380 [DATALAD] Recorded changes	4 years ago
CHANGELOG.md	d90296728b Apply YODA dataset setup	4 years ago
LICENSE	495aad2eb7 add LICENSE, file to create DOI and update README.md	4 years ago
README.md	696822fe6b add license information to README	4 years ago
datacite.yml	495aad2eb7 add LICENSE, file to create DOI and update README.md	4 years ago
highspeed-fmriprep.Rproj	ae4aee2b5f add highspeed-fmriprep project documentation	4 years ago

Faster than thought: Detecting sub-second activation sequences with sequential fMRI pattern analysis - fMRIPrep data

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 file) 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). 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-NonCommercial-ShareAlike 4.0. Please see the LICENSE file and https://creativecommons.org/licenses/by-nc-sa/4.0/ for details.

datacite.yml
Title	Faster than thought: Detecting sub-second activation sequences with sequential fMRI pattern analysis
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;ResearcherID:X-1234-5678
Description	Neural computations are often anatomically localized and executed on sub-second time scales. Understanding the brain therefore requires methods that offer sufficient spatial and temporal resolution. This poses a particular challenge for the study of the human brain because non-invasive methods have either high temporal or spatial resolution, but not both. Here, we introduce a novel multivariate analysis method for conventional blood-oxygen-level dependent functional magnetic resonance imaging (BOLD fMRI) that allows to study sequentially activated neural patterns separated by less than 100 ms with anatomical precision. Human participants underwent fMRI and were presented with sequences of visual stimuli separated by 32 to 2048 ms. Probabilistic pattern classifiers were trained on fMRI data to detect the presence of image-specific activation patterns in early visual and ventral temporal cortex. The classifiers were then applied to data recorded during sequences of the same images presented at increasing speeds. Our results show that probabilistic classifier time courses allowed to detect neural representations and their order, even when images were separated by only 32 ms. Moreover, the frequency spectrum of the statistical sequentiality metric distinguished between sequence speeds on sub-second versus supra-second time scales. These results survived when data with high levels of noise and rare sequence events at unknown times were analyzed. Our method promises to lay the groundwork for novel investigations of fast neural computations in the human brain, such as hippocampal replay.
License	Creative Commons Attribution-NonCommercial-ShareAlike 4.0 (https://creativecommons.org/licenses/by-nc-sa/4.0/)
References	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 (M.TN.A.BILD0004) Max Planck Institute for Human Development
Keywords	Neuroscience functional magnetic resonance imaging hippocampal replay
Resource Type	Dataset

README.md