DataLad dataset containing code and data for statistical analyses used in Wittkuhn & Schuck, 2020, Nature Communications. For details, see:

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Lennart Wittkuhn cffefd2c6c fix minor typos in README.md		3 年之前
.datalad	34eeffcc6b [DATALAD] new dataset	3 年之前
code	2058381548 minor fix in code/highspeed-analysis-sequence.Rmd	3 年之前
data	46b800876f restructure resting-state analysis and add temporary data	3 年之前
figures	10107351d8 add empty /figures directory	3 年之前
sourcedata	79045ed4df add empty sourcedata/ folder	3 年之前
.gitattributes	00e7b42aaa add LICENSE, datacite.yml and update README.md	3 年之前
.gitignore	a749c7b172 create source data files for behavioral analyses	3 年之前
.gitmodules	afbee304b0 [DATALAD] Recorded changes	3 年之前
CHANGELOG.md	fc7f5d5f06 Apply YODA dataset setup	3 年之前
LICENSE	00e7b42aaa add LICENSE, datacite.yml and update README.md	3 年之前
README.md	cffefd2c6c fix minor typos in README.md	3 年之前
datacite.yml	00e7b42aaa add LICENSE, datacite.yml and update README.md	3 年之前
highspeed-analysis.Rproj	97652d6089 add behavioral analysis file	3 年之前

Highspeed Analysis

Overview

This repository contains all code for statistical analyses used in Wittkuhn & Schuck, 2020, Nature Communications.

Please visit https://wittkuhn.mpib.berlin/highspeed/ for the project website and https://gin.g-node.org/lnnrtwttkhn/highspeed-analysis to get the actual data.

Dataset structure

/code contains all project-specific code, mainly .Rmd notebooks. The rendered versions of the notebooks can be found on the project website at https://wittkuhn.mpib.berlin/highspeed/
/data contains relevant input datasets: the behavioral events.tsv files in the BIDS dataset (highspeed-bids) and the decoding results of (highspeed-decoding)
/figures and /sourcedata are empty subdirectories that are populated with the Figures and Source Data produced by the .Rmd scripts in /code

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 - Statistical analyses
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

README.md