DataLad dataset containing the MVPA decoding data used Wittkuhn & Schuck, 2020, Nature Communications

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README.md 64ce1bed7a add LICENSE and datacite.yml, update README.md 1 year ago
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README.md

Highspeed Decoding

Overview

This repository contains results of the multivariate pattern classification pipeline used in Wittkuhn & Schuck, 2020, Nature Communications.

Dataset structure

  • /code contains all project-specific code with sub-directories /docs for project-specific documentation and /decoding for the code relevant to run the deoding pipeline
  • /decoding contains the results of the decoding pipeline
  • /bids contains the defaced BIDS-converted MRI dataset
  • /fmriprep contains pre-processed MRI data
  • /glm contains results of the first-level GLM pipeline used for feature selection
  • /masks contains binarized anatomical masks and smoothed fMRI data

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 - Multivariate pattern classification results
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