DataLad dataset containing anatomical masks used for feature selection in Wittkuhn & Schuck, 2020, Nature Communications. For details, see: https://wittkuhn.mpib.berlin/highspeed

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README.md

Highspeed Masks

Overview

This repository contains binarized anatomical masks used in Wittkuhn & Schuck, 2020, Nature Communications.

Please see the paper and the project website at https://wittkuhn.mpib.berlin/highspeed/ for further details about the study. The meta-data of this dataset are available at https://github.com/lnnrtwttkhn/highspeed-masks and the data contents at https://gin.g-node.org/lnnrtwttkhn/highspeed-masks.

Dataset structure

  • Anatomical binarized masks are in masks/ with subdirectories for hippocampus masks (mask_hippocampus), medial-temporal lobe masks (mask_mtl) and visual and ventrotemporal cortex masks (mask_visual)
  • The masks/ directory also contains smoothed fMRI data in a smooth/ subdirectory
  • All inputs (i.e. building blocks from other sources) are located in fmriprep/ and /bids.
  • All custom code is located in code/.

Run code

Install required packages:

mkvirtualenv -p $(which python3) highspeed-masks
pip install -r requirements.txt

Contact

License

Please see the LICENSE file for details.

datacite.yml
Title Dynamics of fMRI patterns reflect sub-second activation sequences and reveal replay in human visual cortex - Anatomical binarized masks
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