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highspeed-bids
forked from lnnrtwttkhn/highspeed-bids
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10.12751/g-node.4ivuv8
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DataLad dataset containing BIDS-converted MRI data used in Wittkuhn & Schuck, 2020, Nature Communications. For details, see:

Branch: synced/master
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highspeed-bids
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README.md

Highspeed BIDS

Overview

This repository contains meta data of the DataLad dataset of the BIDS-converted MRI data 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-bids to get the actual data.

Usage

Get data

$ datalad clone https://gin.g-node.org/lnnrtwttkhn/highspeed-bids
[INFO   ] Scanning for unlocked files (this may take some time) [INFO   ]
install(ok): highspeed-bids (dataset)
$ cd highspeed-bids
$ datalad get participants.tsv
get(ok): participants.tsv (file) [from origin...]

Run code

Please install the required packages listed in requirements.txt:

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 - MRI data according to the Brain Imaging Data Structure (BIDS)
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