highspeed-glm/datacite.yml

# Metadata for DOI registration according to DataCite Metadata Schema 4.1.
# For detailed schema description see https://doi.org/10.5438/0014

## Required fields

# The main researchers involved. Include digital identifier (e.g., ORCID)
# if possible, including the prefix to indicate its type.
authors:
  -
    firstname: "Lennart"
    lastname: "Wittkuhn"
    affiliation: "Max Planck Institute for Human Development, Max Planck UCL Centre for Computational Psychiatry and Ageing Research, Berlin, Germany"
    id: "ORCID:0000-0001-2345-6789"
  -
    firstname: "Nicolas W."
    lastname: "Schuck"
    affiliation: "Max Planck Institute for Human Development, Max Planck UCL Centre for Computational Psychiatry and Ageing Research, Berlin, Germany"
    id: "ORCID:0000-0002-0150-8776"

# A title to describe the published resource.
title: "Dynamics of fMRI patterns reflect sub-second activation sequences and reveal replay in human visual cortex - First-level GLM results"

# Additional information about the resource, e.g., a brief abstract.
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.

# Lit of keywords the resource should be associated with.
# Give as many keywords as possible, to make the resource findable.
keywords:
  - cognitive neuroscience
  - functional magnetic resonance imaging
  - hippocampal replay

# License information for this resource. Please provide the license name and/or a link to the license.
# Please add also a corresponding LICENSE file to the repository.
license:
  name: "Creative Commons Attribution-ShareAlike 4.0"
  url: "https://creativecommons.org/licenses/by-sa/4.0/"



## Optional Fields

# Funding information for this resource.
# Separate funder name and grant number by comma.
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"


# Related publications. reftype might be: IsSupplementTo, IsDescribedBy, IsReferencedBy.
# Please provide digital identifier (e.g., DOI) if possible.
# Add a prefix to the ID, separated by a colon, to indicate the source.
# Supported sources are: DOI, arXiv, PMID
# In the citation field, please provide the full reference, including title, authors, journal etc.
references:
  -
    reftype: "IsSupplementTo"
    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"
  -
    id: "doi:10.1101/2020.02.15.950667"
    reftype: "IsSupplementTo"
    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"





# Resource type. Default is Dataset, other possible values are Software, DataPaper, Image, Text.
resourcetype: Dataset

# Do not edit or remove the following line
templateversion: 1.2