highspeed-fmriprep/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: "ResearcherID:X-1234-5678"

# A title to describe the published resource.
title: "Faster than thought: Detecting sub-second activation sequences with sequential fMRI pattern analysis"

# Additional information about the resource, e.g., a brief abstract.
description: |
  Neural computations are often anatomically localized and executed on sub-second time scales.
  Understanding the brain therefore requires methods that offer sufficient spatial and temporal resolution.
  This poses a particular challenge for the study of the human brain because non-invasive methods have either high temporal or spatial resolution, but not both.
  Here, we introduce a novel multivariate analysis method for conventional blood-oxygen-level dependent functional magnetic resonance imaging (BOLD fMRI) that allows to study sequentially activated neural patterns separated by less than 100 ms with anatomical precision.
  Human participants underwent fMRI and were presented with sequences of visual stimuli separated by 32 to 2048 ms.
  Probabilistic pattern classifiers were trained on fMRI data to detect the presence of image-specific activation patterns in early visual and ventral temporal cortex.
  The classifiers were then applied to data recorded during sequences of the same images presented at increasing speeds.
  Our results show that probabilistic classifier time courses allowed to detect neural representations and their order, even when images were separated by only 32 ms.
  Moreover, the frequency spectrum of the statistical sequentiality metric distinguished between sequence speeds on sub-second versus supra-second time scales.
  These results survived when data with high levels of noise and rare sequence events at unknown times were analyzed.
  Our method promises to lay the groundwork for novel investigations of fast neural computations in the human brain, such as hippocampal replay.

# Lit of keywords the resource should be associated with.
# Give as many keywords as possible, to make the resource findable.
keywords:
  - 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-NonCommercial-ShareAlike 4.0"
  url: "https://creativecommons.org/licenses/by-nc-sa/4.0/"



## Optional Fields

# Funding information for this resource.
# Separate funder name and grant number by comma.
funding:
  - "Max Planck Society (M.TN.A.BILD0004)"
  - "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:
  -
    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