# 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: "Evangelos"
    lastname: "Paraskevopoulos"
    affiliation: "Department of Psychology, University of Cyprus, Nicosia, Cyprus"
    id: "0000-0003-4063-4182"
  -
    firstname: "Alexandra"
    lastname: "Anagnostopoulou"
    affiliation: "School of Medicine, Faculty of Health Sciences, Aristotle University of Thessaloniki, Greece"
    id: "0000-0001-9578-7552"
  -
    firstname: "Nikolas"
    lastname: "Chalas"
    affiliation: "Institute for Biomagnetism and Biosignal Analysis, University of Münster, Germany"
    id: "0000-0001-5769-0055"
  -
    firstname: "Maria"
    lastname: "Karagianni"
    affiliation: "School of Medicine, Faculty of Health Sciences, Aristotle University of Thessaloniki, Greece"
  -
    firstname: "Panagiotis"
    lastname: "Bamidis"
    affiliation: "School of Medicine, Faculty of Health Sciences, Aristotle University of Thessaloniki, Greece"
    id: "0000-0002-9936-5805"

# A title to describe the published resource.
title: "Unravelling the multisensory learning advantage: Different patterns of within and across frequency-specific interactions drive uni- and multisensory neuroplasticity"

# Additional information about the resource, e.g., a brief abstract.
description: "In the field of learning theory and practice, the superior efficacy of multisensory learning over uni-sensory is well-accepted. However, the underlying neural mechanisms at the macro-level of the human brain remain largely unexplored. This study addresses this gap by providing novel empirical evidence and a theoretical framework for understanding the superiority of multisensory learning. Through a cognitive, behavioral, and electroencephalographic assessment of carefully controlled uni-sensory and multisensory training interventions, our study uncovers a fundamental distinction in their neuroplastic patterns. The outcomes confirm the superior efficacy of multisensory learning in enhancing cognitive processes and improving multisensory processing. A multilayered network analysis of pre- and post- training EEG data allowed us to model connectivity within and across different frequency bands at the cortical level. Pre-training EEG analysis unveils a complex network of distributed sources communicating through cross-frequency coupling, while comparison of pre- and post-training EEG data demonstrates significant differences in the reorganizational patterns of uni-sensory and multisensory learning. Uni-sensory training primarily modifies cross-frequency coupling between lower and higher frequencies, whereas multisensory training induces changes within the beta band in a more focused network, implying the development of a unified representation of audiovisual stimuli. In combination with behavioural and cognitive findings this suggests that, multisensory learning benefits from an automatic top-down transfer of training, while uni-sensory training relies mainly on limited bottom-up generalization. Our findings offer a compelling theoretical framework for understanding the advantage of multisensory learning."
# Lit of keywords the resource should be associated with.
# Give as many keywords as possible, to make the resource findable.
keywords:
  - Neuroscience
  - Multisensory learning

# 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 CC0 1.0 Public Domain Dedication"
  url: "https://creativecommons.org/publicdomain/zero/1.0/"



## Optional Fields

# Funding information for this resource.
# Separate funder name and grant number by comma.
funding:
  - "Hellenic Foundation for Research & Innovation; 2089"



# 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:10.1016/j.neuroimage.2024.120582."
    reftype: "IsSupplementTo"
    citation: "Paraskevopoulos, E., Anagnostopoulou, A., Chalas, N., Karagianni, M., & Bamidis, P. (2024). Unravelling the multisensory learning advantage: Different patterns of within and across frequency-specific interactions drive uni-and multisensory neuroplasticity. NeuroImage, 120582. doi:10.1016/j.neuroimage.2024.120582."


# 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