# 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: "UCY" id: "" - firstname: "Alexandra" lastname: "Anagnostopoulou" affiliation: "AUTH" id: "" - firstname: "Nikolas" lastname: "Chalas" affiliation: "IBB" - firstname: "Maria" lastname: "Karagianni" affiliation: "AUTH" - firstname: "Panagiotis" lastname: "Bamidis" affiliation: "AUTH" # 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: - "HFRI: 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.xxx/zzzz" reftype: "IsSupplementTo" citation: "Citation1" # 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