Dataset Information

Interaction within and between cortical networks subserving multisensory learning and its reorganization due to musical expertise.

ABSTRACT: Recent advancements in the field of network science allow us to quantify inter-network information exchange and model the interaction within and between task-defined states of large-scale networks. Here, we modeled the inter- and intra- network interactions related to multisensory statistical learning. To this aim, we implemented a multifeatured statistical learning paradigm and measured evoked magnetoencephalographic responses to estimate task-defined state of functional connectivity based on cortical phase interaction. Each network state represented the whole-brain network processing modality-specific (auditory, visual and audiovisual) statistical learning irregularities embedded within a multisensory stimulation stream. The way by which domain-specific expertise re-organizes the interaction between the networks was investigated by a comparison of musicians and non-musicians. Between the modality-specific network states, the estimated connectivity quantified the characteristics of a supramodal mechanism supporting the identification of statistical irregularities that are compartmentalized and applied in the identification of uni-modal irregularities embedded within multisensory stimuli. Expertise-related re-organization was expressed by an increase of intra- and a decrease of inter-network connectivity, showing increased compartmentalization.

SUBMITTER: Paraskevopoulos E

PROVIDER: S-EPMC9098427 | biostudies-literature | 2022 May

REPOSITORIES: biostudies-literature

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Interaction within and between cortical networks subserving multisensory learning and its reorganization due to musical expertise.

Paraskevopoulos Evangelos E Chalas Nikolas N Anagnostopoulou Alexandra A Bamidis Panagiotis D PD

Scientific reports 20220512 1

Recent advancements in the field of network science allow us to quantify inter-network information exchange and model the interaction within and between task-defined states of large-scale networks. Here, we modeled the inter- and intra- network interactions related to multisensory statistical learning. To this aim, we implemented a multifeatured statistical learning paradigm and measured evoked magnetoencephalographic responses to estimate task-defined state of functional connectivity based on c ...[more]

PMID: 35552516

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Project description:Music can act as a mnemonic device that can elicit multiple memories. How musical and non-musical information integrate into complex cross-modal memory representations has however rarely been investigated. Here, we studied the ability of human subjects to associate visual objects with melodies. Musical laypersons and professional musicians performed an associative inference task that tested the ability to form and memorize paired associations between objects and melodies ("direct trials") and to integrate these pairs into more complex representations where melodies are linked with two objects across trials ("indirect trials"). We further investigated whether and how musical expertise modulates these two processes. We analyzed accuracy and reaction times (RTs) of direct and indirect trials in both groups. We reasoned that the musical and cross-modal memory demands of musicianship might modulate performance in the task and might thus reveal mechanisms that underlie the association and integration of visual information with musical information. Although musicians showed a higher overall memory accuracy, non-musicians' accuracy was well above chance level in both trial types, thus indicating a significant ability to associate and integrate musical with visual information even in musically untrained subjects. However, non-musicians showed shorter RTs in indirect compared to direct trials, whereas the reverse pattern was found in musicians. Moreover, accuracy of direct and indirect trials correlated significantly in musicians but not in non-musicians. Consistent with previous accounts of visual associative memory, we interpret these findings as suggestive of at least two complimentary mechanisms that contribute to visual-melodic memory integration. (I) A default mechanism that mainly operates at encoding of complex visual-melodic associations and that works with surprising efficacy even in musically untrained subjects. (II) A retrieval-based mechanism that critically depends on an expert ability to maintain and discriminate visual-melodic associations across extended memory delays. Future studies may investigate how these mechanisms contribute to the everyday experience of music-evoked memories.

Dataset Information

Interaction within and between cortical networks subserving multisensory learning and its reorganization due to musical expertise.

Publications

Interaction within and between cortical networks subserving multisensory learning and its reorganization due to musical expertise.

Similar Datasets

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