Connectivity patterns during music listening: Evidence for action-based processing in musicians

Research output: Contribution to journalArticleScientificpeer-review

Researchers

  • Vinoo Alluri
  • Petri Toiviainen
  • Iballa Burunat
  • Marina Kliuchko
  • Peter Vuust
  • Elvira Brattico

Research units

  • University of Helsinki
  • Aarhus University & The Royal Academy of Music Aarhus/Aalborg
  • University of Jyväskylä

Abstract

Musical expertise is visible both in the morphology and functionality of the brain. Recent research indicates that functional integration between multi-sensory, somato-motor, default-mode (DMN), and salience (SN) networks of the brain differentiates musicians from non-musicians during resting state. Here, we aimed at determining whether brain networks differentially exchange information in musicians as opposed to non-musicians during naturalistic music listening. Whole-brain graph-theory analyses were performed on participants' fMRI responses. Group-level differences revealed that musicians' primary hubs comprised cerebral and cerebellar sensorimotor regions whereas non-musicians' dominant hubs encompassed DMN-related regions. Community structure analyses of the key hubs revealed greater integration of motor and somatosensory homunculi representing the upper limbs and torso in musicians. Furthermore, musicians who started training at an earlier age exhibited greater centrality in the auditory cortex, and areas related to top-down processes, attention, emotion, somatosensory processing, and non-verbal processing of speech. We here reveal how brain networks organize themselves in a naturalistic music listening situation wherein musicians automatically engage neural networks that are action-based while non-musicians use those that are perception-based to process an incoming auditory stream.

Details

Original languageEnglish
Pages (from-to)2955–2970
JournalHuman Brain Mapping
Volume38
Issue number6
Publication statusPublished - 2017
MoE publication typeA1 Journal article-refereed

    Research areas

  • FMRI, Functional connectivity, Graph theory, Music, Musical training

ID: 12136953