Similarity of individual functional brain connectivity patterns formed by music listening quantified with a data-driven approach

Christof Karmonik, Anthony Brandt, Saba Elias, Jennifer Townsend, Elliott Silverman, Zhaoyue Shi, J. Todd Frazier

Research output: Contribution to journalArticlepeer-review

Abstract

Introduction: This study aims to explore the similarities in functional connectivity (FC) patterns in individuals when listening to different music genres and, in comparison, to the spoken word, using a novel data-driven approach. Our model and findings can potentially be utilized for evaluating the neurological effects of therapeutic music interventions. Materials and methods: Twelve healthy volunteers listened to seven different sound tracks while undergoing functional magnetic resonance imaging (fMRI) scans: music of the volunteer’s choice with positive emotional attachment, two selections of unfamiliar classical music, one classical piece repeated with visual guidance and three spoken language tracks. FC network graphs were created, and selected graph properties were evaluated toward their commonalities across sound tracks. For comparison, FC patterns represented by the graph adjacency matrices were directly compared for high and low BOLD activation during listening. Results: Graph properties averaged across subjects showed similar values for the same sound track compared to different sound tracks (p < 0.003). For high BOLD activation involving most areas in the auditory cortex, FC patterns for the same sound track correlated highly (0.74 ± 0.11), whereas FC patterns for different sound tracks did not (0.09 ± 0.07; p < 6e−5). For low BOLD activation involving additional brain regions, correlation of FC patterns for the sound tracks was still higher (0.43 ± 0.07) than for different sound tracks (0.09 ± 0.05; p < 8e−6). Conclusion: Similar music creates similar functional activation and connectivity patterns in the brain of healthy individuals as does listening to the spoken word. Direct comparison of FC patterns yielded higher correlations than indirect comparisons of graph properties derived from corresponding FC networks.

Original languageEnglish (US)
Pages (from-to)703-713
Number of pages11
JournalInternational Journal of Computer Assisted Radiology and Surgery
Volume15
Issue number4
DOIs
StateE-pub ahead of print - Oct 26 2019

Keywords

  • Brain functional connectivity
  • Functional magnetic resonance imaging
  • Graph network analysis
  • Music listening
  • Music medicine

ASJC Scopus subject areas

  • Surgery
  • Biomedical Engineering
  • Radiology Nuclear Medicine and imaging
  • Computer Vision and Pattern Recognition
  • Health Informatics
  • Computer Science Applications
  • Computer Graphics and Computer-Aided Design

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