Skip to main content
bioRxiv
  • Home
  • About
  • Submit
  • ALERTS / RSS
Advanced Search
New Results

Musicianship can be decoded from magnetic resonance images

Tuomas Puoliväli, Tuomo Sipola, Anja Thiede, Marina Kliuchko, Brigitte Bogert, Petri Toiviainen, Asoke K. Nandi, Lauri Parkkonen, Elvira Brattico, Tapani Ristaniemi, Tiina Parviainen
doi: https://doi.org/10.1101/2020.07.19.210906
Tuomas Puoliväli
aFaculty of Information Technology, University of Jyväskylä, Finland
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • For correspondence: tuomas.a.b.puolivali@jyu.fi tuomas.puolivali@ieee.org
Tuomo Sipola
aFaculty of Information Technology, University of Jyväskylä, Finland
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Anja Thiede
bDepartment of Neuroscience and Biomedical Engineering, Aalto University, Finland
eCognitive Brain Research Unit, Department of Psychology and Logopedics, University of Helsinki, Finland
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Marina Kliuchko
eCognitive Brain Research Unit, Department of Psychology and Logopedics, University of Helsinki, Finland
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Brigitte Bogert
eCognitive Brain Research Unit, Department of Psychology and Logopedics, University of Helsinki, Finland
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Petri Toiviainen
cFinnish Centre for Interdisciplinary Music Research, Department of Music, Art and Culture Studies, University of Jyväskylä, Finland
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Asoke K. Nandi
dDepartment of Electronic and Computer Engineering, Brunel University London, United Kingdom
iCollege of Electronic and Information Engineering, Tongji University, Shanghai, China
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Lauri Parkkonen
bDepartment of Neuroscience and Biomedical Engineering, Aalto University, Finland
jAdvanced Magnetic Imaging Centre, Aalto Neuroimaging, School of Science, Aalto University, Espoo, Finland
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Elvira Brattico
hCenter for Music in the Brain (MIB), Department of Clinical Medicine, Aarhus University & Royal Academy of Music Aarhus / Aalborg (RAMA), Denmark
eCognitive Brain Research Unit, Department of Psychology and Logopedics, University of Helsinki, Finland
jAdvanced Magnetic Imaging Centre, Aalto Neuroimaging, School of Science, Aalto University, Espoo, Finland
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Tapani Ristaniemi
aFaculty of Information Technology, University of Jyväskylä, Finland
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Tiina Parviainen
fDepartment of Psychology, University of Jyväskylä, Finland
gCentre for Interdisciplinary Brain Research, University of Jyväskylä, Finland
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • Abstract
  • Full Text
  • Info/History
  • Metrics
  • Preview PDF
Loading

Abstract

Learning induces structural changes in the brain. Especially repeated, long-term behaviors, such as extensive training of playing a musical instrument, are likely to produce characteristic features to brain structure. However, it is not clear to what extent such structural features can be extracted from magnetic resonance images of the brain. Here we show that it is possible to predict whether a person is a musician or a non-musician based on the thickness of the cerebral cortex measured at 148 brain regions en-compassing the whole cortex. Using a supervised machine-learning technique, we achieved a significant (κ = 0.321, p < 0.001) agreement between the actual and predicted participant groups of 30 musicians and 85 non-musicians. The areas contributing to the prediction were mostly in the frontal, parietal, and occipital lobes of the left hemisphere. Our results suggest that decoding musicianship from magnetic resonance images of brain structure is feasible. Further, the distribution of the areas that were informative in the classification, which mostly, but not entirely, overlapped with earlier findings on areas relevant for musical skills, implies that decoding-based analyses of structural properties of the brain can reveal novel aspects of musical aptitude. In particular, our results highlight differences in visual areas in addition to the already more established differences located in motor networks and networks of higher-order cognitive function.

Competing Interest Statement

The authors have declared no competing interest.

Footnotes

  • ↵1 T. Sipola left the University of Jyväskylä during the preparation of this manuscript, and currently works at JAMK University of Applied Sciences.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission.
Back to top
PreviousNext
Posted December 14, 2021.
Download PDF
Email

Thank you for your interest in spreading the word about bioRxiv.

NOTE: Your email address is requested solely to identify you as the sender of this article.

Enter multiple addresses on separate lines or separate them with commas.
Musicianship can be decoded from magnetic resonance images
(Your Name) has forwarded a page to you from bioRxiv
(Your Name) thought you would like to see this page from the bioRxiv website.
CAPTCHA
This question is for testing whether or not you are a human visitor and to prevent automated spam submissions.
Share
Musicianship can be decoded from magnetic resonance images
Tuomas Puoliväli, Tuomo Sipola, Anja Thiede, Marina Kliuchko, Brigitte Bogert, Petri Toiviainen, Asoke K. Nandi, Lauri Parkkonen, Elvira Brattico, Tapani Ristaniemi, Tiina Parviainen
bioRxiv 2020.07.19.210906; doi: https://doi.org/10.1101/2020.07.19.210906
Digg logo Reddit logo Twitter logo Facebook logo Google logo LinkedIn logo Mendeley logo
Citation Tools
Musicianship can be decoded from magnetic resonance images
Tuomas Puoliväli, Tuomo Sipola, Anja Thiede, Marina Kliuchko, Brigitte Bogert, Petri Toiviainen, Asoke K. Nandi, Lauri Parkkonen, Elvira Brattico, Tapani Ristaniemi, Tiina Parviainen
bioRxiv 2020.07.19.210906; doi: https://doi.org/10.1101/2020.07.19.210906

Citation Manager Formats

  • BibTeX
  • Bookends
  • EasyBib
  • EndNote (tagged)
  • EndNote 8 (xml)
  • Medlars
  • Mendeley
  • Papers
  • RefWorks Tagged
  • Ref Manager
  • RIS
  • Zotero
  • Tweet Widget
  • Facebook Like
  • Google Plus One

Subject Area

  • Neuroscience
Subject Areas
All Articles
  • Animal Behavior and Cognition (4087)
  • Biochemistry (8762)
  • Bioengineering (6479)
  • Bioinformatics (23341)
  • Biophysics (11750)
  • Cancer Biology (9149)
  • Cell Biology (13247)
  • Clinical Trials (138)
  • Developmental Biology (7416)
  • Ecology (11369)
  • Epidemiology (2066)
  • Evolutionary Biology (15087)
  • Genetics (10399)
  • Genomics (14009)
  • Immunology (9121)
  • Microbiology (22040)
  • Molecular Biology (8779)
  • Neuroscience (47367)
  • Paleontology (350)
  • Pathology (1420)
  • Pharmacology and Toxicology (2482)
  • Physiology (3704)
  • Plant Biology (8050)
  • Scientific Communication and Education (1431)
  • Synthetic Biology (2208)
  • Systems Biology (6016)
  • Zoology (1249)