Modifications in the Topological Structure of EEG Functional Connectivity Networks during Listening Tonal and Atonal Concert Music in Musicians and Non-Musicians

  1. González, Almudena
  2. Pereda, Ernesto
  3. Gamundí, Antoni
  4. Santapau, Manuel
  5. González, Julián J.
  1. 1 Universidad de La Laguna
    info

    Universidad de La Laguna

    San Cristobal de La Laguna, España

    ROR https://ror.org/01r9z8p25

Revista:
Brain Sciences

ISSN: 2076-3425

Año de publicación: 2021

Volumen: 11

Número: 2

Páginas: 159

Tipo: Artículo

DOI: 10.3390/BRAINSCI11020159 GOOGLE SCHOLAR lock_openAcceso abierto editor

Otras publicaciones en: Brain Sciences

Objetivos de desarrollo sostenible

Resumen

The present work aims to demonstrate the hypothesis that atonal music modifies the topological structure of electroencephalographic (EEG) connectivity networks in relation to tonal music. To this, EEG monopolar records were taken in musicians and non-musicians while listening to tonal, atonal, and pink noise sound excerpts. EEG functional connectivities (FC) among channels assessed by a phase synchronization index previously thresholded using surrogate data test were computed. Sound effects, on the topological structure of graph-based networks assembled with the EEG-FCs at different frequency-bands, were analyzed throughout graph metric and network-based statistic (NBS). Local and global efficiency normalized (vs. random-network) measurements (NLE|NGE) assessing network information exchanges were able to discriminate both music styles irrespective of groups and frequency-bands. During tonal audition, NLE and NGE values in the beta-band network get close to that of a small-world network, while during atonal and even more during noise its structure moved away from small-world. These effects were attributed to the different timbre characteristics (sounds spectral centroid and entropy) and different musical structure. Results from networks topographic maps for strength and NLE of the nodes, and for FC subnets obtained from the NBS, allowed discriminating the musical styles and verifying the different strength, NLE, and FC of musicians compared to non-musicians.

Referencias bibliográficas

  • 10.1523/JNEUROSCI.0411-11.2011
  • 10.1098/rstb.2013.0393
  • 10.1037/a0034695
  • 10.1093/acprof:oso/9780195148367.001.0001
  • 10.1038/nrn3666
  • 10.3389/fnhum.2015.00436
  • 10.1016/j.neuropsychologia.2017.01.004
  • 10.1016/j.ijpsycho.2011.12.010
  • 10.1111/j.1756-8765.2012.01214.x
  • 10.1037/a0031126
  • 10.1016/j.tics.2014.12.001
  • 10.3758/s13414-015-1042-y
  • 10.1177/0305735607070381
  • 10.1038/srep00694
  • 10.1371/journal.pone.0027241
  • Krumhansl, (2010), 10.1007/978-1-4419-6114-3_3
  • 10.1080/09658211.2013.770871
  • Imberty, (1969)
  • 10.1016/j.tics.2010.01.002
  • 10.1111/nyas.12677
  • Koelsch, (2012)
  • 10.1016/j.ijpsycho.2007.03.004
  • 10.1523/JNEUROSCI.21-16-06329.2001
  • 10.1038/35003577
  • 10.1098/rspb.2001.1802
  • 10.1016/j.ijpsycho.2005.04.007
  • 10.1007/s11055-005-0170-6
  • 10.1016/j.ijpsycho.2007.10.002
  • 10.1111/j.1749-6632.2001.tb05764.x
  • 10.1080/02699930126048
  • 10.1016/S0028-3932(02)00107-0
  • 10.1111/1467-9450.00228
  • 10.1038/nrn3241
  • 10.1016/j.neulet.2014.05.003
  • 10.1016/j.chb.2016.01.005
  • 10.3389/fnhum.2017.00384
  • 10.1371/journal.pone.0134211
  • 10.1016/j.neulet.2017.03.022
  • 10.2307/40285613
  • 10.1016/j.brainres.2012.09.014
  • 10.1016/j.neuroimage.2015.03.057
  • 10.1016/j.neuroscience.2010.11.039
  • 10.1016/j.clinph.2016.06.023
  • 10.1016/j.cnp.2017.09.003
  • 10.1371/journal.pone.0188629
  • 10.1002/hbm.23343
  • 10.7551/mitpress/6575.001.0001
  • 10.2307/3680135
  • Schubert, (2006), Acustica, 92, pp. 820
  • 10.1063/1.5145005
  • 10.2307/1513213
  • 10.1088/0967-3334/22/4/305
  • 10.1088/0967-3334/26/3/003
  • 10.1007/s00422-011-0456-4
  • 10.1371/journal.pone.0201660
  • 10.1209/epl/i2006-10147-0
  • 10.1098/rsta.2007.2109
  • 10.1063/1.3072784
  • 10.1016/S0167-2789(00)00190-1
  • 10.1093/cercor/bhk002
  • 10.1103/PhysRevE.60.4970
  • 10.1103/PhysRevLett.87.198701
  • 10.1371/journal.pcbi.0030017
  • 10.3389/fpsyt.2018.00046
  • 10.1126/science.1065103
  • 10.3389/fnins.2016.00308
  • 10.3389/neuro.11.037.2009
  • 10.1371/journal.pone.0055347
  • 10.1016/j.neuroimage.2009.10.003
  • 10.1098/rstb.2013.0521
  • 10.1016/j.neuroimage.2010.06.041
  • Mult_Comp_Perm_T1. MATLAB Central File Exchangehttps://www.mathworks.com/matlabcentral/fileexchange/29782-mult_comp_perm_t1-data-n_perm-tail-alpha_level-mu-reports-seed_state
  • Mult_Comp_Perm_T2. MATLAB Central File Exchangehttps://www.mathworks.com/matlabcentral/fileexchange/29782-mult_comp_perm_t2-data-n_perm-tail-alpha_level-mu-reports-seed_state
  • f_PlotEEG_BrainNetwork. MATLAB Central File Exchangehttps://www.mathworks.com/matlabcentral/fileexchange/57372-easy-plot-eeg-brain-network-matlab
  • 10.1016/j.neuroscience.2013.06.021
  • 10.1093/scan/nsx038
  • 10.1002/hbm.20104
  • 10.1016/j.ijpsycho.2011.09.007
  • 10.1152/jn.2001.85.5.1969
  • 10.1126/science.1117256
  • 10.1016/0013-4694(94)90148-1
  • 10.1016/S0167-8760(00)00172-0
  • 10.1016/j.heliyon.2019.e01315
  • 10.1017/S1355617711000695
  • 10.1093/cercor/10.3.295
  • 10.1371/journal.pone.0036568
  • 10.1155/2014/180138
  • 10.1002/hbm.23682
  • 10.1093/cercor/bhw120