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Center for Music in the Brain

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Stewart, L. (2002). Zoning in on music and the brain. Trends in Cognitive Sciences, 6(11), 451.

Fairhurst, M. T., Tajadura-Jiménez, A., Keller, P. E. & Deroy, O. (2023). You, me, and us: Maintaining self-other distinction enhances coordination, agency, and affect. iScience, 26(12), Article 108253. https://doi.org/10.1016/j.isci.2023.108253

Møller, C., Højlund, A., Bærentsen, K. B., Hansen, N. C., Skewes, J. & Vuust, P. (2019). You get what you need: individual differences in visually-induced amplification of the auditory mismatch negativity. Poster session presented at Neuroscience Day 2019, Aarhus, Denmark.

Klarlund, M., Brattico, E., Pearce, M., Wu, Y., Vuust, P., Overgaard, M. & Du, Y. (2023). Worlds apart? Testing the cultural distance hypothesis in music perception of Chinese and Western listeners. Cognition, 235, Article 105405. https://doi.org/10.1016/j.cognition.2023.105405

Bonetti, L., Haumann, N. T., Brattico, E., Kliuchko, M., Vuust, P. & Näätänen, R. (2017). Working memory regulates frontal Mismatch Negativity to sound intensity and slide deviants. Abstract from Neuroscience Day - Let's rethink memory 2017, Aarhus, Denmark.

Bonetti, L., Haumann, N. T., Brattico, E., Kliuchko, M., Vuust, P. & Näätänen, R. (2017). Working memory performance predicts the neural discrimination of sound deviants as indexed by frontal mismatch negativity: an MEG study. In Working memory performance predicts the neural discrimination of sound deviants as indexed by frontal mismatch negativity: an MEG study

Ahonen, L., Huotilainen, M. & Brattico, E. (2016). Within- and between-session replicability of cognitive brain processes: An MEG study with an N-back task. Physiology & Behavior, 158, 43-53. https://doi.org/10.1016/j.physbeh.2016.02.006

Ravignani, A., Thompson, B., Lumaca, M. & Grube, M. (2018). Why do durations in musical rhythms conform to small integer ratios? Frontiers in Computational Neuroscience, 12, Article 86.

Leonetti, S., Cimarelli, G., Hersh, T. A. & Ravignani, A. (2024). Why do dogs wag their tails? Biology Letters, 20(1), Article 20230407. https://doi.org/10.1098/rsbl.2023.0407

Escrichs, A., Sanz Perl, Y., Fisher, P. M., Martínez-Molina, N., G-Guzman, E., Frokjaer, V. G., Kringelbach, M. L., Knudsen, G. M. & Deco, G. (2025). Whole-brain turbulent dynamics predict responsiveness to pharmacological treatment in major depressive disorder. Molecular Psychiatry, 30(3), 1069-1079. Article 110074. https://doi.org/10.1038/s41380-024-02690-7

Deco, G., Cruzat, J., Cabral, J., Knudsen, G. M., Carhart-Harris, R. L., Whybrow, P. C., Logothetis, N. K. & Kringelbach, M. L. (2018). Whole-Brain Multimodal Neuroimaging Model Using Serotonin Receptor Maps Explains Non-linear Functional Effects of LSD. Current Biology, 28(19), 3065-+. https://doi.org/10.1016/j.cub.2018.07.083

Mindlin, I., Herzog, R., Belloli, L., Manasova, D., Monge-Asensio, M., Vohryzek, J., Escrichs, A., Alnagger, N., Núñez, P., Gosseries, O., Kringelbach, M. L., Deco, G., Tagliazucchi, E., Naccache, L., Rohaut, B., Sitt, J. D. & Sanz Perl, Y. (2024). Whole brain modelling for simulating pharmacological interventions on patients with disorders of consciousness. Communications Biology, 7(1), Article 1176. https://doi.org/10.1038/s42003-024-06852-9

Patow, G., Martin, I., Sanz Perl, Y., Kringelbach, M. L. & Deco, G. (2024). Whole-brain modelling: an essential tool for understanding brain dynamics. Nature Reviews Methods Primers, 4, Article 53. https://doi.org/10.1038/s43586-024-00336-0

Toiviainen, P., Alluri, V., Burunat, I. & Brattico, E. (2015). Whole-brain functional connectivity during naturalistic music listening: Effect of musical training. Abstract from Ninth Triennial Conference of the European Society for the Cognitive Sciences of Music, Manchester, United Kingdom.

Uribe, C., Escrichs, A., de Filippi, E., Sanz-Perl, Y., Junque, C., Gomez-Gil, E., Kringelbach, M. L., Guillamon, A. & Deco, G. (2022). Whole-brain dynamics differentiate among cisgender and transgender individuals. Human Brain Mapping, 43(13), 4103-4115. https://doi.org/10.1002/hbm.25905

Bonetti, L., Carlomagno, F., Kliuchko, M., Gold, B. P., Palva, S., Trusbak Haumann, N., Tervaniemi, M., Huotilainen, M., Vuust, P. & Brattico, E. (2022). Whole-brain computation of cognitive versus acoustic errors in music: A mismatch negativity study. NeuroImage: Reports, 2(4), Article 100145. https://doi.org/10.1016/j.ynirp.2022.100145

Lumaca, M., Baggio, G. & Vuust, P. (2021). White matter variability in auditory callosal pathways contributes to variation in the cultural transmission of auditory symbolic systems. Brain Structure & Function, 226(6), 1943–1959. https://doi.org/10.1007/s00429-021-02302-y

Mårup, S. H., Kleber, B. A., Møller, C. & Vuust, P. (2024). When direction matters: Neural correlates of interlimb coordination of rhythm and beat. Cortex, 172, 86-108. https://doi.org/10.1016/j.cortex.2023.11.019

Duffy, S. & Pearce, M. (2018). What makes rhythms hard to perform? An investigation using Steve Reich’s Clapping Music. PLOS ONE, 13(10), e0205847. Article e0205847. https://doi.org/10.1371/journal.pone.0205847

Soler-Toscano, F., Galadí, J. A., Escrichs, A., Perl, Y. S., López-González, A., Sitt, J. D., Annen, J., Gosseries, O., Thibaut, A., Panda, R., Esteban, F. J., Laureys, S., Kringelbach, M. L., Langa, J. A. & Deco, G. (2022). What lies underneath: Precise classification of brain states using time-dependent topological structure of dynamics. PLOS Computational Biology, 18(9), Article e1010412. https://doi.org/10.1371/journal.pcbi.1010412

Stewart, L. & Williamon, A. (2008). What are the implications of neuroscience for musical education? Educational Research, 50(2), 177-186. https://doi.org/10.1080/00131880802082666

Lumaca, M., Trusbak Haumann, N., Brattico, E., Grube, M. & Vuust, P. (2019). Weighting of neural prediction error by rhythmic complexity: A predictive coding account using Mismatch Negativity. 1597-1609. Poster session presented at Organization for Human Brain Mapping (OHBM) Annual Meeting 2019, Rome, Italy.

Lumaca, M., Haumann, N. T., Brattico, E., Grube, M. & Vuust, P. (2019). Weighting of neural prediction error by rhythmic complexity: A predictive coding account using Mismatch Negativity. European Journal of Neuroscience, 49(12), 1597-1609. https://doi.org/10.1111/ejn.14329

Kleber, B., Veit, R., Moll, C. V., Gaser, C., Birbaumer, N. & Lotze, M. (2016). Voxel-based morphometry in opera singers: Increased gray-matter volume in right somatosensory and auditory cortices. NeuroImage, 133, 477-83. https://doi.org/10.1016/j.neuroimage.2016.03.045

Ravignani, A. & Herbst, C. T. (2023). Voices in the ocean. Science (New York, N.Y.), 379(6635), 881-882. https://doi.org/10.1126/science.adg5256

Duengen, D., Jadoul, Y. & Ravignani, A. (2024). Vocal usage learning and vocal comprehension learning in harbor seals. BMC Neuroscience, 25, Article 48. https://doi.org/10.1186/s12868-024-00899-4

Goncharova, M., Jadoul, Y., Reichmuth, C., Fitch, W. T. & Ravignani, A. (2024). Vocal tract dynamics shape the formant structure of conditioned vocalizations in a harbor seal. Annals of the New York Academy of Sciences, 1538(1), 107-116. https://doi.org/10.1111/nyas.15189

De Reus, K., Carlson, D., Lowry, A., Gross, S., Garcia, M., Rubio-Garcia, A., Salazar-Casals, A. & Ravignani, A. (2022). Vocal tract allometry in a mammalian vocal learner. Journal of Experimental Biology, 225(8), Article jeb243766. https://doi.org/10.1242/jeb.243766

Rosselló, J., Celma-Miralles, A. & Dias Martins, M. (2020). Visual recursion develops in absence of linguistic recursion. A case-report. In Proceedings of the 13th International Conference Evolution of Language (pp. 371-373). https://brussels.evolang.org/proceedings/evolang13_proceedings.pdf

Møller, C., Højlund, A., Bærentsen, K. B., Hansen, N. C., Skewes, J. C. & Vuust, P. (2018). Visually induced gains in pitch discrimination: Linking audio-visual processing with auditory abilities. Attention, Perception & Psychophysics, 80(4), 999-1010. https://doi.org/10.3758/s13414-017-1481-8

Fink, L. K., Warrenburg, L. A., Howlin, C., Randall, W. M., Hansen, N. C. & Wald-Fuhrmann, M. (2021). Viral tunes: changes in musical behaviours and interest in coronamusic predict socio-emotional coping during COVID-19 lockdown. Humanities and social sciences communications, 8, Article 180. https://doi.org/10.1057/s41599-021-00858-y

Deco, G., Lynn, C. W., Sanz Perl, Y. & Kringelbach, M. L. (2023). Violations of the fluctuation-dissipation theorem reveal distinct nonequilibrium dynamics of brain states. Physical Review E, 108(6), Article 064410. https://doi.org/10.1103/PhysRevE.108.064410

Fjaeldstad, A. W., Stiller-Stut, F., Gleesborg, C., Kringelbach, M. L., Hummel, T. & Fernandes, H. M. (2021). Validation of Olfactory Network Based on Brain Structural Connectivity and Its Association With Olfactory Test Scores. Frontiers in Systems Neuroscience, 15, Article 638053. https://doi.org/10.3389/fnsys.2021.638053

McDonald, C. & Stewart, L. (2008). Uses and functions of music in congenital amusia. Music Perception, 25(4), 345-355. https://doi.org/10.1525/MP.2008.25.4.345

Kaasgaard, M., Rasmussen, D. B., Hjerrild Andreasson, K., Hilberg, O., Løkke, A., Vuust, P. & Bodtger, U. (2022). Use of Singing for Lung Health as an alternative training modality within pulmonary rehabilitation for COPD: a randomised controlled trial. European Respiratory Journal, 59(5), Article 2101142. https://doi.org/10.1183/13993003.01142-2021, https://doi.org/10.1183/13993003.01142-2021

Escrichs, A., Perl, Y. S., Uribe, C., Camara, E., Türker, B., Pyatigorskaya, N., López-González, A., Pallavicini, C., Panda, R., Annen, J., Gosseries, O., Laureys, S., Naccache, L., Sitt, J. D., Laufs, H., Tagliazucchi, E., Kringelbach, M. L. & Deco, G. (2022). Unifying turbulent dynamics framework distinguishes different brain states. Communications Biology, 5(1), Article 638. https://doi.org/10.1038/s42003-022-03576-6

Zamorano, A. M., Kleber, B., Arguissain, F., Vuust, P., Flor, H. & Graven-Nielsen, T. (2024). Un entraînement sensori-moteur intensif améliore les réponses corticales nociceptives chez les personnes en bonne santé. Douleur et Analgesie, 37(4), 228-248. https://doi.org/10.1684/dea.2024.0305

Parsons, C., Stark, E. A., Young, K. S., Stein, A. & Kringelbach, M. L. (2013). Understanding the human parental brain. A critical role of the orbitofrontal cortex. Social Neuroscience, 8(6), 525-43. https://doi.org/10.1080/17470919.2013.842610

Lord, L.-D., Stevner, A. B., Deco, G. & Kringelbach, M. L. (2017). Understanding principles of integration and segregation using whole-brain computational connectomics: implications for neuropsychiatric disorders. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 375(2096), Article 20160283. https://doi.org/10.1098/rsta.2016.0283

Heng, J. G., Zhang, J., Bonetti, L., Lim, W. P. H., Vuust, P., Agres, K. & Chen, S. H. A. (2024). Understanding music and aging through the lens of Bayesian inference. Neuroscience and Biobehavioral Reviews, 163, Article 105768. https://doi.org/10.1016/j.neubiorev.2024.105768

Vohryzek, J., Cabral, J., Vuust, P., Deco, G. & Kringelbach, M. L. (2022). Understanding brain states across spacetime informed by whole-brain modelling. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 380(2227), Article 20210247. https://doi.org/10.1098/rsta.2021.0247

Saenger, V. M., Kahan, J., Foltynie, T., Friston, K., Aziz, T. Z., Green, A. L., van Hartevelt, T. J., Cabral, J., Stevner, A. B. A., Fernandes, H. M., Mancini, L., Thornton, J., Yousry, T., Limousin, P., Zrinzo, L., Hariz, M., Marques, P., Sousa, N., Kringelbach, M. L. & Deco, G. (2017). Uncovering the underlying mechanisms and whole-brain dynamics of deep brain stimulation for Parkinson's disease. Scientific Reports, 7(1), 9882. Article 9882. https://doi.org/10.1038/s41598-017-10003-y

Vila-Vidal, M., Capouskova, K., Atasoy, S., Kringelbach, M. L. & Deco, G. (2020). Uncovering the spatiotemporal scales of common neuro-mental constructs: Comment on “Is temporo-spatial dynamics the ‘common currency’ of brain and mind? In Quest of ‘Spatiotemporal Neuroscience’ ” by Georg Northoff et al. Physics of Life Reviews, 33, 64-66. https://doi.org/10.1016/j.plrev.2019.10.004

Stark, E., Stacey, J., Mandy, W., Kringelbach, M. L. & Happé, F. (2021). 'Uncertainty attunement' has explanatory value in understanding autistic anxiety. Trends in Cognitive Sciences, 25(12), 1011-1012. https://doi.org/10.1016/j.tics.2021.09.006

Cheung, V. K. M., Harrison, P. M. C., Meyer, L., Pearce, M. T., Haynes, J. D. & Koelsch, S. (2019). Uncertainty and Surprise Jointly Predict Musical Pleasure and Amygdala, Hippocampus, and Auditory Cortex Activity. Current Biology, 29(23), 4084-4092.e4. https://doi.org/10.1016/j.cub.2019.09.067

Thaler, H., Skewes, J. C., Gebauer, L., Christensen, P., Prkachin, K. M. & Jegindø Elmholdt, E.-M. (2018). Typical pain experience but underestimation of others’ pain: Emotion perception in self and others in autism spectrum disorder. Autism, 22(6), 751-762. https://doi.org/10.1177/1362361317701269

Deco, G. & Kringelbach, M. L. (2020). Turbulent-like Dynamics in the Human Brain. Cell Reports, 33(10), Article 108471. https://doi.org/10.1016/j.celrep.2020.108471

Deco, G., Perl, Y. S., Jerotic, K., Escrichs, A. & Kringelbach, M. L. (2025). Turbulence as a framework for brain dynamics in health and disease. Neuroscience and Biobehavioral Reviews, 169, Article 105988. https://doi.org/10.1016/j.neubiorev.2024.105988

Smit, E. A., Rathcke, T. & Keller, P. E. (2023). Tuning the Musical Mind: Next Steps in Solving the Puzzle of the Cognitive Transfer of Musical Training to Language and Back. Music & Science, 6. https://doi.org/10.1177/20592043231175251

Farrugia, N., Jakubowski, K., Cusack, R. & Stewart, L. (2015). Tunes stuck in your brain: The frequency and affective evaluation of involuntary musical imagery correlate with cortical structure. Consciousness and Cognition, 35, 66-77. https://doi.org/10.1016/j.concog.2015.04.020

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Hella Kastbjerg