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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.

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.

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

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 and Function, 226(6), 1943–1959. https://doi.org/10.1007/s00429-021-02302-y

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. Advance online publication. 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

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. Advance online publication. 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

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. The European Respiratory Journal, 59(5), Article 2101142. Advance online publication. 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

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

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. Advance online publication. 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. (2017). 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

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

Høgholt, N. F. & Kringelbach, M. L. (2019). Treating pregnancy-related insomnia with music and light: A neuroimaging study. In Frontiers Abstract Book (pp. 62-64). Frontiers Media SA.

Heggli, O. A., Konvalinka, I., Cabral, J., Brattico, E., Kringelbach, M. & Vuust, P. (2021). Transient brain networks underlying interpersonal strategies during synchronized action. Social Cognitive and Affective Neuroscience, 16(1-2), 19-30. Advance online publication. https://doi.org/10.1093/scan/nsaa056

Stewart, L., Walsh, V., Frith, U. & Rothwell, J. (2001). Transcranial magnetic stimulation produces speech arrest but not song arrest. Annals of the New York Academy of Sciences, 930, 433-5.

Omigie, D., Pearce, M. T. & Stewart, L. (2012). Tracking of pitch probabilities in congenital amusia. Neuropsychologia, 50(7), 1483-93. https://doi.org/10.1016/j.neuropsychologia.2012.02.034

Brandl, S., Trusbak Haumann, N., Brattico, E., Vuust, P. & Grube, M. (2019). Tracing rhythmic regularity processing in EEG and MEG.. Poster session presented at Engineering in Medicine & Biology Conference (EMBC) .

Liu, C., Abu-Jamous, B., Brattico, E. & Nandi, A. K. (2017). Towards tunable consensus clustering for studying functional brain connectivity during affective processing. International Journal of Neural Systems, 27(2), 1650042 . Article 1650042. https://doi.org/10.1142/S0129065716500428

Brattico, E., Bogert, B. & Jacobsen, T. (2013). Toward a neural chronometry for the aesthetic experience of music. Frontiers in Psychology, 4, Article 206. https://doi.org/10.3389/fpsyg.2013.00206

Stewart, L., Walsh, V., Frith, U. & Rothwell, J. C. (2001). TMS produces two dissociable types of speech disruption. NeuroImage, 13(3), 472-8. https://doi.org/10.1006/nimg.2000.0701

Kringelbach, M. L. & Rapuano, K. M. (2016). Time in the orbitofrontal cortex. Brain, 139(Pt 4), 1010-3. https://doi.org/10.1093/brain/aww049

Menouti, K., Akiva-Kabiri, L., Banissy, M. J. & Stewart, L. (2015). Timbre-colour synaesthesia: exploring the consistency of associations based on timbre. Cortex, 63, 1-3. https://doi.org/10.1016/j.cortex.2014.08.009

Brattico, E. & Vuust, P. (2017). The urge to judge: Why the judgmental attitude has anything to do with the aesthetic enjoyment of negative emotions. Behavioral and Brain Sciences, 40, e353. Article 353. https://doi.org/10.1017/S0140525X17001613

Petersen, B. & Friis Andersen, A. S. (2018). The Times They Are A Changin’: Two Novel EEG Paradigms for Studying Music Discrimination in Electric Hearing. In Annual report 2017 (pp. 42-47) http://musicinthebrain.au.dk/fileadmin/user_upload/Annual_Report_2017_2.pdf

Stupacher, J., Matthews, T. E., Pando-Naude, V., Foster Vander Elst, O. & Vuust, P. (2022). The sweet spot between predictability and surprise: Musical groove in brain, body, and social interactions. Frontiers in Psychology, 13, Article 906190. https://doi.org/10.3389/fpsyg.2022.906190

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Hella Kastbjerg