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Center for Music in the Brain

Our publications

Perl, Y. S., Zamora-Lopez, G., Montbrió, E., Monge-Asensio, M., Vohryzek, J., Fittipaldi, S., Campo, C. G., Moguilner, S., Ibañez, A., Tagliazucchi, E., Yeo, B. T. T., Kringelbach, M. L. & Deco, G. (2023). The impact of regional heterogeneity in whole-brain dynamics in the presence of oscillations. Network Neuroscience, 7(2), 632-660. https://doi.org/10.1162/netn_a_00299

Deco, G., Sanz Perl, Y., Bocaccio, H., Tagliazucchi, E. & Kringelbach, M. L. (2022). The INSIDEOUT framework provides precise signatures of the balance of intrinsic and extrinsic dynamics in brain states. Communications Biology, 5(1), Article 572. https://doi.org/10.1038/s42003-022-03505-7

Wells, C. A., Salvage-Jones, J. A., Li, X., Hitchens, K., Butcher, S., Murray, R. Z., Beckhouse, A. G., Lo, Y-L-S., Manzanero, S., Cobbold, C., Schroder, K., Ma, B., Orr, S., Stewart, L., Lebus, D., Sobieszczuk, P., Hume, D. A., Stow, J., Blanchard, H. & Ashman, R. B. (2008). The macrophage-inducible C-type lectin, Mincle, is an essential component of the innate immune response to Candida albicans. Journal of Immunology, 180(11), 7404-7413.

Liu, F., Jiang, C., Thompson, W. F., Xu, Y., Yang, Y. & Stewart, L. (2012). The mechanism of speech processing in congenital amusia: evidence from Mandarin speakers. PLOS ONE, 7(2), e30374. https://doi.org/10.1371/journal.pone.0030374

De Filippi, E., Uribe, C., Avila-Varela, D. S., Martínez-Molina, N., Gashaj, V., Pritschet, L., Santander, T., Jacobs, E. G., Kringelbach, M. L., Sanz Perl, Y., Deco, G. & Escrichs, A. (2021). The Menstrual Cycle Modulates Whole-Brain Turbulent Dynamics. Frontiers in Neuroscience, 15, Article 753820. https://doi.org/10.3389/fnins.2021.753820

Näätänen, R., Petersen, B., Torppa, R., Lonka, E. & Vuust, P. (2017). The MMN as a viable and objective marker of auditory development in CI users. Hearing Research, 353(353), 57-75. https://doi.org/10.1016/j.heares.2017.07.007

Deco, G., Van Hartevelt, T. J., Fernandes, H. M., Stevner, A. & Kringelbach, M. L. (2016). The most relevant human brain regions for functional connectivity: Evidence for a dynamical workspace of binding nodes from whole-brain computational modelling. NeuroImage, 146, 197-210. https://doi.org/10.1016/j.neuroimage.2016.10.047

Müllensiefen, D., Gingras, B., Musil, J. & Stewart, L. (2014). The musicality of non-musicians: an index for assessing musical sophistication in the general population. PLOS ONE, 9(2), e89642. https://doi.org/10.1371/journal.pone.0089642

Young, K. S., Parsons, C. E., Stein, A., Vuust, P., Craske, M. G. & Kringelbach, M. L. (2017). The neural basis of responsive caregiving behaviour: Investigating temporal dynamics within the parental brain. Behavioural Brain Research, 325, 105-116. Advance online publication. https://doi.org/10.1016/j.bbr.2016.09.012

Zou, L-Q., van Hartevelt, T. J., Kringelbach, M. L., Cheung, E. F. C. & Chan, R. C. K. (2016). The neural mechanism of hedonic processing and judgment of pleasant odors: An activation likelihood estimation meta-analysis. Neuropsychology, 30(8), 970-979. https://doi.org/10.1037/neu0000292

Duengen, D. & Ravignani, A. (2023). The paradox of learned song in a semi-solitary mammal. Ethology, 129(9), 445-453. https://doi.org/10.1111/eth.13385

Kringelbach, M. L. (2015). The pleasure of food: underlying brain mechanisms of eating and other pleasures. Flavour, 4(20).

Matrone, C. & Brattico, E. (2015). The Power of music on Alzheimer ’s Disease and the need to understand the underlying molecular mechanisms. Journal of Alzheimer's disease and parkinsonism, 5(3). https://doi.org/10.4172/2161-0460.1000196

Stark, E. A., Cabral, J., Riem, M. M. E., Van IJzendoorn, M. H., Stein, A. & Kringelbach, M. L. (2020). The Power of Smiling: The Adult Brain Networks Underlying Learned Infant Emotionality. Cerebral cortex (New York, N.Y. : 1991), 30(4), 2019-2029. https://doi.org/10.1093/cercor/bhz219

Kringelbach, M. L., McIntosh, A. R., Ritter, P., Jirsa, V. K. & Deco, G. (2015). The Rediscovery of Slowness: Exploring the Timing of Cognition. Trends in Cognitive Sciences, 19(10), 616-28. https://doi.org/10.1016/j.tics.2015.07.011

Fjaeldstad, A. W., Konieczny, D. T., Fernandes, H., Gaini, L. M., Vejlø, M. & Sandberg, K. (2022). The relationship between individual significance of olfaction and measured olfactory function. Current Research in Behavioral Sciences, 3, Article 100076. https://doi.org/10.1016/j.crbeha.2022.100076

Williamson, V. J., Cocchini, G. & Stewart, L. (2011). The relationship between pitch and space in congenital amusia. Brain and Cognition, 76(1), 70-6. https://doi.org/10.1016/j.bandc.2011.02.016

Burunat, I., Toiviainen, P., Alluri, V., Bogert, B., Ristaniemi, T., Sams, M. & Brattico, E. (2016). The reliability of continuous brain responses during naturalistic listening to music. NeuroImage, 124(Pt A), 224-31. https://doi.org/10.1016/j.neuroimage.2015.09.005

Costa, M., Bonetti, L., VIgnali, V., Lantieri, C. & Simone, A. (2018). The role of peripheral vision in vertical road sign identification and discrimination. Ergonomics, 61(12), 1619-1634. https://doi.org/10.1080/00140139.2018.1508756

Matthews, T. E., Witek, M. A. G., Heggli, O. A., Penhune, V. B. & Vuust, P. (2019). The sensation of groove is affected by the interaction of rhythmic and harmonic complexity. PLOS ONE, 14(1), Article e0204539. https://doi.org/10.1371/journal.pone.0204539

Yan, X., Joshi, A., Zang, Y., Assunção, F., Fernandes, H. M. & Hummel, T. (2022). The Shape of the Olfactory Bulb Predicts Olfactory Function. Brain sciences, 12(2), Article 128. https://doi.org/10.3390/brainsci12020128

Jakubowski, K., Farrugia, N., Halpern, A. R., Sankarpandi, S. K. & Stewart, L. (2015). The speed of our mental soundtracks: Tracking the tempo of involuntary musical imagery in everyday life. Memory & Cognition. https://doi.org/10.3758/s13421-015-0531-5

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

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

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

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

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

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

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.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Displaying results 651 to 700 out of 708

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Hella Kastbjerg