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Center for Music in the Brain

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Martínez-Molina, N., Siponkoski, S. T., Pitkäniemi, A., Moisseinen, N., Kuusela, L., Pekkola, J., Laitinen, S., Särkämö, E. R., Melkas, S., Kleber, B., Schlaug, G., Sihvonen, A. & Särkämö, T. (2022). Neuroanatomical correlates of speech and singing production in chronic post-stroke aphasia. Brain Communications, 4(1), Article fcac001. https://doi.org/10.1093/braincomms/fcac001

Kliuchko, M., Puoliväli, T., Heinonen-Guzejev, M., Tervaniem, M., Toiviainen, P., Sams, M. & Brattico, E. (2018). Neuroanatomical substrate of noise sensitivity. NeuroImage, 167, 309-315. https://doi.org/10.1016/j.neuroimage.2017.11.041

Colafiglio, T., Lombardi, A., Sorino, P., Brattico, E., Lofu, D., Danese, D., Di Sciascio, E., Di Noia, T. & Narducci, F. (2024). NeuroSense: A Novel EEG Dataset Utilizing Low-Cost, Sparse Electrode Devices for Emotion Exploration. IEEE Access, 12, 159296-159315. https://doi.org/10.1109/ACCESS.2024.3487932

Vuust, P., Brattico, E., Glerean, E., Seppänen, M., Pakarinen, S., Tervaniemi, M. & Näätänen, R. (2011). New fast mismatch negativity paradigm for determining the neural prerequisites for musical ability. Cortex, 47(9), 1091-8. https://doi.org/10.1016/j.cortex.2011.04.026

Celma-Miralles, A. & Toro, J. M. (2020). Non-human animals detect the rhythmic structure of a familiar tune. Psychonomic Bulletin and Review, 27(4), 694-699. https://doi.org/10.3758/s13423-020-01739-2

Hansen, N. C., Sadakata, M. & Pearce, M. (2016). Nonlinear Changes in the Rhythm of European Art Music: Quantitative Support for Historical Musicology. Music Perception, 33(4), 414. Article 2. https://doi.org/10.1525/mp.2016.33.4.414

Tewarie, P. K. B., Hindriks, R., Lai, Y. M., Sotiropoulos, S. N., Kringelbach, M. & Deco, G. (2023). Non-reversibility outperforms functional connectivity in characterisation of brain states in MEG data. NeuroImage, 276, Article 120186. https://doi.org/10.1016/j.neuroimage.2023.120186

Fernandes, H. M., van Hartevelt, T. J., Boccard, S. G. J., Owen, S. L. F., Cabral, J., Deco, G., Green, A. L., Fitzgerald, J. J., Aziz, T. Z. & Kringelbach, M. L. (2015). Novel fingerprinting method characterises the necessary and sufficient structural connectivity from deep brain stimulation electrodes for a successful outcome. New Journal of Physics, 17(1), 015001. http://stacks.iop.org/1367-2630/17/i=1/a=015001

Deco, G., Tagliazucchi, E., Laufs, H., Sanjuán, A. & Kringelbach, M. L. (2017). Novel Intrinsic Ignition Method Measuring Local-Global Integration Characterizes Wakefulness and Deep Sleep. eNeuro , 4(5), Article e0106-17.2017. https://doi.org/10.1523/ENEURO.0106-17.2017

Vuust, P., Dietz, M., Witek, M. & Kringelbach, M. L. (2018). Now you hear it: a predictive coding model for understanding rhythmic incongruity. Annals of the New York Academy of Sciences, 1423(1), 19-29. https://doi.org/10.1111/nyas.13622

Nijhuis, P., Keller, P. E., Nozaradan, S. & Varlet, M. (2022). Null effects of musical groove on cortico-muscular coherence during isometric contraction. NeuroImage: Reports, 2(1), Article 100075. https://doi.org/10.1016/j.ynirp.2021.100075

Hansen, N. C. (2015). Nye perspektiver på studiet af musikalsk ekspertise. Dansk Musikforskning Online, Special Issue 2015: Music and Brain Research, 69-102. http://danishmusicologyonline.dk/arkiv/arkiv_dmo/dmo_saernummer_2015/dmo_saernummer_2015_musik_hjerneforskning_04.pdf

Fjaeldstad, A., Kjaergaard, T., van Hartevelt, T. J., Moeller, A., Kringelbach, M. L. & Ovesen, T. (2015). Olfactory screening: Validation of Sniffin' Sticks in Denmark. Clinical Otolaryngology, 40(6), 545-550. https://doi.org/10.1111/coa.12405

Kringelbach, M. L., Stark, E. A., Alexander, C., Bornstein, M. H. & Stein, A. (2016). On Cuteness: Unlocking the Parental Brain and Beyond. Trends in Cognitive Sciences, 20(7), 545-58. https://doi.org/10.1016/j.tics.2016.05.003

Deco, G., Sanz Perl, Y., Ponce-Alvarez, A., Tagliazucchi, E., Whybrow, P. C., Fuster, J. & Kringelbach, M. L. (2023). One ring to rule them all: The unifying role of prefrontal cortex in steering task-related brain dynamics. Progress in Neurobiology, 227, Article 102468. https://doi.org/10.1016/j.pneurobio.2023.102468

Hoegholt, N. F., Buus, S., Fernandes, H. M., Sui, J., Vuust, P. & Kringelbach, M. L. (2023). On screen experiment showed that becoming a parent for the first time shifted people's priorities from themselves to their infant at 1 year of age. Acta Paediatrica, International Journal of Paediatrics, 112(1), 85-92. https://doi.org/10.1111/apa.16561

Criscuolo, A., Bonetti, L., Sarkamo, T., Kliuchko, M. & Brattico, E. (2019). On the Association Between Musical Training, Intelligence and Executive Functions in Adulthood. Frontiers in Psychology, 10(JULY), Article 1704. https://doi.org/10.3389/fpsyg.2019.01704

Bihari, A., Nárai, Á., Kleber, B., Zsuga, J., Hermann, P. & Vidnyánszky, Z. (2024). Operatic voices engage the default mode network in professional opera singers. Scientific Reports, 14(1), Article 21313. https://doi.org/10.1038/s41598-024-71458-4

Shestakova, A., Brattico, E., Soloviev, A., Klucharev, U. & Huotilainen, M. (2004). Orderly cortical representation of vowel categories presented by multiple exemplars. Brain Research, 21(3), 342-350. https://doi.org/10.1016/j.cogbrainres.2004.06.011

Kleber, B., Birbaumer, Veit, Trevorrow & Lotze (2007). Overt and imagined singing of an Italian aria. NeuroImage, 36(3), 889-900.

Luppi, A. I., Rosas, F. E., Noonan, M. A. P., Mediano, P. A. M., Kringelbach, M. L., Carhart-Harris, R. L., Stamatakis, E. A., Vernon, A. C. & Turkheimer, F. E. (2024). Oxygen and the Spark of Human Brain Evolution: Complex Interactions of Metabolism and Cortical Expansion across Development and Evolution. Neuroscientist, 30(2), 173-198. https://doi.org/10.1177/10738584221138032

Hansen, N. C. & Keller, P. E. (2021). Oxytocin as an allostatic agent in the social bonding effects of music. Behavioral and Brain Sciences, 44, 61-64. Article 75. https://doi.org/10.1017/S0140525X20001235

Gebauer, L., Witek, M., Hansen, N. C., Thomas, J., Konvalinka, I. & Vuust, P. (2016). Oxytocin improves synchronisation in leader-follower interaction. Scientific Reports, 6, Article 38416. https://doi.org/10.1038/srep38416

Matthews, T. E., Witek, M. A. G., Thibodeau, J. L. N., Vuust, P. & Penhune, V. B. (2022). Perceived Motor Synchrony With the Beat is More Strongly Related to Groove Than Measured Synchrony. Music Perception, 39(5), 423-442. https://doi.org/10.1525/MP.2022.39.5.423

Williamson, V. J., Liu, F., Peryer, G., Grierson, M. & Stewart, L. (2012). Perception and action de-coupling in congenital amusia: sensitivity to task demands. Neuropsychologia, 50(1), 172-80. https://doi.org/10.1016/j.neuropsychologia.2011.11.015

Marin, M. M., Gingras, B. & Stewart, L. (2012). Perception of musical timbre in congenital amusia: categorization, discrimination and short-term memory. Neuropsychologia, 50(3), 367-78. https://doi.org/10.1016/j.neuropsychologia.2011.12.006

Lumaca, M., Dietz, M., Hansen, N. C., Quiroga Martinez, D. R. & Vuust, P. (2021). Perceptual learning of tone patterns changes the effective connectivity between Heschl's gyrus and planum temporale. Human Brain Mapping, 42(4), 941-952. https://doi.org/10.1002/hbm.25269

Deco, G., Cabral, J., Saenger, V. M., Boly, M., Tagliazucchi, E., Laufs, H., Van Someren, E., Jobst, B., Stevner, A. & Kringelbach, M. L. (2018). Perturbation of whole-brain dynamics in silico reveals mechanistic differences between brain states. NeuroImage, 169, 46-56. https://doi.org/10.1016/j.neuroimage.2017.12.009

Perl, Y. S., Pallavicini, C., Ipiña, I. P., Demertzi, A., Bonhomme, V., Martial, C., Panda, R., Annen, J., Ibañez, A., Kringelbach, M., Deco, G., Laufs, H., Sitt, J., Laureys, S. & Tagliazucchi, E. (2021). Perturbations in dynamical models of whole-brain activity dissociate between the level and stability of consciousness. PLOS Computational Biology, 17(7), Article e1009139. https://doi.org/10.1371/journal.pcbi.1009139

Ahrends, C., Bravo, F., Kringelbach, M. L., Vuust, P. & Rohrmeier, M. A. (2019). Pessimistic outcome expectancy does not explain ambiguity aversion in decision-making under uncertainty. Scientific Reports, 9, Article 12177. https://doi.org/10.1038/s41598-019-48707-y

Kaasgaard, M., Rasmussen, D. B., Løkke, A., Vuust, P., Hilberg, O. & Bodtger, U. (2022). Physiological changes related to 10 weeks of singing for lung health in patients with COPD. B M J Open Respiratory Research, 9(1), Article e001206. https://doi.org/10.1136/bmjresp-2022-001206

Gold, B. P., Frank, M. J., Bogert, B. & Brattico, E. (2013). Pleasurable music affects reinforcement learning according to the listener. Frontiers in Psychology, 4, Article 541. https://doi.org/10.3389/fpsyg.2013.00541

Berridge, K. C. & Kringelbach, M. L. (2015). Pleasure Systems in the Brain. Neuron, 86(3), 646-664. https://doi.org/10.1016/j.neuron.2015.02.018

Stark, E. A., Parsons, C. E., Hartevelt, T. J. V., Charquero-Ballester, M., McManners, H., Ehlers, A., Stein, A. & Kringelbach, M. L. (2015). Post-traumatic stress influences the brain even in the absence of symptoms: A systematic, quantitative meta-analysis of neuroimaging studies. Neuroscience & Biobehavioral Reviews, 56, 207-221. https://doi.org/10.1016/j.neubiorev.2015.07.007

Harrison, P. M. C., Bianco, R., Chait, M. & Pearce, M. T. (2020). PPM-Decay: A computational model of auditory prediction with memory decay. PLOS Computational Biology, 16(11), Article e1008304. https://doi.org/10.1371/journal.pcbi.1008304

Seppanen, M., Brattico, E. & Tervaniemi, M. (2007). Practice strategies of musicians modulate neural processing and the learning of sound-patterns. Neurobiology of Learning and Memory, 87(2), 236-247. https://doi.org/10.1016/j.nlm.2006.08.011

Takegata, R., Brattico, E., Tervaniemi, M., Varyagina, O., Naatanen, R. & Winkler, M. (2005). Preattentive representation of feature conjunctions for concurrent spatially distributed auditory objects. Brain Research, 25(1), 169-179. https://doi.org/10.1016/j.cogbrainres.2005.05.006

Gold, B. P., Pearce, M. T., Mas-Herrero, E., Dagher, A. & Zatorre, R. J. (2019). Predictability and Uncertainty in the Pleasure of Music: A Reward for Learning? The Journal of neuroscience : the official journal of the Society for Neuroscience, 39(47), 9397-9409. https://doi.org/10.1523/JNEUROSCI.0428-19.2019

Mencke, I., Quiroga-Martinez, D. R., Omigie, D., Michalareas, G., Schwarzacher, F., Haumann, N. T., Vuust, P. & Brattico, E. (2021). Prediction under uncertainty: Dissociating sensory from cognitive expectations in highly uncertain musical contexts. Brain Research, 1773, Article 147664. https://doi.org/10.1016/j.brainres.2021.147664

Gebauer, L., Kringelbach, M. L. & Vuust, P. (2015). Predictive coding links perception, action, and learning to emotions in music. Comment on "The quartet theory of human emotions: An integrative and neurofunctional model" by S. Koelsch et al. Physics of Life Reviews, 13, 50-52. https://doi.org/10.1016/j.plrev.2015.04.023

Hansen, N. C., Kragness, H. E., Vuust, P., Trainor, L. & Pearce, M. T. (2021). Predictive Uncertainty Underlies Auditory Boundary Perception. Psychological Science, 32(9), 1416-1425. https://doi.org/10.1177/0956797621997349

Nwebube, C., Glover, V. & Stewart, L. (2017). Prenatal listening to songs composed for pregnancy and symptoms of anxiety and depression: A pilot study. BMC Complementary and Alternative Medicine, 17(1), Article 256. https://doi.org/10.1186/s12906-017-1759-3

Omigie, D. & Stewart, L. (2011). Preserved statistical learning of tonal and linguistic material in congenital amusia. Frontiers in Psychology, 2, 109. https://doi.org/10.3389/fpsyg.2011.00109

Stewart, L. (2001). Priming: a tool for imaging. Trends in Cognitive Sciences, 5(12), 511.

Kaplan, T., Jamone, L. & Pearce, M. (2023). Probabilistic modelling of microtiming perception. Cognition, 239, Article 105532. https://doi.org/10.1016/j.cognition.2023.105532

Jakubowski, K., Farrugia, N. & Stewart, L. (2016). Probing imagined tempo for music: Effects of motor engagement and musical experience. Psychology of Music, 44(6), 1274-1288. https://doi.org/10.1177/0305735615625791

Stewart, L. (2002). Probing perceptual asynchrony. Trends in Cognitive Sciences, 6(4), 153.

Jadoul, Y., Duengen, D. & Ravignani, A. (2023). PyGellermann: a Python tool to generate pseudorandom series for human and non-human animal behavioural experiments. BMC Research Notes, 16, Article 135. https://doi.org/10.1186/s13104-023-06396-x

Bonetti, L., Brattico, E., Carlomagno, F., Donati, G., Cabral, J., Haumann, N. T., Deco, G., Vuust, P. & Kringelbach, M. L. (2021). Rapid encoding of musical tones discovered in whole-brain connectivity. NeuroImage, 245, Article 118735. https://doi.org/10.1016/j.neuroimage.2021.118735

Deco, G., Sanz Perl, Y., Vuust, P., Tagliazucchi, E., Kennedy, H. & Kringelbach, M. L. (2021). Rare long-range cortical connections enhance human information processing. Current Biology, 31(20), 4436-4448.e5. https://doi.org/10.1016/j.cub.2021.07.064

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Hella Kastbjerg