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Center for Music in the Brain

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Kringelbach, M. L., Cruzat, J., Cabral, J., Knudsen, G. M., Carhart-Harris, R., Whybrow, P. C., Logothetis, N. K. & Deco, G. (2020). Dynamic coupling of whole-brain neuronal and neurotransmitter systems. Proceedings of the National Academy of Sciences of the United States of America, 117(17), 9566-9576. https://doi.org/10.1073/pnas.1921475117

Lumaca, M., Vuust, P. & Dietz, M. (2019). Dynamic causal modelling of effective connectivity during music perception. Poster session presented at Organization for Human Brain Mapping (OHBM) Annual Meeting 2019, Rome, Italy.

Niranjan, D., Toiviainen, P., Brattico, E. & Alluri, V. (2019). Dynamic Functional Connectivity in the Musical Brain. In P. Liang, V. Goel & C. Shan (Eds.), Brain Informatics : 12th International Conference, BI 2019 Haikou, China, December 13–15, 2019 Proceedings (pp. 82-91). Springer. Lecture Notes in Computer Science Vol. 11976 LNAI https://doi.org/10.1007/978-3-030-37078-7_9

Parsons, C. E., Young, K. S., Petersen, M. V., Jegindoe Elmholdt, E-M., Vuust, P., Stein, A. & Kringelbach, M. L. (2017). Duration of motherhood has incremental effects on mothers' neural processing of infant vocal cues: a neuroimaging study of women. Scientific Reports, 7(1), [1727]. https://doi.org/10.1038/s41598-017-01776-3

Costa, M., Bonetti, L., VIgnali, V., Bichicchi, A., Lantieri, C. & Simone, A. (2019). Driver's visual attention to different categories of roadside advertising signs. Applied Ergonomics, 78, 127-136. https://doi.org/10.1016/j.apergo.2019.03.001

Starcke, K., von Georgi, R., Tiihonen, T. M., Laczika, K. F. & Reuter, C. (2019). Don't drink and chill: Effects of alcohol on subjective and physiological reactions during music listening and their relationships with personality and listening habits. International Journal of Psychophysiology, 142, 25-32. https://doi.org/10.1016/j.ijpsycho.2019.06.001

Stewart, L. (2008). Do musicians have different brains? Clin Ther, 8(3), 304-8.

Heggli, O. A., Stupacher, J. & Vuust, P. (2021). Diurnal fluctuations in musical preference. Royal Society Open Science, 8(11). https://doi.org/10.1098/rsos.210885

Daffertshofer, A., Ton, R., Kringelbach, M. L., Woolrich, M. & Deco, G. (2018). Distinct criticality of phase and amplitude dynamics in the resting brain. NeuroImage, 180, 442-447. https://doi.org/10.1016/j.neuroimage.2018.03.002

Harrison, P. & Pearce, M. (2018). Dissociating sensory and cognitive theories of harmony perception through computational modeling. In Proceedings of the 15th International Conference on Music Perception & Cognition http://webprojects.eecs.qmul.ac.uk/marcusp/papers/HarrisonPearce2018a.pdf

Ross, S. & Hansen, N. C. (2016). Dissociating Prediction Failure: Considerations from Music Perception. The Journal of Neuroscience, 36(11), 3103. https://doi.org/10.1523/JNEUROSCI.0053-16.2016

Bruzzone, S. E. P., Lumaca, M., Brattico, E., Vuust, P., Kringelbach, M. L. & Bonetti, L. (2022). Dissociated brain functional connectivity of fast versus slow frequencies underlying individual differences in fluid intelligence: a DTI and MEG study. Scientific Reports, 12(1), [4746]. https://doi.org/10.1038/s41598-022-08521-5

Jakubowski, K., Finkel, S., Stewart, L. & Müllensiefen, D. (2017). Dissecting an earworm: Melodic features and song popularity predict involuntary musical imagery. Psychology of Aesthetics, Creativity, and the Arts, 11(2), 122-135. https://doi.org/10.1037/aca0000090

Fernandes, H. M., Cabral, J., van Hartevelt, T. J., Lord, L. D., Gleesborg, C., Møller, A., Deco, G., Whybrow, P. C., Petrovic, P., James, A. C. & Kringelbach, M. L. (2019). Disrupted brain structural connectivity in Pediatric Bipolar Disorder with psychosis. Scientific Reports, 9(1), [13638]. https://doi.org/10.1038/s41598-019-50093-4

Stewart, L., Von Kriegstein, K., Bella, S. D., Warren, J. D. & Griffiths, T. D. (2008). Disorders of Musical Cognition. In The Oxford Handbook of Music Psychology Oxford University Press. https://doi.org/10.1093/oxfordhb/9780199298457.013.0017

Stevner, A. B. A., Vidaurre, D., Cabral, J., Rapuano, K., Nielsen, S. F. V., Tagliazucchi, E., Laufs, H., Vuust, P., Deco, G., Woolrich, M. W., Van Someren, E. & Kringelbach, M. L. (2019). Discovery of key whole-brain transitions and dynamics during human wakefulness and non-REM sleep. Nature Communications, 10(1), [1035]. https://doi.org/10.1038/s41467-019-08934-3

Tsatsishvili, V., Cong, F., Puoliväli, T., Alluri, V., Toiviainen, P., Nandi, A. K., Brattico, E. & Ristaniemi, T. (2013). Dimension reduction for individual ICA to decompose FMRI during real-world experiences: Principal component analysis vs. canonical correlation analysis. In ESANN 2013 proceedings, 21st European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (pp. 137-142)

Sipola, T., Cong, F., Ristaniemi, T., Alluri, V., Toiviainen, P., Brattico, E. & Nandi, A. K. (2013). Diffusion map for clustering fMRI spatial maps extracted by Indipendent Component Analysis. IEEE International Workshop on Machine Learning for Signal Processing. https://doi.org/10.1109/MLSP.2013.6661923

Moorthigari, V., Carlson, E., Toiviainen, P., Brattico, E. & Alluri, V. (2020). Differential Effects of Trait Empathy on Functional Network Centrality. In M. Mahmud, S. Vassanelli, M. S. Kaiser & N. Zhong (Eds.), Brain informatics (pp. 107-117). Springer. Lecture Notes in Computer Science Vol. 12241 https://doi.org/10.1007/978-3-030-59277-6_10

Garrido, L., Eisner, F., McGettigan, C., Stewart, L., Sauter, D., Hanley, J. R., Schweinberger, S. R., Warren, J. D. & Duchaine, B. (2009). Developmental phonagnosia: a selective deficit of vocal identity recognition. Neuropsychologia, 47(1), 123-31. https://doi.org/10.1016/j.neuropsychologia.2008.08.003

O'Kelly, J. & Bodak, R. (2016). Development of the music therapy assessment Tool for advanced Huntington’s disease: A pilot validation study. Journal of Music Therapy, 53(3), 232-256. https://doi.org/10.1093/jmt/thw006

Kujala, T. & Brattico, E. (2009). Detrimental noise effects on brain's speech functions. Biological Psychology, 81(3), 135-143. https://doi.org/10.1016/j.biopsycho.2009.03.010

Chen, J. L., Kumar, S., Williamson, V. J., Scholz, J., Griffiths, T. D. & Stewart, L. (2015). Detection of the arcuate fasciculus in congenital amusia depends on the tractography algorithm. Frontiers in Psychology, 6, 9. https://doi.org/10.3389/fpsyg.2015.00009

Young, K., Parsons, C., Stein, A. & Kringelbach, M. L. (2015). Depression alters movement in response to salient emotional sounds and in social interactions. Poster session presented at Neuroscience 2015, Chicago, IL, United States.

Quiroga Martinez, D. R., Hansen, N. C., Højlund, A., Pearce, M., Brattico, E. & Vuust, P. (2020). Decomposing neural responses to melodic surprise in musicians and non-musicians: evidence for a hierarchy of predictions in the auditory system. NeuroImage, 215, [116816]. https://doi.org/10.1016/j.neuroimage.2020.116816

Rué-Queralt, J., Stevner, A., Tagliazucchi, E., Laufs, H., Kringelbach, M. L., Deco, G. & Atasoy, S. (2021). Decoding brain states on the intrinsic manifold of human brain dynamics across wakefulness and sleep. Communications Biology, 4(1), [854]. https://doi.org/10.1038/s42003-021-02369-7

Saari, P., Burunat, I., Brattico, E. & Toiviainen, P. (2018). Decoding Musical Training from Dynamic Processing of Musical Features in the Brain. Scientific Reports, 8(1), [708]. https://doi.org/10.1038/s41598-018-19177-5

Abu-Jamous, B., Liu, C., Roberts, D. J., Brattico, E. & Nandi, A. (2017). Data-driven analysis of collections of big datasets by the Bi-CoPaM method yields field-specific novel insights. In Frontiers in Electronic Technologies Springer.

Perl, Y. S., Pallavicini, C., Ipiña, I. P., Kringelbach, M., Deco, G., Laufs, H. & Tagliazucchi, E. (2020). Data augmentation based on dynamical systems for the classification of brain states. Chaos, Solitons and Fractals, 139, [110069]. https://doi.org/10.1016/j.chaos.2020.110069

Ahrends, C., Stevner, A., Pervaiz, U., Kringelbach, M. L., Vuust, P., Woolrich, M. W. & Vidaurre, D. (2022). Data and model considerations for estimating time-varying functional connectivity in fMRI. NeuroImage, 252, [119026]. https://doi.org/10.1016/j.neuroimage.2022.119026

Niklassen, A. S., Ovesen, T., Fernandes, H. & Fjaeldstad, A. (2019). Danish Validation of Sniffin’ Sticks Olfactory Test for Threshold, Discrimination, and Identification. Poster session presented at Dansk Selskab for Oto-rhino-laryngologi, Hoved-Halskirurgi: DSOHH årsmøde, Nyborg, Denmark.

Stupacher, J., Witek, M. A. G., Vuoskoski, J. K. & Vuust, P. (2020). Cultural familiarity and individual musical taste differently affect social bonding when moving to music. Scientific Reports, 10, [10015]. https://doi.org/10.1038/s41598-020-66529-1

Lumaca, M. & Baggio, G. (2017). Cultural Transmission and Evolution of Melodic Structures in Multi-generational Signaling Games. Artificial Life, 23(3), 406-423.

Morrison, S., Demorest, S. & Pearce, M. (2019). Cultural Distance: A Computational Approach to Exploring Cultural Influences on Music Cognition. In M. Thaut & D. Hodges (Eds.), Oxford Handbook of Music and the Brain Oxford University Press. https://doi.org/10.1093/oxfordhb/9780198804123.013.3

Brattico, E. & Tervaniemi, M. (2010). Creativity in musicians: Evidence from cognitive neuroscience. In Studies in Systematic Musicology: Festschrift in Honor of Professor Albrecht Schneider (pp. 233-244). Verlag Peter Lang.

Overath, T., Kumar, S., Stewart, L., von Kriegstein, K., Cusack, R., Rees, A. & Griffiths, T. D. (2010). Cortical mechanisms for the segregation and representation of acoustic textures. The Journal of neuroscience : the official journal of the Society for Neuroscience, 30(6), 2070-6. https://doi.org/10.1523/JNEUROSCI.5378-09.2010

Brattico, E., Tervaniemi, M., Valimaki, V., Van Zuijen, T. & Peretz, T. (2003). Cortical correlates of acquired deafness to dissonance. Annals of the New York Academy of Sciences, 158-160. https://doi.org/10.1196/annals.1284.019

Mårup, S. H., Møller, C. & Vuust, P. (2022). Coordination of voice, hands and feet in rhythm and beat performance. Scientific Reports, 12, [8046 ]. https://doi.org/10.1038/s41598-022-11783-8

Brattico, E., Naatanen, R. & Tervaniemi, M. (2001). Context effects on pitch perception in musicians and nonmusicians: Evidence from event-related-potential recordings. Music Perception, 19(2), 199-222.

Tiihonen, M., Brattico, E., Maksimainen, J., Wikgren, J. & Saarikallio, S. (2017). Constituents of music and visual-art related pleasure - A critical integrative literature review. Frontiers in Psychology, 8(JUL), [1218]. https://doi.org/10.3389/fpsyg.2017.01218

Atasoy, S., Roseman, L., Kaelen, M., Kringelbach, M. L., Deco, G. & Carhart-Harris, R. L. (2017). Connectome-harmonic decomposition of human brain activity reveals dynamical repertoire re-organization under LSD. Scientific Reports, 7(1), [17661]. https://doi.org/10.1038/s41598-017-17546-0

Alluri, V., Toiviainen, P., Burunat, I., Kliuchko, M., Vuust, P. & Brattico, E. (2017). Connectivity patterns during music listening: Evidence for action-based processing in musicians. Human Brain Mapping, 38(6), 2955. https://doi.org/10.1002/hbm.23565

Williamson, V. J. & Stewart, L. (2013). Congenital amusia. Handbook of Clinical Neurology, 111, 237-9. https://doi.org/10.1016/B978-0-444-52891-9.00024-5

Stewart, L. (2006). Congenital amusia. Current Biology, 16(21), R904-6. https://doi.org/10.1016/j.cub.2006.09.054

Stewart, L. & Walsh, V. (2002). Congenital amusia: all the songs sound the same. Current Biology, 12(12), R420-1.

Vuust, P., Liikala, L., Näätänen, R., Brattico, P. & Brattico, E. (2016). Comprehensive auditory discrimination profiles recorded with a fast parametric musical multi-feature mismatch negativity paradigm. Clinical Neurophysiology, 4(127), 2065-2077. https://doi.org/10.1016/j.clinph.2015.11.009

Haumann, N. T., Parkkonen, L., Kliuchko, M., Vuust, P. & Brattico, E. (2016). Comparing the Performance of Popular MEG/EEG Artifact Correction Methods in an Evoked-Response Study. Computational Intelligence and Neuroscience, 2016, 1-10. https://doi.org/10.1155/2016/7489108

Atasoy, S., Vohryzek, J., Deco, G., Carhart-Harris, R. L. & Kringelbach, M. L. (2018). Common neural signatures of psychedelics: Frequency-specific energy changes and repertoire expansion revealed using connectome-harmonic decomposition. In T. Calvey (Ed.), PSYCHEDELIC NEUROSCIENCE (pp. 97-120). Elsevier. Progress in Brain Research Vol. 242 https://doi.org/10.1016/bs.pbr.2018.08.009

Hansen, N. C., Dietz, M. & Vuust, P. (2017). Commentary: Predictions and the brain: how musical sounds become rewarding. Frontiers in Human Neuroscience, 11, [168]. https://doi.org/10.3389/fnhum.2017.00168

Brattico, E. (2017). Commentary on" Aesthetic Experience Explained by the Affect-Space Framework" by E. Schubert, AC North & DJ Hargreaves. Empirical Musicology Reviews, 11(3-4), 350-353. https://doi.org/10.18061/emr.v11i3-4.5572

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Revised 26.03.2021 -

Hella Kastbjerg