We investigated neural correlates of musical feature processing with a decoding approach. To this end, we used a method that combines computational extraction of musical features with regularized multiple regression (LASSO). Optimal model parameters were determined by maximizing the decoding accurac...
We investigated neural correlates of musical feature processing with a decoding approach. To this end, we used a method that combines computational extraction of musical features with regularized multiple regression (LASSO). Optimal model parameters were determined by maximizing the decoding accuracy using a leave-one-out cross-validation scheme. The method was applied to functional magnetic resonance imaging (fMRI) data that were collected using a naturalistic paradigm, in which participants' brain responses were recorded while they were continuously listening to pieces of real music. The dependent variables comprised musical feature time series that were computationally extracted from the stimulus. We expected timbral features to obtain a higher prediction accuracy than rhythmic and tonal ones. Moreover, we expected the areas significantly contributing to the decoding models to be consistent with areas of significant activation observed in previous research using a naturalistic paradigm with fMRI. Of the six musical features considered, five could be significantly predicted for the majority of participants. The areas significantly contributing to the optimal decoding models agreed to a great extent with results obtained in previous studies. In particular, areas in the superior temporal gyrus, Heschl's gyrus, Rolandic operculum, and cerebellum contributed to the decoding of timbral features. For the decoding of the rhythmic feature, we found the bilateral superior temporal gyrus, right Heschl's gyrus, and hippocampus to contribute most. The tonal feature, however, could not be significantly predicted, suggesting a higher inter-participant variability in its neural processing. A subsequent classification experiment revealed that segments of the stimulus could be classified from the fMRI data with significant accuracy. The present findings provide compelling evidence for the involvement of the auditory cortex, the cerebellum and the hippocampus in the processing of musical features during continuous listening to music.
We investigated neural correlates of musical feature processing with a decoding approach. To this end, we used a method that combines computational extraction of musical features with regularized multiple regression (LASSO). Optimal model parameters were determined by maximizing the decoding accuracy using a leave-one-out cross-validation scheme. The method was applied to functional magnetic resonance imaging (fMRI) data that were collected using a naturalistic paradigm, in which participants' brain responses were recorded while they were continuously listening to pieces of real music. The dependent variables comprised musical feature time series that were computationally extracted from the stimulus. We expected timbral features to obtain a higher prediction accuracy than rhythmic and tonal ones. Moreover, we expected the areas significantly contributing to the decoding models to be consistent with areas of significant activation observed in previous research using a naturalistic paradigm with fMRI. Of the six musical features considered, five could be significantly predicted for the majority of participants. The areas significantly contributing to the optimal decoding models agreed to a great extent with results obtained in previous studies. In particular, areas in the superior temporal gyrus, Heschl's gyrus, Rolandic operculum, and cerebellum contributed to the decoding of timbral features. For the decoding of the rhythmic feature, we found the bilateral superior temporal gyrus, right Heschl's gyrus, and hippocampus to contribute most. The tonal feature, however, could not be significantly predicted, suggesting a higher inter-participant variability in its neural processing. A subsequent classification experiment revealed that segments of the stimulus could be classified from the fMRI data with significant accuracy. The present findings provide compelling evidence for the involvement of the auditory cortex, the cerebellum and the hippocampus in the processing of musical features during continuous listening to music.
Front. Psychol. Brattico 2 2011 A functional MRI study of happy and sad emotions in music with and without lyrics
PLoS One Chapin 5 12 e13812 2010 10.1371/journal.pone.0013812 Dynamic emotional and neural responses to music depend on performance expression and listener experience
Magn. Reson. Imaging Formisano 26 7 921 2008 10.1016/j.mri.2008.01.052 Multivariate analysis of fMRI time series: classification and regression of brain responses using machine learning
Lartillot 2007 Proceedings of 8th International Conference on Music Information Retrieval 2007 MIR in Matlab (II): a toolbox for musical feature extraction from audio
Soc. Cogn. Affect. Neurosci Lehne 2013 Tension-related activity in the orbitofrontal cortex and amygdala: an fMRI study with music
NeuroImage McIntosh 23 S250 2004 10.1016/j.neuroimage.2004.07.020 Partial least squares analysis of neuroimaging data: applications and advances
NeuroImage Tzourio-Mazoyer 15 1 273 2002 10.1006/nimg.2001.0978 Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain
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