article i nfo Weinvestigated neural correlates of musical feature processing with a decoding approach. Tothisend, 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 fea- turetimeseriesthatwere computationally extractedfromthe stimulus.Weexpectedtimbral features to obtaina higher prediction accuracy than rhythmic and tonal ones. Moreover, we expected the areas significantly contrib- uting to the decoding models to be consistent with areas of significant activation observed in previous research usinganaturalisticparadigmwithfMRI.Ofthesixmusicalfeaturesconsidered, fivecouldbesignificantlypredict- ed 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 hip- pocampus 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 find- ings provide compelling evidence for the involvement of the auditory cortex, the cerebellum and the hippocam- pus in the processing of musical features during continuous listening to music.
['Petri Toiviainen', 'Vinoo Alluri', 'Elvira Brattico', 'Mikkel Wallentin', 'Peter Vuust']
Capturing the musical brain with Lasso: Dynamic decoding of musical features from fMRI data