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Keeping time is fundamental for our everyday existence. Various isochronous activities, such as locomotion, require us to use internal timekeeping. This phenomenon comes into play also in other human pursuits such as dance and music. When listening to music, we spontaneously perceive and predict its beat. The process of beat perception comprises both beat inference and beat maintenance, their relative importance depending on the salience of beat in the music. To study functional connectivity associated with these processes in a naturalistic situation, we used functional magnetic resonance imaging to measure brain responses of participants while they were listening to a piece of music containing strong contrasts in beat salience. Subsequently, we utilized dynamic graph analysis and psychophysiological interactions (PPI) analysis in connection with computational modelling of beat salience to investigate how functional connectivity manifests these processes. As the main effect, correlation analyses between the obtained dynamic graph measures and the beat salience measure revealed increased centrality in auditory-motor cortices, cerebellum, and extrastriate visual areas during low beat salience, whereas regions of the default mode- and central executive networks displayed high centrality during high beat salience. PPI analyses revealed partial dissociation of functional networks belonging to this pathway indicating complementary neural mechanisms crucial in beat inference and maintenance, processes pivotal for extracting and predicting temporal regularities in our environment., (Copyright © 2019 Elsevier Inc. All rights reserved.)

Recent functional studies suggest that noise sensitivity, a trait describing attitudes towards noise and predicting noise annoyance, is associated with altered processing in the central auditory system. In the present work, we examined whether noise sensitivity could be related to the structural anatomy of auditory and limbic brain areas. Anatomical MR brain images of 80 subjects were parcellated with FreeSurfer to measure grey matter volume, cortical thickness, cortical area and folding index of anatomical structures in the temporal lobe and insular cortex. The grey matter volume of amygdala and hippocampus was measured as well. According to our findings, noise sensitivity is associated with the grey matter volume in the selected structures. Among those, we propose and discuss particular areas, previously linked to auditory perceptual, emotional and interoceptive processing, in which larger grey matter volume seems to be related to higher noise sensitivity., (Copyright © 2017 Elsevier Inc. All rights reserved.)

Low-level (timbral) and high-level (tonal and rhythmical) musical features during continuous listening to music, studied by functional magnetic resonance imaging (fMRI), have been shown to elicit large-scale responses in cognitive, motor, and limbic brain networks. Using a similar methodological approach and a similar group of participants, we aimed to study the replicability of previous findings. Participants' fMRI responses during continuous listening of a tango Nuevo piece were correlated voxelwise against the time series of a set of perceptually validated musical features computationally extracted from the music. The replicability of previous results and the present study was assessed by two approaches: (a) correlating the respective activation maps, and (b) computing the overlap of active voxels between datasets at variable levels of ranked significance. Activity elicited by timbral features was better replicable than activity elicited by tonal and rhythmical ones. These results indicate more reliable processing mechanisms for low-level musical features as compared to more high-level features. The processing of such high-level features is probably more sensitive to the state and traits of the listeners, as well as of their background in music., (Copyright © 2015 Elsevier Inc. All rights reserved.)

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., (Copyright © 2013 Elsevier Inc. All rights reserved.)

We aimed at predicting the temporal evolution of brain activity in naturalistic music listening conditions using a combination of neuroimaging and acoustic feature extraction. Participants were scanned using functional Magnetic Resonance Imaging (fMRI) while listening to two musical medleys, including pieces from various genres with and without lyrics. Regression models were built to predict voxel-wise brain activations which were then tested in a cross-validation setting in order to evaluate the robustness of the hence created models across stimuli. To further assess the generalizability of the models we extended the cross-validation procedure by including another dataset, which comprised continuous fMRI responses of musically trained participants to an Argentinean tango. Individual models for the two musical medleys revealed that activations in several areas in the brain belonging to the auditory, limbic, and motor regions could be predicted. Notably, activations in the medial orbitofrontal region and the anterior cingulate cortex, relevant for self-referential appraisal and aesthetic judgments, could be predicted successfully. Cross-validation across musical stimuli and participant pools helped identify a region of the right superior temporal gyrus, encompassing the planum polare and the Heschl's gyrus, as the core structure that processed complex acoustic features of musical pieces from various genres, with or without lyrics. Models based on purely instrumental music were able to predict activation in the bilateral auditory cortices, parietal, somatosensory, and left hemispheric primary and supplementary motor areas. The presence of lyrics on the other hand weakened the prediction of activations in the left superior temporal gyrus. Our results suggest spontaneous emotion-related processing during naturalistic listening to music and provide supportive evidence for the hemispheric specialization for categorical sounds with realistic stimuli. We herewith introduce a powerful means to predict brain responses to music, speech, or soundscapes across a large variety of contexts., (© 2013.)

We investigated the neural underpinnings of timbral, tonal, and rhythmic features of a naturalistic musical stimulus. Participants were scanned with functional Magnetic Resonance Imaging (fMRI) while listening to a stimulus with a rich musical structure, a modern tango. We correlated temporal evolutions of timbral, tonal, and rhythmic features of the stimulus, extracted using acoustic feature extraction procedures, with the fMRI time series. Results corroborate those obtained with controlled stimuli in previous studies and highlight additional areas recruited during musical feature processing. While timbral feature processing was associated with activations in cognitive areas of the cerebellum, and sensory and default mode network cerebrocortical areas, musical pulse and tonality processing recruited cortical and subcortical cognitive, motor and emotion-related circuits. In sum, by combining neuroimaging, acoustic feature extraction and behavioral methods, we revealed the large-scale cognitive, motor and limbic brain circuitry dedicated to acoustic feature processing during listening to a naturalistic stimulus. In addition to these novel findings, our study has practical relevance as it provides a powerful means to localize neural processing of individual acoustical features, be it those of music, speech, or soundscapes, in ecological settings., (Copyright © 2011 Elsevier Inc. All rights reserved.)