Biology subjects

Toiviainen, P.

Publications and source records attributed to Toiviainen, P..

5 recordsLinked to original sources

bioRxiv2021

Trait empathy modulates music-related functional connectivity.

It has been well established through behavioural studies that empathy significantly influences music perception. Such individual differences typically manifest as variability in whole brain functional connectivity patterns. To date, nobody has examined the modulatory effect of empathy on functional connectivity patterns during continuous music listening. In the present study, we seek to investigate the global and local connectivity patterns of 36 participants whose fMRI scanning was done by employing the naturalistic paradigm wherein they listened to a continuous piece of music. We used graph-based measures of functional connectivity to identify how cognitive and affective components of empathy modulate functional connectivity. Partial correlation between Eigenvector centrality and measures of empathy showed that cognitive empathy is associated with higher centrality in the sensorimotor regions responsible for motor mimicry while affective empathy showed higher centrality in regions related to auditory affect processing. We furthermore identified a left-hemispheric dominance in the modulatory effect of affective empathy particularly in the Orbitofrontal cortex and the temporal pole. Results are discussed in relation to various theoretical models of empathy and music cognition.

neuroscience↗

bioRxiv2020

Beauty and the brain: Investigating the neural and musical attributes of beauty during a naturalistic music listening experience

Evaluative beauty judgments are very common, but in spite of this commonality, are rarely studied in cognitive neuroscience. Here we investigated the neural and musical attributes of musical beauty using a naturalistic free-listening paradigm applied to behavioral and neuroimaging recordings and validated by experts judgments. In Study 1, 30 Western healthy adult participants rated continuously the perceived beauty of three musical pieces using a motion sensor. This allowed us to identify the passages in the three musical pieces that were inter-subjectively judged as beautiful or ugly. This informed the analysis for Study 2, where additional 36 participants were recorded with functional magnetic resonance imaging (fMRI) while they listened attentively to the same musical pieces as in Study 1. In Study 3, in order to identify the musicological features characterizing the passages that were consistently rated as beautiful or ugly in Study 1, we collected post-hoc questionnaires from 12 music-composition experts. Results from Study 2 evidenced focal regional activity in the orbitofrontal brain structure when listening to beautiful passages of music, irrespectively of the subjective reactions and individual listening biographies. In turn, the moments in the music that were consistently rated as ugly were associated with bilateral supratemporal activity. Effective connectivity analysis also discovered inhibition of auditory activation and neural communication with orbitofrontal cortex, especially in the right hemisphere, during listening to beautiful musical passages as opposed to intrinsic activation of auditory cortices and decreased coupling to orbitofrontal cortex during listening to ugly musical passages. Experts questionnaires indicated that the beautiful passages were more melodic, calm, sad, slow, tonal, traditional and simple than the ones negatively valenced. In sum, we identified a neural mechanism for inter-subjective beauty judgments of music in the supratemporal-orbitofrontal circuit, irrespectively of individual taste and listening biography. Furthermore, some invariance in objective musical attributes of beautiful and ugly passages was evidenced. Future studies might address the generalizability of the findings to non-Western listeners.

neuroscience↗

bioRxiv2020

Musical skills can be decoded from magnetic resonance images

Learning induces structural changes in the brain. Especially repeated, long-term behaviors, such as extensive training of playing a musical instrument, are likely to produce characteristic features to brain structure. However, it is not clear to what extent such structural features can be extracted from magnetic resonance images of the brain. Here we show that it is possible to predict whether a person is a musician or a non-musician based on the thickness of the cerebral cortex measured at 148 brain regions en-compassing the whole cortex. Using a supervised machine-learning technique, we achieved a significant ({kappa} = 0.321, p < 0.001) agreement between the actual and predicted participant groups of 30 musicians and 85 non-musicians. The areas contributing to the prediction were mostly in the frontal, parietal, and occipital lobes of the left hemisphere. Our results suggest that decoding musicianship from magnetic resonance images of brain structure is feasible. Further, the distribution of the areas that were informative in the classification, which mostly, but not entirely, overlapped with earlier findings on areas relevant for musical skills, implies that decoding-based analyses of structural properties of the brain can reveal novel aspects of musical aptitude. In particular, our results highlight differences in visual areas in addition to the already more established differences located in motor networks and networks of higher-order cognitive function.

neuroscience↗

bioRxiv2019

Personality modulates brain responses to emotion in music: Comparing whole-brain and regions-of-variance approaches

Whether and how personality traits explain the individual variance in neural responses to emotion in music remains unclear. The sparse studies on this topic report inconsistent findings. The present study extends previous work using regions of variance (ROVs) as regions of interest, compared with whole-brain analysis. Fifty-five subjects listened to happy, sad, and fearful music during functional Magnetic Resonance Imaging. Personality was measured with the Big Five Questionnaire. Results confirmed previous observations of Neuroticism being positively related to activation during sad music, in the left inferior parietal lobe. In an exploratory analysis, Openness was positively related to activation during Happy music in an extended cluster in auditory areas, primarily including portions of the left Heschls gyrus, superior and middle temporal gyri, supramarginal gyrus, and Rolandic operculum. In the whole-brain analysis, similar results were found for Neuroticism but not for Openness. In turn, we did not replicate previous findings of Extraversion associated to activity during happy music, nor Neuroticism during fearful music. These results support a trait-congruent link between personality and emotion-elicited brain activity, and further our understanding of the action-observation network during emotional music listening. This study also indicates the usefulness of the ROV method in individual-differences research.

neuroscience↗

bioRxiv2019

Exploring Frequency-dependent Brain Networks from ongoing EEG using Spatial ICA during music listening

Recently, exploring brain activity based on functional networks during naturalistic stimuli especially music and video represents an attractive challenge because of the low signal-to-noise ratio in collected brain data. Although most efforts focusing on exploring the listening brain have been made through functional magnetic resonance imaging (fMRI), sensor-level electro- or magnetoencephalography (EEG/MEG) technique, little is known about how neural rhythms are involved in the brain network activity under naturalistic stimuli. This study exploited cortical oscillations through analysis of ongoing EEG and musical feature during free-listening to music. We used a data-driven method that combined music information retrieval with spatial Independent Components Analysis (ICA) to probe the interplay between the spatial profiles and the spectral patterns. We projected the sensor data into cortical space using a minimum-norm estimate and applied the Short Time Fourier Transform (STFT) to obtain frequency information. Then, spatial ICA was made to extract spatial-spectral-temporal information of brain activity in source space and five long-term musical features were computationally extracted from the naturalistic stimuli. The spatial profiles of the components whose temporal courses were significantly correlated with musical feature time series were clustered to identify reproducible brain networks across the participants. Using the proposed approach, we found brain networks of musical feature processing are frequency-dependent and three plausible frequency-dependent networks were identified; the proposed method seems valuable for characterizing the large-scale frequency-dependent brain activity engaged in musical feature processing.

neuroscience↗