Toiviainen, P. | Search bioRxiv

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↗