Biology subjects

Brattico, E.

Publications and source records attributed to Brattico, E..

41 records · Page 3Linked to original sources

bioRxiv2019

Musical prediction error responses similarly reduced by predictive uncertainty in musicians and non-musicians

Auditory prediction error responses elicited by surprising sounds can be reliably recorded with musical stimuli that are more complex and realistic than those typically employed in EEG or MEG oddball paradigms. However, these responses are reduced as the predictive uncertainty of the stimuli increases. In this study, we investigate whether this effect is modulated by musical expertise. Magnetic mismatch negativity (MMNm) responses were recorded from 26 musicians and 24 non-musicians while they listened to low-and high-uncertainty melodic sequences in a musical multi-feature paradigm that included pitch, slide, intensity, and timbre deviants. When compared to non-musicians, musically trained participants had significantly larger pitch and slide MMNm responses. However, both groups showed comparable reductions of pitch and slide MMNm amplitudes in the high-uncertainty condition compared to the low-uncertainty condition. In a separate, behavioral deviance detection experiment, musicians were more accurate and confident about their responses than non-musicians, but deviance detection in both groups was similarly affected by the uncertainty of the melodies. In both experiments, the interaction between uncertainty and expertise was not significant, suggesting that the effect is comparable in both groups. Consequently, our results replicate the modulatory effect of predictive uncertainty on prediction error; show that it is present across different types of listeners; and suggest that expertise-related and stimulus-driven modulations of predictive precision are dissociable and independent.

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

Applying stochastic spike train theory for high-accuracy MEG/EEG

The accuracy of electroencephalography (EEG) and magnetoencephalography (MEG) is challenged by overlapping sources from within the brain. This lack of accuracy is a severe limitation to the possibilities and reliability of modern stimulation protocols in basic research and clinical diagnostics. As a solution, we here introduce a theory of stochastic neuronal spike timing probability densities for describing the large-scale spiking activity in neural networks, and a novel spike density component analysis (SCA) method for isolating specific neural sources. Three studies are conducted based on 564 cases of evoked responses to auditory stimuli from 94 human subjects each measured with 60 EEG electrodes and 306 MEG sensors. In the first study we show that the large-scale spike timing (but not non-encephalographic artifacts) in MEG/EEG waveforms can be modeled with Gaussian probability density functions with high accuracy (median 99.7%-99.9% variance explained), while gamma and sine functions fail describing the MEG and EEG waveforms. In the second study we confirm that SCA can isolate a specific evoked response of interest. Our findings indicate that the mismatch negativity (MMN) response is accurately isolated with SCA, while principal component analysis (PCA) fails supressing interference from overlapping brain activity, e.g. from P3a and alpha waves, and independent component analysis (ICA) distorts the evoked response. Finally, we confirm that SCA accurately reveals inter-individual variation in evoked brain responses, by replicating findings relating individual traits with MMN variations. The findings of this paper suggest that the commonly overlapping neural sources in single-subject or patient data can be more accurately separated by applying the introduced theory of large-scale spike timing and method of SCA in comparison to PCA and ICA. Significance statementElectroencephalography (EEG) and magnetoencelopraphy (MEG) are among the most applied non-invasive brain recording methods in humans. They are the only methods to measure brain function directly and in time resolutions smaller than seconds. However, in modern research and clinical diagnostics the brain responses of interest cannot be isolated, because of interfering signals of other ongoing brain activity. For the first time, we introduce a theory and method for mathematically describing and isolating overlapping brain signals, which are based on prior intracranial in vivo research on brain cells in monkey and human neural networks. Three studies mutually support our theory and suggest that a new level of accuracy in MEG/EEG can achieved by applying the procedures presented in this paper.

neuroscience↗

bioRxiv2018

Precision weighting of musical prediction error: converging neurophysiological and behavioral evidence

Theories of predictive processing propose that prediction error responses are modulated by the certainty of the predictive model or precision. While there is some evidence for this phenomenon in the visual and, to a lesser extent, the auditory modality, little is known about whether it operates in the complex auditory contexts of daily life. Here, we examined how prediction error responses behave in a more complex and ecologically valid auditory context than those typically studied. We created musical tone sequences with different degrees of pitch uncertainty to manipulate the precision of participants auditory expectations. Magnetoencephalography was used to measure the magnetic counterpart of the mismatch negativity (MMNm) as a neural marker of prediction error in a multi-feature paradigm. Pitch, slide, intensity and timbre deviants were included. We compared high-entropy stimuli, consisting of a set of non-repetitive melodies, with low-entropy stimuli consisting of a simple, repetitive pitch pattern. Pitch entropy was quantitatively assessed with an information-theoretic model of auditory expectation. We found a reduction in pitch and slide MMNm amplitudes in the high-entropy as compared to the low-entropy context. No significant differences were found for intensity and timbre MMNm amplitudes. Furthermore, in a separate behavioral experiment investigating the detection of pitch deviants, similar decreases were found for accuracy measures in response to more fine-grained increases in pitch entropy. Our results are consistent with a precision modulation of auditory prediction error in a musical context, and suggest that this effect is specific to features that depend on the manipulated dimension--pitch information, in this case.\n\nHighlightsO_LIThe mismatch negativity (MMNm) is reduced in musical contexts with high pitch uncertainty\nC_LIO_LIThe MMNm reduction is restricted to pitch-related features\nC_LIO_LIAccuracy during deviance detection is reduced in contexts with higher uncertainty\nC_LIO_LIThe results suggest a feature-selective precision modulation of prediction error\nC_LI\n\nMaterials, data and scripts can be found in the Open Science Framework repository: http://bit.ly/music_entropy_MMN\n\nDOI: 10.17605/OSF.IO/MY6TE

neuroscience↗

bioRxiv2017

Functional connectivity of music-induced analgesia in fibromyalgia

Listening to self-chosen, pleasant and relaxing music reduces pain in fibromyalgia (FM), a chronic central pain condition. However, the neural correlates of this effect are fairly unknown and could be regarded as a more direct measure of analgesia. In our study, we wished to investigate the neural correlates of music-induced analgesia (MIA) in fibromyalgia patients. To do this, we studied 20 FM patients and 20 matched healthy controls (HC) acquiring rs-fMRI with a 3T MRI scanner, and pain data before and after two 5-min auditory conditions: music and noise. We performed resting state functional connectivity (rs-FC) seed-based correlation analyses (SCA) using pain and analgesia-related ROIs to determine the effects before and after the music intervention in FM and HC, and its correlation with pain reports. We found significant differences in baseline rs-FC between FM and HC. Both groups showed changes in rs-FC in several ROIs after the music condition between different areas, that were left lateralized in FM and right lateralized in HC. FM patients reported MIA that was significantly correlated with rs-FC decrease between the angular gyrus, posterior cingulate cortex and precuneus, and rs-FC increase between amygdala and middle frontal gyrus. These areas are related to autobiographical and limbic processes, and auditory attention, suggesting MIA may arise as a consequence of top-down modulation, probably originated by distraction, relaxation, positive emotion, or a combination of these mechanisms.

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