bioRxiv Science⌕ Search

Biology subjects

Vuust, P.

Publications and source records attributed to Vuust, P..

83 records · Page 5Linked to original sources

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↗

A Kuramoto model of self-other integration across interpersonal synchronization strategies

Human social behaviour is complex, and the biological and neural mechanisms underpinning it remain debated1,2. A particularly interesting social phenomenon is our ability and tendency to fall into synchrony with other humans3,4. Our ability to coordinate actions and goals relies on the ability to distinguish between and integrate self and other, which when impaired can lead to devastating consequences. Interpersonal synchronization has been a widely used framework for studying action coordination and self-other integration, showing that in simple interactions, such as joint finger tapping, complex interpersonal dynamics emerge. Here we propose a computational model of self-other integration via within- and between-person action-perception links, implemented as a simple Kuramoto model with four oscillators. The model abstracts each member of a dyad as a unit consisting of two connected oscillators, representing intrinsic processes of perception and action. By fitting this model to data from two separate experiments we show that interpersonal synchronization strategies rely on the relationship between within- and between-unit coupling. Specifically, mutual adaptation exhibits a higher between-unit coupling than within-unit coupling; leading-following requires that the follower unit has a low within-unit coupling; and leading-leading occurs when two units jointly exhibit a low between-unit coupling. These findings are consistent with the theory of interpersonal synchronization emerging through self-other integration mediated by processes of action-perception coupling4. Hence, our results show that chaotic human behaviour occurring on a millisecond scale can be modelled using coupled oscillators.

neuroscience↗

Poorer auditory sensitivity is related to stronger visual enhancement of the human auditory mismatch negativity (MMNm)

Multisensory processing facilitates perception of our everyday environment and becomes particularly important when sensory information is degraded or close to the discrimination threshold. Here, we used magnetoencephalography and an audiovisual oddball paradigm to assess the complementary role of visual information in subtle pitch discrimination at the neural level of participants with varying levels of pitch discrimination abilities, i.e., musicians and nonmusicians. The amplitude of the auditory mismatch negativity (MMNm) served as an index of sensitivity. The gain in amplitude resulting from compatible audiovisual information was larger in participants whose MMNm amplitude was smaller in the condition deviating only in the auditory dimension, in accordance with the multisensory principle of inverse effectiveness. These findings show that discrimination of even a sensory-specific feature as pitch is facilitated by multisensory information at a pre-attentive level, and they highlight the importance of considering inter-individual differences in uni-sensory abilities when assessing multisensory processing.

neuroscience↗

Domain-relevant auditory expertise modulates the additivity of neural mismatch responses in humans

It is unknown whether domain-relevant expertise is associated with more independent or more dependent predictive processing of acoustic features. Here, mismatch negativity (MMNm) was recorded with magnetoencephalography (MEG) from 25 musicians and 25 non-musicians, exposed to complex musical multi-feature and simple oddball control paradigms. Deviants differed in frequency (F), intensity (I), perceived location (L), or any combination of these (FI, IL, LF, FIL). Neural processing overlap was assessed through MMNm additivity by comparing double- and triple-deviant MMNms ("empirical") to summed constituent single-deviant MMNms ("modelled"). Significantly greater subadditivity was present in musicians compared to non-musicians, specifically for frequency-related deviants in complex contexts. Despite using identical sounds, expertise effects were absent from the simple paradigm. This novel finding supports the dependent processing hypothesis whereby experts recruit overlapping neural resources facilitating more integrative representations of domain-relevant stimuli. Such specialized predictive processing may enable experts such as musicians to capitalise on complex acoustic cues.

neuroscience↗

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↗

Reduced structural connectivity in Insomnia Disorder

Insomnia Disorder is the most prevalent sleep disorder and it involves both sleep difficulties and daytime complaints. The neural underpinnings of Insomnia Disorder are poorly understood. Existing neuroimaging studies are limited by their focus on local measures and specific regions of interests. To address this shortcoming, we applied a data-driven approach to assess differences in whole-brain structural connectivity between adults with Insomnia Disorder and matched controls without sleep complaints. We used diffusion tensor imaging and probabilistic tractography to assess whole-brain structural connectivity and examined group differences using Network-Based Statistics. The results revealed a significant difference in the structural connectivity of the two groups. Participants with Insomnia Disorder showed reduced connectivity in a subnetwork that was largely left lateralized, including mainly fronto-subcortical connections with the insula as a key region. By taking a whole-brain network perspective, our study succeeds at integrating previous inconsistent findings, and our results reveal that reduced structural connectivity of the left insula and the connections between frontal and subcortical regions are central neurobiological features of Insomnia Disorder. The importance of these areas for interoception, emotional processing, stress responses and the generation of slow wave sleep may help guide the development of neurobiology-based models of the highly prevalent condition of Insomnia Disorder.

neuroscience↗

Musical interaction is influenced by underlying predictive models and musical expertise

Musical interaction is a unique model for understanding humans ability to align goals, intentions, and actions, which also allows for the manipulation of participants internal predictive models of upcoming events. Here we used polyrhythms to construct two joint finger tapping tasks that even when rhythmically dissimilar resulted in equal inter-tap intervals (ITIs). Thus, behaviourally a dyad of two musicians tap isochronously at the same rate, yet with their own distinct rhythmical context model (RCM). We recruited 22 highly skilled musicians (in 11 dyads) and contrasted the effect of having a shared versus non-shared RCM on dyads synchronization behaviour. As expected, tapping synchronization was significantly worse at the start of trials with non-shared models compared to trials with a shared model. However, the musicians were able to quickly recover when holding dissimilar predictive models. We characterised the directionality in the tapping behaviour of the dyads and found patterns mostly of mutual adaptation. Yet, in a subset of dyads primarily consisting of drummers, we found significantly different synchronization patterns, suggesting that instrument expertise can significantly affect synchronization strategies. Overall, this demonstrates that holding different predictive models impacts synchronization in musicians performing joint finger tapping.\n\nPublic significance statementThis study shows that when a pair of musicians thinks differently about a rhythm they play together, their performance is worse. However, they are able to recover back to normal performance levels after a few taps for which they use different strategies. Interestingly, we find that the strategies used by drummers may be different from other musicians.

neuroscience↗

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↗

The sensation of groove is affected by the interaction of rhythmic and harmonic complexity

Groove is defined as the pleasurable desire to move to music. Research has shown that rhythmic complexity modulates the sensation of groove but how other musical features, such as harmony, influence groove is less clear. To address this, we asked people with a range of musical experience to rate stimuli that varied in both rhythmic and harmonic complexity. Rhythm showed an inverted U-shaped relationship with ratings of pleasure and wanting to move, whereas medium and low complexity chords were rated similarly. Pleasure mediated the effect of harmony on wanting to move and high complexity chords attenuated the effect of rhythm. While rhythmic complexity is the primary driver, harmony both modulates the effect of rhythm and makes a unique contribution via its effect on pleasure. These results may be accounted for by predictive processes based on rhythmic and harmonic expectancies that are known to contribute to musical pleasure or reward.

neuroscience↗

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.

neuroscience↗

Music-induced analgesia in chronic pain conditions: a systematic review and meta-analysis

Music is increasingly used as an adjuvant for chronic pain management as it is not invasive, inexpensive, and patients usually report positive experiences with it. However, little is known about its clinical efficacy in chronic pain patients. In this systematic review and meta-analysis, we investigated randomized controlled trials (RCTs) of adult patients that reported any type of music intervention for chronic pain, chosen by the researcher or patient, lasting for any duration. Searches were performed using PsycINFO, Scopus and PubMed for RTCs published until the end of May 2016. The primary outcome was reduction in self-reported pain using a standardized pain measurement instrument reported post-intervention. The secondary outcomes were: quality of life measures, depression and anxiety measures, among others. The study was pre-registered with PROSPERO (CRD42016039837) and the meta-analysis was done using RevMan. We identified 768 titles and abstracts, and we included 14 RTCs that fulfilled our criteria. The sample size of the studies varied between 25 and 200 participants. We found that music reduced chronic pain, and depression, with higher effect size on pain and depression. We also found music had a higher effect when the participant chose the music in contrast with researcher-chosen music. The sample size of RCTs was small and sometimes with different outcome measures. Our analysis suggests that music may be beneficial as an adjuvant for chronic pain patients, as it reduces self-reported pain and its common co-morbidities. Importantly, the analgesic effect of music appears higher with self-chosen over researcher-chosen music.

neuroscience↗