Cortical processing of musical pitch as reflected by behavioural and electrophysiological evidence

In a musical context, the pitch of sounds is encoded according to domain-general principles not confined to music or even to audition overall but common to other perceptual and cognitive processes (such as multiple pattern encoding and feature integration), and to domain-specific and culture-specific properties related to a particular musical system only (such as the pitch steps of the Western tonal system). The studies included in this thesis shed light on the processing stages during which pitch encoding occurs on the basis of both domain-general and music-specific properties, and elucidate the putative brain mechanisms underlying pitch-related music perception. Study I showed, in subjects without formal musical education, that the pitch and timbre of multiple sounds are integrated as unified object representations in sensory memory before attentional intervention. Similarly, multiple pattern pitches are simultaneously maintained in non-musicians' sensory memory (Study II). These findings demonstrate the degree of sophistication of pitch processing at the sensory memory stage, requiring neither attention nor any special expertise of the subjects. Furthermore, music- and culture-specific properties, such as the pitch steps of the equal-tempered musical scale, are automatically discriminated in sensory memory even by subjects without formal musical education (Studies III and IV). The cognitive processing of pitch according to culture-specific musical-scale schemata hence occurs as early as at the sensory-memory stage of pitch analysis. Exposure and cortical plasticity seem to be involved in musical pitch encoding. For instance, after only one hour of laboratory training, the neural representations of pitch in the auditory cortex are altered (Study V). However, faulty brain mechanisms for attentive processing of fine-grained pitch steps lead to inborn deficits in music perception and recognition such as those encountered in congenital amusia (Study VI). These findings suggest that predispositions for exact pitch-step discrimination together with long-term exposure to music govern the acquisition of the automatized schematic knowledge of the music of a particular culture that even non-musicians possess.Musiikkia kuunnellessa äänenkorkeuden (melodian) prosessointiin osallistuvat sekä yleiset kognitiiviset prosessit että pelkästään tietylle kulttuurille tyypilliset musiikin kuunteluun erikoistuneet prosessit. Ensin mainittuun kategoriaan kuuluvat yleiset hahmontunnistusmekanismit sekä musikaalisten piirteiden keskinäinen integraatio, jälkimmäiseen esimerkiksi länsimaisen sävelasteikkojen tunnistaminen ja niiden prosessointi. Tässä väitöskirjassa tarkastellaan sekä yleisiä että alakohtaisia musiikin prosessointiin liittyvä mekanismeja ja tarkastellaan niiden taustalla olevia aivomekanismeja. Kuudessa erillisessä käyttäytymistä ja aivojen toimintaa mittaavassa tutkimuksessa saatiin seuraavat tulokset. Äänen korkeutta ja sointia koodaavat piirteet (tutkimus I) sekä lyhyitä melodioita muodostavat sävelet (tutkimus II) yhdistetään toisiinsa sensorisessa mustissa jo ennen tietoista prosessointia. Myös musiikin kulttuuriset piirteet, kuten sävelasteikot länsimaisessa musiikissa, tunnistetaan automaattisesti sensorisessa muistissa ennen kuin ne tulevat tarkkaavaisuuteen (tutkimukset III ja IV). Muusikkojen lisäksi (tutkimus III) samat tulokset saatiin koehenkilöiltä, joilla ei ollut musiikkiin liittyvää aikaisempaa koulutusta. Tutkimuksessa V havaittiin lisäksi, että äänen korkeuteen erikoistuneet neuraaliset järjestelmät muuttuvat jo tunnin harjoittelun seurauksena, mutta toisaalta myös synnynnäisillä tekijöillä on suuri vaikutus musiikin kokemiseen (tutkimus VI). Yhdessä nämä tulokset pyrkivät kuvaamaan sitä, miten monimutkaisia äänen prosessointiin liittyviä ominaisuuksia sensorinen muisti pystyy käsittelemään riippumatta tarkkaavaisuudesta, tietoisesta prosessoinnista, tai musiikkiin liittyvästä aikaisemmasta koulutuksesta. Lisäksi tutkimus valottaa opittujen ja synnynnäisten tekijöiden vaikutuksia musiikin kokemisessa

Neural Correlates of Music Listening: Does the Music Matter?

The last decades have seen a proliferation of music and brain studies, with a major focus on plastic changes as the outcome of continuous and prolonged engagement with music. Thanks to the advent of neuroaesthetics, research on music cognition has broadened its scope by considering the multifarious phenomenon of listening in all its forms, including incidental listening up to the skillful attentive listening of experts, and all its possible effects. These latter range from objective and sensorial effects directly linked to the acoustic features of the music to the subjectively affective and even transformational effects for the listener. Of special importance is the finding that neural activity in the reward circuit of the brain is a key component of a conscious listening experience. We propose that the connection between music and the reward system makes music listening a gate towards not only hedonia but also eudaimonia, namely a life well lived, full of meaning that aims at realizing one’s own “daimon” or true nature. It is argued, further, that music listening, even when conceptualized in this aesthetic and eudaimonic framework, remains a learnable skill that changes the way brain structures respond to sounds and how they interact with each other

Music and Brain Plasticity: How Sounds Trigger Neurogenerative Adaptations

This contribution describes how music can trigger plastic changes in the brain. We elaborate on the concept of neuroplasticity by focussing on three major topics: the ontogenetic scale of musical development, the phenomenon of neuroplasticity as the outcome of interactions with the sounds and a short survey of clinical and therapeutic applications. First, a distinction is made between two scales of description: the larger evolutionary scale (phylogeny) and the scale of individual development (ontogeny). In this sense, listeners are not constrained by a static dispositional machinery, but they can be considered as dynamical systems that are able to adapt themselves in answer to the solicitations of a challenging environment. Second, the neuroplastic changes are considered both from a structural and functional level of adaptation, with a special focus on the recent findings from network science. The neural activity of the medial regions of the brain seems to become more synchronised when listening to music as compared to rest, and these changes become permanent in individuals such as musicians with year-long musical practice. As such, the question is raised as to the clinical and therapeutic applications of music as a trigger for enhancing the functionality of the brain, both in normal and impaired people

Diffusion map for clustering fMRI spatial maps extracted by independent component analysis

Functional magnetic resonance imaging (fMRI) produces data about activity inside the brain, from which spatial maps can be extracted by independent component analysis (ICA). In datasets, there are n spatial maps that contain p voxels. The number of voxels is very high compared to the number of analyzed spatial maps. Clustering of the spatial maps is usually based on correlation matrices. This usually works well, although such a similarity matrix inherently can explain only a certain amount of the total variance contained in the high-dimensional data where n is relatively small but p is large. For high-dimensional space, it is reasonable to perform dimensionality reduction before clustering. In this research, we used the recently developed diffusion map for dimensionality reduction in conjunction with spectral clustering. This research revealed that the diffusion map based clustering worked as well as the more traditional methods, and produced more compact clusters when needed.Comment: 6 pages. 8 figures. Copyright (c) 2013 IEEE. Published at 2013 IEEE International Workshop on Machine Learning for Signal Processin

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 …Non peer reviewe

Applying Acoustical and Musicological Analysis to Detect Brain Responses to Realistic Music: A Case Study

Music information retrieval (MIR) methods offer interesting possibilities for automatically identifying time points in music recordings that relate to specific brain responses. However, how the acoustical features and the novelty of the music structure affect the brain response is not yet clear. In the present study, we tested a new method for automatically identifying time points of brain responses based on MIR analysis. We utilized an existing database including brain recordings of 48 healthy listeners measured with electroencephalography (EEG) and magnetoencephalography (MEG). While we succeeded in capturing brain responses related to acoustical changes in the modern tango piece Adios Nonino, we obtained less reliable brain responses with a metal rock piece and a modern symphony orchestra musical composition. However, brain responses might also relate to the novelty of the music structure. Hence, we added a manual musicological analysis of novelty in the musical structure to the computational acoustic analysis, obtaining strong brain responses even to the rock and modern pieces. Although no standardized method yet exists, these preliminary results suggest that analysis of novelty in music is an important aid to MIR analysis for investigating brain responses to realistic music.Peer reviewe

Melodic multi-feature paradigm reveals auditory profiles in music-sound encoding

Peer reviewe

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

Background The accuracy of electroencephalography (EEG) and magnetoencephalography (MEG) in measuring neural evoked responses (ERs) is challenged by overlapping neural sources. This lack of accuracy is a severe limitation to the application of ERs to clinical diagnostics. New method We here introduce a theory of stochastic neuronal spike timing probability densities for describing the large-scale spiking activity in neural assemblies, and a spike density component analysis (SCA) method for isolating specific neural sources. The method is tested in three empirical studies with 564 cases of ERs to auditory stimuli from 94 humans, each measured with 60 EEG electrodes and 306 MEG sensors, and a simulation study with 12,300 ERs. Results The first study showed that neural sources (but not non-encephalic artifacts) in individual averaged MEG/EEG waveforms are modelled accurately with temporal Gaussian probability density functions (median 99.7 %–99.9 % variance explained). The following studies confirmed that SCA can isolate an ER, namely the mismatch negativity (MMN), and that SCA reveals inter-individual variation in MMN amplitude. Finally, SCA reduced errors by suppressing interfering sources in simulated cases. Comparison with existing methods We found that gamma and sine functions fail to adequately describe individual MEG/EEG waveforms. Also, we observed that principal component analysis (PCA) and independent component analysis (ICA) does not consistently suppress interference from overlapping brain activity in neither empirical nor simulated cases. Conclusions These findings suggest that the overlapping neural sources in single-subject or patient data can be more accurately separated by applying SCA in comparison to PCA and ICA.Peer reviewe

Distinct neural responses to chord violations: A multiple source analysis study.

The human brain is constantly predicting the auditory environment by representing sequential similarities and extracting temporal regularities. It has been proposed that simple auditory regularities are extracted at lower stations of the auditory cortex and more complex ones at other brain regions, such as the prefrontal cortex. Deviations from auditory regularities elicit a family of early negative electric potentials distributed over the frontal regions of the scalp. In this study, we wished to disentangle the brain processes associated with sequential vs. hierarchical auditory regularities in a musical context by studying the event-related potentials (ERPs), the behavioral responses to violations of these regularities, and the localization of the underlying ERP generators using two different source analysis algorithms. To this aim, participants listened to musical cadences constituted by seven chords, each containing either harmonically congruous chords, harmonically incongruous chords, or harmonically congruous but mistuned chords. EEG was recorded and multiple source analysis was performed. Incongruous chords violating the rules of harmony elicited a bilateral ERAN, whereas mistuned chords within chord sequences elicited a right-lateralized MMN. We found that the dominant cortical sources for the ERAN were localized around Broca's area and its right homolog, whereas the MMN generators were localized around the primary auditory cortex. These findings suggest a predominant role of the auditory cortices in detecting sequential scale regularities and the posterior prefrontal cortex in parsing hierarchical regularities in music

Musical training predicts cerebello-hippocampal coupling during music listening.

Cerebello-hippocampal interactions occur during accurate spatiotemporal prediction of movements. In the context of music listening, differences in cerebello-hippocampal functional connectivity may result from differences in predictive listening accuracy. Using functional MRI, we studied differences in this network between 18 musicians and 18 nonmusicians while they listened to music. Musicians possess a predictive listening advantage over nonmusicians, facilitated by strengthened coupling between produced and heard sounds through lifelong musical experience. Thus, we hypothesized that musicians would exhibit greater functional connectivity than nonmusicians as a marker of accurate online predictions during music listening. To this end, we estimated the functional connectivity between cerebellum and hippocampus as modulated by a perceptual measure of the predictability of the music. Results revealed increased predictability-driven functional connectivity in this network in musicians compared with nonmusicians, which was positively correlated with the length of musical training. Findings may be explained by musicians’ improved predictive listening accuracy. Our findings advance the understanding of cerebellar integrative function.Peer reviewe