Biology subjects

Haumann, N. T.

Publications and source records attributed to Haumann, N. T..

9 recordsLinked to original sources

bioRxiv2025

It takes experience to tango: Experienced cochlear implantusers show cortical evoked potentials to naturalistic music

Approximately 30% of cochlear implant (CI) users report that restoring their ability to enjoy music is a primary goal. However, music perception in CI users has mostly been investigated in controlled laboratory settings using simplified stimuli, such as pure tones or monophonic melodies. There is an increasing interest in developing objective measures of CI outcomes in everyday listening situations, particularly in music listening, which involves complex stimuli rich in timbre, pitch, rhythm, and overlapping sounds. One promising approach is to measure cortical auditory evoked responses (ERs) in CI users. We investigated whether ERs to sound on-sets in a naturalistic four-minute music piece could be measured in adult CI users (N: 25; ages 18-80; CI experience: 0.3-14 years). We assumed that the accumulation of CI experience might be reflected in the morphology of the ERs. The results confirmed that P2 responses to sound onsets embedded in a whole piece of music can be detected in experienced CI users. Compared to a control group with normal hearing, the CI users showed P2 responses with lower amplitudes and longer latencies. Exploratory linear regression models suggested that the logarithmic duration of CI experience significantly predicted both perceived quality of musical sounds and P2 amplitude, explaining 38% and 28% of the variance, respectively. The findings suggest that music perception outcomes may continue to improve for up to 2-4 years post-implantation. Altogether, the results are consistent with the use of ERs to track CI adaptation to music listening.

neuroscience↗

bioRxiv2025

Embodied Singing: Dual Role of Interoception in Vocal Expertise and Musical Competence

Musical expertise is often associated with heightened perceptual sensitivity to external sensory stimuli, yet its relationship with internal bodily awareness (interoception) remains elusive. This study examined whether interoceptive ability relates differentially to varying levels of singing expertise and explored if interoception could predispose individuals to musical skills. Professional singers, amateur singers, and non-singers completed a heartbeat discrimination task (interoceptive accuracy; IAcc), self-reported interoceptive sensibility assessment, and comprehensive musical competence measures. Results demonstrated a significant positive association between singing expertise and IAcc, which notably emerged only in professional singers, who significantly outperformed both amateur singers and non-singers. Regression analyses indicated a moderate predictive role of accumulated singing practice for IAcc among trained singers, though emotional awareness partially mediated the relationship between expertise and IAcc. Critically, higher IAcc in non-singers significantly correlated with superior singing accuracy, suggesting enhanced interoception may facilitate singing competence independently of formal vocal training. Altogether, these findings highlight a dual role of interoception, linking it to expert-level singing--partially mediated by emotional awareness-- and independently to musical competence in non-singers. These results reconcile prior inconsistencies by highlighting the embodied nature of singing and the distinct role of musical expertise, while underscoring inherent musically relevant mechanisms beyond practice alone.

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bioRxiv2022

Whole-brain computation of cognitive versus acoustic errors in music

Previous studies have evidenced how the local prediction of physical stimulus features may affect the neural processing of incoming stimuli. Less known are the effects of cognitive priors on predictive processes, and how the brain computes local versus cognitive predictions and their errors. Here, we determined the differential brain mechanisms underlying prediction errors related to high-level, cognitive priors for melody (rhythm, contour) versus low-level, local acoustic priors (tuning, timbre). We measured with magnetoencephalography the mismatch negativity (MMN) prediction error signal in 104 adults having varying levels of musical expertise. We discovered that the brain regions involved in predictive processes for local priors were primary and secondary auditory cortex and insula, whereas cognitive brain regions such as cingulate and orbitofrontal cortices were recruited for melodic errors in cognitive priors. The involvement of higher-level brain regions for computing cognitive errors was enhanced in musicians, especially in cingulate cortex, inferior frontal gyri, and supplementary motor area. Overall, the findings expand knowledge on whole-brain mechanisms of predictive processing and the related MMN generators, previously mainly confined to the auditory cortex, to a frontal network that strictly depends on the type of priors that are to be computed by the brain.

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bioRxiv2021

Prediction Under Uncertainty: Dissociating Sensory from Cognitive Expectations in Highly Uncertain Musical Contexts

Predictive models in the brain rely on the continuous extraction of regularities from the environment. These models are thought to be updated by novel information, as reflected in prediction error responses such as the mismatch negativity (MMN). However, although in real life individuals often face situations in which uncertainty prevails, it remains unclear whether and how predictive models emerge in high-uncertainty contexts. Recent research suggests that uncertainty affects the magnitude of MMN responses in the context of music listening. However, musical predictions are typically studied with MMN stimulation paradigms based on Western tonal music, which are characterized by relatively high predictability. Hence, we developed an MMN paradigm to investigate how the high uncertainty of atonal music modulates predictive processes as indexed by the MMN and behavior. Using MEG in a group of 20 subjects without musical training, we demonstrate that the magnetic MMN in response to pitch, intensity, timbre, and location deviants is evoked in both tonal and atonal melodies, with no significant differences between conditions. In contrast, in a separate behavioral experiment involving 39 non-musicians, participants detected pitch deviants more accurately and rated confidence higher in the tonal than in the atonal musical context. These results indicate that contextual tonal uncertainty modulates processing stages in which conscious awareness is involved, although deviants robustly elicit low-level pre-attentive responses such as the MMN. The achievement of robust MMN responses, despite high tonal uncertainty, is relevant for future studies comparing groups of listeners MMN responses to increasingly ecological music stimuli.

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bioRxiv2021

Fourier SPoC: A costumised machine-learning analysis pipeline for auditory beat-based entrainment in the MEG

We propose here (the informed use) of a customised, data-driven machine-learning pipeline to analyse magnetoencephalography (MEG) in a theoretical source space, with respect to the processing of a regular beat. This hypothesis- and data-driven analysis pipeline allows us to extract the maximally relevant components in MEG source-space, with respect to the oscillatory power in the frequency band of interest and, most importantly, the beat-related modulation of that power. Our pipeline combines Spatio-Spectral Decomposition as a first step to seek activity in the frequency band of interest (SSD, [1]) with a Source Power Co-modulation analysis (SPoC; [2]), which extracts those components that maximally entrain their activity with the given target function, that is here with the periodicity of the beat in the frequency domain (hence, f-SPoC). MEG data (102 magnetometers) from 28 participants passively listening to a 5-min long regular tone sequence with a 400 ms beat period (the "target function" for SPoC) were segmented into epochs of two beat periods each to guarantee a sufficiently long time window. As a comparison pipeline to SSD and f-SpoC, we carried out a state-of-the-art cluster-based permutation analysis (CBPA, [3]). The time-frequency analysis (TFA) of the extracted activity showed clear regular patterns of periodically occurring peaks and troughs across the alpha and beta band (8-20 Hz) in the f-SPoC but not in the CBPA results, and both the depth and the specificity of modulation to the beat frequency yielded a significant advantage. Future applications of this pipeline will address target the relevance to behaviour and inform analogous analyses in the EEG, in order to finally work toward addressing dysfunctions in beat-based timing and their consequences. Author summaryWhen listening to a regular beat, oscillations in the brain have been shown to synchronise with the frequency of that given beat. This phenomenon is called entrainment and has in previous brain-imaging studies been shown in the form of one peak and trough per beat cycle in a range of frequency bands within 15-25 Hz (beta band). Using machine-learning techniques, we designed an analysis pipeline based on Source-Power Co-Modulation (SPoC) that enables us to extract spatial components in MEG recordings that show these synchronisation effects very clearly especially across 8-20 Hz. This approach requires no anatomical knowledge of the individual or even the average brain, it is purely data driven and can be applied in a hypothesis-driven fashion with respect to the "function" that we expect the brain to entrain with and the frequency band within which we expect to see this entrainment. We here apply our customised pipeline using "f-SPoC" to MEG recordings from 28 participants passively listening to a 5-min long tone sequence with a regular 2.5 Hz beat. In comparison to a cluster-based permutation analysis (CBPA) which finds sensors that show statistically significant power modulations across participants, our individually extracted f-SPoC components find a much stronger and clearer pattern of peaks and troughs within one beat cycle. In future work, this pipeline can be implemented to tackle more complex "target functions" like speech and music, and might pave the way toward rhythm-based rehabilitation strategies.

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bioRxiv2020

Rapid encoding of temporal sequences discovered in brain dynamics

Information encoding has received a wide neuroscientific attention, but the underlying rapid spatiotemporal brain dynamics remain largely unknown. Here, we investigated the rapid brain mechanisms for encoding and prediction of sounds forming a complex temporal sequence. Specifically, we used magnetoencephalography (MEG) to record the brain activity of 68 participants while they listened to a highly structured musical prelude. Advanced analysis of the phase synchronisation and graph theoretical measures showed the rapid transition of brain activity from primary auditory cortex to higher order association areas including insula and superior temporal pole within a whole-brain network, occurring during the first 220 ms of the encoding process. We discovered individual differences, revealing the rapid unfolding of brain network dynamics responsible for the processing of the current sounds and the prediction of the forthcoming events of the sequence. This provides a first glimpse of the general mechanisms underlying pattern encoding in the human brain.

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bioRxiv2020

Applying SCA for high-accuracy cortical auditory ERPs in children

Overlapping neurophysiological signals are the main obstacle preventing from using cortical event-related potentials (ERPs) in clinical settings. Children ERPs are particularly affected by this problem, as their cerebral cortex is still maturing. To overcome this problem, we applied a new version of Spike-density Component Analysis (SCA), an analysis method recently introduced, to isolate with high accuracy the neural components of auditory ERP responses (AEPs) in 8-year-old children. Electroencephalography was used with 33 children to record AEPs to auditory stimuli varying in spectrotemporal features. Three different analysis approaches were adopted: the standard ERP analysis procedure, SCA with template-match (SCA-TM), and SCA with half-split average consistency (SCA-HSAC). SCA-HSAC most successfully allowed the extraction of AEPs for each child, revealing that the most consistent components were P1 and N2. An immature N1 component was also detected. Superior accuracy in isolating neural components at the individual level even in children was demonstrated for SCA-HSAC over other SCA approaches. Reliable methods of extraction of neurophysiological signals at the individual level are crucial for the application of cortical AEPs for routine diagnostic exams in clinical settings both in children and adults. HighlightsO_LISpike-density component analysis (SCA) was validated on children ERPs C_LIO_LISCA extracted overlapping neural components from auditory ERPs (AEPs) C_LIO_LIChild AEPs were modelled at the individual level C_LI

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bioRxiv2020

Association between COMT gene Val158Met heterozygote polymorphism and enhanced brain predicting processes

Predicting events in the ever-changing environment is a fundamental survival function intrinsic to the physiology of sensory systems, whose efficiency varies among the population. Even though it is established that a major source of such variations is genetic heritage, there are no studies tracking down auditory predicting processes to genetic mutations. Thus, we examined the neurophysiological responses to deviant stimuli recorded with magnetoencephalography (MEG) in 108 healthy participants carrying different variants of the Val158Met single-nucleotide polymorphism (SNP) within the catechol-O-methyltransferase (COMT) gene, which is responsible for the majority of catecholamines degradation in the prefrontal cortex. Our results showed significant amplitude enhancement of neural responses localized within inferior frontal gyrus, superior and middle temporal cortices to deviant auditory stimuli in heterozygote genotype carriers (Val/Met) vs homozygote (Val/Val and Met/Met) carriers. Integrating neurophysiology and genetics, this study provided new and broader insights into the brain mechanisms underlying optimal deviant detection.

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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.

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