Publications by authors named "Niels Trusbak Haumann"

6 Publications

  • Page 1 of 1

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

Brain Res 2021 Sep 21:147664. Epub 2021 Sep 21.

Center for Music in the Brain, Department of Clinical Medicine, Aarhus University & The Royal Academy of Music, Aarhus/Aalborg, Nørrebrogade 44, 8000 Aarhus C, Denmark; Department of Education, Psychology and Communication, University of Bari Aldo Moro, Piazza Umberto I, 70121 Bari, Italy.

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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http://dx.doi.org/10.1016/j.brainres.2021.147664DOI Listing
September 2021

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

J Neurosci Methods 2020 07 25;340:108743. Epub 2020 Apr 25.

Center for Music in the Brain, Department of Clinical Medicine, Aarhus University and Royal Academy of Music, Aarhus/Aalborg, Nørrebrogade 44, 8000 Aarhus C, Denmark; BioMag Laboratory, HUS Medical Imaging Center, University of Helsinki and Helsinki University Hospital, 00100 Helsinki, Finland; Department of Education, Psychology, Communication, University of Bari Aldo Moro, Italy.

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.
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http://dx.doi.org/10.1016/j.jneumeth.2020.108743DOI Listing
July 2020

The CI MuMuFe - A New MMN Paradigm for Measuring Music Discrimination in Electric Hearing.

Front Neurosci 2020 23;14. Epub 2020 Jan 23.

Center for Music in the Brain, Department of Clinical Medicine, Aarhus University and The Royal Academy of Music, Aarhus/Aalborg, Aarhus, Denmark.

Cochlear implants (CIs) allow good perception of speech while music listening is unsatisfactory, leading to reduced music enjoyment. Hence, a number of ongoing efforts aim to improve music perception with a CI. Regardless of the nature of these efforts, effect measurements must be valid and reliable. While auditory skills are typically examined by behavioral methods, recording of the mismatch negativity (MMN) response, using electroencephalography (EEG), has recently been applied successfully as a supplementary objective measure. Eleven adult CI users and 14 normally hearing (NH) controls took part in the present study. To measure their detailed discrimination of fundamental features of music we applied a new multifeature MMN-paradigm which presented four music deviants at four levels of magnitude, incorporating a novel "no-standard" approach to be tested with CI users for the first time. A supplementary test measured behavioral discrimination of the same deviants and levels. The MMN-paradigm elicited significant MMN responses to all levels of deviants in both groups. Furthermore, the CI-users' MMN amplitudes and latencies were not significantly different from those of NH controls. Both groups showed MMN strength that was in overall alignment with the deviation magnitude. In CI users, however, discrimination of pitch levels remained undifferentiated. On average, CI users' behavioral performance was significantly below that of the NH group, mainly due to poor pitch discrimination. Although no significant effects were found, CI users' behavioral results tended to be in accordance with deviation magnitude, most prominently manifested in discrimination of the rhythm deviant. In summary, the study indicates that CI users may be able to discriminate subtle changes in basic musical features both in terms of automatic neural responses and of attended behavioral detection. Despite high complexity, the new CI MuMuFe paradigm and the "no-standard" approach provided reliable results, suggesting that it may serve as a relevant tool in future CI research. For clinical use, future studies should investigate the possibility of applying the paradigm with the purpose of assessing discrimination skills not only at the group level but also at the individual level.
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http://dx.doi.org/10.3389/fnins.2020.00002DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6990974PMC
January 2020

Weighting of neural prediction error by rhythmic complexity: A predictive coding account using mismatch negativity.

Eur J Neurosci 2019 06 20;49(12):1597-1609. Epub 2019 Jan 20.

Department of Clinical Medicine, Center for Music in the Brain, Aarhus University & The Royal Academy of Music, Aarhus C, Denmark.

The human brain's ability to extract and encode temporal regularities and to predict the timing of upcoming events is critical for music and speech perception. This work addresses how these mechanisms deal with different levels of temporal complexity, here the number of distinct durations in rhythmic patterns. We use electroencephalography (EEG) to relate the mismatch negativity (MMN), a proxy of neural prediction error, to a measure of information content of rhythmic sequences, the Shannon entropy. Within each of three conditions, participants listened to repeatedly presented standard rhythms of five tones (four inter-onset intervals) and of a given level of entropy: zero (isochronous), medium entropy (two distinct interval durations), or high entropy (four distinct interval durations). Occasionally, the fourth tone was moved forward in time that is it occurred 100 ms (small deviation) or 300 ms early (large deviation). According to the predictive coding framework, high-entropy stimuli are more difficult to model for the brain, resulting in less confident predictions and yielding smaller prediction errors for deviant sounds. Our results support this hypothesis, showing a gradual decrease in MMN amplitude as a function of entropy, but only for small timing deviants. For large timing deviants, in contrast, a modulation of activity in the opposite direction was observed for the earlier N1 component, known to also be sensitive to sudden changes in directed attention. Our results suggest the existence of a fine-grained neural mechanism that weights neural prediction error to the complexity of rhythms and that mostly manifests in the absence of directed attention.
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http://dx.doi.org/10.1111/ejn.14329DOI Listing
June 2019

From random to regular: neural constraints on the emergence of isochronous rhythm during cultural transmission.

Soc Cogn Affect Neurosci 2018 09;13(8):877-888

SISSA International School for Advanced Studies, 34136 Trieste, Italy.

A core design feature of human communication systems and expressive behaviours is their temporal organization. The cultural evolutionary origins of this feature remain unclear. Here, we test the hypothesis that regularities in the temporal organization of signalling sequences arise in the course of cultural transmission as adaptations to aspects of cortical function. We conducted two experiments on the transmission of rhythms associated with affective meanings, focusing on one of the most widespread forms of regularity in language and music: isochronicity. In the first experiment, we investigated how isochronous rhythmic regularities emerge and change in multigenerational signalling games, where the receiver (learner) in a game becomes the sender (transmitter) in the next game. We show that signalling sequences tend to become rhythmically more isochronous as they are transmitted across generations. In the second experiment, we combined electroencephalography (EEG) and two-player signalling games over 2 successive days. We show that rhythmic regularization of sequences can be predicted based on the latencies of the mismatch negativity response in a temporal oddball paradigm. These results suggest that forms of isochronicity in communication systems originate in neural constraints on information processing, which may be expressed and amplified in the course of cultural transmission.
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http://dx.doi.org/10.1093/scan/nsy054DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6123518PMC
September 2018

Comparing the Performance of Popular MEG/EEG Artifact Correction Methods in an Evoked-Response Study.

Comput Intell Neurosci 2016 21;2016:7489108. Epub 2016 Jul 21.

Center for Music in the Brain, Department of Clinical Medicine, Aarhus University and Royal Academy of Music, Aarhus/Aalborg, Nørrebrogade 44, Building 10G, 4th and 5th floor, 8000 Aarhus C, Denmark.

We here compared results achieved by applying popular methods for reducing artifacts in magnetoencephalography (MEG) and electroencephalography (EEG) recordings of the auditory evoked Mismatch Negativity (MMN) responses in healthy adult subjects. We compared the Signal Space Separation (SSS) and temporal SSS (tSSS) methods for reducing noise from external and nearby sources. Our results showed that tSSS reduces the interference level more reliably than plain SSS, particularly for MEG gradiometers, also for healthy subjects not wearing strongly interfering magnetic material. Therefore, tSSS is recommended over SSS. Furthermore, we found that better artifact correction is achieved by applying Independent Component Analysis (ICA) in comparison to Signal Space Projection (SSP). Although SSP reduces the baseline noise level more than ICA, SSP also significantly reduces the signal-slightly more than it reduces the artifacts interfering with the signal. However, ICA also adds noise, or correction errors, to the waveform when the signal-to-noise ratio (SNR) in the original data is relatively low-in particular to EEG and to MEG magnetometer data. In conclusion, ICA is recommended over SSP, but one should be careful when applying ICA to reduce artifacts on neurophysiological data with relatively low SNR.
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http://dx.doi.org/10.1155/2016/7489108DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4972935PMC
February 2017
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