Publications by authors named "N T Haumann"

10 Publications

Applying Spike-density component analysis for high-accuracy auditory event-related potentials in children.

Clin Neurophysiol 2021 Aug 29;132(8):1887-1896. Epub 2021 May 29.

Center for Music in the Brain, Department of Clinical Medicine, Aarhus University and Royal Academy of Music, Aarhus/Aalborg, Universitetsbyen 3, 8000 Aarhus C, Denmark; Department of Education, Psychology, Communication, University of Bari Aldo Moro, Italy.

Objective: Overlapping neurophysiological signals are the main obstacle preventing from using cortical auditory event-related potentials (AEPs) in clinical settings. Children AEPs 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 developed, to isolate with high accuracy the neural components of auditory responses of 8-year-old children.

Methods: Electroencephalography was used with 33 children to record AEPs to auditory stimuli varying in spectrotemporal features. Three different analysis approaches were adopted: the standard AEP analysis procedure, SCA with template-match (SCA-TM), and SCA with half-split average consistency (SCA-HSAC).

Results: 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.

Conclusion: Superior accuracy in isolating neural components at the individual level was demonstrated for SCA-HSAC over other SCA approaches even for children AEPs.

Significance: 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.
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http://dx.doi.org/10.1016/j.clinph.2021.05.007DOI Listing
August 2021

Brain predictive coding processes are associated to COMT gene Val158Met polymorphism.

Neuroimage 2021 06 11;233:117954. Epub 2021 Mar 11.

Center for Music in the Brain, Department of Clinical Medicine, Aarhus University & The Royal Academy of Music Aarhus/Aalborg, Denmark; Department of Education, Psychology, Communication, University of Bari Aldo Moro, Italy.

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 Val158Met single-nucleotide polymorphism (SNP) within the catechol-O-methyltransferase (COMT) gene, responsible for the majority of catecholamines degradation in the prefrontal cortex. Our results showed significant amplitude enhancement of prediction error responses originating from the inferior frontal gyrus, superior and middle temporal cortices in heterozygous genotype carriers (Val/Met) vs homozygous (Val/Val and Met/Met) carriers. Integrating neurophysiology and genetics, this study shows how the neural mechanisms underlying optimal deviant detection vary according to the gene-determined cathecolamine levels in the brain.
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http://dx.doi.org/10.1016/j.neuroimage.2021.117954DOI Listing
June 2021

Extracting human cortical responses to sound onsets and acoustic feature changes in real music, and their relation to event rate.

Brain Res 2021 03 6;1754:147248. Epub 2021 Jan 6.

Center for Music in the Brain, Department of Clinical Medicine, Aarhus University & The Royal Academy of Music, Aarhus/Aalborg, Denmark; Department of Education, Psychology, Communication, University of Bari Aldo Moro, Italy. Electronic address:

Evoked cortical responses (ERs) have mainly been studied in controlled experiments using simplified stimuli. Though, an outstanding question is how the human cortex responds to the complex stimuli encountered in realistic situations. Few electroencephalography (EEG) studies have used Music Information Retrieval (MIR) tools to extract cortical P1/N1/P2 to acoustical changes in real music. However, less than ten events per music piece could be detected leading to ERs due to limitations in automatic detection of sound onsets. Also, the factors influencing a successful extraction of the ERs have not been identified. Finally, previous studies did not localize the sources of the cortical generators. This study is based on an EEG/MEG dataset from 48 healthy normal hearing participants listening to three real music pieces. Acoustic features were computed from the audio signal of the music with the MIR Toolbox. To overcome limits in automatic methods, sound onsets were also manually detected. The chance of obtaining detectable ERs based on ten randomly picked onset points was less than 1:10,000. For the first time, we show that naturalistic P1/N1/P2 ERs can be reliably measured across 100 manually identified sound onsets, substantially improving the signal-to-noise level compared to <10 trials. More ERs were measurable in musical sections with slow event rates (0.2 Hz-2.5 Hz) than with fast event rates (>2.5 Hz). Furthermore, during monophonic sections of the music only P1/P2 were measurable, and during polyphonic sections only N1. Finally, MEG source analysis revealed that naturalistic P2 is located in core areas of the auditory cortex.
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http://dx.doi.org/10.1016/j.brainres.2020.147248DOI Listing
March 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
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