Risk of depression enhances auditory Pitch discrimination in the brain as indexed by the mismatch negativity

Bonetti, Leonardo; Haumann, Niels Trusbak; Vuust, Peter; Kliuchko, Marina; Brattico, Elvira

doi:10.1016/j.clinph.2017.07.004

Cited by 48 publications

(62 citation statements)

References 46 publications

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“…Presence of the MMNm Significant differences were found between standard and deviant ERFs in the 100-250 ms poststimulus time window for all features in both conditions (Figure 3). The differences were present bilaterally, were largest over right temporal gradiometers, and showed a polarity-as observed in the magnetometers-consistent with previous reports of the MMNm (e.g., Bonetti, Haumann, Vuust, Kliuchko, & Brattico, 2017) (Figure 4).…”

Section: Meg Experimentssupporting

confidence: 91%

Reduced prediction error responses in high- as compared to low-uncertainty musical contexts

Quiroga-Martinez

Hansen

Hoejlund

et al. 2018

Preprint

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Highlights:• The mismatch negativity (MMNm) is reduced in musical contexts with high pitch uncertainty • The MMNm reduction is restricted to pitch-related features • Accuracy during deviance detection is reduced in contexts with higher uncertainty • The results suggest a feature-selective precision modulation of prediction error Materials, data and scripts can be found in the Open Science Framework repository: AbstractTheories 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.

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Section: Meg Experimentssupporting

confidence: 91%

Reduced prediction error responses in high- as compared to low-uncertainty musical contexts

Quiroga-Martinez

Hansen

Hoejlund

et al. 2018

Preprint

Self Cite

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“…Deficits in mismatch processing were confirmed in subsequent investigations (Chen et al, 2015;Qiao et al, 2013). In contrast, other studies reported increased MMN amplitudes in patients with depression (He et al, 2010;Kähkönen et al, 2007) as well as an association between the risk for depression and higher MMN amplitudes in a subclinical sample (Bonetti, Haumann, Vuust, Kliuchko, & Brattico, 2017). In contrast, other studies reported increased MMN amplitudes in patients with depression (He et al, 2010;Kähkönen et al, 2007) as well as an association between the risk for depression and higher MMN amplitudes in a subclinical sample (Bonetti, Haumann, Vuust, Kliuchko, & Brattico, 2017).…”

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confidence: 67%

“…For instance, Pang et al (2014) found impaired MMN to sad syllables, whereas happy, angry, and neutral deviants were not processed differently in patients compared to healthy individuals. In contrast, other studies reported increased MMN amplitudes in patients with depression (He et al, 2010;Kähkönen et al, 2007) as well as an association between the risk for depression and higher MMN amplitudes in a subclinical sample (Bonetti, Haumann, Vuust, Kliuchko, & Brattico, 2017). Mu et al (2016) investigated mismatch responses to musical features and found increased timbre-MMN in depression but no differences for the remaining five sound features.…”

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confidence: 92%

Fronto‐parietal and temporal brain dysfunction in depression: A fMRI investigation of auditory mismatch processing

Zweerings

Zvyagintsev

Turetsky

et al. 2019

Human Brain Mapping

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Mismatch responses reflect neural mechanisms of early cognitive processing in the auditory domain. Disturbances of these mechanisms on multiple levels of neural processing may contribute to clinical symptoms in major depression (MD). A functional magnetic resonance imaging (fMRI) study was conducted to identify neurobiological foundations of altered mismatch processing in MD. Twenty‐five patients with major depression and 25 matched healthy individuals completed an auditory mismatch paradigm optimized for fMRI. Brain activity during mismatch processing was compared between groups. Moreover, seed‐based connectivity analyses investigated depression‐specific brain networks. In patients, mismatch processing was associated with reduced activation in the right auditory cortex as well as in a fronto‐parietal attention network. Moreover, functional coupling between the right auditory cortex and frontal areas was reduced in patients. Seed‐to voxel analysis on the whole‐brain level revealed reduced connectivity between the auditory cortex and the thalamus as well as posterior cingulate. The present study indicates deficits in sensory processing on the level of the auditory cortex in depression. Hyposensitivity in a fronto‐parietal network presumably reflects altered attention mechanisms in depression. The observed impairments may contribute to psychopathology by reducing the ability of the affected individuals to orient attention toward important environmental cues.

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“…While Study 1 and Study 2 showed that the SCA method can be applied to isolate a specific component of interest in single-subject measurements, it remains to be verified whether SCA is a reliable method for the study of inter-individual differences measurable in a specific component. In particular, we tested whether the previous findings of increased MMN amplitude to pitch and slide deviants measured in the MEG gradiometers in subjects with higher traits of depression (Bonetti et al, 2017) could be replicated with the SCA method.…”

Section: Interim Discussionmentioning

confidence: 99%

“…Furthermore, in our third study we tested whether previous findings of individual differences in MMN amplitude related to depressive traits analyzed using conventional functional localizers (Bonetti et al, 2017) could be replicated with the novel SCA method.…”

Section: Research Questions and Hypothesesmentioning

confidence: 96%

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

Haumann

Huotilainen²,

Vuust

et al. 2019

Preprint

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Acknowledgments: Center for Music in the Brain is funded by the Danish National Research Foundation (DNRF117). Comments:The theory and spike density component analysis with Gaussian functions was initially developed, tested and reported by the first author in an early version of this manuscript in July 2016.High-accuracy MEG/EEG 2 AbstractThe 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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Risk of depression enhances auditory Pitch discrimination in the brain as indexed by the mismatch negativity

Cited by 48 publications

References 46 publications

Reduced prediction error responses in high- as compared to low-uncertainty musical contexts

Reduced prediction error responses in high- as compared to low-uncertainty musical contexts

Fronto‐parietal and temporal brain dysfunction in depression: A fMRI investigation of auditory mismatch processing

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

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