Identification of the Mismatch Negativity in the Responses of Individual Listeners

Dalebout, Susan D.; Fox, Lisa G.

doi:10.1055/s-0042-1748004

“…On a related point, Mathew et al (2017) , found correspondence between behavioral electrode discrimination and objective auditory change complex responses in recently implanted CI users. However, several papers have also reported on MMN and behavioral results that fail to correspond ( Dalebout & Fox, 2000 ; Kurtzberg et al, 1995 ; Ortmann et al, 2017 ; Uwer & von Suchodoletz, 2000 ). Thus, significant MMN responses do not preclude poor behavioral performances, and likewise, nonsignificant MMN responses do not preclude good behavioral performances ( Bishop & Hardiman, 2010 ).…”

Section: Discussionmentioning

confidence: 99%

Adapting to the Sound of Music — Development of Music Discrimination Skills in Recently Implanted CI Users

Seeberg

¹

,

Haumann

²

,

Højlund

³

et al. 2023

Trends in Hearing

3

0

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Cochlear implants (CIs) are optimized for speech perception but poor in conveying musical sound features such as pitch, melody, and timbre. Here, we investigated the early development of discrimination of musical sound features after cochlear implantation. Nine recently implanted CI users (CIre) were tested shortly after switch-on (T1) and approximately 3 months later (T2), using a musical multifeature mismatch negativity (MMN) paradigm, presenting four deviant features (intensity, pitch, timbre, and rhythm), and a three-alternative forced-choice behavioral test. For reference, groups of experienced CI users (CIex; n = 13) and normally hearing (NH) controls ( n = 14) underwent the same tests once. We found significant improvement in CIre's neural discrimination of pitch and timbre as marked by increased MMN amplitudes. This was not reflected in the behavioral results. Behaviorally, CIre scored well above chance level at both time points for all features except intensity, but significantly below NH controls for all features except rhythm. Both CI groups scored significantly below NH in behavioral pitch discrimination. No significant difference was found in MMN amplitude between CIex and NH. The results indicate that development of musical discrimination can be detected neurophysiologically early after switch-on. However, to fully take advantage of the sparse information from the implant, a prolonged adaptation period may be required. Behavioral discrimination accuracy was notably high already shortly after implant switch-on, although well below that of NH listeners. This study provides new insight into the early development of music-discrimination abilities in CI users and may have clinical and therapeutic relevance.

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“…The MMN is a rather robust component observable in virtually all human participants, but in some occasions it might fail to be detected even in healthy participants with intact sound discrimination [ 82 , 83 ]. We used a simple automatic procedure to identify those participants that did not show a detectable MMN.…”

Section: Methodsmentioning

confidence: 99%

Time-resolved dynamic computational modeling of human EEG recordings reveals gradients of generative mechanisms for the MMN response

Poublan-Couzardot,

Lecaignard,

Fucci

et al. 2023

PLoS Comput Biol

1

0

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Despite attempts to unify the different theoretical accounts of the mismatch negativity (MMN), there is still an ongoing debate on the neurophysiological mechanisms underlying this complex brain response. On one hand, neuronal adaptation to recurrent stimuli is able to explain many of the observed properties of the MMN, such as its sensitivity to controlled experimental parameters. On the other hand, several modeling studies reported evidence in favor of Bayesian learning models for explaining the trial-to-trial dynamics of the human MMN. However, direct comparisons of these two main hypotheses are scarce, and previous modeling studies suffered from methodological limitations. Based on reports indicating spatial and temporal dissociation of physiological mechanisms within the timecourse of mismatch responses in animals, we hypothesized that different computational models would best fit different temporal phases of the human MMN. Using electroencephalographic data from two independent studies of a simple auditory oddball task (n = 82), we compared adaptation and Bayesian learning models’ ability to explain the sequential dynamics of auditory deviance detection in a time-resolved fashion. We first ran simulations to evaluate the capacity of our design to dissociate the tested models and found that they were sufficiently distinguishable above a certain level of signal-to-noise ratio (SNR). In subjects with a sufficient SNR, our time-resolved approach revealed a temporal dissociation between the two model families, with high evidence for adaptation during the early MMN window (from 90 to 150-190 ms post-stimulus depending on the dataset) and for Bayesian learning later in time (170-180 ms or 200-220ms). In addition, Bayesian model averaging of fixed-parameter models within the adaptation family revealed a gradient of adaptation rates, resembling the anatomical gradient in the auditory cortical hierarchy reported in animal studies.

show abstract

“…The MMN is a rather robust component observable in virtually all human participants, but in some occasions it might fail to be detected even in healthy participants with intact sound discrimination [80,81]. We used a 10/61 simple automatic procedure to identify those participants that did not show a detectable MMN.…”

Section: Validation Of Individual Mmnmentioning

confidence: 99%

Time-resolved dynamic computational modeling of human EEG recordings reveals gradients of generative mechanisms for the MMN response

Poublan-Couzardot

¹

,

Lecaignard

²

,

Fucci

³

et al. 2022

Preprint

1

0

View full text Add to dashboard Cite

Despite attempts to unify the different theoretical accounts of the mismatch negativity (MMN), there is still an ongoing debate on the neurophysiological mechanisms underlying this complex brain response. On one hand, neuronal adaptation to recurrent stimuli is able to explain many of the observed properties of the MMN, such as its sensitivity to controlled experimental parameters. On the other hand, several modeling studies reported evidence in favor of Bayesian learning models for explaining the trial-to-trial dynamics of the human MMN. However, direct comparisons of these two main hypotheses are scarce, and previous modeling studies suffered from methodological limitations. Based on reports indicating spatial and temporal dissociation of physiological mechanisms within the timecourse of mismatch responses in animals, we hypothesized that different computational models would best fit different temporal phases of the human MMN. Using electroencephalographic data from two independent studies of a simple auditory oddball task (n = 82), we compared adaptation and Bayesian learning models’ ability to explain the sequential dynamics of auditory deviance detection in a time-resolved fashion. We first ran simulations to evaluate the capacity of our design to dissociate the tested models and found that they were sufficiently distinguishable above a certain level of signal-to-noise ratio (SNR). In subjects with a sufficient SNR, our time-resolved approach revealed a temporal dissociation between the two model families, with high evidence for adaptation during the early MMN window (from 90 to 150-190 ms post-stimulus depending on the dataset) and for Bayesian learning later in time (170-180 ms or 200-220ms). In addition, Bayesian model averaging of fixed-parameter models within the adaptation family revealed a gradient of adaptation rates, resembling the anatomical gradient in the auditory cortical hierarchy reported in animal studies.

show abstract

Identification of the Mismatch Negativity in the Responses of Individual Listeners

Cited by 24 publications

References 22 publications

Adapting to the Sound of Music — Development of Music Discrimination Skills in Recently Implanted CI Users

Adapting to the Sound of Music — Development of Music Discrimination Skills in Recently Implanted CI Users

Time-resolved dynamic computational modeling of human EEG recordings reveals gradients of generative mechanisms for the MMN response

Time-resolved dynamic computational modeling of human EEG recordings reveals gradients of generative mechanisms for the MMN response

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