Do rat auditory event related potentials exhibit human mismatch negativity attributes related to predictive coding?

Jalewa, Jaishree; Todd, Juanita; Michie, Patricia T.; Hodgson, Deborah M.; Harms, Lauren

doi:10.1016/j.heares.2020.107992

“…Our findings suggest that a negative component in the MMR with a peak latency of approximately 55 ms is the most human-like rat MMR in the awake alert animal, as this component exhibits adaptation independent prediction error (Harms et al, 2014;Nakamura et al, 2011) and is substantially reduced with NMDAr antagonism (Harms et al, 2018). Furthermore, we and others have also found that the size of the negative MMR component at approximately 55 ms (referred as N54 component hereafter in this manuscript) scales in response to stimulus paradigm differences, where larger differences between the frequency (in Hz and perceived pitch) of the DEV and STD (Jalewa et al, 2021;Ruusuvirta et al, 2015;Shiramatsu et al, 2013), less probable DEV stimuli (Jalewa et al, 2021;Jung et al, 2013;Sivarao et al, 2014), and more temporally stable sound sequences individually induce larger MMRs in control rats (Astikainen et al, 2011;Jalewa et al, 2021). Taken together, these findings strengthen the notion that the rat brain can produce an MMR that shares many features with human MMN and that the N54 MMR component is the most human-like, facilitating further work aimed at observing schizophrenia-like MMR deficits in rat models of the disorder.…”

Section: Introductionsupporting

confidence: 55%

“…The latency ranges of individual ERP components were determined based on the morphologies of the ERPs, taken from grand averages and thereafter, their mean amplitude extracted for each rat. While multiple peaks were evident in the ERPs, only a negative component peaking at 54 ms, N54, was measured here as it is the most MMN like of the rat ERP peaks (Jalewa et al, 2021). Mean amplitude measures for N54 MMR component were extracted over latency windows 41.5-67.5 ms and 44-67 ms for the deviance difference experiment and the deviant probability experiment, respectively, using EEG Display (version 6.4.1).…”

Section: Data Extractionmentioning

confidence: 99%

The effect of schizophrenia risk factors on mismatch responses in a rat model

Jalewa

¹

,

Todd

²

,

Michie

³

et al. 2022

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Reduced mismatch negativity (MMN), a robust finding in schizophrenia, has prompted interest in MMN as a preclinical biomarker of schizophrenia. The rat brain can generate human‐like mismatch responses (MMRs) which therefore enables the exploration of the neurobiology of reduced MMRs. Given epidemiological evidence that two developmental factors, maternal infection and adolescent cannabis use, increase the risk of schizophrenia, we determined the effect of these two developmental risk factors on rat MMR amplitude in different auditory contexts. MMRs were assessed in awake adult male and female Wistar rats that were offspring of pregnant dams treated with either a viral infection mimetic (poly I:C) inducing maternal immune activation (MIA) or saline control. In adolescence, subgroups of the prenatal treatment groups were exposed to either a synthetic cannabinoid (adolescent cannabinoid exposure: ACE) or vehicle. The context under which MMRs were obtained was manipulated by employing two different oddball paradigms, one that manipulated the physical difference between rare and common auditory stimuli, and another that manipulated the probability of the rare stimulus. The design of the multiple stimulus sequences across the two paradigms also allowed an investigation of context on MMRs to two identical stimulus sequences. Male offspring exposed to each of the risk factors for schizophrenia (MIA, ACE or both) showed a reduction in MMR, which was evident only in the probability paradigm, with no effects seen in the physical difference. Our findings highlight the importance of contextual factors induced by paradigm manipulations and sex for modeling schizophrenia‐like MMN impairments in rats.

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“…Recent reviews-combined with novel empirical work-confirmed a high degree of homology between human MMN and rodent mismatch responses [101][102][103], justifying the use of translational research to better characterize the neurocognitive mechanisms of auditory perception. The gradient of generative mechanisms revealed by the present computational study is strongly reminiscent of electrophysiological results obtained in rats.…”

Section: Relationship To Results From Animal Studiesmentioning

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

2

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.

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“…Recent reviews-combined with novel empirical work-confirmed a high degree of homology between human MMN and rodent mismatch responses [97][98][99], justifying the use of translational research to better characterize the neurocognitive mechanisms of auditory perception. The gradient of generative mechanisms revealed by the present computational study is strongly reminiscent of electrophysiological results obtained in rats.…”

Section: Relationship To Results From Animal Studiesmentioning

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.

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Do rat auditory event related potentials exhibit human mismatch negativity attributes related to predictive coding?

Cited by 10 publications

References 194 publications

The effect of schizophrenia risk factors on mismatch responses in a rat model

The effect of schizophrenia risk factors on mismatch responses in a rat model

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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