A Predictive Coding Perspective on Mismatch Negativity Impairment in Schizophrenia

The auditory mismatch negativity (MMN) has been considered a preattentive index of auditory processing and/or a signature of prediction error computation. This study tries to demonstrate the presence of an MMN to deviant trials included in complex auditory stimuli sequences, and its possible relationship to predictive coding. Additionally, the transfer of information between trials is expected to be represented by stimulus-preceding negativity (SPN), which would possibly fit the predictive coding framework. To accomplish these objectives, the EEG of 31 subjects was recorded during an auditory paradigm in which trials composed of stimulus sequences with increasing or decreasing frequencies were intermingled with deviant trials presenting an unexpected ending. Our results showed the presence of an MMN in response to deviant trials. An SPN appeared during the intertrial interval and its amplitude was reduced in response to deviant trials. The presence of an MMN in complex sequences of sounds and the generation of an SPN component, with different amplitudes in deviant and standard trials, would support the predictive coding framework.

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Ketamine Affects Prediction Errors about Statistical Regularities: A Computational Single-Trial Analysis of the Mismatch Negativity

10.1101/528372 ◽

2019 ◽

Cited By ~ 5

Author(s):

Lilian A. Weber ◽

Andreea O. Diaconescu ◽

Christoph Mathys ◽

André Schmidt ◽

Michael Kometer ◽

...

Keyword(s):

Mismatch Negativity ◽

Transition Probability ◽

Predictive Coding ◽

Prediction Errors ◽

Hierarchical Bayesian ◽

Single Trial ◽

Perceptual Inference ◽

Trial Analysis ◽

Statistical Regularities ◽

High Level

AbstractThe auditory mismatch negativity (MMN) is significantly reduced in schizophrenia. Notably, a similar MMN reduction can be achieved with NMDA receptor (NMDAR) antagonists. Both phenomena have been interpreted as reflecting an impairment of predictive coding or, more generally, the “Bayesian brain” notion that the brain continuously updates a hierarchical model to infer the causes of its sensory inputs. Specifically, predictive coding views perceptual inference as an NMDAR-dependent process of minimizing hierarchical precision-weighted prediction errors (PEs). Disturbances of this putative process play a key role in hierarchical Bayesian theories of schizophrenia.Here, we provide empirical evidence for this clinical theory, demonstrating the existence of multiple, hierarchically related PEs in a “roving MMN” paradigm. We applied a computational model, the Hierarchical Gaussian Filter (HGF), to single-trial EEG data from healthy volunteers that received the NMDAR antagonist S-ketamine in a placebo-controlled, double-blind, within-subject fashion. Using an unrestricted analysis of the entire time-sensor space, our computational trial-by-trial analysis indicated that low-level PEs (about stimulus transitions) are expressed early (102-207ms post-stimulus), while high-level PEs (about transition probability) are reflected by later components (152-199ms, 215-277ms) of single-trial responses. Furthermore, we find that ketamine significantly diminished the expression of high-level PE responses, implying that NMDAR antagonism disrupts inference on abstract statistical regularities.Our findings are consistent with long-standing notions that NMDAR dysfunction may cause positive symptoms in schizophrenia by impairing hierarchical Bayesian inference about the world’s statistical structure. Beyond their relevance for schizophrenia, our results illustrate the potential of computational single-trial analyses for assessing potential disease mechanisms.

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Brain Signals Related to Change Detection

MEG-EEG Primer ◽

10.1093/med/9780190497774.003.0017 ◽

2017 ◽

pp. 262-276

Author(s):

Riitta Hari ◽

Aina Puce

Keyword(s):

Change Detection ◽

Mismatch Negativity ◽

Predictive Coding ◽

Evoked Responses ◽

Stimulus Probability ◽

Error Related Negativity ◽

Brain Signals ◽

Low Probability

This chapter discusses, in the context of the predictive-coding framework, evoked responses to various changes in the environment and describes how the responses are related to variations in stimulus probability and the subject’s expectations. The focus is on three well-known responses: (a) the mismatch negativity peaking at 100 to 250 ms and elicited to changes in stimulus attributes, even when the stimuli are not attended to, (b) the P300 response peaking about 300 ms after attended low-probability “oddball” stimuli, and (c) the N400 peaking about 400 ms after semantic or lexical violations of sentences presented either visually or auditorily. Continent negative variation and error-related negativity are introduced as well.

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Auditory Mismatch Negativity Under Predictive Coding Framework and Its Role in Psychotic Disorders

Frontiers in Psychiatry ◽

10.3389/fpsyt.2020.557932 ◽

2020 ◽

Vol 11 ◽

Author(s):

Chun Yuen Fong ◽

Wai Him Crystal Law ◽

Takanori Uka ◽

Shinsuke Koike

Keyword(s):

Mismatch Negativity ◽

Psychotic Disorders ◽

Predictive Coding

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Parsing components of auditory predictive coding in schizophrenia using a roving standard mismatch negativity paradigm

Psychological Medicine ◽

10.1017/s0033291718004087 ◽

2019 ◽

Vol 49 (07) ◽

pp. 1195-1206 ◽

Cited By ~ 8

Author(s):

Amanda McCleery ◽

Daniel H. Mathalon ◽

Jonathan K. Wynn ◽

Brian J. Roach ◽

Gerhard S. Hellemann ◽

...

Keyword(s):

Mismatch Negativity ◽

Prediction Error ◽

Memory Trace ◽

Predictive Coding ◽

Event Related Potential ◽

Group Differences ◽

Difference Wave ◽

The Third ◽

Intraclass Correlations ◽

Test Retest Reliability

AbstractBackgroundMismatch negativity (MMN) is an event-related potential (ERP) component reflecting auditory predictive coding. Repeated standard tones evoke increasing positivity (‘repetition positivity’; RP), reflecting strengthening of the standard's memory trace and the prediction it will recur. Likewise, deviant tones preceded by more standard repetitions evoke greater negativity (‘deviant negativity’; DN), reflecting stronger prediction error signaling. These memory trace effects are also evident in MMN difference wave. Here, we assess group differences and test-retest reliability of these indices in schizophrenia patients (SZ) and healthy controls (HC).MethodsElectroencephalography was recorded twice, 2 weeks apart, from 43 SZ and 30 HC, during a roving standard paradigm. We examined ERPs to the third, eighth, and 33rd standards (RP), immediately subsequent deviants (DN), and the corresponding MMN. Memory trace effects were assessed by comparing amplitudes associated with the three standard repetition trains.ResultsCompared with controls, SZ showed reduced MMNs and DNs, but normal RPs. Both groups showed memory trace effects for RP, MMN, and DN, with a trend for attenuated DNs in SZ. Intraclass correlations obtained via this paradigm indicated good-to-moderate reliabilities for overall MMN, DN and RP, but moderate to poor reliabilities for components associated with short, intermediate, and long standard trains, and poor reliability of their memory trace effects.ConclusionMMN deficits in SZ reflected attenuated prediction error signaling (DN), with relatively intact predictive code formation (RP) and memory trace effects. This roving standard MMN paradigm requires additional development/validation to obtain suitable levels of reliability for use in clinical trials.

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Enhanced mismatch negativity responses in harmonic compared to inharmonic sound sequences

10.1101/2021.10.15.464562 ◽

2021 ◽

Author(s):

David Ricardo Quiroga-Martinez ◽

Krzysztof Basinski ◽

Jonathan Nasielski ◽

Barbara Tillmann ◽

Elvira Brattico ◽

...

Keyword(s):

Information Entropy ◽

Mismatch Negativity ◽

Predictive Coding ◽

Predictive Processing ◽

Oddball Paradigm ◽

Auditory Information ◽

Prediction Errors ◽

Frequency Spectra ◽

Auditory Information Processing ◽

The Difference

Many natural sounds have frequency spectra composed of integer multiples of a fundamental frequency. This property, known as harmonicity, plays an important role in auditory information processing. However, the extent to which harmonicity influences the processing of sound features beyond pitch is still unclear. This is interesting because harmonic sounds have lower information entropy than inharmonic sounds. According to predictive processing accounts of perception, this property could produce more salient neural responses due to the brain weighting of sensory signals according to their uncertainty. In the present study, we used electroencephalography to investigate brain responses to harmonic and inharmonic sounds commonly occurring in music: piano tones and hi-hat cymbal sounds. In a multi-feature oddball paradigm, we measured mismatch negativity (MMN) and P3a responses to timbre, intensity, and location deviants in listeners with and without congenital amusia, an impairment of pitch processing. As hypothesized, we observed larger amplitudes and earlier latencies for harmonic compared to inharmonic sounds for both MMN and P3a responses. These harmonicity effects were modulated by sound feature. Moreover, the difference in P3a latency between harmonic and inharmonic sounds was larger for controls than amusics. We propose an explanation of these results based on predictive coding and discuss the relationship between harmonicity, information entropy, and precision weighting of prediction errors.

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Standard Tone Stability as a Manipulation of Precision in the Oddball Paradigm: Modulation of Prediction Error Responses to Fixed-Probability Deviants

Frontiers in Human Neuroscience ◽

10.3389/fnhum.2021.734200 ◽

2021 ◽

Vol 15 ◽

Author(s):

Iria SanMiguel ◽

Jordi Costa-Faidella ◽

Zulay R. Lugo ◽

Elisabet Vilella ◽

Carles Escera

Keyword(s):

Correlation Analysis ◽

Mismatch Negativity ◽

Prediction Error ◽

Pure Tone ◽

Predictive Coding ◽

Multiple Comparisons ◽

Oddball Paradigm ◽

High Confidence ◽

Low Probability ◽

Electroencephalogram Eeg

Electrophysiological sensory deviance detection signals, such as the mismatch negativity (MMN), have been interpreted from the predictive coding framework as manifestations of prediction error (PE). From a frequentist perspective of the classic oddball paradigm, deviant stimuli are unexpected because of their low probability. However, the amount of PE elicited by a stimulus can be dissociated from its probability of occurrence: when the observer cannot make confident predictions, any event holds little surprise value, no matter how improbable. Here we tested the hypothesis that the magnitude of the neural response elicited to an improbable sound (D) would scale with the precision of the prediction derived from the repetition of another sound (S), by manipulating repetition stability. We recorded the Electroencephalogram (EEG) from 20 participants while passively listening to 4 types of isochronous pure tone sequences differing in the probability of the S tone (880 Hz) while holding constant the probability of the D tone [1,046 Hz; p(D) = 1/11]: Oddball [p(S) = 10/11]; High confidence (7/11); Low confidence (4/11); and Random (1/11). Tones of 9 different frequencies were equiprobably presented as fillers [p(S) + p(D) + p(F) = 1]. Using a mass-univariate non-parametric, cluster-based correlation analysis controlling for multiple comparisons, we found that the amplitude of the deviant-elicited ERP became more negative with increasing S probability, in a time-electrode window consistent with the MMN (ca. 120–200 ms; frontal), suggesting that the strength of a PE elicited to an improbable event indeed increases with the precision of the predictive model.

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