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Furthermore, the duration MMN was proposed as an index trait marker of schizophrenia, whereas the frequency MMN was proposed as an index of progressive brain pathology related to the disorder [160, 161].

3.2 The mechanisms of MMN generation

The mechanisms underlying the MMN response are highly debated and centered around three major conceptual frameworks: the neuronal adaptation, the sensory memory and regularity violation and the predictive coding hypotheses [133].

Over the past two decades, the theoretical focus has shifted from predominantly feed-forward hypotheses, such as the neuronal adaptation hypothesis, where the lower sensory areas propagate information to higher level of analysis, to models where feedback projections to lower level areas apply considerable top-down modulation, such as the predictive coding hypothesis.

Adaptation hypothesis

Sensory auditory processing is dynamic and neurons throughout the auditory pathways alter their response properties to external stimuli depending on the contextual statistics; therefore, by adjusting the response strength to repetitive stimuli, the auditory neurons increase sensitivity to changes in those stimuli [162]. The most prevalent form of neuronal adjustment is proposed to happen through “stimulus-specific adaptation”, a mechanism that may serve to maximize information transmission and may enhance the ability to differentiate auditory stimuli [162].

The neuronal adaptation hypothesis proposes that the MMN is generated due to an enhanced sensitivity to unexpected stimuli following a specific decrease in the response to a stream of repetitive stimuli [163]. The stimulus specific adaptation occurs along the auditory pathway at different levels in the auditory hierarchy. It has been measured across multiple neurons within inferior colliculi in the midbrain, thalamic medial geniculate nuclei, and primary auditory cortices [164].

Further, Jääskeläinen et al. (2004) proposed that the cortical neuronal adaptation may also generate supressed and delayed early sensory responses, such as N100 [165]. In their view, the MMN response is an enhanced N100 generated as differential adaptation of cortical sources, like anterior and posterior auditory cortex, to changes within the auditory stream that creates an illusory difference between the N100 and MMN [165].

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Nevertheless, intra-cortical recordings in primates and rodents can distinguish the effects of deviance detection from those of stimulus adaptation and indicate that these two phenomena are produced by distinct thalamocortical circuits [107, 157].

One point of controversy is that the MMN can be elicited also in the absence of a repetitive input that could cause adaptation, such as ascending and descending tones pair that violate an abstract rule of complex inter-stimulus relationships [166]. The MMN is also generated by silent gaps replacing the deviants [167], by phonemic processing and discrimination [168] and by a sequence of paired stimuli that differ in intensity (Low-High Low-High)[169]. These studies challenge the adaptation hypothesis and strengthen the idea that the MMN might be a genuine deviance detector. Furthermore, many studies suggested that MMN response generation relies on perceptual awareness [170] and triggers a reflexive reorientation attention switch engaging a widespread temporo-frontal network [138].

Regularity violation (Deviance detection)

The early work by Winkler and Näätänen [171] suggests that the MMN may appear due to the incongruency between the current sound input and the previous inputs stored in the short-memory trace. An argument proposed in favour of this hypothesis is that the MMN response is decreased by long (>10s) and short (>150ms) inter-stimulus-intervals, meaning that MMN may be caused by the mismatch between the expected stimulus represented in the sensory-memory trace and a deviant input [172].

Based on the sensory memory hypothesis, the authors [171, 173, 174] propose that MMN is elicited in response to a rule violation. The brain constructs an internal neural representation of patterns or regularities based on the input it receives and when there is a mismatch between the incoming stimulus and the brain expectation, the MMN is elicited. In the context of the regularity-violation hypothesis, the main function of the MMN is to accurately form short-memory traces based on the incoming stimulus, and to detect errors caused by deviations from that learned regularity [175].

The predictive coding hypothesis

The predictive coding hypothesis was proposed by Garrido et al. (2009) as a framework that merge both the neural adaptation hypothesis and the regularity violation or model-adjustment hypothesis [133].

One of the first theoretical concepts of predictive coding was proposed in 1867 by the German physicist Hermann von Helmholtz as unconscious inference, a term referring to the influence of prior experiences on the perception of ongoing sensory input. The contemporary

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version of Helmholtz’s theory is generally incorporated in the predictive coding hypothesis [176].

Central to this hypothesis is the hierarchical Bayesian model of perceptual learning, a probabilistic model that generates predictions about the incoming sensory input based on internal neural representations built through prior experiences [177]. The MMN would thus result from a mismatch between the predicted input (higher-level areas) and the incoming sensory information (lower-level areas) that triggers a prediction error signal used to update higher-level representations to optimize future predictions [112, 133]. In this view, the brain is processing the input actively and based on the internal representation stored in the sensory memory as prior distribution it predicts what is the most likely cause of the input. It is worth mentioning that alongside with the internal causal representation of the input, most of the MMN paradigms also involve an internal timing prediction of when the next stimulus should come.

This hypothesis was proposed by Arnal and Giraud (2012) and suggests specific linkages between sensory predictions of when and what, and neuronal oscillatory activity in defined frequency bands, such as delta\theta and gamma [178].

The prediction error framework suggests that both predictions and prediction errors are generated by the interactions among auditory hierarchy levels including midbrain, thalamus and auditory cortex [157]. Using bottom-up and top-down reciprocal connections, higher cortical areas create internal neural representations of the world to be fitted to the data received from lower cortical areas; therefore, the brain is constantly generating through high order cortical areas and updating through low level sensory areas input a mental model of the environment. The predictive activity has been described at the cortical level by a neurobiologically informed theory, the canonical microcircuit proposed by Bastos et al. (2012).

In this model the prediction errors generated at lower levels send feedforward connections to the granular layer (L4) and passed forward to superficial layers (L2) where the new predictions are encoded and send forward to deep cortical layers (L5\6) which in turn send feedback projections to lower areas [112]. This schematic representation might be applied also to interpret the MMN response. Previous animal studies measured intracortical deviance detection both within subcortical areas and within the superficial cortical layers [107, 157, 170, 179], supporting the hypothesis that the prediction error signals underlying the mismatch responses are hierarchically organized.

A model-based prediction error assessment used to explain competing theories about neuronal circuits underlying the neurophysiological mechanisms of evoked responses, such as MMN is the dynamic causal modeling proposed by Karl Friston [180], where the model is designed to

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infere the mechanisms generating the observed data by modelling both the local neuronal circuits and the coupling among distant brain areas.

In summary, there are three major hypotheses that contribute to the mechanistic understanding of the auditory MMN elicitation, with the last one, predictive coding proposed as unifying framework. According to these theories the repeated stimulation of standards may strongly suppress the evoked responses under repetition and enhance the generation of a mismatch response when a new stimulus is presented.