Deviance Detection and Encoding Acoustic Regularity in the Auditory Midbrain
Abstract and Keywords
In the past, there was a rather corticocentric conception of the processing of relationships between sounds that used to mostly relegate the midbrain function to a mere relay. However, increasing neurophysiological evidence demonstrates that the midbrain is, in fact, playing a crucial role in encoding some sorts of regularities present in the flow of acoustic stimulation, adapting the neuronal response for processing efficiency. Midbrain neurons are capable of responding more rapidly and strongly when a new stimulus is not matching to a previously encoded regularity; a phenomenon referred to as deviance detection. This chapter discusses deviance detection evidence in the midbrain, mainly describing the characteristics and mechanisms of stimulus-specific adaptation (SSA), and closing with an interpretation from the standpoint of the predictive coding theory.
Keywords: SSA/stimulus-specific adaptation, MMN/mismatch negativity, MLR/middle-latency responses, deviance detection, repetition suppression, prediction error, GABA, acetylcholine, endocannabinoids, predictive coding
Introduction: Processing an Auditory World in Constant Change
In the everyday life of humans and animals, there is a plethora of events occurring simultaneously. Most of those events are routine, recurrent, or meaningless; whereas others might be of great importance. But all of them generate a profuse amount of sounds, mixing in an acoustic jumble that eventually reaches the eardrum. The tympanic membrane is hit by an incessant flow of fine air-pressure oscillations at every fraction of a second, and each oscillation is mechanically transduced into electrochemical impulses that the central nervous system has to process and interpret. This gargantuan sum of inputs could easily overwhelm the capacities of the auditory brain, which must implement processing strategies to avoid the loss of relevant sensory information.
Thus, the auditory system is equipped with efficient filtering mechanisms that progressively prune the original acoustic signal into more manageable and useful inputs, starting at very early stages of the auditory processing. Already at the auditory nerve, fibers respond vigorously when a stimulus is switched on, but after few tens of milliseconds their firing rate slows down until reaching a steady-state rate that follows the sound to its end (Feng et al., 1991; Nomoto et al., 1964; Sumner & Palmer, 2012). This decline over time from the initially high firing rate to a lower, “adapted” rate is known as spike-frequency adaptation, and its presumed function is to spare processing resources without losing sensory information. It has also been reported in nuclei of the auditory brainstem, like the superior olivary complex (Finlayson & Adam, 1997) and the inferior colliculus (Ingham & McAlpine, 2004).
Nonetheless, if spike-frequency adaptation were the only filter at play, auditory neurons would still have to respond to every sound. Overabundant auditory representations would be conveyed, ceaselessly and with similar saliency, to superior levels of processing. Higher brain regions would have to strive for distinguishing amongst all those representations which ones are the relevant perceptions for the ongoing tasks. A discriminative endeavor that could easily flood attention and affect cognition, thereby hampering an adaptive behavior, and eventually imperiling the survival of the hearing being.
Fortunately, the auditory system relies on yet another, more refined filtering strategy that makes it possible to discern the informative value of a sound based on its probability of occurrence, without fully processing it. Imagine, for example, the sudden unexpected burst of a droplet hitting the bottom of the sink. The bursts of the next couple of droplets are enough to deduce that the faucet is leaking. However, the ensuing droplet bursts are just providing confirmatory evidence, adding no new meaning to the represented event. Hence, every new droplet is less informative than the preceding one, but the processing cost remains constant. Only a change in that sequence of sounds would provide new information. If the droplets bursts suddenly become a stream, the water pipe may have broken! Therefore, it would be more efficient to just process the changes in the auditory scene, instead of fully representing each and every single sound in it.
Nature complies with this heuristic logic, as some neurons in the auditory system diminish or even cease their firing to repetitive stimulation, while still responding to the occurrence of new stimuli (Bibikov, 1977; Malone & Semple, 2001). Such filtering is appropriately referred to as stimulus-specific adaptation (SSA). The most widely-used paradigm to study SSA is the oddball sequence (Figure 1A), which consists in the repetition of a tone (named standard) featuring the random irruption of a rare tone (named deviant). During the oddball paradigm, SSA neurons keep the auditory system from overloading with mostly irrelevant input by preventing the representation of standard stimuli, thereby automatically providing perceptual saliency to deviant stimuli, which are potentially more informative. It is not surprising to find such refined selective processing widespread in the auditory cortex (Nieto-Diego & Malmierca, 2016; Ulanovsky et al., 2003). What might be somewhat unexpected, however, is to find neurons displaying SSA as early in the ascending auditory pathway as to in the midbrain level (Malmierca et al., 2009; Pérez-González et al., 2005; Duque et al., 2018). More precisely, robust and consistent SSA is present in the cortices of the inferior colliculus (IC), which reveals that this subcortical brain region is involved in higher-order functions of auditory processing.
General Properties of SSA in the Midbrain
The magnitude of SSA displayed by a neuron can be measured as an index of the difference between its respective responses to a given stimulus when presented as deviant or as standard in the oddball paradigm (Figure 1B). By systematically varying the physical parameters of the oddball sequence, it has been determined that SSA in the auditory midbrain is very sensitive to (1) the probability of occurrence of the deviant stimulus, (2) the pitch and the intensity of the stimuli, (3) the frequency contrast between standard and deviant, and (4) the repetition rate of the stimuli.
First, one important factor modulating SSA is the magnitude of the probability unbalance between standard and deviant. The rarity of the deviant is positively correlated with the magnitude of the SSA index. The differences between standard and deviant responses progressively diminish as their probability of occurrence in an oddball sequence tends to equalize. This means that higher SSA indices can be elicited when, e.g., deviant tones appear just 10% of the times than when they do it in 30% of the trials (Ayala et al., 2013; Malmierca et al., 2009). In this regard, it is worth mentioning that most studies report an effect of probability, not only in the total firing of a neuron, but also in the latency of the first spike fired to the stimulus (Malmierca et al., 2009; Zhao et al., 2011). However, this adaptation of the first spike latency was only observed at relatively fast presentation rates (≥ 4 Hz). It could be speculated that, under demanding sensory conditions, the repetition of a stimulus provokes a decline and delay of some neuronal responses, in order to prioritize a swift and favored processing of the rare events that might occur.
Second, SSA levels do not remain constant across the whole receptive field of a neuron. The strength of SSA is biased within the frequency response area (FRA) of IC neurons toward high-frequency and low-intensity regions (Duque et al., 2012). In other words, IC neurons adapt better to relatively softer and higher-pitched tones (Figure 2). The fact that SSA strength is not homogeneous within the receptive field indicates that SSA is not an intrinsic property of the neuron, but rather depends on its synaptic activity. Also regarding the neuronal receptive field, it is worth mentioning that there is a positive correlation between the width of the FRA of the neuron and the strength of its SSA (see Figure 4E, F for examples; Duque et al., 2012; Malmierca et al., 2009). This suggests that SSA is an integrative property of the network, conducted by neurons with extensive dendritic arbors and wide receptive fields that act as converges of auditory input.
Third, SSA strengthens as frequency contrast between standard and deviant increases (Ayala et al., 2013; Malmierca et al., 2009). Nevertheless, the excellent hyperacuity of the IC neurons enables SSA at frequency contrasts as small as 0.057 octaves (Malmierca et al., 2009). Furthermore, SSA modulates the receptive field of auditory neurons in a way that could facilitate frequency discrimination (Ayala et al., 2013). Most neurons in the IC have an identifiable best frequency, which is the frequency that elicits the strongest firing in a tuning curve (Figure 3A). When the frequency of a standard tone is close to the best frequency of an SSA neuron, it shifts away that maximal response in the resulting adapted receptive field toward a contiguous frequency band (Figure 3B; Shen et al., 2015). Conversely, when the frequency of the standard tone is distant from the best frequency of an SSA neuron, the maximal response of the resulting receptive field is attracted closer to the adapted frequency band (Figure 3C; Shen et al., 2015).
Lastly, SSA also varies with the rate at which the stimuli are presented. SSA indices are positively correlated with presentation rate, up to a certain optimal point. Increasing the number of tones presented per second beyond that optimal point produces a reduction of SSA strength, due to temporal masking effects (Faure et al., 2003). The strongest SSA levels in the rat IC are reported at a rate of 4 Hz (inter-stimulus interval of 250 ms). Notwithstanding, notable SSA can be elicited presenting just one stimulus per second (Pérez-González et al., 2005; Zhao et al., 2011). In the case of the barn owl IC, SSA has been reported at inter-stimulus intervals as long as 10 seconds (Netser et al., 2011). This time scale of seconds allows to properly establish a connection between SSA and echoic memory already at the level of the midbrain. Furthermore, significant SSA could be appreciated at stimulation rates of 1 stimulus per minute in the barn owl optic tectum (OT, analogous to the mammalian superior colliculus, also in the midbrain), as well as in several nuclei of the avian forebrain compounded in the gaze control circuitry. The identification of long-lasting SSA in those kind of neural structures hints at a possible implication in behavioral habituation (Gutfreund, 2012; Netser et al., 2011).
SSA Dynamics: Sensitivity to Stimulus History
Stimulus history has an impact in SSA that can be decomposed in two components: short-term and long-term dynamics (Ulanovsky et al., 2004).
Only one repetition of a stimulus suffices to induct SSA. In consequence, SSA can emerge from a local effect prompted by the influence of the immediately preceding stimulus. Therefore, the response to a repeated stimulus A (rAA) will always be weaker than if a different stimulus B would have come before A (rBA), as it is the empirical case (rBA > rAA) in the auditory cortex (Ulanovsky et al., 2004) and the IC (Zhao et al., 2011). According to this one-trial effect, it does not matter if a stimulus is common (standard) or rare (deviant), just if it is repeated. This is known as the short-term dynamic of SSA (Ulanovsky et al., 2004; Zhao et al., 2011).
However, the short-term dynamic does not fully predict what can be observed in the context of an oddball paradigm. Even disregarding the global probabilities of the stimuli (e.g., 50% or 90% of the presentations in an oddball sequence), repetition creates a long-term accumulative effect. A stimulus will elicit a weaker response after several repetitions than after just one (rA > rAA > rAAA > rAAAA), implying an n-trial effect (Ulanovsky et al., 2004). Therefore, after enough presentations, the unbalanced probabilities of the oddball paradigm will have a great influence over the neuronal response. This global effect is referred to as the long-term dynamics of SSA (Ulanovsky et al., 2004; Zhao et al., 2011).
The long-term dynamic of SSA also explains why, during an oddball sequence, appearance of a deviant tone does not fully restore the response to the standard tone. SSA is taking into account several preceding trials, not just the last one. However, the standard response indeed recovers to some extent (Zhao et al., 2011), indicating that not only the global statistics of the stimuli are considered. If that were the case, after enough repetitions of the standard, a new standard coming after a deviant would display a similar level of SSA as the standard that came previously to that deviant tone. Inasmuch as the standard response partially remerges after a deviant tone, it implies that the one-trial effect is still at play throughout the whole oddball sequence (Figure 1C). The magnitude of the SSA that a neuronal response undergoes at each stimulus presentation is determined by the interplay of both local and global components, which represent two time scales of integration in the SSA dynamics (Ulanovsky et al., 2004).
SSA Time Course
As a rule of thumb for the rat IC, the response to the repetitive stimulus describes a rapid decay over the first 10 presentations, which is followed by a slower exponential decay that reached a maximum within 25 trials, and finally ensued by a late steady-state component (Figure 1B). During the oddball paradigm, interleaved deviant stimuli are not significantly affected by SSA (Figure 1B). However, its irruption partially restores the response to the subsequent standard stimulus (Figure 1C; Malmierca et al., 2009).
SSA Mechanics: The Model of Adaptation in Narrow Channels
The possible mechanisms underlying SSA are still under investigation and debate. Most types of adaptation can be understood as rather basic physiological mechanisms governed by the neuronal output. Thus, spike-frequency adaptation depends on the discharge history of the neuron, accounted for by activation of voltage-dependent conductance (Sánchez-Vives et al., 2000a, 2000b) or tonic hyperpolarization (Carandini & Ferster, 1997). However, since these mechanisms operate at the level of the somatic membrane potential, they cannot be stimulus specific (Ulanovsky et al., 2004), nor can they vary across the neuronal receptive field as SSA does (Duque et al., 2012). Considering that, SSA emergence must rely on physiological mechanisms operating at the input of the neuron, based on its synaptic history. Mechanisms such as synaptic depression and facilitation (Abbott et al., 1997; Tsodyks & Markram, 1997) or inhibition (Zhang et al., 2003) would affect differentially certain regions of the dendritic tree, thereby exerting stimulus-specific effects over the eventual response of the neuron.
The model of narrowly-tuned adaptation channels provides a functional explanation on how SSA may emerge from the differential adaptation of dendritic inputs (Taaseh et al., 2011). The repetition of a standard tone would adapt a particular frequency channel of the neuronal receptive field independently, leaving the rest mostly unaffected. Those channels have been estimated to stretch about ⅓ octaves on either side of the repeating frequency (Taaseh et al., 2011). Therefore, if the frequency contrast between the standard and the deviant tone is more than ⅓ octaves, the neuronal response to the deviant will not be adapted. Conversely, if the frequency contrast is less than ⅓ octaves, the deviant response will subdue some cross-frequency adaptation. This is why wider frequency separations between standard and deviant tones result in stronger SSA. Interestingly, the width of the adaptation channels in IC neurons seems even narrower at high frequencies and low intensities (Duque et al., 2016).
The model of adaptation channels, despite very comprehensive, suffers from some limitations to account for the empirical SSA data, revealing that complementary mechanisms may be at play (Hershenhoren et al., 2014; Taaseh et al., 2011). In this regard, recent efforts have attempted to reinterpret SSA from a more general framework based on the predictive coding theory (Carbajal & Malmierca, 2018a; Parras et al., 2017), as discussed in the last section of this chapter.
Neuroanatomical Features that Enable SSA Emergence in the Midbrain
Neuroanatomically speaking, the midbrain is the first structure in the ascending auditory pathway where SSA can be found (Ayala et al., 2013). The main auditory center in the midbrain is the IC, where nearly all ascending auditory pathways converge before passing the processed information to the auditory cortex via the thalamus. Besides, the IC receives rich descending auditory and non-auditory projections, and it possess a dense and complex microcircuitry of local and commissural connections. The integration of excitatory, inhibitory, and neuromodulatory input, coming from multiple sources, makes the IC a perfect gate for controlling the flow of relevant acoustic information before reaching the cortex. Neurons showing SSA have been found in the IC of frogs (Bibikov, 1977), gerbils (Malone & Semple, 2001), rats (Pérez-González et al., 2005), mice (Duque & Malmierca, 2015), bats (Thomas et al., 2012), and barn owls (Reches & Gutfreund, 2008), findings that reveal this filtering function is preserved across species. Additional evidence obtained from non-invasive approaches confirm that the IC may be the first gate of salient acoustic information also in humans (Cacciaglia et al., 2015; Shiga et al., 2015). The cytoarchitecture of the IC and its pattern of projections allows to differentiate at least two main structures: a central nucleus (Figure 4A) that is wrapped by a shell or cortices (Figure 4D).
The central nucleus of the IC receives most of its input from the lower auditory brainstem. It is organized in a tonotopic fashion, where sharply-tuned neurons (Figure 4B, C) are arranged in approximately 150 frequency laminae (Malmierca et al., 2008; Malmierca et al., 1993, 1995). They send tonotopically-arranged projections to the thalamus and cortex, forming the so-called lemniscal pathway. The response of lemniscal neurons is primarily determined by the physical features of the sound. Neurons located in the central nucleus of the IC tend to show only partial or rather poor SSA (Malmierca et al., 2009; Parras et al., 2017).
On the other hand, the neurons in the cortices of the IC are broadly-tuned (Figure 4E, F), with frequency-response areas so wide that any tonotopic arrangement is diffuse at best.
As oppose to the central nucleus, the IC cortices receive less input from the main brainstem and more from a diversity of sources that include intrinsic connections from the central nucleus, the auditory cortex and other multimodal nuclei (Ito & Malmierca, 2018). Neurons in the IC cortices send ascending projections mainly to other non-lemniscal divisions of the auditory thalamus and cortex, constituting an adjunct ascending pathway known as the nonlemniscal pathway. Nonlemnical IC neurons possess large dendritic arbors (Malmierca et al., 2011), morphologically consistent with the wideness of their receptive fields. The capacity of the nonlemniscal neurons in the IC to converge inputs across many frequencies, along with the heavy cortical modulation these neurons are subjected to, denotes their integrative role in the auditory pathway (Carbajal & Malmierca, 2018b). In line with this, the neurons in the IC cortices show the highest and most robust levels of SSA in the midbrain to the extent that many neurons cease firing completely after just few repetitions of a stimulus.
Three Classes of SSA Neurons in the Inferior Colliculus
The majority of IC neurons show at least some degree of SSA, varying in magnitude in a continuum from negligible to absolute. Three loosely defined functional groups of neurons can be distinguished in such continuum: non-adapting, partially-adapting and novelty neurons (Malmierca et al., 2009).
In one extreme of the continuum, non-adapting neurons are those lacking SSA in any configuration of the oddball paradigm. Non-adapting neurons will respond similarly to a tone when presented as standard as when presented as deviant (Figure 4B, C). As a rule of thumb, non-adapting neurons tend to have a narrow, V-shaped FRA, show more sustained responses during the whole presentation of the sound, and have shorter latencies than the adapting neurons. All of which suggests that the role of non-adapting neurons is to accurately represent the physical features of the stimulus, disregarding its context. Non-adapting neurons are commonly found in the central nucleus, i.e. the lemniscal IC (Figure 4A). In fact, these general trends seem particularly true in bats, whose central nucleus is greatly developed and resilient to SSA, probably due to the fundamental role of repetitive sounds in echolocation (Thomas et al., 2012).
On the other extreme, novelty neurons is the classic denomination given to those neurons that show great or even absolute levels of SSA consistently throughout their entire receptive field. Thus, while novelty neurons quickly cease to response to repeated stimuli they retain their full responses to deviant stimuli (Figure 4E, F). The FRAs of novelty neurons are always very broad, indicating they congregate a wide range of acoustic inputs. Most of novelty neurons are located in the cortices of the IC, i.e. in the nonlemniscal IC, where neurons have characteristically broadly-oriented dendritic arbors. Novelty neurons tend to be onset responders, or at least have a very pronounced onset component in their response, which is also the component most affected by SSA (Figure 4E, F). Furthermore, novelty neurons have virtually no spontaneous activity, so their firing reliably signals a change in the ongoing acoustic environment. In that regard, the term novelty neurons has lost popularity in recent years, in favor of other formulas like deviance detectors. The concept of deviance detection better emphasizes the sensitivity to a change in the stimulation, and it is better suited for interpretation from other more general theoretical frameworks about perception like the predictive coding theory (Carbajal & Malmierca, 2018a).
Between the extremes of non-adapting neurons and novelty neurons, there is a well-stocked and diverse group of neurons showing some degree of SSA. Those neurons are usually referred to as partially-adapting neurons, because they exhibit some levels of SSA, but not under all conditions. At certain oddball parameters (e.g., stimulation rate, rarity of the deviant, etc.) and at specific areas of the receptive field, these neurons may show SSA, whereas at other areas they may lose their deviance detection capacities completely. In general, partially-adapting neurons are not as finely tuned as the non-adapting neurons, but the broadness and shape of their FRAs are very variable. Partially-adapting neurons respond with a variety of discharge patterns. Nevertheless, the effects of SSA present in the partially-adapting neurons are most obvious in the onset portion of their response (the first 20-30 ms; Duque et al., 2012; Malmierca et al., 2009; Zhao et al., 2011). Indeed, this fact represents an interesting difference with the SSA observed in the auditory cortex, where SSA seems stronger in the sustained and late portions of the neuronal response (Nieto-Diego & Malmierca, 2016). The most characteristic feature of the partially-adapting neurons is their sensitivity to the internal context of the neuronal network. Whereas novelty neurons operate consistently filtering out redundant input, the function of partially-adapting neurons changes considerably depending on neuromodulation (Ayala & Malmierca, 2015), state of awareness (Duque & Malmierca, 2015), or cortical influences (Anderson & Malmierca, 2013). This suggests that, whereas novelty neurons behave as hard static filters against redundancy, partially-adapting neurons may act as finer dynamic filters that adjust their function dependent on other contextual aspects besides purely acoustic stimulus relationships. Partially-adapting neurons can be found in all subdivisions of the IC, but they tend to show stronger levels of SSA in the cortices.
The Origin of SSA in the Midbrain: Generated in Situ or Inherited from the Cortex?
The anatomical source of subcortical SSA has been a matter of discussion for the last 15 years. Selective adaptation had been described in the auditory midbrain of animal models long ago (Bibikov, 1977; Malone & Semple, 2001), implicitly assuming that such adaptation was being generated in situ. However, those pioneering studies were overlooked and their assumptions overwritten when a study using the oddball paradigm found SSA in primary auditory cortex of the cat (Ulanovsky et al., 2003). This seminal work was the first to propose that SSA could be the neuronal correlate of the mismatch negativity (MMN), a change-specific component of the auditory event-related potentials (ERP) recorded from the human scalp (Näätänen et al., 1978). The MMN is traditionally elicited using the oddball paradigm, although it can appear in response to any perceivable change in stimulation (Winkler & Schröger, 2015). The MMN has proven to be a central non-invasive tool for neurocognitive research (Näätänen et al., 2007), even showing promising potential diagnostic applications (Näätänen et al., 2012), as it appears altered in a great variety of clinical conditions. As a result, the suggested link between cortical SSA and MMN soon captured great scientific attention.
Unfortunately, this seminal study not find significant traces of SSA in the auditory thalamus (Ulanovsky et al. 2003). Inasmuch as the anatomical sources of MMN had been firmly pinned down to the auditory cortex in humans (Alho, 1995), it was concluded that SSA was a purely cortical mechanism as well, leaving a profound and enduring impression in the literature. This conception had to be revised after the consistent confirmation of strong SSA using the oddball paradigm present in the nonlemniscal divisions of the IC (Ayala et al., 2015; Ayala & Malmierca, 2015, 2018; Duque et al., 2012, 2016; Duque & Malmierca, 2015; Gao et al., 2014; Herrmann et al., 2015; Lumani & Zhang, 2010; Malmierca et al., 2009; Netser et al., 2011; Parras et al., 2017; Patel et al., 2012; Pérez-González et al., 2005, 2012; Pérez-González & Malmierca, 2012; Reches & Gutfreund, 2008; Shen et al., 2015; Thomas et al., 2012; Valdés-Baizabal et al., 2017; Zhao et al., 2011) and thalamus (Anderson & Malmierca, 2013; Anderson et al., 2009; Antunes et al., 2010; Antunes & Malmierca, 2014; Duque et al. 2014; Parras et al., 2017; Yu et al., 2009). Notwithstanding, the corticocentric approach to understanding deviance detection was still not completely abandoned. It was suggested then that SSA had a cortical origin, to be passed down via corticofugal projections to the nonlemniscal divisions of the subcortical auditory nuclei (Nelken & Ulanovsky, 2007). Indeed, all SSA neurons located in the IC cortices receive descending cortical projections (Ayala et al., 2015). But it is not possible to determine the prime anatomical source of SSA just by investigating connectivity.
To properly address the question, studies of reversible deactivation of the auditory cortex using a cooling technique were conducted while recording from the nonlemniscal auditory thalamus (Antunes & Malmierca, 2011) and the IC cortices (Anderson & Malmierca, 2013). The general results demonstrated that, although the auditory cortex clearly modulated the firing rate of the nonlemniscal subcortical neurons in a gain-control manner (Malmierca et al., 2015), increasing the contrast between standard and deviant stimuli by affecting the response to both proportionally, the auditory cortex was not generating SSA by itself. Remarkable alterations were observed in many other response properties of the subcortical neurons during cortical deactivation, such as their FRA, spontaneous activity, and first spike latencies. However, the overall strength and dynamics of subcortical SSA were mostly unaffected by the deactivation of the auditory cortex. Only about half of the adapting neurons in the IC suffered changes in their SSA sensitivity. This confirms a prominent cortical modulation, but also demonstrates that SSA is emerging in the midbrain, independently from coticocollicular input. As a matter of fact, novelty neurons in the IC cortices remained mostly unaffected. Even the partially-adapting IC neurons that underwent reductions of their SSA sensitivity did not lose it completely. Furthermore, almost no changes in SSA sensitivity were detected in the non-lemniscal auditory thalamus after the cortical deactivation.
In light of these results, it is more plausible that the SSA present in the auditory cortex is inherited from subcortical structures than vice versa, albeit the possibility of SSA being generated de novo at the intrinsic microcircuitry of each station independently cannot be ruled out. Notwithstanding, since no other auditory nuclei lower than the midbrain seems to manifest SSA (Ayala et al., 2013), and given that cortical deactivation does not eliminate SSA from the midbrain, the IC cortices can be considered the most probable anatomical origin of SSA.
The Physiology of SSA: How Is Synaptic Adaptation Modulated?
Research in animal models has tried to disentangle the neurochemistry behind the emergence of SSA in IC neurons using microiontophoresis. During the extracellular recording of neuronal activity, the microiontophoresis technique is used to permeate the vicinity of the recorded neuron with neurotransmitter agonists or antagonists. By means of this local neuropharmacological manipulation, we have been able to characterize the contribution of some membrane receptors to the emergence of deviance detection in the auditory midbrain.
The Role of Inhibition: A Gain Control of Deviance Detection
Synaptic inhibition has been pointed at for long in the literature as one of the most probable mechanisms that could generate SSA (Figure 5B). The main inhibitory neurotransmitter in the IC is GABA, which binds to two main classes of receptors. GABAA receptors are ionotropic, part of a ligand-gated ion channel. GABAB receptors are metabotropic, regulating the opening or closing of ion channels via intermediate G proteins. In addition to GABA, in the mammalian IC inhibitory activity is also exerted by glycine through its ionotropic glycine receptors. Several studies have unraveled the role of inhibition in midbrain SSA emergence, using the oddball paradigm and the microiontophoresis technique.
The inhibition exerted through GABAA receptors, which regulate the postsynaptic membrane potential (Sivaramakrishnan et al. 2004), was analyzed using the antagonist gabazine in the rat IC (Pérez-González et al., 2012). Gabazine slowed down adaptation to the standard, having a profound effect on the magnitude and time course of SSA (Figure 5C). However, blocking GABAA receptors failed to abolish SSA, since the application of gabazine also increased the firing rate of the deviant response. Therefore, the response magnitude and latency still depended on the probability of the stimulus when GABAA receptors were blocked, maintaining the absolute difference between the responses to deviant and standard stimuli. What gabazine application actually achieved was the reduction of the proportional difference between the deviant and standard responses, whereby SSA indices dropped (Figure 5C). Thus, that general increase of neuronal responsiveness resulted in a reduction of SSA sensitivity. This reflects a gain control mechanism mediated by GABAA receptors in the IC. GABAA-mediated inhibition facilitates the relative saliency of rare auditory events over the redundant ones by sharpening the contrast between their responses, as shown in the so-called iceberg effect (Ayala & Malmierca, 2018; Pérez-González et al., 2012). The iceberg effect describes the observation whereby the spike output of a neuron under inhibition is more sharply tuned than the underlying membrane potential, since only the strongest excitatory input sufficiently depolarizes the membrane to reach threshold for spike generation (Figure 5A; Isaacson & Scanziani, 2011). Such gain-control mechanism of SSA starting in the midbrain has been demonstrated to continue in the auditory thalamus (Duque et al., 2014), which suggests a progressive GABAA-mediated filtering of redundant information before reaching the auditory cortex.
More subtle gain control effects were found to be mediated by GABAB presynaptic receptors in the rat IC (Ayala & Malmierca, 2018), but in the opposite manner. Application of CGP 36216, a selective antagonist for presynaptic GABAB receptors (Ong et al., 2001), prompted a decrease of the neuron’s overall excitability, producing an increase of SSA (Figure 5D). This is thought to occur because GABAB receptors act as autoreceptors at GABAergic terminals in the rat IC (Ma et al., 2002; Zhang & Wu, 2000). Activation of presynaptic GABAB receptors suppresses GABAergic inhibition, augmenting the overall responsiveness of the neuron, leading to a decrease in the relative difference of standard and deviant responses. When GABAB presynaptic receptors are blocked by CGP 36216, the continuous release of GABA on SSA neurons decreases its firing rate, maximizing the relative contrast between standard and deviant responses, therefore augmenting SSA indices (Figure 5D; Ayala & Malmierca, 2018).
Another preparation used CGP 35348, an antagonist that blocks both pre- and post-synaptic GABAB receptors (Luo et al., 2011; Magnusson et al., 2008; Sun et al., 2006), but shows a much higher affinity for the post-synaptic ones (Stäubli et al., 1999). Interestingly, that kind of blockade of GABAB-mediated inhibition prompted an increase in the response to the standard tones that did not affect the response to the deviant ones, therefore reducing the total amount of SSA (Figure 5E). This selective effect can be explained by assuming that GABAB postsynaptic receptors on SSA neurons are mainly allocated extrasynaptically. Repetitive stimulation would generate larger amounts of GABA release than rare tones, which end up saturating the synaptic cleft. The spillovers would get to reach the additional extrasynaptic GABAB receptors. Therefore, blocking those GABAB postsynaptic receptors only diminishes the adaptation to the repetitive sound, and not the rare one (Figure 5E; Ayala & Malmierca, 2018).
Glycine-mediated inhibition displayed subtle paradoxical effects depending on the strength of SSA to different sets of frequencies in each partially-adapting neuron. Injections of strychnine, an antagonist of ionotropic glycine receptors, caused particular areas within the receptive field that showed strong levels of SSA to decrease it, while provoking enhancements of the salience of deviant responses in other areas with partial levels of SSA. This dual effect could be due to the differential effect of glycine in different parts of the receptive field of the IC neurons (Williams & Fuzessery, 2011), or a co-expression with other kinds of inputs in a frequency-dependent manner (e.g., with GABA for low intensities). The spread of strychnine in the surroundings of the SSA neuron, affecting also its sources of inputs, could be another possibility accounting for these paradoxical effects (Ayala & Malmierca, 2018). In any case, glycinergic receptors are expressed mainly in the ventral part of the central nucleus of the IC (Choy Buentello et al., 2015; Merchán et al., 2005), where SSA levels are weaker. Therefore, the implication of glycinergic inhibition in the generation of SSA must be secondary, if any.
Finally, this study tried all mixed inhibitory effects by co-applying different combinations of GABAA, GABAB and glycinergic antagonists to block multiple receptors types at the same time. The augmented effects observed showed that the relative increase in neuronal responsiveness mostly affected the response to repetitive stimuli, and not all the sound-evoked responses. GABAA-mediated inhibition proved to exert the most evident and profound modulatory effect on the SSA. Since GABAA receptors are ionotropic, their blockade rapidly affected the SSA time course from the beginning (Figure 5C). On the other hand, GABAB receptors are metabotropic, necessarily slower since they are coupled to ion channels through second messengers (Mott, 2015). Thus, GABAB-mediated inhibition antagonists subtly affected the late steady-state component of the SSA time course only (Figure 5D, E), leaving the initial fast decay of the standard response untouched (Ayala & Malmierca, 2018).
In conclusion, local inhibition could account for about half of the relative difference between standard and deviant responses, therefore acting as an important modulator of SSA in the IC. Notwithstanding, inhibition alone could not fully account for SSA emergence, so other mechanisms must be contributing to its generation (Ayala & Malmierca, 2018; Pérez-González & Malmierca, 2012).
The Role of Acetylcholine: Enhancing Repetition Sensitivity
Cholinergic projections are known to play and important role in arousal, attention, and memory. MMN literature suggests that cholinergic modulation favors the encoding of ongoing stimulation (Hasselmo & McGaughy, 2004; Jääskeläinen et al., 2007; Moran et al., 2013; Sarter et al., 2005) and that it enhances responses to afferent sensory input in the auditory cortex (Hsieh et al. 2000; Metherate & Ashe 1993). In consequence, there is a possibility that acetylcholine (ACh) could be playing an important role in SSA.
This hypothesis was tested in the rat IC (Ayala & Malmierca, 2015) using microiontophoretic applications of Ach chloride to activate the two main kinds of cholinergic receptors: Nicotinic (ionotropic) and muscarinic (metabotropic) receptors. The infusion of the cholinergic agonist provoked a decrease in SSA levels on partially adapting neurons, whereas leaving the excitability of non-adapting neurons and novelty neurons mostly unaffected. SSA reduction affected the late steady-state component of its time course, and it was caused by a differential increase in the response to repetitive stimulation that did not generalized to the deviant responses (as in Figure 5E, middle and right charts). A decrease in SSA prompted by cholinergic input is coherent with the lower levels of SSA observed in awake animals (von der Behrens et al., 2009; Duque et al., 2014) in which ACh concentrations are higher (Kametani & Kawamura, 1990; Marrosu et al., 1995). Therefore, it is apparent that cholinergic modulation in the IC contributes to persistence of the encoding of repetitive acoustic stimulation by decreasing adaptation to high probability sounds.
In addition, preparations with antagonists for the two types of cholinergic receptors were also injected. Scopolamine was used for blocking muscarinic receptors, and mecamylamine for the nicotinic receptors. As expected, affected neurons tended to augment their SSA levels for both cholinergic antagonists. Notwithstanding, only scopolamine exhibited a significant increase of SSA at population level, implying a prominent role in cholinergic modulation of muscarinic receptors, most likely via M1-receptor subtype (Ayala & Malmierca, 2015). The activation of the M1-type receptor induces changes in potassium conductance that could act as an activity-dependent adaptation mechanism (Abolafia et al., 2011; Sánchez-Vives et al., 2000a, 2000b), making K+-mediated adaptation a potential mechanism underlying SSA (Abolafia et al., 2011; Ayala & Malmierca, 2015; Malmierca et al., 2014).
The Role of Endocannabinoids: Modulating the Modulators
Unlike most neurotransmitters, endocannabinoids action is mostly presynaptic, and they are not stored in vesicles but rather synthesized “on demand,” when and where they are needed (Mechoulam & Parker, 2013). The release of endocannabinoids can be stimulated by activity-dependent mechanisms (Di et al., 2005) and they play a role in short-term neural plasticity (Castillo et al., 2012), so their retrograde signaling could be also involved in the modulation of SSA (Figure 6A).
This was demonstrated by the application of two different agonists of CB1 cannabinoid receptors, anandamide (intravenously) and O-2545 (microiontophoretically), during the oddball paradigm (Valdés-Baizabal et al., 2017). Both systemic and local injections prompted a decrease of SSA in a subset of neurons in the rat IC, exerting its effects in the late steady-state component of the adaptation. This reduction was caused by the increased response to the standard condition, while the spike count of the deviant response remained unaffected (Figure 6B). The blockade of CB1 receptors, via microiontophoretic application of the antagonist AM251, leads to non-significant population effects. Nevertheless, there was a coherent tendency of some neurons to decrease their firing rate in response to the standard stimulus, increasing SSA levels in those neurons (Valdés-Baizabal et al., 2017).
Retrograde regulation of inhibitory and excitatory inputs by cannabinoids is found along the auditory pathway for both glutamatergic and GABAergic synapses (Zhao et al., 2009), describing a protective mechanism that could modulate SSA. Neurons in the IC displaying cannabinoid-mediated modulation of SSA probably receive inhibitory input from GABAergic neurons expressing CB1 receptors in their presynaptic terminals (Figure 6A; Merchán et al., 2005). The injection of CB1 agonists would decrease GABA release of presynaptic inhibitory neurons, exerting especially significant effects in the postsynaptic response to the standard condition, insofar as the repetitive stimulation is expected to elicit much more GABA release than rare tones (Figure 6B).
Mixed and complex interactions of the endocannabinoid system with other neuromodulators could be also at play. In any case, the capacity of the endocannabinoid system to exert modulation on SSA would depend on the strength and nature of the inputs that each adapting neuron receives (Valdés-Baizabal et al., 2017).
The Effects of Anesthesia: SSA During Deep Sleep
Most experiments on SSA are performed under anesthesia. The most common preparation uses urethane, for its stability and minimal disruptive impact on neuronal functioning (Hara & Harris, 2002; Maggi & Meli, 1986; Sceniak & MacIver, 2006). Some studies even show that the effects of moderate urethane doses on neurotransmission mimic those of natural sleep (Clement et al., 2008; Pagliardini et al., 2012, 2013). When compared, SSA levels in the IC of anaesthetized and awake rodents seem rather similar (Duque & Malmierca, 2015; Parras et al., 2017), confirming the automatic nature of SSA as a hardwired redundancy-filter.
Notwithstanding, both preparations are not identical, being the most obvious difference the drastic reduction of spontaneous neuronal activity caused by anesthesia (Duque & Malmierca, 2015; Parras et al., 2017). Such quietening of spontaneous activity may further provide perceptual salience to deviant events that might take place during sleep-like states. Therefore, SSA could have an essential role during sleep, which is further supported by the large body of evidence demonstrating the existence of human MMN during sleep (Atienza et al., 2001). One of the functional roles of the deviance detection system, associated to MMN and by SSA, is driving attention involuntarily to unexpected stimuli (Escera et al., 1998; Escera et al., 2000). So during sleep, SSA could preserve the ability to filter out potentially disturbing noises, and thereby improve resting. But likewise, in a background of minimal spontaneous activity, SSA can prompt arresting responses to deviant sounds that might reveal some danger, eventually triggering awakening.
Auditory Deviance Detection in the Human Midbrain
Once firmly demonstrated with the oddball paradigm that SSA has presence (Malmierca et al., 2009) and origin (Anderson & Malmierca, 2013) in the midbrain of animal models, the suggested connection between SSA and the MMN (Ulanovsky et al., 2003) indirectly implied that deviance detection activity in humans could have subcortical roots as well (Carbajal & Malmierca, 2018a; Escera & Malmierca, 2014; Manuel S. Malmierca et al., 2014). In spite of the great technical challenge of recording non-invasively and correctly locating the source of a signal originated in such profound regions of the brain, several studies have since tried to find traces of deviance detection activity coming from the human midbrain.
Initial attempts to observe deviance detection used simple tones to analyze the classic auditory brainstem response (ABR) of the human ERP. No differences between standard and deviant tones could be identified at wave V (Althen et al., 2011; Slabu et al., 2010), which is the portion of the ABR that is thought to be generated in the IC (Picton, 2010; Stockard et al., 1979). Since SSA is present in the IC of animal models, contextual differences in wave V could be expected. However, the very short latency of the wave V (5–10 ms) contrasts with the timing of the changes in the firing rate observed at neuronal level in the IC, which spans throughout the first 20–30 ms (Malmierca et al., 2009; Pérez-González et al., 2005). This short latency of wave V indicates that it may be generated in the lemniscal portion of the IC, the central nucleus, where SSA population levels are rather low, when significant.
Using consonant–vowel vocalizations (Figure 7A) to build the oddball sequence made possible to elicit a “complex” ABR (Slabu et al., 2012; Skoe & Kraus, 2010). Contextual differences were detectable at the frequency-following responses (FFR; Figure 7B) that come after wave V (Shiga et al., 2015; Skoe et al., 2014; Slabu et al., 2012), indicating that the human midbrain is indeed contributing to deviance detection (Figure 7C). Research based in FFR analysis had for long emphasized an active and critical involvement of the brainstem in auditory plasticity regarding complex forms of sound processing (for review see Chandrasekaran et al., 2014). However, these claims might have been overestimated, since recent studies have discovered varying but significant cortical contributions to FFR, at least for eliciting stimuli of low fundamental frequency (F0) circa 100 Hz (Bidelman, 2018; Coffey et al., 2016, 2017), and therefore FFR evidence cannot be solely imputed to brainstem activity. Notwithstanding, deviance detection in the human IC has been also confirmed using fMRI (Cacciaglia et al., 2015), which could dissipate any possible doubts about the contribution of the human midbrain to the perceptual saliency of rare stimuli (Figure 8).
Future Directions: Predictive Coding in the Auditory Midbrain
The proposal of cortical SSA as the neuronal correlate of the MMN (Ulanovsky et al., 2003) meant a great breakthrough in deviance detection research, boosting its exploration from a neurophysiological perspective, and improving our understanding of the early stages of perception emergence (Escera & Malmierca, 2014; Malmierca et al., 2014). However, the MMN clearly exceeds the explanatory potential of SSA as currently defined. The MMN can be elicited by any violation of an established regularity in the flow of stimulus (Winkler & Schröger, 2015). This includes even complex regularities that are well beyond the standard repetition featuring in the oddball paradigm. Even in sequences organized by abstract rules (e.g., tone pairs where the first tone can have any frequency but the second tone is always higher in pitch), the occurrence of deviant events (e.g., the second tone turns out to be lower in pitch) can prompt an MMN, something impossible to account for in terms of SSA. Whereas all SSA reflects a process of deviance detection at cellular level, deviance detection beyond sheer repetition cannot emerge from SSA as currently defined. Therefore, if MMN and SSA are indeed related, SSA must be just a specific manifestation of a more general mechanism of deviance detection. These limitations have encouraged a revision of SSA data from a more global approach, capable of accounting for MMN evidence as well. A new common framework arises based on the predictive coding theory (Carbajal & Malmierca, 2018a).
The predictive coding theory interprets the brain basically as a generative mechanism of Bayesian hierarchical inference (Friston, 2005; Friston, 2009). According to this general theory of neuronal function, higher-order processing stations send predictions (or expectations) to lower hierarchical levels based on a model extracted from the regularities in the flow of stimuli. The aim is to reduce or suppress any ascending input that can be anticipated by the perceptual model already represented in the system, in order to save processing resources. The suppressive effects on the firing rate of the neurons are resultant of short-term plasticity changes, exerted through mechanisms like synaptic adaptation. In turn, lower stations forward prediction errors to the higher hierarchical levels whenever their expectations fail, in order to favor the processing of those inputs that could not be anticipated, and mobilize resources to update the perceptual model. Hence, perception emerges from a dynamic multilevel interaction between top-down expectations and bottom-up prediction errors.
Based on the aforementioned, we can explain deviance detection resultant of the violation of both simple (repetition) and complex regularities (abstract rules). In the case of the oddball sequence, the representation of the repeating standard stimulus will quickly generate top-down predictions (i.e., “the upcoming stimulus will be similar to the previous one encoded”), efficiently explaining away the ascending sensory input and suppressing prediction error. But when the random deviant appears, its unpredictable occurrence will release the bottom-up input from suppression and forward a prediction error signal prompting an update of the predictive model. Hence, from a predictive coding standpoint, SSA would result from two processes: repetition suppression affecting the standard response, and prediction error emerging from deviances (Carbajal & Malmierca, 2018a). This has been empirically confirmed in a recent study, where it was possible to disentangle both components in the IC of anaesthetized rats and awake mice (Parras et al., 2017) using control conditions directly borrowed from MMN methodology for human research (Ruhnau et al., 2012). Prediction errors were only detected in the IC cortices, the parts which receive the strongest descending inputs from neocortex (Malmierca & Ryugo, 2011). Furthermore, a very recent study suggests that the IC could indeed have an important role in the early processing of complex regularities (Malmierca et al., 2018).
Hence, predictive coding provides a common framework to study both SSA and MMN as respectively the microscopic and macroscopic measurements of the same deviance detection mechanism, which generative roots can be trace down to the midbrain. The cortices of the IC are the first auditory nuclei to display such sophisticated predictive processing, as part of a larger multilevel modelling network. All of which highlights the pivotal role that the midbrain plays in the composition of meaningful perceptual representations of complex acoustic events. As a general conclusion, taking into account all the evidence discussed throughout this chapter, there is no doubt that the auditory midbrain is endowed with wondrous processing capacities, and its function is definitely more important in the attainment of complex and refined auditory perceptions than previous literature estimated.
Manuel S. Malmierca and Guillermo V. Carbajal have contributed equally. This work has been funded by Spanish MINECO (SAF2016-75803-P) and The European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 722098 (ITN-MCSA-LISTEN) to MSM and by MINECO/FEDER (PSI2015-63664-P), the Generalitat de Catalunya (SGR2017-974), and the ICREA Acadèmia Distinguished Professorship award to CE. GVC held a fellowship from the Spanish MICINN (BES-2017-080030).
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