Mismatch negativity (MMN): an objective measure of sensory memory and long-lasting memories during sleep

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1 International Journal of Psychophysiology 46 (2002) Mismatch negativity (MMN): an objective measure of sensory memory and long-lasting memories during sleep Mercedes Atienza*, Jose L. Cantero, Elena Dominguez-Marin Laboratory of Sleep and Cognition, Seville, Spain Received 7 July 2002; received in revised form13 August 2002; accepted 3 September 2002 Abstract Sleep, unlike wakefulness, facilitates the internal stimulus generation and hinders the processing of external stimulation. Nevertheless, evidence yielded by physiological studies in animals and event-related potential (ERP) studies in humans suggest that basic functions of the central auditory system are still preserved during sleep. This review is focused on the automatic change-detection function of the auditory system as revealed by a negative ERP component called mismatch negativity (MMN). MMN mainly originates in the auditory cortex, although it also receives an important contribution from subcortical areas (especially at thalamic level), as well as frontal areas. We discuss recent experiments supporting the use of MMN as an objective measure of sensory memory and long-lasting memories not only during wakefulness, but also during sleep. The outcome of the activation of MMN generating systemduring sleep highly differs fromthat in waking, especially when there is no previous information about the stimulus sequence in the neuronal network as a result of learning. We discuss these differences in MMN generation in terms of a dynamicist view of the brain that emphasizes the importance of the integration between bottom-up and top-down influences on sensory processing, independently of the processing level in the auditory hierarchy Elsevier Science B.V. All rights reserved. Keywords: Mismatch negativity (MMN); Brain generators; Sleep; Sensory memory; Learning; Bottom-up mechanisms; Top-down mechanisms 1. Introduction Automatic detection of any discernible change in the surrounding auditory environment is an adaptive function of the auditory nervous system that persists to some degree under different states *Corresponding author. Division de Neurociencias, Laboratorio Andaluz de Biologıa, Universidad Pablo de Olavides. Carretera de Utrera Km. 1, Sevilla (Spain). Tel.yfax: q address: mercedes_atienza@hms.harvard.edu (M. Atienza). of consciousness. Because of the change-detection mechanisms, any input can potentially reach awareness in the absence of or during low states of consciousness, for example during sleep. Automatic change-detection processes have been extensively studied during wakefulness using the mismatch negativity (MMN) as electrophysiological index. The MMN is a component of the auditory event-related potentials (ERPs) typically elicited when a repetitive sound (standard) is substituted by another low-probability sound /02/$ - see front matter 2002 Elsevier Science B.V. All rights reserved. PII: S Ž

2 216 M. Atienza et al. / International Journal of Psychophysiology 46 (2002) Fig. 1. (a) Frontal (Fz) grand average waveforms to frequent tones (80%, thick line) of 1000 Hz and infrequent tones (20%, thin line) of different frequencies (as indicated on the left side). (b) Difference waveforms resulting from subtracting ERPs elicited by the infrequent tone fromthose elicited by the frequent tone. Adapted, with permission, from Sams et al. (1985). (deviant) that differs in some physical feature (e.g. frequency, loudness, duration, location, see Fig. 1). MMN elicitation is claimed to not require attention and is thought to be the result of a preperceptual process able to detect changes in a repetitive sound sequence based on a regularity detection process (Winkler et al., 1996) and an automatic comparison process (Naatanen, 1990). The presentation of a sound sequence activates neural analyzers located at different levels of the ascending auditory pathway in order to extract information about physical, temporal, and spatial features of single sounds and regularities of the sound sequence, such as stimulus repetition or stimulus order. This information is maintained in sensory memory for a short time period extending up to several seconds. Each time that a new input reaches the auditory cortex, the result of its analysis is compared with the contents of that sensory representation. If the sound matches the representation of the foregoing sound sequence, it may be incorporated into the memory representation; on the contrary, neural mechanisms responsible for the automatic change detection are activated, as revealed by MMN elicitation. MMN generator mechanisms have been further hypothesized to be involved in the involuntary switching of attention to sound changes (Naatanen, 1990). Accordingly, once the change-detection mechanism is activated, it can trigger a chain of neural events whereby attention is shifted toward the deviant stimulus. This automatic attention shift may result in conscious awareness of the stimulus. MMN reflects, therefore, a pre-attentive process, i.e. it is elicited by the deviant sound even when subjects attention is directed elsewhere, for example when individuals are engaged in a different task. Its elicitation in the absence of attention has motivated the use of MMN as a valid tool in studying automatic change-detection processes during brain states different fromwakefulness. Physiological studies in animals have demonstrated that auditory function is preserved during sleep to some extent (see Coenen and Drinkenburg, in press), supporting the hypothesis that basic preattentive processes such as auditory change detection could also be preserved during sleep. The main goal of the present review is to determine to what extent the auditory discrimination process is feasible during human sleep as revealed by the MMN component. Initially, we review the main brain areas involved in MMN generation. The knowledge of MMN generators may help to understand better the effects of sleep on its elicitation. The relationship between MMN and different forms of memory are addressed in order to determine the influence of sleep on some aspects of memory function. Finally, differences in MMN generation between wakefulness and sleep are discussed on the basis of bottom-up and top-down mechanisms that might be affected during sleep. 2. MMN generation relies on the activation of a temporofrontal neural network MMN elicitation requires, as mentioned above, several parallel andyor sequential processes: (1) the pre-attentive analysis of sound features; (2) the extraction of regularities fromthe auditory sequence on the basis of the organization of sound; (3) the maintenance in memory of a neural rep-

3 M. Atienza et al. / International Journal of Psychophysiology 46 (2002) resentation of the sound sequence; and finally (4) a comparison process between the current sensory input and the memory traces of the foregoing sound sequence. All these processes are thought to be confined to areas located at or near the temporal auditory cortex (Naatanen, 1992). According to results fromintracranial ERP recordings in humans, different areas within the temporal cortex appear to have distinctive roles in sound discrimination. Specifically, Kropotov et al. (2000) found that responses fromarea 41 are especially sensitive to changes in the sound frequency, suggesting that the neuronal circuits in the primary auditory cortex are mainly responsible for the analysis of stimulus features. ERPs recorded from secondary auditory cortex (area 42) were significantly affected by stimulus rate, probably reflecting the formation of memory traces on the basis of information provided by the analysis of the repetitive sound in the primary auditory cortex. Finally, specific responses to the deviant sound were recorded fromthe auditory association cortex (area 22). Evidence for the temporal component of MMN further comes from scalp current-density analysis of electroencephalographic (EEG) (Deouell et al., 1998) and electromagnetic (MEG) recordings (Alho et al., 1998); fromhigh-spatial-resolution techniques, such as positron emission tomography (PET) (Dittmann-Balcar et al., 2001), functional magnetic resonance imaging (fmri) (Celsis et al., 1999), event-related optical signal (EROS) recordings (Rinne et al., 1999); and fromhuman lesion studies (Alain et al., 1998). However, the exact location of the MMN generators within the auditory cortex and within each hemisphere appears to depend on which specific single-sound feature is changed (Levanen et al., 1996), on whether a single feature or a conjunction of two or more features are changed (Schairer et al., 2001), and on the complexity of the sound (Alho et al., 1996). Results fromanimal studies also support the involvement of the temporal cortex in the changedetection mechanism. Kraus and colleagues demonstrated that an MMN in response to tonal contrasts (Kraus et al., 1994a) and synthesized speech syllables (Kraus et al., 1994b) can be recorded in anesthetized guinea pigs fromthe surface midline and caudiomedial division of the medial geniculate body (MGcm), both cerebral locations associated with the non-primary auditory pathway. These results further indicate distinct contributing sources for duration and spectral speech contrasts in the MGcm, but not at the surface midline (Kraus et al., 1994b), supporting the existence of different generators to detect changes in different features, even at the subcortical level. In addition, it was found that the midline evoked response to changes in the interaural phase differed, depending on the context in which the deviant stimulus was presented (King et al., 1995). In this study, responses to stimuli presented in-phase and out-of-phase to both ears were very similar at the midline surface and significantly different fromthe temporal response, suggesting that differences in the phase are represented in the primary auditory cortex. In contrast, the midline response to stimuli out-of-phase was different when this stimulus was presented in a sequence of standard stimuli or in a series alone, supporting the non-primary pathway origin for the MMN. King and colleagues draw the conclusion that both primary and non-primary pathways are necessary for MMN elicitation, but neither is sufficient alone. In addition to auditory cortex and thalamus, MMN has also been recorded in inferior colliculus and hippocampus (for a review Csepe, 1995). These findings together make it clear that independent processing of features and the change detection process reflected by MMN can occur at lower levels of the auditory system. The MMN may also play a role in triggering involuntary attention switches toward the sound changes (Naatanen, 1990). Current evidence suggests that MMN recorded fromthe scalp in humans receives an important contribution from the frontal cortex, especially fromthe right frontal lobe (e.g. Levanen et al., 1996; but see also Deouell et al., 1998 for bilateral frontal contribution). Patients with dorsolateral prefrontal lesions showed a marked reduction of the MMN, especially over the damaged hemisphere (Alain et al., 1998). However, the only direct evidence of a frontal MMN generator, located in the opercular part of the right inferior frontal gyrus in adults, has recently been reported by Opitz et al. (2002) using ERPs and

4 218 M. Atienza et al. / International Journal of Psychophysiology 46 (2002) fmri measures. This frontal activation occurs several milliseconds later than the temporal activation (Rinne et al., 2000), which is in agreement with the hypothesis that the change detection (temporal areas) may lead to subsequent initiation of an involuntary attention switch toward the sound eliciting MMN (frontal areas). Together, these studies suggest that MMN reflects the activation of a temporofrontal neural network mainly involved in stimulus-driven change-detection processes that may otherwise facilitate involuntary switches of attention. 3. MMN as an objective index of sensory memory in wakefulness and sleep Pre-attentive change detection reflected by MMN requires that a neural representation of the auditory context is maintained in auditory sensory memory for a period of time long enough to compare it with the incoming stimulus. This neural representation is based, in turn, on the information provided by feature-extraction processes along the afferent auditory pathway (Naatanen and Winkler, 1999). According to this view, MMN elicitation depends on whether or not auditory sensory memory contains a strong (or activated) representation of the immediately previous sound sequence at the moment that the deviant stimulus is delivered. Thus, the stronger this representation, the larger is the MMN amplitude. The probability of the repetitive sound is presumed to be a key factor of the strength of the neural representation in sensory memory (Naatanen, 1992). In a recent study, Sato et al. (2000) found that the frontal MMN component increased as the probability of the deviant stimulus was decreased. In contrast, the temporal generators were equally activated, independently of the deviant probability of occurrence. On the basis of these results, the authors draw the conclusion that a weak representation of the standard stimulus is enough to activate the automatic change-detection process reflected by MMN, but that activation of the frontal component and subsequent initiation of the attention switch to the auditory change depend on the strength of such a neural representation. These findings, coupled with the intrinsic pre-attentive nature of the changedetection process reflected by MMN, make its elicitation during sleep feasible. Sleep is not a homogeneous brain state. It is characterized by tonic and phasic fluctuations of the background electroencephalographic (EEG) activity. The sleep state consists of the well-known stages 1, 2, 3, 4 and REM (where the four first stages are often grouped as non-rem wnremx sleep and stages 3 and 4 are known as slow-wave sleep wswsx). These stages are, in turn, characterized by different transient phasic events. In addition to changes in the EEG activity, neuromodulation and cognition also vary dramatically fromwakefulness to sleep and across stages within sleep itself (for an extensive review see Hobson et al., 2000). All these changes are thought to affect information processing of external stimuli. Nevertheless, in agreement with the notion that auditory function is preserved to some extent during sleep, MMN, as for other auditory waking- ERP components, has been demonstrated to be elicited during sleep, especially during REM sleep (for a review see Atienza et al., 2001). Csepe et al. (1987) were the first to observe an attenuated MMN during SWS in cats. However, Paavilainen et al. (1987) failed to record MMN in any stage of NREM sleep in humans (subjects did not reach REM sleep), probably as a result of the small frequency deviance (1000 vs Hz) employed in their study. Campbell et al. (1992) demonstrated that a large frequency deviation (1000 vs Hz) may elicit a small MMN in late stage 2 and REM sleep. In the years following the publication of these results, other human studies using the oddball paradigmduring stages 2, 3, or 4 of NREM sleep also failed to record MMN (Nielsen-Bohlman et al., 1991; Niiyama et al., 1994; Sallinen et al., 1994; Winter et al., 1995; Loewy et al., 1996; Nordby et al., 1996; Nashida et al., 2000). Sallinen et al. (1994) did, however, record an MMN-like wave during stage 2 that was only evident when the deviant stimulus was followed by a K-complex. Nevertheless, this result demands caution because it could not be replicated in a subsequent study by the same authors using similar experimental conditions (Sallinen et al., 1996). It is possible that this MMN was over-

5 M. Atienza et al. / International Journal of Psychophysiology 46 (2002) Fig. 2. Grand average waveforms recorded from frontal (Fz) and right mastoid (RM) sites to frequent tones (90%, thick line) of 1000 Hz and infrequent tones (10%, thin line) of 2000 Hz. Stimuli were presented in wakefulness and all stages of sleep with stimulus onset asynchrony (SOA) of 500 ms to the left ear at an intensity of 60 db SPL. Adapted, with permission, fromnashida et al. (2000). lapped by other negative waves unique to sleep, such as the N350 and N550. It would therefore appear that in general the MMN cannot easily be elicited in NREM sleep when simple oddball paradigms are employed. In contrast, several studies have replicated the result first reported by Campbell et al. (1992) during REM sleep under similar conditions (Loewy et al., 1996; Atienza et al., 1997, 2000; Nashida et al., 2000). Fig. 2 shows ERPs to single tones differing in frequency recorded fromfz and right mastoid in wakefulness and all stages of sleep (Nashida et al., 2000). In this study, MMN was elicited by the deviant tone in wakefulness, stage 1 and REM sleep. Interestingly in these studies, the interstimulus interval (ISI) varied between 450 and 600 ms. When slower stimulus rates were used (ISI of s), no MMN was recorded to similar frequency changes (one octave) during REM sleep (Niiyama et al., 1994; Nordby et al., 1996). Stimulus rate is another factor thought to determine the strength of stimulus representation in sensory memory. The longer the interstimulus interval, the weaker is the neural representation as a consequence of its progressive decay in sensory memory, which in turn may lead to decreases in the MMN amplitude (see Muller-Gass and Campbell, in press). However, against this assumption, manipulations of stimulus-onset asynchrony (SOA) in wakefulness did not lead to significant differences in the MMN amplitude (e.g. Schroger and Winkler, 1995). On the basis of results obtained by varying SOA, it has been concluded that auditory sensory memory duration is no longer than 4 10 s, with MMN disappearing abruptly when this time period is exceeded. The absence of a progressive decrease in MMN amplitude as the rate is slowed could be due to the fact that variations in the time between standard stimuli also run parallel to variations in the temporal probability of the deviant, which is known to be inversely related with MMN amplitude (e.g. Sabri and Campbell, 2001). Results fromstudies using alternative strategies in which stimuli are presented at a constant SOA within trains separated by a longer silent interval do not support the hypothesized progressive deterioration of the stimulus representation in sensory memory with the passage of time. For instance, Winkler et al. (2001) found MMNs for durationdeviant tones in all their subjects when the stimuli were presented at regular intervals of 7 s, but not when the same stimuli were presented at fast rate (SOAs0.5 s) within trains and the silent interval preceding the deviant stimulus was 7 s. These results cannot be explained by a rapid decay in the stimulus representation in sensory memory. An alternative explanation is provided by the modeladjustment hypothesis proposed to explain MMN elicitation (Winkler et al., 1996). According to this hypothesis, the auditory systemcontinuously updates the model of the auditory environment,

6 220 M. Atienza et al. / International Journal of Psychophysiology 46 (2002) generating as many MMNs as regularities (e.g. stimulus repetition, stimulus-feature repetition, alternating presentation of two stimuli) are violated. To date, only one study has evaluated MMN elicitation during sleep using the stimulus train paradigm (Atienza et al., 2000). In this study, subjects were tested during wakefulness and REM sleep. They were presented with single tones occurring at relatively fast rate (SOAs650 ms) within trains separated by a silent interval of 3, 6 or 9 s. The deviant tone could occur in position 1, 2, 4 or 6 within each train. As in previous waking studies, MMN elicitation did not occur when the silent interval preceding the deviant stimulus was 9 s. However, MMN was elicited to deviant tones presented in any other position within the train, independently of the silent interval. These results support the predictions of the model-adjustment hypothesis. Stimuli presented within trains are grouped as belonging to a specific context. Thus, when the deviant stimulus occurs in position 1 after a long silent interval, no reference is available for the comparison process. However, if the same deviant stimulus occurs in position 2, the first stimulus works as a reference capable of anticipating the subsequent acoustic event. The MMNgenerating systemis activated only if the second stimulus violates this prediction. During REM sleep, the MMN was elicited to the deviant tones occurring in all positions within the train only when the silent interval between trains was 3 s. This MMN was, however, attenuated relative to the waking state. Unexpectedly, the MMN-generating systemwas not activated to any deviant tone within the train when the silent interval was longer than 3 s, suggesting that sensory memory during REM sleep is much shorter than in wakefulness. The results of studies examining the MMN during sleep indicate that it occurs much more often during REM compared with NREM sleep. Parameters such as the rate presentation and the stimulus features are further determinant factors of MMN elicitation during sleep. For instance, while the MMN does occur in response to frequency changes, large changes in intensity elicited no MMN during stage 2 or REM sleep (Loewy et al., 2000), and interstimulus intervals longer than 1.5 s also prevented MMN elicitation during REM sleep (Nielsen-Bohlman et al., 1991; Nordby et al., 1996). However, the duration of sensory memory during REM sleep can be extended up to 3 s, provided that stimuli are rapidly presented within trains (Atienza et al., 2000). 4. MMN as an objective index of long-lasting memories in wakefulness and sleep The MMN-generating systemdoes not only access information stored in sensory memory, but also information contained in other, more permanent forms of memory. Naatanen et al. (1993b) presented subjects with two complex patterns of stimuli. If they could not discriminate between, themno MMN was generated. However, after they learned the discrimination, the deviant pattern now elicited the MMN. This MMN response progressively increased as performance became more accurate. No changes in amplitude were observed in those subjects who were initially very good in the discrimination task. MEG recordings have shown that this training-dependent MMN is generated in the auditory cortex (Alho et al., 1996; Tervaniemi et al., 2001). Subsequent studies have demonstrated changes in MMN not only immediately after training, but also several days later (Kraus et al., 1995; Tremblay et al., 1998; Menning et al., 2000; Atienza et al., 2002), suggesting a training-dependent long-termeffect on pre-attentive sensory processing. The ability of the brain to generate a permanent neural representation of a complex auditory pattern as a consequence of learning is also determined by the previous experience. For instance, musicians have demonstrated to have a special capability to extract information out of musically relevant stimuli as revealed by MMN (Tervaniemi et al., 2001). Interestingly, these authors showed that none of the non-musicians in their study learned to discriminate changes in a melodic pattern presented at several frequency levels, and that the previous experience of those musicians who showed accurate performance was more related to auditory than visual information. These effects of long-termexperience on pre-attentive auditory processing have also been reported for language-

7 M. Atienza et al. / International Journal of Psychophysiology 46 (2002) specific phonemes in infancy (Cheour et al., 1998) and in adulthood (Naatanen et al., 1997). Evidence fromanimal studies indicate that information learned during wakefulness can be recovered during REM sleep when the same stimulation is delivered (Hennevin et al., 1998). Likewise, a recent study conducted in adult humans has demonstrated that MMN can be recorded during REM sleep to small changes in a complex auditory pattern if subjects previously learned to behaviorally discriminate the two patterns (Atienza and Cantero, 2001). Fig. 3 shows frontal (Fz) ERPs to the standard and deviant patterns and the corresponding difference waveforms during the preand post-training phase, as well as during REM sleep. None of the subjects in that study showed MMN to the deviant pattern during wakefulness before training in the discrimination task. After learning, however, the MMN emerged during wakefulness and REM sleep with similar amplitudes. These results suggest that the neural changes initiated during wakefulness, probably during the training session, appeared to be accessible during REM sleep 2 days after training. On the basis of these findings, MMN might be used as a valid electrophysiological index to evaluate whether information acquired during training is stabilized in more permanent forms of memory during diurnal naps (Mednick et al., 2002) or nocturnal sleep (for a review see Maquet, 2001; Stickgold et al., 2001). Interestingly, Cheour et al. (2002) have recently demonstrated that newborns are able to learn to discriminate speech sounds during sleep. However, this result cannot be extrapolated to the adult human population. The main reason is that sleep in newborns is very different fromthat in adults, and it could subserve different functions. In fact, MMN in newborns can be recorded during wakefulness, quiet (equivalent to SWS in adults) and active (equivalent to REM sleep in adults) sleep, and its amplitude does not vary across these states (Cheour et al., 2000). In contrast, when MMN is recorded during sleep in adult humans, its amplitude is very small, even in REM sleep, compared with that obtained during wakefulness, suggesting that the MMN-generating systemis affected during sleep in a different way in newborns and adult humans. 5. Differences in sensory processing between wakefulness and sleep as revealed by MMN elicitation: bottom-up and top-down mechanisms 5.1. Bottom-up mechanisms The change-detection process underlying MMN elicitation mainly stems from the activity of ascending connections in the auditory hierarchy. Accordingly, MMN is elicited even when the subjects do not pay attention to the eliciting stimuli (Naatanen et al., 1993a), or control the occurrence of the infrequent deviant sounds (Rinne et al., 2001). Fromthis perspective, the much-reduced MMN during sleep can be explained by an inhibition of sensory input prior to entry in the MMN generators located in the temporal lobe. Indeed, single-unit studies in animals have reported a decrease in responsiveness to single tones during SWS for a high percentage of cells in different subthalamic and thalamic nuclei (see Coenen and Drinkenburg, in press). However, changes in the auditory cortex only partially reflect those occurring at subcortical level (Edeline et al., 2001), suggesting that cortical circuits might process auditory information relatively independently. Therefore, although the subcortical response is clearly diminished for a high percentage of cells, a high number of cortical cells show similar responses in both wakefulness and sleep. In agreement with the overall responsiveness level showed by the cortical cells in sleep and wakefulness, PET studies have shown no significant decreases in regional blood flow or glucose metabolism in temporal areas when wakefulness was compared with SWS and REM sleep (Braun et al., 1997). These results, coupled with the fact that MMN has been recorded at thalamic and subthalamic level (see Section 2 on MMN brain generators), support the notion that changes in bottom-up mechanisms may partially explain the reduced MMN observed during sleep Top-down mechanisms Classical theories of sensory processing assume that perception is the result of a serial bottom-up processing that requires a full and detailed analysis

8 222 M. Atienza et al. / International Journal of Psychophysiology 46 (2002) Fig. 3. Frontal (Fz) grand average waveforms to frequent (87%, thick line) and infrequent (13%, thin line) auditory patterns during wakefulness before training (pre-training), just after training (post-training) and during REM sleep. A schematic illustration of auditory patterns is shown below the vertical dotted line, which indicates the moment at which the frequency change was introduced within the pattern (sixth segment within the pattern, black bar). Adapted fromatienza and Cantero (2001). of the different units within the hierarchy. As a result of this ascending processing, the complexity of neural representations depends on the processing level within the hierarchy. In contrast to these classical theories, current approaches emphasize the functional importance of integration between bottom-up and top-down influences on sensory processing (for a review see Engel et al., 2001). According to dynamicist models, the idea of topdown is broader than the notion of feedback signal flow and does not necessarily require a processing hierarchy. The neural representation of a sensory event within a neuronal assembly could be modulated or modified by a dynamic process of neuronal recruiting into a larger assembly. A core idea in the top-down dynamicist models is that the recruiting process could occur between areas at the same processing level or within one area (Varela et al., 2001). For instance, incoming afferent signals induce a specific stimulus-driven pattern of synchronized activity in cortical areas. As a result of the spreading of this synchronized activity pattern through lateral connections, other neuronal populations in the same area andyor in other areas become part of the same overall assembly. These synchronizing (or desynchronizing) influences carry predictions about the sensory events. The specific pattern of synchronized activity that better expresses a successful match of the input to the predicted event will be a salient signal to other neuronal populations, recruiting of which will support the stabilization of a temporal coherence pattern (Engel et al., 2001). Fromthis perspective, the processing of an incoming stimulus depends on the previous experience. Accordingly, the pre-attentive detection of an auditory deviant event would depend on the representation of the previous stimulus sequence. In fact, MMN is only elicited when a stimulus is sorted as an infrequent event that happens among other frequent events, but not when it is presented in a series alone. The representation of the sound sequence could be modified on the basis of changes in the synchronicity of activity within a specific neuronal assembly, which in turn might be determined by the general state of the brain. A feasible hypothesis is that the background activity during sleep does not facilitate the spreading of synchronization within the auditory cortex with stimulus repetition, which in turn may affect the changedetection process underlying MMN elicitation. Fromthis viewpoint, differences in the self-generated synchronization within the auditory cortex

9 M. Atienza et al. / International Journal of Psychophysiology 46 (2002) could explain differences in MMN elicitation between wakefulness and sleep. As mentioned in previous sections, MMN receives an important contribution from the frontal cortex, supporting its role in triggering involuntary attention switches toward the infrequent auditory event. The outcome of this process might, through long-range interactions, modulate the synchronized activity pattern that arises fromlocal computations (sensory areas) operating on the input, affecting the neural representation of the auditory event. The deactivation of prefrontal areas during sleep as revealed by PET (Maquet et al., 1996; Braun et al., 1997) and fmri studies (Portas et al., 2000) could prevent the frontal MMN component elicitation contributing, at least partially, to the sleep- MMN attenuation. Indeed, a reduction of the later portion of MMN was reported during REM sleep by Loewy et al. (1996), which was interpreted by the authors as a result fromdeactivation of the frontal MMN generators. Similarly, changes in alertness seem to mainly affect the temporal component of MMN (Sallinen and Lyytinen, 1997), reflected by the reversal polarity in mastoids when the nose is used as a reference. Together, these results support the notion that sleep-mmn attenuation might partially stem from the lack of topdown influences originating in the prefrontal cortex. Experience-dependent learning constitutes another source of top-down processing. As in wakefulness, improved sensory processing, revealed by enhanced MMN, was reported to occur during REM sleep 2 days after subjects were trained to behaviorally discriminate two complex auditory patterns (Atienza and Cantero, 2001). This result is strong evidence that learning-induced changes affect the stimulus-driven change-detection process reflected in the MMN component. Furthermore, the effects of learning on MMN during sleep strongly support the notion drawn in the present study, i.e. the lack of dynamic topdown influences on the sensory processing mainly affects the formation and maintenance of the neural representation, which in turn could affect the MMN amplitude. Obviously, these top-down influences do not include attention or expectancy factors, because evidence reviewed earlier demonstrated that these factors may modulate the MMN generating process but do not eliminate it (Naatanen et al., 1993a; Rinne et al., 2001; Muller- Gass and Campbell, in press). Instead, these topdown influences involve a wide variety of brain signals that convey information related to immediately previous experience and learning. 6. Conclusion MMN studies conducted in animals and humans during different brain states support the notion that the automatic detection of a change in a sound sequence is a basic function of the central auditory systemthat is also preserved during sleep. In adult humans, this function is especially evident during REM sleep, a brain state in which the sensory input and motor output gates are hardly open and internal stimulus generation is enhanced (Hobson et al., 2000). Despite these adverse conditions for sensory processing, the auditory cortex maintains the ability to organize information supplied by the ascending auditory systeminto a neural representation. The maintenance in sensory memory of such a representation activates the MMN generating system, provided that the information extracted from the incoming input does not match the information related to immediate experience. The outcome of this process highly differs from that in wakefulness, which we have hypothesized to be the result not only of weakened bottom-up influences, but also of altered top-down processing during sleep. Only under a brain model including the integration of self-generated temporal dynamics in neuronal networks at different processing levels with the view of a continuously adaptive brain is it possible to understand the reduced sleep- MMN to changes in a sequence of single tones and the enhanced sleep-mmn to changes in complex auditory patterns as a result of previous learning. References Alain, C., Woods, D.L., Knight, R.T., A distributed cortical network for auditory sensory memory in humans. Brain Res. 812, Alho, K., Tervaniemi, M., Huotilainen, M., et al., Processing of complex sounds in the human auditory cortex

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