This study compared MMN features among compensated tinnitus subjects, decompensated tinnitus subjects, and normal controls. We found that MMN amplitude and area under the curve for higher frequency and silent gap deviants were significantly lower in decompensated tinnitus subjects compared to the other studied groups.

4.1 MMN differences among groups

A few studies have investigated MMN in tinnitus, among them a few looked at MMN using auditory stimuli with different deviants and frequencies. To our knowledge, this is the first study comparing MMN among compensated, decompensated tinnitus subjects, and normal controls and also using auditory stimuli in the frequency range of tinnitus pitch. MMN reveals the process of automatic change detection in sensory stimulus (Escera et al., 1998; Escera, Yago, Corral, Corbera, & Nuñez, 2003) by comparing the deviant stimulus with the sensory memory trace of the preceding standard stimuli. To date, the mechanisms underlying tinnitus and its persistence remain unclear but it is accepted that the perception of tinnitus is a result of aversive brain central processing (Eggermont, 2003; Lockwood et al., 2002).

MMN deficit in tinnitus subjects have been reported in previous studies and our results are consistent with them. Weisz et al., 2004 reported that the tinnitus subjects demonstrated significant abnormalities in MMNs specific to frequencies located at the audiometrically normal lesion‐edge as compared to healthy controls. Mahmoudian et al., 2013 indicated lower MMN amplitude and area under the curve for frequency, duration, and silent gap deviants in tinnitus subjects compared to normal controls. They concluded a possible deficit in auditory preattentive change detection processing. Our results agree with them in significant amplitude and area under the curve differences for high frequency and silent gap deviants. The main difference between our study and Mahmoudian et al., 2013 was that we divided tinnitus subjects into two groups of compensated and decompensated, so to be able to investigate tinnitus mechanism more precisely. Moreover, we used auditory stimuli with frequency adjusted to the subjects’ tinnitus pitch range. It is expected that auditory event‐related potentials characteristics differ when auditory stimuli correspond with tinnitus pitch or edge frequency of hearing loss (Sereda, Adjamian, Edmondson‐Jones, Palmer, & Hall, 2013). Limited studies in tinnitus subjects are designed in this way. However, pitch matching data suggest that most patients do not have a pitch‐match frequency just below the maximum hearing threshold loss frequency. This argues against the brain reorganization model in most subgroups of tinnitus (Pan et al., 2009).

In the current study, MMN amplitudes and area under the curves in all studied groups seem to be lower than the previous studies which used low‐frequency stimuli (Mahmoudian et al., 2013; Weisz et al., 2004). Wunderlich & Cone‐Wesson, 2001 reported that the amplitude of MMN decreased as the frequency of auditory stimuli increased in normal healthy subjects. This might be due to the facts that low‐frequency sounds activate a larger portion of the basilar membrane and that neural generators for lower frequencies are positioned at the higher level on the surface of the cortex compared to high‐frequency generators in the cortex; so larger amplitudes of MMN are recorded (May et al., 1999). Consistent with previous studies, our results confirm that the detection of differences in high‐frequency stimuli is more difficult than the low‐frequency stimuli. It may be related to the decrease in sensation level at high frequencies (Wier, Jesteadt, & Green, 1977). So we suggest that using auditory stimuli matching the tinnitus pitch may better reveal the processing deficit in MMN.

Using high‐frequency stimuli, we found that MMN amplitude and area under the curve for higher frequency and silent gap deviants were significantly lower in decompensated tinnitus subjects compared to the other studied groups. The lower amplitude and area under the curve of MMN for silent gap deviant in decompensated tinnitus group may be due to gap detection deficit in tinnitus. Studies on gap detection indicated a consistent deficit in gap processing in tinnitus subjects (Fournier‐Viger, Faghihi, Nkambou, & Nguifo, 2012). Our result on MMN supports this hypothesis that tinnitus may fill in the silent gaps and makes it difficult for the auditory cortex to detect the silent gap (Mahmoudian et al., 2013). Another recent study reported that deficit in gap processing in tinnitus subjects is linked to deficient timing cues and deficient temporal discrimination caused by processing alterations in tinnitus (Ku et al., 2017). Whereas we tried to minimize the effect of hearing loss in the studied subjects, but we cannot ignore the presence of up to 40 dB HL hearing loss in frequencies of 4,000 and 12,000 Hz in some subjects. Therefore, the deficit in gap processing in tinnitus subjects may relate to the probable hearing loss, as gap detection problems are evident in many with hearing loss (Tyler, Summerfield, Wood, & Fernandes, 1982).

However, what can be concluded is that the presence of a decompensated tinnitus may cause a deficit in the perception of the silent gap. The gap detection deficit might be an index of abnormal cortical auditory processing in tinnitus.

The lower amplitude and area under the curve of MMN for higher frequency deviant in decompensated tinnitus group may be due to enhanced frequency resolution and frequency discrimination in tinnitus subjects. Thai‐Van, Micheyl, Moore, and Collet (2003) suggested that persistent auditory stimulation results in enhancing frequency discrimination and resolution. It has been shown that frequency discrimination training results in cortical reorganization and increases the numbers of neurons respond to the trained frequency (Recanzone, Schreiner, & Merzenich, 1993). The significant result in higher frequency deviant may be due to this fact that frequency discrimination training resulted from persisting tinnitus sound had modulated the brain synchronous activities. It has been reported that tinnitus subjects with a mild hearing loss at tinnitus pitch have a more amplitude‐dependent N1–P2 response in the tinnitus frequency relative to controls (Kadner et al., 2002). The enhancement of frequency discrimination and resolution due to tinnitus results in facilitated comparisons between the deviant and standard stimuli and the decrease of MMN amplitudes. Our results for duration deviant were near to be significant. They might statistically be significant if the number of subjects was increased.

According to the Bayesian brain model, the brain relies on internal probabilities to adjust function in situations of uncertainty (Friston, 2010; Ostwald et al., 2012). In this model, the incoming sensory input is compared with the existing prior knowledge in memory to predict. Unitary sensory memory representations are then created and used to form predictions and create auditory objects (Winkler & Czigler, 2012; Winkler & Schröger, 2015). The incoming sensory input is compared with the existing internal memory representation and if they were different, a prediction error occurs. The brain only allows the prediction errors to pass onto the next level of processing. This Bayesian prediction has been verified by electrophysiology. MMN (Näätänen et al., 2011) and P300 (Polich, 2007) are known as neurobiological indicators associated with Bayesian brain hypothesis (Baldi & Itti, 2010; Itti & Baldi, 2009). The N100 is an index of sound detection and sensation (Parasuraman & Beatty, 1980; Winkler, Tervaniemi, & Näätänen, 1997). MMN reflects the automated change detection based on prediction error processing; however, the P300 might involve attention orientation toward deviant stimuli and context perception (King, Gramfort, Schurger, Naccache, & Dehaene, 2014; Polich, 2007; Schwartze, Tavano, Schröger, & Kotz, 2012). MMN detects the difference between the repetitive standard stored in the sensory memory and the oddball deviant. It works based on detecting the changes between the incoming stimuli and the existing memory representations. So, it can show the processing according to the Bayesian model.

Consistent with Durai, O'Keeffe, and Searchfield (2018), our results provide evidence that sensory memory is occupied by the intrinsic tinnitus signal, so the change detection mechanism is not able to retain the incoming signal to use it for comparison and detect the changes. Constant updating of tinnitus percept from memory as a result of deficient sensory memory prevents habituation (De Ridder et al., 2006). Normal sensory memory and change detection processes are needed for detecting the tinnitus signal as a prediction error and habituating to tinnitus. Using the Bayesian model, we propose that abnormal sensory memory function prevents prediction error caused by the tinnitus signal. The tinnitus signal cannot be maintained to the existing prior template in memory, so it is persistently detected as a prediction error and passes the tinnitus signal onto the next level of processing. This is why tinnitus signal is consistently detected as a new signal and activates the brain salience network and prevents habituating to tinnitus. This indicates that the dishabituation to tinnitus in decompensated tinnitus subjects is not only related to characteristics of the sensory input, that is, the bottom‐up processing, but rather a top‐down processing associated with detection of a mismatch between the internal expectations and the incoming information. It can be hypothesized that sensory memory and prediction error mechanisms in compensated tinnitus subjects are similar to normal subjects but deficits of sensory memory and prediction error prevent habituating to tinnitus in decompensated tinnitus subjects.

De Ridder, Vanneste, Weisz et al. (2014)) applying the Bayesian model to tinnitus proposed that tinnitus perception results from the underlying neural reorganization of the tonotopic areas caused by auditory deafferentation. They believe that auditory deafferentation leads to topographically restricted prediction errors, although the absence of an expected stimulus induces a cortical prediction error signal. If uncertainty cannot be reduced by getting information from the adjacent cortical regions, the missing information can be recalled from the memory stored in the parahippocampal region (De Ridder, Vanneste, & Freeman, 2014). The involvement of the parahippocampus in tinnitus might be related to the constant updating of the tinnitus percept from memory, thereby preventing habituation (De Ridder et al., 2006).

Although the hypothesis proposed by De Ridder, Vanneste, Weisz et al. (2014) can apply to tinnitus perception, but considering that not all the tinnitus subjects have necessarily hearing loss and consequently, auditory deafferentations; we suggest that prediction errors caused by the tinnitus signal is related to the deficient brain comparator in top‐down processing, so it cannot identify tinnitus as a repetitive signal in the memory. As a result, tinnitus is continuously updated from memory regardless of its origin (being due to peripheral deafferentation or cortical regions). It is detected as a prediction error and is sent to higher order processing of the brain and finally prevents habituation. This is why decompensated tinnitus subjects have the best MMNs in the frequency range of tinnitus pitch confirmed by higher frequency deviant MMN, but it has significantly lower amplitudes in other deviants, meaning that the process of comparing deviants to standards is not working properly as shown in Figure 3 waveforms. From the figure, it is clear that although MMN for higher frequency deviants showed desirable amplitudes and waveforms in all groups the response to standard and the deviant significantly decreased in decompensated tinnitus groups. It reveals that although the comparisons of standard stimuli to deviants are happening but possibly due to difficulty of access to memory during the comparisons, the response to this deviant decreased compared to the other groups.

Neuroimaging studies supported this hypothesis in tinnitus showing that dorsal anterior cingulate cortex and insula that are brain regions related to MMN and P300 are activated during filling in mechanisms to restore missing information in tinnitus subjects (Shahin, Bishop, & Miller, 2009). The neural networks, activated when a stimulus is predicted closely, are similar to memory retrieval processes (Albright, 2012). Persisting tinnitus causes involvement of memory networks (De Ridder et al., 2006; Mahmoudian et al., 2013). The presence of tinnitus led to the involvement of working memory which can hypothetically affect the accuracy of predictive processing (Durai et al., 2018). Previous studies have shown that MMN is elicited by auditory cortex, frontoparietal brain areas, the dorsal anterior cingulate cortex, insula and dorsolateral prefrontal cortex, (i.e., salience network) (Molholm, Martinez, Ritter, Javitt, & Foxe, 2005; Takahashi et al., 2013). Interestingly, many of the brain areas involved in tinnitus overlay the MMN‐related regions. It can be concluded that lower MMN amplitudes and area under the curve in decompensated tinnitus subjects may be due to abnormal activity of salience network. The activity of other annoyance and awareness neural networks that emphasize on thoughts and emotions are also emphasized in the Tinnitus Primary Functions Questionnaire (Tyler et al., 2014). Also, it can be concluded that the presence of tinnitus cannot be a problem by itself but the involvement of other nonauditory neural networks like salient network causes tinnitus annoyance and awareness.