Skip to main content
NCBI home page
NIHPA Author Manuscripts logoLink to NIHPA Author Manuscripts
. Author manuscript; available in PMC: 2019 Jan 1.
Published in final edited form as: Schizophr Res. 2017 Jul 11;191:25–34. doi: 10.1016/j.schres.2017.07.009

Meta-analysis of mismatch negativity to simple versus complex deviants in schizophrenia

Michael Avissar 1,*, Shanghong Xie 2,*, Blair Vail 1, Javier Lopez-Calderon 1, Yuanjia Wang 2, Daniel C Javitt 1,3
PMCID: PMC5745291  NIHMSID: NIHMS892302  PMID: 28709770

Abstract

Mismatch negativity (MMN) deficits in schizophrenia (SCZ) have been studied extensively since the early 1990s, with the vast majority of studies using simple auditory oddball task deviants that vary in a single acoustic dimension such as pitch or duration. There has been a growing interest in using more complex deviants that violate more abstract rules to probe higher order cognitive deficits. It is still unclear how sensory processing deficits compare to and contribute to higher order cognitive dysfunction, which can be investigated with later attention-dependent auditory event-related potential (ERP) components such as a subcomponent of P300, P3b. In this meta-analysis, we compared MMN deficits in SCZ using simple deviants to more complex deviants. We also pooled studies that measured MMN and P3b in the same study sample and examined the relationship between MMN and P3b deficits within study samples. Our analysis reveals that, to date, studies using simple deviants demonstrate larger deficits than those using complex deviants, with effect sizes in the range of moderate to large. The difference in effect sizes between deviant types was reduced significantly when accounting for magnitude of MMN measured in healthy controls. P3b deficits, while large, were only modestly greater than MMN deficits (d = 0.21). Taken together, our findings suggest that MMN to simple deviants may still be optimal as a biomarker for SCZ and that sensory processing dysfunction contributes significantly to MMN deficit and disease pathophysiology.

Keywords: mismatch negativity, complex deviant, P300, P3b, schizophrenia, meta-analysis

1. Introduction

1.1. Mismatch negativity deficits in schizophrenia

Mismatch negativity (MMN), a component of the auditory event-related potential (ERP) is among the most widely studied biomarker of cognitive dysfunction in schizophrenia. It is typically elicited in response to an auditory oddball paradigm in which repeating standard stimuli are interrupted by infrequent deviants, which can range from simple deviants such as changes in pitch to violations of complex patterns or abstract rules (Näätänen et al., 2001). MMN is elicited even when the stimulus is not attended and when no behavioral response is required such as during sleep or coma (Kane et al., 1993; Sallinen et al., 1994), suggesting that it indexes a primarily pre-attentive stage of auditory information processing. At the local circuit level, recent theories of MMN generation suggest critical involvement of N-methyl-D-aspartate (NMDA)-type glutamate receptors, somatostatin interneurons, and theta-frequency generation mechanisms (Javitt, 2000; Javitt and Sweet, 2015; Lavoie et al., 2008; Michie et al., 2016; Umbricht et al., 2000).

Despite the well-replicated findings regarding MMN dysfunction in schizophrenia (Erickson et al., 2016; Umbricht and Krljes, 2005a), ideal approaches for eliciting MMN in clinical settings remain to be determined. In addition, relatively few studies have addressed the relationship between auditory sensory dysfunction and higher order cognitive impairment. Finally, while several studies have addressed potential contributions of neuroscientific constructs such as top-down vs. bottom-up processing to across-study heterogeneity of MMN findings, few have evaluated the potential contributions of technical issues such as absolute MMN amplitude or signal-to-noise. Here we use a meta-analytic approach to evaluate optimal utilization of MMN in the investigation of schizophrenia.

1.2. Deviant complexity

The earliest studies of MMN deficit in SCZ used simple deviants such as duration (Lembreghts and Timsit-Berthier, 1993; Shelley et al., 1991) and pitch (Javitt et al., 1993). Since then, MMN deficits to both pitch and duration have been extensively replicated. For example, two meta-analyses have examined MMN deficits to simple deviants and found moderate to large effect sizes with duration deviants demonstrating the greatest deficit (Erickson et al., 2016; Umbricht and Krljes, 2005). Newer studies have also used more complex deviant types to assess MMN deficit in SCZ (Chen et al., 2016; Haigh et al., 2016; Hay et al., 2015; Kantrowitz et al., 2015; Rudolph et al., 2015; Salisbury and McCathern, 2016).

In both humans and animal models, neurophysiological evidence of deviance detection exists along much of the central auditory pathway, including brainstem structures such as inferior colliculus, medial geniculate nucleus, primary and secondary auditory cortex, and inferior frontal regions (Escera and Malmierca, 2014; Recasens et al., 2012). In general, responses to deviants in physical stimulus parameters are thought to activate deviant detectors at lower levels of the auditory system, while more complex MMN paradigms activate higher brain regions.

Prior meta-analyses in schizophrenia have focused primarily on MMN to simple physical deviants. However, several studies have now evaluated MMN from complex paradigms as well. Given an increased interest in studies using complex deviants, one goal of this meta-analysis is to evaluate the relative utility of simple vs. complex paradigms for evaluation of MMN dysfunction in SCZ.

1.3. Relationship between MMN and cognitive impairments in SCZ

A second set of unanswered questions concerns the relationship between MMN deficits and higher order cognitive impairments. Despite the simplicity of the MMN paradigm, effect sizes for MMN dysfunction are similar to those for impairments in overall cognitive function probed through standard neuropsychological measures (Schaefer et al., 2013) or with late ERP components such as P300 that require both attention and cognitive control (Linden, 2005; Perlman et al., 2015; Polich, 2007). A subcomponent of P300, P3b, is elicited when subjects are asked to respond to rare deviants in the auditory oddball paradigm and is also impaired in SCZ (Jeon and Polich, 2003; Leitman et al., 2010; Perlman et al., 2015). Pre-attentive deviance detection is a processing step required for subsequent planned responses to attended deviants (Novak et al., 1992). Therefore, deficits in MMN may limit the ability to generate a normal P3b response (Javitt et al., 1995).

In some studies, MMN and P3b have been measured in the same subjects and this within-subject design promises to reveal the relationship between the two measures and their relative deficits. For example, Leitman et al. (2010) used structural equation modeling and a combination of traditional and adaptive threshold paradigms to quantify the contribution of MMN to P3b. MMN deficits to pitch deviants accounted for about 50% of the deficit in P3b. We performed an additional meta-analysis specific to studies that obtained both MMN and P3b in parallel to evaluate the relative magnitude of deficit and the relationship between the two measures.

1.4. The influence of signal-to-noise

MMN paradigms have yet to be standardized across research sites studying SCZ. Even in HC, there can be considerable variability in MMN amplitude and signal-to-noise based on differences in data collection (recording equipment, environment, auditory stimulus paradigm) and data analysis methods (electrode referencing, peak detection routine). Some of the variation between studies in MMN deficit is likely due to these differences in signal-to-noise detection of the MMN component in HC. We hypothesize that techniques and paradigms that generate more robust MMN in HC are more likely to detect deficits in SCZ. Conversely, any loss of MMN signal could result in reduced group differences. Therefore, we also examined the relationship between MMN amplitude in HC and MMN deficit in SCZ across all studies and conditions.

To date, approaches to eliciting MMN in SCZ have varied substantially across research groups. An overall goal of this meta-analysis is to facilitate increased convergence and standardization toward optimal paradigms in SCZ.

2. Materials and methods

2.1. Literature search

We searched PubMed and a recent meta-analysis on mismatch negativity in schizophrenia (Erickson et al., 2016). In addition, one study under submission for this issue was also included (Perrin et al., 2017). Studies were only included if they were peer-reviewed original articles with data not previously reported elsewhere, if they included both a healthy control group and a patient group (>75% schizophrenia spectrum disorders group), and if mismatch negativity during a passive auditory oddball task was quantified using EEG in both groups and reported as mean ± SD/SE (or attainable visually from published figures) or groups were compared pairwise using t-test/ANOVA (t-stat, F-stat, Cohen’s d, or p-value). Twin studies were excluded with the exception of a separate search for studies measuring both MMN and P3b in HC and SCZ. Studies were also excluded if reporting of methodology was inadequate (e.g. electrode site or latency window not reported) or if there was significant evidence that data quality was poor. Only one study was excluded for both incomplete reporting of methodology and poor data quality (Li et al., 2013).

Literature search was performed in multiple steps. First, all studies included in Erickson et al. (2016) were reviewed. We included all articles from that meta-analysis except those that did not examine a schizophrenia spectrum disorder group, as that was beyond the scope of this study (e.g. studies that only examined bipolar disorder, relatives of schizophrenia patients, or clinical high risk group). We also excluded studies in which MMN was measured under an active paradigm in which the subject attended to or responded to the deviants used to calculate the MMN waveform.

With respect to psychotic disorders, we were more inclusive than the Erickson et al. (2016) meta-analysis, allowing for inclusion of schizoaffective disorder or studies in which a patient group consisted of more than 75% of patients in a schizophrenia spectrum disorder category (brief psychotic disorder, schizophreniform disorder, delusional disorder, schizoaffective disorder, schizophrenia, or first episode psychosis later confirmed to be schizophrenia spectrum). Given this inclusivity, we also reviewed all studies excluded by Erickson et al. (2016) for reason of “unclear diagnostic group” and included those that met our diagnostic criteria.

In some cases, MMN means and SDs were obtainable from visual measurement of published bar graphs/scatter plots that showed means and variance information. Therefore, we also reviewed studies excluded for “insufficient data” and included those in which MMN mean and SD was ascertainable. In one case of “insufficient data” we were able to contact the study author who provided data tables for inclusion into our meta-analysis (Laton et al., 2014).

Next, we performed our own PubMed keyword search limited to 2015 and later to include more recent studies, as some of these were studies that would not have been captured by Erickson et al. (2016). We used a Boolean logic combination of the following keyword terms: [“schizophrenia” OR “schizoaffective” OR “psychosis”] AND [“mismatch negativity” OR “MMN” OR “N2a”]. This yielded 73 hits of which 61 were excluded, leaving 12 studies included from 2015 to present, including Perrin et al. (2017).

We were also interested in including studies examining more complex deviant types beyond simple duration, pitch, and intensity deviants. Therefore, we performed a PubMed keyword search of all years up to the present (January 2017) that focused on complex deviants using the following terms: [“schizophrenia” OR “schizoaffective” OR “psychosis”] AND [“mismatch negativity” OR “MMN” OR “N2a”] AND [“complex” OR “abstract” OR “pattern” OR “missing”]. We also included any studies from Erickson et al. (2016) that included complex deviants as well as Perrin et al. (2017). This resulted in 24 studies included in the analysis of complex deviants.

Lastly, we wanted to examine the within-study relationship between MMN and P3b. Therefore, we performed a separate search of all years up to the present for studies that measured both ERPs in HC and SCZ. Our search consisted of the following combination of terms: [“schizophrenia” OR “schizoaffective” OR “psychosis”] AND [“mismatch negativity” OR “MMN” OR “N2a”] AND [“P300*” OR “P3” OR “P3a” OR “P3b”]. Only studies that measured P3b under an active auditory oddball paradigm where the deviant was a target stimulus requiring a response were included. Studies were only included if they measured both P3b and MMN in the same subject groups. Out of 100 studies found, 87 were excluded leading to 17 included.

Overall, a total of 117 studies were included. The number of population samples used in each analysis is reported in Section 3 (Results).

2.2. Data encoding

We encoded a number of variables of interest. First, we were interested in the effect size of MMN deficits in SCZ. Therefore, where available, we used the mean and SD of MMN for HC and SCZ (in µV). Preference was given to MMN measured in frontal and midline sites (electrode Fz) which for some studies included the mean of frontal or midline electrode chains but in some cases included the mean of all electrodes or the global field potential (GFP). Preference was also given to MMN measured in the difference waveform (deviant minus standard stimulus ERP) but in rare cases, studies only reported MMN from the deviant waveform. In some cases, the mean and SD was not available for each group but outcomes of a statistical analysis comparing HC vs. SCZ was reported (such as t-test or ANOVA). In such cases, we encoded in order of preference the F-stat, t-stat, Cohen’s d, or p-value which in combination with the sample sizes could be used to derive the effect size and its confidence intervals (see Section 2.3). Some studies measured MMN using different deviant types, different analyses conditions, or in multiple patient groups. These were all entered separately to keep track of different patient samples and conditions. For a sub-group of studies that also measured P3b, a similar methodology was used to encode P3b results but preference was given to parietal and midline electrode sites and to analyses using the target waveform (in rare cases, only the difference waveform P3b was reported).

Mean age, percent male, and deviant type were all recorded for each sample. For deviant type, we encoded whether the deviant was a simple pitch, duration, or intensity deviant. There was significant variation in whether duration deviant tones were longer or shorter than the standard tones. Therefore, we also encoded duration deviant polarity (i.e. longer/shorter). We also encoded whether a deviant was a more complex sensory deviant such as a simple double, SOA, intra- or inter-tone gap, white noise, omission/missing, location, frequency glide/modulation, or non-vocal prosody deviant. In addition to complex sensory deviants, some deviants were more abstract, naturalistic, or involved changes in stimulation pattern. These were encoded separately as complex abstract/pattern deviants and included pattern (extra tone in a tone train or ascending vs descending frequency tone pairs), novel environmental sounds, or language deviants (vowel sounds).

2.3. Statistical analysis

Effect size was calculated by deviant type for simple deviants (pitch, duration, intensity). If MMN was measured under multiple deviance levels and probabilities for a given study and deviant type, preference was given to the condition with a deviance level and deviance probability closest to 10%. For each comparison of HC and SCZ, effect size was calculated by the difference between the means divided by the pooled standard deviation. The overall effect size was calculated using a meta-analysis fixed effects model (Berkey et al., 1995; Hedges and Olkin, 1985; Raudenbush, 2009; van Houwelingen et al., 2002), which provides an estimate of the overall effect size pooled across studies by properly averaging and weighting the uncertainty of effect sizes estimated from each individual study. For duration deviants, we calculated the overall effect size in longer and shorter deviants separately; in cases where both were measured for a given study, both were included in the calculation.

In addition to simple deviants, we computed overall effect size for complex sensory and complex abstract/pattern deviants separately. In some studies, the same sample was subjected to multiple complex deviants. Since we were interested in determining whether complex deviants could outperform simple deviants in detecting deficits in SCZ, we selected the specific deviant type and condition that maximized effect size. Because the complex deviant parameter space is larger and more multi-dimensional, this ensured that only complex deviants which optimized deficit detection were included. Some studies included both simple and complex deviants. To better account for across study variability, we performed an additional analysis of effect size measured using only studies in which both simple and complex deviants were used. For each study, the simple and complex deviant that maximized effect size was included only. This was done separately for studies that included complex sensory and complex abstract/pattern deviants. There were nine studies available for the complex sensory and five for the complex abstract/pattern analysis. The overall effect size was calculated for the simple deviant and corresponding complex deviant condition using the meta-analysis fixed effects model.

To examine the relationship between deficits in MMN and P3b in SCZ, we only included studies in which both were measured in the same study sample. Individual study effect size for P3b was calculated in the same way as for MMN, using the difference between the means divided by the pooled standard deviation. Only studies using simple deviants were included for this comparison. Overall effect size was calculated for MMN and P3b separately as well as an effect size comparing P3b deficits vs MMN deficits using the meta-analysis fixed effects model. A weighted linear regression with study sizes as weights was performed between P3b deficits and MMN deficits to obtain their correlation.

Finally, we examined the relationship between magnitude of MMN in HC and the deficit in MMN found in SCZ. For each study sample and condition comparing MMN in HC and SCZ, we calculated a normalized mean of MMN in HC (mean MMN divided by standard deviation) and the individual study effect size of MMN between SCZ and HC as the standardized mean difference between groups (mean difference divided by pooled standard deviation). This was possible when mean and SD were reported for the MMN of HC or the effect size could be derived from the reported t-statistic or F-statistic. Samples and conditions missing the needed HC data were not included. There were 202 data points included out of the 241 sample/condition combinations available. In addition, we ran both an unweighted analysis of variance (ANOVA) and analysis of covariance (ANCOVA) to test whether MMN amplitude in HC accounted for the difference in effect sizes between deviant types. We limited each sample to one deviant probability and level (closest to 10%) to better compare deviant categories. Deviant category was used as a factor and the normalized mean for MMN in HC was used as a covariate for the ANCOVA. We calculated the unadjusted and adjusted effect sizes for each deviant type. A similar but separate ANOVA/ANCOVA pair was run on duration deviant data points with one factor (longer/shorter duration deviant). In a separate analysis, we grouped simple deviants (pitch, duration, intensity) and complex deviants (abstract/pattern and complex sensory) together and performed a univariate ANOVA comparing simple versus complex. This was repeated after adjusting for MMN in HC (ANCOVA).

Meta-analyses were performed using R package “metafor” (Viechtbauer, 2010). All tests were two-sided at a significance level of 0.05. SPSS version 23 (IBM) was used to run ANOVA and ANCOVA tests. All other analyses were performed in R version 3.3.2.

3. Results

3.1. MMN deficits to simple deviants

The overall effect size of MMN to simple physical deviants (pitch, duration, and intensity) in SCZ vs HC was measured. The deficit to each deviant was analyzed separately (Fig. 1). There were 64, 88, and 8 samples included for pitch, duration, and intensity deviants, respectively (see Supplemental Table 1 for list of all studies included in this meta-analysis). Results are consistent with previous reports with all simple deviants demonstrating deficits in SCZ with moderate to large effect sizes. Also consistent with previous reports (Erickson et al., 2016; Umbricht and Krljes, 2005), duration deviants showed the largest effect size. The effect size to duration deviants was 24% larger than for pitch deviants.

Figure 1.

Figure 1

Open in a new tab

MMN deficits in SCZ by deviant category. The overall pooled effect size calculated using a fixed effects model is shown with 95% confidence intervals (horizontal error bars) for each simple deviant category. Exact values are reported on the right column including confidence intervals in brackets. Longer and shorter duration deviants were also analyzed separately. Positive values indicate lower MMN magnitudes in SCZ.

Duration deviants can be presented as longer or shorter than standard stimuli. We examined whether the polarity of the duration deviance influenced effect size. As shown in Figure 1, long duration deviants demonstrated a 26% larger effects size than shorter duration deviants. This result should be interpreted with caution, however, since the level of deviance may also influence the effect size. Long duration deviants differed from standards more than short duration deviants in both absolute duration and percent change (short duration: −43.9 ± 8.25 ms, −60.7 ± 13.3%; long duration: 69.0 ± 38.3 ms, 113 ± 28.7%; p = 0.0025 for absolute duration, p = 0.001 for % change).

3.2. MMN deficits to complex deviants

Some studies have used more complex deviant types to elicit MMN. We divided these into two main categories. “Complex sensory” included paradigms in which deviants 1) varied in multiple sensory dimensions simultaneously, 2) depended on binaural vs. monaural processing or 3) utilized complex sounds. “Complex abstract/pattern” included paradigms in which simple physical features did not differentiate standards from deviants. Instead, for these paradigms deviance used to elicit MMN consisted of violations in patterns of stimulation, variation in abstract features or rules, or representation of changes in more naturalistic features (see Section 2.2). Deficits in MMN to complex deviants likely rely more heavily on higher tier regions of the auditory stream, including secondary auditory regions and frontal cortex. In contrast, MMN deficits to deviation in sensory features likely rely on auditory cortical or even subcortical deviance detection. We measured the overall effect size of schizophrenia to complex sensory and complex pattern/abstract deviants (Fig. 2, 3). For population samples that were tested under multiple complex deviants, the deviant that maximized effect size was chosen. For both complex sensory and complex abstract/pattern deviants, effect sizes were moderate and comparable to those elicited by simple deviants. No evidence of preferential utility of these paradigms was detected.

Figure 2.

Figure 2

Open in a new tab

MMN deficits in SCZ using complex sensory deviants. Effect sizes for individual HC vs SCZ study samples are shown for studies using complex sensory deviants (as defined in Section 2.2) with 95% confidence intervals plotted as horizontal error bars. The pooled effect size using a fixed effects model is shown at the bottom including its 95% confidence interval. Positive values indicate lower MMN magnitudes in SCZ.

Figure 3.

Figure 3

Open in a new tab

MMN deficits in SCZ using complex abstract/pattern deviants. Effect sizes for individual HC vs SCZ samples are shown for studies using complex abstract/pattern deviants (as defined in Section 2.2) with 95% confidence intervals plotted as horizontal error bars. The pooled effect size using a fixed effects model is shown at the bottom including its 95% confidence interval. Positive values indicate lower MMN magnitudes in SCZ.

To better account for across-study variability, we also calculated MMN effect size to simple and complex deviants using only studies that included both types of deviants. Only the simple and complex deviant conditions that maximized effect size were included to draw a fair comparison. For complex sensory deviants, there were nine studies available that also used simple deviants. The maximal overall effect size was 0.78 (95% CI 0.59–0.97) for simple deviants versus 0.46 (95% CI 0.27–0.64). There were only five studies that included both complex abstract and simple deviants. For those studies, the maximal overall effect size was 0.66 (95% CI 0.36–0.96) for simple deviants and 0.75 (95% CI 0.45–1.05). Therefore, although less studies were available, the within-study analysis was consistent with the broader analysis in showing no evidence of preferential utility of complex paradigms. More studies that include both simple and complex deviants are needed to confirm these findings in a larger sample.

3.3. P3b versus MMN deficits

We compared deficits in MMN to those of P3b in SCZ. We have previously suggested that a substantial component of deficits in higher order processing in SCZ, as reflected in P3b, may be attributable to sensory-level dysfunction (Leitman et al., 2010). Studies in which both were measured in the same population samples were included. The difference in effect size between P3b and MMN was calculated in each sample as well as the overall difference for all included 17 samples.

Only studies using simple deviants were included. In this sub-sample of studies, the overall effect size for MMN was 0.65 (95% CI: 0.53–0.78) and for P3b was 0.87 (0.74–1.00). As expected, the relative deficit in P3b was larger than that in MMN (Fig. 4). However, also consistent with prior expectation, the difference in effect size, 0.2 ± 0.16, is in the range of “small” as opposed to the deficit in MMN itself, which is in the range of moderate to large, demonstrating that sensory function and higher order processing deficits are similarly impaired in schizophrenia.

Figure 4.

Figure 4

Open in a new tab

A comparison of MMN and P3b deficits in SCZ. An effect size representing the difference in effect sizes for MMN and P3b in SCZ vs HC is shown for individual studies that measured both ERP components. 95% confidence are depicted as horizontal error bars. Exact values are reported on the right column including 95% confidence intervals in brackets. Positive values indicate greater deficits for SCZ in P3b than in MMN.

We also examined the degree to which P3b amplitude depended on prior MMN generation across studies (Fig. 5). As expected, there was a significant correlation between deficits in the two neurophysiological measures after adjusting for age and gender reflected by a correlation coefficient of 0.43 (p = 0.04). Thus, across studies MMN deficits accounted for 18.7% of the variance in P3b deficits. In general, MMN amplitude has shown correlation with some subsequent potentials such as the N2, but findings regarding MMN vs. P3 amplitude have been variable (e.g. Leitman et al., 2010; Umbricht et al., 2006). The present study confirms a significant association between MMN and P3 based upon cross-study correlation.

Figure 5.

Figure 5

Open in a new tab

P3b deficits correlate with MMN deficits in SCZ. The effect size of P3b deficits is plotted against the effect size of MMN deficits in each SCZ vs HC sample in which both components were measured. The dashed line represents the results of a correlation between the two variables computed from a weighted linear regression. Symbol sizes depict the weight given to each sample as determined by the variance estimate of MMN and P3b effect sizes. R is the correlation coefficient after adjusting for age and sex.

3.4. MMN deficits in SCZ vs. MMN signal-to-noise

As detailed above, MMN can be measured in different patient populations, under different stimulation conditions (e.g. deviant types), and with different ERP analysis methods. Although differences in effect size across paradigms is typically interpreted relative to neuroscientific constructs, purely technical aspects such as magnitude and signal-to-noise of the MMN measure within the HC population may also contribute to differences in effect size of the observed deficit. It is axiomatic that to observe a deficit of MMN in SCZ, there must be a measurable level of MMN in HC. A larger MMN in HC leaves more room to detect a deficit in clinical groups. Therefore, we examined the relationship between MMN deficit (effect size in MMN in HC vs SCZ) and the normalized amplitude of MMN in HC (Fig. 6A).

Figure 6.

Figure 6

Open in a new tab

The relationship between MMN deficits in SCZ and MMN magnitude in HC. (A) The effect size for SCZ vs HC sample comparison is plotted against the normalized mean of MMN in HC. Symbol colors denote the different deviant type categories (see key). The solid line is an unweighted linear regression. (B) Unweighted means for the effect sizes shown in A by deviant type as filled circles (duration also shown as longer and shorter duration separately). Empty circles represent the means adjusted for normalized amplitude of MMN in HC and error bars represent SD.

MMN measurements from all studies under all conditions were included (223 data points). As predicted, larger normalized MMN amplitude in HC predicted more deficit in SCZ. This suggests that detecting deficits in SCZ using MMN is optimized by selecting conditions under which MMN is maximally elicited in HC. It also may indicate that some of the differences observed between studies and deviant types could be due to the ability to detect MMN in HC.

Before accounting for the effect of MMN in HC, the effect of deviant type on this all-inclusive sample was statistically significant (F4,179 = 3.283, p = 0.013). However, following co-variation for normalized MMN amplitude in HC, the effect of deviant type was no longer significant (F4,179 = 1.951, p = 0.104). Similarly, an analysis of effect sizes to longer versus shorter duration deviants revealed significant differences prior to adjustment for the normalized MMN amplitude in HC (F1,84 = 7.311, p = 0.008). This difference also lost significance following co-variation for normalized MMN amplitude in HC (F1,83 = 2.049, p = 0.156). Figure 6B displays adjusted and unadjusted mean effect sizes (unweighted and pooled across all available study samples and conditions). The increase in uniformity when accounting for MMN in HC between deviant types appears driven by increases in effect size for intensity and more complex deviants.

In an additional analysis, we grouped simple (pitch, intensity, and duration) and complex (abstract/pattern and complex sensory) deviants together. A univariate ANOVA with complex versus simple deviant as the factor revealed that effect size was larger using simple deviants than complex deviants (unadjusted means 0.82 ± 0.69 versus 0.51 ± 0.38; F1,182 = 5.40, p = 0.029). After co-varying for MMN in HC, the difference in effect size between simple and complex deviants lost significance (F1,181 = 0.911, p = 0.341). The results suggest that the difference in effect sizes observed between simple and complex deviants is largely driven by the ability of each deviant type to elicit MMN in HC.

4. Discussion

4.1. The case for keeping it simple

Mismatch negativity has long shown promise as a biomarker for predicting conversion to, diagnosing, “bio-typing”, “staging”, individualizing treatment for, and tracking response to treatment of schizophrenia (Javitt and Freedman, 2015; Javitt and Sweet, 2015; Light and Swerdlow, 2015). Given the broad range of deviant types that can elicit MMN, questions remain as to which paradigm makes the “best” biomarker or whether different deviant types could serve disparate functions in the evaluation and management of the disorder. Related to this question is the continued exploration of the neural mechanisms underlying MMN deficits and what those mechanisms may say about the etiology and pathophysiology of schizophrenia (see Section 4.3). The current meta-analysis explores some of the conditions under which MMN deficits can be elicited and to our knowledge is the first to pool data from studies using complex deviants in SCZ.

One of our major findings is that MMN generation to complex deviants is significantly reduced in SCZ, parallel to observations with simple physical deviants. Moreover, reductions are observed both to complex sensory deviants, such as double deviants, gaps or omissions and to conceptual deviants, such as change in pattern or repeating stimuli. Nevertheless, the magnitude of deficit is no larger than the magnitude of deficit observed to simple sensory features (Figs. 1–3). These findings thus suggest that use of more complex deviants may be useful for testing specific neuroscientific hypotheses, but do not necessarily assist further in differentiation of schizophrenia spectrum disorders from healthy controls. These findings also argue that deviance detection mechanisms are likely uniformly impaired throughout the brain, potentially involving subcortical as well as cortical mechanisms (Lee et al., 2017) with each paradigm type likely emphasizing deviance detection at different stages in the auditory pathway.

In unadjusted analyses, MMN elicited by complex patterns showed a lesser degree of impairment than MMN elicited in more standard paradigms (Fig. 6). Nevertheless, these paradigms also tend to elicit relatively “noisy” MMN components even in healthy controls. Following adjustment for MMN signal-to-noise, MMN deficits to pattern deviants in SCZ were no longer reduced compared to MMN deficits to simple frequency or duration deviants.

4.2. Automatic vs. controlled processing

Schizophrenia is marked by several deficits along the auditory processing chain characterized by separate components of the auditory ERP. The current meta-analysis focused on MMN, a long-latency automatic component that reflects pre-attentive novelty detection. To our knowledge, we are the first to pool together studies that also examined an even later component, P3b, in the same patient samples, which reflects a later processing step involving controlled attention. This within-study approach was especially important in trying to understand the relationship between these two measures in the same samples and to better evaluate their relative deficits.

We found that while P3b deficits are larger than MMN deficits, this was only a small effect size difference between the two sets of measures, whereas the MMN deficit itself was in the range of moderate to large (0.5–0.8 SD). It is tempting to think of MMN as reflective of a processing step necessary to generate P3b, as both depend on successful discrimination between standards and deviants. If this were completely true, we would conclude that most of the deficit in P3b reflects dysfunction in an earlier pre-attentive processing step represented by MMN (based on their relative effect sizes reported in Section 3.6, MMN would account for 75% of the deficit).

However, the generation of P3b is more complex and likely involves other sensory, memory, and attentional processes not captured by MMN (Polich, 2007). In fact, when we examined the within-sample correlation between P3b and MMN (Fig. 5), we found that only 18.7% of the variance in P3b deficits could be accounted for by MMN deficits across studies, and some of this variance may reflect correlations due to study design rather than individual subject differences.

Therefore, P3b and MMN deficits likely represent separate dysfunctions in auditory processing that share some common mechanisms, with MMN deficit contributing to a small portion of P3b deficits. The estimate of the MMN’s contribution is lower, however, than in a previous study in which structural equation modeling in the same sample of patients demonstrated that MMN accounted for about 50% of P3b deficit (Leitman et al., 2010). Our underestimate may be due to being unable to examine within-subject correlations directly. Overall, therefore, these findings suggest that not all of cognitive dysfunction in SCZ may be attributable to sensory dysfunction alone, and that deficits in additional higher order regions, such as frontal and parietal cortices (presumed generators of P3b) may contribute as well. At the same time, they support significant MMN contributions to P3b deficits, consistent with distributed hierarchical models (Leitman et al., 2010; Krishnan et al., 2009).

4.3. Neural mechanisms underlying MMN dysfunction in SCZ

On a cellular and micro-circuit level, MMN deficits in SCZ have been indirectly linked to NMDA receptor and GABA interneuron dysfunction (Javitt, 2000; Javitt and Sweet, 2015; Lavoie et al., 2008; Michie et al., 2016), also supported by more direct neuropharmacology evidence using NMDA-R antagonists in animals and healthy controls (Javitt et al., 1996; Umbricht et al., 2002, 2000). On a brain network level, it is still unclear how much of MMN deficit is due to bottom-up sensory discrimination deficits at the auditory cortex level or below versus higher-order top-down processing deficits. Micro-circuit and NMDA receptor dysfunction could be involved in either type of deficit and probably contribute to broad cortical dysfunction, consistent with disrupted network connectivity in SCZ.

The current meta-analysis does not directly probe the potential neural mechanisms underlying MMN deficits. That being said, we can infer that bottom-up sensory processes must play a significant role in explaining MMN deficits. First, there appears to be no difference in effect sizes between studies using simple versus complex deviants, and in fact simple deviants may offer an advantage in detecting robust deficits. Therefore, much of the dysfunction could lie in lower order processing localized to sensory areas of cortex or subcortical regions. This is also supported by our finding that P3b deficits are only modestly elevated compared to MMN deficits. Since P3b is representative of later stage attention and task relevancy dependent processing whereas MMN is known to be pre-attentive and largely generated in sensory regions, it is likely that bottom-up automatic sensory errors contribute to auditory pathway dysfunction reflected in late ERP components.

It has been argued that MMN deficits in SCZ must be due to higher order processing dysfunction since lower sensory impairments would be expected to generate more deficit as deviants become smaller and harder to detect (Erickson et al., 2016). However, it is known that healthy controls generate smaller MMN to lower deviance levels (e.g. May et al., 1999) and our results demonstrate that it is harder to detect group differences when MMN is smaller in HC (Fig. 6). This suggests that MMN deficits in SCZ would appear smaller at lower deviance levels which has been demonstrated in studies using within-subject designs (Horton et al., 2011; Javitt et al., 1998). This effect would occur regardless of the relative contribution of sensory and cognitive deficits to MMN reduction in SCZ.

4.4. Conclusions

MMN impairments in schizophrenia can be detected effectively using simple deviant oddball paradigms without the need for more complex rule violations. Moreover, in general, based on this meta-analysis, it appears that MMN deficits elicited by simpler deviant types are as large or larger than those observed in more complex paradigms. Furthermore, differences in MMN deficit effect size between deviant types is eliminated when accounting for the signal-to-noise of MMN in HC. Lastly, P3b, which reflects higher order task-dependent auditory processing is only modestly more impaired than MMN in SCZ.

The most current framework for understanding MMN generation is the predictive coding account, which allows both early sensory memory and higher order model adjustments to modulate MMN amplitude (Garrido et al., 2009). The results of this meta-analysis argue that sensory processing deficits play a significant role in lower MMN amplitudes in SCZ. There continue to be major unknowns regarding MMN deficits in SCZ, including how current psychological-level models (e.g. predictive coding) map onto underlying neural structures such as somatostatin interneurons or local circuit interactions (Javitt and Sweet, 2015; Lee et al. 2017). The present studies suggest that these can be adequately tested using simple deviant types, such as pitch or duration, that are most readily translated to animal models, and most easily probed at the intracortical level.

Supplementary Material

supplement

Acknowledgments

The authors would like to thank Jorne Laton and his co-authors for sharing data not reported explicitly in their publication (Laton et al., 2014) so that it could be included in this meta-analysis.

Funding

Funded by NIH grant MH49334 to DCJ.

Abbreviations

MMN

mismatch negativity

SCZ

Schizophrenia

HC

Healthy controls

ERP

evoked response potential or event-related potential

SOA

stimulus onset asynchrony

Footnotes

Publisher's Disclaimer: This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final citable form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

Conflicts of Interest

Dr. Javitt reports having received consulting payments within the last 36 months from Sunovion, Forum, and Takeda. He has received research support from Roche. He holds intellectual property rights for use of NMDA modulators in treatment of neuropsychiatric disorders. He holds equity in Glytech, AASI, and NeuroRX, and serves on the advisory board of Promentis and NeuroRx. All other co-authors report no conflicts.

Contributors

MA designed the study, collected the data, performed some of the basic analysis, and wrote the first draft of the manuscript. SX and YW were responsible for the majority of the statistical analyses and meta-analytic techniques. BV helped with the analysis, figure creation/preparation, and tables. JLC helped edit the manuscript and advised on technical details related to ERPs. DCJ contributed to all portions of study design, analysis, and writing and served as a supervisor and mentor on all aspects of the project. All authors contributed to and have approved the final manuscript.

References

  1. Alain C, Hargrave R, Woods DL. Processing of auditory stimuli during visual attention in patients with schizophrenia. Biol. Psychiatry. 1998;44(11):1151–1159. doi: 10.1016/s0006-3223(97)00478-2. [DOI] [PubMed] [Google Scholar]
  2. Atkinson RJ, Michie PT, Schall U. Duration mismatch negativity and P3a in first-episode psychosis and individuals at ultra-high risk of psychosis. Biol. Psychiatry. 2012;71(2):98–104. doi: 10.1016/j.biopsych.2011.08.023. [DOI] [PubMed] [Google Scholar]
  3. Baldeweg T, Hirsch SR. Mismatch negativity indexes illness-specific impairments of cortical plasticity in schizophrenia: a comparison with bipolar disorder and Alzheimer’s disease. Int. J. Psychophysiol. 2015;95(2):145–155. doi: 10.1016/j.ijpsycho.2014.03.008. [DOI] [PubMed] [Google Scholar]
  4. Baldeweg T, Klugman A, Gruzelier J, Hirsch SR. Mismatch negativity potentials and cognitive impairment in schizophrenia. Schizophr. Res. 2004;69(2):203–217. doi: 10.1016/j.schres.2003.09.009. [DOI] [PubMed] [Google Scholar]
  5. Baldeweg T, Klugman A, Gruzelier JH, Hirsch SR. Impairment in frontal but not temporal components of mismatch negativity in schizophrenia. Int. J. Psychophysiol. 2002;43(2):111–122. doi: 10.1016/s0167-8760(01)00183-0. [DOI] [PubMed] [Google Scholar]
  6. Ballesteros A, Summerfelt A, Du X, Jiang P, Chiappelli J, Tagamets M, O’Donnell P, Kochunov P, Hong LE. Electrophysiological intermediate biomarkers for oxidative stress in schizophrenia. Clin. Neurophysiol. 2013;124(11):2209–2215. doi: 10.1016/j.clinph.2013.05.021. [DOI] [PMC free article] [PubMed] [Google Scholar]
  7. Berkey CS, Hoaglin DC, Mosteller F, Colditz GA. A random-effects regression model for meta-analysis. Stat. Med. 1995;14(4):395–411. doi: 10.1002/sim.4780140406. [DOI] [PubMed] [Google Scholar]
  8. Bodatsch M, Ruhrmann S, Wagner M, Mller R, Schultze-Lutter F, Frommann I, Brinkmeyer J, Gaebel W, Maier W, Klosterktter J, Brockhaus-Dumke A. Prediction of psychosis by mismatch negativity. Biol. Psychiatry. 2011;69(10):959–966. doi: 10.1016/j.biopsych.2010.09.057. [DOI] [PubMed] [Google Scholar]
  9. Bramon E, Croft RJ, McDonald C, Virdi GK, Gruzelier JG, Baldeweg T, Sham PC, Frangou S, Murray RM. Mismatch negativity in schizophrenia: a family study. Schizophr. Res. 2004;67(1):1–10. doi: 10.1016/s0920-9964(03)00132-4. [DOI] [PubMed] [Google Scholar]
  10. Brockhaus-Dumke A, Tendolkar I, Pukrop R, Schultze-Lutter F, Klosterkötter J, Ruhrmann S. Impaired mismatch negativity generation in prodromal subjects and patients with schizophrenia. Schizophr. Res. 2005;73(2):297–310. doi: 10.1016/j.schres.2004.05.016. [DOI] [PubMed] [Google Scholar]
  11. Catts SV, Shelley AM, Ward PB, Liebert B, McConaghy N, Andrews S, Michie PT. Brain potential evidence for an auditory sensory memory deficit in schizophrenia. Am. J. Psychiatry. 1995;152(2):213–219. doi: 10.1176/ajp.152.2.213. [DOI] [PubMed] [Google Scholar]
  12. Chen C, Liu C-C, Weng P-Y, Cheng Y. Mismatch negativity to threatening voices associated with positive symptoms in schizophrenia. Front. Hum. Neurosci. 2016;10:362. doi: 10.3389/fnhum.2016.00362. [DOI] [PMC free article] [PubMed] [Google Scholar]
  13. Davalos DB, Kisley MA, Freedman R. Behavioral and electrophysiological indices of temporal processing dysfunction in schizophrenia. J. Neuropsychiatry Clin. Neurosci. 2005;17(4):517–525. doi: 10.1176/jnp.17.4.517. [DOI] [PubMed] [Google Scholar]
  14. Davalos DB, Kisley MA, Polk SD, Ross RG. Mismatch negativity in detection of interval duration deviation in schizophrenia. Neuroreport. 2003;14(9):1283–1286. doi: 10.1097/00001756-200307010-00019. [DOI] [PubMed] [Google Scholar]
  15. Devrim-Üçok M, Keskin-Ergen HY, Üçok A. P50 gating at acute and post-acute phases of first-episode schizophrenia. Prog. Neuro-Psychopharmacology Biol. Psychiatry. 2008;32(8):1952–1956. doi: 10.1016/j.pnpbp.2008.09.018. [DOI] [PubMed] [Google Scholar]
  16. Domján N, Csifcsák G, Drótos G, Janka Z, Szendi I. Different patterns of auditory information processing deficits in chronic schizophrenia and bipolar disorder with psychotic features. Schizophr. Res. 2012;139(1):253–259. doi: 10.1016/j.schres.2012.06.002. [DOI] [PubMed] [Google Scholar]
  17. Dulude L, Labelle A, Knott VJ. Acute nicotine alteration of sensory memory impairment in smokers with schizophrenia. J. Clin. Psychopharmacol. 2010;30(5):541–548. doi: 10.1097/JCP.0b013e3181f0c9c6. [DOI] [PubMed] [Google Scholar]
  18. Erickson MA, Ruffle A, Gold JM. A meta-analysis of mismatch negativity in schizophrenia: from clinical risk to disease specificity and progression. Biol. Psychiatry. 2016;79(12):980–987. doi: 10.1016/j.biopsych.2015.08.025. [DOI] [PMC free article] [PubMed] [Google Scholar]
  19. Escera C, Malmierca MS. The auditory novelty system: an attempt to integrate human and animal research. Psychophysiol. 2014;51(2):111–123. doi: 10.1111/psyp.12156. [DOI] [PubMed] [Google Scholar]
  20. Fisher DJ, Grant B, Smith DM, Borracci G, Labelle A, Knott VJ. Effects of auditory hallucinations on the mismatch negativity (MMN) in schizophrenia as measured by a modified “optimal” multi-feature paradigm. Int. J. Psychophysiol. 2011;81(3):245–251. doi: 10.1016/j.ijpsycho.2011.06.018. [DOI] [PubMed] [Google Scholar]
  21. Fisher DJ, Labelle A, Knott VJ. Alterations of mismatch negativity (MMN) in schizophrenia patients with auditory hallucinations experiencing acute exacerbation of illness. Schizophr. Res. 2012;139:237–245. doi: 10.1016/j.schres.2012.06.004. [DOI] [PubMed] [Google Scholar]
  22. Fisher DJ, Smith DM, Labelle A, Knott VJ. Attenuation of mismatch negativity (MMN) and novelty P300 in schizophrenia patients with auditory hallucinations experiencing acute exacerbation of illness. Biol. Psychol. 2014;100(1):43–49. doi: 10.1016/j.biopsycho.2014.05.005. [DOI] [PubMed] [Google Scholar]
  23. Friedman T, Sehatpour P, Dias E, Perrin M, Javitt DC. Differential relationships of mismatch negativity and visual P1 deficits to premorbid characteristics and functional outcome in schizophrenia. Biol. Psychiatry. 2012;71(6):521–529. doi: 10.1016/j.biopsych.2011.10.037. [DOI] [PMC free article] [PubMed] [Google Scholar]
  24. Fulham WR, Michie PT, Ward PB, Rasser PE, Todd J, Johnston PJ, Thompson PM, Schall U. Mismatch negativity in recent-onset and chronic schizophrenia: a current source density analysis. PLoS One. 2014;9(6):e100221. doi: 10.1371/journal.pone.0100221. [DOI] [PMC free article] [PubMed] [Google Scholar]
  25. Garrido MI, Kilner JM, Stephan KE, Friston KJ. The mismatch negativity: a review of underlying mechanisms. Clin. Neurophysiol. 2009;120(3):453–463. doi: 10.1016/j.clinph.2008.11.029. [DOI] [PMC free article] [PubMed] [Google Scholar]
  26. Gjini K, Arfken C, Boutros NN. Relationships between sensory “gating out” and sensory “gating in” of auditory evoked potentials in schizophrenia: a pilot study. Schizophr. Res. 2010;121(1):139–145. doi: 10.1016/j.schres.2010.04.020. [DOI] [PMC free article] [PubMed] [Google Scholar]
  27. Grzella I, Müller BW, Oades RD, Bender S, Schall U, Zerbin D, Wolstein J, Sartory G. Novelty-elicted mismatch negativity (MMN) in patients with schizophrenia on admission and discharge. J Psychiatry Neurosci. 2001;26(3):235–46. [PMC free article] [PubMed] [Google Scholar]
  28. Haigh SM, Coffman BA, Murphy TK, Butera CD, Salisbury DF. Abnormal auditory pattern perception in schizophrenia. Schizophr. Res. 2016;176(2):473–479. doi: 10.1016/j.schres.2016.07.007. [DOI] [PMC free article] [PubMed] [Google Scholar]
  29. Hall MH, Rijsdijk F, Picchioni M, Schulze K, Ettinger U, Toulopoulou T, Bramon E, Murray RM, Sham P. Substantial shared genetic influences on schizophrenia and event-related potentials. Am. J. Psychiatry. 2007;164(5):804–812. doi: 10.1176/ajp.2007.164.5.804. [DOI] [PubMed] [Google Scholar]
  30. Hay RA, Roach BJ, Srihari VH, Woods SW, Ford JM, Mathalon DH. Equivalent mismatch negativity deficits across deviant types in early illness schizophrenia-spectrum patients. Biol. Psychol. 2015;105:130–137. doi: 10.1016/j.biopsycho.2015.01.004. [DOI] [PMC free article] [PubMed] [Google Scholar]
  31. Hayakawa YK, Kirino E, Shimoji K, Kamagata K, Hori M, Ito K, Kunimatsu A, Abe O, Ohtomo K, Aoki S. Anterior cingulate abnormality as a neural correlate of mismatch negativity in schizophrenia. Neuropsychobiology. 2013;68(4):197–204. doi: 10.1159/000355296. [DOI] [PubMed] [Google Scholar]
  32. Hedges LV, Olkin I. Statistical methods for meta-analysis. Phytochemistry. 1985;72(13):369. [Google Scholar]
  33. Higuchi Y, Seo T, Miyanishi T, Kawasaki Y, Suzuki M, Sumiyoshi T. Mismatch negativity and p3a/reorienting complex in subjects with schizophrenia or at-risk mental state. Front. Behav. Neurosci. 2014;8:172. doi: 10.3389/fnbeh.2014.00172. [DOI] [PMC free article] [PubMed] [Google Scholar]
  34. Hirayasu Y, Potts GF, O’Donnell BF, Kwon JS, Arakaki H, Akdag SJ, Levitt JJ, Shenton ME, McCarley RW. Auditory mismatch negativity in schizophrenia: Topographic evaluation with a high-density recording montage. Am. J. Psychiatry. 1998;155(9):1281–1284. doi: 10.1176/ajp.155.9.1281. [DOI] [PubMed] [Google Scholar]
  35. Hong LE, Moran LV, Du X, O’Donnell P, Summerfelt A. Mismatch negativity and low frequency oscillations in schizophrenia families. Clin. Neurophysiol. 2012;123(10):1980–1988. doi: 10.1016/j.clinph.2012.03.011. [DOI] [PMC free article] [PubMed] [Google Scholar]
  36. Horacek M, Kärgel C, Scherbaum N, Müller BW. The effect of deviance predictability on mismatch negativity in schizophrenia patients. Neurosci. Lett. 2016;617:76–81. doi: 10.1016/j.neulet.2016.02.010. [DOI] [PubMed] [Google Scholar]
  37. Horton J, Millar A, Labelle A, Knott VJ. MMN responsivity to manipulations of frequency and duration deviants in chronic, clozapine-treated schizophrenia patients. Schizophr. Res. 2011;126(1):202–211. doi: 10.1016/j.schres.2010.11.028. [DOI] [PubMed] [Google Scholar]
  38. Hsieh MH, Shan JC, Huang WL, Cheng WC, Chiu MJ, Jaw FS, Hwu HG, Liu CC. Auditory event-related potential of subjects with suspected pre-psychotic state and first-episode psychosis. Schizophr. Res. 2012;140(1):243–249. doi: 10.1016/j.schres.2012.06.021. [DOI] [PubMed] [Google Scholar]
  39. Hsu JL, Hsieh MH, Tseng YL, Chiu MJ, Liu CM, Jaw FS, Hwu HG. Time-frequency analysis of mismatch negativity in schizophrenia patients in Taiwan. Biomed. Eng. Appl. Basis Commun. 2011;23(04):287–293. [Google Scholar]
  40. Inami R, Kirino E, Inoue R, Suzuki T, Arai H. Nicotine effects on mismatch negativity in nonsmoking schizophrenic patients. Neuropsychobiology. 2008;56(2–3):64–72. doi: 10.1159/000111536. [DOI] [PubMed] [Google Scholar]
  41. Jahshan C, Cadenhead KS, Rissling AJ, Kirihara K, Braff DL, Light GA. Automatic sensory information processing abnormalities across the illness course of schizophrenia. Psychol Med. 2012;42(1):85–97. doi: 10.1017/S0033291711001061. [DOI] [PMC free article] [PubMed] [Google Scholar]
  42. Jahshan C, Wynn JK, Green MF. Relationship between auditory processing and affective prosody in schizophrenia. Schizophr. Res. 2013;143(2):348–353. doi: 10.1016/j.schres.2012.11.025. [DOI] [PMC free article] [PubMed] [Google Scholar]
  43. Jahshan C, Wynn JK, Mathis KI, Altshuler LL, Glahn DC, Green MF. Cross-diagnostic comparison of duration mismatch negativity and P3a in bipolar disorder and schizophrenia. Bipolar Disord. 2012;14(3):239–248. doi: 10.1111/j.1399-5618.2012.01008.x. [DOI] [PMC free article] [PubMed] [Google Scholar]
  44. Javitt DC, Doneshka P, Grochowski S, Ritter W. Impaired mismatch negativity generation reflects widespread dysfunction of working memory in schizophrenia. Arch. Gen. Psychiatry. 1995;52(7):550–558. doi: 10.1001/archpsyc.1995.03950190032005. [DOI] [PubMed] [Google Scholar]
  45. Javitt DC, Doneshka P, Zylberman I, Ritter W, Vaughan HG. Impairment of early cortical processing in schizophrenia: an event-related potential confirmation study. Biol. Psychiatry. 1993;33(7):513–519. doi: 10.1016/0006-3223(93)90005-x. [DOI] [PubMed] [Google Scholar]
  46. Javitt DC, Grochowski S, Shelley A-M, Ritter W. Impaired mismatch negativity (MMN) generation in schizophrenia as a function of stimulus deviance, probability, and interstimulus/interdeviant interval. Electroencephalogr. Clin. Neurophysiol. Potentials Sect. 1998;108(2):143–153. doi: 10.1016/s0168-5597(97)00073-7. [DOI] [PubMed] [Google Scholar]
  47. Javitt DC, Shelley AM, Ritter W. Associated deficits in mismatch negativity generation and tone matching in schizophrenia. Clin. Neurophysiol. 2000;111(10):1733–1737. doi: 10.1016/s1388-2457(00)00377-1. [DOI] [PubMed] [Google Scholar]
  48. Javitt DC, Shelley AM, Silipo G, Lieberman JA. Deficits in auditory and visual context-dependent processing in schizophrenia: defining the pattern. Arch. Gen. Psychiatry. 2000;57(12):1131–1137. doi: 10.1001/archpsyc.57.12.1131. [DOI] [PubMed] [Google Scholar]
  49. Javitt DC, Steinschneider M, Schroeder CE, Arezzo JC. Role of cortical N-methyl-D-aspartate receptors in auditory sensory memory and mismatch negativity generation: implications for schizophrenia. Proc. Natl. Acad. Sci. U. S. A. 1996;93(21):11962–11967. doi: 10.1073/pnas.93.21.11962. [DOI] [PMC free article] [PubMed] [Google Scholar]
  50. Javitt DC, Sweet RA. Auditory dysfunction in schizophrenia: integrating clinical and basic features. Nat. Rev. Neurosci. 2015;16(9):535–550. doi: 10.1038/nrn4002. [DOI] [PMC free article] [PubMed] [Google Scholar]
  51. Jeon Y, Polich J. Meta-analysis of P300 and schizophrenia: patients, paradigms, and practical implications. Psychophysiol. 2003;40(5):684–701. doi: 10.1111/1469-8986.00070. [DOI] [PubMed] [Google Scholar]
  52. Jessen F, Fries T, Kucharski C, Nishimura T, Hoenig K, Maier W, Falkai P, Heun R. Amplitude reduction of the mismatch negativity in first-degree relatives of patients with schizophrenia. Neurosci. Lett. 2001;309(3):185–188. doi: 10.1016/s0304-3940(01)02072-9. [DOI] [PubMed] [Google Scholar]
  53. Kane NM, Curry SH, Butler SR, Cummins BH. Electrophysiological indicator of awakening from coma. Lancet. 1993;341(8846):688–689. doi: 10.1016/0140-6736(93)90453-n. [DOI] [PubMed] [Google Scholar]
  54. Kantrowitz JT, Epstein ML, Beggel O, Rohrig S, Lehrfeld JM, Revheim N, Lehrfeld NP, Reep J, Parker E, Silipo G, Ahissar M, Javitt DC. Neurophysiological mechanisms of cortical plasticity impairments in schizophrenia and modulation by the NMDA receptor agonist D-serine. Brain. 2016;139(12):3281–3295. doi: 10.1093/brain/aww262. [DOI] [PMC free article] [PubMed] [Google Scholar]
  55. Kantrowitz JT, Hoptman MJ, Leitman DI, Moreno-Ortega M, Lehrfeld JM, Dias E, Sehatpour P, Laukka P, Silipo G, Javitt DC. Neural substrates of auditory emotion recognition deficits in schizophrenia. J. Neurosci. 2015;35(44):14909–14921. doi: 10.1523/JNEUROSCI.4603-14.2015. [DOI] [PMC free article] [PubMed] [Google Scholar]
  56. Kärgel C, Sartory G, Kariofillis D, Wiltfang J, Muller BW. Mismatch negativity latency and cognitive function in schizophrenia. PLoS One. 2014;9(4):e84536. doi: 10.1371/journal.pone.0084536. [DOI] [PMC free article] [PubMed] [Google Scholar]
  57. Kasai K, Nakagome K, Itoh K, Koshida I, Hata A, Iwanami A, Fukuda M, Kato N. Impaired cortical network for preattentive detection of change in speech sounds in schizophrenia: a high-resolution event-related potential study. Am. J. Psychiatry. 2002;159(4):546–553. doi: 10.1176/appi.ajp.159.4.546. [DOI] [PubMed] [Google Scholar]
  58. Kaser M, Soltesz F, Lawrence P, Miller S, Dodds C, Croft R, Dudas RB, Zaman R, Fernandez-Egea E, Muller U, Dean A, Bullmore ET, Nathan PJ. Oscillatory underpinnings of mismatch negativity and their relationship with cognitive function in patients with schizophrenia. PLoS One. 2013;8(12):e83255. doi: 10.1371/journal.pone.0083255. [DOI] [PMC free article] [PubMed] [Google Scholar]
  59. Kathmann N, Wagner M, Rendtorff N, Engel RR. Delayed peak latency of the mismatch negativity in schizophrenics and alcoholics. Biol. Psychiatry. 1995;37(10):754–757. doi: 10.1016/0006-3223(94)00309-Q. [DOI] [PubMed] [Google Scholar]
  60. Kaur M, Battisti RA, Lagopoulos J, Ward PB, Hickie IB, Hermens DF. Neurophysiological biomarkers support bipolar-spectrum disorders within psychosis cluster. J. Psychiatry Neurosci. 2012;37(5):313–321. doi: 10.1503/jpn.110081. [DOI] [PMC free article] [PubMed] [Google Scholar]
  61. Kaur M, Battisti RA, Ward PB, Ahmed A, Hickie IB, Hermens DF. MMN/P3a deficits in first episode psychosis: comparing schizophrenia-spectrum and affective-spectrum subgroups. Schizophr. Res. 2011;130(1):203–209. doi: 10.1016/j.schres.2011.03.025. [DOI] [PubMed] [Google Scholar]
  62. Kiang M, Braff DL, Sprock J, Light GA. The relationship between preattentive sensory processing deficits and age in schizophrenia patients. Clin. Neurophysiol. 2009;120(11):1949–1957. doi: 10.1016/j.clinph.2009.08.019. [DOI] [PMC free article] [PubMed] [Google Scholar]
  63. Kiang M, Light Ga, Prugh J, Coulson S, Braff DL, Kutas M. Cognitive, neurophysiological, and functional correlates of proverb interpretation abnormalities in schizophrenia. J Int Neuropsychol Soc. 2007;13(4):653–663. doi: 10.1017/S1355617707070816. [DOI] [PubMed] [Google Scholar]
  64. Kim M, Cho KIK, Yoon YB, Lee TY, Kwon JS. Aberrant temporal behavior of mismatch negativity generators in schizophrenia patients and subjects at clinical high risk for psychosis. Clin. Neurophysiol. 2017;128(2):331–339. doi: 10.1016/j.clinph.2016.11.027. [DOI] [PubMed] [Google Scholar]
  65. Kim M, Kim SN, Lee S, Byun MS, Shin KS, Park HY, Jang JH, Kwon JS. Impaired mismatch negativity is associated with current functional status rather than genetic vulnerability to schizophrenia. Psychiatry Res. Neuroimaging. 2014;222(1):100–106. doi: 10.1016/j.pscychresns.2014.02.012. [DOI] [PubMed] [Google Scholar]
  66. Korostenskaja M, Dapsys K, Maciulis V, Ruksenas O. Evaluation of new MMN parameters in schizophrenia. Acta Neurobiol. Exp. (Wars) 2003;63(4):383–388. doi: 10.55782/ane-2003-1478. [DOI] [PubMed] [Google Scholar]
  67. Korostenskaja M, Dapsys K, Siurkute A, Maciulis V, Ruksenas O, Kähkönen S. Effects of olanzapine on auditory P300 and mismatch negativity (MMN) in schizophrenia spectrum disorders. Prog. Neuro-Psychopharmacology Biol. Psychiatry. 2005;29(4):543–548. doi: 10.1016/j.pnpbp.2005.01.019. [DOI] [PubMed] [Google Scholar]
  68. Krishnan RR, Keefe R, Kraus M. Schizophrenia is a disorder of higher order hierarchical processing. Med. Hypotheses. 2009;72(6):740–4. doi: 10.1016/j.mehy.2008.12.039. [DOI] [PubMed] [Google Scholar]
  69. Laton J, Van Schependom J, Gielen J, Decoster J, Moons T, De Keyser J, De Hert M, Nagels G. Single-subject classification of schizophrenia patients based on a combination of oddball and mismatch evoked potential paradigms. J. Neurol. Sci. 2014;347(1):262–267. doi: 10.1016/j.jns.2014.10.015. [DOI] [PubMed] [Google Scholar]
  70. Lavoie S, Murray MM, Deppen P, Knyazeva M, Berk M, Boulat O, Bovet P, Bush AI, Conus P, Copolov D, Fornari E, Meuli R, Solida A, Vianin C, Bucliln T, Do KQ. Glutathione precursor, N-Acetyl-Cysteine, improves mismatch negativity in schizophrenia patients. Neuropsychopharmacology. 2008;33(9):2187–2199. doi: 10.1038/sj.npp.1301624. [DOI] [PubMed] [Google Scholar]
  71. Lee M, Sehatpour P, Hoptman MJ, Lakatos P, Dias EC, Kantrowitz JT, Martinez AM, Javitt DC. Neural mechanisms of mismatch negativity dysfunction in schizophrenia. Mol. Psychiatry. 2017;00:1–9. doi: 10.1038/mp.2017.3. [DOI] [PMC free article] [PubMed] [Google Scholar]
  72. Lee SH, Sung K, Lee KS, Moon E, Kim CG. Mismatch negativity is a stronger indicator of functional outcomes than neurocognition or theory of mind in patients with schizophrenia. Prog. Neuro-Psychopharmacology Biol. Psychiatry. 2014;48:213–219. doi: 10.1016/j.pnpbp.2013.10.010. [DOI] [PubMed] [Google Scholar]
  73. Leitman DI, Sehatpour P, Higgins BA, Foxe JJ, Silipo G, Javitt DC. Sensory deficits and distributed hierarchical dysfunction in schizophrenia. Am. J. Psychiatry. 2010;167(7):818–827. doi: 10.1176/appi.ajp.2010.09030338. [DOI] [PubMed] [Google Scholar]
  74. Lembreghts M, Timsit-Berthier M. The value of cognitive psychophysiological studies in a comprehensive approach to schizophrenia (treatment of information and attention capacities in acute-stage schizophrenia) Acta Psychiatr. Belg. 1993;93(Spec No):322–342. [PubMed] [Google Scholar]
  75. Li Z, Zheng B, Deng W, Liu X, Zheng Z, Li T. Multi-components of evoked-brain potentials in deficit and nondeficit schizophrenia. Asia-Pacific Psychiatry. 2013;5(2):69–79. doi: 10.1111/appy.12030. [DOI] [PubMed] [Google Scholar]
  76. Light GA, Braff DL. Mismatch negativity deficits are associated with poor functioning in schizophrenia patients. Arch. Gen. Psychiatry. 2005;62(2):127–136. doi: 10.1001/archpsyc.62.2.127. [DOI] [PubMed] [Google Scholar]
  77. Light GA, Braff DL. Stability of mismatch negativity deficits and their relationship to functional impairments in chronic schizophrenia. Am. J. Psychiatry. 2005;162(9):1741–1743. doi: 10.1176/appi.ajp.162.9.1741. [DOI] [PubMed] [Google Scholar]
  78. Light GA, Swerdlow NR. Future clinical uses of neurophysiological biomarkers to predict and monitor treatment response for schizophrenia. Ann. N. Y. Acad. Sci. 2015;1344(1):105–119. doi: 10.1111/nyas.12730. [DOI] [PMC free article] [PubMed] [Google Scholar]
  79. Light GA, Swerdlow NR, Rissling AJ, Radant A, Sugar CA, Sprock J, Pela M, Geyer MA, Braff DL. Characterization of neurophysiologic and neurocognitive biomarkers for use in genomic and clinical outcome studies of schizophrenia. PLoS One. 2012;7(7):e39434. doi: 10.1371/journal.pone.0039434. [DOI] [PMC free article] [PubMed] [Google Scholar]
  80. Light GA, Swerdlow NR, Thomas ML, Calkins ME, Green MF, Greenwood TA, Gur RE, Gur RC, Lazzeroni LC, Nuechterlein KH, Pela M, Radant AD, Seidman LJ, Sharp RF, Siever LJ, Silverman JM, Sprock J, Stone WS, Sugar CA, Tsuang DW, Tsuang MT, Braff DL, Turetsky BI. Validation of mismatch negativity and P3a for use in multi-site studies of schizophrenia: characterization of demographic, clinical, cognitive, and functional correlates in COGS-2. Schizophr. Res. 2015;163(1):63–72. doi: 10.1016/j.schres.2014.09.042. [DOI] [PMC free article] [PubMed] [Google Scholar]
  81. Lin YT, Liu CM, Chiu MJ, Liu CC, Chien YL, Hwang TJ, Jaw FS, Shan JC, Hsieh MH, Hwu HG. Differentiation of schizophrenia patients from healthy subjects by mismatch negativity and neuropsychological tests. PLoS One. 2012;7(4):e34454. doi: 10.1371/journal.pone.0034454. [DOI] [PMC free article] [PubMed] [Google Scholar]
  82. Lin YT, Hsieh MH, Liu CC, Hwang TJ, Chien YL, Hwu HG, Liu CM. A recently-discovered NMDA receptor gene, GRIN3B, is associated with duration mismatch negativity. Psychiatry Res. 2014;218(3):356–358. doi: 10.1016/j.psychres.2014.04.032. [DOI] [PubMed] [Google Scholar]
  83. Linden DEJ. The p300: Where in the brain is it produced and what does it tell us? Neuroscientist. 2005;11(6):563–576. doi: 10.1177/1073858405280524. [DOI] [PubMed] [Google Scholar]
  84. Magno E, Yeap S, Thakore JH, Garavan H, De Sanctis P, Foxe JJ. Are auditory-evoked frequency and duration mismatch negativity deficits endophenotypic for schizophrenia? high-density electrical mapping in clinically unaffected first-degree relatives and first-episode and chronic schizophrenia. Biol. Psychiatry. 2008;64(5):385–391. doi: 10.1016/j.biopsych.2008.03.019. [DOI] [PMC free article] [PubMed] [Google Scholar]
  85. Matthews N, Todd J, Budd TW, Cooper G, Michie PT. Auditory lateralization in schizophrenia - mismatch negativity and behavioral evidence of a selective impairment in encoding interaural time cues. Clin. Neurophysiol. 2007;118(4):833–844. doi: 10.1016/j.clinph.2006.11.017. [DOI] [PubMed] [Google Scholar]
  86. May P, Tiitinen H, Ilmoniemi RJ, Nyman G, Taylor JG, Näätänen R. Frequency change detection in human auditory cortex. J. Comput. Neurosci. 1999;6(2):99–120. doi: 10.1023/a:1008896417606. [DOI] [PubMed] [Google Scholar]
  87. Michie PT, Budd T, Todd J, Rock D, Wichmann H, Box J, Jablensky A. Duration and frequency mismatch negativity in schizophrenia. Clin. Neurophysiol. 2000;111(6):1054–1065. doi: 10.1016/s1388-2457(00)00275-3. [DOI] [PubMed] [Google Scholar]
  88. Michie PT, Innes-Brown H, Todd J, Jablensky AV. Duration mismatch negativity in biological relatives of patients with schizophrenia spectrum disorders. Biol. Psychiatry. 2002;52(7):749–758. doi: 10.1016/s0006-3223(02)01379-3. [DOI] [PubMed] [Google Scholar]
  89. Michie PT, Malmierca MS, Harms L, Todd J. The neurobiology of MMN and implications for schizophrenia. Biol. Psychol. 2016;116:90–97. doi: 10.1016/j.biopsycho.2016.01.011. [DOI] [PubMed] [Google Scholar]
  90. Milovan DL, Baribeau J, Roth RM, Stip E. ERP study of pre-attentive auditory processing in treatment-refractory schizophrenia. Brain Cogn. 2004;55(2):355–357. doi: 10.1016/j.bandc.2004.02.045. [DOI] [PubMed] [Google Scholar]
  91. Minami Y, Kirino E. Schizophrenic patients are impaired in memory reinstatement underlying mismatch negativity system. Clin. Neurophysiol. 2005;116(1):120–128. doi: 10.1016/j.clinph.2004.07.013. [DOI] [PubMed] [Google Scholar]
  92. Miyanishi T, Sumiyoshi T, Higuchi Y, Seo T, Suzuki M. LORETA current source density for duration mismatch negativity and neuropsychological assessment in early schizophrenia. PLoS One. 2013;8(4):e61152. doi: 10.1371/journal.pone.0061152. [DOI] [PMC free article] [PubMed] [Google Scholar]
  93. Mondragón-Maya A, Solís-Vivanco R, León-Ortiz P, Rodríguez-Agudelo Y, Yáñez-Téllez G, Bernal-Hernández J, Cadenhead KS, de la Fuente-Sandoval C. Reduced P3a amplitudes in antipsychotic naïve first-episode psychosis patients and individuals at clinical high-risk for psychosis. J. Psychiatr. Res. 2013;47(6):755–761. doi: 10.1016/j.jpsychires.2012.12.017. [DOI] [PubMed] [Google Scholar]
  94. Näätänen R, Tervaniemi M, Sussman E, Paavilainen P, Winkler I. “Primitive intelligence” in the auditory cortex. Trends Neurosci. 2001;24(5):283–288. doi: 10.1016/s0166-2236(00)01790-2. [DOI] [PubMed] [Google Scholar]
  95. Nagai T, Tada M, Kirihara K, Yahata N, Hashimoto R, Araki T, Kasai K. Auditory mismatch negativity and P3a in response to duration and frequency changes in the early stages of psychosis. Schizophr. Res. 2013;150(2):547–554. doi: 10.1016/j.schres.2013.08.005. [DOI] [PubMed] [Google Scholar]
  96. Narr KL, Leaver AM. Connectome and schizophrenia. Curr. Opin. Psychiatry. 2015;28(3):229. doi: 10.1097/YCO.0000000000000157. [DOI] [PubMed] [Google Scholar]
  97. Novak G, Ritter W, Vaughan HG. Mismatch detection and the latency of temporal judgments. Psychophysiology. 1992;29(4):398–411. doi: 10.1111/j.1469-8986.1992.tb01713.x. [DOI] [PubMed] [Google Scholar]
  98. Oades RD, Wild-Wall N, Juran SA, Sachsse J, Oknina LB, Röpcke B. Auditory change detection in schizophrenia: sources of activity, related neuropsychological function and symptoms in patients with a first episode in adolescence, and patients 14 years after an adolescent illness-onset. BMC Psychiatry. 2006;6(1):7. doi: 10.1186/1471-244X-6-7. [DOI] [PMC free article] [PubMed] [Google Scholar]
  99. Opitz B, Rinne T, Mecklinger A, von Cramon DY, Schröger E. Differential contribution of frontal and temporal cortices to auditory change detection: fMRI and ERP results. Neuroimage. 2002;15(1):167–174. doi: 10.1006/nimg.2001.0970. [DOI] [PubMed] [Google Scholar]
  100. Perez VB, Woods SW, Roach BJ, Ford JM, McGlashan TH, Srihari VH, Mathalon DH. Automatic auditory processing deficits in schizophrenia and clinical high-risk patients: Forecasting psychosis risk with mismatch negativity. Biol. Psychiatry. 2014;75(6):459–469. doi: 10.1016/j.biopsych.2013.07.038. [DOI] [PMC free article] [PubMed] [Google Scholar]
  101. Perlman G, Foti D, Jackson F, Kotov R, Constantino E, Hajcak G. Clinical significance of auditory target P300 subcomponents in psychosis: Differential diagnosis, symptom profiles, and course. Schizophr. Res. 2015;165(2):145–151. doi: 10.1016/j.schres.2015.04.013. [DOI] [PMC free article] [PubMed] [Google Scholar]
  102. Perrin MA, Kantrowitz J, Silipo G, Dias E, Jabado O, Javitt DC. Mismatch negativity (MMN) as a tool for investigating neural mechanisms underlying auditory verbal hallucinations (AVH) in schizophrenia. Schizophr. Res. 2017 doi: 10.1016/j.schres.2017.05.012. In press. [DOI] [PubMed] [Google Scholar]
  103. Polich J. Updating P300: an integrative theory of P3a and P3b. Clin. Neurophysiol. 2007;118(10):2128–2148. doi: 10.1016/j.clinph.2007.04.019. [DOI] [PMC free article] [PubMed] [Google Scholar]
  104. Price GW, Michie PT, Johnston J, Innes-Brown H, Kent A, Clissa P, Jablensky AV. A multivariate electrophysiological endophenotype, from a unitary cohort, shows greater research utility than any single feature in the Western Australian Family Study of Schizophrenia. Biol. Psychiatry. 2006;60(1):1–10. doi: 10.1016/j.biopsych.2005.09.010. [DOI] [PubMed] [Google Scholar]
  105. Rasser PE, Schall U, Todd J, Michie PT, Ward PB, Johnston P, Helmbold K, Case V, Søyland A, Tooney PA, Thompson PM. Gray matter deficits, mismatch negativity, and outcomes in schizophrenia. Schizophr. Bull. 2011;37(1):131–140. doi: 10.1093/schbul/sbp060. [DOI] [PMC free article] [PubMed] [Google Scholar]
  106. Raudenbush SW. Analzying effect sizes: random-effects models. In: Cooper H, Hedges LV, Valentine JC, editors. The Handbook of Research Synthesis and Meta-Analysis. Russell Sage Foundation; New York: 2009. pp. 295–315. [Google Scholar]
  107. Recasens M, Grimm S, Capilla A, Nowak R, Escera C. Two sequential processes of change detection in hierarchically ordered areas of the human auditory cortex. Cereb. Cortex. 2012;24(1):143–153. doi: 10.1093/cercor/bhs295. [DOI] [PubMed] [Google Scholar]
  108. Rentzsch J, Buntebart E, Stadelmeier A, Gallinat J, Jockers-Scherübl MC. Differential effects of chronic cannabis use on preattentional cognitive functioning in abstinent schizophrenic patients and healthy subjects. Schizophr. Res. 2011;130(1):222–227. doi: 10.1016/j.schres.2011.05.011. [DOI] [PubMed] [Google Scholar]
  109. Rentzsch J, Shen C, Jockers-Scherübl MC, Gallinat J, Neuhaus AH. Auditory mismatch negativity and repetition suppression deficits in schizophrenia explained by irregular computation of prediction error. PLoS One. 2015;10(5):e0126775. doi: 10.1371/journal.pone.0126775. [DOI] [PMC free article] [PubMed] [Google Scholar]
  110. Revheim N, Corcoran CM, Dias E, Hellmann E, Martinez A, Butler PD, Lehrfeld JM, DiCostanzo J, Albert J, Javitt DC. Reading deficits in schizophrenia and individuals at high clinical risk: relationship to sensory function, course of illness, and psychosocial outcome. Am. J. Psychiatry. 2014;171(9):949–959. doi: 10.1176/appi.ajp.2014.13091196. [DOI] [PMC free article] [PubMed] [Google Scholar]
  111. Rissling AJ, Braff DL, Swerdlow NR, Hellemann G, Rassovsky Y, Sprock J, Pela M, Light GA. Disentangling early sensory information processing deficits in schizophrenia. Clin. Neurophysiol. 2012;123(10):1942–1949. doi: 10.1016/j.clinph.2012.02.079. [DOI] [PMC free article] [PubMed] [Google Scholar]
  112. Rissling AJ, Miyakoshi M, Sugar CA, Braff DL, Makeig S, Light GA. Cortical substrates and functional correlates of auditory deviance processing deficits in schizophrenia. NeuroImage Clin. 2014;6:424–437. doi: 10.1016/j.nicl.2014.09.006. [DOI] [PMC free article] [PubMed] [Google Scholar]
  113. Rowland LM, Summerfelt A, Wijtenburg A, Du X, Chiappelli JJ, Krishna N, West J, Muellerklein F, Kochunov P, Hong LE, Wijtenburg SA, Du X, Chiappelli JJ, Krishna N, West J, Muellerklein F, Kochunov P, Hong LE. Frontal glutamate and gamma-aminobutyric acid levels and their associations with mismatch negativity and digit sequencing task performance in schizophrenia. JAMA Psychiatry. 2016;73(2):166–174. doi: 10.1001/jamapsychiatry.2015.2680. [DOI] [PMC free article] [PubMed] [Google Scholar]
  114. Rudolph ED, Ells EML, Campbell DJ, Abriel SC, Tibbo PG, Salisbury DF, Fisher DJ. Finding the missing-stimulus mismatch negativity (MMN) in early psychosis: altered MMN to violations of an auditory gestalt. Schizophr. Res. 2015;166(1):158–163. doi: 10.1016/j.schres.2015.05.028. [DOI] [PMC free article] [PubMed] [Google Scholar]
  115. Salisbury DF, Kuroki N, Kasai K, Shenton ME, McCarley RW. Progressive and interrelated functional and structural evidence of post-onset brain reduction in schizophrenia. Arch. Gen. Psychiatry. 2007;64(5):521–529. doi: 10.1001/archpsyc.64.5.521. [DOI] [PMC free article] [PubMed] [Google Scholar]
  116. Salisbury DF, McCathern AG. Abnormal complex auditory pattern analysis in schizophrenia reflected in an absent missing stimulus mismatch negativity. Brain Topogr. 2016;29(6):867–874. doi: 10.1007/s10548-016-0514-2. [DOI] [PMC free article] [PubMed] [Google Scholar]
  117. Salisbury DF, Polizzotto NR, Nestor PG, Haigh SM, Koehler J, McCarley RW. Pitch and duration mismatch negativity and premorbid Intellect in the first hospitalized schizophrenia spectrum. Schizophr. Bull. 2016;43(2):407–416. doi: 10.1093/schbul/sbw074. [DOI] [PMC free article] [PubMed] [Google Scholar]
  118. Salisbury DF, Shenton ME, Griggs CB, Bonner-Jackson A, McCarley RW. Mismatch negativity in chronic schizophrenia and first-episode schizophrenia. Arch Gen Psychiat. 2002;59(8):686–694. doi: 10.1001/archpsyc.59.8.686. [DOI] [PubMed] [Google Scholar]
  119. Sallinen M, Kaartinen J, Lyytinen H. Is the appearance of mismatch negativity during stage 2 sleep related to the elicitation of K-complex? Electroencephalogr. Clin. Neurophysiol. 1994;91(2):140–148. doi: 10.1016/0013-4694(94)90035-3. [DOI] [PubMed] [Google Scholar]
  120. Sato Y, Yabe H, Todd J, Michie PT, Shinozaki N, Sutoh T, Hiruma T, Nashida T, Matsuoka T, Kaneko S. Impairment in activation of a frontal attention-switch mechanism in schizophrenic patients. Biol Psychol. 2003;62(1):49–63. doi: 10.1016/s0301-0511(02)00113-8. [DOI] [PubMed] [Google Scholar]
  121. Schaefer J, Giangrande E, Weinberger DR, Dickinson D. The global cognitive impairment in schizophrenia: consistent over decades and around the world. Schizophr. Res. 2013;150(1):42–50. doi: 10.1016/j.schres.2013.07.009. [DOI] [PMC free article] [PubMed] [Google Scholar]
  122. Schall U, Catts SV, Karayanidis F, Ward PB. Auditory event-related potential indices of fronto-temporal information processing in schizophrenia syndromes: valid outcome prediction of clozapine therapy in a three-year follow-up. Int. J. Neuropsychopharmacol. 1999;2(2):83–93. doi: 10.1017/S1461145799001418. [DOI] [PubMed] [Google Scholar]
  123. Şevik AE, Anıl Yağcıoğlu AE, Yağcıoğlu S, Karahan S, Gürses N, Yıldız M. Neuropsychological performance and auditory event related potentials in schizophrenia patients and their siblings: a family study. Schizophr. Res. 2011;130(1):195–202. doi: 10.1016/j.schres.2011.04.018. [DOI] [PubMed] [Google Scholar]
  124. Shelley AM, Silipo G, Javitt DC. Diminished responsiveness of ERPs in schizophrenic subjects to changes in auditory stimulation parameters: implications for theories of cortical dysfunction. Schizophr. Res. 1999;37(1):65–79. doi: 10.1016/s0920-9964(98)00138-8. [DOI] [PubMed] [Google Scholar]
  125. Shelley AM, Ward PB, Catts SV, Michie PT, Andrews S, McConaghy N. Mismatch negativity: an index of a preattentive processing deficit in schizophrenia. Biol. Psychiatry. 1991;30(10):1059–1062. doi: 10.1016/0006-3223(91)90126-7. [DOI] [PubMed] [Google Scholar]
  126. Shinozaki N, Yabe H, Sato Y, Hiruma T, Sutoh T, Nashida T, Matsuoka T, Kaneko S. The difference in mismatch negativity between the acute and post-acute phase of schizophrenia. Biol. Psychol. 2002;59(2):105–119. doi: 10.1016/s0301-0511(01)00129-6. [DOI] [PubMed] [Google Scholar]
  127. Shutara Y, Koga Y, Fujita K, Takeuchi H, Mochida M, Takemasa K. An event-related potential study on the impairment of automatic processing of auditory input in schizophrenia. Brain Topography. 1996;8(3):28–289. doi: 10.1007/BF01184786. [DOI] [PubMed] [Google Scholar]
  128. Solís-Vivanco R, Mondragón-Maya A, León-Ortiz P, Rodríguez-Agudelo Y, Cadenhead KS, de la Fuente-Sandoval C. Mismatch negativity reduction in the left cortical regions in first-episode psychosis and in individuals at ultra high-risk for psychosis. Schizophr. Res. 2014;158(1):58–63. doi: 10.1016/j.schres.2014.07.009. [DOI] [PubMed] [Google Scholar]
  129. Thönnessen H, Zvyagintsev M, Harke KC, Boers F, Dammers J, Norra C, Mathiak K. Optimized mismatch negativity paradigm reflects deficits in schizophrenia patients. a combined EEG and MEG study. Biol Psychol. 2008;77(2):205–216. doi: 10.1016/j.biopsycho.2007.10.009. [DOI] [PubMed] [Google Scholar]
  130. Todd J. Impaired detection of silent interval change in schizophrenia. Neuroreport. 2006;17(8):785–789. doi: 10.1097/01.wnr.0000220126.93973.31. [DOI] [PubMed] [Google Scholar]
  131. Todd J, Michie PT, Budd TW, Rock D, Jablensky AV. Auditory sensory memory in schizophrenia: inadequate trace formation? Psychiatry Res. 2000;96(2):99–115. doi: 10.1016/s0165-1781(00)00205-5. [DOI] [PubMed] [Google Scholar]
  132. Todd J, Michie PT, Jablensky AV. Do loudness cues contribute to duration mismatch negativity reduction in schizophrenia? Neuroreport. 2001;12(18):4069–4073. doi: 10.1097/00001756-200112210-00042. [DOI] [PubMed] [Google Scholar]
  133. Todd J, Michie PT, Schall U, Karayanidis F, Yabe H, Näätänen R. Deviant matters: duration, frequency, and intensity deviants reveal different patterns of mismatch negativity reduction in early and late schizophrenia. Biol. Psychiatry. 2008;63(1):58–64. doi: 10.1016/j.biopsych.2007.02.016. [DOI] [PubMed] [Google Scholar]
  134. Todd J, Whitson L, Smith E, Michie PT, Schall U, Ward PB. What’s intact and what’s not within the mismatch negativity system in schizophrenia. Psychophysiology. 2014;51(4):337–347. doi: 10.1111/psyp.12181. [DOI] [PubMed] [Google Scholar]
  135. Turetsky BI, Bilker WB, Siegel SJ, Kohler CG, Gur RE. Profile of auditory information-processing deficits in schizophrenia. Psychiatry Res. 2009;165(1):27–37. doi: 10.1016/j.psychres.2008.04.013. [DOI] [PMC free article] [PubMed] [Google Scholar]
  136. Umbricht D, Javitt DC, Novak G, Bates J, Pollack S, Lieberman J, Kane J. Effects of risperidone on auditory event-related potentials in schizophrenia. Int. J. Neuropsychopharmacology. 1999;2(4):299–304. doi: 10.1017/S1461145799001595. [DOI] [PubMed] [Google Scholar]
  137. Umbricht D, Javitt DC, Novak G, Bates J, Pollack S, Lieberman J, Kane J. Effects of clozapine on auditory event-related potentials in schizophrenia. Biol. Psychiatry. 1998;44(8):716–725. doi: 10.1016/s0006-3223(97)00524-6. [DOI] [PubMed] [Google Scholar]
  138. Umbricht D, Koller R, Schmid L, Skrabo A, Grübel C, Huber T, Stassen H. How specific are deficits in mismatch negativity generation to schizophrenia? Biol Psychiatry. 2003;53(12):1120–1131. doi: 10.1016/s0006-3223(02)01642-6. [DOI] [PubMed] [Google Scholar]
  139. Umbricht D, Koller R, Vollenweider FX, Schmid L. Mismatch negativity predicts psychotic experiences induced by NMDA receptor antagonist in healthy volunteers. Biol. Psychiatry. 2002;51(5):400–406. doi: 10.1016/s0006-3223(01)01242-2. [DOI] [PubMed] [Google Scholar]
  140. Umbricht D, Krljes S. Mismatch negativity in schizophrenia: a meta-analysis. Schizophr. Res. 2005;76(1):1–23. doi: 10.1016/j.schres.2004.12.002. [DOI] [PubMed] [Google Scholar]
  141. Umbricht DSG, Bates JA, Lieberman JA, Kane JM, Javitt DC. Electrophysiological indices of automatic and controlled auditory information processing in first-episode, recent-onset and chronic schizophrenia. Biol. Psychiatry. 2006;59(8):762–772. doi: 10.1016/j.biopsych.2005.08.030. [DOI] [PubMed] [Google Scholar]
  142. Valkonen-Korhonen M, Purhonen M, Tarkka IM, Sipilä P, Partanen J, Karhu J, Lehtonen J. Altered auditory processing in acutely psychotic never-medicated first-episode patients. Cogn. Brain Res. 2003;17(3):747–758. doi: 10.1016/s0926-6410(03)00199-x. [DOI] [PubMed] [Google Scholar]
  143. van Houwelingen HC, Arends LR, Stijnen T. Advanced methods in meta-analysis: multivariate approach and meta-regression. Stat. Med. 2002;21(4):589–624. doi: 10.1002/sim.1040. [DOI] [PubMed] [Google Scholar]
  144. Viechtbauer W. R package, version 1.4-0. 2010. Metafor: Meta-Analysis Package for R. [Google Scholar]
  145. Waters F, Jablensky A. Time discrimination deficits in schizophrenia patients with first-rank (passivity) symptoms. Psychiatry Res. 2009;167(1):12–20. doi: 10.1016/j.psychres.2008.04.004. [DOI] [PubMed] [Google Scholar]
  146. Wynn JK, Sugar C, Horan WP, Kern R, Green MF. Mismatch negativity, social cognition, and functioning in schizophrenia patients. Biol. Psychiatry. 2010;67(10):940–947. doi: 10.1016/j.biopsych.2009.11.024. [DOI] [PMC free article] [PubMed] [Google Scholar]
  147. Yuan G, Zhou Z, Yao J. Effect of quetiapine on cognitive function in schizophrenia: a mismatch negativity potentials study. Acta Neuropsychiatry. 2009;21(1):26–33. doi: 10.1111/j.1601-5215.2008.00337.x. [DOI] [PubMed] [Google Scholar]
  148. Zhou Z, Zhu H, Chen L. Effect of aripiprazole on mismatch negativity (MMN) in schizophrenia. PLoS One. 2013;8(1):e52186. doi: 10.1371/journal.pone.0052186. [DOI] [PMC free article] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

supplement
NIHMS892302-supplement.docx (51.5KB, docx)

RESOURCES