Mismatch negativity (MMN) as a tool for translational investigations into early psychosis: A review

International Journal of Psychophysiology 145 (2019) 5–14

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Review

Mismatch negativity (MMN) as a tool for translational investigations into early psychosis: A review

T

Mariko Tadaa,b, Kenji Kiriharaa, Shunsuke Mizutanic, Takanori Ukad, Naoto Kuniie, Daisuke Koshiyamaa, Mao Fujiokaa, Kaori Usuia, Tatsuya Nagaia, Tsuyoshi Arakia, ⁎ Kiyoto Kasaia,b, a

Department of Neuropsychiatry, Graduate School of Medicine, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8655, Japan International Research Center for Neurointelligence (WPI-IRCN), UTIAS, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8654, Japan c Department of Cellular Neurobiology, Graduate School of Medicine, Faculty of Medicine, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8655, Japan d Department of Integrative Physiology, Graduate School of Medicine, University of Yamanashi, 1110 Shimokato, Chuo, Yamanashi 409-3898, Japan e Department of Neurosurgery, The University of Tokyo Hospital, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8655, Japan b

A R T I C LE I N FO

A B S T R A C T

Keywords: MMN Schizophrenia Electroencephalogram (EEG) Animal-model Nonhuman primate (NHP) Intracranial EEG (iEEG)

Mismatch negativity (MMN) reduction is one of the most robust findings among several neurophysiological and neurocognitive measures in patients with schizophrenia. MMN is a promising biomarker for schizophrenia because of the following properties: 1) its relationship with early psychosis, including clinical high-risk (CHR); 2) its relationship with the functional abilities of patients; and 3) its translatability into basic research using animal models. Specifically, the utility of the passive auditory oddball paradigm that does not require subjects to make behavioral responses enables identical physiological activities to be obtained from both experimental animals and patients. This advantage has contributed to clarifying the generating mechanism of MMN in various animal studies. We reviewed clinical reports focused on early psychosis; specifically differential effects of deviance type and relationships to clinical and functional outcome. For the utility of MMN as a tool for translational research, we next reviewed recent MMN studies in rodents and nonhuman primates (NHP) as well as studies using intracranial recordings in humans, a rare opportunity to detect neural signals in vivo in humans. Neural computations of MMN, such as adaptation, deviance detection, and predictive coding, have been recent topics for understanding MMN generating mechanisms. Finally, several significant research questions were provided for future directions. MMN research could contribute to innovative, novel, therapeutic strategies in the future by becoming a bridge between basic and clinical research.

1. Introduction Biomarkers are necessary to achieve precision medicine in psychiatry (Insel and Cuthbert, 2015), and mismatch negativity (MMN) is a promising candidate of biomarkers for schizophrenia. MMN is an electrophysiological response elicited in an auditory oddball paradigm. Conventionally, there are two types of auditory stimuli: a frequent, standard stimulus and an infrequent, deviant stimulus (Fig. 1a). Participants listen to repeated presentation of the tones, but are instructed to ignore the auditory stimuli. MMN is the differential response to the standard and deviant stimuli (Fig. 1b; Nagai et al., 2013a). The MMN waveform is maximized at frontocentral sites, with phase reversal at mastoids. The deviant types used commonly in schizophrenia are frequency and duration changes of pure tones. MMN is important because

⁎

it is reliable across repeated experiments (Light and Braff, 2005; Light et al., 2012). In patients with psychosis or schizophrenia, reduction of the MMN amplitude is one of the most robust findings among several neurophysiological and neurocognitive measures (Light and Swerdlow, 2015). In addition, MMN has advantages as a biomarker for schizophrenia because of the following properties: 1) its relationship with early psychosis, including clinical high-risk (CHR); 2) its relationship with the functional abilities of patients; and 3) its translatability into basic research using animal models. Elucidating the pathophysiology of early psychosis is important to improve the outcome of young people because the pathological process of schizophrenia is considered to be progressive. Most studies before 2000 were conducted in chronic stage, middle-aged patients whose

Corresponding author at: Department of Neuropsychiatry, Graduate School of Medicine, University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8655, Japan. E-mail address: [email protected] (K. Kasai).

https://doi.org/10.1016/j.ijpsycho.2019.02.009 Received 12 October 2018; Received in revised form 9 February 2019; Accepted 25 February 2019 Available online 01 March 2019 0167-8760/ © 2019 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/BY-NC-ND/4.0/).

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a.

Fig. 1. a. Auditory tasks utilized for MMN. MMN is a difference waveform between the eventrelated potential (ERP) responses to deviant stimuli and standard stimuli. a) The conventional task is the “oddball” paradigm, which consisted of two types of auditory stimuli, a frequent standard stimulus (white block) and an infrequent deviant stimulus (black block). b) The control task is the “many standards” paradigm, which consisted of several types of auditory stimuli, including identical standard and deviant stimuli to the oddball paradigm. b. MMN waveform. MMN relates to the difference wave obtained by subtracting the ERP elicited by the standard stimuli (Es) from the ERP elicited by the deviant stimuli (Ed) (adapted from Nagai et al., 2013a).

sound smuli standard e.g. 1000 Hz

deviant e.g. 1200 Hz

a) Convenonal task “oddball “

䞉䞉䞉䞉䞉䞉䞉 me

comparison 䠄deviance detecon䠅

comparison 䠄adaptaon䠅 b) Control task “many standards”

䞉䞉䞉䞉䞉䞉䞉 standard e.g. 1000 Hz

0

b.

me

deviant e.g. 1200 Hz 2

1 Standard smulus

Standard smulus

Standard smulus

ERP elicited by deviant smuli (Ed)

ERP elicited by standard smuli (Es)

μV

Deviant smulus

Standard Smulus

sec

μV

4

4

2

2

0

Difference waveform (Ed - Es)

-2

0 -2

MMN

-4

-4 -100

0

100

200

300

400 msec

-100

0

100

200

300

400 msec

2. Differential characteristics of MMN in early and later stages of psychosis

durations of illness were approximately 20–30 years. Since the 2000s, research has focused on the earlier stages, i.e., patients with first-episode schizophrenia (FES) and pre-onset CHR. CHR, sometimes referred to as ‘ultra-high risk (UHR)’, is defined only by clinical features that are characterized by attenuated psychotic symptoms using several diagnostic tools. Genetic risk and deteriorations are also considered as highrisk for psychosis that is sometimes discussed separately from CHR. Translational research is a powerful strategy for understanding pathophysiology in early psychosis. For example, invasive recording methods can acquire high-precision neuronal signals. Multiscale recording methods, such as single-unit recording and local field potential (LFP) in animal models and intracranial EEG (iEEG) and scalp EEG in humans, can be applied to obtain MMN or MMM-related activity. Specifically, the utility of the passive auditory oddball paradigm that does not require subjects to make behavioral responses enables identical physiological activities to be obtained from both experimental animals and patients (Nagai et al., 2013a). This advantage has contributed to clarifying the generating mechanism of MMN in various animal studies, which are described later in this review.

The prediction of transition to psychosis is essential to establish the prevention for psychosis and, later, functional decline. Recently, several studies reported MMN amplitude reduction in patients with CHR for psychosis as well as first-episode psychosis (Haigh et al., 2017) and chronic stage of schizophrenia (Light and Braff, 2005). Several CHR criteria were used such as the Bonn scale for the assessment of basic symptoms (BSABS) (Gross et al., 1987) (MMN studies: BrockhausDumke et al., 2005; Bodatsch et al., 2011), the structured interview for prodromal symptoms (SIPS) (Miller et al., 2002) (MMN studies: Jahshan et al., 2012; Nagai et al., 2013b; Perez et al., 2014; SolísVivanco et al., 2014; Lavoie et al., 2018) or the comprehensive assessment of at-risk mental state (CAARMS) (Yung et al., 2005) (MMN studies: Shin et al., 2009; Atkinson et al., 2012; Hsieh et al., 2012; Shaikh et al., 2012; Higuchi et al., 2013; Higuchi et al., 2014; Atkinson et al., 2017). One study (Murphy et al., 2013) reported that the duration MMN (dMMN) was reduced in adolescents (11–13 years) with psychotic symptoms using the Schedule for Affective Disorders and Schizophrenia for School Aged Children, Present and Lifetime versions (K-SADSPL) (Kelleher et al., 2011). 6

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In patients at chronic stages, smaller dMMN amplitude was associated with reduced glutamate level, a higher ratio of glutamine to glutamate, and a lower GABA level using 1H MRS in the anterior cingulate cortex (Rowland et al., 2016). In FES, increased plasma levels of glutamate were associated with smaller dMMN amplitudes (Nagai et al., 2017). These results were not found in CHR, indicating that increments of the plasma levels of glutamate and their relationships with MMN might reflect the pathophysiology after the onset of psychosis. Increased peripheral glutamate levels were reported in patients with schizophrenia in a meta-analysis (Song et al., 2014). Increased glutamate release has also been considered to result from disinhibition of pyramidal cells, dependent on blockage of N-methyl-D-aspartate (NMDA)mediated drive on gamma-aminobutyric acid (GABA)-ergic interneurons that normally inhibit pyramidal cells (Moghaddam and Krystal, 2012). We reported that dMMN and fMMN were related to different clinical characteristics in early stages of schizophrenia (Koshiyama et al., 2018). We explored the relationships among dMMN, fMMN, global functioning, and cognitive function in early stages of psychosis. Structural equation modeling indicated that dMMN had a direct effect on global functioning in patients with recent-onset schizophrenia and CHR individuals, whereas fMMN had a direct effect on working memory only in patients with recent-onset schizophrenia (Fig. 2). Another study found that dMMN reduction was related to the ability to identify common environmental sounds, i.e., “real-world environmental sounds” in patients with schizophrenia (Joshi et al., 2018). These findings suggest that different neural mechanisms exist between dMMN and fMMN generation that may affect different aspects of pathophysiology in schizophrenia.

A meta-analysis regarding CHR reported a modest effect size of 0.40 (CI = 0.23 to 0.58) for MMN amplitude reduction in 16 studies (Erickson et al., 2016). Although the effect size for MMN reduction in CHR was smaller than that for the chronic stage (0.99, CI = 0.79 to 1.29 reported by Umbricht and Krljes, 2005; 0.95, CI = 0.85 to 1.04 reported by Erickson et al., 2016), MMN reduction in CHR is important because it has been reported that it predicts conversion to psychosis (Bodatsch et al., 2011; Perez et al., 2014). Additional longitudinal studies in early psychosis should enhance the potential usage of MMN as a predictive marker for psychosis outcome. 2.1. Differential effect of deviance type Since the first report of MMN (Näätänen et al., 1978), attention has been paid to the effects of deviant type in the auditory oddball paradigm. There are several variations of deviant type for auditory stimuli, including duration, frequency, intensity and location (Näätänen et al., 2012). Todd and colleagues explored the differential effect of deviance type such as duration, frequency and intensity (Todd et al., 2008). They found that both intensity and duration MMN were reduced in patients with short length of illness (within 5 years from the diagnosis of schizophrenia). In CHR study, reduction of double deviant (duration and frequency) MMN significantly predicted the time to psychosis onset (Perez et al., 2014). Because frequency and duration deviants were commonly used in schizophrenia, we focused on these effects in the following review. A meta-analysis (Erickson et al., 2016) reported that the effect size of dMMN reduction (0.94; CI = 0.85 to 1.04) was significantly larger than the frequency MMN (fMMN) reduction (0.72; CI = 0.57 to 0.87) (p < 0.05). A more recent meta-analysis also confirmed that dMMN reduction, especially long deviant duration, showed a larger effect size than the fMMN reduction (Avissar et al., 2018). Moreover, the dMMN reduction achieved the highest effect size measures among other eventrelated potentials (ERP), such as N100 or P50 deficits, as well as fMMN reduction (Bodatsch et al., 2015). A magnetoencephalography (MEG) study also confirmed this result (Suga et al., 2016). Our previous studies have reported that dMMN was significantly reduced in patients with FES and in CHR individuals compared to healthy controls (Nagai et al., 2013a; Nagai et al., 2013b). In contrast, significant fMMN reductions were not observed in the same FES patients or CHR individuals (Nagai et al., 2013a; Nagai et al., 2013b). Another study reported that dMMN reductions were observed in both inpatients and outpatients; in contrast, an fMMN reduction was found only in a chronic inpatient setting (Lee et al., 2018a). In CHR and first-episode psychosis, we did not find any progressive change in either dMMN or fMMN (Koshiyama et al., 2017). One study reported that the fMMN amplitude was reduced progressively in a subpopulation of CHR (N = 6) individuals who transition to psychosis within a 12-month follow-up period. There were no changes in fMMN in participants who did not transition (N = 21) (Lavoie et al., 2018). The difference between this study and our previous study (Koshiyama et al., 2017) might be explained by the fact that only one participant developed psychosis during the follow-up period in our sample. These findings suggest that dMMN reduction may reflect the pathophysiology of early stages of illness or an altered developmental process. Alternatively, fMMN reduction may reflect progressive brain changes or illness chronicity. The relationship between dMMN, fMMN and other in vivo measurements, such as MRI, proton magnetic resonance spectroscopy (1H MRS) and plasma levels of amino acids, has been investigated. A pioneering MRI study reported that fMMN reduction in patients with schizophrenia at first hospitalization was associated with Heschl gyrus gray matter volume reduction (Salisbury et al., 2007). Another study reported that fMMN amplitude reduction was correlated with gray matter volume reduction not only in the bilateral Heschl gyrus but also in the frontal cortex, while dMMN amplitude reduction was correlated with reduced gray matter in the right Heschl gyrus (Rasser et al., 2011).

2.2. Relationship to clinical and functional outcome The relationship between MMN reduction and the clinical features of psychosis has attracted much attention. A seminal study reported that MMN amplitude reduction was associated with lower Global Assessment of Functioning (GAF) Scale ratings (Light and Braff, 2005) in patients with chronic schizophrenia (mean duration of illness was 17.7 years). In this study, MMN reduction was not associated with symptom severity. Other groups confirmed the relationships between MMN and functioning (Kiang et al., 2009; Rasser et al., 2011; Wynn et al., 2010; Friedman et al., 2012) and found higher sensitivity of MMN to functioning in schizophrenia compared to P300 measures (Hamilton et al., 2018). Although a meta-analysis reported that symptomatic severity did not relate simply to MMN reduction (Erickson et al., 2017), a recent study thoroughly explained the illness pathway in terms of MMN, cognition, clinical symptoms and functional outcome (Thomas et al., 2017). They found that MMN and other early auditory information processing (EAP)-related measures, such as P3a and reorienting negativity (RON), affected functional outcome indirectly via a single pathway running through cognition and negative symptoms. This large study (N = 1415) was done as a part of the Consortium on the Genetics of Schizophrenia 2 (COGS-2), suggesting that MMN and other EAP-related measures could be surrogate end points in early-stage precognitive intervention studies for schizophrenia. Relationships between MMN and functioning were also reported in the early stages of psychosis, including CHR individuals (Koshiyama et al., 2017), although this is still controversial (Jahshan et al., 2012). This may be because, in early stages, especially high-risk individuals have higher heterogeneity compared to chronic stages of schizophrenia. Indeed, a recent study reported that MMN could predict remission in CHR regardless of transition to psychosis (Kim et al., 2018). In this study, CHR subjects were divided into remitter and nonremitter groups by GAF scores (> 60) and a symptomatic scale (Scale of Prodromal Symptoms (SOPS) positive scores < 3), and baseline MMN amplitudes in nonremitters were reduced compared to the remitters. These findings will help us apply MMN in predicting functional outcomes in CHR, as 7

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Fig. 2. Path diagrams among duration mismatch negativity (dMMN), frequency mismatch negativity (fMMN), Working Memory score, and Global Assessment of Functioning-Functioning (GAF-F) score in the recent-onset schizophrenia (ROSZ) (Model 1a) and ultra-high risk (UHR) (Model 1b) groups. The asterisks and thick lines indicate statistical significance (p ≤ 0.05) (adapted from Koshiyama et al., 2018).

also been achieved by in vivo patch clamp methods (Chen et al., 2015). This elegant mouse study showed that deviance detection existed in the late latency range (200–400 ms after tone onset). Another study also showed deviance detection in the mouse visual cortex, and silencing SOM-interneurons inhibited deviance detection using a chemogenetic approach, DREADD (Designer Receptors Exclusively Activated by Designer Drug). (Hamm and Yuste, 2016). These studies highlight the relationships between PV- and SOM- positive GABAergic interneurons and MMN generation. A more recent study acquired single-neuron activity from subcortical to cortical auditory pathways by combining large samples from anesthetized rats and smaller samples from awake mice to validate their results across rodent species and arousal levels (Parras et al., 2017). This study suggested that both adaptation and deviance detection existed at subcortical levels, as well as cortical areas. To translate this basic knowledge to human EEG, analogous ERPs between mice and humans are useful; P20, N40, P80 and P120 in mice are thought to be comparable to P50, N100, P200 and P300 in humans (Siegel et al., 2003). Meanwhile, careful consideration is also recommended to determine whether genuine MMN-like responses exist in rodent models (Harms et al., 2016). The auditory task is recommended to be the same as previous human clinical studies when compared with patients. Specifically, the probability of the deviant stimuli in the sequence is known to affect MMN amplitude, and 10–20% is commonly used in previous patient studies (reviewed as Nagai et al., 2013a, 2013b). In a sophisticated preclinical study, a NMDA receptor-related pharmacological test was done using MMN in freely moving rats (Lee et al., 2018b). This study acquired both dMMN and fMMN in a passive auditory oddball paradigm. Only dMMN showed a significant difference between the standard stimuli and the deviant stimuli from rat epidural recordings over the bilateral auditory cortex. The application of the NMDAR antagonist phencyclidine (PCP) reduced dMMN, while concurrent glycine treatment did not result in an amplitude reduction of dMMN. The task and analysis methods in this rat study were in line with a human patient study (Lee et al., 2017) done by the same authors; accordingly, a high compatibility between the rat preclinical and the human clinical studies was achieved.

well as chronic stages. 3. MMN as a tool for translational research MMN has been explored not only in clinical samples but also in basic research. One major goal has been using MMN as a translatable brain marker (Nagai et al., 2013a, 2013b) for developing new drugs or therapeutic methods. Another has been to clarify the pathophysiology of psychosis or schizophrenia through understanding the generating mechanism of MMN. MMN reflects early auditory information processing, and one of its main sources is considered to be the auditory cortex. Auditory dysfunction is a basic feature of schizophrenia (Javitt and Sweet, 2015). At the same time, higher executive functioning is also considered to be involved in MMN generation. Recently, computational approaches are adopted for understanding the generating mechanism of MMN, and are explained by the neural adaptation hypothesis and/or the deviance detection hypothesis (for details, see Section 4. Neural computation: adaptation, deviance detection and predictive coding). In this framework, “adaptation” indicates reduction of neural response to specific repetitive sounds (mostly “standard stimuli”), while “deviance detection” indicates neural responses that detect the difference between the up-coming sound and past sounds based on the context of the auditory stimuli (i.e. comparison between “deviant stimuli” and “standard stimuli” in the oddball sequence). Many basic researches focus on the involvement of adaptation or deviance detection as described in the following subheadings. We reviewed recent MMN studies in rodents and NHP, as well as studies using intracranial recordings in humans, a rare opportunity to detect neural signals in vivo in humans. 3.1. Rodents Rodent models have great advantages for exploring the molecular mechanism of MMN. Many studies have reported MMN in rat and mouse models that are already referred to in a comprehensive review (Todd et al., 2013). Molecular genetic techniques, including optogenetics, are powerful tools, and it is still difficult to apply these methods to NHP. Using the optogenetic approach, Natan et al. (2015) reported that PV-positive interneurons provide nonspecific inhibition in the auditory oddball sequence, while SOM-interneurons selectively inhibit the standard response in mouse A1. Whole-cell recordings of MMN have 8

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10 Frequency 10 Frequency 20 Intensity Local-global paradigmb Roving-oddball sequenceb Number and sequence changeb Roving-oddball sequenceb 11 different tones make “random” and “regular”b Rhythmic stimulus patternsb Local-global paradigmb Macaque Macaque Macaque Macaque Marmoset Macaque Macaque Macaque Macaque Macaque Fishman and Steinschneider (2012) Honing et al. (2012) Gil-da-Costa et al. (2013) Uhrig et al. (2014) Komatsu et al. (2015) Wang et al. (2015) Takaura and Fujii (2016) Teichert (2016) Honing et al. (2018) Chao et al. (2018)

LFP in A1 Scalp EEG Scalp EEG fMRI Epidural fMRI Subdural Scalp EEG Scalp EEG Subdural

Chimpanzee Ueno et al. (2008)

Scalp EEG

20

Intensity Intensity Intensity Frequency Frequency 15–20 5–20 5–15 Epidural MUA in A1 LFP in A1 Macaque Macaque Macaque Javitt et al. (1992) Javitt et al. (1994) Javitt et al. (1996)

3.2. Nonhuman primates In addition to rodent studies, MMN-like responses have also been found in other animal models, such as cats (Csépe et al., 1987; Ulanovsky et al., 2003), guinea pigs (Kraus et al., 1994), rabbits (Ruusuvirta et al., 1996) and NHP (Table 1). Since the NHP model shares high homology with humans, especially in the structure of the prefrontal and auditory cortices, it is potentially more useful than other models (Sweet et al., 2005). Beginning with the pioneering work by Javitt et al. (1992), several groups have investigated MMN in NHP using various recording methods, including multiunit activity (MUA), local field potential (LFP), intracranial EEG (epidural and subdural), scalp EEG and fMRI (Table 1). Intensity and frequency MMN have been reported repeatedly, whereas dMMN has not been reported yet in NHP. Fishman and Steinschneider (2012) investigated whether adaptation or deviance detection was found in A1, using a well-designed control condition, the “many-standards paradigm” (Fig. 1b), and found that adaptation explained MMN in A1. From a translational point of view, Gil-da-Costa et al. (2013) developed a homologous scalp EEG acquiring system between macaque monkeys and humans. Noninvasive EEG caps were used in both species, and the obtained MMNs resembled each other in their latency and topography. Subanesthetic administration of ketamine, an NMDA receptor antagonist, reduced MMN amplitude consistent with a previous study (Javitt et al., 1996). Other variations of the MMN paradigm other that the typical twotone oddball sequence, such as the “local-global paradigm” (Uhrig et al., 2014; Chao et al., 2018) or the “roving paradigm” (Komatsu et al., 2015; Takaura and Fujii, 2016) are also useful to investigate MMN generating mechanism. The “local-global paradigm” has been employed more often in intracranial EEG study, discussed in the next subsection. The “roving paradigm” has increasingly been used in MMN research including clinical studies in schizophrenia (Braeutigam et al., 2018; McCleery et al., 2019). In this paradigm, trains of repetitive tones (e.g. 3 or 5) of several different frequencies are presented randomly. Tones are identical within each train, but differ between trains. When one train changes to another train, the first tone of the new train is considered to be a deviant and the final tone of the preceding train is considered to be a standard. This unique paradigm enables us to use identical tone as deviant and standard tones.

Abbreviations: MUA, multiunit activity; A1, primary auditory cortex; LFP, local field potential; BF, best frequency; SSA, stimulus-specific adaptation; EEG, electroencephalogram; ECoG, electrocorticogram. a Both stimuli were used as deviants and as standards (flip flop). b Variation paradigm different from typical two-tone oddball sequence.

SSA in A1 MMN and rhythm Comparative EEG study in human and monkey Test auditory regularities cognition First in marmoset Test auditory regularities cognition Test of SSA effect Homolog of fMMN in rhesus monkey Rhythm cognition Prediction and prediction error in large-scale network

First in chimpanzee None

None None Ketamine None None None None None None None

First in monkey First in monkey A1 First pharmacological test in monkey None None PCP, MK801, CGS19755,kynurenic acid, bicuculline

65 dB, 85 dB 65 dB, 85 dBa 80–85 dB 60–65 dB Non-BF BF 500 Hz, 1500 Hza 500 Hz, 2000 Hza BF, non-BFa 500 Hz, 1500 Hza 60 dB, 80 dBa

Deviant

a

Standard (%)

Probability Recording Species Publication

Table 1 MMN in nonhuman primates (all studies were done in awake states).

Deviant

Stimulus characteristics

Pharmacology

Comments

M. Tada, et al.

3.3. Human intracranial EEG (iEEG) Human iEEG presents a special opportunity to investigate MMN generation mechanisms with high spatial and temporal resolution (Parvizi and Kastner, 2018). Electrocorticogram (ECoG) is obtained from patients with refractory epilepsy who underwent subdural electrode implantation for diagnostic purposes. LFP recorded using depth electrodes is also useful to explore human brain activity, especially in areas deep in the sulcus, such as A1. Localization of MMN has been investigated using source estimation of EEG (Oknina et al., 2005; Miyanishi et al., 2013) or fMRI (Molholm et al., 2005; Gaebler et al., 2015). These studies suggested that primary generators of MMN existed in the bilateral auditory cortex, and secondary generators were proposed to exist in the frontal cortex. The accuracy of these studies, however, was limited due to their low spatial resolution of EEG and temporal resolution of fMRI. Higher spatial and temporal resolutions of iEEG would enable us to explore the precise localization of MMN, and recent studies have tested more complex auditory processing underlying MMN in the same way as in recent NHP studies (Table 2). In the earlier studies, the localization of ERP deflections was analyzed to make for an easy comparison with MMN in scalp EEG (Halgren et al., 1995; Kropotov et al., 1995; Rosburg et al., 2005; Molholm et al., 2014). These studies reported MMN in the temporal cortex as expected. Interestingly, MMN was observed in the lateral inferior frontal cortex (Rosburg et al., 2005) or at an unspecified frontal electrode 9

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Table 2 MMN in human intracranial EEG (except for single case studies). Publication

N

Recording

Area

Probability

Deviant

Stimulus characteristics

(%) Halgren et al. (1995) Kropotov et al. (1995) Rosburg et al. (2005)

28 17 6 13

Depth electrode

TPF

Edwards et al. (2005)

3

Depth Depth Dense Dense

electrode electrode array array

T TF

Bekinschtein et al. (2009) Molholm et al. (2014)

2 3

Depth electrode Dense array

TF T

King et al. (2013) El Karoui et al. (2015) Dürschmid et al. (2016) Ishishita et al. (2019)

9 9 5 10

Unspecified Depth electrode Dense array Dense array

TFO TFO TF TPF

TF

11 13 10 16.7%b

Frequency

Standard

Deviant

670 Hz 1000 Hz 1000 Hz 1500 Hz 6.6 ms

Unspecified 2500 Hz See belowa 2000 Hz 12.8 ms 550 Hz 550 Hz

Frequency Frequency & Duration 500 Hz 15% Frequency & omission Omission c Local-global paradigm 20 Phoneme Phoneme Nonphoneme Frequency 720 Hz, 800 Hzd Local-global paradigmc Local-global paradigmc predictable & unpredictable paradigmc 10 Frequency 1000 Hz 1200 Hz

Comments

Three-tone oddball paradigm, including target stimuli Patients with Parkinson's disease and OCD MMN in superior temporal lobe HGA in temporal cortex Test auditory regularities in cognition Mapping phoneme-related areas in the STG Test auditory regularities in cognition Test auditory regularities in cognition Test prediction of frontal cortex Deviance detection in the STG

T, temporal cortex; P, parietal cortex; F, frontal cortex; O, occipital; OCD, obsessive compulsive disorders; STG, superior temporal gyrus. a Different among patients, depending on their discrimination ability (1100 Hz, 1200 Hz, 1300 Hz). b The first five stimuli were standard, and the sixth stimulus was deviant. c Variation paradigm different from typical two-tone oddball sequence. d Both stimuli were used as deviants and as standards (flip flop).

ECoG acquired from multiple participants (Kunii et al., 2013a, 2013b; Ishishita et al., 2019).

(Bekinschtein et al., 2009; Ishishita et al., 2019) in addition to the superior temporal lobe in a small minority of patients, while another study did not find MMN-like activity in the frontal site (Edwards et al., 2005). Phase reversal of ERP across a sulcus or major fishers, however, sometimes makes it difficult to estimate the precise signal source. High gamma activity (HGA) is considered to reflect more local activity just below the electrode (Lachaux et al., 2012) and has been increasingly adopted in recent literature (El Karoui et al., 2015; Dürschmid et al., 2016; Ishishita et al., 2019). Recently, using HGA, we found that deviance detection but not adaptation was the dominant component of MMN in the lateral superior temporal gyrus (STG) (Ishishita et al., 2019). Here we employed the oddball task compatible with our previous clinical MMN study (Nagai et al., 2013a, 2013b) (Table 2). Thus, our ECoG study implies that disruption of deviance detection in the STG underlies MMN reduction in psychosis and patients with schizophrenia. Some studies have investigated auditory regularity cognition using MMN elicited by the “local-global paradigm” (Bekinschtein et al., 2009; King et al., 2013; El Karoui et al., 2015). This paradigm contains 2 hierarchical compositions in a series of 5 brief sounds (A or B): a local deviant (e.g., B in AAAAB) and a global deviant (e.g., AAAAA in a series of frequent AAAABs). This unique paradigm showed that MMN was evoked by the local deviant regardless of the attentional condition, whereas global deviant detection induced P3b depending on the participants' attention. One NHP study also used this paradigm (Uhrig et al., 2014). Moreover, a recent study showed frontal predictive activity using a fully predictable condition that consisted of four standards always followed by a deviant and an unpredictable condition where deviants were randomly embedded in trains of standard stimuli (Dürschmid et al., 2016). The literature discussed above enhances the understanding of how MMN or auditory regularity and its deviant cognition is generated in the brain; however, the interpretations should be carefully applied to clinical MMN studies because of the difference in task setting. Although a high signal-to-noise (S/N) ratio is a distinguished feature of ECoG, there are some limitations for ECoG research (Lachaux et al., 2012). Electrode implantation areas are limited by clinical necessity, and accordingly, comprehensive investigation of the whole brain is difficult, unlike other noninvasive neuroimaging methods, such as EEG, MEG or fMRI. It is important that reproducibility is confirmed across subjects whose epileptic foci are different from each other. Spatial normalization methods are useful to validate the spatial-temporal dynamics of

4. Neural computation: adaptation, deviance detection and predictive coding The neural computation underlying MMN is still under debate (Garrido et al., 2009). Several hypotheses, such as pre-attentive sensory memory (Tiitinen et al., 1994; Näätänen et al., 2012), neural adaptation, deviance detection, and predictive coding, have been explored. The neural adaptation hypothesis and the deviance detection hypothesis have been tested intensively in recent basic studies discussed in detail later as we discussed in an earlier section. Regarding the adaptation hypothesis, neurons in cat primary auditory cortex showed a specific decrease in response to repetitive auditory stimuli (Ulanovsky et al., 2003). This neural adaptation occurred more strongly for the standard stimuli, which is presented frequently, compared to the deviant stimuli during an auditory oddball paradigm. In line with the adaptation hypothesis, MMN is thought to reflect the difference between the deviant stimuli and adapted neural responses to the standard stimuli (May and Tiitinen, 2010). Other animal studies (in rodents, Farley et al., 2010; in nonhuman primates, Fishman and Steinschneider, 2012) have also suggested that adaptation components contributed to MMN in the primary auditory cortex (A1). In humans, Jääskeläinen et al. (2004) also reported that adaptation contributed to MMN generation. Adaptation is also considered to affect other ERPs, such as N1 and P2 (Herrmann et al., 2015), and the difference between N1 and MMN has been well-documented in previous studies (Todd et al., 2013; Javitt and Sweet, 2015). The adaptation hypothesis is significant but insufficient to interpret MMN generation. To explore the deviance detection hypothesis, Jacobsen and Schröger (2003) applied the “many-standards paradigm” (Fig. 1b) as a control condition for the classical oddball paradigm in human EEG. In the “many-standards paradigm”, there were multiple control sounds in addition to the standard and deviant sounds in the oddball paradigm. The presentation probability of the standard and deviant sounds in the many-standards paradigm was equal to the probability of the deviant sound in the oddball paradigm. They found that the neural response to the deviant sound in the oddball paradigm was larger than the neural response to the identical sound in the manystandards paradigm. These results and another MEG study (Maess et al., 2007) suggest the existence of neural processes that detect deviance 10

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been provided to separate MMN into subcomponents, such as adaptation, deviance detection and predictive coding. Although these paradigms are useful in obtaining new knowledge on MMN, major differences from the conventional oddball paradigm utilized in clinical MMN studies should be considered carefully. More compatible translational studies are needed to move on to the next step, which is understanding the aberrant neural computation in psychosis or schizophrenia.

based on the context of auditory stimuli (e.g., oddball sequence) and that MMN may reflect this comparison process between the deviant and the standard. Evidence for deviance detection, in addition to adaptation, was recently reported in mouse primary auditory cortex (Chen et al., 2015) as we discussed in an earlier section. In this whole-cell recording study, adaptation was present in all cell types (excitatory neurons, parvalbumin (PV)- and somatostatin (SOM)-positive GABAergic interneurons) at early latencies, whereas deviance detection was reflected in excitatory neurons at late latencies (200–400 ms after tone onset). Another study reported that the NMDA receptor antagonist MK-801 affected deviance detection in awake, freely moving rats (Harms et al., 2018). The contribution of both adaptation and deviance detection has been discussed not only at the cortical level but also at the subcortical level (Malmierca et al., 2014). Predictive coding is the other neural computation for MMN generation. Recent studies suggest that altered predictive coding may be related with the pathophysiology of schizophrenia and interpret the MMN as a prediction error signal (Garrido et al., 2008; Corlett et al., 2011). In this framework, the standard stimuli are regarded as the “predicted stimuli”, and the deviant stimuli are regarded as the “unpredicted stimuli”. Dynamic causal model (DCM) studies have reported that a hierarchical organization among the lateral prefrontal cortex, bilateral primary cortex and superior temporal gyrus is related to the predictive coding of different auditory stimuli during various MMN paradigms (Garrido et al., 2008; Phillips et al., 2015). DCM was developed for the connectivity analysis of fMRI (Friston et al., 2003) and can account for the causal relationships among brain areas, which are separate for the standard and deviant stimuli in the MMN paradigm. Clinical DCM studies have reported that patients with schizophrenia showed reduced intrinsic connectivity within the primary auditory cortex, as well as reduced forward and backward extrinsic connectivity underlying the MMN (Dima et al., 2012). A model-based study suggested that predictive coding computed through the interaction of excitatory and inhibitory neurons mediated by NMDA receptors can account for the major features of MMN (Wacongne et al., 2012). In this way, MMN generation has begun to attract much attention from the computational neuroscience field, as well as the clinical psychiatric field.

5.3. Circuit-level mechanisms The localization and global network analysis of MMN has focused on the auditory cortex and frontal cortex. Functional connectivity between temporal and frontal sources has also been explored (Garrido et al., 2008; Dima et al., 2012; MacLean and Ward, 2014). Networklevel generators of MMN were also explored using a combination of EEG and resting-state functional connectivity analysis in fMRI (Lee et al., 2017). In this study, EEG response to deviant stimuli occurred in the theta (4–7 Hz) frequency range, which is thought to reflect corticocortical processing, whereas standard stimuli increased the alpha (8–12 Hz) response, which is considered to reflect thalamocortical connectivity. Patients with schizophrenia showed reductions in both responses that were related to somatomotor, ventral attention and default networks in functional connectivity by fMRI. Future translational studies can use these clinical works as a reference to overcome the limited resolutions of human noninvasive neuroimaging methods. Taking advantage of optogenetics or chemogenetics might reveal causal relationships among brain networks regarding MMN. 5.4. Cellular mechanisms MMN is of particular interest for its relationships with the glutamatergic dysfunction hypothesis (Moghaddam and Javitt, 2012) in schizophrenia. As a translational investigation, Javitt and colleagues have reported that NMDA receptor antagonists reduced MMN amplitude in the awake, nonhuman primate (Javitt et al., 1996), although it is unclear how the local injection dosage in this study relates to the subanesthetic systemic injection that induces psychosis in humans. Further studies are needed to clarify the cellular mechanism of MMN for drug development based on the molecular hypothesis of schizophrenia. For example, the NMDA receptor contains a D-serine/glycine binding site on the NR1 subunit that is considered to be a potential therapeutic target. Recently, several studies reported an increment of MMN amplitudes by the low- to moderate-affinity NMDA receptor antagonist memantine (Korostenskaja et al., 2007; Swerdlow et al., 2016), glycine (Greenwood et al., 2018) and D-serine (Kantrowitz et al., 2018). Novel molecular technologies, such as calcium imaging, may enable us to unveil the cellular mechanisms of MMN by showing neural activity directly in a wide view.

5. Future directions Finally, we discuss several significant research questions concerning how MMN may contribute to the development of innovative solutions for future study in the psychiatric field. 5.1. Mechanisms underlying the differential effects of deviance type To utilize MMN as a translatable brain marker for preclinical and clinical studies, we can never put aside the mechanisms underlying the differential effects of deviance type, i.e., the difference between duration and frequency MMN. It is well known that dMMN reduction precedes fMMN reduction in schizophrenia, as we reviewed in earlier section; however, the investigation of the differential mechanisms of dMMN and fMMN has been insufficient. A meta-analysis showed that the NMDA receptor antagonist ketamine attenuated MMN (Rosburg and Kreitschmann-Andermahr, 2016), although this study did not find any difference in ketamine effects on the duration and frequency deviants of MMN. Multiple scale investigations, such as neural computation, circuit and cellular level analyses, are warranted to clarify differences between dMMN and fMMN.

6. Conclusion MMN amplitude reduction is a reliable neurophysiological finding in patients with schizophrenia. Relationships with clinical significance, such as early illness, functioning, cognition and clinical outcomes, indicate the importance to clarify the generating mechanism of MMN. In addition to its localization (conventionally, in the auditory cortex, whereas the frontal and temporo-frontal networks have been proposed recently), its molecular mechanism (NMDA receptor and glutamatergic regulation) and neural computation (adaptation, deviance detection and predictive coding) have also been actively discussed. This trend involves more general broad interest from basic neuroscientists over the traditional psychiatric field. Fascinating investigations using animal models (rodent and NHP) and intracranial EEG from humans are continuing to be reported. These basic studies take advantage of their unique characteristics and gradually unveil the mechanisms underlying MMN reduction in patients. MMN research could contribute to innovate

5.2. Translational investigations for neural computation mechanisms underlying MMN Neural computational mechanisms underlying MMN have recently been of increased interest. Several unique variation paradigms have 11

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novel therapeutic strategies in the future by becoming a bridge between basic and clinical research.

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Acknowledgements This work was supported, in part, by the Japan Society for the Promotion of Science (JSPS) KAKENHI (15H06166, M.T.; 18K07588; K.Ki), by the Brain Mapping by Integrated Neurotechnologies for Disease Studies (Brain/MINDS) from the Japan Agency for Medical Research and Development (AMED) (JP17dm0207004, N.K., K.Ka), the International Research Center for Neurointelligence (WPI-IRCN) at the University of Tokyo Institutes for Advanced Study (UTIAS) (M.T., K.Ka), the University of Tokyo Center for Integrative Science of Human Behavior (CiSHuB) (K.Ka), the SENSHIN Medical Research Foundation (M.T.), the Takeda Science Foundation (M.T.), the Naito Foundation (M.T.), and the Kurata Grants (M.T.). References Atkinson, R.J., Michie, P.T., Schall, U., 2012. Duration mismatch negativity and P3a in first-episode psychosis and individuals at ultra-high risk of psychosis. Biol. Psychiatry 71 (2), 98–104. 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