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Mismatch Negativity（MMN）as a PromisingTranslational Neurophysiological Biomarker in
Schizophrenia
Patricia T. MICHIE1,3,4）, Jaishree JALEWA1）, Lauren HARMS2,3,4）,
Juanita TODD1,3,4）, Deborah HODGSON1,3,4）
1）School of Psychological Sciences, College of Engineering, Science and Environment, University of Newcastle2）School of Biomedical Science and Pharmacy, College of Health, Medicine and Wellbeing, University of Newcastle3）Centre for Brian and Mental Health Research, University of Newcastle
4）Hunter Medical Research InstituteMismatch negativity（MMN）is a neurophysiological response measured as an auditory
event‒related potential（ERP）generated automatically by the brain when a deviant sound occurs
rarely and unpredictably amongst regular background sounds. A reduction in the amplitude of
MMN in schizophrenia is one of the most replicated results in schizophrenia studies first reported
approximately 30 years ago. Considering the large effect size of the MMN reduction and othercharacteristics of MMN in schizophrenia, it has been proposed that MMN have the status of aneurophysiological biomarker of the disorder. In this paper, we review the evidence that has
accrued to support the assertion of biomarker status, and then evaluate what that means for diagnosis, prognosis, and prediction of medication response, but most importantly, its use as a translational tool in pre‒clinical studies. Although considerable knowledge has accrued over the last
decades about the optimal stimulus conditions for exposing MMN impairments in schizophrenia,
the association with cognitive deficits and functional status, and the involvement of the glutamate
NMDAr system, little is known about the neurobiological basis of MMN reduction in schizophrenia. Pre‒clinical research using animal models is essential to make advances in this area. Fortunately, paradigms have been developed for use in the rat that strongly suggest that the rat brain iscapable of generating the equivalent of a human MMN response or MMR. We review evidence
supporting that a late component of MMR in the rat exhibits many of the characteristics of thehuman MMR. What is needed now is an animal model of schizophrenia that produces a robust andreplicable reduction in this component that can be investigated using molecular approaches to support the development of novel pharmaceuticals to rescue MMR in these animals.

Keywords

824

mismatch negativity, MMN, schizophrenia, animal models of schizophrenia, pre—clinical

精神経誌（2021）第 123 巻 第 12 号

dard electroencephalography（EEG）systems. There is a

Introduction

magnetoencephalography（MEG）equivalent‒the MMNm.
It is an automatic response to stimulus change‒that is, its

The first publication reporting that the mismatch nega-

occurrence is not reliant on active attention being paid totivity（MMN）component of the auditory event‒related

the attributes of the eliciting stimulus sequence. This attri-

potential was reduced in schizophrenia was by an Austra-bute of automaticity is one of the reasons why it has been82）

lian group in 1991 , followed shortly afterwards by a sim39）

such a useful tool for investigating brain function in clini-in 1993. In the inter-cal patients with cognitive impairments. For auditory stim-vening 30 years, MMN amplitude reduction was noted inuli, MMN consists of a negative potential of approximately

over 500 separate reports（Web of Science count excluding

1‒4μV peaking at approximately 150‒250 ms after thereviews）in patients with a diagnosis of a schizophrenia

deviant event, and is most often visualized and measured

spectrum disorder from a variety of laboratories and patient

by subtracting the ERP response to the regular standard

sources（hospitalized, outpatients, supported accommoda-

sounds from the ERP response to deviant sounds（Figuretion, etc.）. Numerous meta‒analyses estimated the effect

1B）. MMN is elicited by any deviant that represents a dis-size（EF）to be of the order of 0.8‒1.0 depending on thecriminable violation of a regularity in regular sounds, such

ilar report from a New York group

14,97）. The

as a change in a simple attribute〔e. g. frequency（pitch）,

robustness of this finding along with other attributes ofduration, spatial location〕, or a violation of a more com-

MMN in schizophrenia have led to assertions that reduced

plex regularity such as a pattern violation（e. g. regular tone

MMN meets the criteria for a biomarker of the disor-

pairs of low‒high pitch interrupted by a deviant pair ofexact methodology used to generate the response

4,55）. The purpose of this review is to summarize the evi-high‒low where the absolute frequencies of sounds making

dence that has accrued to support the assertion of biomark-up the pair are randomized77））, speech sounds, or stimulus

er status, and then evaluate what that means for diagnosis,

omission in a repeating sound sequence64）. MMN is gener-

prognosis, or prediction of medication response 99）, butally maximal（most negative）at frontocentral sites andmost importantly, its use as a translational tool in pre‒clini-

reverses in polarity over mastoid and more posterior sites

der

78）

cal studies . First, a brief description of MMN, the stimu-

when recorded with a nose reference（Figure 1D）. Therelus conditions that generate a mismatch response, andare at least two sources of activity contributing to the scalp

models of mechanisms proposed as explanations of thepotential‒a bilateral supratemporal source contributing to

MMN signal are provided.

the polarity reversed potential recorded over temporal andposterior sites and a right hemisphere frontal source in gen-What is MMN?

eral, the degree of laterality depending upon the nature of
the eliciting stimulus, with language stimuli producing a

MMN was first described by a Finnish researcher, Ris-

more left lateralized response64）.

to Näätänen, and his colleagues in the late 70s60,61） in thecontext of a program of research to understand the effects

Neuronal mechanisms of MMNof selective attention on event‒related potentials（ERPs）of
the brain. MMN is an electrophysiological response to rare

Several models of the neuronal mechanisms underpin-

deviant or oddball stimuli that interrupt regularity in back-ning the generation of the MMN have been proposed. The

ground standard stimulation（Figure 1A）. It has beenclassic interpretation of the MMN proposes that a sensory

observed in auditory, visual, and somatosensory modali-memory（or echoic memory）of acoustic regularities storedties, but is more frequently examined in audition, mainlyin the auditory cortex supports the detection of sensory

because of ease of stimulus control. MMN can be readily

deviance between the memory and sensory input 62,64）.

recorded with scalp electrodes（Figure 1C37））using stan-

Fronto‒temporal networks are then engaged to assess theMichie・他：Mismatch Negativity（MMN）as a Promising Translational Neurophysiological Biomarker in Schizophrenia

825

A. Schematic oddball sequence

C. Standard locations of scalp
electrodes using 10-20 system
Front

FP1
Regular（standard）short tones（e.g., 50 ms）

F7

Rare（deviant）longer tones（e.g., 100 ms）
A1
0.5

1.0

1.5

2.0

2.5

3.0

T3

Left

Time（in seconds）

T5

Side

FP2
F8

F3

FZ

F4

C3

CZ

C4

P3

PZ

P4 T6

O1

T4

A2
Right
Side

O2

Back
B. MMN＝deviant minus standard ERPs

D. Most negative MMN at frontocentral
sites, reverses in polarity over mastoid

Deviant（100 ms）ERP
Standard（50 ms）ERP
FPZ
MMN

FZ

Fz

－3μV

0 100 200 300 400 500
ms

CZ

M1

PZ

M2

OZ

－
1μV
＋
0

170

400

0

170

400

Figure 1 A schematic of a stimulus sequence, stylized ERPs, to standard and deviant and MMN difference wave
along with human scalp distribution
A：Schematic oddball sequence with standard tones in red and deviant tones in blue.
B：MMN is the difference waveform（in yellow）obtained by subtracting standard ERPs from the deviant ERPs.
C：10—20 system of standard scalp locations.
D：Human scalp topography of the MMN, with maximal MMN at frontocentral sites and phase reversal at mastoid sites（M1and M2）when recorded with a nose reference. D Adapted from20）with permission.

relevance of the deviance and trigger an attention switch

standard）34）. This model denied the necessity for any spe-

towards the change15）. It is assumed that the sum of thecial sensory memory or genuine deviance detection, but

neural activity supporting these processes is reflected in thewas firmly rebutted on a variety of grounds63）, but mostrecorded scalp MMN and its topography100）. However, thisimportantly that MMN can be elicited even in the absence

model was challenged by the proposal that the MMNof an N1（as in the stimulus omission paradigm101））or rep-

waveform is generated as a result of different levels of neu-

etition of the same sounds77）. However, both explanations

ronal adaptation of an exogenous auditory ERP compo-of MMN generation can be integrated under a single unify-

nent, namely the N1 component, generated in response to a

ing framework of a predictive coding model21）. Predictive

novel sound（the deviant）versus a repeated sound（thecoding is a general theory of brain function underpinning826

精神経誌（2021）第 123 巻 第 12 号

perceptual inference and learning19） that proposes that theanalysis14） suggests a small but significant reduction ofbrain is a hierarchically organized system with feedforward

MMN in bipolar patients）. There is some evidence that aand feedback connections between levels of the system thatduration deviant is a more sensitive dimension than fre-attempt to minimize discrepancies between bottom‒up sen-quency based on the same group of patients in whom fre-sory inputs provided from a lower level, such as auditory

quency MMN was unaffected, whereas duration MMN was

areas located in temporal regions, and top‒down predic-

substantially reduced in amplitude57）. The first meta‒analy-

tions from a higher level（frontal areas）21）. According to

sis of 62 studies of MMN in schizophrenia offered some

this framework, a novel sound that violates predictions

support for the notion that MMN to duration deviants was（based on an environmental model of past regularities）

more impaired in schizophrenia than that to frequency

about what the next stimulus will be, generates a prediction

deviants, although it failed to reach significance with anerror necessitating an updated environmental model,

effect size of 1.01 for duration and 0.47 for frequency97）.

reflected in MMN. A repeated sound that matches predic-

On the other hand, a subsequent meta‒analysis of an eventions results in a suppression of prediction errors through alarger set of 104 unique studies found evidence of a signifi-

process akin to adaptation. Hence, the predictive coding

cantly larger MMN deficit to duration than frequency devi-

account incorporates both adaptation mechanisms and a

ants14）. However, the real situation may be more complex,

model（or memory）of past regularities. Furthermore, theas we reported that reduction of MMN to duration deviants

predictive coding framework emphasizes the importance ofis evident even in the early stages of the illness, whereas

short‒term plasticity of glutamatergic synaptic connections

the reduction of MMN to frequency deviants occurs in later

between lower and higher brain regions that underpins

stages92）. We57）previously suggested that extraction of themodel updating, the latter regulated by glutamate N‒meth-duration dimension of sound requires more complex com-81）yl‒D‒aspartate receptors（NMDARs） . Numerous phar-putational processing within multiple auditory networks31）

macological studies in humans and animals revealed thatthan frequency, which is robustly coded in multiple tonoto-

the MMN amplitude is reduced by the administration ofpically mapped regions of the auditory cortex86）. Thus,

NMDAR antagonists40,95,94）.

duration may be more vulnerable to even subtle anatomicalor connectivity changes that occur early in the course of

MMN as a biomarker for a diagnosis of

schizophrenia.

schizophrenia

Considering the impressive effect size of 1.0, particularly for a duration deviant, in distinguishing between a

As noted earlier, reduced MMN in medicated patients

healthy control group and a group with a diagnosis ofwith a diagnosis of schizophrenia was first observed in theschizophrenia, how useful is MMN likely to be as a diag-

early 1990s using simple paradigms in which the deviant

nostic marker? Admittedly, such an effect size means that,

82）

39）

or frequency（pitch） .

on average, 84％ of a patient group will have an MMN

Shortly afterwards, the Australian group reported that theamplitude lower than the control group, but there are still

reduced MMN to a duration deviant also occurred inmany impediments to MMN amplitude being used as a

unmedicated patients with a schizophrenia diagnosis, but

diagnostic biomarker or to move it into clinical use56,80）.

was absent in patients with a bipolar diagnosis6）, demon-

Studies reporting MMN data in schizophrenia populations

strating not only that reduced MMN occurred irrespective

vary substantially in terms of their methods：physicalof medication status in schizophrenia patients, but also that

stimulus parameters〔frequency（Hz）, duration, probabili-

the effect was restricted to patients with a diagnosis of

ty of deviant, etc.〕, recording system parameters（filter set-schizophrenia（Figure 2）, suggesting specificity of thetings, digitization rate, and amplification）, recording mon-marker to schizophrenia rather than a general marker oftage（location of active sites and reference location）, andpsychosis（see also96）for a similar result, although meta‒

post‒processing algorithms（artefact rejection criteria）80）.

was a variation either in duration

Michie・他：Mismatch Negativity（MMN）as a Promising Translational Neurophysiological Biomarker in Schizophrenia

827

LONG DURATION DEVIANTSHORT DURATION DEVIANT

MEDICATED SCHIZOPHRENIA PATIENTSUNMEDICATED SCHIZOPHRENIA PATIENTSBIPOLAR PATIENTS

－2μV

－200

－2μV

0

200

400

－200

0

200

400 msHEALTHY COMPARISON GROUPPATIENT GROUP

Figure 2

MMN reduction in schizophrenia patients

Catts et al. demonstrated that the MMN amplitude is significantly lower in individuals with schizophrenia, but not in the individuals with bipolar affective disorder, when compared with healthy subjects. Reprinted with permission fromAm J Psychiatry,（Copyright 1995）. American Psychiatric Association6）.

This lack of standardization and normative data, including

leading to high‒levels of life long disability22,23）. Although

age norms, combined with lack of extensive data on sensi-

the number of published MMN studies in schizophrenia

tivity and specificity to schizophrenia are a major impedi-that examine correlations between MMN and cognitive

ment to using MMN as a diagnostic biomarker.

performance is limited（for a review93））, a large multi‒cen-

There are, however, other attributes of MMN reduc-ter study of 1415 patients found, using structural equation

tion that strengthen the case for it being designated as amodelling, that an early auditory processing measure driv-

diagnostic biomarker of schizophrenia. The MMN measureen largely by reduced MMN has a direct effect on cogni-

has utility for investigating the cognitive aspects of audito-tion. Poor functioning is also associated with reduced

65）

ry processing , a link that is strengthened by reframing

MMN in patients with functioning assessed using a variety

MMN within a predictive coding framework of brain func-of scales, including the Global Assessment of Function

21）

tioning . Impaired cognition is a central feature of schizo-（GAF）53,54） and Social and Occupational Assessment ofphrenia and is largely responsible for the poor functional

Functioning（SOFAS）73）. The large multi‒center study cit-

outcomes of patients in work, education, and relationships

ed earlier89） found that MMN amplitude predicted func-828

精神経誌（2021）第 123 巻 第 12 号

tional outcomes and cognition, the authors estimating that

however is stronger, with meta‒analysis14） producing aan intervention that resulted in a 1‒μV increase in MMN

modest and significant reduction relative to healthy con-amplitude will be theoretically linked to improvements introls（EF＝0.40）.

both cognition and functional outcome（effect sizes of 0.78

However, is there evidence that MMN size can predict

and 0.28, respectively）. Therefore, the development of

who will develop psychosis? The first report5）that this may

interventions that increase MMN amplitude is an important

be the case used a Cox regression model to demonstrategoal for clinical researchers.

that MMN to duration deviants（but not frequency devi-

NMDAR deficiency has been implicated in the cogni-ants）predicted who will develop psychosis, mostly with ative deficits and symptoms of schizophrenia. A direct com-schizophrenia diagnosis. A later report71）demonstrated thatparison of the effects of ketamine, an NMDAR antagonist,

the size of the MMN elicited by a double deviant differingand amphetamine, a DA agonist, administered to healthy

in duration and pitch from standards not only predictedvolunteers revealed that only ketamine produced cognitive

who will develop, but also the time from ERP assessment

deficits and negative symptoms that mimic those exhibitedto psychosis onset in clinically at‒risk individuals. In sum-50）

by schizophrenia patients . This and other evidence chal-mary, there is evidence that the degree of MMN amplitudelenged the dominant model of schizophrenia as being due

reduction in at‒risk individuals is useful as a prognostic

to a dopaminergic imbalance. Ketamine was also demon-

biomarker in predicting those with low‒level psychotic

94）

strated to reduce MMN in healthy volunteers . Further-

symptoms who will develop a schizophrenic disorder, butmore, the amplitude of MMN prior to ingestion predicted

the same deficiencies that limit the usefulness of MMN as a95）

the level of psychotic experiences induced by ketamine .

diagnostic biomarker also apply to MMN as a prognostic

MMN therefore indexes the degree to which the NMDAr

marker, i. e., the lack of standardization of methods andsystem is vulnerable to disruption by NMDAr antagonists

normative data.

and may reflect the functional state of the glutamate
NMDAr neurotransmission. Thus, reduced MMN in

MMN as a biomarker of treatment outcome

schizophrenia has been touted as a marker of aberrant glutamatergic neurotransmission67）.

It has been argued that one of the most promisingareas for investment in schizophrenia biomarkers is in pre-MMN as a prognostic biomarker

dicting treatment response‒not only existing therapies, but
most importantly for developing novel therapies99）. There

Individuals can be at‒risk of developing schizophrenia

are a number of characteristics of MMN that are relevant toeither on the basis of genetic risk（having a first‒degree rel-the issue of treatment effects. First, does MMN in patients

ative with the disorder）or on the basis of sub‒thresholdrespond to current neuroleptic therapies? The answer is inclinical symptoms（low‒level symptoms below the thresh-

general no. Neither first nor second generation neuroleptics

old for a diagnosis of a psychotic disorder accompanied byameliorate MMN deficits in schizophrenia patients. The

a decline in social or occupational functioning）, referred to

one exception is aripiprazole, a relatively new antipsychot-as a clinically defined high‒risk group. There is mixed evi-

ic with a unique receptor binding profile that combines

dence on whether first‒degree relatives exhibit reduced

partial agonistic activity at D2 receptors and 5‒HT 1A

MMN with early evidence suggesting that MMN is as

receptors, and potent antagonism at 5‒HT 2A receptors104）.

reduced in first‒degree relatives as in diagnosed patients58）,

After 4 weeks of treatment with aripiprazole, MMN ampli-

but the most recent meta‒analysis

14）

based on 8 separate

tudes to both frequency and duration deviants improved

reports suggested that MMN in this group is quantitatively

with further improvement after 8 weeks104）. In addition,（EF＝0.26）, but not significantly, reduced. Evidence forincreased MMN was accompanied by reduced positive,

reduced MMN in clinically defined high‒risk individuals

negative and total PANSS ratings, and correlated with

Michie・他：Mismatch Negativity（MMN）as a Promising Translational Neurophysiological Biomarker in Schizophrenia829decreased total PANSS ratings, suggesting that MMN

improved pharmacotherapies for patients, primarily

improvement is a possible biomarker of treatment effica-

because human studies are limited in terms of probing the

104）

cy

. These results contrast with the lack of effects of oth-biological mechanisms of disrupted MMN generation.

er atypical antipsychotics, such as clozapine, risperidone,

Hence, the need for translational studies of MMN reduc-and olanzapine, on MMN（reviewed in104））.

tion in animal models, which is the focus of the remainder

Second, is there evidence that novel treatments areof this review.

effective in increasing MMN in patients? There have beenreports of heightened MMN with novel pharmacotherapies

MMN as a translational biomarker inthat target glutamatergic neurotransmission. Administra-schizophrenia

tion of the glutathione precursor N‒acetyl‒cysteine（NAC）
as adjunctive therapy to standard neuroleptic medication

Evaluation of translational biomarkers in schizophre-significantly increased MMN in schizophrenia patientsnia assumes that a valid animal model of this complex het-

51）

after 6 weeks of treatment . In addition, glycine, whicherogeneous psychiatric disorder can be developed. The fact

functions as a co‒agonist with glutamate of NMDA recep-that schizophrenia is a uniquely human disorder has com-

tor activity in excitatory glutamatergic neurotransmission,plicated the development of suitable animal models. Ani-

again administered as adjunctive therapy, increased MMN

mal homologues of positive and negative symptoms, such

24）

acutely in patients, but not after 6 weeks of treatment .

as hallucinations, delusions, alogia, or affective flattening,

Similar acute effects of a high dose of memantine, a mod-are clearly problematic with behavioral assessments uti-

erate affinity NMDAr antagonist, delivered as adjunctive

lized as animal equivalents of positive45） and negative12）

therapy, significantly increased MMN in patients, althoughsymptoms requiring further validation. In recent years,

the degree of enhancement was smaller in patients than in

there has been a greater focus on developing animal mod-85）

healthy subjects . Although the memantine effects appear

els that mimic the cognitive deficits that patients exhibit.

paradoxical considering its NMDAr antagonism, it has

However, this approach also has limitations because of dif-been used as adjunctive therapy in schizophrenia to

ficulties in mapping particular human cognitive constructs.

46）

improve cognition, although with limited success .

For example, working memory is usually assessed inThird, is there any evidence that MMN can predict thehumans by archetypal tasks such as digit span backwards

clinical response to current therapies? This important ques-or the n‒back task. In animal models, considering the con-tion is infrequently investigated. A relatively old but pre-straints imposed by species differences in terms of shaping

79）

on a three‒year follow‒up of clinical（instructing）and measuring behavior, there is uncertainty

response to clozapine treatment in patients found thatabout whether the selected animal task targets the same

MMN amplitude measured prior to clozapine therapy com-construct in the animal and human. The advantage of using

mencement is related to treatment response, although per-an electrophysiological measure, such as MMN, is that it

haps not in the direction expected：a relatively intact

can be measured in exactly the same manner in animals

liminary report

79）

MMN was associated with a better prognosis . The inter-and humans using the same stimulus sequences, and thepretation of this result is that a good clinical response tosame methodology for recording and processing data.

clozapine is associated with relatively intact brain func-

There is still an issue of determining whether the animaltioning as indicated by a larger MMN. Although unexpect-

brain can produce the equivalent of an adaptation‒indepen-ed, it nonetheless suggests that MMN has value in clinicaldent mismatch response（MMR）that reflects prediction

decisions about medication choice.

error. This issue is discussed below. First however, we

Can MMN itself be used to develop novel pharmaco-

address the question of what is the primary impetus fortherapies? Although reduced MMN in schizophrenia wasmoving into animal models of this complex neuropsychiat-

first observed approximately 30 years ago, it has not led toric disorder.

830

精神経誌（2021）第 123 巻 第 12 号

1．Whydoweneedanimalmodelsofschizophrenia?

that leads to MMN reduction. Among other species,

Although animal model studies provide evidence forrodents are well‒suited for preclinical studies due to theirwhat is changed, and how it is changed in schizophrenia,

small size, easy handling, and faster breeding cycles, which

they can more importantly shed light on mechanisms of

makes them cost‒efficient models. In general, rats are often

how symptoms or behaviors are induced, and how suchthe preferred species for behavioral and neuroscience

outcomes can be ameliorated or treated. For example,

research because of their relatively larger brains, but micealthough the glutamatergic hypothesis of schizophrenia

are more suited to molecular studies 13）. Initial studies

was triggered by the clinical observations that NMDAr

observed MMN‒like ERPs 63‒253 ms post stimulus onset,

antagonists, such as phencyclidine（PCP）, or ketamine canrecorded epidurally from the auditory cortex of anesthe-induce a syndrome that resembles schizophrenia in

tized rats76）. In addition, dependence of MMN generation

humans10,42,49,50,93）, it was animal model studies that pro-on the presence of standard tones in a sequence was foundvided evidence for the mechanisms underlying thesein anesthetized rats using DEV‒alone condition, where

effects using pharmacological and genetic approach-human‒like MMN was not observed in response to DEV

33,52,72）. Another example of the significance of animal

tones in the DEV‒alone paradigm vs an oddball paradigm,

models is demonstrated by the treatment studies performed

consistent with human studies demonstrating the depen-with compounds acting directly at the NMDAR glycine

dence of human MMN on the echoic trace76）. In anothersite, such as glycine, D‒serine, or D‒cycloserine, and,study on urethane‒anesthetized rats, an MMN‒likemore recently, with high‒affinity glycine transport inhibi-

response was observed at 60‒100 ms in response to ascend-

es

42）

tors . A significant improvement in schizophrenia‒like

ing DEV from stimulus onset during an auditory oddballnegative symptoms in a NMDAR antagonist rat model

sequence 2）. Several studies demonstrated MMN‒likeusing dizocilpine（MK‒801）was demonstrated in the pres-responses

inanesthetized

rats1～3） andawake

27,30,68,74,84）

ence of D‒serine or by blocking glycine transporter‒1 thatrats

stimulates the NMDAr function directly or indirectly,

the presence of MMN‒like responses in both anesthetized

respectively44）. Administration of D‒cycloserine, a co‒ago-

and awake rats.

nist at the glycine site on the NMDAr, attenuated attentional deficits in rats treated with MK‒801

32）. Overall, there is strong evidence to support

A methodological feature making MMN a valuable. However,candidate for translation into animals is that the MMN inalthough the current gold standard method for evaluating

humans is elicited without instructing the participant to

the face validity of“schizophrenia‒ness”of a given animal

attend to the stimuli. Similarly, the recording of mismatchmodel is behavioral, behavioral measurements are limited

responses（MMR）in animals does not require the activein their ability to translate animal model outcomes toparticipation of the animal（no prior training is required）,

18）

humans . Therefore, recent attention has turned to search-

provided the animal can discriminate the sounds in theing for biomarkers of schizophrenia that can be measured

sequences that are used to generate MMR. Second, thein a similar manner in rodents and humans.

auditory patterns that elicit MMN can be simple, for example, auditory stimuli of different tone frequencies, as fre-

2．AnimalmodelsofMMN

quency discrimination thresholds have been well estab-

Due to clinical relevance of the MMN for evaluating

lished in rodents87）. However, the face validity of an animal

normal and pathological auditory processing, the search formodel of MMN needs to be established：how does a rat

evidence of MMN in several mammalian species, including

MMR resemble human MMN? For preclinical research, it

8）

48）

38）

76）

98）

cats , guinea pigs , monkeys , rats , and mice , isis essential to show the adaptation‒independent element ofunderway in order to facilitate further understanding of thehuman MMN in rodents. Harms and colleagues provided

neurophysiological and neurochemical mechanisms under-

evidence that the rat brain is capable of generating human‒lying MMN generation, and for exploring the mechanism

like MMN, at least in terms of having some element ofMichie・他：Mismatch Negativity（MMN）as a Promising Translational Neurophysiological Biomarker in Schizophrenia

831

adaptation‒independent prediction error（a criteria of
29,68）

To overcome these issues, the cascade control, initially, by employing three different control

proposed by Schroger’s group75）, was used by Harms andsequences―1. the flip‒flop control（to control for differen-colleagues. In the cascade control, stimuli are presented

tial responses to the physical characteristics of standards

varying from low to high frequency in a regular pattern,

and deviants as in35））；2. the many‒standards control（towith the highest frequency stimulus corresponding to thehuman MMN）

35）

control for differential adaptation as in ）, and 3. the cas27）

deviant and the second highest frequency stimulus corre-cade control（to control for differential adaptation） . In

sponding to the standard within an ascending oddball

the flip‒flop oddball sequences, two oddball sequences aresequence（Figure 3C, D）27）. Unexpectedly, there were

present such that the identity of the standard and deviant

inconclusive results in the cascade control design, possibly

reverse, which means that a given tone frequency appears

because rats may be unable to encode the complex pattern

as standard in one sequence and a deviant in the otherof the ascending and descending arms of the cascade con-sequence（Figure 3A）. Use of the flip‒flop sequences

trol sequence. However, other researchers recording single

enables the control of the physical characteristics of theunit and multiple unit activity in oddball paradigms fromdeviant and standard stimuli. In the many‒standards con-auditory brain regions found no difference in outcomestrol sequence, the equivalent of the deviant tone from thebetween the cascade control and the many‒standards con-oddball sequence is presented among other equiprobable

trol69,70）, although only ascending or descending cascade

tones equal to the probability of the deviant in the oddballsequences were used. Therefore, adaptation‒independent

sequence and the tones are presented pseudo‒randomly

deviance detection was only evident for the many‒stan-such that no pattern of regularity is established（Figure

dards control sequence, not for the cascade sequence in27）

3B）. Thus, no prediction can be set due to lack of regulari-（Figure 4）. As shown in Figures 4A and 4B, larger ERPs

ty and no violation can occur. A comparison of theare generated in response to high frequency deviants, but

responses to the deviant in the oddball sequence to the

not low frequency deviants, suggesting the lack of effect onsame sound at the same probability of occurrence in thedescending deviants27）. Studies conducted in our laborato-

many‒standards control sequence excludes the explanationry68）and other groups7,26,43,83,84）demonstrated that rodents

that lower levels of adaptation of neural populations

exhibit an adaptation‒independent MMR, similar to human

responding to the deviant are responsible for the larger

MMN, which can even be observed in local recordings

response to a deviant stimulus. Instead, this comparison

from multiple auditory processing regions69）. Of note, ityields a measure of adaptation‒independent deviancehas only recently been demonstrated that this adaptation‒

27,69）

independent component of human MMN is reduced in

detection or prediction error. However, the many‒stan-dards control has been subjected to two criticisms. First, as

patients with schizophrenia47）.

the many‒standards sequence contains stimuli from a
broad frequency range, larger responses are produced than

3．Do animal mismatch responses（MMRs）exhibitwith those from a narrower range, as the deviant responsefeatures of human MMN?

from oddball undergoes more adaptation（thus, reduced

To establish the face validity of an animal model ofsize amplitude）than the deviant response to the many‒stan-

MMN, there are simple requirements, the first being that adards control, which is not affected much by adaptation

deviant response＞standard response. To date, animal stud-（thus, increased in amplitude）. Overall, this potential

ies have reported this, as described above, although not for

imbalance may result in an underestimation of deviancelow‒frequency deviants in our laboratory. Some of the key

detection. Second, the oddball sequence establishes afeatures of human MMN are adaptation independence（asrepetitive and predictable sequence that is violated, but thedescribed above）, sensitivity to the NMDAR perturbation,

many‒standards control, by design, does not violate a pre-sensitivity to deviance difference, sensitivity to deviant

diction because one is never established.

probability, and sensitivity to environmental uncertainty

832

精神経誌（2021）第 123 巻 第 12 号

A. Oddball Sequences-Flip-Flop Design
Ascending

Stimulus
type

Presentationrate（P ）Frequency（Hz）deviant
standard

0.125
0.875

8137
6636

standard
deviant

0.875
0.125

8137
6636

control
control

0.125
0.125
0.125
0.125
0.125
0.125
0.125
0.125

15000
12233
9977
8137
6636
5412
4414
3600

deviant
standard

0.125
0.875

15000
12233

standard
deviant

0.875
0.125

8137
6636

control

0.125
0.250
0.250
0.250
0.125

15000
12233
9977
8137
6636

Descending

B. Control Sequence－Many－Standards Design

C. Oddball sequences-Cascade Design
Ascending

Descending

D. Control Sequence-Cascade Design

control
1

2

3

4

5

Figure 3

67

8 Seconds

Design of control sequences

A：Flip—flopoddballsequence,whichenablesthecomparisonoftheresponsetoastimuluswhenitisaDEVorwhenthesametoneisaSTD,thusthisdesigncontrolsforthedifferentialresponsestothephysicalcharacteristicsofthestimuli.
B：Many—standardscontrolsequenceenablesmeasurementofadaptation—independentdeviancedetection.
C：Oddballsequenceforthecascadedesign.
D：Cascadecontrolsequence.Adaptedfrom27）withpermission.

（‘jitter’）. In order to establish the face validity of an ani-ketamine and phencyclidine25,41,42,88,93）. Human MMN

mal model of MMN, there is a need to determine how wellamplitude increases in response to the higher degree ofthe animal model exhibits these features. Studies havedeviance difference between the standard and the deviant

demonstrated consistent reductions in the MMN amplitude

stimuli, lower probability of occurrence of the deviant, andin humans in the presence of NMDAr antagonists such asa stable stimulus onset asynchrony（SOA）in between theMichie・他：Mismatch Negativity（MMN）as a Promising Translational Neurophysiological Biomarker in Schizophrenia

833

A.

μV

B.

－20

－10

N55
N18

0

μV

－20

N85

0

High Deviant

P30

－100

－50

50

100

150

200

－100

20

－50

High Standard
50

0

100

200

150

Time（ms）

Time（ms）

C.

D.

Oddball effectNo signiﬁcant effects

Oddball effectDeviance-

DevianceNo signiﬁcant effects

detection effectdetection effect

Adaptation

Adaptation
No signiﬁcant effects

effect
－20

－20

－15

－15

－10
－5
0
Low Deviant
5
10

Figure 4

Low Control
Low Standard
P13

N18

P30

N55

N85

＊

＊＊

＊＊＊

＊

＊

effectMean Amplitude（μV）

Mean Amplitude（μV）

High Control

P30High Standard
0

High Deviant

10 P13High Control
20

N85

N18

－10

P13

10

N55

＊＊

＊

＊＊＊

＊＊＊

＊＊＊

－10
－5
0
High Deviant
5
10High Control
High Standard
P13

P18

P30

P55

P85

Rat event related potentials（ERPs）demonstrating effects of stimulus type and frequency

Averaged ERPs in response to deviant（red）,the many—standards control（black）, and the standard（blue）for the low—（A）and high—（B）frequency stimuli. Mean amplitudes（±standard error, SE）of MMR components to low—（C）and
high—（D）frequency stimuli. Adapted from27）with permission.

two auditory stimuli（reviewed in 36））. Several studies

evidence of the sensitivity of the rodent MMR to thereported that NMDAr hypofunction also reduces MMRs indegree of deviance, DEV probability, and environmental

rodents

11,17,28,30,70,84,90,91）. We found that the hypothesized

uncertainty.

NMDAr antagonism exclusively affects the N55 MMR

Considering the growing research interest in thecomponent. There was a reduction in the size of MMR

rodent MMRs as a translatable‘biomarker’for schizophre-

amplitude for late negative potential（N55）at the highest

nia, it is important to examine how reliably rat MMRs

dose of the antagonist, but not on an early positive poten-

mimic the attributes of human MMN. An extensive review

tial（P13）（Figure 5）. This suggests that N55 is the moston how well the MMN features recapitulate human fea-

MMN‒like component30）. However, there is only limited

tures in the studies of rodent MMRs is presented in36）.

834

精神経誌（2021）第 123 巻 第 12 号

N18

－20

A.

－10

－20

0
Deviance Detection（μV）

N55
－10

10

N85

20

P30
10

20

30

40

P13
0
0.0 mg/kg
0.1 mg/kg
0.3 mg/kg

10

0.5 mg/kg

20
－50

10

50

100

150

200

Time（ms）

Deviance Detection（μV）

B. P13－5

C. N18＊0

D. P30－8

E. N55

0

－8

2

5

－2

0

0.1 0.3 0.5

0

－4

0

4

－2

－2

2
0

0.1 0.3 0.5

60

0.1 0.3 0.5

4

－8
－6

－4

－4
10

20

＊

－6

－6

15

F. N850

0.1 0.3 0.5

00

0.1 0.3 0.5

NK－801 Dose（mg/kg）

Figure 5

NMDAr antagonism effects on the MMR amplitude

A：Grand—averageddeviancedetection（deviant—control）differencewavesinthedrug—freecondition（black）,andafterescalatingdosesof0.1（blue）,0.3（green）,and0.5mg／kgMK—801（red）.
（B—F）：Graphsshowtheextracteddeviancedetectiondifferenceamplitudes（mean±SEM）foreachofthecomponentsmeasured［P13（B）,N18（C）,P30（D）,N55（E）,andN85（F）］overincreasingMK—801doses30）.

Although there are increasing preclinical studies on

ability of occurrence of deviant tone among regular stan-

MMRs, some gaps in understanding remain. To fill some ofdards, and 3. stable SOA（no jitter）. Screw electrodes werethese gaps, our laboratory used a rat model of MMN tosurgically‒implanted epidurally, and using wireless telem-

determine the degree to which the rat brain can recapitulate

etry in freely‒moving rats, we observed human‒like alter-features of MMN found in healthy human controls. We

ations of MMRs36）. Rat MMRs decreased in size with aasked whether MMRs in rats exhibit features of human

reduced degree of difference between the deviant and stan-

MMN, such as larger MMR amplitude, in response to 1. adard stimuli, with increased probability of the deviant stim-higher degree of deviance difference between the regularuli, and with increased variability in the SOA of soundstandard tones and surprising deviant tones, 2. lower prob-

sequences（Figure 6）. This suggested that N54 MMR com-

Michie・他：Mismatch Negativity（MMN）as a Promising Translational Neurophysiological Biomarker in Schizophrenia

835Mismatch responses to Pitch difference（Deviant-Standard）
A.

B.
MMR amplitude（μV）

Amplitude（μV）

N18

－20

N54
N86

0
20
40
－100

Low Dev diff

－40

－40

P32
－50

0

50

100

150

High Dev diff

－20

＊
＊

0
＊

20
40

200

Mid Dev diff

＊
＊

N18

＊
＊

P32

N54

N86

Time（ms）
Mismatch responses to Deviant Probability（Deviant－Standard）
C.

D.

－20

MMR amplitude（μV）

Amplitude（μV）

－30

－10
0
10
20
30
－100

－50

0

50

100

150

－30

Low Probability－20High Probability

－10
＊

0

＊

10

＊

20
30

200

Mid ProbabilityTime（ms）

N18

P32

N54

N86

Mismatch responses under Jitter conditions（Deviant-Standard）
F.
MMR amplitude（μV）

Amplitude（μV）

E.
－5

0

5

－100

－50

0

50

100

150

200

－5

0
＊

No Jitter
5

Low Jitter
High Jitter
N18

P32

N54

N86

Time（ms）

Figure 6

Effects of deviance difference, deviant probability, and jitter on event—related potentials averaged over auditory
and frontal cortex of the control male and female rats

（A—B）：
  MMRstopitchdifference.A.MMRwaveform.Differencewaveobtainedbysubtractingtheresponsestostandardstimulus
fromtheresponsestothedeviantstimulusforlow（blue）,mid（grey）,andhigh（red）deviancedifferences.B.MMRmean
amplitude.Meanamplitudes（±standarderror,SE）ofN18,P32,N54,andN86componentsofratMMRs.ThereisasignificantlylargerMMRamplitudeinresponsetothehighdeviancedifference（inred）comparedwiththelowdeviancedifference（inblue）ormiddeviancedifference（ingrey）
.
（C—D）
：
  MMRstodeviantprobability.C.MMRwaveform.Differencewaveobtainedbysubtractingtheresponsestostandardstimulus
fromtheresponsestothedeviantstimulusforlow（red）,mid（grey）,andhigh（blue）probability.D.MMRmeanamplitude.Meanamplitudes（±standarderror,SE）ofN18,P32,N54,andN86componentsofratMMRs.ThereisasignificantprobabilityeffectonN54andP32MMRcomponents.
（E—F）： MMRsunderjitterconditions.E.MMRwaveform.Differencewaveobtainedbysubtractingtheresponsestostandardstimulusfromtheresponsestothedeviantstimulusforno（blue）,low（grey）,andhigh（red）jitter.F.MMRmeanamplitude.Mean
amplitudes（±standarderror,SE）ofN18,P32,N54,andN86componentsofratMMRs.N54MMRamplitudeislargerunder
nojitterconditionsthanunderhighjitterconditions.Adaptedfrom36）withpermission.
836

精神経誌（2021）第 123 巻 第 12 号

Table 1 Summary of findings suggesting N54 MMR
component as the most human—like
MMRcomponents

Deviance
Difference

Deviant
Probability

N18

Y

P32

Y

Y

N54

Y

Y

N86

Y

Jittereffectreviewed earlier demonstrates that there is much that westill do not know about MMN in schizophrenia. Animalmodels are required in order to advance our understandingof the neuro‒circuitry of MMN generation and to provide
knowledge about the neurobiological basis of MMN reduc-Ytion in schizophrenia59）. This knowledge is essential for thedevelopment of novel pharmaceuticals that can rescue theimpairments in function that underlie the reduced amplitude of MMN. As noted earlier, a 1‒μV increase in MMN
amplitude will likely result in improvements in both cogni-

ponent in rats is highly‘MMN‒like’, as it was significant-tion and functional outcome89）.

ly influenced by all 3 alterations（Table 1）36）. The strongest

Conclusion

effects of stimulus type were previously observed in
response to high‒frequency stimuli for the N55 componentof MMR27）. Furthermore, Harms et al. noted MK‒801‒

There are several approaches to creating a rodent

induced N55 MMR amplitude reduction, similar to humanmodel of MMN reduction in schizophrenia, including acute30）

MMN reductions, elicited by NMDAr antagonism . The

pharmacological（NMDAR antagonists）challenge11,30,40,90）

N54 component by Jalewa et al.36）is equivalent to the N55

and genetic manipulations16）, and endogenous causes suchcomponent reported by Harms et al.30）. The different label-as exposure to neurodevelopmental insults associated withling is the result of a naming convention of using the meanschizophrenia risk9）. The purpose of such approaches is notpeak latency across all animals in a study. Overall, theseto recapitulate schizophrenia in an animal model, but tostudies suggest that rodent MMR mimics human MMN

explore the details（the what, why, and how）of the under-

attributes and that N54/N55 is the most‒human like MMN

lying neurobiological mechanisms that result in attenuation

analogue in rats.

of MMN amplitude. Although we have learnt much fromacute pharmacological challenges based on NMDAR

4．What are the implications of animal MMRs forantagonism, it is likely that a model based on endogenous

schizophrenia?

causes producing a large, enduring, and reproducible

Although MMN impairment in schizophrenia patients

reduction of MMN will be more informative. It may enable

is one of the most highly replicated neurophysiologicalmultiple approaches, including molecular approaches, to66）

features of the disorder , there are few studies demon-

exploring the causes of MMN reduction, and the develop-

strating that MMN reduction is well‒treated by current

ment of new pharmaceuticals targeting increased MMN

24,104）. Compounds that pass the pre‒based on this knowledge. Pre‒clinical research of this typeclinical stages in developing novel psychiatric treatments

may also offer insights into the optimal paradigms for use

often fail in human clinical trials due to lack of suitablein humans‒are there particular features of the oddball para-animal models of disease with associated biomarkers of

digm that are more likely to generate a reduced MMN in

antipsychotic drugs

neural‒circuit function across species

55,102,103）. Moreover,animal models and why?

examining MMRs in rodents is a fairly new field of
research, and has only been pursued for the past 10 years［Acknowledgements］

or so, with relatively few studies having looked for MMRs

JJ is supported by a University of Newcastle Postgraduate Research
Scholarship funded by the Australian Government’s Research Training Program. Clinical research was funded by the Australian National
Health and Medical Research through project grants #880840,#900663, #960579, and #990701. Pre‒clinical research was funded byin rats or mice, model species for which a wealth of existing knowledge, techniques, and relevant methodologies isavailable 17,90）. Examination of the clinical literature

Michie・他：Mismatch Negativity（MMN）as a Promising Translational Neurophysiological Biomarker in Schizophrenia

837the Australian National Health and Medical Research grants ID
1026070 and ID 1109283.
［Disclosure Statement］

None of the co‒authors have conflicts of interest with any companies or organizations that may influence the study reported here.
［Author Contributions］

PM and JJ wrote the manuscript. JJ prepared Figure 1. All authors
reviewed the content, and contributed to and approved the final manuscript.

References

1）Astikainen, P., Ruusuvirta, T., Wikgren, J., et al.：Memory‒
based detection of rare sound feature combinations in anesthetized rats. Neuroreport, 17（14）；1561‒1564, 2006
2）Astikainen, P., Stefanics, G., Nokia, M., et al.：Memory‒based
mismatch response to frequency changes in rats. PLoS One, 6（9）；e24208, 20113）Astikainen, P., Mällo, T., Ruusuvirta, T., et al.：Electrophysiological evidence for change detection in speech sound patterns
by anesthetized rats. Front Neurosci, 8；374, 20144）Belger, A., Yucel, G. H., Donkers, F. C.：In search of psychosis
biomarkers in high‒risk populations：is the mismatch negativity the one we’ve been waiting for? Biol Psychiatry, 71（2）；94‒
95, 2012
5）Bodatsch, M., Ruhrmann, S., Wagner, M., et al.：Prediction of
psychosis by mismatch negativity. Biol Psychiatry, 69（10）；959‒966, 2011
6）Catts, S. V., Shelly, A. M., Ward, P. B., et al.：Brain potential
evidence for an auditory sensory memory deficit in schizophrenia. Am J Psychiatry, 152（2）；213‒219, 1995
7）Chen, I. W., Helmchen, F., Lütcke, H.：Specific early and late
oddball‒evoked responses in excitatory and inhibitory neuronsof mouse auditory cortex. J Neurosci, 35（36）；12560‒12573,
20158）Csepe, V., Karmos, G., Molnar, M.：Evoked potential correlatesof stimulus deviance during wakefulness and sleep in cat：animal model of mismatch negativity. Electroencephalogr Clin
Neurophysiol, 66（6）；571‒578, 1987
9）Dunn, A. L., Michie, P. T., Hodgson, D. M., et al.：Adolescentcannabinoid exposure interacts with other risk factors in schizophrenia：a review of the evidence from animal models. Neurosci Biobehav Rev, 116；202‒220, 202010）Egerton, A., Grace, A. A., Stone, J., et al.：Glutamate in schizophrenia：neurodevelopmental perspectives and drug development. Schizophr Res, 223；59‒70, 202011）Ehrlichman, R. S., Maxwell, C. R., Majumdar, S., et al.：Deviance‒elicited changes in event‒related potentials are attenuatedby ketamine in mice. J Cogn Neurosci, 20（8）；1403‒1414,
200812）Ellenbroek, B. A., Cools, A. R.：Animal models for the negative

838

symptoms of schizophrenia. Behav Pharmacol, 11（3‒4）；223‒
233, 2000
13）Ellenbroek, B., Youn, J.：Rodent models in neuroscience
research：is it a rat race? Dis Model Mech, 9（10）；1079‒1087,
201614）Erickson, M. A., Ruffle, A., Gold, J. M.：A meta‒analysis ofmismatch negativity in schizophrenia：from clinical risk to disease specificity and progression. Biol Psychiatry, 79（12）；980‒
987, 201615）Escera, C., Alho, K., Winkler, I., et al.：Neural mechanisms of
involuntary attention to acoustic novelty and change. J Cogn
Neurosci, 10（5）；590‒604, 199816）Featherstone, R. E., Shin, R., Kogan, J. H., et al.：Mice withsubtle reduction of NMDA NR1 receptor subunit expression
have a selective decrease in mismatch negativity：implications
for schizophrenia prodromal population. Neurobiol Dis, 73；289‒295, 201517）Featherstone, R. E., Melnychenko, O., Siegel, S. J.：Mismatch
negativity in preclinical models of schizophrenia. Schizophr
Res, 191；35‒42, 201818）Forrest, A. D., Coto, C. A., Siegel, S. J.：Animal models of psychosis：current state and future directions. Curr Behav Neurosci Rep, 1（2）；100‒116, 201419）Friston, K.：Learning and inference in the brain. Neural Netw,16（9）；1325‒1352, 200320）Fulham, W. R., Michie, P. T., Ward, P. B., et al.：Mismatch negativity in recent‒onset and chronic schizophrenia：a current
source density analysis. PLoS One, 9（6）；e100221, 201421）Garrido, M. I., Kilner, J. M., Stephan, K. E., et al.：The mismatch negativity：a review of underlying mechanisms. Clin
Neurophysiol, 120（3）；453‒463, 200922）Green, M. F.：What are the functional consequences of neurocognitive deficits in schizophrenia? Am J Psychiatry, 153（3）；321‒330, 199623）Green, M. F.：Cognitive impairment and functional outcome inschizophrenia and bipolar disorder. J Clin Psychiatry, 67（10）；e12, 200624）Greenwood, L. M., Leung, S., Michie, P. T., et al.：The effects
of glycine on auditory mismatch negativity in schizophrenia.
Schizophr Res, 191；61‒69, 201825）Gunduz‒Bruce, H., Reinhart, R. M. G., Roach, B. J., et al.：Glutamatergic modulation of auditory information processing in
the human brain. Biol Psychiatry, 71（11）；969‒977, 201226）Hamm, J. P., Yuste, R.：Somatostatin interneurons control a key
component of mismatch negativity in mouse visual cortex. Cell
Rep, 16（3）；597‒604, 201627）Harms, L., Fulham, W. R., Todd, J., et al.：Mismatch negativity（MMN）in freely‒moving rats with several experimental controls. PLoS One, 9（10）；e110892, 201428）Harms, L.：Mismatch responses and deviance detection in N‒
methyl‒D‒aspartate（NMDA）receptor hypofunction and developmental models of schizophrenia. Biol Psychol, 116；75‒81,201629）Harms, L., Michie, P. T., Näätänen, R.：Criteria for determining
whether mismatch responses exist in animal models：focus on

精神経誌（2021）第 123 巻 第 12 号

rodents. Biol Psychol, 116；28‒35, 201630）Harms, L., Fulham, W. R., Todd, J., et al.：Late deviance detection in rats is reduced, while early deviance detection is augmented by the NMDA receptor antagonist MK‒801. Schizophr
Res, 191；43‒50, 201831）He, J.：Long‒latency neurons in auditory cortex involved in
temporal integration：theoretical analysis of experimental data.
Hear Res, 121（1‒2）；147‒160, 199832）Howe, W. M., Burk, J. A.：Dizocilpine‒induced accuracy deficits in a visual signal detection task are not present followingD‒cycloserine administration in rats. Eur J Pharmacol, 577（1‒3）；87‒90, 2007
33）Inta, D., Monyer, H., Sprengel, R., et al.：Mice with geneticallyaltered glutamate receptors as models of schizophrenia：a comprehensive review. Neurosci Biobehav Rev, 34（3）；285‒294,
201034）Jääskeläinen, I. P., Ahveninen, J., Bonmassar, G., et al.：Human
posterior auditory cortex gates novel sounds to consciousness.
Proc Natl Acad Sci U S A, 101（17）；6809‒6814, 200435）Jacobsen, T., Schröger, E.：Measuring duration mismatch negativity. Clin Neurophysiol, 114（6）；1133‒1143, 200336）Jalewa, J., Todd, J., Michie, P. T., et al.：Do rat auditory event
related potentials exhibit human mismatch negativity attributes
related to predictive coding? Hear Res, 399；107992, 2021
37）Jasper, H.：Report of the committee on methods of clinicalexamination in electroencephalography. Clin Neurophysiol, 112（4）；713‒719, 195838）Javitt, D. C., Schroeder, C. E., Steinschneider, M., et al.：Demonstration of mismatch negativity in the monkey. Electroencephalogr Clin Neurophysiol, 83（1）；87‒90, 1992
39）Javitt, D. C., Doneshka, P., Zylberman, I., et al.：Impairment ofearly cortical processing in schizophrenia：an event‒related
potential confirmation study. Biol Psychiatry, 33（7）；513‒519,
199340）Javitt, D. C., Steinschneider, M., Schroeder, C. E., et al.：Roleof cortical N‒methyl‒D‒aspartate receptors in auditory sensory
memory and mismatch negativity generation：implications forschizophrenia. Proc Natl Acad Sci U S A, 93（21）；11962‒
11967, 199641）Javitt, D. C.：Intracortical mechanisms of mismatch negativity
dysfunction in schizophrenia. Audiol Neurootol, 5（3‒4）；207‒215, 2000
42）Javitt, D. C., Zukin, S. R., Heresco‒Levy, U., et al.：Has anangel shown the way? Etiological and therapeutic implicationsof the PCP/NMDA model of schizophrenia. Schizophr Bull, 38（5）；958‒966, 201243）Jung, F., Stephan, K. E., Backes, H., et al.：Mismatch responses
in the awake rat：evidence from epidural recordings of auditory
cortical fields. PLoS One, 8（4）；e63203, 201344）Kawaura, K., Koike, H., Kinoshita, K., et al.：Effects of a glycine transporter‒1 inhibitor and D‒serine on MK‒801‒induced
immobility in the forced swimming test in rats. Behav Brain
Res, 278；186‒192, 201545）Kesby, J. P., Eyles, D. W., McGrath, J. J., et al.：Dopamine, psychosis and schizophrenia：the widening gap between basic andclinical neuroscience. Transl Psychiatry, 8（1）；30, 201846）Kishi, T., Ikuta, T., Oya, K., et al.：Anti‒dementia drugs for psychopathology and cognitive impairment in schizophrenia：asystematic review and meta‒analysis. Int J Neuropsychopharmacol, 21（8）；748‒757, 201847）Koshiyama, D., Kirihara, K., Tada, M., et al.：Reduced auditory
mismatch negativity reflects impaired deviance detection inschizophrenia. Schizophr Bull, 46（4）；937‒946, 202048）Kraus, N., McGee, T., Littman, T., et al.：Nonprimary auditory
thalamic representation of acoustic change. J Neurophysiol, 72（3）；1270‒1277, 1994
49）Krystal, J. H., D’Souza, D. C., Mathalon, D., et al.：NMDA
receptor antagonist effects, cortical glutamatergic function, andschizophrenia：toward a paradigm shift in medication development. Psychopharmacology（Berl）, 169（3‒4）；215‒233, 2003
50）Krystal, J. H., Perry, E. B. Jr., Gueorguieva, R., et al.：Comparative and interactive human psychopharmacologic effects ofketamine and amphetamine：implications for glutamatergic anddopaminergic model psychoses and cognitive function. ArchGen Psychiatry, 62（9）；985‒994, 2005
51）Lavoie, S., Murray, M. M., Deppen, P., et al.：Glutathione precursor, N‒acetyl‒cysteine, improves mismatch negativity inschizophrenia patients. Neuropsychopharmacology, 33（9）；2187‒2199, 2008
52）Lee, G., Zhou, Y.：NMDAR Hypofunction Animal Models of
Schizophrenia. Front Mol Neurosci, 12；185, 201953）Light, G. A., Braff, D. L.：Mismatch negativity deficits areassociated with poor functioning in schizophrenia patients. ArchGen Psychiatry, 62（2）；127‒136, 2005
54）Light, G. A., Braff, D. L.：Stability of mismatch negativity deficits and their relationship to functional impairments in chronic
schizophrenia. Am J Psychiatry, 162（9）；1741‒1743, 2005
55）Light, G. A., Näätänen, R.：Mismatch negativity is a breakthrough biomarker for understanding and treating psychotic disorders. Proc Natl Acad Sci U S A, 110（38）；15175‒15176,
201356）Light, G. A., Swerdlow, N. R., Thomas, M. L., et al.：Validationof mismatch negativity and P3a for use in multi‒site studies ofschizophrenia：characterization of demographic, clinical, cognitive, and functional correlates in COGS‒2. Schizophr Res,
163（1‒3）；63‒72, 201557）Michie, P. T., Budd, T. W., Todd, J., et al.：Duration and frequency mismatch negativity in schizophrenia. Clin Neurophysiol, 111（6）；1054‒1065, 2000
58）Michie, P. T., Innes‒Brown, H., Todd, J., et al.：Duration mismatch negativity in biological relatives of patients with schizophrenia spectrum disorders. Biol Psychiatry, 52（7）；749‒758,
200259）Michie, P. T., Malmierca, M. S., Harms, L., et al.：The neurobiology of MMN and implications for schizophrenia. Biol Psychol, 116；90‒97, 201660）Näätänen, R., Gaillard, A. W., Mantysalo, S.：Early selective‒
attention effect on evoked potential reinterpreted. Acta Psychol（Amst）, 42（4）；313‒329, 1978
61）Näätänen, R., Michie, P. T.：Early selective‒attention effects on

Michie・他：Mismatch Negativity（MMN）as a Promising Translational Neurophysiological Biomarker in Schizophrenia

839

the evoked potential：a critical review and reinterpretation. Biol
Psychol, 8（2）；81‒136, 1979
62）Näätänen, R., Alho, K.：Mismatch negativity：a unique measure of sensory processing in audition. Int J Neurosci, 80（1‒4）；317‒337, 1995
63）Näätänen, R., Jacobsen, T., Winkler, I.：Memory‒based orafferent processes in mismatch negativity（MMN）：a review ofthe evidence. Psychophysiology, 42（1）；25‒32, 200564）Näätänen, R., Paaviliainen. P., Rinne, T., et al.：The mismatch
negativity（MMN）in basic research of central auditory processing：a review. Clin Neurophysiol, 118（12）；2544‒2590, 2007
65）Näätänen, R., Sussman, E. S., Salisbury, D., et al.：Mismatch
negativity（MMN）as an index of cognitive dysfunction. Brain
Topogr, 27（4）；451‒466, 201466）Näätänen, R., Todd, J., Schall, U.：Mismatch negativity（MMN）as biomarker predicting psychosis in clinically at‒risk
individuals. Biol Psychol, 116；36‒40, 2016
67）Nagai, T., Kirihara, K., Tada, M., et al.：Reduced Mismatch
Negativity is Associated with Increased Plasma Level of Glutamate in First‒episode Psychosis. Sci Rep, 7（1）；2258, 2017
68）Nakamura, T., Michie, P. T., Fulham, W. R., et al.：Epidural
auditory event‒related potentials in the rat to frequency andduration deviants：evidence of mismatch negativity? Front Psychol, 2；367, 201169）Parras, G. G., Nieto‒Diego, J., Carbajal, G. V., et al.：Neurons
along the auditory pathway exhibit a hierarchical organizationof prediction error. Nat Commun, 8（1）；2148, 201770）Parras, G. G., Valdés‒Baizabal, C., Harms, L., et al.：The effectof NMDA‒R antagonist, MK‒801, on neuronal mismatch along
the rat auditory thalamocortical pathway. Sci Rep, 10（1）；12391, 2020
71）Perez, V. B., Woods, S. W., Roach, B. J., et al.：Automatic auditory processing deficits in schizophrenia and clinical high‒risk
patients：forecasting psychosis risk with mismatch negativity.
Biol Psychiatry, 75（6）；459‒469, 201472）Ramsey, A. J.：NR1 knockdown mice as a representative modelof the glutamate hypothesis of schizophrenia. Prog Brain Res,
179；51‒58, 200973）Rasser, P. E., Schall, U., Todd, J., et al.：Gray matter deficits,
mismatch negativity, and outcomes in schizophrenia. Schizophr
Bull, 37（1）；131‒140, 201174）Roger, C., Hasbroucq, T., Rabat, A., et al.：Neurophysics oftemporal discrimination in the rat：a mismatch negativity study.
Psychophysiology, 46（5）；1028‒1032, 200975）Ruhnau, P., Herrmann, B., Schröger, E.：Finding the right control：the mismatch negativity under investigation. Clin Neurophysiol, 123（3）；507‒512, 201276）Ruusuvirta, T., Penttonen, M., Korhonen, T.：Auditory corticalevent‒related potentials to pitch deviances in rats. Neurosci
Lett, 248（1）；45‒48, 1998
77）Saarinen, J., Paavilainen, P., Schöger, E., et al.：Representationof abstract attributes of auditory stimuli in the human brain.
Neuroreport, 3（12）；1149‒1151, 1992
78）Sahin, M., Jones, S. R., Sweeney, J. A., et al.：Discoveringtranslational biomarkers in neurodevelopmental disorders. Nat

840

Rev Drug Discov, 18；235‒236, 201979）Schall, U., Catts, S. V., Karayanidis, F., et al.：Auditory event‒related potential indices of fronto‒temporal information processing in schizophrenia syndromes：valid outcome predictionof clozapine therapy in a three‒year follow‒up. Int J Neuropsychopharmacol, 2（2）；83‒93, 1999
80）Schall, U.：Is it time to move mismatch negativity into the clinic? Biol Psychol, 116；41‒46, 201681）Schmidt, A., Diaconescu, A. O., Kometer, M., et al.：Modelingketamine effects on synaptic plasticity during the mismatch
negativity. Cereb Cortex, 23（10）；2394‒2406, 201382）Shelley, A. M., Ward, P. B., Catts, S. V., et al.：Mismatch negativity：an index of a preattentive processing deficit in schizophrenia. Biol Psychiatry, 30（10）；1059‒1062, 1991
83）Shiramatsu, T. I., Kanzaki, R., Takahashi, H.：Cortical mappingof mismatch negativity with deviance detection property in rat.
PLoS One, 8（12）；e82663, 201384）Sivarao, D. V., Chen, P., Yang, Y., et al.：NR2B antagonist CP‒
101,606 abolishes pitch‒mediated deviance detection in awake
rats. Front Psychiatry, 5；96, 201485）Swerdlow, N. R., Bhakta, S., Chou, H. H., et al.：Memantine
effects on sensorimotor gating and mismatch negativity inpatients with chronic psychosis. Neuropsychopharmacology, 41（2）；419‒430, 201686）Talavage, T. M., Sereno, M. I., Melcher, J. R., et al.：Tonotopic
organization in human auditory cortex revealed by progressionsof frequency sensitivity. J Neurophysiol, 91（3）；1282‒1296,
200487）Talwar, S. K., Musial, P. G., Gerstein, G. L.：Role of mammalian auditory cortex in the perception of elementary sound properties. J Neurophysiol, 85（6）；2350‒2358, 2001
88）Thiebes, S., Leicht, G., Curic, S., et al.：Glutamatergic deficit
and schizophrenia‒like negative symptoms：new evidence fromketamine‒induced mismatch negativity alterations in healthy
male humans. J Psychiatry Neurosci, 42（4）；273‒283, 201789）Thomas, M. L., Green, M. F., Hellemann, G., et al.：Modeling
deficits from early auditory information processing to psychosocial functioning in schizophrenia. JAMA Psychiatry, 74（1）；37‒46, 201790）Tikhonravov, D., Neuvonen, T., Pertovaara, A., et al.：Effects ofan NMDA‒receptor antagonist MK‒801 on an MMN‒like
response recorded in anesthetized rats. Brain Res, 1203；97‒
102, 200891）Tikhonravov, D., Neuvonen, T., Pertovaara, A., et al.：Dose‒
related effects of memantine on a mismatch negativity‒like
response in anesthetized rats. Neuroscience, 167（4）；1175‒
1182, 201092）Todd, J., Michie, P. T., Schall, U., et al.：Deviant matters：duration, frequency, and intensity deviants reveal different patternsof mismatch negativity reduction in early and late schizophrenia. Biol Psychiatry, 63（1）；58‒64, 200893）Todd, J., Harms, L., Schall, U., et al.：Mismatch negativity：translating the potential. Front Psychiatry, 4；171, 201394）Umbricht, D., Schmid, L., Koller, R., et al.：Ketamine‒induced
deficits in auditory and visual context‒dependent processing in

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Powered by TCPDF (www.tcpdf.org)

healthy volunteers：implications for models of cognitive deficits in schizophrenia. Arch Gen Psychiatry, 57（12）；1139‒
1147, 2000
95）Umbricht, D., Koller, R., Vollenweider, F. X., et al.：Mismatch
negativity predicts psychotic experiences induced by NMDA
receptor antagonist in healthy volunteers. Biol Psychiatry, 51（5）；400‒406, 200296）Umbricht, D., Koller, R., Schmid, L., et al.：How specific are
deficits in mismatch negativity generation to schizophrenia?
Biol Psychiatry, 53（12）；1120‒1131, 2003
97）Umbricht, D., Krljes, S.：Mismatch negativity in schizophrenia：a meta‒analysis. Schizophr Res, 76（1）；1‒23, 2005
98）Umbricht, D., Vyssotki, D., Latanov, A., et al.：Deviance‒related electrophysiological activity in mice：is there mismatch
negativity in mice? Clin Neurophysiol, 116（2）；353‒363, 200599）Weickert, C. S., Weickert, T. W., Pillai, A., et al.：Biomarkers inschizophrenia：a brief conceptual consideration. Dis Markers,
35（1）；3‒9, 2013100）Winkler, I., Karmos, G., Näätänen, R.：Adaptive modeling of
the unattended acoustic environment reflected in the mismatch
negativity event‒related potential. Brain Res, 742（1‒2）；239‒
252, 1996
101）Yabe, H., Tervaniemi, M., Reinikainen, K., et al.：Temporalwindow of integration revealed by MMN to sound omission.
Neuroreport, 8（8）；1971‒1974, 1997
102）Young, J. W., Geyer, M. A.：Developing treatments for cognitive deficits in schizophrenia：the challenge of translation. J
Psychopharmacol, 29（2）；178‒196, 2015
103）Young, J. W., Light, G. A.：Cross‒species neurophysiologicalbiomarkers of attentional dysfunction in schizophrenia：bridging the translational gap. Neuropsychopharmacology, 43（1）；230‒231, 2018
104）Zhou, Z., Zhu, H., Chen, L.：Effect of aripiprazole on mismatch
negativity（MMN）in schizophrenia. PLoS One, 8（1）；e52186,
2013

Michie・他：Mismatch Negativity（MMN）as a Promising Translational Neurophysiological Biomarker in Schizophrenia

841