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The Use of the Mismatch Negativity in Studies of Sound

Processing

Since the earliest days of experimental psychology

introspective and behavioural measures have been used in

the study of auditory processes (Wiseman, 2012).

Neuroimaging allows these investigations to be taken

further with measurements of the underlying activity of

brain regions providing spacial and temporal analysis of

functioning. Electroencephalography (EEG) records

electrical activity from neuronal dipoles expressed at

scalp level, Magnetoencephalography (MEG) measures the

faint magnetic fields created by neuronal activity and

Functional Magnetic Resonance Imaging (fMRI) images

hemodynamic response, the deoxygenation of blood within

neuronal tissue. Each neuroimaging method has its own

relative benefits and problems, at a general level and in

application to the field of sound processing. Considering

one measurable response, the Mismatch Negativity, this

essay examines the use of each method and the potential

for combinations in furthering research.

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Manifesting as EEG and MEG components the mismatch

negativity (MMN and MMNm respectively) provides a measure

of neuronal response to deviations. fMRI imaging studies

allow further investigation into the phenomena and may be

combined with or compared to MMN recordings. Though

manifesting in other modalities, such as visual and

olfactory (Pazo-Alvarez, Cadaveira and Amenedo, 2003),

this essay will focus on its use within auditory

research, taking a number of examples from linguistics

and stream segregation to clinical to examine the scope

of measures of this component examining the methodologies

involved.

Discovered by Näätänen et al in 1978 the MMN component is

generated by discriminable deviations from repetition in

a sequence of auditory stimuli giving an objective

measure of auditory perception. Early investigations

looked at the differences in response to two tones of

perceptively different frequencies. The first tone (A)

would be used as the standard, presented in a repeating

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sequence, while a second tone (B) was used as a deviant,

occasionally replacing A. Though EEG responses to tones A

and B would be expected to differ slightly relative to

their respective frequencies it was found that a much

larger negative EEG response occurred to the deviant than

standard stimuli, remaining after counterbalancing

frequency of the standards and deviant.

This difference was thought to reflect a perceptual

change detection process which was necessary, though not

sufficient, for conscious detection of deviation within a

presented auditory sequence. The response was

consequently taken to be an objective measure of just

noticeable differences in stimuli, improving upon

previous introspective measures (Wolf and Schroger,

2001). MMN responses were found in deviations of a large

range of different stimuli characteristics from simple

deviations in intensity, duration and timbre to complex

deviations among abstract patterns of ascending and

descending tones, silences where tones would be expected

and breaches of grammatical rules in mother tongue

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(Näätänen et al., 2007). The perceptual processing of

these more abstract patterns gave an insight into how the

brain perceives sound and how basic perceptions may

interact with interpretations in higher brain regions.

MMN responses were found in patients who were not

consciously aware of deviations and measurable in

sleeping and coma patients while fetal, newborn and

developing children also show mismatch responses allowing

insights into perceptual developments over time (Lowery

et al, 2006). To understand some of these uses we must

first take a step back to examine ERP recordings in

general and how MMN is expressed within EEG recordings.

EEG

EEG recordings involve the application of multiple

electrodes to the scalp recording expressed electrical

potentials in many channels. Various layouts and

densities of electrode placement (such as the 10-20

system (Jurcak et al 2007)) are possible depending on

experimental requirements with locations standardised

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between participants to give comparable recordings.

Measurements track changes of electrical amplitude in

near-real time as waveforms, averaged across many

repeats, from which a series of negative and positive

peaks can be derived. Scalp level recordings refer to

dipoles formed by the postsynaptic potentials of large

numbers of similarly orientated neurons firing

concurrently with recordings primarily reflecting

activity in the neural cortex. Interference of the scalp

leads to lowered spacial accuracy while deeper brain

regions and neuronal firings that do not produce mass

dipoles are inaccessible. Despite limitations EEG

recordings are thought to give good access to activity in

the neural cortex including the auditory cortex and

frontal areas involved in sound processing (Luck and

Kappenman, 2012).

Averaged EEGs evoked in response to a stimulus are termed

Event-Related Potentials (ERP) with those produced in

response to auditory stimuli distinguished as Auditory-

Related Potentials (ARP). Components can be extracted

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from ERPs. These are inferred to reflect underlying

neurological and psychological process. Extraction is not

a straightforward process as multiple components can co-

occur with amplitude recorded at a single electrode

influenced by multiple components. Further to this the

inverse problem of tracing EEG recordings to their

neuronal areas of origin requires complex mathematical

modeling involving the comparisons of electrode

recordings from multiple scalp locations. This allows

extracted components to be traced back to their location

of generation. The MMN component is measured as an

elevated negative peak within the response to an auditory

stimuli acting as a deviant when compared to the response

to the same stimuli acting as a standard (Luck and

Kappenman, 2012).

Taking the simple frequency of tone experiment explained

above as an example we will now examine where MMN fits

into the EEG response to sound. On presentation of a

repeating auditory tone each instance generates a

waveform showing P1, N1 and P2 peaks. The N1 is a

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negative peak occurring at approximately 100ms from

stimulus onset. The P2, a positive measure peaking at

approximately 180-220ms from onset follows.

The deviant tone presented within the pattern of

standards in our example evokes a MMN. This in generated

as an elevated negativity beginning at the peak of the N1

wave lasting approximately 100-250ms. Consequently a more

negative N2 peak is produced while the positive P2 is

reduced, offset by the negativity. If the deviant tone is

consciously detected then a further P3 component is

generated, theorized by Donchin’s context updating

hypothesis to reflect updating of expectations (Näätänen

et al., 2007), and N2b and N2c components may also be

evoked (Pritchard, Sahppell and Brandt, 1991). As the P3,

Nb2 and Nb3 are not generated when attention is directed

away from stimuli some studies choose to provide a

reading task or silent video to distract conscious

attention. In addition to the expression of the MMN as a

negative peak, the positive area of the neuronal dipole

can be recorded in nose-referenced electrodes placed in

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the mastoid region allowing extraction of the component

in tasks where attention is necessary or uncontrollable

(Näätänen et al, 2007).

Within the ARP data for each participant the MMN is

extracted through a process of subtracting the averaged

EEG waveform from a tone when presented as a standard

from the averaged waveform of the same tone presented as

a deviant (Friston, 2005). This produces a subtraction

waveform showing the additional negativity. It should be

noted that the difference in ERP response to two stimuli

based on characteristics of sound are often small when

compared to that of the MMN response allowing response to

a standard to be subtracted from that of a different

deviant in some experimental contexts and

counterbalancing performed between participants rather

than within. Common measures include presence/absence of

MMN, variations in amplitude and latency from stimulus

onset (Luck and Kappenman, 2012). We will now examine

some of the applications of MMN in research in an attempt

to highlight the scope of EEG measures of the component.

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COGNITIVE APPLICATIONS

As previously touched upon, presence of the MMN is

thought to give a measure of just noticeable differences.

This can be studied either by manipulating

characteristics of a stimuli or measuring individual

differences in reactions to a single stimuli. These same

formats are taken in studies investigating echoic memory.

Approximately 10s in normal participants (Cowan et al,

1984) deviations within repeating stimuli with inter

stimulus intervals (ISI) beyond this critical duration do

not generate MMNs. This is thought to be due to a fading

of sensory memory trace over time ( Kujala, Tervaniemi

and Schröger. 2007). Consequently MMN provides an

objective measure of auditory memory. Patients with

Alzheimer’s can be studied in this way. As would be

expected the critical ISI for MMN response is much lower

than average in Alzheimer’s patients and decreases as the

condition progresses (Pekkonen et al, 1994).

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MMN has been used to study linguistics with stable long

term phoneme perceptions linked to MMN production. In

Finnish /e/ and / ä/ are separate phonemes while in

Hungarian they are not. Hungarians who did not speak

Finnish were not seen to produce MMNs to the distinction

while Finnish speakers did (Winkler et al. 1999). This

may suggest that high-level top down expectations based

upon permanent memory traces can impact on processing

even when attention is directed elsewhere.

While some studies examine the presence or absence of MMN

response, others study variations in its size and

duration. Shorter ISIs lead to larger MMN responses

(Javitt et al, 1998) and larger amplitude MMNs are evoked

when a deviant occurs after more repetitions of the

standard stimuli (Haenschel et al. 2005). Increases in

numbers of deviant instances leads to a smaller MNN

response both to a deviant that repeats (Ritter et al

1992) and with multiple varying deviants varying along

the same dimension (Sams et al 1984).

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As well as the presence and waveform characteristics of

MMNs, recordings have given an insight into the temporal

course of auditory processing. Shown to occur in two

streams with an early auditory cortex response followed

by a later frontal element, temporal differences between

these responses are thought to relate to the multiple

stages of neuronal processing with low level perceptual

elements processed by the auditory cortex followed by

higher level analysis in frontal regions. Within this

differences can be seen between language and musical

sounds with second stage localisation occurring in the

right hemisphere in music and left in speech processing.

Phoneme generated MMNs are larger over the left

hemisphere than the right while responses to musical

tones show the opposite localisation (Sharma and Kraus,

1995). An example of the MMNs applicability to studies of

the temporal course of processing is found in Sussman et

al (1999, 1998) use of the component to investigate

auditory stream analysis.

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When in complex sound environments with multiple sound

streams it is necessary to cognitively separate input

into streams and attend only to those of relevance. This

separation can be elicited both through conscious

attentional choice and unconsciously as a consequence of

stimuli features. Firstly, examining how unconscious

characteristics of sound can lead to perceptual

separation Sussman et al (1999) used an oscillating sound

of alternating high and low pitches with occasional

deviants in both. When presented at slow speeds (750ms

intervals between high and low pitches) no MMN response

was evoked by deviants in the high or low pitches. At

high speeds (100ms intervals) MMNs were evoked by

deviants in both streams. This was taken as evidence that

high-speed oscillations are pre-attentively perceived as

two separate streams of repeating notes while slow-speed

are not separated, perceived instead as a single

oscillating tone.

In a follow up study, conscious stream segregation was

investigated. Between the two extremes of oscillation

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speed investigated above is a region of ambiguity where

stream separation is thought to occur dependent upon top

down processes. In Sussman et al (1998) participants were

presented with an ambiguous tone changing from high to

low pitch. In a control condition participants were asked

to listen to the stimuli while EEG recordings were made.

Deviants were introduced in both the high and low pitches

but neither evoked MMN responses. In a second condition

participants were asked only to attend to the high

pitches looking for deviants. In this condition MMNs were

shown for the attended high pitch and for the unattended

low pitch. This suggests that top down processes are able

to impact upon perception at a pre-attentive level of

processing.

CLINICAL APPLICATIONS

MMN has shown much value in clinical contexts. Its

production is predictive of recovery to consciousness in

early stage coma patients (Wijnen et al, 2007). The

passive pre-attentive aspect of the measure has further

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been put to use in assessment of functioning in newborns

including those born prematurely (Näätänen et al 2012).

MMNs are thought to be the earliest discriminable ERP

component (Cheour-Luhtanen et al, 1996) and are seen

developmentally to originate as a positive rather than

negative change-dependent response flipping in early

maturation (Leppänen et al, 2004). At later stages of

childhood development MMN studies have been used to

investigate dyslexia (Schulte-Körne, 1998), dysphasia

(Korpilahti and Lang, 1994), ADHD (Rothenberger at al,

2000) and hearing problems (Huttunen-Scott et al, 2008).

Language problems, both extreme and more minor can be

investigated through MMN recordings. Patients with

aphasia do not produce phoneme MMN but do produce general

frequency change MMNs to non-phoneme stimuli (Aaltonen et

al, 1993). MMN response to frequency deviants is of a

lower amplitude in dyslexic adults, with this amplitude

attenuation showing correlation to levels of reading

disturbance (Baldeweg et al. 1999). The production of

MMNs to the contrast between /da/ and /ga/ is only seen

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in those children who are able to behaviourally

discriminate between the two (Kraus et al. 1996). Many of

the children who could not distinguish were in ‘problem-

learner’ categories and one possible interpretation would

be that their inability to distinguish phonemes was

causal in this. Phoneme discrimination training such as

FastForWord (Hook, Jones and Macaruzo, 2001) may help

with this and MMN studies would be able to measure

changes in discrimination ability. Experimental phoneme

distinguishing training was seen to produce behavioural

discriminations between two variations of /ba/. Over the

course of training MMNs to these variations arose earlier

in the learning process than behavioural abilities to

distinguish (Tremblay, Kraus and McGee, 1998).

A limitation found in oddball EEG paradigms using sparse

deviants is session duration. Each block required many

standards to be presented for each deviant meaning long

duration experiments are required to generate gather even

small amounts of data. Näätänen, Pakarinen, Rinne and

Rika (2004) showed that it was possible to record many

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different types of stimuli deviance in a single sequence.

It was found to be possible to present different deviants

concurrently. If each deviated by a different dimension

then standardisation among other dimensions would

continue to build. The design used a repeating cycle with

every second sound deviating along one of five

alternating characteristics (D1-5). This means each

deviant type is 10 stimuli apart (see figure 1). The

paradigm increases the scope for investigations and is

especially relevant to clinical patients about whom

profiles of performance along a number of attributes may

be quickly created.

MEG

MEG recordings, though similar to EEG in scope are not

susceptible to interference of the skull or scalp

allowing better spacial resolution. The MEG measures

mismatch negativity fields (MMNm). As with EEG these

S D1 S D2 S D3 S D4 S D5 S D1 S D2 S D3 S D4 S D5 S D1

(S:Standard , D:Deviant)Figure 1: Stimuli presentation in multiple deviants paradigm

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reflect dipoles after neuronal firings. Less common than

EEG recordings due to lower availability and increased

price MEG is theoretically more suited to the measure of

mismatch negativity giving better imaging of the auditory

cortex (Thönnessen et al, 2008).

Herrmann et al (2009) used MEG in an attempt to localise

the effect of syntactic deviance on MMNm. Syntactically

correct deviations are seen to produce smaller mismatch

negativities than those deviations which do not conform

to the syntactical structure of the stimuli sequence

(Shtyrov et al, 2003). Rare but grammatically valid

stimuli do not produce a MMN (Pulvemüller and

Assadollahi, 2007) so this effect is thought to

demonstrate the application of syntactical rules at an

early stage of speech processing. In the study MMNm

activation was found in the Sylvian fissure and superior

temporal sulcus with stronger activation in the left

hemisphere. No frontal response was found. Frontal

responses had been seen in similar EEG studies (eg.

Pulvermüller and Shtyrov, 2003) and this is thought to be

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a demonstration of the differences in sensitivity between

the two methods with increased error seen in EEG relative

to MEG recordings (Leahy et al, 1998).

In studying ERP components such as the MMN it is possible

to take advantage of the capacity for concurrent

recording from both methods that is offered. EEG and MEG

recordings compliment each other with EEG giving

increased sensitivity to radial generators while MEG

allows for a greater measure of tangential dipoles and

hemispherical localisation (Thonnessen et al 2008).

fMRI

fMRI recordings measure the increased blood flow to

cerebral areas of high metabolic activity. Differing

magnetic resonances between oxygenated and deoxygenated

hemoglobin are detected within a fluctuating magnetic

field. These are reduced to patterns of voxel activations

allowing standardisation for analysis and comparison

between participants. The hemodynamic response is

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relatively slow leading to a more limited temporal

accuracy compared to the previously discussed methods.

Spacial activity however is much higher and all brain

areas can be accessed. Though showing much potential in

the study of visual perception progress in fMRI studies

of sound was initially slow with the main obstacle being

scanner noise (Di Salle at al, 2003).

fMRI scanners produce a loud pulsed noise of

approximately 100dB (Hedeen and Edelstein, 1997).

Generated by the gradient switches of magnetic field this

noise leads to a BOLD response in the auditory cortex

that may remain throughout the session causing a

saturation of response against which stimuli dependent

responses can be difficult to extract. Beyond this

scanner noise can also interfere with perception of

auditory stimuli in unpredictable ways modulating

responses (Seifritz et al, 2006).

Mustovic et al (2004) overcame this problem by using

scanner noise as the experimental stimuli. In an

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experiment attempting to locate the source of echoic

memory, deviants of increased volume and absence of

expected scan noise were used, achieved by adding

additional artificial scanner noise and skipping gradient

switches. Use of silence and additional noise captured a

cognitive conjunction (Price and Friston, 1997) in that a

common neurological process is shared between the

radically different stimuli of silence and noise. The

right planum temporale and temporopariental junction were

seen to be involved suggesting their importance in the

formation of echoic memory with the planum temporale

involved in segregation of sound streams. Right

lateralisation preference was shown to these non-speech

sounds. Though demonstrating the potential of fMRI

studies in the area, this experimental design is limited

in the stimuli characteristics available for

investigation. To fully utilise the potential of fMRI

scans clearer methods for presenting auditory stimuli

were required.

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One proposed solution was use of a quasi-continuous

gradient switch pattern (Seifritz et al, 2006) giving a

perceptually continuous sound. This use of continuous

sound leads to reduced baseline BOLD response relative to

pulsed scanner sound. An alternative approach is to use

Sparse Temporal Sampling (STS) with long gaps between

single scans. This use of infrequent gradient switches

allows presentation of auditory stimuli in low noise

periods. However this limits the amount of data that can

be collected and reduces experimental design

possibilities as time of peak response must be predicted

(Josephs and Henson, 1999) but variations exist which aim

to tackle these problems.

Interleaved Silent Steady State (ISSS) paradigms consist

of longer low volume periods for stimuli presentation

followed by periods of multiple scans. This reduces the

need for prior predictions of time course of BOLD

response and allows changes in response to be tracked

temporally to a limited extent. It is achieved by

producing silent slice selective pulses throughout the

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period of stimuli presentation. These dummy scans allow

acquisition scans to be made at the same short gradient

switch intervals. Variations exist which allow, different

fidelity of acquisition, for example 5 volumes of 15

slices or 2 volumes of 32 slices with a trade off between

the number of slices per scan and the number of scans

made. Mueller et al. (2011) found that the ISSS has

increased sensitivity when compared to STS and allowing

acquisition of more data in the same time periods while

retaining the advantages of noise reduction of that

method. (Mueller et al, 2011). These periods of silence

within fMRI recordings open up the possibility for

concurrent EEG recordings. Truly concurrent EEG

recordings made at the time of fMRI image acquisition

would be effectively obliterated by the scanner but as

EEG measured activity occurs at the time of stimuli

presentation and slightly after while the BOLD response

measured by fMRI occurs after this point the techniques

can be combined under STS or ISSS paradigms.

fMRI and EEG

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Broadly speaking fMRI and EEG recordings can be combined

in two ways. Firstly it is possible to record each

separately with data collected from one method informing

the other. This allows the multiple repeats required for

a reliable measure of EEG to be made without the cost of

multiple fMRI studies, an expensive waste as comparable

correlation on a trial-by-trial basis is not required

(Debener et al, 2006).

In this approach participants are required to perform the

same experiment under each measure. As well as order

effects such as task learning, the impact of differences

in experimental design such as short durations in fMRI

recordings and multiple repeats under EEG must be

considered. For example fMRI recordings are made while

the participant lies horizontally and so any EEG

recordings taken for comparison should attempt to match

this. As discussed above fMRI noise can affect stimuli

perception and so artificial fMRI sounds should be played

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while collecting EEG measurements, even though poorer

quality of EEG recording may be a consequence.

An example of this is Opitz et al (2002). The study

looked at the involvement of the temporal and frontal

lobes in connection to MMN evoking stimuli. Three levels

of deviants were used, with changes in volume of 10%, 30%

and 100%. In silent EEG recordings MMNs were produced by

all three but in further EEG recordings in the presence

of artificial scanner noise MMN only occurred in response

to 30% and 100% deviants. In consequence 10% deviations

were not included in the fMRI study. Later fMRI scans of

the same participants were compared with the EEG

recordings using strength of activation correlations

whereby ERP distributions were modeled to fMRI

activations. It was found that the main MMN generators

were around the auditory cortex with temporal activity

related to size of deviance in the right temporal cortex

and right inferior frontal gyrus.

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The second method for combining is to make EEG recordings

in the fMRI scanner. Many issues arise from this. fMRI

images are warped slightly by the metal in electrodes

while vibration of the scanner and the BA artifact caused

by the QRS complex of blood flow occurs as an artifact on

the EEG waveform and must be modeled and subtracted. This

combined with the effect of noise means that combination

is quite a challenge and currently rare in studies of

audition where EEG variations are relatively small.

An example can be found in Eichele et al (2008) which

argues against separate EEG as a predictor of fMRI

activity as neither the EEG nor the fMRI responses

recorded at any one location necessarily relate to a

single neurological event. Instead it advocates unmixing

both modalities concurrently at a single-trail level.

Using Infomax independent component analysis (ICA) where

ICAs were collected as spacial maps for fMRI (sICA) and

temporal ICA for EEG (tICA).

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The above essay has covered some of the uses of MMN in

examining auditory perception. From insight into how

phonemes are perceived to prognostic measures of coma

outcome the MMN component demonstrates the importance of

preattentive perceptual processes and with it gives

insights into the role of conscious attention. These

findings, while specific to the auditory modality, may

give some insight into other modalities, most notably

visual perception. While research into a visual MMN is at

a much earlier stage, evidence may support its existence

(Pazo-Alvarez, 2003). Future technological developments

in EEG, MEG and fMRI technology will likely lead to a

more definitive answer and increase the potential of this

component to inform understanding in the area.

(3997 words)

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