ORIGINAL PAPER

Stimulus-Specific Adaptation in Field Potentials and Neuronal

Responses to Frequency-Modulated Tones in the Primary

Auditory Cortex

Carsten Klein • Wolfger von der Behrens •

Bernhard H. Gaese

Received: 23 July 2013 / Accepted: 8 May 2014

� Springer Science+Business Media New York 2014

Abstract In order to structure the sensory environment

our brain needs to detect changes in the surrounding that

might indicate events of presumed behavioral relevance. A

characteristic brain response presumably related to the

detection of such novel stimuli is termed mismatch nega-

tivity (MMN) observable in human scalp recordings. A

candidate mechanism underlying MMN at the neuronal

level is stimulus-specific adaptation (SSA) which has

several characteristics in common. SSA is the specific

decrease in the response to a frequent stimulus, which does

not generalize to an interleaved rare stimulus in a sequence

of events. SSA was so far mainly described for changes in

the response to simple pure tone stimuli differing in tone

frequency. In this study we provide data from the awake rat

auditory cortex on adaptation in the responses to fre-

quency-modulated tones (FM) with the deviating feature

being the direction of FM modulation. Adaptation of

cortical neurons to the direction of FM modulation was

stronger for slow modulation than for faster modulation. In

contrast to pure tone SSA which showed no stimulus

preference, FM adaptation in neuronal data differed

sometimes between upward and downward FM. This,

however, was not the case in the local field potential data

recorded simultaneously. Our findings support the role of

the auditory cortex as the source for change-related activity

induced by FM stimuli by showing that dynamic stimulus

features such as FM modulation can evoke SSA in the rat

in a way very similar to FM-induced MMN in the human

auditory cortex.

Keywords SSA � Stimulus-specific adaptation �Mismatch negativity � Auditory cortex � Extracellular

recording � Frequency-modulated tone � Awake rat

Introduction

The separation of infrequent, novel acoustic stimuli from a

repetitive background is of great importance for all ani-

mals. Such unexpected changes in the auditory environ-

ment often indicate events of particular behavioral

relevance. In order to detect them, the brain represents

important stimulus features such as frequency and ampli-

tude in sensory memory. This memory system can store

such information for a few seconds (Sperling 1960) and is

independent of selective attention (Escera et al. 1998). The

search for a possible neural substrate of such novelty

detection and for an automatic, stimulus-driven mechanism

activating attention has a long history and was advanced at

very different levels of investigation. A presumed neural

indicator was found in EEG recordings in humans as a

negative-going component (hence its name: mismatch

This is one of several papers published together in Brain Topography

in the ‘‘Special Issue: Mismatch Negativity’’.

C. Klein � B. H. Gaese (&)

Institute for Cell Biology and Neuroscience, Goethe University

Frankfurt, Max-von-Laue-Str. 13, 60438 Frankfurt/Main,

Germany

e-mail: gaese@bio.uni-frankfurt.de

C. Klein

e-mail: carsten.klein@tuebingen.mpg.de

Present Address:

C. Klein

Max Planck Institute for Biological Cybernetics, Tuebingen,

Germany

W. von der Behrens

Institute of Neuroinformatics, ETH and University of Zurich,

Zurich, Switzerland

e-mail: wolfger@ini.phys.ethz.ch

123

Brain Topogr

DOI 10.1007/s10548-014-0376-4

negativity, MMN) when a stimulus deviated from a pre-

ceding sequence of stimuli (Naatanen et al. 1978). This

additional negativity could be related to bilateral sources in

the supratemporal cortex (Alho 1995; Huotilainen et al.

1998). However, both the functional significance of MMN

and the mechanism of its generation are under debate in the

literature. As an alternative view to the assumed relation of

MMN to sensory memory, a model of MMN based on

neuronal adaptation is proposed (May and Tiitinen 2010).

This leads to a different level of investigation. Namely,

several studies tried to pinpoint the neuronal substrate of

MMN by means of extracellular recordings in the auditory

cortex (e.g. Ulanovsky et al. 2003) and at subcortical levels

(e.g. Anderson et al. 2009; Antunes et al. 2010; Malmierca

et al. 2009). Oddball paradigms, originally used to investi-

gate MMN, were applied and showed that neurons in the

auditory cortex adapted specifically to frequent standard

stimuli but not to rare deviants. The degree of adaptation in

these oddball paradigms was negatively related to deviant

probability and positively related to the difference in tone

frequency between standard and the deviant stimuli (von der

Behrens et al. 2009), which resembles closely the stimulus

dependence of human MMN potentials (Nelken and Ula-

novsky 2007). As a result, such stimulus-specific adaptation

(SSA) of neurons was suggested as a candidate mechanism

underlying the generation of MMN. While SSA and MMN

have several characteristics in common, the importance of

SSA for true change detection has not been shown so far.

The rat auditory cortex is probably the brain structure in

which the encoding of SSA has most extensively been

studied in the auditory domain. Almost always pure tones

were used as adapting stimuli with standard and deviant

stimuli differing in tone frequency. MMN effects in

humans, on the other hand, were tested using all different

kinds of acoustic stimuli. One might consider this limita-

tion being irrelevant, as repetitive stimulation of a given

stimulus almost always leads to response reduction (i.e.

adaptation), while a change in stimulation leads to an

increase in response and/or additional response compo-

nents (May and Tiitinen 2010). A closer look at the liter-

ature, however, reveals that even slightly more complex

acoustic stimuli such as frequency-modulated tones (FM)

can elicit heterogeneous response patterns when recorded

from the human scalp. The degree of mismatch, for

example, could strongly depend on specific stimulus

parameters, e.g. the modulation range of FM stimuli (Sams

and Naatanen 1991). While a deviance in FM modulation

direction was found to result in a mismatch signature in

magnetoencephalography (MEG) recordings in humans,

this signature occurred earlier than the typically described

MMN (Pardo and Sams, 1993). In a recent study we

investigated stimulus selectivity specific to FM stimuli in

change-related paradigms focusing again on FM

modulation direction. Interestingly, we observed repetition

enhancement instead of adaptation for frequency-modu-

lated but not unmodulated sounds (Heinemann et al. 2010).

Such a signal enhancement, which was also found for

complex FM-sweeps, indicates a strong spectro-temporal

interaction between successive FM tones rather than a

change-related signature (Altmann et al. 2011). These

findings led to the question how sequences of FM stimuli

and changes in FM modulation direction might in detail be

encoded at the neuronal level.

Frequency modulation is an ubiquitous sound feature

present in communicative sounds of several animal species

and in human speech. Phoneme identification, for example,

is strongly dependent on the decoding of frequency varia-

tions and FM sweeps (Liberman and Mattingly 1989). Not

surprisingly, many aspects of FM encoding are explicitly

represented in the human brain (reviewed in: Altmann and

Gaese 2014). In the present study we focus on the encoding

of changes in FM direction as this parameter is categorized

in the auditory cortex (Konig et al. 2008). This encoding is

investigated in the rat auditory cortex as lesion studies

demonstrated that the rodent auditory cortex (as a whole) is

important for discrimination of FMs but not pure tones

(Ohl et al. 1999). FM direction selectivity is systematically

organized in the rat primary auditory cortex along the

tonotopic representation (Zhang et al. 2003) and rats are

well able to discriminate and categorize FM direction

(Gaese et al. 2006). Based on this, we used FM tones to

investigate SSA depending on a more dynamic, second

order stimulus feature in the rat auditory cortex, in order to

clarify the characteristics of SSA as a possible upstream

substrate of MMN. Data are included from both individual

neurons and local field potentials in order to more closely

relate single neuron encoding to the representation at a

mass neural scale.

Materials and Methods

Animals and Preparation

Five female adult Sprague–Dawley rats (180–250 g,

Charles River) were implanted with electrodes into the left

auditory cortex as described before (von der Behrens et al.

2009). Briefly, three to six tungsten electrodes (impedance

before black plating 9–12 MOhm; FHC) attached to a

custom-built microdrive were implanted under general

anesthesia (chloral hydrate, 300 mg/kg, i.p., Sigma–

Aldrich and ketamine 10 mg/kg, i.p., Ketavet, Pfizer).

Electrodes were oriented in a tangential approach, thereby

moving through the auditory cortex in parallel to the cor-

tical layers. Experimental procedures were in full compli-

ance with the ‘‘Principles of laboratory animal care’’ (NIH

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publication no. 86–23, revised 1985) and with current

federal regulations, and were approved by the local animal

care committee.

Electrophysiological Recording

All experiments were performed in a sound-attenuating

chamber (IAC) during the wake phase of the animals.

Single unit activity and local field potentials (LFP) were

recorded in parallel. All electrodes were in layers III–IV of

the left primary auditory cortex (AI) as determined by

electrolytic lesions in histological sections and according to

response latencies and tonotopic arrangement (Rutkowski

et al. 2003). A Cheetah-32 system (Neuralynx) was used

for recordings (LFP channels: 1–475 Hz, spike channels:

600–3,000 Hz). Only clearly separable and stable clusters

with uniform spike waveforms were defined as single units

(SU) in the off-line spike sorting (SpikeSort, Neuralynx),

other clusters were regarded as multi units (MU) presum-

ably including a small number of related neurons.

Sound Generation, Stimulus Design and Presentation

Stimuli were computed (RX6, TDT), attenuated (PA5,

TDT) and presented free-field via electrodynamic speaker

(ScanSpeak R2904/7000, Tymphany). Speakers were cal-

ibrated (microphone 4135, Bruel & Kjær) and their fre-

quency response was corrected in real-time with a finite

impulse response filter (402 taps). The frequency response

characteristic for all units was determined at 50 dB SPL by

presenting pure tones (PT, rise/fall-time 5 ms) in a fre-

quency range of 1–45 kHz, logarithmically spaced (0.25

octaves) at a rate of 1 Hz (see Fig. 1c). FM stimuli

(modulation range 1–32 kHz) of different modulation rates

and directions (-100–?100 oct/s, step size 10 oct/s, 20

repetitions) were presented in pseudo-random order at

50 dB SPL and a rate of 1 Hz to determine modulation

direction preferences. The modulation direction selectivity

index (DSI: Zhang et al. 2003) for each unit was calculated

as: DSI = (rup - rdown) / (rup ? rdown), with rup being the

response to an upward modulated FM (FMup) and rdown the

response to a downward modulated FM (FMdown).

Responses were taken as the mean of three bins centered on

the bin with maximum activity (binwidth 5 ms).

In order to determine neural adaptation to simple stim-

ulus features, two PT stimuli (f1 and f2, 100 ms duration,

inter stimulus interval ISI = 500 ms, 0.5 octaves differ-

ence in frequency) were mainly centered around the best

frequency (BF) and used as deviant and standard in an

oddball paradigm (Fig. 1a) with a 10 % deviant probability

in pseudo-randomized order (for a detailed description see:

von der Behrens et al. 2009). The adaptation to complex

stimulus features was measured with frequency-modulated

tones (modulation range 1–32 kHz) presented in an oddball

paradigm. FM deviants (10 % probability) had a different

modulation direction than the FM standards but the same

modulation rate. FMs were either slowly modulated (slow

FM, ±50 oct/s) or fast modulated (fast FM, ±100 oct/s),

all at a sound pressure level of 50 dB SPL. This FM-par-

adigm (Fig. 1b; ISI = 1,000 ms) was presented only to

units with a DSI between -0.5 and 0.5.

Data Analysis

The basis of spike data analysis were peri-stimulus time

histograms (PSTHs; 5-ms bins). To quantify the degree of

spike adaptation a normalized spike adaptation index (sAI)

was calculated (Ulanovsky et al. 2003):

sAI ¼Dev1 þ Dev2ð Þ � St1 þ St2ð Þ

Dev1 + Dev2 þ St1 + St2;

where Dev1 and St1 represent spike rate responses to

stimulus 1 as deviant and standard, respectively. The same

nomenclature applies for the second stimulus 2. For PTs

the calculated response was the mean activity in a 15-ms

window centered around the maximum bin determined in

the range 0–50 ms after stimulus onset giving rise to a

‘‘frequency spike adaptation index’’ (freq-sAI). Because

spike responses to FM-stimuli were more heterogeneous,

the average response to standard and deviant was used for

finding the bin with the highest activity. Again, deviant-

and standard-response rates were calculated from a 15-ms

window centered around this bin. This procedure was

performed for stimuli 1 and 2 separately. On the basis of

these responses a ‘‘direction-spike adaptation index’’ (dir-

sAI) was computed. In all cases an additional sAI was

calculated for adaptation to each stimulus separately. This

yielded sAIf1 and sAIf2 for the two pure tone stimulus

frequencies and sAIup and sAIdown for the two FM direc-

tions composing an oddball sequence.

Analysis of local field potentials included several steps

comparable to the spike analysis. The continuously sam-

pled LFP signal was bandpass filtered to 1–50 Hz by

applying a fast Fourier transformation (function FFT,

Matlab) to the continuous signal, then removing unwanted

frequency components and then recovering the signal with

an inverse FFT (function IFFT, Matlab). Adaptation was

quantified on the basis of the response amplitude measured

as Nd, i.e. the largest negative going component in the

response. Comparable to the spike data, a normalized

adaptation index of the LFP was calculated as pAI (anal-

ogous to sAI) by including the respective values from

responses to upward and downward FMs as standard and as

deviant stimuli. Indices for each stimulus type separately

were calculated for LFP responses as well (for pure tones:

pAIf1, pAIf2, for FM stimuli: pAIup, pAIdown).

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Results

Activity from 48 MU and 13 SU was recorded for the PT-

adaptation paradigm (frequency deviant), 84 MU and 12 SU

for the slow FM-paradigm (?50/-50 oct/s direction devi-

ant) and 52 MU and 3 SU for the fast FM-paradigm

(?100/-100 oct/s). Typically, the firing-rate of a neuron

showed a sharp, phasic response to the deviant onset in the

PT-paradigm (Fig. 1c, d), followed by a phase with very low

activity, below the rate of spontaneous firing. Stimulus off-

set, again, elicited a phasic component of excitation. Stimuli

used for the PT-adaptation paradigm differed in frequency

by 0.5 octaves. They were arranged symmetrically (log

scale) around the best frequency and their frequencies were

always inside the unit’s frequency response area at 50 dB

SPL. Thereby it was ensured that the levels of activation by

the two PT stimuli were fairly comparable, resulting in

reliable adaptation data.

At the population level, the grand mean averages of PT

stimuli showed significant differences between standard

and deviant responses shortly after stimulus onset (Wil-

coxon rank sum test, Bonferroni-corrected, p = 0.0006;

Fig. 2a). In addition, the first negative deflection of the LFP

was stronger when the PT stimulus was presented as

deviant (Fig. 1d, dashed line) than as a standard (solid

line). All this is in line with previous findings in the rat

fre

q.

time

fre

q.

A

time

B

0

0.2

0.4

0.6

0.8

1

1.2

22.595 kHz15.977 kHz

[Sp

ike

s/B

in]

D

3000Time [ms]

3000Time [ms]

0

400

-400

[µV

]

FMup FMdown

0

0.2

0.4

0.6

0.8

1

1.2E

3000Time [ms]

3000Time [ms]

0

400

-400

4 8 16 32

frequency [kHz]

C

2 s

pik

es/b

in

Fig. 1 Adaptation paradigm with example neuron and LFP record-

ing. a The oddball paradigm to determine adaptation consisted of 720

standard (gray) and 80 deviant (black) pure tone stimuli (PT).

Deviant/Standard positions were swapped in a second block. b FM-

paradigm with standard (gray) and deviant (black) positions being the

same as in the PT-paradigm. Deviant and standard FMs differed only

in modulation direction. FMs were logarithmically modulated at ±50

or ±100 oct/s. c Frequency response curve and underlying PSTHs at

50 dB SPL of an example neuron with a best frequency (BF) at

22.6 kHz. Average responses were determined based on 25 pure tone

repetitions with frequencies between 1 and 45 kHz with a 0.25-octave

spacing. Calibration bar on the left indicates 2 spikes/bin. d Average

response of the example neuron in an oddball paradigm. The

responses to a 16 and a 22.6 kHz pure tone stimulus are shown

when presented as deviant (dashed line) or standard (solid line).

Upper row shows PSTHs (5-ms bins), lower row shows correspond-

ing LFP responses (horizontal bar at bottom indicates the stimulus).

While test frequencies were in most cases centred around BF, in this

example the higher test frequency was at BF. e Average responses of

the same neuron to a ?50 and a -50 oct/s FM when presented as a

deviant (dashed line) or standard (solid line). Upper row shows

PSTHs with 5-ms bins, lower row LFP responses

Brain Topogr

123

primary auditory cortex, which showed similar effects in

awake and anesthetized animals (von der Behrens et al.

2009; Taaseh et al. 2011).

After demonstrating stimulus-specificity of adaptation to

PTs we wanted to know whether cortical neurons are able to

adapt to dynamic stimuli (FM) and a stimulus feature like FM

modulation direction. Responses to FMstimuliwere different

fromPT stimuli in twomajorways. First, response latency for

a given neuron could vary strongly depending on modulation

direction and the tuning characteristics of the neurons, as

different FMs reached the neurons’ receptive fields earlier or

later. Neurons tuned to low frequencies responded with

shorter latency to upward FMs and with longer latency to

downward FMs. Latencies were reversed for neurons with

high frequency BFs (example in Fig. 1e). As a second effect,

response latencies to slow FMs were on average longer and

had a higher variation (mean = 71.6 ms ± 57 ms) than for

fast FMs (mean = 50 ms ± 34 ms STD). In contrast, PT

responses had the most consistent response latencies

(mean = 20.2 ms ± 6.5 ms STD). As mentioned above,

responses to FMup and FMdown stimuli were different from

each other. As shown in the example tuned to a high

frequency in Fig. 1e, the FMdown stimulus could elicit an

early and single peaked response while the FMup stimulus

elicited a late and double peak response. Due to this response

variability an adjusted time window for analyzing SSA to

FMs had to be defined. As SSA for PTs is most prominent at

highest activity, responses to FM stimuli were analyzed as

well around dominant activity peaks (see ‘‘Materials and

Methods’’ section). Typically, neurons adapted stronger to

one of the two modulation directions of FMs as shown in

Fig. 1e. In that example only FMdown elicited a strong and

clear adaptation effect in spikes responses and LFPs, while

responses to FMup did not show any adaptation.

Quantification of SSA for the population of neurons

showed significant adaptation in responses to PT and FM

stimuli. Significant adaptation was always based on the

differences between standard- and deviant-related respon-

ses that were most obvious in the prominent phasic

response peaks that could be seen after both, PT and FM

stimulation. Responses to PT stimulation always included a

phasic response induced by stimulus onset (on-response).

Significant adaptation occurred only during this phasic

component (Fig. 2a). The degree of adaptation was

A

-50 0 50 100 150 200 250

time [ms]

0

0.2

0.4

0.6

0.8

1norm

. activity

0.188 **

0.113 **

freq-sAI

# U

nits

0.4

6

4

2

00 0.2

B

-0.2 0 0.2 0.4

dir-sAI

PT

dir-sAI

0

4

8

12

16

20

0.067 *

0.043 **

FM -50/50C

0

4

8

12

16 0.028 **

0.021

-0.2 0 0.2 0.4

D FM -100/100

0

2

4

6

8

10 0,106 **

# U

nits

-0.4 0 0.80.4 -0.4 0 0.80.40

2

4

6

8

0.067 **0.106 **

dir-pAIdir-pAI

Fig. 2 PT grand mean and average strength of stimulus-specific

adaptation in responses to PTs and FMs. a Normalized grand mean of

all units measured with the PT-paradigm. Responses to standard

(solid line) and deviant (dashed line) PT stimuli (duration 100 ms)

were significantly different in the phasic on-component (gray-shaded

area, bin-wise comparison). b Distribution of freq-sAIs for PT

adaptation are given for MU indices (gray) and SU indices (black) as

histograms and medians (dashed lines). Median values are given for

MU and SU data with the level of significance for the difference to

zero indicated (*p\ 0.05; **p\ 0.01). c Distribution of direction-

specific indices to slow FMs (±50 oct/s) as derived from unit

response data (left, dir-sAI) and from local field potentials (right, dir-

pAI). Distributions of dir-sAIs are given for MU indices (gray) and

SU indices (black) as histograms and medians (dashed lines). d Same

as in (c) but for responses to fast FMs (±100 oct/s)

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significant for both pure tones as well as for slow and fast

FMs (50 and 100 oct/s; Fig. 2b–d). Median freq-sAIs for

pure tones were 0.113 and 0.188 (SU and MU, respec-

tively, Wilcoxon signed rank test, p\ 0.05, Fig. 2b). The

median freq-pAI derived from LFP data was even higher

with 0.409 (not shown).

Both responses to slow and fast FMs exhibited adapta-

tion in a stimulus-specific manner. The median dir-sAIs

were lower than the freq-sAIs for PTs, but still significantly

different from zero (slow FM dir-sAI: 0.043 [SU], 0.067

[MU], Wilcoxon signed rank test, p\ 0.05, Fig. 2c; fast

FM dir-sAI: 0.028 [MU], Wilcoxon signed rank test,

p\ 0.05, Fig. 2d). Comparing dir-sAI distributions for

slow and fast FMs showed that SSA was weaker for the

faster modulation rate (p = 0.086; Wilcoxen ranksum test).

This general pattern was also obvious in the LFP data that

were recorded in parallel. The median dir-pAI values were

smaller than the median freq-pAI of 0.409, but, again,

significantly different from zero for both slow FM stimuli

(dir-pAI: 0.106, Wilcoxon signed rank test, p\ 0.01,

Fig. 2c) and fast FM stimuli (dir-pAI: 0.067, Wilcoxon

signed rank test, p\ 0.01, Fig. 2d). Again, stimulus-spe-

cific adaptation for the faster modulation rate was not as

strong as for the slower FM rates. All these data were also

consistent with the prominent observation in the literature

that SSA measured in LFP data was stronger than SSA

measured in neuronal unit responses (von der Behrens et al.

2009; Taaseh et al. 2011).

Since response latencies to FM stimuli depended on the

units’ tuning properties and on FM direction, a deviant

could also be considered a kind of ‘‘time deviant’’ and not

only a direction deviant. To rule out such an interpretation,

we correlated the latency and the degree of SSA to FM

stimuli in case of different response latencies depending on

FM direction. There was no significant correlation between

the neurons’ response latency and the degree of SSA

(spearman’s rho: n.s.).

In order to investigate how strongly adaptation depen-

ded on specific stimulus features or how general the indi-

cated change detection was, we quantified asymmetries in

adaptation to the two stimuli presented in each oddball

paradigm by comparing separate adaptation indices

(Fig. 3). For pure tone stimuli, adaptation indices for both

frequencies (sAIf1 and sAIf2) were positive as indicated by

points in the upper right quadrant (Fig. 3a). This shows that

only the lower probability but not the tone frequency of the

deviant stimulus was causing the increase in firing rate.

This was even more prominent in adaptation indices

derived from LFP responses. The pAIf1 and pAIf2-values

were well correlated and even more restricted to the upper

right quadrant (Fig. 3a, bottom).

For FMs, however, this pattern changed: most units

adapted only to one of the two stimulus variants presented,

either FMup or FMdown. This dependency on specific

stimulus features in FM adaptation is shown by plotting the

adaptation indices for FMup and FMdown separately (sAIupvs sAIdown). The resulting scatter plot of data points for

slow FMs (50 oct/s; Fig. 3b) showed a strong stimulus-

dependency as indicated by the alignment of dots along the

falling diagonal (rs: -0.35 and -0.84 for MU and SU,

respectively; Fig. 3b). This means that adaptation was

usually strong for one direction of FM modulation but not

the other (see Fig. 1b). However, when FM modulation

rate was increased to -100/100 oct/s this dependency on

specific stimulus features became weaker and resembled

more the stimulus-independent adaptation as seen for PT

(rs: -0.45 for MU, Fig. 3c). Taken together, there was a

clear component of stimulus-specific adaptation depending

on FM modulation direction, especially for fast FMs and

more obvious in the LFP data. In addition, however, a

feature-specific/stimulus-dependent component was obvi-

ous, most clearly in the unit responses to slow FM stimuli.

The described feature-specific component in FM adap-

tation might be related to the underlying mechanisms for

the generation of FM direction selectivity. This hypothesis

was tested by correlating DSI values with direction-specific

sAI-values. There was no significant correlation between

the units’ FM direction selectivity (DSI) and SSA to the

FM directions FMup and FMdown (Spearman’s rho, n.s.).

For example, a unit selective for FMup (positive DSI) did

not automatically exhibit a high sAIup.

This minor influence of specific stimulus features on

adaptation on was also obvious in the stimulus-dependent

indices derived from field potentials (pAIup, pAIdown). The

distribution of these indices for slow (-50/50 oct/s) and for

fast (-100/100 oct/s) FM stimuli was broad with a center

in the upper right quadrant (Fig. 3b, c, bottom part). The

arrangement of data points was, as a tendency, more along

the rising diagonal, indicating that often at least some

adaptation was found to both upward FM and downward

FM stimuli. The pattern of feature-dependent adaptation as

it was found for slow FM stimulation in the unit data was,

in summary, a form of adaptation specific to the stimulus

type tested.

Finally we wanted to know if there was any correlation

between adaptation to PTs and FMs. In a subset of units

we recorded SSA to both PT and FM stimuli. This

enabled us to directly test whether the degree of adapta-

tion was a general property of an individual neuron or

depended mainly on stimulus features. As it turned out

there was no significant correlation between freq-sAIs

from PT and dir-sAIs from FM stimuli (Fig. 4). This was

true for both the slower modulation rate (rs = 0.27, n.s.;

Fig. 4a) and the faster modulation rate (rs = 0.24, n.s.;

Fig. 4b). This indicates that SSA is not a general property

of a neuron but depends on the type of stimulus and

Brain Topogr

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originates from several different processes. This aspect

can be extended further into the comparison of these data

on adaptation at different levels of description. The data

on SSA that were recorded in parallel as unit data and

field potentials were astonishingly different. The most

striking difference can be seen by comparing the

respective distributions of adaptation indices in Fig. 3

between top and bottom row. The differences for all three

types of stimuli between unit data (top row) and the

corresponding field potential data (bottom row) are

obvious.

The importance of stimulus statistics for SSA is a strong

indicator for the question, to what extent adaptation can

serve as a mechanism underlying general deviance detec-

tion. The respective influence of stimulus statistics was

tested for the data on adaptation to FM stimuli by com-

paring the results from the oddball paradigms (deviant

probability of 10 %) as shown above (Figs. 2, 3) to a

control condition with equal probability (50 %) of standard

and deviant stimuli (Fig. 5). The control condition, tested

in a random subset of neurons and recording sites, revealed

no significant SSA to both slow FM stimuli (-50/50 oct/s)

and fast FM stimuli (-100/100 oct/s) for both unit data and

LFPs. All median indices from the different stimulus types

and levels of recording (unit data, LFP) were comparably

small and non-significant.

sAI f1

0

n = 48

n = 13

0

0.5

-0.5

sA

I f2

A PT

0.5

sA

I up

0.5

0

-0.5

n = 84

n = 12

sAIdown

0

B FM -50/50

0.5 0-0,5

0

0,5

n = 52

n = 3

sA

I up

sAIdown

C FM -100/100

0.5

0-1

0

1

n = 84

pA

I up

0-1

0

1

n = 52

pAI down pAI down

0

1

-10

pAI

pA

I f2

n = 48

f1

Fig. 3 Stimulus-dependency of adaptation. a Separately calculated

adaptation indices for the two PT stimuli (f1 and f2) plotted against

each other. Indices from unit data (sAI) are depicted in the upper row

(black dots: MU; gray dots: SU), indices from LFP data (pAI) are

shown in the bottom row. Note the differences in scaling between top

and bottom row. b, c Separately calculated adaptation indices for

FMup versus FMdown for slow FMs (±50 oct/s; part B) and for fast

FMs (±100 oct/s; part C). Again, unit data indices (sAI) are depicted

in the upper row (black dots: MU; gray dots: SU), field potential

indices (pAI) in the bottom row

0

0.1

0.2

0.3

dir-sAI

freq-s

AI

-50/50 vs. PTA0.4

dir-sAI

-100/100 vs. PT

0 0.1 0.2-0.1-0.2 0 0.04 0.08-0.04 0.12

B

Fig. 4 Correlation between PT and FM adaptation. a, b, SSA-indices

from PTs (freq-sAI) and from FMs (dir-sAI) determined from units

where both, SSA to PTs and to FMs was measured. They are plotted

against each other for FM adaptation in (A) responses to the slow

modulation rate (50 oct/s; rs = 0.273, n = 21) and FM adaptation in

(B) responses to fast modulation rates (100 oct/s; rs = 0.24, n = 25)

Brain Topogr

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Discussion

Several studies have recently investigated the auditory

MMN marker and the presumably related physiological

phenomenon of SSA in the rat cortex. Almost all of them

were using sequences of pure tones with a deviation in tone

frequency as a basic stimulation paradigm. Only few

studies did not focus on this primary parameter of orga-

nization in the auditory pathway and went on to test more

derived stimulus parameters in more complex sounds. Data

on SSA in oddball paradigms using FM stimuli are pre-

sented in this study as an example for such an approach.

Stimulus-related activity in the human brain related to FM

stimulation in oddball sequences and other forms of

repetitive stimulation differs from responses to simpler

stimuli. The data provided here might help to understand

these more complex response patterns.

Possible Mechanism Generating SSA to FM Tones

The response latency to FMs is determined by the speed

with which an FM enters the neuron’s response area

(Felsheim and Ostwald 1996). A change in modulation-

direction or –rate is therefore accompanied by a shift in

latency (e.g. Fig. 1d), i.e. latency increases with decreasing

modulation rate. This leads to the hypotheses that actually

two processes are underlying the observed FM adaptation,

as follows: The first process is activated along the direction

of FM modulation by recruiting corresponding afferent

neurons and networks. These networks extract the second

order stimulus feature ‘FM direction’ and detect deviations

from the prior stimulus sequence as it is shown for MMN

(Leitman et al. 2011). The second possible process is dri-

ven by the temporal irregularities in the sequence of

responses to FMs that occur as different FM stimuli are

presented at a constant rate (fixed onset-to-onset interval),

however eliciting responses at varying latencies. As the ISI

was fixed, stimuli followed a predictive temporal pattern, a

rhythm. Therefore it might be possible that the neurons

adapt to this rhythm. Such entrainment of a rhythm has

been shown before in the monkey (Lakatos et al. 2005).

Deviations in the inter-stimulus intervals elicit MMN as

well, as shown with sound omissions (Yabe et al. 1997). In

our PT paradigm the adaptation to the rhythmic informa-

tion cannot become visible because both stimuli elicited

responses with almost identical latencies. In the FM para-

digm, the deviant with a different FM direction elicited

responses at a different latency, leading to an additional

A# U

nits

control FM -50/50 control FM -100/100

1

2

4

0

3

5

0-0.5

0

0.5

0.006

0.065

n = 16n = 4

sA

I up

-0.2 0 -0.2 00.2 0.4 0.2 0.4sAIdownsAIdown

pAIdownpAIdown

1

2

0

1

2

3

4

0-0.5

0

0.5

0.029

-0.014

n = 11n = 1

B

# U

nits

sA

I up

4

0

3

0-1

0

1

0.044

# U

nits

pA

I up

n = 150

1

2

3

4

0-1

0

1

0.028

n = 11

# U

nits

pA

I up

-0.4 0 0.80.4 -0.4 0 0.80.4

dir-pAIdir-pAI

dir-sAI dir-sAI

Fig. 5 Lack of stimulus-specific adaptation for FM stimuli in a

control condition with equal probability (50 %) for standard and

deviant stimuli. Shown are distributions of SSA-indices (left part in

each subfigure) and stimulus-dependency of adaptation (right part in

each subfigure) from both unit data (upper row) and local field

potentials (bottom row). Again, MU data (gray) and SU data (black)

are differentiated and the respective values for the medians of the

distributions are given separately. a Control condition data for

responses to slow FM stimuli (50/-50 oct/s). b, Control condition

data for responses to fast FM stimuli (100/-100 oct/s)

Brain Topogr

123

aspect of a ‘time-deviant’. This time-deviant is larger for

slow FMs and smaller for fast FMs. Adaptation at faster

modulation rates should be dominated by the mechanism

related to FM direction and not by the ISI of the paradigm

because the direction-dependent differences in latency are

smaller. This is consistent with our data: SSA to fast FMs

was very low while SSA to slow FMs was clearly higher.

However, we could not find a correlation between response

latency and SSA to FMs. This does not completely rule out

the possibility that timing of the stimuli is important for

FM SSA, as neurons do not adjust their responses to

varying ISIs according to a simple pattern of excitatory and

inhibitory influences (Brosch and Schreiner 1997). Fur-

thermore, a possible SSA effect of a ‘‘time-deviant’’ might

be counterbalanced by the influence of temporal expecta-

tion on the primary auditory cortex. Jaramillo and Zador

(2011) demonstrated that neurons in the primary auditory

cortex had enhanced stimulus representation during periods

of heightened expectation (corresponding to no ‘‘time-

deviant’’ stimulus) compared to periods of low expectation

(corresponding to a ‘‘time deviant’’ stimulus).

Interestingly, FM-selective neurons in the bat inferior

colliculus which are specialized for the processing of

echolocation calls show no SSA. Non-specialized neurons

in this structure, on the other hand, exhibited various levels

of SSA with a subset of these cells displaying strong

adaptation (Thomas et al. 2012). Auditory cortical neurons

as they were described here are rather non-specialized

according to the classification applied in the bat inferior

colliculus. Not only might the neural mechanisms for FM

generation be different, but also the process underlying

adaptation.

Is SSA in the Auditory Cortex Neuron- or Stimulus-

Specific?

When we compared SSA to simple and complex stimulus

features (PTs and FMs, respectively) one of the main dif-

ferences was the stimulus-dependency of adaptation

occurring for complex stimulation. SSA in PTs was mostly

depending on stimulus probability (standard vs. deviant), as

shown before (von der Behrens et al. 2009) and was

equally strong for high and low frequency tones. The

exhibited amount of adaptation is not a general character-

istic of a given neuron in the auditory cortex. This is

indicated by the missing correlation between PT-induced

and FM-induced SSA shown above (Fig. 4). In addition,

SSA to FMs, especially at slow modulation rates, showed a

significant dependency on stimulus features: many neurons

indicated strong direction-specific SSA to one FM direction

but not the other (Fig. 3b). This indicates that FM pro-

cessing in the primary auditory cortex may contribute only

indirectly to FM-induced MMN-like phenomena of more

general character. Nevertheless, the net effect of adaptation

to both FM directions remained positive. Such a difference

in PT and FM representation might be a direct consequence

of cortical processing. While PTs activate only small

groups of neurons tuned to a given frequency, FMs activate

far larger populations of neurons in a defined temporal

order. Furthermore, while in FMs there is no spectral dif-

ference between standard and deviant stimuli, thereby

always activating the same part of the tonotopic map, PT

standards and deviants always recruit a different set of

afferent fibers. The resulting mismatch responses for pure

tones differing in frequency recorded from the surface of

the rat brain show consistently no stimulus-dependence

(Astikainen et al. 2011). A study on MMN related to the

presence or absence of FM stimuli in humans, on the other

hand, found differences depending on the standard/deviant

configuration. This indicates a stronger contribution of the

specific stimulus-encoding entities (Timm et al. 2011).

The data on SSA of FM stimuli provided here were in

several aspects stimulus-dependent and cannot be used to

decide if true deviance detection (independent of specific

stimulus features) was actually also involved. This aspect,

however, is a matter of debate also for other types of

stimuli, e.g. pure tones. Farley et al. (2010) concluded that

neurons in the rat auditory cortex show no true deviance

detection. More recently, the presence of SSA for pure

tones was confirmed and carefully selected control exper-

iments were included which led to a different conclusion

(Taaseh et al. 2011; Yaron et al. 2012). These experiments

allowed for a separation of the different aspects of stimulus

statistics on deviant-related activity. Especially, the influ-

ence of stimulus rarity could be separated from the

dependence on stimulus regularity.

How is SSA in the Auditory Cortex Related

to Adaptation Along the Auditory Pathways?

This leads to the general question, how SSA at the cor-

tical level is either resulting from processes of adaptation

along the ascending auditory pathway or, on the other

hand, how SSA in the auditory cortex is influencing the

non-primary ascending or the descending pathway.

Automatic deviance detection is stimulus-derived, which

means that, although the source for MMN might be some

higher cortical area, it is the auditory pathway that is

probably the necessary substrate for adaptation of more

basic acoustic stimuli. The recent evidence for potentials

related to deviance detection at early stages of the human

auditory pathway is strongly supporting this view (Grimm

and Escera 2011).

As a result, the strong adaptation in the auditory cortex

might be integrated from the sum of effects along the

primary (tonotopically organized) ascending pathway,

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which is the way how stimulus information mainly reaches

the primary auditory cortex. This straightforward expla-

nation, however, cannot be the whole truth for the rat

auditory system where adaptation at the midbrain level (i.e.

the central nucleus of the inferior colliculus; Malmierca

et al. 2009) and in the respective structure of the auditory

thalamus (i.e. ventral division of the medial geniculate

body; Anderson et al. 2009; Bauerle et al. 2011) is very

weak. Neurons in non-primary, non-tonotopic subnuclei at

these levels (Lee and Sherman 2011), on the other hand,

show often a high degree of adaptation. For the case of FM

adaptation this pattern has to be compared to the presumed

sites of origin of FM selectivity. Selectivity for FM mod-

ulation direction can, again, be found along a major portion

of the rat primary auditory pathway, from where it might

be transferred to the auditory cortex (Kuo and Wu 2012).

Opposing results indicate that FM selectivity is generated

in cortical neurons through differential synaptic input

(Zhang et al. 2003). The latter result would require a cor-

tical mechanism of adaptation in order to fully explain the

results presented above. Indeed, a possible mechanism in

rat cortical neurons based on changes in potassium currents

is described in the literature (Abolafia et al. 2010). Our

recordings were mainly from cortical layers III-IV where

one would still expect a strong thalamic influence. While

some studies found MMN-like responses in more superfi-

cial layers (e.g. Javitt et al. 1994), recent data are more

consistent with this study (Fishman and Steinschneider

2012).

Differences in SSA Between Awake and Anesthetized

Preparations

While one would expect that physiological markers rela-

ted to novelty detection should be the more prominent the

more awake or vigilant a test person or an animal is, one

has to note that the amount of SSA determined in the

awake rat auditory cortex (von der Behrens et al. 2009;

present study) is much smaller than the effects described

in anesthetized preparations. While this difference might

partially be related to the selection of less optimized

oddball paradigms, an additional strong component is

related to the general difference in the level of neuronal

activation as measured in the firing rate of individual

neurons. Spontaneous firing rates and also stimulus-

induced rates were found to be much higher in awake

compared to anesthetized rat auditory cortex (Gaese and

Ostwald 2001). If one just assumes that stimulus-related

responses are simply added to a given level of underlying

spontaneous firing rate, then the amount of SSA, as it is

determined based on adaptation indices, is strongly

reduced by the high spontaneous firing in awake prepa-

rations, as shown below.

The influence of firing rate on index-quantified SSA can

be illustrated by comparing the freq-sAIs and dir-sAIs

presented in the Results section to recalculated indices after

subtracting average spontaneous rates that were determined

from the time window before stimulation. This procedure

resulted in much higher sAI indices for PT and FM stimuli.

Average freq-sAI for PT stimulation (always given for

multi units) as shown in Fig. 3b increased from 0.133 to

0.41. The average dir-sAIs determined for slow (-50/

50 oct/s) and fast (-100/100 oct/s) FM stimulation

increased from 0.043 to 0.16 and from 0.028 to 0.109 (see

Figs. 3c, d), respectively. Interestingly, these recalculated

indices were then in the range of values determined for the

local field potentials that were recorded in parallel. These

potentials, by the way, do not reflect any spontaneous

activity unrelated to any stimuli.

The strong influence of spontaneous rate on adaptation

quantified by sAI-values is straightforward. One can

assume that each value included in the formula (see

‘‘Material and Methods’’ section) is the sum of the spon-

taneous rate (always same level!) plus the stimulus-induced

firing. While spontaneous rates are canceled down in the

numerator in the formula, they are added four times to the

denominator. As a result, higher activity will always result

in lower amounts of adaptation.

These assumptions are confirmed by looking at the

influence of stimulus intensity in the rat IC. This revealed a

strong reduction in the amount of SSA with increasing

absolute SPL (Duque et al. 2012; e.g. Figs. 4e, 6f). This

reduction can easily explained by the strong increase in

firing rate with increasing SPL as it is obvious from the

shape of the typical frequency response areas in the IC. The

dependence of SSA on firing rate when quantified with an

adaptation index would be exactly as described for the

anesthetized-awake comparison.

Taking this seriously, it is important to note that the

amount of SSA determined so far for the auditory cortex in

awake preparations is for the majority of recorded neurons

comparably small (von der Behrens et al. 2009) compared

to SSA under anesthesia (Taaseh et al. 2011). MMN-like

components in epidural auditory ERP recordings are, on

the other hand, more prominent in the awake rat (Na-

kamura et al. 2011). Open questions derived from this are:

How much adaptation is necessary as a reliable basis for

detecting individual deviations from a series of stimuli? Is

the (rat) auditory cortex actually involved in the process of

deviance/novelty detection? What other functional signifi-

cance might be behind the phenomenon described? Or

asking more fundamentally: Is the adaptive change in

neuronal representation just resulting from exhausted

physiological processes? This leads again to a question of

stimulus encoding: How is the brain dealing with this

change in representation?

Brain Topogr

123

How is SSA to FM Stimuli Related to MMN Measured

in the Human Auditory Cortex?

If one accepts the possibility of a contribution of SSA

phenomena to MMN generation then the following aspects

of FM-elicited activity indicate a comparable processing at

different levels of description, spanning from more stim-

ulus-specific features in the process of adaptation in rats to

more generalized characteristics of change-related activity

in MMN-type components in humans.

These MMN-type components were determined, com-

parable to the experimental approach presented here, in

neuromagnetic responses recorded during oddball stimu-

lation paradigms with FM tones differing in FM modula-

tion direction (Sams and Naatanen 1991; Pardo and Sams

1993). Both studies find the source of FM-related activity

in the supratemporal (auditory) cortex. A high percentage

of neurons with FM-direction preference and with SSA

depending on modulation direction, as described in this

study from the rat auditory cortex, support this. In addition,

Paavilainen et al. (1999) presented divers tone pairs where

the invariant feature was only the direction of tone pair

frequency (ascending or descending). A change in direction

evoked the characteristic MMN wave. Essentially, these

tone pairs can be seen as simplified frequency modulated

tones with only the start- and end-frequency present and no

continuous sweep as we used it in our FM stimuli. All

approaches together indicate that the auditory cortex is

essential for the detection of changes in FM modulation

direction.

This change-related activity, however, was not fully

independent of unrelated stimulus features. Especially SSA

to slow FM stimuli in the rat cortex differed in the neuronal

responses to some extend between up- and downward FM

stimuli, while SSA to fast FM and the encoding in LFPs

was rather independent. This is comparable to the stimulus

dependence of MMN-type components in humans. The

size of these components was depending on the FM mod-

ulation range (Sams and Naatanen 1991), a parameter that

is directly related to FM speed when the range is changed

in stimuli of constant duration as it was done here. In

addition, the latencies of MMN-type components were

slightly shorter than usually expected for MMN and these

latencies depended on modulation direction (Pardo and

Sams 1993), as was also shown in this study for SSA

encoding.

On the other hand, the MMN-type components elicited

by the change in FM modulation direction were indepen-

dent of other aspects of stimulation as one would expect for

generalized deviance detection. MMN was independent of

the specific frequency content of individual stimuli (Pardo

and Sams 1993). This relates to an important aspect of SSA

encoding of FM stimuli. Both, SSA and MMN were in the

vast majority of studies shown for arrangements where the

detected change in stimulation was related to a change in

the underlying neural substrate that was activated by the

stimuli (e.g. change from low frequency neurons to high

frequency neurons in pure tone-elicited MMN). FM stim-

uli, on the other hand, differing only in modulation direc-

tion, activate always the same set of afferent fibers in the

tonotopically arranged central auditory pathway. This

strongly suggests that the change-related activity is actually

originating from cortical neurons, both in SSA and MMN.

One might also take this as evidence against the idea that

SSA is just resulting from exhausted physiological pro-

cesses. The extent to which the differential responses to

frequent and rare FM stimuli in the primary auditory cortex

reflect SSA or true deviance detection, as thought to be

reflected by MMN in humans, remains to be fully inves-

tigated in future studies.

Acknowledgments The authors thank the guest editors, Drs. Val-

erie Shafer and Elyse Sussman, for supporting the submission of this

paper to the special issue on MMN. Two anonymous reviewers

provided helpful comments and suggestions on a previous version of

the manuscript.

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