Cortical activation during Pavlovian fear conditioning depends on heart rate response patterns: An...

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Cognitive Brain Research

Research Report

Cortical activation during Pavlovian fear conditioning depends on heart

rate response patterns: An MEG study

Stephan Moratti*, Andreas Keil

Department of Psychology, University of Konstanz, P.O. Box D25, D-78457 Konstanz, Germany

Accepted 21 July 2005

Available online 2 September 2005

Abstract

In the present study, we examined stimulus-driven neuromagnetic activity in a delayed Pavlovian aversive conditioning paradigm using

steady state visual evoked fields (SSVEF). Subjects showing an accelerative heart rate (HR) component to the CS+ during learning trials

exhibited an increased activation in sensory and parietal cortex due to CS+ depiction in the extinction block. This was accompanied by a

selective orientation response (OR) to the CS+ during extinction as indexed by HR deceleration. However, they did not show any differential

cortical activation patterns during acquisition. In contrast, subjects not showing an accelerative HR component but rather unspecific HR

changes during learning were characterized by greater activity in left orbito-frontal brain regions in the acquisition block but did not show

differential SSVEF patterns during extinction. The results suggest that participants expressing different HR responses also differ in their

stimulus-driven neuromagnetic response pattern to an aversively conditioned stimulus.

D 2005 Elsevier B.V. All rights reserved.

Theme: Neural basis of behavior

Topic: Motivation and emotion

Keywords: Pavlovian conditioning; Cortical activation; MEG; Heart rate; Fear response; Steady state visual evoked field

1. Introduction

Learning that certain events in an individual’s environ-

ment signal potential threat or danger is an important ability

to ensure survival of the organism. Pavlovian fear con-

ditioning [69] as a laboratory model of the aforementioned

ability represents a simple form of associative learning that

is part of most mammalian defensive behavior systems [21].

The neural systems underlying fear conditioning have

been elucidated by animal models as well as human imaging

studies using functional magnetic resonance imaging (fMRI)

and positron emission tomography (PET) [13,58]. The

amygdala has been regarded as the key structure processing

aversive stimuli via afferents from sensory thalamus [55,84],

enabling fast evaluation of noxious stimuli without complex

computations of the cortex. As the amygdaloidal complex is

0926-6410/$ - see front matter D 2005 Elsevier B.V. All rights reserved.

doi:10.1016/j.cogbrainres.2005.07.006

* Corresponding author. Fax: +49 7531 884601.

E-mail address: [email protected] (S. Moratti).

highly interconnected with the temporal, orbito-frontal, and

insular cortices [1,2,64,74], the amygdala is anatomically

well placed for stimulus association learning in various

sensory domains. Given its fast access to sensory informa-

tion and its neuroanatomical location, the amygdala has been

assigned a central role in mediating synaptic changes at the

cortical level [59] necessary for the association between the

conditioned (CS) and the unconditioned stimulus (US). For

example, Buchel and colleagues [14] demonstrated in an

fMRI study that the amygdala activation was most prominent

during acquisition of the conditioned response (CR) and

LaBar and collaborators [50] showed a decline of amygdala

activation over trials possibly reflecting that amygdala

activation is only necessary until associations between the

CS and US have been formed.

In addition to amygdala circuitry and highly intercon-

nected with it, a distributed network of cortical areas seems

to be involved in classical fear conditioning as well. For

example, standard fear conditioning paradigms using

25 (2005) 459 – 471

S. Moratti, A. Keil / Cognitive Brain Research 25 (2005) 459–471460

auditory CS not only reported enhanced spike firing rates in

neurons of the amygdala and hippocampus, but also of

neurons in auditory cortex [5,52,75]. In the visual system of

primates, the amygdala projects back to various stages in the

ventral visual path and receives itself highly processed

sensory information [3]. This neural architecture may be the

basis of a mechanism, which enhances stimulus processing

in visual sensory systems. In line with this notion, imaging

studies in humans have shown greater activations for the

reinforced visual CS (CS+) as compared to the non-

reinforced CS (CS�) in temporal and occipital cortices

[15,39].

Additional support to the involvement of cortical areas in

fear conditioning comes from cognitive theories of Pavlo-

vian conditioning that emphasize anticipation or attentional

processes [70]. For instance, Rescorla [77] has emphasized

that the CS+ in a Pavlovian conditioning paradigm gains

predictive value that enables the organism to anticipate an

aversive event. The ability to obtain such a predictive value

has been associated with the salience of a stimulus [57].

Thus, at a neurophysiological level, cortical systems media-

ting attentional and evaluative processes should come into

play.

There is good evidence for the involvement of a

widespread fronto-parietal cortical network in orienting to

a stimulus and integrating its features [16,54,68,80]. Human

imaging studies of Pavlovian conditioning have consistently

reported enhancement of cortical activity for visual rein-

forced CSs in brain regions like the frontal, temporal,

parietal cortices, and the anterior cingulate [14,22,23,

39,47,50] possibly reflecting the involvement of a neural

network mediating attention/emotion aspects of that stimu

lus. In a recent fMRI study, Armony and Dolan [4]

demonstrated that a visual context stimulus modulated

conditioned BOLD responses in auditory cortex associated

with activity in parietal cortex, further illustrating the

importance of cortical structures being part of a cortical

attention network.

In the present work, we investigated changes of cortical

processes over time using the steady state visual evoked

field (SSVEF) technique. SSVEFs (a counterpart of the

SSVEP in EEG research) reflect widely distributed func-

tional networks oscillating coherently at the driving stimulus

frequency and are sensitive to attentional and complex

cognitive processes [61,66,71,85].

As expectancy and attentional processes may be

involved in Pavlovian conditioning as outlined above,

the SSVEP/SSVEF paradigm seems to be an adequate tool

in order to track ongoing neural activity associated with

processes of aversive conditioning. A recent SSVEP study

by Gray and coworkers [26] investigated cortical oscil-

latory activity during the anticipation of an electric shock

and revealed the involvement of frontal, temporal, and

occipital electrode sites during anticipatory anxiety.

Recently, Moratti and collaborators [60] have identified

a fronto-parietal network during visual affective stimulus

processing using magnetoencephalography (MEG) and the

SSVEF technique. This finding supports the involvement

of networks traditionally related to attention processes in

processing of emotional information. In the same vein, it

has been demonstrated that cued spatial attention and

motivated attention to affective stimuli possibly interact

within the same neuronal networks of attention and

stimulus processing [44]. This raises the question whether

similar neurophysiological processes are associated with

viewing arbitrary visual stimuli which are associated with

aversive responses as a consequence of classical fear

conditioning. There is, however, no study to date using

the SSVEP/SSVEF technique in combination with a

discriminative aversive conditioning design which allows

the direct comparison between the stimulus-driven neuro-

magnetic response of a reinforced (CS+) and nonrein-

forced (CS�) stimulus during acquisition and extinction

blocks.

Therefore, the aim of the present study was to examine

cortical sources of the SSVEF as modulated by the

predictive value of a visual CS during acquisition and

extinction, using MEG. Since the SSVEF response is a

stimulus-driven ongoing oscillatory response in cortical

networks responsible for processing the visual CS+ and

CS�, estimating the underlying sources should elucidate the

involvement of relevant cortical structures in Pavlovian

conditioning. Applying the minimum norm estimate (MNE)

[34,35] to the SSVEF response to determine the cortical

sources of the neuromagnetic field, we hypothesized that

after an association between the CS+ and the US has been

established, the motivationally relevant CS+ will generate

greater amplitudes in fronto-parietal and extrastriate cortex

responsible for allocating attention and feature extraction,

respectively. As the MEG has sufficient temporal resolution,

we were interested at what time interval during CS depiction

a differential activation pattern in the aforementioned

cortical structures could be observed for the CS+ and

CS�, respectively.

Heart rate (HR) was recorded to validate the effective-

ness of the conditioning procedure. Further, we wanted to

evaluate the brain responses of subjects who reacted with

accelerative or decelerative HR change components in

response to the CS+ during acquisition as has been observed

by various authors [28,32,38]. Hodes et al. [38] hypothe-

sized that HR accelerators learn a fear response whereas HR

decelerators just learn that two events are related in time.

Hamm and Vaitl [28] demonstrated that only subjects

responding with an accelerative HR component displayed

potentiated startle responses to the CS+. We wanted to

explore if attentional and sensory cortical networks during

Pavlovian conditioning are involved in HR accelerators and

decelerators to the same extent. Subjective judgments of the

US were obtained in order to assess the aversiveness of the

US. Further, we collected questionnaire data in order

to evaluate state/trait anxiety and depressiveness of the

participants.

S. Moratti, A. Keil / Cognitive Brain Research 25 (2005) 459–471 461

2. Materials and methods

2.1. Participants

Nineteen paid volunteers (10 females, 9 males, all right

handed [67]) gave written consent to participate in the study.

Their mean age was 27.1 years (range: 20–49 years). They

had normal or corrected to normal vision and no family

history of photic epilepsy. They did not report a recent

critical live event or any history of psychotherapy. On the

German version of the state–trait anxiety inventory (STAI)

[53], subjects reported a mean state anxiety score of 31.1

(T1.1) and a mean trait anxiety score of 34.2 (T1.8). Theirmean depression score measured by a German version of the

Beck depression inventory (BDI) [36] was 3.8 (T0.8). Dueto equipment malfunction, we lost HR data of one subject.

Another subject had to be excluded from the analysis

because of noise-contaminated MEG data. The MEG data of

the participant with the missing HR data was excluded from

further processing because the MEG data of groups

associated with different HR responses were analyzed

separately (see below). Finally, HR data of eighteen [10

females, 8 males, state anxiety: 31.1 (T1.1), trait anxiety:34.4 (T1.9), BDI: 3.8 (T0.8)] subjects and MEG data of

seventeen subjects [9 females, 8 males, state anxiety: 31.3

(T1.2), trait anxiety: 34.4 (T2.0), BDI: 3.4 (T0.7)] will bereported.

2.2. Stimuli

Two gray shaded 45- gratings perpendicularly oriented

to each other served as conditioned stimuli (CS). Using a

video projector (JVCi, DLA-G11E) and a mirror system,

the CSs were projected on a screen in the magnetically

shielded room in random order, subtending a visual angle

of 8- both horizontally and vertically. A red fixation cross

was shown in the middle of the screen and was present

throughout the whole experiment. In each trial, a grating

was shown in a luminance-modulated mode of 12.5 Hz for

a time period of 5000 ms resulting in 63 on/off cycles, the

grating being shown for 40 ms, followed by 40 ms black

screen during each cycle. During the intertrial interval,

which varied randomly between 6 and 8 s, a black screen

was shown. The luminance modulation was done in order

to elicit visual evoked steady state fields that can be

recorded with MEG.

One of the two gratings was selected as the CS+ or CS�,

counterbalanced across subjects. Using an air tube system

attached to a sound amplifier (BTi \ ASG, 1996), the

unconditioned stimulus (US) consisting of a 95-dB white

noise with instantaneous onset was presented binaurally for

1 s during the last second of CS+ depiction, and terminated

together with CS+ offset. In 15% of the trials (see below),

the red fixation cross turned from blue into red color for 500

ms either 1000, 2500, or 4000 ms after CS+ and CS� onset,

each time lag occurring with the same probability.

2.3. Procedure

Upon arriving at the laboratory, subjects were familiar-

ized with the MEG chamber in which the recording took

place. Subsequently, participants were instructed about the

experimental procedure (see below). Then, subjects gave

written consent and completed the STAI and the BDI.

Thereafter, participants were prepared for the recordings.

For artifact control, four electrodes for the electrooculo-

gram (EOG) were attached, two at the left and right outer

canthi and two above and below the right eye. Additionally,

two electrodes for the electrocardiogram (ECG) were placed

at the left lower costal arch and the right collarbone.

Participants were then seated in the MEG chamber, their

head shapes were digitized, and index points (left and right

periauricular points, nasion, a pseudo-vertex and a pseudo

inion point at the forehead) were determined to calculate the

relative head position within the MEG helmet for source

analysis. Finally, they were positioned under the MEG

sensors and a projection screen was placed in front of them.

The experimental session started with participants read-

ing a written instruction on the screen in the MEG chamber

explaining again the task and ensuring subjects that no loud

noise would be delivered during the first block, which

served as a habituation phase. The habituation block

consisted of twenty CS+, twenty CS�, and six target trials

which requested a button press after the red cross had turned

into blue. The stimuli were depicted without pairing of the

CS+ with the US. Thereafter, a short break of 1 min was

allowed and subjects were instructed via text depicted on the

screen that during the next block, they will hear a loud noise

from time to time. After the break, the acquisition block

began, which was identical to the habituation block, except

that the CS+ was always paired with the US. The extinction

phase started without a break after the acquisition trials,

containing the same number of trials as the individual

blocks before. The CS+ and CS� were never paired with

the US during extinction. After the experiment, participants

rated pleasantness/unpleasantness and the level of arousal

on a 10-cm analog scale each, whereas a greater distance

from the left starting point of the analog scale represented a

higher level of unpleasantness and arousal, respectively.

Finally, participants were interviewed if they had detected a

rule behind US presentation and were coded as aware or

unaware of conditioning contingency depending on rule

identification. Subjects were paid C15 (approximately 15

US dollars) for participation.

2.4. Data acquisition and preprocessing

The MEG was recorded continuously, and digitized at a

rate of 254.3 Hz, using a 148-channel whole head system

(MAGNESi 2500 WH, 4D Neuroimage, San Diego,

USA). A bandpass filter of 0.1–50 Hz was applied on line.

The EOG and ECG acquisition was performed by means of

a Synamps amplifier (NEUROSCANi) using Ag/Cl


electrodes. As the EOG and ECG recordings were coupled

with the MEG acquisition, the same sampling rate and

bandpass filter were applied to them.

SSVEFs were derived for each condition (CS+ and CS�)

and every experimental block (habituation, acquisition,

extinction) by averaging the MEG data over 4000 ms

subtracting a 500-ms pre-stimulus baseline. The last 1000

ms of CS presentations were omitted because during CS+

trials in the acquisition block, a loud noise as US was

presented and startling of the subjects caused movement

artifacts. The target trials (subjects were requested to press a

button) were not submitted to further analysis. Eye artifact

topography was corrected using the algorithm implemented

in BESAi software [9]. Additionally, all data were

inspected visually for movement artifacts.

Subspace out-projection [88] of the eye artifact topo-

graphy was applied on the SSVEFs for each condition (CS+

and CS�) and each experimental block (habituation,

acquisition, and extinction) in order to account for topo-

graphy distortions using the eye correction algorithm (see

below). Thereafter, the SSVEFs were complex demodulated

[76] at the stimulation frequency of 12.5 Hz in order to

obtain stimulus-driven amplitudes for the sine and cosine

parts of the demodulated SSVEFs over time.

As with the MEG data, only the first 4000 ms of HR

change during stimulus presentation were analyzed. Target

trials were not submitted to analysis. HR change waveforms

were calculated for 4000 ms of stimulus viewing in 500-ms

steps subtracting a 2000-ms pre-stimulus baseline. The

frequency of the occurrence of an R-wave was derived from

the interbeat-interval and vectorized by weighting it by the

distance to a 500-ms bin within the time vector. The

HR change was transformed in beats per minute (bpm).

Habituation blocks were not analyzed.

2.5. Source analysis

In order to estimate the cortical sources of the complex

demodulated SSVEFs, a minimum norm estimation (MNE)

technique was applied [27]. The MNE procedure utilized in

the present study was described in more detail in Moratti et

al. [60] and will be outlined only in brief, here. The

estimated current density of the sources is obtained by

solving the following equation:

b ¼ Axþ e ð1Þ

where b is the data vector, A is the lead-field matrix

describing the sensor’s sensitivity to the sources located,

e.g., on a shell, x is the solution of the current density of the

sources, and e is the noise component.

The MNE is characterized by minimizing the power of

the estimated solution x (L2-norm). Given the presence of

noise and a high number of sensors, a regularization during

pseudo-inversion of A is necessary to obtain a stable current

density solution [10]. Here, a Tikhonov–Phillips regulari-

zation was applied in order to suppress the noise part of the

solution. For the determination of the regularization para-

meter k, the L-curve method was used [31]. The L-curve

method determines a trade off between a good data fit and

minimizing the power of the solution [31].

For the computation of the lead-field matrix A, we used

the algorithm suggested by Hauk et al. [34]. To this end, A

was calculated for each participant, based on information on

the center of a fitted sphere to the digitized head shape, and

the positions of the MEG sensors relative to the head. A

subspace projection [88] of the eye artifact topography was

applied to the lead-field matrix A in order to account for

topographical distortions caused by the eye artifact correc-

tion (see above). A spherical shell with evenly distributed

350 dipole locations served as source space. The radius of

the shell was 80% of the radius of the fitted sphere.

According to Hauk et al. [34], this radius was considered as

a good approximation of the location of cortical sources.

For the derivation of k and computation of the MNE

solution, we used an L-curve and Tikhonov–Phillips

regularization algorithm implemented in MATLABi by

Hansen and O’Leary [31] available at http://www.imm.

dtu.dk/~pch/. As the phase information of the measured

MEG signal is crucial for the computation of the MNE and

is lacking in a complex demodulated signal, the MNE was

estimated for the sine and cosine parts of the 12.5-Hz

complex demodulated SSVEFs separately for every con-

dition (CS+ and CS�), experimental block (habituation,

acquisition, and extinction), and for each subject. This

procedure was derived from an algorithm calculating the

MNE in the frequency domain suggested by Jensen and

Vanni [40]. The square root of the sum of squares of the

MNE amplitudes for the sine and cosine parts served as a

measure of the total 12.5 Hz current source density over

stimulus presentation time:

Ampi tð Þ ¼ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiAmp2i;sine tð Þ þ Amp2i;cosine tð Þ

q;

ia 1; N ; 350½ �; ta 0 ms; N ; 4000 ms½ � ð2Þ

where Ampi(t) is the total amplitude of the 12.5-Hz

demodulated SSVEF at dipole location i and time t, and

Ampi,sine(t) and Ampi,cosine(t) are the corresponding ampli-

tudes of the sine and cosine parts for time t.

L-curves were calculated for the averaged baseline

topography over the 500-ms pre-stimulus interval for each

condition (CS+, CS�), experimental block (habituation,

acquisition, extinction), and sine/cosine parts within one

subject. The highest k value determined from the resulting

L-curves was used as regularization parameter. This

procedure was chosen in order to prevent that different

regularizations could account for condition effects. Finally,

the MNE solutions were projected on a standard brain

implemented in the EMEGS* analyzing software [41]

freely available at http://www.uni-konstanz.de/win/emegs

and submitted to a statistical mapping procedure (see

below).

http:\\www.imm.dtu.dk\~pch\

http:\\www.uni-konstanz.de\win\emegs


2.6. Statistical analysis

In order to evaluate differences between processing of

the CS+ and CS� over stimulus presentation time, the MNE

amplitudes were averaged across three time windows (t1:

0–1.3 s; t2: 1.3–2.6 s and t3: 2.6–3.9 s) for each condition

(CS+, CS�), each experimental block (habituation, acquis-

ition, and extinction), and each subject. Within every

experimental block, the interaction between condition

(CS+, CS�) and time (t1, t2, t3) was modeled at 273 of

the 350 sources of the MNE shell in order to identify source

clusters of differential activations for the CS+ and CS�.

Sources located at the bottom of the shell were not

considered as meaningful and were omitted in order to

reduce noise and to enhance statistical power (reducing the

number of dipoles from 350 to 273). The P values for the

interaction terms were corrected for 273 multiple tests by

the false discovery rate procedure [8,45] and mapped on a

standard brain implemented in the EMEGS* analyzing

software.

The interactions were modeled using a linear mixed

effects model (LME) applying restricted maximum like-

lihood estimators (REML) for parameter estimation. A

detailed introduction of LME models in psychophysiolog-

ical research is given by Bagiella et al. [6]. As LME models

use vertical data matrices, the degrees of freedom of the

denominator are higher than those obtained by univariate

analysis of variance. A description of the REML estimation

method can be found in Searle et al. [83]. As the factor time

implies dependence of observations, a first-order autore-

gressive [AR(1)] covariance structure was applied that

estimates one correlation parameter between observations

and assumes that the interdependence of observations

decays exponentially. Such serial correlation structures have

been introduced to model dependence in time-series data

and are discussed, for example, in Pinheiro and Bates [73] in

more detail. In the LME model, we chose condition and

time as fixed effects. As we were not interested in mean

differences in MNE amplitudes between subjects over all

conditions and within conditions, we selected the intercept

to be a random effect at the subject and condition within

subject level. The statistical modeling was done using R*

[87], a freely available statistical package.

Source clusters were considered as meaningful whenever

at four or more neighboring source locations the interaction

terms survived the false discovery rate procedure for 273

tests and thus consisted of sources with associated corrected

P values less than 0.05. The mean amplitude across dipoles

of an identified source cluster was submitted to LME

analysis identical to the one described above, except that we

added the fixed effect time2 to the fixed effect time and

considered them as ordered factors in order to assess

different time courses of cortical activations for the CS+

and CS� (linear and quadratic time courses). Whenever the

kind of time course (linear and/or quadratic) interacted with

condition, the time courses were modeled for the CS+ and

CS� condition, separately. Additionally, we conducted

paired t tests in order to assess at which time slots (t1, t2,

t3) the CS+ and CS� generated different cortical source

activations.

Finally, following the procedure described by Hodes et al.

[38], a cluster analysis was performed on heart rate data

obtained in acquisition trials during CS+ presentation. This

approach was chosen because we wanted to identify

subjects who accelerated or decelerated in their HR response

to the CS+ during acquisition as observed by previous

studies [28,32,38].

The HR obtained change time series for groups derived

by cluster analysis was analyzed using an LME model. The

model was identical to the LME model presented above,

except that time, time2, time3, and time4 served as ordered

fixed effects in order to assess a linear, quadratic, cubic, and

quartic time course of the HR data. This was implemented

on the basis of earlier notions [89] suggesting that analyzing

differences in HR waveform components is more mean-

ingful than comparing means. The time factors consisted of

nine time bins containing 500 ms each (see above). As we

were not interested in mean differences or drifts of the HR

response curve per subject over all conditions and within

conditions, we chose the intercept and the linear slope (time)

to be random effects at both the subject and condition within

subject level.

The difference between HR accelerators and decelerators

with respect to the sum scores of the questionnaires and to

the distance from the left starting point of the analog scales

were analyzed using t tests. The frequency distributions of

females and males and of subjects aware or unaware of

conditioning contingency were assessed by v2 tests.

3. Results

3.1. Delineation of different HR change responders by

cluster analysis

The cluster analysis on HR change in acquisition trials

during CS+ presentation revealed three groups. One group

of subjects showed a defensive accelerative HR component

in response to the CS+ (N = 8) and the other two subgroups

depicted a decelerative pattern (N = 6 and N = 4,

respectively). In order to obtain samples of sufficient size

for further analysis, the latter two subgroups were collapsed

into one decelerative group (N = 10). The obtained grouping

structure was applied to the extinction trials in order to

evaluate possible learning differences between accelerators

and decelerators.

As suggested by Fig. 1A and a strong interaction of a

quartic waveform component (suggesting a triphasic pat-

tern) and condition (CS+ and CS�), accelerators showed a

defense response to the CS+ (see Table 1) within a general

HR deceleration as the HR change values were below the

baseline. This was confirmed by separate analysis of the

Fig. 1. (A) Heart rate change (in beats per minute) in acquisition (left panel) and extinction (right panel) blocks during CS+ and CS� depiction for accelerators

(N = 8). (B) Heart rate change in acquisition (left panel) and extinction (right panel) blocks during CS+ and CS� depiction for decelerators (N = 10). The zero

value in the time scale indicates the onset of the visual CS+ and CS�.


CS+ and CS� trials yielding only a quartic and linear

waveform component for the CS+ [quartic: F(1,60) = 28.3,

P < 0.0001; linear: F(1,60) = 12.1; P < 0.001]. At CS�trials, HR response only contained a weak linear component

[F(1,60) = 3.5; P < 0.10] and no other waveform

components. During the extinction block of the experiment,

accelerators reacted with an orientation response to the CS+

Table 1

Interaction between waveform components (linear, quadratic, cubic, and

quartic trends) and condition (CS+, CS�) during acquisition for subjects

with an accelerative component in acquisition trials

Interaction with condition numDF denDF F value P value

Linear 1 120 1.1 n.s.

Quadratic 1 120 0.5 n.s.

Cubic 1 120 0.4 n.s.

Quartic 1 120 15.4 <0.0001

n.s. = not significant, numDF = degrees of freedom numerator, denDF =

degrees of freedom denominator.

but not to the CS� [see Fig. 1 and Table 2 for interaction;

linear component CS+: F(1,60) = 18.7, P < 0.0001; CS�:

not significant (n.s.)].

For decelerators, a weak but statistically significant

interaction between a quadratic waveform component and

condition was obtained in the acquisition block (see Table

3). Separate analyses of the acquisition trials during CS+

Table 2


quartic trends) and condition (CS+, CS�) during extinction for subjects

with an accelerative component in acquisition trials


Linear 1 120 9.4 <0.01

Quadratic 1 120 3.6 n.s.

Cubic 1 120 3.7 n.s.

Quartic 1 120 0.5 n.s.



Table 3


quartic trends) and condition (CS+, CS�) during acquisition for

decelerators


Linear 1 152 0.3 n.s.

Quadratic 1 152 4.4 <0.05

Cubic 1 152 0.7 n.s.

Quartic 1 152 1.0 n.s.




and CS� presentation showed that the quadratic component

was more prominent for the CS+ [F(1,76) = 20.6, P <

0.0001] than for the CS� [F(1,76) = 4.0, P < 0.05]. During

extinction, decelerators did not show any differential HR

changes for the CS+ and CS� (see Fig. 1B).

Thus, the HR data indicated that specifically subjects

with an accelerative HR component showed a differential

defense—and orientation response to the CS+ during

acquisition and extinction, respectively. During acquisition,

the other subject group showed a decelerative quadratic time

Fig. 2. Grand mean of the minimum norm estimates (MNE) over all subjects (

extinction phase of the experiment. Left and right hemispheres are shown. The arro

of the depicted MNE topographies is indicated. The color bars on the right show t

and CS� depiction.

course of HR change for both the CS+ and CS� but

somewhat stronger for the CS+. The former group will be

denoted accelerators and the latter decelerators. Within

extinction trials, decelerators did not differentiate between

the CS+ and CS� with respect to HR change.

3.2. MNE data

Fig. 2 depicts the grand mean of the MNE topography

across all subjects (N = 17) in order to give an impression of

visual cortical activation by steady state stimulation for each

condition and across three different time intervals after

stimulus onset. As can be seen, the SSVEF amplitude in

visual areas declines over stimulus presentation time and is

more prominent at left hemispheric sources.

3.2.1. Subjects showing an accelerative HR component

During habituation and acquisition, no interaction

between condition (CS+, CS�) and time (t1, t2, t3) could

be observed in any brain region. However, in extinction

trials, statistical mapping revealed a differentiated SSVEF

response in certain brain areas to the CS+ and CS� for the

N = 17) for CS+ and CS� trials during the habituation, acquisition, and

ws indicate the time of CS presentation. Below the arrows, the time interval

he activation strength in nAm. Time 0.0 s indicates the onset of visual CS+

Fig. 3. (A) Significant (interaction CS presentation time and condition) source clusters for subjects showing an accelerative HR component are depicted. The

color bar depicts corrected P values. (B) Mean amplitudes and standard errors of the means over significant occipito-temporal and parietal source clusters as

identified by the linear mixed effects procedure for three time intervals after stimulus onset. (C) Difference map (CS+ minus CS� condition) for the last time

interval before the US could be expected. The color bar indicates the difference values of cortical SSVEF activation in nAm. *P < 0.05; ~P < 0.10; paired

comparisons.


accelerators. In the left hemisphere, parietal cortical sources

showed a quadratic activation time course only during CS+

presentation [quadratic trend� condition: F(1,28) = 6.4, P <

0.05; quadratic trend CS+: F(1,14) = 20.7, P < 0.001;

quadratic trend CS�: n.s.] indicating an increase in SSVEF

amplitude in the late time segment of CS+ depiction

compared to CS� presentation (t1 and t2: n.s.; t3: t = 2.1,

df = 7, P = 0.07). For left occipito-temporal brain regions, a

similar difference in the time course of activation emerged

[quadratic trend � condition: F(1,28) = 13.7, P < 0.001;

quadratic trend CS+: F(1,14) = 12.8, P < 0.005; quadratic

trend CS�: n.s.]. This effect was not only driven by an

increase of CS+ activation towards the time the US could be

expected, but was also due to a decline of CS+ amplitude

beneath the activity level of the CS� during the second time

segment (t1: n.s.; t2: t = �2.4, df = 7, P < 0.05; t3: t = 3.3,

df = 7, P < 0.05). The results for the accelerators are

summarized in Fig. 3.

3.2.2. Decelerators

In the habituation block, statistical mapping revealed no

brain areas of differentiated SSVEF activation for the CS+

and CS� in the decelerator group. During acquisition,

decelerators showed a strong quadratic time course of

cortical source activation in left orbito-frontal brain regions

only for the CS+ and not for the CS� [quadratic trend �condition: F(1,32) = 10.7, P < .005; quadratic trend CS+:

F(1,16) = 9.4, P < .01; quadratic trend CS�: n.s.]. The CS�activation remained at the same level over all time segments

(linear trend CS�: n.s.). The mean SSVEF amplitude in left

orbito-frontal brain areas elicited by the CS+ was greater

than the activation generated by the CS� during the initial

(t1) and late (t3) stimulus presentation times (t1: t = 3.4, df =

8, P < .01; t2: n.s.; t3: t = 2.3, df = 8, P < .05). The SSVEF

amplitude of left inferior temporal brain regions generated

by the CS+ was characterized by a decline over time,

whereas the amplitude elicited by the CS� remained at the

same level [linear trend � condition: F(1,32) = 10.4, P <

.005; linear trend CS+: F(1,16) = 43.2, P < .0001; linear

trend CS�: n.s.]. However, single comparisons of the

amplitude means of the CS+ and CS� for each time

segments did not reveal any differences (t1, t2, and t3: n.s.).

During extinction, no interaction between condition

(CS+, CS�) and time (t1, t2, t3) could be observed in any

brain region for the decelerators. The results for the

decelerators are summarized in Fig. 4.

3.3. Questionnaires and demographic data

As expected, the state and trait scores of the STAI over

all subjects correlated (r = 0.56, P < 0.05). Considering

accelerators and decelerators separately, both groups did not

show any differences in state or trait scores of the STAI

[state STAI: accelerators mean: 29.9 (T2.0); decelerators

mean: 32.1 (T1.3); trait STAI: accelerators mean: 32.4

(T3.1); decelerators mean: 36.0 (T2.5)]. The two groups also

did not differ in depression scores as measured by the BDI

[accelerators: 2.5 (T1.0); decelerators: 4.9 (T1.2)]. Accele-rators and decelerators rated the white noise that served as

US equally unpleasant and high arousing [valence: acce-

lerators: 9.1 (T0.3); decelerators: 8.4 (T0.4); arousal:

accelerators: 8.1 (T0.6); decelerators: 7.8 (T0.6)]. Accelera-tors and decelerators were of about the same age [accele-

rators: 27.6 (T1.7); decelerators: 27.1 (T2.6)] and females

and males were similarly represented in the accelerator and

decelerator groups (accelerators: 4 females, 4 males;

Fig. 4. (A) Significant (interaction CS presentation time and condition) source clusters for HR decelerators are depicted. The color bar depicts corrected P

values. (B) Mean amplitudes and standard errors of the means over significant left orbito-frontal and inferior temporal source clusters as identified by the linear

mixed effects procedure for three time intervals after stimulus onset. (C) Difference map (CS+ minus CS� condition) for the first time interval after stimulus

onset. The color bar indicates the difference values of cortical SSVEF activation in nAm. *P < 0.05; paired comparisons.


decelerators: 6 females, 4 males). Altogether, only 3 out of

18 subjects could report a contingency awareness. Two

aware subjects were accelerators and one aware subject was

a decelerator. The corresponding t tests and v2 tests were not

significant.

4. Discussion

Examining MEG correlates of Pavlovian fear condition-

ing by means of steady state visual evoked fields

(SSVEF), we studied the modulation of stimulus-driven

magnetocortical responses to visual stimuli differing in

their motivational significance (reinforced CS+ vs. non-

reinforced CS�) after one stimulus had been paired with

an aversive event. Using the techniques of complex

demodulation and minimum norm estimation, we aimed

to investigate the changes in cortical processing over

stimulus presentation time related to the experimental

manipulation that one previous biologically irrelevant

stimulus such as a grating gains predictive value of an

aversive event. Additionally, we recorded the HR change

with respect to CS onset in order to evaluate physiological

responding to the CS+ and CS�.

4.1. Heart rate response

By means of cluster analysis, we separated two groups of

HR responders to the CS+ during acquisition trials. In

general, subjects reacted with an orientation response (OR)

[25,86] to the visual CS as all HR change values were below

the pre-stimulus baseline. However, one group of partici-

pants showed an accelerative component within the OR

response to the CS+ during acquisition as indicated by a

strong interaction of condition (CS+ and CS�) and a quartic

waveform component [89]. Accelerative components within

a general decelerative HR pattern have been observed in HR

recordings before in humans and animals [12,49]. Although

cluster analysis in human Pavlovian conditioning studies

have revealed stronger accelerative reactions above baseline

[28,32,38], we argue that participants of our study depicting

an accelerative HR component acquired a fear response to

the CS+ during acquisition. Accelerative HR responses are

generally associated with fear reactions [33]. A possible

explanation why the accelerative component observed in

our study was not strong enough to push the HR response

above the baseline may lie in the fact that only 3 out of 17

subjects could report the CS–US contingencies after the

experiment although the 45- gratings that were perpendic-

ularly oriented to each other were easy to discriminate. The

reaction task during the conditioning procedure may have

distracted attention and resulted in unawareness of the CS–

US contingencies. Additionally, the reaction task could have

prompted an overall OR generating the HR patterns

observed here. However, subjects of the accelerator group

reacted with a marked OR response (HR deceleration) solely

to the CS+ during extinction supporting that HR condition-

ing took place during learning trials. Pavlovian conditioning

in unaware subjects has been demonstrated by several

studies [19,28,30,46,62,63]. However, as our study was not

designed to address this question and the validity of post-

experimental interviews has been criticized [56], we cannot

draw clear conclusions of the role of awareness in the

present study.

Participants not showing an accelerative component to

the CS+ during acquisition trials strongly decelerated to

both the CS+ and CS�. Although there was a weak but

statistically significant interaction of a quadratic waveform


component, we did not obtain the usually observed selec-

tively decelerative HR pattern to the CS+ obtained by

cluster analysis during acquisition trials [29,32,38]. Addi-

tionally, the decelerators of the present study did not show

any differential HR response to the CS+ and CS� during

extinction reflecting the possibility that the cluster analysis

simply separated subjects that did show conditioning

(accelerators) or not (decelerators of the present study) with

respect to HR.

With respect to behavioral data, HR accelerators and

subjects that unspecifically decelerated their HR response

to both the CS+ and CS� did not differ in the pleasantness

and aversiveness ratings of the US. Thus, differences in

the subjective evaluation of the US could not account for

the discriminative HR responses of the two groups.

Further, accelerators did not report higher levels of state/

trait anxiety and BDI depression scores than decelerators

nor were there any gender differences between the two

groups.

4.2. Cortical activation patterns in HR decelerators

Although the HR decelerators of the current study

possibly did not show successful Pavlovian autonomic

conditioning with respect to HR change, this group showed

a differentiated neuromagnetic SSVEF response with

respect to the CS+ and CS� during acquisition. In left

orbito-frontal regions, the CS+ generated greater SSVEF

activity during early CS+ presentation time (0–1.3 s) and

just before US onset (2.6–3.9 s) compared to CS� elicited

brain activity.

Orbito-frontal brain regions have recently been associ-

ated with emotion regulation in general [17] and with

reversal learning in particular that has been characterized

as changing emotional behavior in response to a previously

punished (or rewarded) stimulus [78,79]. Humans with

higher left frontal baseline activity have been reported to

be more successful in suppressing negative emotional

states [17]. Recently, Ghashghaei and Barbas [24] showed

that the orbito-frontal cortex exercises inhibitory control

onto the central nucleus of the amygdala. The subjects of

our study who did not show an accelerative defense HR

reaction generated more SSVEF activity in response to the

CS+ than to the CS� at orbito-frontal cortical sources

possibly reflecting a suppression of an autonomic fear

response such as an HR accelerative component. Hamm

and Vaitl [28] demonstrated in a Pavlovian fear condition-

ing experiment that conditioned startle potentiation was

stronger for cardiac accelerators than for decelerators

further supporting that HR acceleration indicates learned

fear and that HR decelerators do not acquire a fear/defense

response. Altogether, increased activity in the orbito-frontal

cortex may have suppressed the acquisition of a fear

response as indexed by an accelerative HR component and

possibly prevented successful Pavlovian conditioning with

respect to HR change in that sub-sample.

4.3. Cortical activation patterns in subjects showing an

accelerative HR component

Contrary, subjects showing an accelerative HR waveform

component did not show any differences in the time course

of SSVEF activations with respect to the CS+ and CS� in

the acquisition block. However, they showed marked

differences during extinction. We observed a stimulus-

driven amplitude enhancement in left parietal and left

occipito-temporal brain regions during extinction trials.

Imaging studies in the field of Pavlovian conditioning

reported that stimuli associated with an aversive event have

generated more activity in brain areas of the visual system

[15,39] and parietal cortex [4,23] paralleling our results.

Further, our observations confirmed the hypothesis that

sensory and attentional cortical networks may be involved

in Pavlovian fear conditioning at least for subjects respond-

ing with an accelerative HR component to the CS+ during

acquisition and an OR to the CS+ during extinction.

Therefore, these effects could be caused by attentional

processes reflected by the OR response to the CS+ and by

the amplitude amplification in left occipito-temporal and

parietal regions towards the end of CS+ presentation when

the US could be expected. There are several lines of

argumentation that support this view.

First, the grand average of MNE solutions clearly

showed a left lateralized topography of stimulus processing

for both the CS+ and CS�. The statistical map depicting

activity modulation with respect to condition (CS+, CS�)

and time (t1, t2, t3) in left occipito-temporal regions

demonstrated that the motivational significant stimulus

generated a greater amplitude in areas where the stimulus

was processed. Thus, the lateralization to the left parietal

and left occipito-temporal regions may be due to the grating

perception per se. A similar relationship between topo-

graphical activity distributions and lateralization was ob-

served in a study by Keil et al. [43], who suggested that

motivated attention acts to amplify activity in cortical areas

that are engaged in processing of specific features.

Facilitation of the SSVEF response for a stimulus that has

high motivational relevance is in line with results in the field

of attention research showing that attended visual stimuli

produce greater SSVEPs than unattended [61,65]. Further,

ERP studies have elucidated that attention can modulate

activity in extrastriate and primary visual cortex [18,37].

Finally, studies in the area of emotion research showed that

high arousing biologically significant stimuli generate

greater neural responses in posterior areas [42,43,60].

Second, parietal brain areas often have been associated

with attentional processes [16,54,68,80]. Although these

processes have been linked to the right hemisphere, the left

dominant processing of the stimuli could have caused the

involvement of the left parietal cortex in the present study.

Third, the observed facilitated SSVEF for the CS+ in left

parietal areas were observed towards the end of CS+

depiction when the US could be expected further supporting


the involvement of attentional processes. As the time

courses of activations in left occipito-temporal and left

parietal areas were quite parallel, it can be assumed that

attentional processes may tune feature processing in sensory

areas reflecting a sensory gain mechanism as hypothesized

by authors in the field of selective attention [37].

Taken together, when the OR as indexed by HR

deceleration reached its maximum towards late CS+

depiction just before US presentation, left parietal brain

regions showed enhanced electrocortical activity. This

process may influence the sensory cortex by top–down

processes in order to facilitate stimulus analysis in extras-

triate cortex. However, only three out of 17 subjects were

aware of the contingency between the CS+ and the US,

challenging the argumentation in favor of attentional

processes as long as consciousness is regarded a necessary

prerequisite to attention. However, Hamm and Vaitl [28]

argued that the fear network can be activated automatically

and that overt knowledge of past stimulus relations is not

necessary for the expression of fear responses to the CS+.

But as stated above, our study was not designed to address

this question and we cannot draw definite conclusions about

the participant’s awareness of CS–US contingencies.

Interestingly, the observed left occipito-temporal and

parietal SSVEF amplification by the CS+ did not emerge

during acquisition but during extinction trials in HR

accelerators. This is in line with the notion that extinction

is not a passive but an active learning process (for review,

see [11]). Several recent studies have investigated the

extinction processes in humans reporting that the amygdala

was not only active during early acquisition but also during

early extinction trials [48,51,72] paralleling results from

animal research that showed that blocking of NMDA

receptors within the amygdala prevented extinction [7,20,

82]. Although we did not model amygdala activity as the

neuromagnetic field attenuates by the square of the distance

and deep sources are unlikely to contribute to magnetic

fields measured over the scalp, the amygdala as a highly

interconnected structure with visual cortical areas [2,81]

could be responsible for the facilitated sensory processing of

the CS+ reported here. This could represent an alternative

mechanism to higher-order attentional top–down processes

as outlined above. Knight et al. [48] observed no differential

amygdala activation during acquisition trials but at early

extinction trials paralleling our results in the visual sensory

domain. Knight and collaborators [48] argued that changes

in CS–US contingencies could be responsible for the

amygdala activations after changing the experimental phase

(from acquisition to extinction) and that the amygdala

circuitry might be involved in drawing attention to the CS+.

An alternative and more simple explanation for the

occurrence of the elevated SSVEF activity in sensory and

parietal cortex only during extinction could be that learning

was slow in our HR accelerator group reflecting the possible

unawareness of the CS–US contingency. Therefore, learn-

ing-related changes in cortical processing of the CS+ may

have emerged after acquisition trials, specifically. In our

laboratory, we are currently investigating this hypothesis by

instructing subjects about the CS–US contingency in order

to induce full awareness, with the same experimental

design.

Enhanced neuromagnetic activity in left orbito-frontal

brain regions in HR decelerators were observed during

acquisition trials. As we have outlined above, left orbito-

frontal activation could reflect a suppression process

interfering with autonomic conditioning. The occurrence

of such an activation pattern during the early experimental

phase in subjects showing rather unspecific HR changes

adds evidence that inhibition of fear conditioning may have

taken place in these subjects.

Finally, we found support for our original hypothesis that

motivationally relevant stimuli engage cortical networks

that are involved in sensory and attentional processes at

least in subjects showing an accelerative HR component to

the CS+ during acquisition and selective HR deceleration in

CS+ trials during extinction. Different HR and SSVEF

activation patterns in HR accelerators and decelerators could

not be explained by behavioral data.

Amplification of SSVEF amplitude in left extrastriate

cortex by the CS+ and the parallel time course of that

difference at parietal sources indicated that top–down

processes facilitated the processing of a motivationally

significant stimulus in sensory areas. To what extent

afferents from the amygdala were involved could not be

inferred from our study. The observation of enhanced CS+

driven activity in extrastriate and parietal cortex was

restricted to subjects that showed a fear response in

acquisition trials and an OR response during extinction.

Subjects that had no accelerative component in their HR

response to the CS+ during acquisition responded with

greater orbito-frontal brain activity during learning possibly

reflecting a fear reaction suppression process, thus prevent-

ing autonomic conditioning. Our work added evidence of

different response patterns at the cortical level with respect

to cardiac response patterns.

Acknowledgments

We like to thank Leonie Koban and Ursula Lommen for

help with data acquisition. Research was supported by the

Deutsche Forschungsgesellschaft (DFG) and the Volkswa-

gen Stiftung.

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