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Behavioral/Systems/Cognitive
From Threat to Fear: The Neural Organization of Defensive
Fear Systems in Humans
Dean Mobbs,
1,2
Jennifer L. Marchant,
1
Demis Hassabis,
1
Ben Seymour,
1
Geoffrey Tan,
1
Marcus Gray,
1,5
Predrag Petrovic,
1,3
Raymond J. Dolan,
1
and Christopher D. Frith
1,4
1
Wellcome Trust Centre for Neuroimaging, University College London, London WC1 3BG, United Kingdom,
2
Medical Research Council-Cognition and
Brain Sciences Unit, Cambridge CB2 7EF, United Kingdom,
3
Department of Clinical Neuroscience, Karolinska Institute, 71 76 Stockholm, Sweden,
4
Centre
for Functional Integrative Neuroscience, Aarhus University Hospital, DK-800 Aarhus C, Denmark, and
5
Clinical Imaging Sciences Centre, Brighton and
Sussex Medical School, University of Sussex, Brighton, East Sussex BN1 9PX, United Kingdom
Postencounter and circa-strike defensive contexts represent two adaptive responses to potential and imminent danger. In the context of a
predator,thepostencounterreflectstheinitialdetectionofthepotentialthreat,whereasthecirca-strikeisassociatedwithdirectpredatoryattack.
We used functional magnetic resonance imaging to investigate the neural organization of anticipation and avoidance of artificial predators with
high or low probability of capturing the subject across analogous postencounter and circa-strike contexts of threat. Consistent with defense
systems models, postencounter threat elicited activity in forebrain areas, including subgenual anterior cingulate cortex (sgACC), hippocampus,
and amygdala. Conversely, active avoidance during circa-strike threat increased activity in mid-dorsal ACC and midbrain areas. During the
circa-strike condition, subjects showed increased coupling between the midbrain and mid-dorsal ACC and decreased coupling with the sgACC,
amygdala, and hippocampus. Greater activity was observed in the right pregenual ACC for high compared with low probability of capture during
circa-strike threat. This region showed decreased coupling with the amygdala, insula, and ventromedial prefrontal cortex. Finally, we found that
locomotor errors correlated with subjective reports of panic for the high compared with low probability of capture during the circa-strike threat,
and these panic-related locomotor errors were correlated with midbrain activity. These findings support models suggesting that higher fore-
brain areas are involved in early-threat responses, including the assignment and control of fear, whereas imminent danger results in fast, likely
“hard-wired,” defensive reactions mediated by the midbrain.
Introduction
Evolution has endowed all living organisms with a repertoire of
adaptive responses to circumvent a wide range of ecological dan-
gers (Bolles and Fanselow, 1980). One influential model posits
that distinct types of threat are compartmentalized into several
core contexts along a “threat imminence continuum” (Fanselow
and Lester, 1988; Bouton et al., 2001). In the context of a preda-
tor, the postencounter reflects the initial detection of the poten-
tial threat, whereas the circa-strike is associated with direct
interaction with the predator (i.e., when the predator attacks).
The postencounter is linked with “passive freezing” and elevated
anticipatory anxiety when an aversive stimulus is remote in time
(Bouton et al., 2001). The “circa-strike” is exemplified by fear,
“active escape and avoidance,” and panic surges associated with
imminent threat (Craske, 1999; Gray and McNaughton, 2000;
Bouton et al., 2001; Phelps and LeDoux, 2005; Rau and Fanselow,
2007). Although these biologically potent defense reactions to
postencounter and circa-strike threat are well-characterized in
rodents, no studies have explicitly explored these fear contexts in
humans.
It has been theorized that the inhibitory interactions be-
tween the brain systems supporting the postencounter and
circa-strike threat allow the organism to rapidly switch be-
tween evolutionary conserved defense reactions (Fanselow
and Lester, 1988). Postencounter and circa-strike defensive
states are thought to be topographically organized along a me-
dial prefrontal cortical (mPFC) network (Blanchard et al., 1990a;
Fanselow, 1994; Price, 2005), an hierarchical continuum sup-
ported by brain defense system models (Deakin and Graeff, 1991;
McNaughton and Corr, 2004; Burghardt et al., 2007; Lowry et al.,
2008). In essence, these models posit that when a remote threat is
confronted, specialized higher corticolimbic regions including
the ventral mPFC (vmPFC) and hippocampus gather contin-
gency and contextual information and, via the amygdala instigate
survival actions by controlling midbrain systems [e.g., ventrolat-
eral periaqueductal gray (PAG) evoked freezing] (Fanselow,
1994; LeDoux, 1996; Amat et al., 2005, 2006; Quirk and Beer,
2006; Schiller et al., 2008; Jhou et al., 2009). Conversely, immi-
nent threat in the form of circa-strike corresponds with the inhi-
bition of forebrain circuits, with midbrain regions such as the
dorsolateral PAG becoming dominant, which, in turn, engineer
active defense reactions [e.g., fight or flight (Fanselow and Lester
1988; Blanchard and Blanchard, 1990b; Bandler et al. 2000; Dayan
and Huys, 2009; Robbins and Crockett, 2009)].
Received May 21, 2009; accepted July 1, 2009.
This work was funded by the Wellcome Trust research programme grants. D.M. was supported by a Brain
ResearchTrustPrize studentship andthe Medical ResearchCouncil. We thankC. Hagan, L.Passamonti, C Hutton,and
N. Weiskopf for discussions and help with data analysis.
Correspondence should be addressed to Dr. Dean Mobbs, Medical Research Council-Cognition and Brain Sciences
Unit, 15 Chaucer Road, Cambridge CB2 7EF, UK. E-mail: dean.mobbs@mrc-cbu.cam.ac.uk.
DOI:10.1523/JNEUROSCI.2378-09.2009
Copyright © 2009 Society for Neuroscience 0270-6474/09/2912236-08$15.00/0
12236 •The Journal of Neuroscience, September 30, 2009 •29(39):12236 –12243
In the current study, we build on previous observations
(Mobbs et al., 2007) that have showed simple proximity effects
during active avoidance of a predator, in which brain activity
switches from prefrontal cortical areas to midbrain areas as a
predator comes closer. Here, we explicitly examine distinct contex-
tual fear states along a threat continuum (i.e., postencounter vs
circa-strike context; see Fig. 1A–I). Furthermore, we also manipu-
late probability of capture (i.e., shock), which has previous been
hypothesized to relate to distance along the threat imminence con-
tinuum (Bolles and Fanselow, 1980; Fanselow and Lester, 1988).
Therefore, to gain a clearer picture of these ecologically de-
fined contexts, we combined capture probability with early
danger (i.e., postencounter) versus imminent danger (i.e.,
circa-strike) detection.
Consistent with previous findings (Mobbs et al., 2007), we pre-
dict that the midbrain is associated with immediate circa-strike,
whereas postencounter threat recruits the vmPFC, hippocampus,
and amygdala regions implicated in coding fear contingency, con-
text, vigilance, and behavioral control (LeDoux, 1996; Davis and
Whalen, 2001; Amat et al., 2005; Schiller et al., 2008). We also aimed
to assess a simple putative analog of panic: we reasoned that a
high probability of capture during the circa-strike would result in
increased locomotor errors, psychophysiological arousal (i.e.,
skin conductance), and recruitment of midbrain structures im-
plicated in panic. Last, to further characterize these fear states, we
examined functional coupling between these regions to charac-
terize the hypothesized inhibitory relationship between midbrain
and forebrain areas as determined by threat context.
Materials and Methods
Subjects
Twenty-four healthy subjects (12 males; mean age and SD 27.0 ⫾4.7)
were scanned. All were right-handed, had normal or corrected vision,
and were screened for a history of psychiatric or neurological problems.
All subjects gave informed consent, and the study was approved by the
joint Ethics Committee of the National Hospital for Neurology and Neu-
rosurgery (University College London Hospital National Health Service
Trust) and the Institute of Neurology.
Pain calibration
A cutaneous electrical pain stimulation was applied to the dorsum of the left
hand for 1 or 3 ms via an in-house built functional magnetic resonance
imaging (fMRI) compatible electrical stimulator. Each subject was allowed
to calibrate the shocks to their own tolerance level. The intensity of the shock
was tested before the experiment and set to the maximum tolerable painful
stimulation (⬍20 mA). The average shock intensity was 10.3 ⫾2.6 mA.
Artificial intelligence predator model
The computerized predator was programmed using a standard algorithm
in artificial intelligence. More specifically, we implemented a recursive
breadth-first flood-fill search algorithm (Russell and Norvig, 2003) to
control the behavior of the artificially intelligent predator. This works by
computing the distance to the target prey for each of the valid adjacent
positions (i.e., not wall blocks) to the current predator position and
selecting the one with the shortest distance as the predator’s next move-
ment. Distances are computed by a recursive search algorithm that main-
tains a queue of current search positions. On each pass of the algorithm,
each position in the queue is removed, and in its place, all the valid
adjacent positions (excluding its “parent” position in the search tree) are
added. When one of the search paths reaches the target prey position, all
other searches are terminated, and the path and its distance are returned
(i.e., a breadth-first search). For mazes with no dead ends, as used in this
study, this algorithm yields the optimal strategy for the high probability
of capturing the subject (CS
HI
)orCS
LO
. For both CS
HI
and CS
LO
, the
speed linearly increased after 15 s in the maze until the subject was caught
by the CS
HI
(87.5% of the time) and 12.5% of the CS
LO
trials. Probability
of capture in the circa-strike conditions was achieved by making the
artificial predator disappear when within one-to-three squares away
from the subjects’ blue triangle.
Paradigm
Subjects were presented with a two-dimensional maze containing a 9 ⫻
13 rectangle grid of walls (f) and paths (䡺; see Fig. 1). The paradigm
consisted of three core contexts. All experimental contexts commenced
with a pre-encounter context (PrE) in which a maze appeared sur-
rounded by a gray box. During this context, the subject was asked to
navigate a triangle toward flashing yellow squares presented for 100 ms
and appearing at different locations every 5 s. Next, subjects either moved
to the “postencounter” context, which was separated into two contexts,
each determined by the color of the box surrounding the maze. An or-
ange box (postencounter high probability of capture; PE
HI
) indicated to
the subject that there was a probability (16 blocks ⫽69.6%) of moving on
to the circa-strike contexts with the circa-strike predator with CS
HI
. Like-
wise, a purple box (PE
LO
) signaled that there was a probability (16
blocks ⫽69.6% probability) that the subject would move on to encoun-
ter the circa-strike predator with CS
LO
. A green box (safe context; SC)
indicated to the subjects that they would avoid any interaction with the
artificial predator (14 blocks). The final context was the circa-strike in
which the artificial predator began to chase and attempt to capture the
subject. The subject’s goal was to try and avoid the artificial predator for
as long as possible. The orange CS
HI
predator caught the subject on
87.5% of the trials. Conversely, the purple CS
LO
predator was nonopti-
mal with CS
LO
(i.e., capture on 12.5% of the trials). The difficulty of each
game was set on a person-by-person basis, using performance in the
training session. Capture was manipulated by making the artificial pred-
ator disappear from three to one squares from the subject’s blue triangle.
When the subjects were caught,a2swait was given before one shock
(50% of the time) or three shocks (50% of the time) were administered.
A 2 s rest was given before the subject moved back to the next “pre-
encounter” context. The exact instructions can be found in supplemental
materials (available at www.jneurosci.org).
Questionnaires
After scanning, subjects completed a questionnaire that asked them to
indicate on a 10-point analog scale (1) how much anxiety they felt in the
preferred, pre-encounter, postencounter, and circa-strike contexts and
(2) how much panic they felt in the postencounter and circa-strike con-
texts. An example of a question is, “Did you panic when the orange circle
got close to you in the chase condition?” See supplemental material
(available at www.jneurosci.org) for more examples.
fMRI acquisition
A 3T Allegra head scanner (Siemens Medical Systems) with standard
transmit-receive head coil was used to acquire functional data using
echoplanar imaging (EPI) sequences (matrix size, 64 ⫻64; Fov, 192 ⫻
192 mm; in-plane resolution, 2 ⫻2 mm; 40 slices with interleaved acqui-
sition; slice thickness, 2 mm with a 1 mm gap between slices; repetition
time, 2.6 ms). To maximize statistical power, we used only 40 slices that
were optimized to cover the brainstem and angled at ⫺30° to cover the
whole brain. The slice tilt, z-shim gradient compensation reduced signal
loss in the vmPFC (Weiskopf et al., 2006). In addition, field maps were
acquired for reduction of geometric distortions of the EPI images (Hut-
ton et al., 2002). A high-resolution T1-weighted structural scan was ob-
tained for each subject [1 mm isotropic resolution three-dimensional
modified driven equilibrium Fourier transform (Deichmann et al.
2004)] and coregistered to the subject’s mean EPI. The average of all
structural images permitted the anatomical localization of the functional
activations at the group level.
fMRI analysis
Statistical parametric mapping (SPM5; Wellcome Trust Centre for Neu-
roimaging, www.fil.ion.ucl.ac.uk) was used to preprocess all fMRI data
and included spatial realignment, coregistration, normalization, and
smoothing. To control for motion, all functional volumes were realigned
to the mean volume. Using the FieldMap toolbox, field maps were esti-
mated from the phase difference between the images acquired at the short
and long echo time and unwrapped (Hutton et al., 2002). Voxel displace-
Mobbs et al. •Defensive Fear Systems in Humans J. Neurosci., September 30, 2009 •29(39):12236 –12243 • 12237
ments in the EPI were determined from the
field map and EPI parameters. Distortions
were corrected by applying the inverse dis-
placement to the EPIs. Images were spatially
normalized (Ashburner and Friston, 1999) to
standard space Montreal Neurological Insti-
tute template with a voxel size of 2 ⫻2⫻2mm
and smoothed using a Gaussian kernel with an
isotropic full width at half maximum of 8 mm.
In addition, high-pass temporal filtering with a
cutoff of 128 s was applied to remove low-
frequency drifts in signal, and global changes
were removed by proportional scaling.
After preprocessing, statistical analysis was
conducted using the general linear model.
Analysis was performed to determine each sub-
ject’s voxelwise activation during artificial
predator and yoked contexts. Activated voxels
in each experimental context were identified
using a statistical model containing boxcar
waveforms representing each of the four ex-
perimental contexts, convolved with a ca-
nonical hemodynamic response function,
and mean-corrected (Turner et al., 1991).
The cardiac noise correction was imple-
mented at the level of modeling the mea-
sured signal and not at the level of image
reconstruction, i.e., image data were not
modified. The underlying model assumed
that cardiac effects on a voxel’s signal depend
on the phase of the image slice acquisition
within the cardiac cycle. Sine and cosine se-
ries (ⱕthird order) were used to describe the
phase effect on a single reference slice (pass-
ing through PAG), creating six regressors
(Josephs et al., 1997).
Connectivity analyses
Psychophysiological interaction. In our study,
the connectivity arising from different fear
context is modulated by the following contrast:
[(CS
HI
⫺PE
HI
)⫺(CS
LO
⫺PE
LO
)]. We sought
to identify “target areas,” which had differen-
tial connectivity with the source region in the midbrain. This was
achieved using a moderator variable, derived from the product of source
activation and context. Hence, for the subsequent functional connectiv-
ity analyses, the midbrain was chosen as the source region. For each
participant, we computed the above contrasts to determine the local
maximum that was the nearest voxel to the activation peak in the mid-
brain defined by the whole-group cluster (supplemental Tables 1 and 3,
available at www.jneurosci.org as supplemental material). Analysis used
a standardized 6 mm sphere across all participants for midbrain: seed
location: x⫽8, y⫽⫺26, and z⫽⫺8, which was the maximal voxel.
Using these same procedures, we also examined the connectivity for the
CS
HI
⫺CS
LO
contrast using a right pregenual anterior cingulate cortex
(pgACC) seed (x⫽20, y⫽44, and z⫽6).
Skin conductance response analysis. In parallel to the acquisition of the
fMRI data, we continuously monitored skin conductance level (SCL),
from electrodes placed on the middle and index fingers of the left hand.
However, as a result of technical problems, one subject was dismissed
from the analysis. Thus, we recorded skin conductance data in 23 sub-
jects out of 24. Skin conductance data were segmented into single epochs
containing pre-encounter, postencounter, and circa-strike phases, and
where necessary, individual epochs were rotated to correct for drift.
Mean SCLs were then calculated for each phase within an epoch, and
each subject normalized by setting the maximum and minimum of all
means to 100 and 0, respectively. Finally, to allow for meaningful com-
parison, we adjusted the CS
HI
and CS
LO
conditions so that group means
(PrEs) began at 0.
Results
Subjective ratings of panic and anxiety
We first examined subjective reports of anxiety and fear using
postexperimental questionnaires. Both PE
HI
and PE
LO
predator
interactions were rated as causing significantly more anxiety than
the safe (mean ⫾SD, 1.8 ⫾1.2) and pre-encounter (1.8 ⫾1.9)
contexts (Wilcoxon signed ranks test, p⬍0.001, one-tailed) (Fig.
1I). Greater anxiety was observed when encountering the CS
HI
predator (CS
HI
, 4.5 ⫾2.2; CS
LO
, 3.5 ⫾2.1; Z⫽⫺2.844; p⫽
0.004). No significant differences were found between safe (SC)
and the PrE (Z⫽⫺0.333; p⫽0.739). For subjective ratings of
panic, a significant difference was evident between the CS
HI
(5.5 ⫾2.2) and CS
LO
predator (4.7 ⫾1.8; Z⫽⫺2.473; p⫽
0.013). Panic was also rated as being significantly higher for the
CS
HI
conditions compared with the PE
HI
conditions (2.9 ⫾1.9;
Z⫽⫺3.8; p⫽0.0005). A similar pattern was observed for the
CS
LO
conditions compared with the PE
LO
conditions (2.3 ⫾1.58;
Z⫽⫺4.1; p⫽0.0005).
Panic-related locomotor errors
We also used an indirect measure of panic. Specifically, we tested
if locomotor errors quantified by calculating the amount of
button presses directed into the walls of the maze, during the
circa-strike, were indicative of disorganized behavior typically
Figure 1. Schematic representation of the paradigm and subjective scores for task relating to anxiety, panic, and panic-related
locomotor errors. The experiment commenced with the pre-encounter state (A), in which a maze appeared surrounded by a gray
box.Thesubject’sgoal was to navigate a triangletowardflashing squares. Next, the subject movedwithequalprobability to the SC
(B), which was signaled by a green box around the maze and indicated that the subjects would avoid any interaction with the
artificial predators, or to the postencounter phases, which were divided into two subphases: PE
HI
(C)orPE
HI
(D) signaling that the
subjecthadaprobability of moving to thecirca-strikephasesin which the CS
HI
(E)andtheCS
LO
(F)begantochase the subjects blue
triangle.TheCS
HI
predatorcapturedsubjects on 87.5% of thetrials(G), and the CS
LO
predatorcapturedthe subjects on 12.5% ofthe
trials (H). When the subjects were caught,a2swait occurred before either one or three shocks were administered to the dorsum
of the right hand with 50% probability. Subjective scores for (I) self-reported anxiety for all fear states. J, The correlation between
self-reported panic sensations and locomotor errors (r⫽0.35; p⬍0.05). ⴱp⬍0.05. ns, Not significant.
12238 •J. Neurosci., September 30, 2009 •29(39):12236 –12243 Mobbs et al. •Defensive Fear Systems in Humans
observed during panic (Fanselow, 1988) and conditions where
there is a high probability of capture (McNaughton, 1993). Be-
cause we expected more panic-like locomotor errors when sub-
jects encountered the CS
HI
predator, we first tested if subjects
made more errors for the CS
HI
compared with the CS
LO
(Wil-
coxon signed ranks test, Z⫽0.44; p⫽0.032). No significant
differences were found for the PE
HI
and PE
LO
conditions (Z⫽
⫺0.815; p⫽0.415). We next subtracted the number of errors
for the CS
LO
from the CS
HI
predator and
correlated the residual locomotor errors
(divided by time to account for time dif-
ferences between the CS
HI
and CS
LO
condition) with subjective ratings of
panic. We found a positive correlation
between amount of panic-like errors
and self-reported panic for the CS
HI
condition (Spearman, r⫽0.35; p⫽
0.048) (Fig. 1J).
Skin conductance levels
Concomitant recordings of SCLs were
taken during the whole experiment. We
ran a repeated-measures ANOVA on
probability of capture and postencoun-
ter and circa-strike conditions. In addi-
tion to significant main effects for
conditions [F
(22)
⫽66.275; p⫽0.0005]
and capture probability [F
(22)
⫽28.868;
p⫽0.0005], we found an interaction
[F
(22)
⫽32.129; p⫽0.0005], indicating
that SCL increases from postencounter
to circa-strike were considerably larger for the encounter with
the CS
HI
predator (Fig. 2B).
fMRI results
Postencounter versus circa-strike contexts
For the fMRI analysis, we first examined the interaction high-
lighting postencounter contexts [i.e. (PE
HI
⫺CS
HI
)⫺(PE
LO
⫺
Figure 2. Theoretical model of defense avoidance, SCLs, and fMRI results. A, McNaughton and Corr’s defense avoidance model (McNaughton and Corr, 2004). B, Mean-normalized SCLs for the
pre-encounter and postencounter and circa-strike contexts. C, BOLD signal for the interaction between circa-strike (shown in orange) and postencounter contexts (shown in purple); parameter
estimates for activity in the sgACC (0, 26, ⫺12; p⬍0.005svc) (D), right amygdala (24, ⫺8, ⫺24; p⬍0.0005svc) (E), hypothalamus (⫺2, 2, ⫺12; p⬍0.002svc) (F), and midbrain (8, ⫺26, ⫺8;
p⬍0.0005 (G); family wise error corrected for whole brain (FWEcorr).
Figure 3. PPIs from the midbrain seed. A, Positive connectivity with the dACC and lateral midbrain. B, Midbrain seed (seed
location, 8, ⫺26, ⫺8). C, Negative PPIs with the sgACC, pgACC, PCC, insula, amygdala, ventral striatum (VS), and hippocampus.
Blue arrow indicates negative connectivity. Red arrow indicates positive coupling.
Mobbs et al. •Defensive Fear Systems in Humans J. Neurosci., September 30, 2009 •29(39):12236 –12243 • 12239
CS
LO
)]. In this analysis, we observed increased posterior cingu-
late cortex (PCC), bilateral hippocampus, hypothalamus, amyg-
dala, vmPFC, and subgenual ACC (sgACC) activity (Fig. 2C–G;
supplemental Table S1, available at www.jneurosci.org as supple-
mental material).
Circa-strike versus postencounter contexts
For the interaction highlighting circa-strike context [i.e. (CS
HI
⫺
PE
HI
)⫺(CS
LO
⫺PE
LO
)] increased activity was observed in the
midbrain, mediodorsal thalamus, right striatum, right insula,
and dorsal ACC (dACC) (Fig. 2C–G, supplemental Table S1,
available at www. jneurosci.org as supplemental material). To
investigate activity in these regions further, we examined the
psychophysiological interaction (PPI) (i.e., functional cou-
pling) with the PAG for the contrast [(CS
HI
⫺PE
HI
)⫺(CS
LO
⫺PE
LO
)]. A midbrain seed region revealed positive connec-
tivity with the dACC, ventral striatum, medial dorsal thala-
mus, anterior insula, and lateral midbrain (Fig. 3A) and negative
connectivity with the right amygdala, hippocampus, insula,
vmPFC, PCC, and sgACC (Fig. 3B, supplemental Table S2, avail-
able at www.jneurosci.org as supplemental material).
CS
HI
versus CS
LO
conditions
To examine the neural systems associated with probability of
capture, we next directly compared CS
HI
and CS
LO
conditions.
The main effect of CS
HI
⬎CS
LO
revealed increased vmPFC activ-
ity, namely the pgACC. Another cluster was observed in the dor-
sal mPFC (Fig. 4A, supplemental Table S3, available at
www.jneurosci.org as supplemental material). Using the right
pgACC peak coordinate as a seed, we also conducted a PPI anal-
ysis showing this region to have decreased connectivity with the
amygdala, insula, and vmPFC (Fig. 4B, supplemental Table S4,
available at www. jneurosci.org as supplemental material). To
further interrogate this activity, we examined the covariation be-
tween State Anxiety Inventory trait anxiety scores and blood ox-
ygenation level-dependent (BOLD) signal for the main effect of
CS
HI
⬎CS
LO
showing increased correlation with the bilateral
amygdala and sgACC (supplemental Table S5, available at ww-
w.jneurosci.org as supplemental material). Supporting the puta-
tive role of the pgACC in high shock probability, we also observed
activity in this region for the interaction between CS
HI
and PE
HI
conditions (pgACC, 20, 44, 6; p⬍0.001).
CS
HI
versus CS
LO
: panic-related locomotor errors
To probe the relationship between panic and shock probability
more directly, we next subtracted the CS
LO
condition locomotor
errors from the CS
HI
and correlated the residual errors with the
BOLD signal for the CS
HI
⫺CS
LO
comparison. This analysis
showed the left PAG, dACC, and right insula activity correlated
Figure 4. Direct comparison between CS
HI
⫺CS
LO
conditions and panic-related locomotor errors. A, Render showing right pgACC activity (20, 44, 6; p⬍0.024svc) and parameter estimates for
the CS
IS
⫺CS
ES
comparison. B, PPI analysis showing decreased coupling, most notably the left amygdala (⫺20, 2, ⫺24; p⬍0.05svc). The gray arrows denote negative coupling. C, How
panic-related motors errors were quantified. Red arrows equate to bumps in the wall (i.e., locomotor errors). Green arrows indicate smooth uninterrupted movements through the maze. D,
correlations between midbrain activity and panic-related locomotor errors (0, ⫺28, ⫺8; p⬍0.05svc).
12240 •J. Neurosci., September 30, 2009 •29(39):12236 –12243 Mobbs et al. •Defensive Fear Systems in Humans
with panic-related locomotor errors (supplemental Fig. S6, avail-
able at www.jneurosci. org as supplemental material). Decreased
panic-like errors elicited activity in the ventrolateral prefrontal
cortex and pgACC, albeit somewhat weaker ( p⬍0.005).
Discussion
We set out to characterize the neural systems associated with etho-
logically defined postencounter and circa-strike threat contexts, as
well as how these systems are influenced by capture probability. Our
key neurobiological findings show that an early anticipation of a
possible nociceptive event (i.e., the postencounter) increased ac-
tivity in a set of forebrain structures, most prominently the
vmPFC, hippocampus, hypothalamus, and amygdala. Imminent
threat in the form of circa-strike elicited activity in midbrain
regions, including the PAG and cortical regions, known to be
involved in analgesia and panic (i.e., dACC) (Petrovic et al., 2002;
Tamburin et al., 2008). Encountering the CS
HI
elicited pgACC
activity consistent with the notion that this region is involved in
behavioral control and analgesia (Petrovic et al., 2002; Amat et
al., 2005; Schiller et al., 2008). Finally, we show for the first time a
neurobehavioral index of panic in which elevated locomotor er-
rors were associated with increased with midbrain activity. Our
observations have strong resonance to theoretical models of
threat imminence and demonstrate that threat context evokes
distinct parts of the fear system the human brain (Fanselow and
Lester, 1988; Deakin and Graeff, 1991; Gray and McNaughton, 2000;
McNaughton and Corr, 2004).
The so-called postencounter, which involves the detection but
not interaction with a threat, is characterized in the rodent by
passive defensives such as freezing, although flight is sometimes
observed when escape is possible (Rau and Faneslow, 2007). Our
results indicate that a postencounter threat preferentially engages
the vmPFC, sgACC, pgACC, hippocampus, amygdala, and hypo-
thalamus. Although other structures (e.g., ventrolateral PAG) are
also engaged during a real “life-endangering” postencounter
threat, the forebrain regions we describe are known to play a
critical role in a postencounter threat by influencing visceral
functions (Critchley et al., 2001), prediction, and prefiguring an-
algesic and strategic responses (Fanselow and Lester, 1988;
Petrovic et al. 2002). These forebrain areas also have dense con-
nections to the basolateral amygdala as well as the hypothalamus,
hippocampus, and PAG, forming a critical component of a
mPFC network that is known to exert control over these emotion
systems (Price, 2005). The amygdala receives contextual input
from the hippocampus (Phillips and LeDoux, 1992; LeDoux,
1996; Phelps and LeDoux, 2005) and is an integral component of
the postencounter instigating behavioral reactions (e.g., ventro-
lateral PAG evoked freezing), vigilance (Whalen, 1998), as well as
encoding information about the threat stimulus (Fanselow,
1994). The precise role of these forebrain structures is likely to en-
compass complex reactions to ecological dangers (Price, 2005), in-
cluding the assignment and control of fear (Schiller et al., 2008).
The circa-strike is characterized by direct predator attack,
which results in reactive defensive strategies. Self-report panic
was significantly higher for the circa-strike than postencounter
conditions as were SCLs, presumably reflecting increased auto-
nomic sympathetic arousal (Critchley, 2002). Moreover, increased
midbrain activity was observed, again supporting previous theory
(Deakin and Graeff, 1991; Gorman et al., 2000; McNaughton and
Corr, 2004). A previous study from our group showed that the mid-
brain is more active when a threat is spatially close during circa-strike
attack (Mobbs et al., 2007). Nonetheless, the exact role of this region
still remains unresolved. It is known that overactivity of the mid-
brain PAG results in maladaptive responses such as panic, which
manifest as uncoordinated behavior and loss of control (Graeff,
2004). Panic is defined as an overwhelming surge in behavior
with robust flight (or fight) reactions (Bouton et al., 2001). Sup-
porting the notion that panic is associated with uncoordinated
behavior during inescapable threat (McNaughton, 1993), we
found that midbrain activity increased with the amount of panic-
related locomotor errors for the CS
HI
⫺CS
LO
threat (Fig. 4).
Indeed, chemical stimulation of the rodent dorsolateral PAG elic-
its uncoordinated panic-like behaviors such as uncontrolled ac-
tivity bursts (e.g., vigorous running and jumping) (Deakin and
Graeff, 1991; Bandler et al., 2000; Vianna et al., 2001), whereas
lesions to the same region eradicate such activity bursts to threat
(Fanselow, 1991). We also observed increased activity in the mid-
dACC, a region with strong connectivity to the midbrain and
implicated in panic (Asami et al., 2008). Indeed, damage to this
region can cause panic attacks (Tamburin et al., 2008). Although
future studies need to probe the role of these regions with different
aversive stimuli, our observations suggest that the midbrain may
reflect uncoordinated flight or panic-like behaviors.
The high-level processes instantiated in forebrain regions
involving predictive coding, monitoring, and encoding of con-
tingencies and uncertainty means that the time course of their
response is likely to be slow, and contrast with an obligatory
response profile of midbrain regions evoked during circa-strike
(Fanselow and Lester, 1988; Mobbs et al., 2007; Ochsner et al.,
2009). It follows that when circa-strike is initiated, it is optimal if
these forebrain regions are inhibited (Fanselow and Lester, 1988;
Butler et al., 2007; Martel et al., 2008). In support of this, we
found decreased forebrain activity for the interaction between
circa-strike contexts. Moreover, the amygdala and hippocampus,
along with other regions of the forebrain, showed negative con-
nectivity with the midbrain. However, the amygdala also showed
negative connectivity with pgACC during the CS
HI
condition.
These two findings are important in light of studies showing, on
one hand, that the midbrain PAG results in inhibition of the
amygdala during conditioned fear (Fanselow et al., 1995),
whereas stimulation of the pgACC results in similar inhibition of
the amygdala (Quirk et al., 2003). Our findings are in line with the
notion that distinct divisions of the fear system are evoked during
postencounter versus circa-strike contexts (Fanselow, 1994).
It has previously been suggested that shock probability essen-
tially models distance on the predatory imminence continuum
(Bolles and Fanselow, 1980; Fanselow and Lester, 1988). Com-
pared with low probability, high probability of capture resulted in
increased right vmPFC (i.e., pgACC) activity. We also found that
the pgACC was primarily linked with decreased panic-related
locomotor errors during the CS
HI
⫺CS
LO
threat. Thus, when the
subjects thought there was a low probability of shock, they had
more controlled locomotor behaviors, yet the knowledge they
were likely to be caught increased locomotor errors. The mPFC
also regulates the amygdala and expression of fear (Phelps and
LeDoux, 2005; Schiller et al., 2008) and extinction (Phelps et al.,
2004) and augments hypothalamic stress hormones (Figueiredo
et al., 2003). Stimulation of the mPFC homolog decreases activity
in the rodent central nucleus of the amygdala (CeA) (Quirk et al.,
2003). The CeA projects to the midbrain PAG and hypothalamus
and acts as a control hub for fear responses (LeDoux, 1996). It is
proposed that via GABAergic-intercalated cells, mPFC mediates
the expression of fear by gating transmission from the basolateral
amygdala to the CeA (Quirk et al., 2003; Bermpohl et al., 2006).
Indeed, these regions have been shown to control stress reactions
(Salomons et al., 2004; Amat et al., 2005; Salomons et al., 2007)
Mobbs et al. •Defensive Fear Systems in Humans J. Neurosci., September 30, 2009 •29(39):12236 –12243 • 12241
and to be abnormal in patients with posttraumatic stress disorder
and panic disorder (Zubieta et al., 1999; Asami et al., 2008;
Uchida et al., 2008). Although one might argue that this activity
reflects cognitive predictive process, which function indepen-
dently from the emotional system, our findings support the no-
tion that the vmPFC regulates the fear systems possibly via the
amygdala (Reiman et al., 1989; Schiller et al., 2008). Similarly, it
could be suggested that prefrontal cortex exerts inhibitory con-
trol on the fear system in the midbrain.
Although the current results only present contexts analogous
to real defensive states, they are strongly consistent with brain
antipredator defensive systems models developed in rodents
(Deakin and Graeff, 1991; Gorman et al., 2000; McNaughton and
Corr, 2004) and human psychiatric models of panic (Gorman et
al., 2000). It is conceivable that distinct parts of the fear system are
modulated by contextual factors expounded by the threat immi-
nence continuum (Fanselow, 1995). For example, when a threat
is spotted, slow, but accurate, higher parts of the fear system
organize fear and preparatory responses. This higher threat sys-
tem, however, is seemingly inhibited when the organism shifts to
a circa-strike level of threat, which evokes responses associated
with fast hard-wired defenses in the midbrain. Although our con-
clusions remain tentative and need further empirical verification,
these evolutionary conserved systems are critical to the rapid
switch in adaptive behavior, and we speculate that different
symptoms associated with anxiety and panic are modulated by
disruption to differential components of the fear circuitry.
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