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J Neurosci. Author manuscript; available in PMC Mar 30, 2010.
Published in final edited form as:
PMCID: PMC2782300

From Threat to Fear: The neural organization of defensive fear systems in humans


Post-encounter and circa-strike defensive contexts represent two adaptive responses to potential and imminent danger. In the context of a predator, the post-encounter reflects the initial detection of the potential threat, whilst the circa-strike is associated with direct predatory attack. We used fMRI to investigate the neural organization of anticipation and avoidance of artificial predators with high or low probability of capturing the subject across analogous post-encounter and circa-strike contexts of threat. Consistent with defense systems models, post-encounter 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 to 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 to 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 forebrain areas are involved in early threat responses, including the assignment and control of fear, whereas as imminent danger results in fast, likely “hard-wired”, defensive reactions mediated by the midbrain.

Keywords: Fear, fMRI, Midbrain, Anxiety, Defense, Pain


Evolution has endowed all living organisms with a repertoire of adaptive responses to circumvent a wide range of ecological dangers (Bolles, 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 predator, the post-encounter reflects the initial detection of the potential threat, whilst the circa-strike is associated with direct interaction with the predator (i.e. when the predator attacks). The post-encounter 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, 2000; Bouton, et al. 2001; Phelps, and LeDoux 2005; Rau and Fanselow, 2007). Although these biologically potent defense reactions to post-encounter 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 between the brain systems supporting the post-encounter and circa-strike threat allow the organism to rapidly switch between evolutionary conserved defense reactions (Fanselow, 1988). Post-encounter and circa-strike defensive states are thought to be topographically organized along a medial prefrontal cortical (mPFC) network (Blanchard, et al. 1990a; Fanselow, 1994; Price, 2005), an hierarchical continuum supported 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 medial prefrontal cortex (vmPFC) and hippocampus gather contingency and contextual information and, via the amygdala instigate survival actions by controlling midbrain systems (e.g. ventrolateral 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, imminent threat in the form of circa-strike corresponds with the inhibition 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.)

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 contextual fears states along a threat continuum (i.e. post-encounter vs. circa-strike context; see Fig. 1A-I). Furthermore, we also manipulate probability of capture (i.e. shock), which has previous been hypothesized to relate to distance along the threat imminence continuum (Bolles, 1980; Fanselow and Lester, 1988). We therefore to gain a clearer picture of these ecologically defined contexts by combining capture probability with early danger detection (i.e. post-encounter) vs. imminent danger (i.e. circa-strike).

Fig. 1
Schematic representation of the paradigm and subjective scores for task relating to anxiety, panic and panic-related locomotor errors

Consistent with previous findings (Mobbs, et al. 2007), we predict that the midbrain is associated with immediate circa-strike, while post-encounter threat recruits the vmPFC, hippocampus and amygdala regions implicated in coding fear contingency, context, 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 analogue 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 implicated in panic. Lastly, to further characterize these fear states, we examined functional coupling between these regions to characterize the hypothesized inhibitory relationship between midbrain and forebrain areas as determined by threat context.


Methods and Materials


Twenty-four healthy subjects (12 males; mean age and s.d. 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 Neurosurgery (UCLH NHS 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 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 (below 20mA). The average shock intensity was 10.3mA ± 2.6mA.

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) was implemented 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 movement. Distances are computed by a recursive search algorithm that maintains 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 CSHI or CSLO. For both CSHI and CSLO, the speed linearly increased after 15s in the maze until the subject was caught by the CSHI (87.5% of the time) and 12.5% of the CSLO 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.


Subjects were presented with a 2D maze containing a 9 × 13 rectangle grid of walls (black squares) and paths (white squares; Fig. 1). The paradigm consisted of three core contexts. All experimental contexts commenced with a pre-encounter context (PrE) where a maze appeared surrounded by a grey box. During this context, the subject was asked to navigate a triangle towards flashing yellow squares presented for 100ms and appearing at different locations every 5s. Next, subjects either moved to the “post-encounter” context which was separated into two contexts, each determined by the color of the box surrounding the maze. An orange box (post-encounter high probability of capture; PEHI) 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 high probability of capturing the subject (CSHI). Likewise, a purple box (post-encounter low probability of capture; PELO) signaled that there was a probability (16 blocks = 69.6% probability) that the subject would move on to encounter the circa-strike predator with low probability of capturing the subject (CSLO). 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” where 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 CSHI predator caught the subject on 87.5% of the trials. Conversely, the purple CSLO predator was non-optimal with low probability of capturing the subject (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 predator disappear from three to one squares from the subject’s blue triangle. When the subjects where caught a 2 sec wait 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 supplementary materials.


After scanning, subjects completed a questionnaire which asked them to indicate on a 10-point analog scale how much (i) anxiety they felt in the preferred, pre-encounter, post-encounter and circa-strike contexts and (ii) how much panic they felt in the post-encounter and circa-strike contexts. An example of a question is “Did you panic when the orange circle got close to you in the chase condition?”. See supplementary material for more examples.

fMRI acquisition

A 3T Allegra head scanner (Siemens Medical Systems, Erlangen, Germany) with standard transmit-receive head coil was used to acquire functional data using EPI sequences (matrix size: 64 × 64; Fov: 192 × 192mm; in-plane resolution: 2 × 2mm; 40 slices with interleaved acquisition; slice thickness: 2 mm with 1mm gap between slices; TR: 2.6 ms). In order 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 (Hutton, et al. 2002). A high-resolution T1-weighted structural scan was obtained for each subject (1 mm isotropic resolution 3D MDEFT, (Deichmann, et al. 2004) and co-registered to the subject’s mean EPI image. 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 Neuroimaging, www.fil.ion.ucl.ac.uk) was used to preprocess all fMRI data and included spatial realignment, co-registration, normalization and smoothing. To control for motion, all functional volumes were realigned to the mean volume. Using the FieldMap toolbox, field maps were estimated from the phase difference between the images acquired at the short and long TE and unwrapped (Hutton, et al. 2002). Voxel displacements in the EPI image were determined from the field map and EPI imaging parameters. Distortions were corrected by applying the inverse displacement to the EPI images. Images were spatially normalized (Ashburner and Friston, 1999) to standard space Montreal Neurological Institute (MNI) template (Mazziotta, et al. 1995) with a voxel size of 2 × 2 × 2 mm and smoothed using a Gaussian kernel with an isotropic full width at half maximum (FWHM) of 8 mm. In addition, high-pass temporal filtering with a cut-off of 128 s was applied to remove low-frequency drifts in signal and global changes were removed by proportional scaling.

Following preprocessing, statistical analysis was conducted using the general linear model. Analysis was performed to determine each subject’s voxel-wise 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 experimental contexts, convolved with a canonical hemodynamic response function and mean-corrected (Turner, et al. 1991). The cardiac noise correction was implemented at the level of modeling the measured 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 series (≤third order) were used to describe the phase effect on a single reference slice (passing through PAG), creating six regressors (Josephs, et al. 1997).

Connectivity analyses

Psycho-Physiological Interaction

In our study, the connectivity arising from different fear context is modulated by the following contrast: [[CSHI – PEHI] - [CSLO – PELO]). We sought to identify ‘target areas’ which had differential 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 connectivity 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 midbrain defined by the whole-group cluster (Supplementary Tables 1 and 3). Analysis employed a standardised 6-mm sphere across all participants for midbrain: seed location: x = 8, y = −26, z = −8 which was the maximal voxel. Using these same procedures, we also examined the connectivity for the CSHI – CSLO contrast using a right pgACC seed (x = 20, y= 44, z= 6).

Skin Conductance Responses 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, due to technical problems, one subject was dismissed from the analysis. Thus, we recorded skin conductance data in 23 subjects out of 24. Skin conductance data were segmented into single epochs containing pre-encounter, post-encounter and circa-strike phases, and where necessary individual epochs were rotated to correct for drift. Mean SCL’s were then calculated for each phase within an epoch, and each subject normalised by setting the maximum and minimum of all means to 100 and 0 respectively. Finally, to allow for meaningful comparison, we adjusted the CSHI and CSLO conditions so that group means (pre-encounter contexts) began at zero.


Subjective Ratings of Panic and Anxiety

We first examined subjective reports of anxiety and fear using post-experimental questionnaires. Both PEHI and PELO predator interactions were rated as causing significantly more anxiety than the safe (Mean ± s.d: 1.8 ± 1.2) and pre-encounter (1.8 ± 1.9) contexts (Wilcoxon signed ranks test: P<0.001 one-tailed. see Fig. 1I). Greater anxiety was observed when encountering the CSHI predator (CSHI = 4.5 ± 2.2; CSLO = 3.5 ± 2.1; Z=−2.844; P = 0.004). No significant differences were found between safe (SC) and the pre-encounter context (PrE; Z=−.333; P = 0.739). For subjective ratings of panic, a significant difference was evident between the CSHI (5.5 ± 2.2) and CSLO predator (4.7 ± 1.8; Z=−2.473; P = 0.013). Panic was also rated as being significantly higher for the CSHI conditions compared to the PEHI conditions (2.9 ± 1.9; Z=−3.8; P = 0.0005). A similar pattern was observed for the CSLO conditions compared to the PELO 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 observed during panic (Fanselow, 1988) and conditions were there is a high probability of capture (McNaughton, 1993). Because we expected more panic-like locomotor errors when subjects encountered the CSHI predator, we first tested if subjects made more errors for the CSHI compared to the CSLO (Wilcoxon Signed Ranks Test: Z=.44; P = 0.032). No significant differences were found for the PEHI and PELO conditions (Z=−.815; P = 0.415). We next subtracted the number of errors for the CSLO from the CSHI predator and correlated the residual locomotor errors (divided by time to account for time differences between the CSHI and CSLO condition) with subjective ratings of panic. We found a positive correlation between amount of panic-like errors and self-reported panic for the CSHI condition (Spearman: r=.35; P = 0.048; Fig. 1J).

Skin Conductance Levels

Concomitant recordings of skin conductance levels (SCL) were taken during the whole experiment. We ran a repeated-measures ANOVA on probability of capture and post-encounter and circa-strike conditions. In addition 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 post-encounter to circa-strike were considerably larger for the encounter with the CSHI predator (Fig. 2B).

Fig. 2
Theoretical model of defense avoidance, skin conductance levels and fMRI results

fMRI Results

Post-Encounter vs. Circa-Strike Contexts

For the fMRI analysis, we first examined the interaction highlighting post-encounter contexts (i.e. [PEHI - CSHI] - [PELO - CSLO]). In this analysis we observed increased posterior cingulate cortex (PCC), bilateral hippocampus, hypothalamus, amygdala, vmPFC, and subgenual anterior cingulate cortex (sgACC) activity (Fig. 2C-G; Supplementary Materials: Table S1).

Circa-Strike vs. Post-Encounter Contexts

For the interaction highlighting circa-strike context (i.e. [CSHI – PEHI] - [CSLO – PELO]) increased activity was observed in the midbrain, mediodorsal thalamus, right striatum, right insula, dorsal ACC (Fig. 2C-G; Table S1). To investigate activity in these regions further, we examined the psychophysiological interaction (PPI) (i.e. functional coupling) with the PAG for the contrast [CSHI – PEHI] - [CSLO – PELO]). A midbrain seed region revealed positive connectivity with the dorsal ACC (dACC), ventral striatum, medial dorsal thalamus, anterior insula and lateral midbrain (Fig. 3A) and negative connectivity with the right amygdala, hippocampus, insula, vmPFC, PCC and sgACC (Fig. 3B; Table S2).

Fig. 3
Psychophysical Interactions (PPIs) from the midbrain seed

CSHI vs. CSLO Conditions

To examine the neural systems associated with probability of capture, we next directly compared CSHI and CSLO conditions. The main-effect of CSHI > CSLO revealed increased vmPFC activity, namely the pregenual ACC (pgACC). Another cluster was observed in the dorsal medial PFC (Fig. 4A; Table S3). Using the right pgACC peak coordinate as a seed, we also conducted a PPI analysis showing this region to have decreased connectivity with the amygdala, insula and vmPFC (Fig. 4BTable S4). To further interrogate this activity, we examined the covariation between STAI trait anxiety scores and BOLD-signal for the main-effect of CSHI > CSLO showing increased correlation with the bilateral amygdala and sgACC (Table S5). Supporting the putative role of the pgACC in high shock probability, we also observed activity in this region for the interaction between CSHI and PEHI conditions (pgACC: 20, 44, 6; P<0.001).

Fig. 4
Direct comparison between CSHI – CSLO conditions and panic-related locomotor errors

CSHI vs. CSLO: Panic–Related Locomotor Errors

To probe the relationship between panic and shock probability more directly, we next subtracted the CSLO condition locomotor errors from the CSHI and correlated the residual errors with the BOLD-signal for the CSHI – CSLO comparison. This analysis showed the left PAG, dACC and right insula activity correlated with panic-related locomotor errors (Fig. S6). Decreased panic-like errors elicited activity in the ventrolateral prefrontal cortex and pgACC, albeit somewhat weaker (P<0.005).


We set out to characterize the neural systems associated with ethologically-defined post-encounter 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 post-encounter) increased activity 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, 2002; Tamburin, 2008). Encountering the CSHI 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 where elevated locomotor errors 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 Faneslow, 1988; Deakin and Graeff, 1991; Gray, 2000; McNaughton and Corr, 2004).

The so-called post-encounter 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 post-encounter threat preferentially engages the vmPFC, sgACC, pgACC, hippocampus, amygdala and hypothalamus. Although other structures (e.g. ventrolateral PAG) are also engaged during real “life-endangering” post-encounter threat, the forebrain regions we describe are known to play a critical role in post-encounter threat by influencing visceral functions (Critchley, et al. 2001), prediction and prefiguring analgesic and strategic responses (Fanselow and Lester, 1988; Petrovic, et al. 2002). These forebrain areas also have dense connections 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, et al. 1992; LeDoux, 1996; Phelps and LeDoux, 2005) and is an integral component of the post-encounter instigating behavioral reactions (e.g. ventrolateral 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 encompass complex reactions to ecological dangers (Price, 2005) including 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 post-encounter conditions as were SCL’s, presumably reflecting increased autonomic sympathetic arousal (Critchley, et al. 2002). Moreover, increased midbrain activity was observed, again supporting previous theory (Deakin, 1991; Gorman, et al. 2000; McNaughton and Corr, 2004). A previous study from our group showed that the midbrain 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 over-activity of the midbrain 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). Supporting the notion that panic is associated with uncoordinated behavior during inescapable threat (e.g. McNaughton, 1993), we found that midbrain activity increased with the amount of panic-related locomotor errors for the CSHI - CSLO threat (Fig. 5). Indeed, chemical stimulation of the rodent dorsolateral PAG elicits uncoordinated panic-like behaviors such as uncontrolled activity bursts (e.g. vigorous running and jumping) (Deakin and Graeff, 1991; Bandler, et al. 2000; Vianna, et al. 2001) while 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). While 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 contingencies 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 (Mobbs et al 2007; Fanselow and Lester 1988)(Ochsner, et al. In press). 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. In press). 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 connectivity with the midbrain. However, the amygdala also showed negative connectivity with pgACC during the CSHI 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, 1995), while 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 post-encounter vs. circa-strike contexts (Fanselow, 1994).

It has previously been suggested that shock probability essentially models distance on the predatory imminence continuum (Bolles, 1980; Fanselow and Lester, 1988). Compared to 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 CSHI - CSLO 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), extinction (Phelps et al 2004) and augments hypothalamic stress hormones (Figueiredo et al 2003). Stimulation of the mPFC homologue 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 BLA to the CeA (Quirk et al. 2003; Bermpohl, et al. 2005). Indeed, these regions have been shown to control stress reactions (Salomons, et al. 2004; Amat, et al. 2005; Salomons, et al. 2007) and to be abnormal in patients with PTSD and panic disorder (Zubieta, et al. 1999; Asami, et al. 2008; Uchida, et al. 2008). While one might argue that this activity reflects cognitive predictive process which function independently from the emotional system, our findings support the notion that the vmPFC regulates the fear systems possibly via the amygdala (Reiman, et al. 1989; Schiller, et al. 2008). Similarly, whereas it could be suggested that prefrontal exerts hierarchical inhibitory control on an otherwise scalar fear system, the differential activity of different subcortical and midbrain areas.

Although the current results only present contexts analogous to real defensive states, they are strongly consistent with brain anti-predator 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 imminence continuum (Fanselow, 1995). For example, when threat is spotted, slow, but accurate, higher parts of the fear system organize fear and preparatory responses. This higher threat system, 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. While our conclusions 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.

Supplementary Material



We thank C. Hagan, L. Passamonti, C Hutton, and N. Weiskopf for discussions and help with data analysis. This work was funded by the Wellcome Trust research programme grants. D.M. supported by a Brain Research Trust Prize studentship and Medical Research Council.


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