NeuroImage 44 (2009) 975–981
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NeuroImage j o u r n a l h o m e p a g e : w w w. e l s e v i e r. c o m / l o c a t e / y n i m g
Dynamic activation of the anterior cingulate cortex during anticipatory anxiety Thomas Straube a,⁎, Stephanie Schmidt a, Thomas Weiss a, Hans-Joachim Mentzel b, Wolfgang H.R. Miltner a a b
Department of Biological and Clinical Psychology, Friedrich Schiller University of Jena, Am Steiger 3 // 1, D-07743 Jena, Germany Institute of Diagnostic and Interventional Radiology, Friedrich Schiller University of Jena, Bachstr. 18; D-07740 Jena, Germany
a r t i c l e
i n f o
Article history: Received 25 June 2008 Revised 6 October 2008 Accepted 7 October 2008 Available online 5 November 2008 Keywords: Brain Anticipation ACC Pain Fear
a b s t r a c t Based on theoretical models, we investigated the dynamics of brain activation during anticipatory anxiety using functional magnetic resonance imaging and a combined parametric/correlational design. Subjects (16 females) anticipated the application of electrical shocks of varying intensity resulting in four different threat levels. The parametric analysis revealed an inverted U-function of activation in the ventral anterior cingulate cortex (ACC) depending on the level of threat. Furthermore, the correlation analysis showed that the association between anxiety and brain activation in the pregenual ACC was, as a tendency, positive during moderate threat but clearly negative during strong threat. Moreover, during strong threat, a positive correlation between anxiety and activation was observed in the dorsal ACC, somatosensory cortex, motor cortex, and hippocampus. These findings suggest threat dependent dynamics of brain activation in the ACC; with increased attentional avoidance during moderate threat and a switch to hypervigilant action readiness in the most anxious subjects during strong threat. © 2008 Elsevier Inc. All rights reserved.
Introduction Anticipatory anxiety during the expectation of threatening stimuli is a negative affective state, with changing behavioral tendencies depending on the level of threat (Gray and McNaughton, 2000; Mogg and Bradley, 1998). During moderate threat, subjects seem able to shift their attention away from the expected source of threat, while increasing a default processing state and prioritizing avoidance behavior (Mogg and Bradley, 1998). By contrast, stronger threat increases vigilance towards the expected threatening stimuli, leading to a preparation for dealing with the threat (Gray and McNaughton, 2000; Mogg and Bradley, 1998). Thus, strong threat increases readiness for action. This relationship between threat and certain behavioral tendencies is modified by individual differences in anxiety. Both the avoidance tendency during moderate threat and the shift to a hypervigilant state, with increased readiness for action, during strong threat are most pronounced in anxious subjects (Mogg and Bradley, 1998). Several efforts have been undertaken to reveal the functional neuroanatomy that is associated with anticipatory anxiety in humans by using functional neuroimaging. While some studies reported that the anticipation of aversive stimuli led to activation of the ventral medial prefrontal cortex (MPFC) including ventral (subgenual and pregenual) anterior cingulate cortex (ACC) (Butler et al., 2005; Nitschke et al., 2006; Ploghaus et al., 2003; Straube et al., 2008), other studies indicated a significant deactivation of these areas during ⁎ Corresponding author. Fax: +49 3641 9 45 142. E-mail address:
[email protected] (T. Straube). 1053-8119/$ – see front matter © 2008 Elsevier Inc. All rights reserved. doi:10.1016/j.neuroimage.2008.10.022
anticipatory anxiety (Ploghaus et al., 1999; Simpson et al., 2001). It has been proposed that pregenual and ventral areas of the MPFC may be a part of the so-called default areas of the brain that are involved in the processing of internally directed mental states (McKiernan et al., 2006; Raichle et al., 2001; Simpson et al., 2001). Activation in these areas seems to be associated with different kinds of mental activity (Morcom and Fletcher, 2007), which have in common that they are widely unrelated to vigilance, behavioral activation and action readiness. During anticipatory anxiety, activation of these areas seems to depend upon the interplay between externally and internally focused attention (Simpson et al., 2001; Straube et al., 2007a). Thus, it has been found that the most anxious subjects show the highest degree of activation/ the least degree of deactivation in the ventral MPFC, including ventral ACC (Simpson et al., 2001; Straube et al., 2007a). The results might indicate that activation in these areas is associated with attentional control of threat or attentional avoidance of external stimuli (Bishop et al., 2004; Etkin et al., 2006; Shin et al., 2004). However, previous findings on brain activation during anticipatory anxiety are based on experiments with fixed and rather moderate threat levels. Although predictability of or distance from threat have been demonstrated to modify the activation in the MPFC (Carlsson et al., 2006; Mobbs et al., 2007), it remains unclear whether and how activation during anticipatory anxiety depends on the anticipated threat level per se. Varying threat levels might result in dynamically changing patterns of brain activation. For example, the Mogg and Bradley theory (Mogg and Bradley, 1998) suggests a curvilinear relationship between threat and avoidance in the case of perceived threat. Although the exact function in the case of anticipatory situations is not clear, one can assume that a curvilinear relationship
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Materials and methods
subcutaneously to the tip of the index finger of the left hand through an isolated golden pin electrode with a diameter of 0.95 mm and a length of 1 mm (Bromm and Meier, 1984; Straube et al., 2007a, 2008). The pin was inserted into a small epidermal cavity of 1 mm diameter and about 1 mm depth, and fixed with adhesive tape. The purpose of this preparation was to reduce skin resistance, thus reducing the current necessary to elicit a pain sensation. A flexible stainless-steel electrode, fixed loosely around the first finger joint, served as a reference electrode. Subjects were grounded by using a broad, flexible, humid band electrode fixed around the wrist of the stimulated hand. Before scanning, individual stimulus intensities were determined by requesting participants to rate each single electrical stimulus on a four-point Likert scale (1= no pain; 2 = mild pain; 3 = moderate pain; 4 = strong pain, but tolerable. For half of the subjects the assignment between intensity and figures was reversed, i.e., 1 = strong pain, etc.). Using a modified method of limits, three series of single electrical pulses with increasing and decreasing intensities were applied. Mean values for each intensity of the last two series were used in the experiment proper. During the scanning session, subjects anticipated the application of electrical stimuli of varying intensity. Each stimulus was preceded by an anticipatory period of variable duration. During the anticipatory period, a cue was presented that consisted of a digit (“1” to “4”, shown in white on a black background) and corresponded to expected pain intensity (see above). Subjects were told that the stimulus could be applied at any time after the occurrence of the cue. Each of the four stimulus intensities was applied after six anticipatory periods, which were presented in pseudorandom order. Anticipatory periods varied between 14 and 22 s with a mean of 18 s. Between application of the shock which terminated the anticipation phase and the next anticipation period, a fixation cross was shown for 16 s. After the scanning-session, participants were requested to rate the anxiety they experienced during the anticipation periods using a nine-point Likert scale (1 = “not anxious at all” to 9 = “very strongly anxious”). The anticipation phases for no, mild, moderate, and strong pain relate to the terms no, mild, moderate, and strong threat in the data analysis. However, one has to consider that we do not equate our terms mild and moderate with the same terms in the Mogg and Bradley theory (1998). Thus mild, moderate, and strong threat might be also replaced by no threat (but very mild pain), mild threat (moderate pain), and moderate threat (strong pain), since the subjective significance of threat is the relevant variable for the definition of threat levels. The subjective significance is indicated by the anxiety ratings in our study. Thus, mild threat here refers to a situation where the pain stimuli do not induce clear signs of anxiety. Behavioral data were analyzed by means of repeated measures analysis of variance (ANOVA) using SPSS (Version 12; SPSS, INC., Chicago). A probability level of P b 0.05 was considered statistically significant. All data are expressed as means ± SEM.
Subjects
fMRI
Sixteen healthy, right-handed female volunteers (mean age: 21.3 years; range: 18–26) with normal or normal-to-corrected vision provided informed consent to participate in the study. All participants were students of the University of Jena each of whom were paid 10 EUR were paid for volunteering. The experimental procedures were approved by the Ethics Committee of the University of Jena. Only women were included in the experiment, because men and women differ in pain processing (see Straube et al., 2008), which would complicate the analysis of the current experiment.
In the 1.5 T magnetic resonance scanner (“Magnetom Vision plus”, Siemens, Medical Systems. Erlangen), one run of 208 volumes was measured using a T2⁎-weighted echo-planar sequence (TE = 50 ms, flip angle = 90°, matrix = 64 × 64, FOV = 192 mm, TR = 3.9 s). Each volume comprised 40 axial slices (thickness = 3 mm, no gap, in plane resolution = 3 × 3 mm), acquired with a tilted slice orientation to minimize susceptibility artifacts in the ventral prefrontal cortex (Deichmann et al., 2003). To ensure that steady-state tissue magnetization was reached the first four volumes of each run were excluded from analysis. Additionally, a high-resolution T1-weighted anatomical volume was recorded. Pre-processing and analysis of functional data was performed using the Brain Voyager QX software (Version 1.9; Brain Innovation, Maastricht, The Netherlands). The volumes were realigned to the first volume in order to minimize the effects of head movements. Further
between threat level and specific brain activation patterns should be found. Furthermore, it seems that anticipation of “moderate” pain or threat seems to activate the so-called prefrontal default areas at least in the highly anxious subjects (Simpson et al., 2001; Mobbs et al., 2007; Straube et al., 2007a). Based on these considerations, the relationship between ventral MPFC activation and level of threat might follow an inverted U-function. Thus, mild to moderate threat, as compared to very mild threat, should increase activation in the ventral MPFC, including the pre- and subgenual ACC, especially in high anxious subjects, due to increased attempts to cope with the threat by means of attentional avoidance. However, during rather strong threat, the most anxious subjects should show a decrease of activation in this brain area. Furthermore, under strong threat, a positive relationship between anxiety and brain activation should be observed in brain areas implicated in vigilance and action preparation such as the dorsal ACC, an area repeatedly found to be activated by anxiety provoking paradigms (Milad et al., 2007; Straube et al., 2006, 2007a,b). Previously it has been proposed that the dorsal ACC has a role in anxiety states that are associated with increased executive functions (Straube et al., 2007a). Since anxiety and attention to highly threatening stimuli are closely associated with each other, anxiety ratings should correlate positively with activation in regions implicated in the sensory processing of the corresponding stimulus, such as the somatosensory cortex in the case of a threatening somatosensory stimulus (Ploghaus et al., 2003). Furthermore, a positive correlation between anxiety and activation should also be observed in the motor cortex as an indicator of action preparation. The current study aimed to investigate the impact of threat level on neural correlates of anticipatory anxiety. The dynamics of neural activation during anticipatory anxiety was investigated with a combined parametric/correlational design. Subjects anticipated the application of four different levels (no pain, mild pain, moderate pain, strong pain) of electrical shocks to their right hand. The design allowed analysis of parametric effects of threat level on brain activation as well as a correlational analysis of the association between anxiety and brain activation for different threat levels. According to models proposing increased avoidance during moderate threat and a shift to increased vigilance and action readiness during stronger threat in the most anxious subjects, we hypothesized that brain activation would follow such models. Concerning the ACC, we further predicted that the activation pattern would not support a currently influential model of emotional versus cognitive functions associated with the ventral versus dorsal ACC (Bush et al., 2000). Rather, activation in these regions should be associated with different processing modes during anxiety states and activation of both areas should correlate with anxiety ratings as a function of threat level.
Stimulation and paradigm Somatosensory electrical stimuli consisted of rectangular pulse of 20 ms duration generated by a constant current stimulator (DS7H; Digitimer, Welwyn Garden City, UK). Stimuli were applied
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Fig. 1. Anxiety ratings across threat levels. There was a linear increase in anxiety ratings across threat levels.
data pre-processing contained a correction for slice time errors and spatial [8 mm full-width half-maximum isotropic Gaussian kernel (FWHMK)] as well as temporal (high pass filter: 0.005 Hz) smoothing. Anatomical and functional images were coregistered and normalized to the Talairach space (Talairach and Tournoux, 1988). Statistical analysis was performed by multiple linear regression of the signal time course at each voxel, including a correction for serial correlations. The expected BOLD signal change for each anticipation phase (= predictors of interest) was modeled by a canonical hemodynamic response function (modified gamma function; delta = 2.5, tau = 1.25). Furthermore, the applied somatosensory stimuli were modeled as events of no interest. Within-group statistical comparisons were conducted using a mixed effect analysis, which considers inter-subject variance and permits population-level inferences. Firstly, voxelwise statistical maps were generated and the relevant planned contrasts of predictor estimates (beta-weights) were computed for each individual. Secondly, a random effect group analysis of these individual contrasts was performed. Analysis was conducted for four different parametric functions (PF). Two PFs modeled linear increases and decreases depending on expected stimulus intensity (balanced contrast values for no pain, mild pain, moderate pain, strong pain: −3, −1, 1, 3 and 3, 1, −1, −3). The other two PFs modeled a U-function or an inverted U-function for the pain phases, irrespective of baseline activation (contrast values: 0, 1, −2, 1 and 0, −1, 2, −1, respectively). Analysis was conducted in specific regions of interest (ROI). Besides those regions for which specific hypotheses were formulated (ventral MPFC, ventral ACC, dorsal ACC and the hand area in primary motor and somatosensory cortex), we also included regions that have been suggested to be involved in anticipatory anxiety, such as the dorsomedial prefrontal cortex (DMPFC), amygdala, bed nucleus of the stria terminalis (BNST), hippocampus and insula (Butler et al., 2005; Milad et al., 2007; Paulus and Stein, 2006; Ploghaus et al., 1999, 2003; Straube et al., 2006, 2007a,b). Coordinates of the ROIs were defined with the help of the Talairach Daemon software (http://ric.uthscsa.edu/projects/ talairachdaemon.html), which determines brain regions according to the stereotactic coordinate system of the Talairach atlas (Talairach and Tournoux, 1988). Statistical parametric maps resulting from
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voxelwise analysis were considered statistically significant for clusters that survived a correction for multiple comparisons. We used the approach as implemented in Brain Voyager (Goebel et al., 2006), which is based on a 3D extension of the randomization procedure described by Forman et al. (1995). Firstly, voxel-level threshold was set at P b 0.005 (uncorrected). Thresholded maps were then submitted to a ROI-based correction for multiple comparisons. The correction criterion was based on the estimate of the map's spatial smoothness and on an iterative procedure (Monte Carlo simulation) for estimating cluster-level false-positive rates. After 1,000 iterations, the minimum cluster size threshold that yielded a cluster-level false-positive rate of 5% was applied to the statistical maps. Due to four different analyses on the data (linear increase and decrease, U- and inverted U shapes) a Bonferroni correction for multiple tests was applied (with an effective P b 0.0125). The minimum cluster size required to satisfy the multiple comparison correction was five (3 × 3 × 3 mm) voxels. All clusters reported in this paper survived the ROI-based control of multiple comparisons. Results Ratings Anxiety ratings increased with increasing threat level: 1.25± 0.11 (no pain), 1.94 ± 1.18 (mild pain), 3.5 ± 2.03 (moderate pain), and 5.06 ± 2.26 (strong pain) (see Fig. 1). Analysis of anxiety ratings revealed a main effect of threat level [F(1.80, 26.95) = 28.68, P b 0.0001, Greenhouse– Geisser corrected) and a significant linear positive trend with increasing threat level [F (1, 15) = 42.16 P b 0.0001; see Fig. 1]. Furthermore, anxiety ratings for moderate and strong threat strongly correlated with each other [r(16) = 0.80; P b 0.0001]. This finding indicates that threat-level dependent changes in correlations between anxiety ratings and brain activation (see below) are indeed due to threat level, but not to changes in structure of the anxiety ratings across subjects. fMRI-data Parametric analysis Firstly, we analyzed brain activation according to different parametric functions (PF) as described in Materials and methods. Two PFs modeled linear increases and decreases depending on expected stimulus intensity. The other two PFs modeled a U-function or an inverted U-function for the pain phases, irrespective of activation during the “no pain” anticipation. There was no linear increase or decrease in activation depending on the threat level. However, there was a strong inverted U-function of activation in the ventral ACC (coordinates of peak voxel [x,y,z]: −3,40,7; t = 3.88). Fig. 2a shows the activation based on this parametric analysis in the ventral ACC. Inspection of the corresponding blood oxygen level-dependent (BOLD) signal scatter plots shows similar levels of activation during
Fig. 2. Inverted U-function of brain activation across threat levels in the ventral ACC. There was an inverted U-function of brain activation across threat levels in the ventral ACC. Statistical parametric maps are overlaid on a T1 scan (x = −3; radiological convention: left = right). The plots show contrasts of parameter estimates according to the parametric function for the peak voxel. (a) Activation across all subjects. (b) Activation shown separately for highly and low anxious subjects indicating that the inverted U-function is driven by the highly anxious subjects.
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Fig. 3. Correlation between anxiety and brain activation in the pregenual ACC during moderate threat. There was a positive correlation between anxiety and brain activation in the pregenual ACC during moderate threat. Statistical parametric maps are overlaid on a T1 scan (x = 9; radiological convention: left = right). The scatter plots show the relationship between parameter estimates (moderate threat) and brain activation.
both the low and the strong threat conditions, but a pronounced increase of activation during moderate threat. Using a median split of the sample based on the anxiety ratings for the strong threat condition, Fig. 2b illustrates that the inverted U-function across the whole group is driven by the highly anxious subjects. A post-hoc comparison between the highly and lowly anxious subjects confirmed a significant difference for the above parametric analysis at the peak voxel (t = 4.38; P b 0.05). Furthermore, an inverted U-function of brain activation with threat level was also detected bilaterally in the insula [coordinates of peak voxel (x,y,z): 45,−3,0 (right) and −37,12,4 (left); t = 3.48 and t = 3.80]. Only activation in the midbrain, including the periaquaeductal grey, followed a U-function of brain activation with respect to the threat levels [coordinates of peak voxel (x,y,z): 1,−28,−12; t = 3.50; see Supplementary Fig. S1, which illustrates that this result is mainly based on the highly anxious subjects]. In accordance with a previous study (Mobbs et al., 2007), this finding indicates a suppression of midbrain activation during moderate threat. Correlation analysis According to the model of brain activation during anticipatory anxiety described in the introduction, we expected a positive correlation between BOLD responses and anxiety during moderate threat in the ventral ACC. During strong threat, however, a negative correlation between anxiety and activation should be found within this area. Furthermore, during strong threat, a correlation between anxiety and brain activation was expected in dorsal ACC and the somatosensory and motor hand area. Correlations between anxiety and brain activation were analyzed for the three different pain levels (mild, moderate, strong). During anticipation of mild pain, there was no association between anxiety and brain activation, even at a voxel wise threshold of P b 0.05. During moderate threat, there was also no association between anxiety and threat on the significance thresholds
chosen a priori. To detect a trend of activation corresponding to our hypothesis, we reanalyzed the data with a more lenient voxelwise threshold. Lowering the voxelwise threshold to P b 0.05, revealed a cluster in the pregenual ACC. This cluster survived correction for multiple comparisons on the cluster level [r(16) = 0.55 for peak voxel (x,y,z): 14,37,13] (see Fig. 3). Note that there were no significant positive or negative correlations on this significance level in any of the other ROIs. During strong threat, significant positive correlations were detected in the dorsal ACC [r(16) = 0.82 for peak voxel (x,y,z): 3,1,38; see Fig. 4], left somatososensory hand area [r(16) = 0.84 for peak voxel (x,y,z): −39,−28,47; see Fig. 5], left motor hand area [r(16) = 0.75 for peak voxel (x,y,z): −34,−18,45; see Fig. 5], and left hippocampus [r(16) = 0.77 for peak voxel (x,y,z): −22,−20,−14; see Fig. 5]. Using the threshold set a priori, there was one single cluster in the somatosensory and motor cortex with peaks as given above. A method to identify centres of gravity within overlapping clusters and to show that the single cluster found in the somatosensory/motor cortex is driven by both somatosensory and motor activation is to increase the threshold for significant activations. Thus, when the voxelwise threshold was increased to P b 0.001, two distinct clusters were found in the motor and somatosensory hand area, respectively (see Fig. 5). In contrast to the positive correlation in the dorsal ACC, there was a significant negative correlation between activation and anxiety in the ventral prefrontal cortex including the ventral ACC [r(16) = −0.84 for peak voxel (x,y,z): −3,34,−2]. Fig. 4 indicates this negative correlation in blue color in contrast to the red color used to indicate the positive correlation in the dorsal ACC in the same figure. Discussion The present study investigated brain activation while subjects anticipated electrical shocks of varying intensities. The results
Fig. 4. Correlation between anxiety and brain activation in the ventral and dorsal ACC during strong threat. The blue cluster indicates a negative correlation in the ventral ACC, the red cluster a positive correlation in the dorsal ACC. Statistical parametric maps are overlaid on a T1 scan (x = 2; radiological convention: left = right). The scatter plots show the relationship between parameter estimates (strong threat) and brain activation (left: ventral ACC; right: dorsal ACC).
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Fig. 5. Positive correlation between anxiety and brain activation in the somatosensory cortex, motor cortex, and hippocampus during strong threat. Positive correlation between anxiety and brain activation were found in the somatosensory cortex (a), motor cortex (b), and hippocampus (c) during strong threat. Statistical parametric maps are overlaid on a T1 scan (A–C: z = 48, 48, − 14; radiological convention: left = right). The scatter plots show the relationship between parameter estimates (strong threat) and brain activation.
provide evidence that the relationship between activation in the ventral MPFC, including the ventral ACC, and the level of threat follow an inverted U-function that is mainly driven by the highly anxious subjects. During mild threat there is a positive correlation between anxiety and activation in this area. During strong threat, however, anxiety is negatively correlated with activation in the ventral ACC, but positively correlated with activation in the dorsal ACC, somatosensory cortex, motor cortex, and hippocampus. The findings clearly show that brain activation changes depending on the threat level. Regarding the ACC, the results challenge the idea that subregions of the ACC are involved in emotional versus cognitive functions (Bush et al., 2000). Rather, brain activation varies with threat level, producing almost completely opposite activation patterns. Dynamically changing brain activation in the ventral MPFC has also been proposed previously in a study that used a manipulation of proximal vs. distal threat (Mobbs et al., 2007). In that study, proximal threat led to deactivation of the ventral MPFC and to activation of the periaquaductal grey, suggesting more reflexive response tendencies in the case of very near threat. In the current study, the distance to the
threat was not manipulated. Nevertheless, the dynamic of activation of the ventral MPFC/ACC is strikingly clear. While moderate threat leads, as a tendency, to a positive association between anxiety and activation within this area, strong threat reverses this relation. Our data might also explain the obviously opposite results of previous studies on the role of the ventral ACC in anxiety and emotion in general. Several studies have found an association between activation in perigenual ACC and subjective emotion (Bush et al., 2000; Lane et al., 1998; Phan et al., 2002). Furthermore, evidence for a specific role for perigenual ACC in anticipation of aversive stimuli (Butler et al., 2005; Nitschke et al., 2006; Ploghaus et al., 2003) and a positive association between anxiety and activation of ventral ACC (Simpson et al., 2001; Straube et al., 2007a,b) has been provided. On the other hand, several authors suggested a role of this ACC area in the control of distracting emotional information (Bishop et al., 2004; Etkin et al., 2006; Shin et al., 2004). Anxiety during performance situations has been shown to correlate negatively with activation of pregenual ACC (Bishop et al., 2004). Furthermore, deactivation of ventral ACC has been reported in patients with anxiety disorders during symptom
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provocation (Shin et al., 2004). Thus, while some authors suggest an association between anxiety level and activation of the ventral ACC, other propose the opposite. Here, we suggest that moderate threat leads to increased activation in the MPFC due to increased attempts of attentional avoidance of the external threat in the most anxious subjects (Mogg and Bradley, 1998). The activation in this area increases during internally focused attention but decreases during externally focused attention, action preparation and action execution (Raichle et al., 2001). As long as anxiety is related to internally oriented attention (this might also include increased awareness of emotional state), a relationship between activation of MPFC and experienced anxiety will be observed (Simpson et al., 2001; Straube et al., 2007a,b, 2008). One has to consider that the so-called default areas of the brain might have different functions in different contexts. During rest, activation simply reflects ongoing non-focused mental activity (Raichle et al., 2001). When detecting potential threat these areas might be partly deactivated as long as there are no attempts of attentional avoidance (Simpson et al., 2001). With increased attentional avoidance of external threat these areas seem to participate in an attentional avoidance network, which may increase activation over a limited range of increasing threat levels. This process starts with objectively lower threat in the highly as compared to the lowly anxious subjects. However, from a certain threshold threat level on (e.g., in our design “strong” in the highly anxious and perhaps some kind of “very strong” in the lowly anxious subjects), the avoidance system is switched off and the vigilance network is switched on, which may include aspects of behavioral response preparation and thus activity in executory areas. Thus, strong threat is associated with hypervigilance and readiness for action in the most anxious subjects who switch to externally focused information processing, which is accompanied by deactivation of the ventral ACC. This processing mode leads to an activation of the dorsal ACC, as indicated by the positive correlation between activation and anxiety in this area during strong threat. In line with this finding, the dorsal ACC has been implicated in fear and anxiety in previous studies across several stimulus modalities (Milad et al., 2007; Ploghaus et al., 2003; Straube et al., 2006, 2007a,b). Thus, activation of the dorsal ACC might be specifically relevant for anticipatory anxiety states characterized by enhanced executive functions, including hyperscanning of the environment and increased drive for action (Straube et al., 2007a). Under strong threat, a positive correlation between anxiety and brain activation was also detected in the somatosensory–motor representation of the hand subjected to electrical stimuli. This finding suggests that with increasing anxiety, subjects attended more intensely to the hand, while displaying increased preparedness to cope with the threat. Furthermore, in accordance with previous studies (Hasler et al., 2007; Ploghaus et al., 2001) an association between anxiety and brain activation was detected in the hippocampal formation. Activation of the hippocampus seems to be associated with those forms of anxiety, which are induced in more sustained anxiety provoking paradigms [in contrast to the amygdala, which is activated during brief anxietyrelated cues (Hasler et al., 2007; Straube et al., 2006, 2007a,b)]. Hippocampal activation was proposed to be related to the behavioral conflict, which is associated with anxiety states (Gray and McNaughton, 2000; Ploghaus et al., 2001). Furthermore, it has been proposed that the hippocampus sends amplification signals to resolve the conflict that is associated with an aversive situation, for example to the ACC (Gray and McNaughton, 2000; Ploghaus et al., 2001, 2003). According to previous studies (Chua et al., 1999; Ploghaus et al., 1999; Straube et al., 2006, 2007a,b), the present study also found some support for an involvement of the insula in anxiety mechanisms. The role of the insula in interoception suggests a relationship between insular hyperactivity, increased awareness of bodily states and anxiety proneness (Craig, 2002; Critchley, 2004; Paulus and Stein, 2006). However, the current study indicates that the relation between insular
activity and threat during anticipatory anxiety might also follow an inverted U-function. This seems to support the proposal that insula activation is a function of the amount of internally focused attention (Critchley, 2004; Straube et al., 2006). It should be noted that we investigated whether brain activation shows a curvilinear relationship with threat under the assumption that, in our design, “moderate threat” represents the suited vertex of this function. However, there might be further functions – for example with mild threat as peak activation – that could explain the data in subregions of the insula, the ACC or in other regions of the brain. Thus, future studies should include the analysis of further functions and, most importantly, relate brain activation to measures of behavioral tendencies, for example to the magnitude of muscle activation or to post-scanning ratings of coping strategies used during the specific threat phases. In conclusion, the present study revealed neural correlates of anticipatory anxiety during different threat levels. As a main finding, our data challenge the idea that ventral and dorsal ACC might be separated by emotional vs. cognitive functions. Rather, depending upon the threat level, these regions are involved in different processing modes during anxiety states. Thus, activation of the ACC changes depending on the threat level and individual differences in experienced anxiety. Acknowledgments The study was supported by a grant of the federal state of Thuringia and the University of Jena awarded to TS. We are thankful to two anonymous reviewers for the advice regarding the potential functions of the ACC during anticipatory anxiety. Appendix A. Supplementary data Supplementary data associated with this article can be found, in the online version, at doi:10.1016/j.neuroimage.2008.10.022. References Bishop, S., Duncan, J., Brett, M., Lawrence, A.D., 2004. Prefrontal cortical function and anxiety: controlling attention to threat-related stimuli. Nat. Neurosci. 7, 184–188. Bromm, B., Meier, W., 1984. The intracutaneous stimulus: a new pain model for algesimetric studies. Methods Find. Exp. Clin. Pharmacol. 6, 405–410. Bush, G., Luu, P., Posner, M.I., 2000. Cognitive and emotional influences in anterior cingulate cortex. Trends Cogn. Sci. 4, 215–222. Butler, T., Pan, H., Epstein, J., Protopopescu, X., Tuescher, O., Goldstein, M., Cloitre, M., Yang, Y., Phelps, E., Gorman, J., Ledoux, J., Stern, E., Silbersweig, D., 2005. Fearrelated activity in subgenual anterior cingulate differs between men and women. NeuroReport 16, 1233–1236. Carlsson, K., Andersson, J., Petrovic, P., Petersson, K.M., Ohman, A., Ingvar, M., 2006. Predictability modulates the affective and sensory-discriminative neural processing of pain. NeuroImage 32, 1804–1814. Chua, P., Krams, M., Toni, I., Passingham, R., Dolan, R., 1999. A functional anatomy of anticipatory anxiety. NeuroImage 9, 563–571. Craig, A.D., 2002. How do you feel? Interoception: the sense of the physiological condition of the body. Nat. Rev. Neurosci. 3, 655–666. Critchley, H.D., 2004. The human cortex responds to an interoceptive challenge. Proc. Natl. Acad. Sci. U. S. A. 101, 6333–6334. Deichmann, R., Gottfried, J.A., Hutton, C., Turner, R., 2003. Optimized EPI for fMRI studies of the orbitofrontal cortex. NeuroImage 19, 430–441. Etkin, A., Egner, T., Peraza, D.M., Kandel, E.R., Hirsch, J., 2006. Resolving emotional conflict: a role for the rostral anterior cingulate cortex in modulating activity in the amygdala. Neuron 51, 871–882. Forman, S.D., Cohen, J.D., Fitzgerald, M., Eddy, W.F., Mintun, M.A., Noll, D.C., 1995. Improved assessment of significant activation in functional magnetic resonance imaging (fMRI): use of a cluster-size threshold. Magn. Reson. Med. 33, 636–647. Goebel, R., Esposito, F., Formisano, E., 2006. Analysis of functional image analysis contest (FIAC) data with brainvoyager QX: from single-subject to cortically aligned group general linear model analysis and self-organizing group independent component analysis. Hum. Brain Mapp. 27, 392–401. Gray, J.A., McNaughton, N., 2000. The Neuropsychology of Anxiety: An Enquiry into the Functions of the Septo-hippocampal system. Oxford University Press, New York. Hasler, G., Fromm, S., Alvarez, R.P., Luckenbaugh, D.A., Drevets, W.C., Grillon, C., 2007. Cerebral blood flow in immediate and sustained anxiety. J. Neurosci. 27, 6313–6319.
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