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. Author manuscript; available in PMC: 2021 Mar 1.
Published in final edited form as: J Psychiatr Res. 2019 Dec 31;122:54–63. doi: 10.1016/j.jpsychires.2019.12.017

Threat bias and resting state functional connectivity of the amygdala and bed nucleus stria terminalis

Samantha K Jenks 1, Sheng Zhang 2, Chiang-shan R Li 2,3,4, Sien Hu 1,*
PMCID: PMC7010552  NIHMSID: NIHMS1548790  PMID: 31927266

Abstract

Background

Previous research has distinguished the activations of the amygdala and bed nucleus of stria terminalis (BNST) during threat-related contingencies. However, how intrinsic connectivities of the amygdala and BNST relate to threat bias remains unclear. Here, we investigated how resting state functional connectivity (rsFC) of the amygdala and BNST in healthy controls (HC) and patients with anxiety-related disorders (PAD) associate with threat bias in a dot-probe task.

Methods

Imaging and behavioral data of 30 PAD and 83 HC were obtained from the Nathan Kline Institute - Rockland sample and processed according to published routines. All imaging results were evaluated at voxel p<0.001 and cluster p<0.05, FWE corrected in SPM.

Results

PAD and HC did not show differences in whole brain rsFC with either the amygdala or BNST. In linear regressions threat bias was positively correlated with amygdala-thalamus rsFC in HC but not PAD, and with BNST-caudate rsFC in PAD but not HC. Slope tests confirmed group differences in the correlations between threat bias and amygdala-thalamus as well as BNST-caudate rsFC.

Limitations

As half of the patients included were diagnosed with comorbid anxiety, the current findings need to be considered with the clinical heterogeneity and require replication in populations specifically with anxiety disorders.

Conclusions

Together, these results suggest amygdala and BNST connectivities as new neural markers of anxiety disorders. Whereas amygdala-thalamus rsFC support adaptive regulation of threat response in the HC, BNST-caudate rsFC may reflect maladaptive neural processes that are dominated by anticipatory anxiety.

Keywords: amygdala, BNST, resting-state connectivity, dot-probe task, threat bias, anxiety

Introduction

The amygdala is best known for its role in processing negative emotions, especially fear and anxiety. The bed nucleus of stria terminalis (BNST), a small structure located dorsal to the amygdala and ventral to the hypothalamus and anatomically connected with the limbic system, including the amygdala, thalamus, and basal ganglia, shares a similar role (Avery et al., 2014). Both the amygdala and BNST support defensive behavior in response to threats (Ahrens et al., 2018; Avery et al., 2016; Fox and Shackman, 2019; Wu et al., 2019). However, the amygdala and BNST can be functionally distinguished; the amygdala is involved in rapid response to threat (Fox et al., 2015) and the BNST responds to sustained fear as a result of increased release of corticotropin-releasing factor by the amygdala (Davis et al., 2010). The functional distinction has also been reported in clinical populations with greater phasic activation of the amygdala and sustained activation of the BNST in patients with posttraumatic stress disorder (PTSD), generalized anxiety disorder, and specific phobia, as compared to healthy individuals (Knight and Depue, 2019). On the other hand, whether or how the intrinsic connectivities of the amygdala and BNST could be dissociated in link with anxiety remains unclear. Despite abundant research on the resting-state functional connectivity (rsFC) of the amygdala and its relationship to emotional processing (Kim et al., 2011), only a handful of studies have focused on the BNST. The current study investigated the rsFC of the amygdala and BNST and how the rsFC may associate with threat bias in patients with anxiety disorders and healthy controls.

Recent advances in high field magnetic resonance imaging has allowed better localization of the BNST in humans (Avery et al., 2014). Although anatomically connected, the amygdala and BNST differ in functional connectivities. The central nucleus of the amygdala is more coupled with regions supporting sensorimotor processing, including the ventral posterior thalamus, middle insula, fusiform gyrus, and postcentral gyrus (Gorka et al., 2018; Torrisi et al., 2015). In contrast, the BNST is more heavily connected with the ventral striatum and posterior cingulate cortex, regions that are central to reward, saliency and cognitive processing (Gorka et al., 2018; Roy et al., 2009). The amygdala and BNST also show subtle but important differences in task-related responses. For example, in participants estimating when electric shocks would be administered at the end of a trial, the BNST but not amygdala showed higher activation as the threat approached (Somerville et al., 2010). In another study with healthy participants associating colored squares with the likelihood of an impending electric shock, the BNST and amygdala showed stronger activations during shock anticipation and administration, respectively (Klumpers et al., 2017). These results suggest that the amygdala supports multisensory and motor integration in reactions toward threat, whereas the BNST engages in modulation of sustained threat and danger.

Numerous studies have characterized the activity and connectivity of the amygdala during resting or task challenges in healthy and clinical populations. In healthy adults, the amygdala showed increases in functional connectivity as measured by psychophysiological interaction with prefrontal cortical regions including the orbitofrontal cortex (OFC), ventromedial prefrontal cortex (vmPFC) and anterior cingulate cortex (ACC) during threat exposure (Gold et al., 2015; Gold et al., 2016; Satterthwaite et al., 2011). Compared to healthy controls, patients with social anxiety disorder (Jung et al., 2018; Zhu et al., 2017) and PTSD (Hahn et al., 2011; Sripada et al., 2012) showed decreased amygdala rsFC with the prefrontal cortex (PFC). A cross-sectional study reported dampened amygdala rsFC with the medial PFC (mPFC) in children exposed to adversity early in their lives (Park et al., 2018). In comparison to healthy adults with past trauma exposure, patients with PTSD showed reduced amygdala rsFC with the OFC, thalamus (Zhu et al., 2017), and ACC (Sripada et al., 2012). Compared with healthy controls, patients with social anxiety demonstrated reduced rsFC between the amygdala and OFC (Hahn et al., 2011) and mPFC (Jung et al., 2018). These results suggest reduced amygdala connectivity with the PFC and thalamus as key neural markers of PTSD and social anxiety. In contrast, little is known about how the intrinsic functional connectivity of the BNST may be altered in individuals with anxiety related disorders.

With open-access imaging data, the current study aimed to explore the rsFC of the amygdala and BNST and their associations with threat bias in a dot-probe task in both healthy adults and individuals with anxiety related disorders. We expect altered amygdala and BNST rsFC in PAD as compared with HC, and differential patterns of amygdala and BNST rsFC in association with threat bias in PAD and HC. As anticipatory anxiety represents a central feature of anxiety disorders (Grupe and Nitschke, 2013; Radoman et al., 2019; Sussman et al., 2016) and the BNST appears to be more involved in the anticipation of threat, we expect to observe a more robust pattern of BNST rsFC in link with threat bias in the PAD.

Methods

Data set

Imaging and behavioral data were collected from the Nathan Kline Institute/Rockland sample (http://www.nitrc.org/projects/fcon_1000/; (Nooner et al., 2012). Data from this sample were used in other studies to examine the rsFC of the BNST (Tillman et al., 2018). Data from 85 healthy adults (HC; 61 women) and 31 adult patients with anxiety related disorders (PAD; 24 women) diagnosed according to ICD-9 were examined. Three participants (2 HC and 1 PAD) were excluded from the current analysis because of extreme negative values of Threat to Neutral Bias and Threat to Happy Bias (>2 standard deviations from the mean) in the dot-probe task, resulting in 83 HC (59 women) and 30 PAD (23 women). HCs reported no history of psychiatric disorder. In PADs, 22 patients were diagnosed with one disorder including depressive disorder (n=14), attention deficit/hyperactivity disorder (ADHD, n=5), post-traumatic stress disorder (PTSD, n=1), social phobia (n=1), and phobia (n=1); the rest of PADs showed comorbidity including ADHD and obsessive compulsive disorder (OCD) and anxiety (n=1), depressive disorder and panic disorder (n=2), phobia and OCD (n=1), depressive disorder and eating disorder (ED) and ADHD (n=1), phobia and ED (n=1), panic and ADHD (n=1), and panic and bereavement (n=1). Age, sex and clinical measures of anxiety are summarized in Table 1.

Table 1.

Demographic and clinical information.

Characteristics HC PAD t or χ2 p
Age (years) 53.5 ± 20.2 45.6 ±15.2 1.990 0.049
Gender (M/W) 24/59 7/23 0.120 0.729
STAI* 49.46 ± 11.59 56.70 ± 11.50 −2.931 0.004
ATQ
 Fear 3.08 ± 0.75 3.66 ± 1.07 −3.195 0.002
 Frustration 3.32 ± 1.04 3.82 ± 0.94 −2.338 0.021
 Sadness 3.78 ± 0.85 4.40 ± 1.07 −3.202 0.002
 Discomfort 3.97 ± 1.28 4.07 ± 1.36 −0.376 0.707
 Negative Affect 3.53 ± 0.64 3.99 ± 0.76 −3.220 0.002
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Note: HC: healthy control; PAD: patients with anxiety-related disorders; STAI: State trait anxiety inventory (*one data point missing in HC; n=82); ATQ: Adult temperament questionnaire. PAD scored higher in all anxiety-related measures except for the Discomfort subscale of the ATQ. HC were older than the PAD, and thus age was included as a covariate in data analyses.

Dot-probe task and threat bias

All participants performed the dot-probe task (Abend et al., 2014; MacLeod et al., 1986) to measure attentional bias to threatening stimulus. In the task, two faces appeared on opposite sides of the screen. One of them was neutral in emotion, and the other one was either threatening or happy. After the faces were presented, a single dot appeared on either side replacing the face and demanding a corresponding button press from participants. The face contained 16 different individuals (8 males, 8 females) from the NimStim set (https://www.macbrain.org/resources.htm). There were 120 trials (48 threatening vs. neutral, 48 happy vs. neutral, and 24 neutral vs. neutral) with the locations and orders of faces counterbalanced across trials (http://people.socsci.tau.ac.il/mu/anxietytrauma/research/). Attentional bias to threat was computed in two ways: Threat to Neutral Bias, the difference in the reaction time (RT) to the dot replacing neutral faces and that replacing threatening faces (i.e., RT to Neutral – RT to Threatening); and Threat to Happy Bias, the difference in RT to the dot replacing threatening face and that replacing happy faces (i.e., RT to Happy – RT to Threatening). The Threat to Neutral Bias is a conventional measure of threat bias (Mogg and Bradley, 1999) whereas the Threat to Happy Bias controls for arousal evoked by the stimulus (Eldar and Bar-Haim, 2010; Mueller et al., 2009; O’Toole and Dennis, 2012). In both measures, shorter RT to threatening faces indicates a greater bias to threat.

Imaging data protocol

T1-weighted anatomical images were acquired using a rapid gradient-echo sequence using a 3T scanner (Siemens Magnetom Trio Tim) (http://fcon_1000.proiects.nitrc.org/indi/enhanced/mri_protocol.html). One hundred and seventy-six sagittal slices were acquired with TR=1900ms, TE=2.52ms, flip angle=9°, FOV=250×250ms, matrix=256×256ms, slice thickness=1mm. Functional blood oxygenation level dependent (BOLD) signals were then acquired with a gradient-echo echo-planar imaging (EPI) sequence. Thirty-eight axial slices covering the whole brain were obtained with TR=2500ms, TE=30ms, bandwidth=2240Hz/pixel, flip angle=80°, field of view=216×216mm, matrix=72×72, slice thickness=3mm and no gap. Each participant completed a 5-minute resting state fMRI scan.

Imaging data preprocessing

Brain imaging data were preprocessed using Statistical Parametric Mapping (SPM 8, Wellcome Department of Imaging Neuroscience, University College London, U.K.), as in our earlier studies (Hu et al., 2018b; Hung et al., 2019; Peterson et al., 2017). Images of each individual participant were realigned and corrected for motion and slice timing. A mean functional image volume was constructed for each participant from the realigned image volumes. These mean images were co-registered with the high-resolution structural image and then segmented for normalization with affine registration followed by nonlinear transformation (Ashburner and Friston, 1999; Friston et al., 1995). The voxel size after normalization was 3×3×3 mm2. The normalization parameters determined for the structure volume were then applied to the corresponding functional image volumes for each participant. Finally, the images were smoothed with a Gaussian kernel of 4mm Full Width at Half Maximum.

Head Motion

Micro head motion (>0.1mm) is an important source of spurious correlations in resting state functional connectivity analysis (Van Dijk et al., 2012). We hence applied a “scrubbing” method to remove time points affected by head motions (Power et al., 2012; Smyser et al., 2010; Tomasi and Volkow, 2014). For every time point t, we computed the framewise displacement given by FD(t) = |Δdx(t)| + |Δdy(t)| + |Δdz(t)| + r|α(t)| + r|β(t)| + r|γ(t)|, where (dx, dy, dz) and (α, β, γ) are the translational and rotational movements, respectively, and r (= 50mm) is a constant that approximates the mean distance between center of MNI space and the cortex and transform rotations into displacements (Power et al., 2012). The second head movement metric was the root mean square variance (DVARS) of the differences in % BOLD intensity I(t) between consecutive time points across brain voxels, computed as follows: DVARS(t)=〈|I(t)−I(t−1)|2〉, where the brackets indicate the mean across brain voxels. Finally, to compute each subject’s correlation map, we removed every time point that exceeded the head motion limit FD(t)>0.5mm or DVARS(t)>0.5% (Power et al., 2012; Tomasi and Volkow, 2014). On average, 1% of the time points were removed across participants. We excluded individuals who moved > 1 voxel in any directions and those with greater than 10-20% of volumes affected by micromovements.

Imaging data analysis

The amygdala and BNST were used as seed regions (Figure 1). The mask of amygdala was obtained from the SPM Anatomy Toolbox Version 2.2c (Eickhoff et al., 2006; Eickhoff et al., 2007; Eickhoff et al., 2005). The mask of BNST was obtained from previous 7T studies using gradient spin echo (GRASE) sequence (Avery et al., 2016; Avery et al., 2014). Previous studies have supported different functional roles of the right and left amygdala (Baas et al., 2004; Hu et al., 2018a). We thus distinguished R and L amygdala seeds and likewise R and L BNST seeds in connectivity analyses.

Figure 1.

Figure 1.

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Seed regions of left and right amygdala (yellow) and BNST (green) shown in a structural image in axial sections from z=−30 to z=−10 and from z=−4 to z=4, respectively.

In first-level analysis, the BOLD time courses were averaged spatially over each seed region for each individual. To compute rsFC, we calculated the correlation coefficient between the average time course of each seed region and the time courses of all other brain voxels. We then converted these image maps, which were not normally distributed, to z score maps by Fisher’s z transform (Berry and Mielke, 2000; Jenkins and Watts, 1968): z=0.5*ln(1+r)/(1−r). In second-level analysis, we correlated the Z map with Threat to Neutral Bias and Threat to Happy Bias, respectively, with age as a covariate (Xiao et al., 2018). We targeted brain regions anatomically connected with the amygdala (Roy et al., 2009) and BNST (Avery et al., 2014), including the thalamus, caudate, putamen, insula, orbitofrontal cortex (OFC), and anterior cingulate cortex (ACC), and performed small volume correction using AAL masks of these regions on whole brain regression. We then used region of interest (ROI) analysis to extract the functional connectivity of areas showing significant correlation with threat bias each in HC and PAD. For the ROIs that showed a significant correlation in either HC or PAD, the slopes of regressions between the rsFC and threat bias were compared between the two groups with a slope test (Hu et al., 2019; Ide et al., 2018; Li et al., 2017; Zar, 1999; Zhornitsky et al., 2018).

In all group analyses, because PAD and HC were significantly different in age, we included age as a covariate. In addition, we performed the same set of group analyses for age-matched 70 HC and 30 PAD. The latter results are similar and presented in the Supplement.

Results

Demographics and behavioral performance

There was no difference in gender composition between HC and PAD (chi-square=0.12; p=0.729). However, HC and PAD were different in age (mean±standard deviation, HC: 53.5±20.2; PAD: 45.5±15.2 years; t=1.990, p=0.049). Age was thus included as a covariate in the following regression analyses.

Performance of the dot-probe task is summarized in Table 2. Although we expected a difference in performance, HC and PAD did not differ in Threat to Neutral Bias or in Threat to Happy Bias. On the other hand, the two measures of threat bias significantly correlated in each group (HC: r=0.423; p=0.001: PAD: r=0.622; p<0.001).

Table 2.

Performance of the dot-probe task.

RT (ms) HC PAD t p
Threat RT in NT 615.6 ± 103.4 587.1 ± 78.1 1.372 0.173
Neutral RT in NT 618.2 ± 115.2 587.7 ± 77.3 1.423 0.158
Happy RT in NH 616.2 ± 106.9 585.8 ± 79.1 1.347 0.181
Neutral RT in NH 621.1 ± 111.9 585.2 ± 84.3 1.595 0.114
Threat vs. Neutral Bias 2.6 ± 37.0 0.5 ± 28.8 0.258 0.780
Threat vs. Happy Bias 0.6 ± 33.0 −1.4 ± 30.9 0.283 0.778
RT across All Conditions 617.8 ± 109.0 586.5 ± 78.7 2.888 0.004
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Note: RT: reaction time; NT: Threatening vs. Neutral faces; NH: Happy vs. Neutral faces; HC: healthy controls; PAD: patients with anxiety-related disorders.

We performed the same analyses on 70 HC and 30 PAD who were age-matched (HC: 48.8 ± 18.3, PAD: 45.5±15.2 years; t=0.863, p=0.390; Supplementary Table 1) and the results are shown in Supplementary Table 2. As with the original sample, PAD and HC did not differ in performance.

Resting state Junctional connectivity (rsFC) of the amygdala and BNST

At voxel p<0.001 uncorrected in combination with cluster p<0.05 corrected for family-wise error (FWE) of multiple comparisons, the results of one-sample t-test on the rsFC of the left and right amygdala, and left and right BNST of each group are shown in Supplementary Figure 1. We performed group × sex analyses of variance (ANOVA) with age as a covariate, which did not reveal any significant main or interaction effects for any seed rsFC. Thus, we combined men and women each for HC and PAD and performed two sample t-tests, which again revealed no significant group differences for any of the seed regions. Therefore, we combined the two groups in one-sample t-tests of rsFC (Figure 2). The left and right amygdala showed positive connectivity with bilateral thalamus, midbrain, basal ganglia, insula, medial prefrontal cortex, lingual gyrus, and cerebellum, and negative connectivity with inferior and superior frontal cortices, paracentral and parietal gyri. The left and right BNST showed positive connectivity with bilateral thalamus, basal forebrain, basal ganglia, and medial prefrontal cortex, and negative connectivity with the occipital and temporal cortices.

Figure 2.

Figure 2.

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Resting state functional connectivity of left and right amygdala and BNST in all participants. Clusters with positive and negative connectivity are shown in red and blue, respectively, and overlaid on a T1 structural image in axial sections, from z=−20 to 60 with 10 mm gaps between sections. A white matter mask was applied to the maps. Voxel p<0.001 uncorrected, in combination with cluster p<0.05 FWE corrected.

For the age-matched groups, a group × sex ANOVA did not show significant main or interaction effects. With men and women combined, two sample t-tests revealed higher right amygdala rsFC with the cerebellum (x=−30, y=−61, z=−35, k=891mm3, Z=4.27) in HC than in PAD, and stronger rsFC between the right BNST and cuneus (x=21, y=−94, z=7, k=2214mm3, Z=4.78) in PAD than in HC (Supplementary Figure 2).

Threat biases and the rsFC

For each seed region, we examined the rsFC with small volume correction for the thalamus, caudate, putamen, pallidum, insula, orbitofrontal cortex (OFC) and anterior cingulate cortex (ACC), and reported clusters surviving p=0.05 family-wise error (FWE) corrected for multiple comparisons. The results are summarized in Table 3. In HC, the regression of left amygdala rsFC against Threat to Neutral Bias (Figure 3a), and that of right amygdala rsFC against Threat to Neutral Bias showed a significant positive correlation in the left thalamus (Figure 3b). The regression of right amygdala rsFC against Threat to Happy Bias showed a significant positive correlation in the ACC (Figure 3c). On the other hand, no significant results were found in PAD for the regression of amygdala rsFC with either measure of threat bias. The regression of right BNST rsFC against Threat to Neutral Bias and Threat to Happy Bias both showed a significant positive correlation with the right caudate in PAD (Figure 3d and 3e), whereas no results were found in HC. Neither HC nor PAD showed significant correlations between left BNST rsFC and threat biases.

Table 3.

Brain regions showing positive activation with rsFC of each seed region in correlation of threat biases.

Seed region Group Bias Region Cluster Peak Voxel MNI Coordinate (mm)
(mm3) Z Value X Y Z
L Amygdala HC TN L Thalamus 324 4.303 −15 −7 −2
TH NS
PAD NS
R Amygdala HC TN L Thalamus 972 4.731 −15 −7 −2
TH ACC 1701 4.391 12 29 25
PAD NS
L BNST HC NS
PAD NS
R BNST HC NS
PAD TN R Caudate 2430 3.895 9 11 4
TH R Caudate 2133 3.823 9 11 7
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Note: L = Left; R = Right: BNST: bed nucleus stria terminalis; HC: all 83 healthy controls; PAD: patients with anxiety disorders; TN: Threat to Neutral Bias; TH: Threat to Happy Bias; NS: none significant.

Figure 3.

Figure 3.

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Brain regions showing rsFC in correlation with threat biases. * in the titles indicates a significant difference in slope test of the correlations between PAD and HC (p<0.05). Overall, HC and PAD each showed a more robust pattern of amygdala and BNST rsFC in correlation with threat bias.

Similar results were found in age-matched HC and PAD (Supplementary Figure 3). In ROI analysis, we extracted the rsFC of ROIs with significant correlations with threat bias in HC or PAD and evaluated the group differences in the slope of regression using a slope test (Zar, 1999). The correlation between left amygdala-thalamus rsFC with Threat to Neutral Bias was significantly different between HC and PAD (t=2.051, p=0.043, Figure 3a), and the same pattern was found for the right amygdala-thalamus rsFC (t=2.0968, p=0.0383, Figure 3b). The correlation between right amygdala-ACC with Threat to Happy Bias showed only a trend level difference between HC and PAD (t=1.925, p=0.057, Figure 3c). Both the correlation between right BNST-caudate with Threat to Neutral Bias and that with Threat to Happy Bias were significantly different between the two groups (Threat to Neutral Bias: t=2.722, p=0.008, Figure 3d; Threat to Happy Bias: t=2.496, p=0.014, Figure 3e).

These results were largely replicated in age-matched HC and PAD. The correlation between right (t=2.118, p=0.037) but not the left (t=0.889, p=0.376) amygdala-thalamus rsFC with Threat to Neutral Bias was significantly different between the two groups. The correlation between right amygdala-ACC with Threat to Happy Bias was not significantly different between HC and PAD (t=1.896, p=0.061). Both the correlation between right BNST-caudate rsFC with Threat to Neutral Bias (t=3.094, p=0.003) and that with Threat to Happy Bias (t=2.761, p=0.007) were significantly different between the two groups. These results are shown in Supplementary Figure 3.

Sex differences in the rsFC and its association with threat bias

As described earlier, we explored sex difference in amygdala and BNST connectivities between HC and PAD, and the two-way ANOVA (group × sex) with age as a covariate showed no group or sex main effects or group × sex interactions for the entire cohort or the age-matched groups. In regions of interest analysis of the clusters with rsFC in correlation with threat bias, we examined sex differences in the correlation with a slope test. Post-hoc analyses showed a significant sex difference in the association between left amygdala-thalamus rsFC and Threat vs. Neutral bias in HC (Supplementary Figure 4); females showed a more significant positive correlation between amygdala-thalamus rsFC and threat bias than males, and this pattern was consistent in all HC (t=2.228, p=0.029) and in age-matched HC (t=2.290, p=0.025). No sex differences were found for other correlations.

Discussion

Healthy adults (HC) and patients with anxiety disorders or clinical conditions with comorbid anxiety (PAD) performed a dot-probe task, and the threat bias was correlated with the resting state functional connectivity (rsFC) of the amygdala and BNST, two subcortical structures of the limbic circuit that play a central role in fear and anxiety response. HC and PAD did not differ in threat bias; however, threat bias was positively correlated with the amygdala-thalamus rsFC and amygdala-anterior cingulate cortex (ACC) rsFC in HC but not in PAD. On the other hand, threat bias was positively correlated with the BNST-caudate rsFC in PAD but not in HC. The group differences in correlation were confirmed with slope tests. These results together support altered amygdala/BNST connectivities that may underlie threat bias and potentially the etiological processes of anxiety across clinical diagnoses.

Behavioral performance

Threat bias was quantified in two ways: RT to neutral faces – RT to threatening faces, and RT to happy faces – RT to threatening faces. Although the latter is thought to be more sensitive as it controls for arousal elicited by the stimulus (Eldar and Bar-Haim, 2010; Mueller et al., 2009; O’Toole and Dennis, 2012), these two measures are highly correlated, as also observed in the current study. Hence, the results were combined for discussion.

Previous research has reported greater threat bias in PAD than in HC, in that PAD responded faster to threatening than to non-threatening stimuli (Mogg and Bradley, 1999). Here, although the RT were generally shorter during threat than happy or neutral trials, we did not observe a group difference in threat bias, as was also the case with earlier studies of obsessive-compulsive disorder (Kyrios and Iob, 1998; van den Heuvel et al., 2005). It is not clear what may account for the discrepancy. A small number of trials for each condition and consequently reduced statistical power is a potential issue.

The amygdala rsFC and threat bias

Threat bias and amygdala-thalamus as well as amygdala-ACC rsFC were positively correlated in HC, suggesting stronger intrinsic connectivities between these structures in healthy individuals with higher sensitivity to threat (Timbie and Barbas, 2015). In animals, the thalamus showed higher activity in mice placed in a looming environment to simulate an approaching predator (Salay et al., 2018), and increased metabolic activity, in correlation with the freezing duration, in monkeys when their living quarters were intruded by humans (Kalin et al., 2005). In humans, both the thalamus and amygdala showed higher activation during experiences of aversive events (Butler et al., 2007; Gold et al., 2015; Mechias et al., 2010). In particular, healthy participants showed increased amygdala-thalamus connectivity viewing words or colors that indicated higher likelihoods of an impending electric shock (Torrisi et al., 2019; Vytal et al., 2014). Thus, the positive correlation between the amygdala-thalamus rsFC and threat bias mirrored these earlier findings.

The amygdala and mPFC, including the ACC, are anatomically and bidirectionally connected (Stujenske and Likhtik, 2017). Healthy adults showed stronger task-related functional connectivity between the amygdala and ACC when viewing fearful faces along with electric shocks delivered to the foot than without shocks, suggesting that the amygdala-ACC connectivity may amplify stress induced by aversive events (Robinson et al., 2012). In healthy participants learning to anticipate an electrical shock, the amygdala-ACC rsFC was positively correlated with the differences in physiological arousal, as indexed by skin conductance, before and after fear conditioning (Schultz et al., 2012). These results are consistent with the current finding of increased amygdala-ACC rsFC in association with threat bias in healthy adults. As the amygdala-ACC connectivity has been implicated in emotion regulation in real-time fMRI (Li et al., 2016), the current finding may provide a biomarker to facilitate the use of biofeedback in the treatment of anxiety.

Unlike HC, PAD did not show significant correlations between threat bias and amygdala-thalamus rsFC. A previous study reported decreased amygdala-thalamus rsFC in link with lower heart rate variability (HRV) in patients with generalized anxiety disorder (GAD) during recalls of recent stressful events (Makovac et al., 2016). HRV reflects autonomic health (Cygankiewicz and Zareba, 2013) and patients with anxiety related disorders show lower HRV (Chalmers et al., 2014). In healthy adults, higher HRV was associated with stronger amygdala-thalamus rsFC (Chang et al., 2013). These findings together suggest a protective role of amygdala-thalamus connectivity in response to emotional distress, and this functional connectivity is disrupted in PAD.

PAD also did not show a significant correlation between threat bias and amygdala-ACC rsFC. Previous research on regional activations and connectivities of the amygdala and ACC reported mixed results in patients with anxiety disorders. In a review, Shin and Liberzon (2010) summarized the patterns of activation of the amygdala and ACC across different diagnostic categories. Increases in amygdala activations were found in patients with PTSD, social phobia and specific phobia during exposure to aversive stimuli, whereas both increases and decreases in amygdala activations have been reported in patients with panic disorder and GAD. Increases in rostral ACC (rACC) activations were reported in patients with panic disorder and GAD, decreases in rACC activations were reported in patients with PTSD, and both increases and decreases in rACC activation were reported in patients with social or specific phobia (Shin and Liberzon, 2010). Clinical as compared to neurotypical populations also showed a mixed pattern of differences in resting state and/or task-related amygdala-frontal connectivities (Brakowski et al., 2017; Duval et al., 2015; Hilbert et al., 2014). Decreased amygdala-PFC rsFC was reported in patients with GAD (Etkin et al., 2009) and major depressive disorder (Tang et al., 2018), while increased amygdala-ACC rsFC was found in patients with PTSD (Brown et al., 2014). These discrepancies may result from the distinct neural processes underlying the mental illnesses and/or the treatments patients have received prior to the study (Zhu et al., 2017).

Taken together, our findings suggest that the amygdala-thalamus and amygdala-ACC rsFC are less relevant in supporting threat bias in PAD than in HC. One needs to be cautioned that in the current study PAD included patients with different diagnoses, which, as discussed above, may be associated with different patterns of changes in regional activations and connectivities.

The rsFC of BNST and threat bias

Threat bias was positively correlated with the BNST-caudate rsFC in PAD but not in HC. This can be considered along with previous reports of increased BNST activation during threat anticipation (Brinkmann et al., 2017a; Brinkmann et al., 2017b; Buff et al., 2017; Munsterkotter et al., 2015) and increased caudate activation during anticipation of uncertain emotional stimuli (Weidt et al., 2016) or tracking of uncertain incentives (Benson et al., 2015; Guyer et al., 2012) in individuals with anxiety disorders. In healthy adults, threat-induced increases in BNST activation was also stronger in people with higher trait anxiety (Brinkmann et al., 2018; Somerville et al., 2010). One study reported increased BNST-caudate rsFC in patients with PTSD as compared to HC, although the differences in connectivity were not related to behavioral measures of anxiety (Rabellino et al., 2018). The caudate nucleus is known for its role in action planning and outcome anticipation, including anticipation of aversive experiences (Andrzejewski et al., 2019; Labrenz et al., 2016; Zou et al., 2018). Activations of the caudate during anticipation of negative emotional stimuli were correlated with neuroticism and other affective measures (Bruhl et al., 2011). During conflict monitoring with contingent delivery of electric shocks, both the caudate and BNST showed higher activation during threat vs. safe cues (Choi et al., 2012). The current finding of BNST-caudate connectivities in correlation with threat bias in PAD is consistent with this literature.

As described earlier, the BNST and amygdala showed higher activation upon anticipation and administration, respectively, of an electric shock (Klumpers et al., 2017; Somerville et al., 2010). These results distinguished the roles of the amygdala and BNST each in sensorimotor reactivity and sustained threat response during aversive conditioning. Uncertainty provokes anxiety and anticipatory anxiety represents a cardinal feature of anxiety disorders. Thus, altered BNST connectivity in link with threat bias appears to support a more significant role of anticipatory anxiety for PAD than for HC. On the other hand, Klumpers et al. (2017) demonstrated that the extent of childhood maltreatment predicted amygdala, but not BNST, hyperactivity during shock anticipation, suggesting that early life stress may contribute to neural responses toward threats. Another study showed that the time course of amygdala activity was more prolonged and less variable throughout speech anticipation in participants with social anxiety disorders, as compared to controls (Davies et al., 2017). More research is warranted to understand the developmental dynamics of amygdala and BNST connectivities during resting state and task challenges and in relation to the etiological processes of various anxiety disorders.

Sex difference in rsFC and its association with threat bias

We found no sex differences in amygdala and BNST rsFCs, consistent with previous research on the rsFC of these seed regions (Tillman et al., 2018; Xiao et al., 2018). However, in regions of interest analyses, there appeared to be a stronger left amygdala-thalamus rsFC associated with threat bias in female than in male HC. Research on sex-related functional lateralization showed stronger left amygdala response to stimuli of negative valence (Stevens and Hamann, 2012) and during exposure to arousing pictures (Cahill et al., 2004) in females than in males. The current findings thus complement this literature by showing stronger intrinsic connectivity of the left amygdala in link with threat bias in healthy women.

Limitations, other considerations and conclusions

A number of limitations need to be discussed. First, half of the patients were diagnosed with comorbid anxiety. The current findings need to be considered with the clinical heterogeneity and require replication in populations specifically with anxiety disorders. Second, no medication or other treatment records were available for the patients, and the analyses did not account for the potential confounds of treatments. Third, the small number of trials in the dot-probe paradigm reduced the sensitivity of the task and power of analysis, which may have contributed to the lack of group difference in threat bias. Finally, HC were older than PAD. Although age was controlled for in the connectivity analyses, we could not entirely rule out the effects of age on behavioral performance or regional connectivities.

The findings of amygdala and BNST rsFC in differential association with threat bias, despite no group differences between PAD and HC, seem intriguing. Notably, this pattern of findings has been reported in other psychiatric populations. For instance, although showing minimal group differences in rsFC, ketamine users but not non-drug using controls demonstrated higher putamen connectivity with the orbitofrontal cortex (OFC) in association with Barratt impulsivity (Hung et al., 2019). Compared to controls, ketamine users did not demonstrate significant differences in subgenual anterior cingulate cortical (sgACC) connectivities but showed less sgACC connectivity with the OFC in positive correlation with depression severity (Li et al., 2017). Likewise, showing little differences in regional responses to cognitive motor inhibition, alcohol users however demonstrated altered activities in a wide swath of cortical and subcortical areas in correlation with the severity of problem alcohol use and impairment in response inhibition (Hu et al., 2016). These findings together suggest the utility of behavioral and clinical measures in capturing cerebral functional changes in neuropsychiatric illnesses.

In sum, the current study was to our knowledge the first to contrast amygdala and BNST rsFC in relation to threat bias in anxiety disorders. Individuals with anxiety disorders or comorbid anxiety demonstrated a diminished relationship between threat bias and amygdala-thalamus and amygdala-ACC rsFC, in contrast with healthy controls, and, conversely, a stronger relationship between threat bias and BNST-caudate connectivity than healthy participants. These findings distinguished amygdala and BNST connectivity in anxiety and supported anticipation of anxiety as a cardinal feature of clinical anxiety.

Supplementary Material

1

Acknowledgements:

The study was supported by the Department of Psychology, SUNY Oswego, and a NIH grant (MH113134). The funding agencies otherwise did not participate in the conceptualization of the study or data collection and analysis, or in the decision to publish the current results.

Footnotes

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Declaration of interests:

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

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NIHMS1548790-supplement-1.docx (4.6MB, docx)

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