In the current study, we demonstrated that the resting functional connectivity between the amygdala and mPFC varies as a function of self-reported anxiety. Across the whole brain, anxiety levels exclusively predicted functional connectivity between the amygdala and mPFC. Specifically, we observed a dissociation in functional connectivity between the amygdala and dorsal and ventral mPFC with respect to anxiety—that is, the negative connectivity normally seen between the amygdala and the dmPFC at rest was attenuated in high anxious subjects, whereas the positive connectivity normally observed between the amygdala and vmPFC at rest, manifested as negative connectivity in high anxious subjects. Importantly, this relationship was observed in the absence of external stimulus presentations.
Previous task-based neuroimaging studies have demonstrated that individual differences in anxiety levels predict amygdala responsivity (Bishop, Duncan and Lawrence 2004
; Etkin et al. 2004
; Somerville et al. 2004
; Pezawas et al. 2005
; Dickie and Armony 2008
). For example, exaggerated amygdala responses to fearful faces have been associated with increased state (Bishop, Duncan and Lawrence 2004
) as well as trait (Etkin et al. 2004
) anxiety. A positron emission tomography (PET) study showed increased amygdala activity to cues that provoke anxiety by predicting threat (e.g., electric shocks; Hasler et al. 2007
). Interestingly, this relationship was observed even when the stimuli used to evoke amygdala activity were unrelated to threat. For instance, Somerville et al. (2004)
reported that higher levels of state anxiety were associated with increased amygdala activity to neutral faces. These data suggest that a relationship between amygdala activity and anxiety need not be examined in a negative experimental context (e.g., threat-related stimuli) but can be generalized to other, nonthreatening, experimental contexts. The present data extend this logic to show that a systematic relationship between amygdala activity and anxiety can also be observed at rest.
Task-based neuroimaging studies have also demonstrated that anxiety levels predict mPFC responses to different types of stimuli, with slightly mixed results (Simpson et al. 2001
; Bishop, Duncan, Brett et al. 2004
; Simmons et al. 2008
; Straube et al. 2009
). For example, higher levels of state anxiety were associated with “decreased” mPFC activity in response to unattended fearful faces in one study (Bishop, Duncan, Brett, and Lawrence 2004
), whereas higher levels of anxiety were accompanied by “increased” activity in a different subregion of the mPFC to cues predicting electric shock in another study (Simpson et al. 2001
). Recent investigations have shown that different subregions of the mPFC may be differentially related to anxiety—for example, during the anticipation of threat (e.g., electric shocks), activity in the dmPFC was positively correlated with anxiety whereas vmPFC activity was negatively correlated with anxiety (Straube et al. 2009
). Another study reported that highly anxious subjects showed increased dmPFC activity and decreased vmPFC activity during a task that involved the viewing of angry and happy faces (Simmons et al. 2008
). Collectively, these findings suggest that the dorsal and ventral regions of the mPFC may play different, if not opposing roles in anxiety and prompt further investigation into the relationship between anxiety and interactions between these brain regions and the amygdala.
We observed that the typical positive correlation between amygdala–vmPFC functional connectivity at rest (Roy et al. 2009
) is compromised in high anxious, psychiatrically healthy subjects. This result complements the findings of Pezawas et al. (2005)
, who demonstrated that stronger amygdala–vmPFC connectivity during the viewing of angry and fearful faces was associated with lower levels of anxious temperament. In our previous study using DTI, we observed that a stronger structural integrity in a pathway linking the amygdala and vmPFC also predicted lower anxiety levels (Kim and Whalen 2009
). Combined with animal studies highlighting the importance of this circuitry in fear extinction (Milad and Quirk 2002
), these findings suggest that a more coherent amygdala–vmPFC connectivity predicts a healthier behavioral outcome (i.e., lower anxiety). Findings from the current study further extend this notion by demonstrating that spontaneous fluctuations in the activity of the amygdala at rest positively correlate with fluctuations in the vmPFC in individuals with lower levels of anxiety. Perhaps positive functional connectivity between the amygdala–vmPFC circuitry (and concomitant negative connectivity with dmPFC) during rest represents an efficient amygdala–mPFC cross talk, which may mitigate the generation of anxious states. This amygdala–mPFC relationship is disrupted in individuals with high levels of anxiety who show a negative relationship between amygdala and vmPFC activity (and no relationship between amygdala and dmPFC activity).
Our data also demonstrated a positive correlation between amygdala–dmPFC functional connectivity during rest and anxiety. A prior resting-state fMRI study (Seeley et al. 2007
) used an independent component analysis approach to define a “salience network” of the brain, which largely consisted of limbic regions, including parts of the amygdala, dmPFC, and the insula. Importantly, this study reported a strong positive correlation between anxiety levels and the strength of the functional connectivity between the dmPFC and the salience network. Our data show a similar positive correlation between dmPFC–amygdala functional connectivity and anxiety, related to the specific amygdala seed-based approach we employed. Moreover, our data suggest that resting negative functional connectivity between the amygdala and the dmPFC may be a healthy, canonical pattern and that individuals with higher levels of anxiety fail to engage the amygdala–dmPFC circuitry during rest. This interpretation of these task-independent data is consistent with a previous suggestion based on task-based studies showing that inhibitory feedback from the dmPFC serves to suppress the anxiogenic effects of amygdala activity (Pezawas et al. 2005
; Hariri and Holmes 2006
These data in healthy, nonpathological subjects may have implications for the study of psychiatric disorders. Anxiety disorders, particularly posttraumatic stress disorder (PTSD), have been characterized by a hyperactive amygdala, hypoactive vmPFC, and hyperactive dmPFC (see Shin and Handwerger 2009
for review). Specifically, individual differences in the degree to which the vmPFC is recruited and, in turn, how reactive the amygdala remains predicts symptom severity in PTSD (Shin et al. 2005
; Rauch et al. 2006
). Also, a PET investigation has shown that exaggerated dmPFC activity at rest predicts a greater likelihood of developing PTSD after being exposed to psychological trauma (Shin et al. 2009
). Further, recent reports have documented abnormalities in other resting-state brain circuitries in generalized anxiety disorder (Etkin et al. 2009
) and obsessive–compulsive disorder (Harrison et al. 2009
). Although our data link resting-state amygdala–mPFC functional connectivity with self-reported anxiety levels within the normal range, they might inform investigations of amygdala–mPFC resting-state data in the anxiety disorders. Perhaps, they could specifically aid in interpreting amygdala–prefrontal connectivity in a control group that is to be compared with a patient group. Specifically, the resting baseline connectivity in healthy and pathological groups could be measured in addition to their responses during specific tasks, and the resting data could be used to explain a portion of the variability observed within the task-based responses.
As documented by the current literature on resting-state functional connectivity, the nature of negative functional connectivity remains unclear. While some research has suggested that negative correlations are artifacts of global signal regression (Murphy et al. 2009
), recent work indicates that negative correlations have a biological, instead of artifactual basis (Fox et al. 2009
). That said, since we chose to regress out the effect of global signal from our data to remove the effects of physiological noise, we are cautious in making strong interpretations of the observed negative functional connectivity between the amygdala and mPFC regions.
In the present study, the dissociation between amygdala–vmPFC and dmPFC connectivity was observed most robustly in relation to state anxiety. The fact that our brain data were more prominently associated with state anxiety relative to trait anxiety raises the possibility that the observed results may be due to the uniqueness of the scanning environment. That is, perhaps the experimental environment itself (i.e., being inside a loud MRI scanner in the dark) may be anxiogenic, affecting state anxiety levels, rather than trait anxiety. Importantly, there is empirical evidence showing that scanning without the presentation of any external stimuli could evoke a different amount of anxiety in each subject (Heinz et al. 2007
However, we point out that both state and trait anxiety measures were highly correlated from our study sample (r
= 0.81, P
< 0.0001) and using trait anxiety also produced a similar dissociation between amygdala–vmPFC and dmPFC connectivity at a lower threshold. Thus, we are careful not to make strong claims that our data solely reflects state anxiety as opposed to trait anxiety. Further, a previous study from our laboratory (Kim and Whalen 2009
) documented a correlation between trait anxiety and the structural integrity of a white matter pathway between the amygdala and prefrontal cortex (with state anxiety showing the trend toward significance). It is interesting to speculate that perhaps the nonspecific “resting” nature of the present study lent itself to be more relevant to state anxiety, while our previous study assessing more static structural measurements was more readily correlated with trait anxiety. Given that numerous previous functional and structural neuroimaging studies have differed in finding relationships with state (present report; Bishop, Duncan, and Lawrence 2004
) or trait (Etkin et al. 2004
; Dickie and Armony 2008
; Kim and Whalen 2009
; Carlson et al. 2010
) anxiety, future studies will be needed to more carefully assess these highly correlated constructs.
One limitation of the current study is that our subjects were mostly women. To address this issue, we have verified that there were no statistically significant differences in terms of self-reported and functional connectivity measures between the 2 genders. We attempted to further control for these effects by removing variances that could be explained by gender from all of our analyses using general linear models. Even so, in order to truly test for potential between-gender differences in amygdala–mPFC functional connectivity measures, future studies should be designed to include equal number of men and women. Given that there are reports showing that the interaction between gender and anxiety is represented in the brain as differential patterns of amygdala activity (Dickie and Armony 2008
), it would be interesting to investigate the potential effects of gender on the relationship between anxiety and amygdala–mPFC functional connectivity during rest.
Taken together, the findings from the current study show that individual differences in anxiety are reflected in the strength of amygdala–mPFC functional connectivity during rest. A closer investigation revealed a dissociation between vmPFC and dmPFC resting functional connectivity with respect to anxiety—individuals with high anxiety showed fluctuations in amygdala activity that were negatively correlated with vmPFC activity and unrelated to dmPFC activity. This altered pattern was observed in the absence of presented anxiety-inducing stimuli, but we note the potential importance of the imaging environment itself. Given that such an environment constitutes the baseline for neuroimaging studies of healthy and pathological anxiety, the present resting-state data strategy could be used as an adjunct to task-based studies to explain response variability in response to presented stimuli.