By mapping temporally correlated patterns of low frequency spontaneous activity during rest, we detected distinct functional networks associated with three amygdala subdivisions. These results demonstrate the potential of resting state fMRI to make fine-tuned distinctions within amygdala circuits in vivo, and as such, contribute to the growing literature supporting translational models of amygdala function.
Analyses of the amygdala as a single region revealed patterns of functional connectivity largely consistent with animal models (Pitkanen, 2000
; Amaral and Price, 1984
), and task-based human neuroimaging findings (Stein et al., 2007
). Spontaneous activity in the amygdala positively predicted activity in regions implicated in identifying the emotional significance of stimuli and producing affective states; these include ACC, insula, medial PFC, striatum, and thalamus. Conversely, activity in regions involved in cognitive processes and effortful regulation of affect, such as superior frontal gyrus, middle frontal gyrus, PCC, and precuneus, was negatively predicted by amygdala activity.
The functional connectivity of individual amygdala subdivisions showed regions of overlap and regions uniquely related to each subdivision. The insular cortex represented a region of convergence for positive functional connectivity maps while the precuneus and lateral occipital cortex represented regions of convergence for negative functional connectivity maps. The unique patterns of connectivity associated with each amygdala subdivision revealed homologies with animal amygdala-based circuits at a level of resolution greater than previously considered by most human imaging studies. Consistent with its role in associative learning processes such as contextual fear conditioning (LeDoux, 2000
; Phelps and LeDoux, 2005
), the laterobasal subdivision was positively associated with activity in the superior temporal gyrus, hippocampus, and parahippocampal gyrus. Additionally, this subdivision positively predicted activity in medial PFC and precentral gyrus; this latter association was significantly stronger for the right LB than the left. These connectivity patterns, along with negative associations between spontaneous fluctuations in the LB subdivision and dorsal and posterior regions such as dorsal ACC, middle frontal gyrus, and precuneus, are consistent with data from previous task-based studies demonstrating the involvement of similar circuits in emotion regulation (Hariri et al., 2000
; Ochsner et al., 2004
; Phelps and LeDoux, 2005
; Phillips et al., 2003
; Zald, 2003
; Blair et al., 2007
). Activity of the centromedial nuclei, which mediate response expression and facilitate attention to salient stimuli (Kapp et al., 1994
), was significantly correlated with thalamus, insula, dorsal ACC, and cerebellum. Significant positive associations were also found with striatal regions (caudate, putamen, GP) which are similar in function, connectivity, and chemistry (neurotransmitter and peptide distribution) to the centromedial nuclei (Swanson and Petrovich, 1998
; Swanson, 2003
). The superficial nuclei, which support olfactory information processing and olfaction-related affective processing in rodents (Kemppainen et al., 2002
; Pitkanen, 2000
), positively predicted activity throughout regions traditionally identified as limbic cortex including the cingulate gyrus extending from subgenual to dorsal regions, insula, and striatum.
The merits of examining individual amygdala subdivisions are further highlighted by findings that some regions showed opposing patterns of connectivity with different amygdala subdivisions. This was particularly evident in patterns of connectivity involving the centromedial and laterobasal subdivisions. Positively-predicted laterobasal and negatively-predicted centromedial networks converged in regions of medial PFC and temporal lobe while negatively-predicted laterobasal and positively-predicted centromedial networks converged in the striatum. Even within the amygdala, resting state activity of the right centromedial subdivision negatively predicted activity in the laterobasal subdivision. This is consistent with reports of reciprocal oscillations in the firing probabilities of lateral and centromedial nuclei observed in animals (Collins and Pare, 1999
) and opposing BOLD activation patterns in these same regions observed in humans (Ball et al., 2007
While our results were generally consistent with the extant literature, there was evidence of functional connectivity patterns that do not have clear anatomic bases. These findings highlight the fact that our methods measure correlated spontaneous activity, which may reflect indirect as well as direct anatomic connections (Greicius, Supekar, Menon, & Dougherty, 2008
; Hagmann et al., 2008
; Vincent et al., 2007
). Further, the mechanisms underlying negative relationships between brain regions detected with resting-state fMRI remain unknown (Fox et al., 2005
; Fransson, 2005
). These findings also suggest that the functional connectivity of human amygdala subdivisions may differ somewhat from that of non-human primates and rodents. Future studies applying these resting state FC methods to translational research could provide the means for more effective comparisons of amygdala circuitry across species.
We observed a high degree of concordance in the functional connectivity of the left and right amygdala. These findings are consistent with previous investigations of resting state connectivity across the brain (Lowe et al., 1998
; Biswal et al., 1995
) and of the amygdala (Zald et al., 1998
). However, lateralized patterns of correlated spontaneous activity were also observed, particularly for the right centromedial subdivision, which demonstrated a unique negative association with activity in the superficial/laterobasal amygdala, medial frontal gyrus, and left middle frontal gyrus. These results may reflect structural differences, as the right centromedial subdivision was 25% larger than the left, allowing for greater power to detect negative associations. Ball et al. (2007)
found a similar lateralization of responses in the centromedial subdivision (positive responses on the right and negative responses on the left) that was not observed in the LB or SF subdivisions. The concordance of these findings suggests that lateralization in regulation of amygdala activity may be observed at a more detailed level. For example, there is some evidence that the right amygdala demonstrates more rapid habituation to fearful faces than the left amygdala (Phillips et al., 2001
; Wright et al., 2002
). This may result from greater negative intrinsic connectivity between the right CM subdivision and regions involved in fear learning (laterobasal nuclei) (LeDoux, 2003
; Phelps and LeDoux, 2005
) as well as the regulation of emotion (medial frontal gyrus) (Hariri et al., 2003
; Irwin et al., 2004
; Berretta, 2003
). Clearly, these ideas are speculative and additional studies of the connectivity of the amygdala subdivisions at rest, as well as during task, are needed to further investigate these questions of laterality.
The current study delineated patterns of connectivity that have been shown to be altered in clinical populations using task-based methods (Heinz et al., 2005
; McClure et al., 2007
; Pezawas et al., 2005
; Quirk and Gehlert, 2003
). This suggests that resting state fMRI can be used to efficiently probe intrinsic differences in these critical circuits without the potential confounds of group differences in task performance. Further, by observing significant differences in the connectivity of amygdala subdivisions implicated in fear learning and extinction, we demonstrate the utility of these methods for testing translational models of fear and anxiety. For example, pharmacological treatments that affect the basolateral amygdala in animals are effective at facilitating extinction in patients with anxiety disorders (Hofmann, 2007
; McNally, 2007
; Davis et al., 2006
). Our methods may provide an opportunity to further examine the impact of these agents on amygdala circuits in humans.
The present study has several limitations. First, our results only apply to resting state data and may not reflect connectivity during task performance. However, the convergence of these findings with those from animal studies suggests that intrinsic activity likely indexes functionally relevant circuits. Furthermore, recent studies using diffusion tensor imaging (DTI; Greicius et al., 2008
) and task-based meta-analyses (Toro et al., 2008
) suggest that functional connectivity reflects structural connectivity and that networks identified in the resting-state mimic those identifiable across a wide array of task paradigms, respectively. Second, the amygdala is susceptible to EPI image distortions, normalization errors, and draining vein effects which may lead to spatial localization errors (Merboldt, Fransson, Bruhn, & Frahm, 2001
). As such, connectivity patterns reported for a given amygdala subdivision could, to some extent reflect surrounding structures. This problem would likely be greatest for the CM as it is the smallest of the three subdivisions. To minimize these effects on localization of amygdala subdivisions, we used probabilistic maps of amygdala ROIs with a 50% threshold (only voxels with a probability of 50% or higher of belonging to that region were included) and probability-weighted each voxel’s contribution to the time series. We also conducted more conservative analyses to further reduce the effect of spatial errors. Using only the twenty voxels for each subdivision that had the highest probability of membership, we found highly similar results (cross-correlations between thresholded maps p < 10−4
). While these analyses do not eliminate the likely impact of distortion and localization errors, they provide initial evidence that independent functional connectivity patterns can be identified within the amygdala. Future fMRI studies using coronal sections, smaller voxel sizes, and EPI distortion correction will help to further confirm the localization of amygdala subdivisions and the current findings. Third, while our results suggest functional connectivity between amygdala subdivisions and anterior regions of the cerebellum, we were not able to extend analyses to more inferior cerebellar structures due to our imaging parameters, which limited coverage in these regions. Fourth, we selected the laterobasal, centromedial, and superficial subdivisions based upon the most recent structural delineation of amygdala subdivisions in humans (Amunts et al., 2005
), which is still less detailed than animal models.
In summary, resting state fMRI was used to interrogate human amygdala-based circuits at a greater level of detail than previously examined. The convergence of these findings with animal models supports the validity of this approach for the translational investigation of amygdala networks and their role in psychopathology and development.