A central role for the functional connectivity (FC) between the amygdala and medial prefrontal cortex (mPFC) is ubiquitous in neurocircuitry models of trauma and PTSD (Rauch et al., 2006; Pitman et al., 2012; Admon et al., 2013). Within the context of these models, amygdala activity is conceptualized as reflecting threat processing, and mPFC activity is conceptualized as reflecting an emotion regulation/inhibition process (Rauch et al., 2006; Pitman et al., 2012; Admon et al., 2013). Weakened FC between these nodes is conceptualized as weakened top-down control of the amygdala by the mPFC and is theorized to mediate over-expression of negative affectivity and under-expression of emotion regulation (e.g., implicit emotion regulation, fear extinction learning, etc) among individuals with PTSD (Rauch et al., 2006; Patel et al., 2012; Pitman et al., 2012). Consistent with these models’ predictions, there is consistent data suggesting that altered amygdala-mPFC FC at rest scales with degree of early life trauma and additionally predicts internalizing symptoms and cortisol levels (Burghy et al., 2012; Herringa et al., 2013; Pagliaccio et al., 2015). As such, the strength of amygdala-mPFC FC appears to be a potent mechanism explaining risk for PTSD and related affective psychopathology among those exposed to early life trauma.

The purpose of the current study is to examine this common finding of altered amygdala-mPFC connectivity among those exposed to early life trauma within the context of large-scale network models of human brain function. Network-level approaches to human functional brain organization demonstrate spatially distributed networks consisting of brain regions with greater within-network temporal covariance compared to between-network temporal covariance (Bullmore and Sporns, 2009; Bressler and Menon, 2010; Rubinov and Sporns, 2010; Menon, 2011). The spatial organization of these networks is remarkably consistent across studies, which demonstrate canonical motor, salience, default mode, frontoparietal, and visual networks (Damoiseaux et al., 2006; Smith et al., 2009). From the perspective of larger-scale networks in the human brain, it is interesting to consider how to conceptualize a single bi-nodal connection, such as between the amygdala and mPFC. That is, the mPFC often is found to be part of a default mode network (DMN) (Raichle, 2015) while the amygdala is found to be part of a limbic network (Smith et al., 2009; Onoda and Yamaguchi, 2013; Cisler et al., 2016; Schlesinger et al., 2017). It is relevant to mention here that, while the amygdala is often regarded intuitively as belonging to the salience network (Bressler and Menon, 2010), the salience network is most consistently characterized by dorsal anterior cingulate cortex and bilateral anterior insular cortex (Uddin, 2015). Nonetheless, the interesting aspect of focusing on bi-nodal connectivity remains: if the two regions belong to different networks, does altered connectivity between them simply reflect connectivity between the larger networks in which the nodes belong? Similarly, modularity is a growing concept in network-level approaches to brain organization in the past few years and refers to the degree to which a network can be sub-divided into functionally specialized modules (Hartwell et al., 1999; Meunier et al., 2009; Rubinov and Sporns, 2010; Onoda and Yamaguchi, 2013; Cisler et al., 2016; Schlesinger et al., 2017). Modularity is formally defined as the difference between the number of observed connections within modules and the number of connections expected given chance distribution (Newman, 2006). Greater values reflect networks with more cleanly segregated modules. From this perspective, the intriguing question regarding amygdala-mPFC FC is whether degree of connection between these nodes belonging to different modules only reflects larger modular organization patterns of the network. For example, a network with greater separation between modules (i.e., more pronounced modular organization) would by definition be expected to have weaker connections between nodes belonging to different modules. As such, perhaps the weaker amygdala-mPFC observed among those exposed to early life trauma simply reflects greater overall modularity.

One means of addressing these questions regarding the role of bi-nodal connectivity within larger-scale network patterns is to examine dynamic functional connectivity (DFC) (Calhoun et al., 2014). DFC is a relatively newer approach to FC analysis and is based on the concept that the degree to which brain regions are connected is not static and instead connectivity between brain regions can fluctuate depending on various factors such as context, experimental task, alertness, etc (Hutchison et al., 2013; Calhoun et al., 2014; Gonzalez-Castillo et al., 2015). This approach frequently uses a sliding window in which FC is defined repeatedly across the scan (Allen et al., 2014). For example, using a window length of 30 TRs, one would define connectivity between regions (or voxels or networks) from TR 1 to TR 30, then shift the window forward and redefine connectivity between TR 6 and 35, and so forth throughout the scan. Using this approach to address the current questions allows for a within-subject analysis of the degree to which temporal fluctuations in amygdala-mPFC connectivity track temporal fluctuations in larger-network patterns. At a second-level (group-level) of analysis, the consistency of these within-subject effects can be tested, as can any moderating role of early life trauma-exposure on these analyses across network-levels. Accordingly, the DFC approach is used here to test the alternative hypothesis that amygdala-mPFC connectivity during rest reflects larger patterns of network organization.

The purpose of this study was to investigate the degree to which a specific bi-nodal functional connectivity pattern, amygdala-mPFC, was associated with larger-scale network patterns. The commonly reported finding and predicted observation based on neurocircuitry models of trauma and PTSD of weakened static FC between amygdala-mPFC and childhood trauma (Rauch et al., 2006; Burghy et al., 2012; Herringa et al., 2013) was replicated in the current sample, such that the median FC between the amygdala and mPFC across the time windows was negatively correlated with the continuous emotional abuse subscale of the CTQ, as has been previously reported (Dannlowski et al., 2012). Additionally, it was also observed that greater emotional abuse was associated with heightened modularity Q-values. Given that modularity Q-values are defined based on the patterns of within- vs. between-network connections, and that the amygdala and mPFC belong to different networks, it would be expected that early life trauma would have opposing relationships with these two indices of brain function. Indeed, these opposing relationships beg the question of how, on a within-subject basis, bi-nodal FC relates to larger-scale patterns of brain function.

With respect to the role of the bi-nodal amygdala-mPFC FC within the larger-network patterns, it was observed that this bi-nodal FC closely tracked both the FC of the networks to which the nodes belong as well as to the larger-network patterns. Moreover, the observed effects were in the expected directions from a network-level perspective. That is, the amygdala and mPFC ROIs belong to the limbic and DMN sub-networks, respectively, and as such, it would be expected that the bi-nodal FC track the overall degree of FC between these networks. Similarly, network modularity Q-values are defined by the degree to which edges lie within, as opposed to between, communities, and as such, it would be expected that as modularity Q-values increase, FC between nodes in different modules would necessarily decrease. These group-level findings ostensibly confirm the larger concept that perhaps one should not interpret a single bi-nodal FC pattern without considering the modules in which the nodes belong and the larger network patterns. From this perspective, one might re-conceptualize weakened amygdala-mPFC FC, such that instead of inferring that weakened FC between these nodes suggests a weakened inhibitory effect of the mPFC on the amygdala, one might instead infer less overall communication between the DMN and the limbic system or greater overall network partitioning. This difference in inference would seemingly have a significant downstream impact on clinical efforts to correct/modify the observed bi-nodal FC findings, such as targeting the larger network patterns rather than targeting anything specific about amygdala-mPFC connectivity.

Nonetheless, it is relevant to note the results of the control analyses with the caudate. On the one hand, the network analyses demonstrated similar relationships between caudate-mPFC FC with the limbic-DMN FC and overall modularity Q-values, supporting the inference that bi-nodal FC is related to larger-scale network patterns. On the other hand, there was no evidence that the caudate-mPFC FC differed between groups. The latter result suggests specificity of the effect of early life trauma on amygdala-mPFC FC and that there is not a generic relationship between any node in the limbic network with the DMN. Future research is clearly needed to continue to probe the degree of bi-nodal FC independence from larger network function, and the associated impact of early life trauma.

As an exploratory analysis, a complimentary data-driven approach was used to characterize which network–network interactions were consistently predictive of degree of amygdala-mPFC connectivity. This approach demonstrated three network–network interactions consistently related to amygdala-mPFC FC: (1) as expected, the limbic-DMN FC was positively predictive, (2) the DMN-superior temporal sulcus FC was positive predictive, while (3) the salience-superior temporal sulcus FC was negatively predictive. There was no evidence that interactions with the frontoparietal network were consistently related to degree of amygdala-mPFC FC. Further, the limbic-DMN FC clearly, and intuitively, was the strongest predictor and the only relationship that would survive correction for multiple comparisons if set stringently with FDR. These network-wide exploratory analyses seemingly confirm inferences from the a priori analyses and demonstrate the importance of understanding bi-nodal FC within the context of the networks in which the individual nodes belong.

There was not consistently strong evidence that the magnitude of cross-level analyses differed between the assaulted and control participants. There was a significantly lesser relationship between amygdala-mPFC FC and limbic-DMN FC among the assaulted girls, and there appeared to be trends toward similar effects on the other cross-level analyses. Nonetheless, the direction of the relationships (e.g., positive relationships between amygdala-mPFC FC and limbic-DMN FC) was consistent in each group, suggesting that the qualitative nature of the relationships did not differ between groups. These within-group effects potentially reinforce the inferences made in the preceding paragraph regarding the importance of interpreting bi-nodal FC within the context of larger-scale network organization.

While the data presented here shed light on the role of a canonical bi-nodal FC findings within larger network patterns and also on how early life trauma might disrupt or alter the role a bi-nodal FC pattern within the larger network, the current study is not without limitations. First, the sample is limited to adolescent girls, and the degree to which the findings generalize to male samples or adult samples needs to be addressed. Second, the cross-sectional nature of the design precludes any causal inferences regarding the relationship between early life trauma exposure and alterations in bi-nodal FC or larger network patterns. Third, a relatively small parcellation of 250 ROIs was used, whereas prior research from this lab has used a larger 824 ROI parcellation (Cisler et al., 2016). The reason for this is the use of the DFC approach, such that repeating the community detection algorithm on an 824 × 824 matrix with 25 iterations at each of the 41 time windows for each of the 56 participants was computationally expensive. In support of the smaller parcellation approach used here, we observed networks that were highly similar, though not identical, to that observed in the previous report. Nonetheless, it remains possible that different results regarding the role of amygdala-mPFC FC within larger network patterns might be different if the larger network patterns were defined using a larger parcellation. Future research is clearly still needed to continue to understand how to interpret bi-nodal FC patterns within the context of a network-level conceptualization of human brain function.

The studies from which these data were collected were approved by the University of Arkansas for Medical Sciences IRB. A parent or caregiver provided written consent and all adolescents provided written assent.

JC was involved in the design, analysis, interpretation, and writing of this manuscript.

The author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.