Default mode network abnormalities in posttraumatic stress disorder: A novel network-restricted topology approach

Highlights

• A network-restricted topology method was implemented to study the relationship between the DMN and PTSD in combat Veterans.

• Veterans suffering from severe PTSD symptoms were found to have decreased within-DMN functional connectivity strength.

• A pattern of prefrontal dysconnectivity but sparing of the posterior DMN was associated with increasing PTSD symptomatology.

• Further topological characterization suggested decreased functional integration and increased segregation in severe PTSD.

• DMN functional dysconnectivity in PTSD appears not to be mediated by white matter anatomical network dysconnectivity.

Abstract

Disruption in the default mode network (DMN) has been implicated in numerous neuropsychiatric disorders, including posttraumatic stress disorder (PTSD). However, studies have largely been limited to seed-based methods and involved inconsistent definitions of the DMN. Recent advances in neuroimaging and graph theory now permit the systematic exploration of intrinsic brain networks. In this study, we used resting-state functional magnetic resonance imaging (fMRI), diffusion MRI, and graph theoretical analyses to systematically examine the DMN connectivity and its relationship with PTSD symptom severity in a cohort of 65 combat-exposed US Veterans. We employed metrics that index overall connectivity strength, network integration (global efficiency), and network segregation (clustering coefficient). Then, we conducted a modularity and network-based statistical analysis to identify DMN regions of particular importance in PTSD. Finally, structural connectivity analyses were used to probe whether white matter abnormalities are associated with the identified functional DMN changes. We found decreased DMN functional connectivity strength to be associated with increased PTSD symptom severity. Further topological characterization suggests decreased functional integration and increased segregation in subjects with severe PTSD. Modularity analyses suggest a spared connectivity in the posterior DMN community (posterior cingulate, precuneus, angular gyrus) despite overall DMN weakened connections with increasing PTSD severity. Edge-wise network-based statistical analyses revealed a prefrontal dysconnectivity. Analysis of the diffusion networks revealed no alterations in overall strength or prefrontal structural connectivity. DMN abnormalities in patients with severe PTSD symptoms are characterized by decreased overall interconnections. On a finer scale, we found a pattern of prefrontal dysconnectivity, but increased cohesiveness in the posterior DMN community and relative sparing of connectivity in this region. The DMN measures established in this study may serve as a biomarker of disease severity and could have potential utility in developing circuit-based therapeutics.

Introduction

Intrinsic connectivity networks (ICNs)—networks of functionally coupled regions with high spatial consistency—have been identified as organizational elements of the human brain (Damoiseaux et al., 2006; Laird et al., 2011). One of the most consistently identifiable ICN is known as the default mode network (DMN), comprising aspects of the ventromedial prefrontal cortex (vmPFC), dorsomedial PFC (dmPFC), posterior cingulate cortex (PCC), precuneus, medial temporal lobe, and medial and lateral parietal cortices (Andrews-Hanna et al., 2010; Buckner et al., 2008; Yeo et al., 2011).

Several groups have seeded known structures of the DMN (e.g., PCC) and found evidence of alterations in DMN functional connectivity in PTSD (Bluhm et al., 2009; Daniels et al., 2011; DiGangi et al., 2016; Lanius et al., 2010; Miller et al., 2017a, 2017b; Qin et al., 2012; Shin et al., 2009; Sripada et al., 2012; Tursich et al., 2015; Wu et al., 2011; Zhou et al., 2012). While these studies generally have many strengths, when used for the purpose of assessing ICNs, seed-based methods suffer from the constraints of having a limited number of regions-of-interest (ROIs). With only a limited number of ROIs, such approaches may not yield an optimal representation of the connectivity in the DMN as a whole. Further, the obtained results may be more prone to noise, since the metrics would be derived from few ROIs/connections, which could lead to false negatives (e.g., by missing important connections) and false positives (due to conclusions being drawn from non-representative connections). Heterogeneity in the choice of the seeds also limits comparison across studies. Although generalization is currently limited by such constraints, individuals with PTSD nonetheless appear to evidence decreased connectivity within the DMN (Akiki et al., 2017; Bluhm et al., 2009; DiGangi et al., 2016; Sripada et al., 2012; Tursich et al., 2015), though not without inconsistencies (Reuveni et al., 2016).

In addition to seed-based studies, our group and others have previously used network approaches to examine whole-brain functional connectivity, using voxel-based global brain connectivity (GBC), or other nodal metrics. These PTSD studies have sometimes identified abnormalities in voxels/nodes that are known to be part of the DMN (e.g., decreased vmPFC nodal strength in PTSD), providing clues of DMN involvement (Abdallah et al., 2017b; Kennis et al., 2016; Koch et al., 2016; Lei et al., 2015; Patel et al., 2012; Suo et al., 2015). While these whole-brain studies have many strengths, a major limitation is the lack of ICN constraints, rendering the assessment of within-ICN topology impractical. The results therefore cannot be interpreted as being ICN-specific.

Considering the increased recognition of the role of ICN alterations in psychiatric disorders, and the need for developing circuit-based treatment (Kressel, 2017; Lui et al., 2016; Ross et al., 2017; Sheynin and Liberzon, 2016), it is critical for the field to develop tools to map ICN abnormalities. The accurate identification of ICN abnormalities could potentially pave the way for rational treatment development and target validation (Lanius et al., 2015). This has been recognized for brain stimulation approaches such as transcranial magnetic stimulation (Opitz et al., 2016), deep brain stimulation (Lozano and Lipsman, 2013), as well as for real time fMRI feedback (Garrison et al., 2013; Whitfield-Gabrieli et al., 2017), and psychotherapy (Lanius et al., 2015).

Meso-scale analysis of network architecture (e.g., communities) is well established in network science, and shifts the analysis to a scale coarser than seed-based and voxel/node-based whole-brain approaches (Fortunato, 2010; Meunier et al., 2010; Newman, 2011; Sporns and Betzel, 2016). Indeed, such methods have been used to define ICNs (e.g., (Power et al., 2011)). In the current study, we implemented an ICN-restricted approach to systematically investigate connectivity within the DMN. To achieve this goal, we used 1) a meta-analytically derived functional brain atlas from Power et al. (2011) to delineate ROIs (also referred to as nodes); 2) a validated definition of the DMN using the ICN map from Yeo et al. (2011); and 3) network tools from graph theory to probe connectivity patterns (Rubinov and Sporns, 2010). Here, it is important to note that the network-restricted methods established in the current study are not limited to a particular ICN or disease. Therefore, they can be easily implemented to investigate other circuits and neuropsychiatric disorders, extending the scientific benefit beyond the study's specific PTSD findings.

We hypothesized that PTSD symptoms would be associated with a pattern of network-wide connectivity dysfunction in the DMN. We aimed: 1) to determine whether previous observations made regarding altered DMN functional connectivity strength in PTSD hold true using a method that is DMN-specific, less model-reliant, and standardized (i.e., the DMN overall strength – the primary outcome of the study); 2) to further characterize DMN dysfunction in terms of network topology (i.e., the ability to integrate and segregate information); 3) to determine the modular architecture of the DMN, and to identify sub-communities relevant to PTSD; 4) to identify subregions in the DMN driving the network-wide abnormalities using edge-based statistics; 5) to determine, using data from diffusion MRI (dMRI), whether structural white matter abnormalities are driving the DMN functional abnormalities; and 6) to assess the robustness of results by repeating the analyses using alternate ROI definitions and network construction techniques. The main analyses were carried out with a dimensional approach based on a continuum of PTSD symptoms severity rather than diagnosis, consistent with the continuous nature of the disorder (further details in an accompanying data article (Akiki et al., in press)), but group comparisons were conducted also on the global metrics to ascertain robustness and enable comparisons with other studies that have used group approaches.