Deconstructing dissociation: a triple network model of trauma-related dissociation and its subtypes

Abstract

Trauma-related pathological dissociation is characterized by disruptions in one's sense of self, perceptual, and affective experience. Dissociation and its trauma-related antecedents disproportionately impact women. However, despite the gender-related prevalence and high individual and societal costs, dissociation remains widely underappreciated in clinical practice. Moreover, dissociation lacks a synthesized neurobiological model across its subtypes. Leveraging the Triple Network Model of psychopathology, we sought to parse heterogeneity in dissociative experience by examining functional connectivity of three core neurocognitive networks as related to: (1) the dimensional dissociation subtypes of depersonalization/derealization and partially-dissociated intrusions; and, (2) the diagnostic category of dissociative identity disorder (DID). Participants were 91 women with and without: a history of childhood trauma, current posttraumatic stress disorder (PTSD), and varied levels of dissociation. Participants provided clinical data about dissociation, PTSD symptoms, childhood maltreatment history, and completed a resting-state functional magnetic resonance imaging scan. We used a novel statistical approach to assess both overlapping and unique contributions of dissociation subtypes. Covarying for age, childhood maltreatment and PTSD severity, we found dissociation was linked to hyperconnectivity within central executive (CEN), default (DN), and salience networks (SN), and decreased connectivity of CEN and SN with other areas. Moreover, we isolated unique connectivity markers associated with depersonalization/derealization in CEN and DN, to partially-dissociated intrusions in CEN, and to DID in CEN. This suggests dissociation subtypes have robust functional connectivity signatures that may serve as targets for PTSD/DID treatment engagement. Our findings underscore dissociation assessment as crucial in clinical care, in particular, to reduce gender-related health disparities.

Fig. 1. Statistical Approach of the Full and Unique Variance models.

Full standard model represents a multiple regression model that includes all diagnostic categorical (CAT) and dimensional (DIM) variables together. In this case, the shared variance between the variables (the areas of overlap in the center of the Venn diagram) are ignored when estimating the regression parameters. In contrast, full variance modeling (Step 1) involves running separate models that estimate the regression parameter of the variable using its full variance (heavy black circles) to yield a set of brain regions (or markers) whose connectivity with the network is associated with that variable. In Step 2, unique variance modeling identifies the unique association between the markers identified in Step 1 and each diagnostic categorical and dimensional variable. Adapted from [38].

Fig. 2. Triple Network Model of Pathological Dissociation.

The Triple Network Model of Pathological Dissociation depicts biomarkers (brain regions) with functional connectivity to our core networks (right central executive, medial temporal default network, and cingulo-opercular salience network) that is associated with the full variance of each pathological dissociation variable (dissociative identity disorder diagnosis, depersonalization/derealization, and partially-dissociated intrusions). Green regions indicate the network of interest (right central executive, medial temporal default network, or cingulo-opercular salience network). Yellow indicates areas with increased connectivity between that region and the network of interest that is associated with pathological dissociation. Blue indicates regions with decreased connectivity between that region and the network of interest that is associated with pathological dissociation. The radial bar graph depicts the number of markers linked with pathological dissociation in each network associated with increased or decreased connectivity either within or outside the network of interest. Images made with MRIcroGL (https://www.nitrc.org/plugins/mwiki/index.php/mricron:MainPage). rCEN right central executive network, cSN cingulo-opercular salience network, tDN medial temporal default network.

Fig. 3. Unique Associations between Connectivity Biomarkers and Depersonalization/Derealization, Partially-Dissociated Intrusions and Dissociative Identity Disorder Diagnosis.

Here we depict biomarkers (brain regions) with functional connectivity to our core networks (right central executive, medial temporal default network, and cingulo-opercular salience network) that is uniquely associated with each of the pathological dissociation variables (dissociative identity disorder diagnosis, depersonalization/derealization, and partially-dissociated intrusions). Green regions indicate the network of interest (right central executive, medial temporal default network). Only two of the three networks are shown because no markers with functional connectivity to salience network were uniquely associated with one of the dissociation variables. Yellow indicates regions with increased connectivity between that region and the network of interest uniquely associated with the dissociation variable. Blue indicates regions with decreased connectivity between that region and the network of interest with the dissociation variable. The radial bar graphs depict the number of markers linked with each type of dissociation in each network. The markers reflect either increased or decreased connectivity of regions within or outside the network of interest. Images made with MRIcroGL (https://www.nitrc.org/plugins/mwiki/index.php/mricron:MainPage). rCEN right central executive network, cSN salience network, tDN medial temporal default network.