Clinical patterns in a neuroimaging-based predictive model of self-reported dissociation

ObjectiveAssessment of trauma-related dissociation has been historically challenging given its subjective nature and the lack of provider education around this topic. Recent work identified a promising neural biomarker of trauma-related dissociation, representing a significant step toward improved assessment and identification of dissociation. However, it is necessary to better understand clinical factors that may be associated with this biomarker. MethodParticipants were 65 women with histories of childhood maltreatment, posttraumatic stress disorder (PTSD), and varying levels of dissociation (e.g., co-occurring dissociative identity disorder, DID). Data were drawn from a previously published work that identified a model predicting Multidimensional Inventory of Dissociation severe pathological dissociation scores on the basis of neural functional connectivity. Here, we conducted a k-means cluster analysis to explore patterns in results of the prediction model. We then investigated differences among the clusters in a range of clinically-relevant variables. ResultsThe clustering analysis identified four distinct groups. The functional connectivity model best predicted those at the low (cluster 1, 82% PTSD) and high (cluster 3, 86% DID) ends of the self-reported dissociation scale. Cluster 2 also largely included participants with DID (67%), but the predictive model was less accurate for these individuals. Follow up analyses revealed that DID participants in cluster 2 reported lower levels of self-state intrusions, a type of DID-specific dissociation, compared to those in cluster 3. ConclusionsThe predictive performance of the functional connectivity biomarker is linked to DID-specific dissociation. This suggests that in the future functional connectivity signatures may improve accurate assessment of DID. Clinical Impact StatementThe present study aimed to examine patterns in a previously identified brain signature of dissociation. We identified two distinct groups of individuals with dissociative identity disorder (DID) who differed in a DID-specific type of dissociation. These findings suggest that the brain signature of dissociation may be linked with DID-specific dissociation and underscore the importance of comprehensively evaluating dissociative symptoms. Dissociative symptoms are difficult to assess because the experience is highly subjective and because many providers do not receive training in dissociation. Objective, brain-based metrics to supplement self-reports would be invaluable in enhancing the assessment and treatment of trauma-related dissociation.