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

Area of Science:

• Neuroscience
• Psychiatry
• Clinical Psychology

Background:

• Trauma-related dissociation assessment is challenging due to subjectivity and limited provider education.
• Recent research identified a neural biomarker for trauma-related dissociation, aiding assessment.
• Understanding clinical factors associated with this biomarker is crucial.

Purpose of the Study:

• To explore patterns in a neural functional connectivity model predicting pathological dissociation.
• To investigate clinical differences among identified clusters based on model performance.
• To determine if the prediction model is linked to dissociative identity disorder (DID)-specific dissociation.

Main Methods:

• K-means cluster analysis was performed on data from 65 women with childhood maltreatment and PTSD.
• The analysis explored model performance patterns in predicting severe pathological dissociation scores.
• Clinically relevant variables were analyzed across identified clusters.

Main Results:

• Four distinct clusters emerged from the analysis.
• The prediction model was most accurate for low (82% PTSD) and high (86% DID) dissociation groups.
• A subgroup of DID participants (Cluster 2) showed less accurate model prediction and reported fewer self-state intrusions compared to high dissociation DID participants (Cluster 3).

Conclusions:

• Functional connectivity patterns may be linked to DID-specific dissociation.
• The identified neural biomarker's performance varies across dissociation severity and presentation.
• Functional connectivity patterns hold potential for improving the accurate assessment of DID.