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 training.
• Recent advancements identified a neural biomarker for trauma-related dissociation.
• Understanding clinical factors associated with this biomarker is crucial for improving assessment.

Purpose of the Study:

• To examine patterns within a previously identified brain signature of dissociation.
• To explore clinical factors associated with a neural biomarker of trauma-related dissociation.
• To investigate differences among distinct groups identified through cluster analysis of the biomarker's predictive model.

Main Methods:

• Utilized data from 65 women with histories of childhood maltreatment and PTSD.
• Performed k-means cluster analysis on a predictive model of pathological dissociation based on neural functional connectivity.
• Investigated differences in clinically-relevant variables among identified clusters.

Main Results:

• Identified four distinct groups based on the functional connectivity model's predictive patterns.
• The model best predicted low (PTSD) and high (DID) dissociation groups.
• Two distinct groups of individuals with dissociative identity disorder (DID) were identified, differing in DID-specific dissociation levels (self-state intrusions).

Conclusions:

• The predictive performance of the neural biomarker is associated with DID-specific dissociation.
• Functional connectivity signatures may enhance the accurate assessment of dissociative identity disorder.
• Comprehensively evaluating dissociative symptoms, including DID-specific presentations, is essential.