ABSTRACT
Background and Purpose Posttraumatic stress disorder (PTSD) is a heterogeneous condition associated with a range of brain imaging abnormalities. Early life stress (ELS) contributes to this heterogeneity, but we do not know how a history of ELS influences traditionally defined brain signatures of PTSD. Here we used a novel machine learning method - evolving partitions to improve classification (EPIC) - to identify shared and unique structural neuroimaging markers of ELS and PTSD in 97 combat-exposed military veterans.
Methods We used EPIC with repeated cross-validation to determine how combinations of cortical thickness, surface area, and subcortical brain volumes could contribute to classification of PTSD (n=40) versus controls (n=57), and classification of ELS within the PTSD (ELS+ n=16; ELS-n=24) and control groups (ELS+ n=16; ELS- n=41). Additional inputs included intracranial volume, age, sex, adult trauma, and depression.
Results On average, EPIC classified PTSD with 69% accuracy (SD=5%), and ELS with 64% accuracy in the PTSD group (SD=10%), and 62% accuracy in controls (SD=6%). EPIC selected unique sets of individual features that classified each group with 75-85% accuracy in post hoc analyses; combinations of regions marginally improved classification from the individual atlas-defined brain regions. Across analyses, surface area in the right posterior cingulate was the only variable that was repeatedly selected as an important feature for classification of PTSD and ELS.
Conclusions EPIC revealed unique patterns of features that distinguished PTSD and ELS in this sample of combat-exposed military veterans, which may represent distinct biotypes of stress-related neuropathology.
Acknowledgments and Disclosure
This work was supported by VISN6 MIRECC, VA Merit 1I01RX000389-01, NIH grants R01 NS086885, K23 MH073091, VA Merit 1I01CX000748, and NIH grants U54 EB020403 (BD2K), MH111671, and P41 EB015922. The authors have no conflicts of interest.