Format

Send to

Choose Destination
J Neuroimaging. 2019 May;29(3):335-343. doi: 10.1111/jon.12600. Epub 2019 Feb 3.

Adaptive Identification of Cortical and Subcortical Imaging Markers of Early Life Stress and Posttraumatic Stress Disorder.

Author information

1
Imaging Genetics Center of the Mark and Mary Stevens Institute for Neuroimaging & Informatics, Keck School of Medicine, University of Southern California, Marina del Rey, CA.
2
Durham VA Medical Center, Durham, NC.
3
Duke University Medical Center, Durham, NC.
4
Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN.
5
Psychiatry Neuroimaging Laboratory, Harvard Medical School, Boston, MA.
6
Stanford Neurodevelopment, Affect, and Psychopathology Laboratory, Stanford, CA.

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 (CV) 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.

KEYWORDS:

PTSD; early life stress; machine learning; neuroimaging

PMID:
30714246
PMCID:
PMC6571150
[Available on 2020-05-01]
DOI:
10.1111/jon.12600

Supplemental Content

Full text links

Icon for Wiley
Loading ...
Support Center