Diagnostic and Predictive Neuroimaging Biomarkers for Posttraumatic Stress Disorder

Sigal Zilcha-Mano, Xi Zhu, Benjamin Suarez-Jimenez, Alison Pickover, Shachaf Tal, Sara Such, Caroline Marohasy, Marika Chrisanthopoulos, Chloe Salzman, Amit Lazarov, Yuval Neria, Bret R. Rutherford

Research output: Contribution to journalArticlepeer-review

Abstract

Background: Comorbidity between posttraumatic stress disorder (PTSD) and major depressive disorder (MDD) has been commonly overlooked by studies examining resting-state functional connectivity patterns in PTSD. The current study used a data-driven approach to identify resting-state functional connectivity biomarkers to 1) differentiate individuals with PTSD (with or without MDD) from trauma-exposed healthy control subjects (TEHCs), 2) compare individuals with PTSD alone with those with comorbid PTSD+MDD, and 3) explore the clinical utility of the identified biomarkers by testing their associations with clinical symptoms and treatment response. Methods: Resting-state magnetic resonance images were obtained from 51 individuals with PTSD alone, 52 individuals with PTSD+MDD, and 76 TEHCs. Of the 103 individuals with PTSD, 55 were enrolled in prolonged exposure treatment. A support vector machine model was used to identify resting-state functional connectivity biomarkers differentiating individuals with PTSD (with or without MDD) from TEHCs and differentiating individuals with PTSD alone from those with PTSD+MDD. The associations between the identified features and symptomatology were tested with Pearson correlations. Results: The support vector machine model achieved 70.6% accuracy in discriminating between individuals with PTSD and TEHCs and achieved 76.7% accuracy in discriminating between individuals with PTSD alone and those with PTSD+MDD for out-of-sample prediction. Within-network connectivity in the executive control network, prefrontal network, and salience network discriminated individuals with PTSD from TEHCs. The basal ganglia network played an important role in differentiating individuals with PTSD alone from those with PTSD+MDD. PTSD scores were inversely correlated with within–executive control network connectivity (p <.001), and executive control network connectivity was positively correlated with treatment response (p <.001). Conclusions: Results suggest that unique brain-based abnormalities differentiate individuals with PTSD from TEHCs, differentiate individuals with PTSD from those with PTSD+MDD, and demonstrate clinical utility in predicting levels of symptomatology and treatment response.

Original languageEnglish
Pages (from-to)688-696
Number of pages9
JournalBiological Psychiatry: Cognitive Neuroscience and Neuroimaging
Volume5
Issue number7
DOIs
StatePublished - Jul 2020

Bibliographical note

Funding Information:
This work was supported by the National Institute of Mental Health (Grant No. R01 MH111596 [to BRR], Grant Nos. R01MH105355 and R01MH072833 [to YN], Grant No. 5T32MH020004-20 [to AP], and Grant No. K01 MH118428-01 [to BS-J]) and the United States-Israel Binational Science Foundation (Grant No. 2017263 [to SZ-M and BRR]).

Publisher Copyright:
© 2020 Society of Biological Psychiatry

Keywords

  • Machine learning
  • Major depressive disorder
  • Posttraumatic stress disorder
  • Resting-state functional MRI
  • Support vector machine
  • Treatment outcome
  • fMRI classification

ASJC Scopus subject areas

  • Radiology Nuclear Medicine and imaging
  • Cognitive Neuroscience
  • Clinical Neurology
  • Biological Psychiatry

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