Posttraumatic Stress Disorder Symptom Network Analysis in U.S. Military Veterans: Examining the Impact of Combat Exposure.

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2018-01

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Abstract

Recent work inspired by graph theory has begun to conceptualize mental disorders as networks of interacting symptoms. Posttraumatic stress disorder (PTSD) symptom networks have been investigated in clinical samples meeting full diagnostic criteria, including military veterans, natural disaster survivors, civilian survivors of war, and child sexual abuse survivors. Despite reliable associations across reported networks, more work is needed to compare central symptoms across trauma types. Additionally, individuals without a diagnosis who still experience symptoms, also referred to as subthreshold cases, have not been explored with network analysis in veterans. A sample of 1,050 Iraq/Afghanistan-era U.S. military veterans (851 males, mean age = 36.3, SD = 9.53) meeting current full-criteria PTSD (n = 912) and subthreshold PTSD (n = 138) were assessed with the Structured Clinical Interview for DSM-IV Disorders (SCID). Combat Exposure Scale (CES) scores were used to group the sample meeting full-criteria into high (n = 639) and low (n = 273) combat exposure subgroups. Networks were estimated using regularized partial correlation models in the R-package qgraph, and robustness tests were performed with bootnet. Frequently co-occurring symptom pairs (strong network connections) emerged between two avoidance symptoms, hypervigilance and startle response, loss of interest and detachment, as well as, detachment and restricted affect. These associations replicate findings reported across PTSD trauma types. A symptom network analysis of PTSD in a veteran population found significantly greater overall connectivity in the full-criteria PTSD group as compared to the subthreshold PTSD group. Additionally, novel findings indicate that the association between intrusive thoughts and irritability is a feature of the symptom network of veterans with high levels of combat exposure. Mean node predictability is high for PTSD symptom networks, averaging 51.5% shared variance. With the tools described here and by others, researchers can help refine diagnostic criteria for PTSD, develop more accurate measures for assessing PTSD, and eventually inform therapies that target symptoms with strong network connections to interrupt interconnected symptom complexes and promote functional recovery.

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10.3389/fpsyt.2018.00608

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Phillips, Rachel D, Sarah M Wilson, Delin Sun, undefined VA Mid-Atlantic MIRECC Workgroup and Rajendra Morey (2018). Posttraumatic Stress Disorder Symptom Network Analysis in U.S. Military Veterans: Examining the Impact of Combat Exposure. Frontiers in psychiatry, 9. p. 608. 10.3389/fpsyt.2018.00608 Retrieved from https://hdl.handle.net/10161/18138.

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Scholars@Duke

Wilson

Sarah M Wilson

Assistant Professor in Psychiatry and Behavioral Sciences

Sarah M. Wilson is an Assistant Professor in the Department of Psychiatry & Behavioral Sciences at the Duke University School of Medicine, with a secondary appointment in the Department of Population Health Sciences and a faculty affiliation in the Duke Center for Health Policy and Inequalities Research. She is a Research Investigator and Co-Lead of the Diversity, Equity, and Inclusion Core at the Veterans Affairs Center of Innovation to Accelerate Discovery and Practice Transformation (ADAPT COIN). She also serves as the Associate Director of the Duke Center for AIDS Research Social and Behavioral Sciences Core.

Dr. Wilson's research focuses on access to care and systems-level healthcare change to improve inequities in populations who experience systemic discrimination, including Black, Indigenous, and People of Color (BIPOC), individuals with low income, sexual and gender minorities, and specific populations of U.S. Military Veterans. Her clinical work as a licensed clinical psychologist focuses on mental health care for patients with comorbid mental health concerns and stress due to systemic discrimination, as well as training for health care professionals on LGBTQ-affirmative care.

Dr. Wilson has expertise in health equity, social determinants of health, community engagement, intervention development, and implementation science. She is a former Fellow in the Implementation Research Institute. She leads VA and NIH research studies related to implementation science and health equity in the areas of tobacco cessation, provider implicit bias, pre-exposure prophylaxis for HIV, and LGBTQ-affirmative mental healthcare.

Sun

Delin Sun

Assistant Professor in Psychiatry and Behavioral Sciences

I am interested in the neuropsychological and behavioral correlates of posttraumatic stress disorder (PTSD) and adolescent trauma. With a multidisciplinary background in psychology, physics, and biology, I have specific training and expertise in neuroimaging methods and secondary data analysis of neural changes in PTSD and childhood trauma. I systematically study PTSD-related structural and functional brain images using methods that focus on different levels including regional changes, connectivity, and brain networks, respectively, to build a complete behavioral-brain map. I also study the relationship between adolescent brain development and trauma as well as alcohol by using brain structure network graphs, longitudinal studies, and machine learning research methods. Besides, I utilize neuroimaging and neurophysiological techniques to investigate the neural underpinnings of social cognitive and affective functions in both healthy volunteers and patients with mental disorders including excessive internet usage, smoking addiction, Alzheimer’s Disease, and depression. I have successfully collaborated with researchers locally or from other institutions and have had several peer-reviewed publications in each project. 

Morey

Rajendra A. Morey

Professor of Psychiatry and Behavioral Sciences

Research in my lab is focused on brain changes associated with posttraumatic stress disorder (PTSD), traumatic brain injury (TBI), and other neuropsychiatric disorders. We apply several advanced methods for understanding brain function including functional MRI, structural MRI, diffusion tensor imaging, and genetic effects.


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