The candidate is a clinical psychologist and neuroscientist with a strong interest in the phenomenology, affective neurocircuitry, and effective treatment of posttraumatic stress disorder (PTSD). PTSD is highly prevalent (7% in males, 12% in females), chronic and highly debilitating. One in every five veterans deployed to Afghanistan and Iraq have clinically significant PTSD symptoms. Empirically supported treatment (ESTs) for PTSD using trauma exposure have very large effect sizes in RCTs; however, recent work finds high rates of refusal and early drop-out among veterans (30-50%). We previously reported deficits in large scale distributed neural networks in PTSD, including increased connectivity between Default Mode Network (DMN) and Salience Network (SN), and we present pilot data that PTSD avoidant symptoms are linked to decreased DMN connectivity with Central Executive Network (CEN). We also reported a mindfulness-based intervention for PTSD decreased avoidance and increased connectivity between DMN and CEN. This K23 training program will allow the candidate to learn and apply the powerful whole-brain connectomic and dynamic connectivity methodologies needed to study the alterations in large scale distributed neural networks underlying PTSD and therapeutic mechanisms. Our hypotheses are H1: Decreased DMN-CEN and increased DMN-SN underlie emotional / behavioral avoidance associated with poor clinical acceptability and outcomes in PTSD patients, H2: Mindfulness-based Cognitive Therapy (MBCT) targets the same DMN-CEN connectivity linked with PTSD avoidance, and H3: MBCT-induced increased DMN-CEN connectivity mediates improvement in PTSD avoidance, emotional / behavioral avoidance, and improved outcomes.
The aims are to: 1.) test if behavioral and emotional avoidance in PTSD are associated with decreased DMN-CEN connectivity, 2.) identify effects of MBCT on DMN-CEN using powerful connectomics analyses, and 3.) explore mediation relationships between DMN-CEN connectivity and behavioral and emotional engagement in subsequent therapy and clinical outcomes. fMRI with N=60 combat PTSD patients with rsFC using dynamic causal modeling, and contextual processing paradigms, will test association with avoidance symptoms and laboratory measures of emotional avoidance. An RCT (N=30 MBCT, N=30 control) with pre- post fMRI and group x time interaction analyses will identify effects of MBCT on connectivity among and between DMN, CEN, and SN. Mediation analyses will test neural targets and longitudinal measures of emotional engagement. This study will be the first targeting functional neural networks underlying avoidance behaviors in PTSD treatment, and may lead to additional strategies to help the ~50% PTSD patients who do not fully engage with existing ESTs, and elucidate neural mechanisms associated with a novel potential component of PTSD treatment (mindfulness training). It will also provide the candidate advanced training and pilot data for R-level funding to identify mechanisms of therapeutic change and novel treatments for PTSD.
. Posttraumatic stress disorder (PTSD) is a major societal problem with high levels of disability and family disruption common in military veterans and the general public, and although empirically-supported ?gold- standard? interventions are very helpful for some patients, there are high drop-out and refusal rates due to patient avoidance. Alterations in connectivity of large-scale neural networks have been implicated in PTSD, and the candidate has pilot data that ?mindfulness-based? intervention decreases avoidance and alters neural network connectivity in PTSD patients. This K23 program will test the effects of mindfulness-based cognitive therapy (MBCT) on emotional avoidance and neural networks in PTSD patients who have declined or dropped out of ?gold-standard? treatment, by learning and applying state-of-the-art connectomics and network analyses.
