Neural Markers of Treatment Mechanisms and Prediction of Treatment Outcomes in Social Anxiety

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PROJECT SUMMARY/ABSTRACT Social anxiety disorder (SAD) is one of the most common mental disorders. For unknown reasons, many patients do not respond to existing treatments. Treatment guidelines and systematic reviews often recommend CBT as the first line treatment, followed by an SSRI adjunctively for patients who show no or only partial response to CBT. A major advance toward personalized medicine would be to identify reliable treatment predictors, and then to clarify the neuromechanism of treatment change. One promising approach toward improving patient outcomes is to examine the key neurocircuitry of SAD that may also serve as neuromarkers predicting treatment response. We have gathered convincing pilot data identifying neuromarkers that predict response to CBT in adults with SAD. The next translational step, and our primary aim, is to apply state of the art computational psychiatry approaches to strengthen the evidence base for these neuromarkers, in line with moving psychiatry toward precision medicine. This aim will be efficiently achieved by collecting state-of-the-art, multimodal neuroimaging data to better elucidate the key neurocircuitry of SAD (compared to controls) in a well powered sample, while also identifying differential treatment-related changes in neural circuitry (target engagement). The ultimate goal is to effectively treat all patients, not only a few and without knowing why, and to illuminate the brain circuitry associated with effective treatments to inform psychopathology, nosology, and therapy of common mental disorders. For these reasons, we propose recruiting a large number of patients with SAD (n = 190) and healthy controls (n = 100) to examine differences in relevant neurocircuitries that will also be used as neuromarkers of treatment response. Patients with SAD will first receive CBT group therapy. Those who show no or only partial response will then receive individual and tailored CBT plus SSRI. In addition to MRI, we will examine EEG and behavioral measures to determine if there are more cost effective correlates of neuropredictors that could be easily implemented in clinical practice. We have assembled a team of skilled researchers with complementary expertise at the Massachusetts Institute of Technology (MIT; John D. E. Gabrieli, Ph.D.), Boston University (BU; Stefan G. Hofmann, Ph.D.), and McLean Hospital (Daniel Dillon, Ph.D.), as well as outstanding consultants in neuroimaging analysis (Northeastern University: Susan Whitfield- Gabrieli, Ph.D.) and machine learning applications in psychiatry (McLean Hospital: Christian Webb, Ph.D.).