First-line interventions for psychiatric disorders do not work for everyone and factors impacting treatment response are poorly understood. There is a clear need to identify reliable predictors of treatment response in order to optimize treatment outcomes, provide more targeted referrals, and reduce healthcare costs. Neuroimaging-based predictors of treatment response offer better predictive utility than clinical variables alone, yet these predictors are largely lacking. This 5-year mentored patient-oriented research career development award will address this need. Specifically, this project will test the hypothesis that individual differences in maladaptive self-focused processing predict treatment response and that neuroimaging-based markers are better predictors than behavioral ones.
Each aim of the study corresponds to specific training goals, which will map onto competency in four main areas: (1) fMRI neuroimaging techniques in terms of event-related design, image acquisition, and data analysis, (2) neuroscience-informed treatment outcome research, (3) statistical methods involving multidimensional classification, and (4) career development. Such training will transform the applicant into an independent translational clinical scientist who examines the neural basis of cognitive dysfunction in anxiety and obsessive-compulsive spectrum disorders to inform personalized formulations of pathology and aid in individual treatment decisions. Training goals will be implemented with the expert guidance of Dr. Sabine Wilhelm (primary mentor), Dr. Dara Manoach (co-mentor), and the advisory team consisting of Drs. Luan Phan, Jamie Feusner, and Mark Vangel. First, we propose to identify the neural correlates of self-focused processing. We will assess baseline resting state connectivity within the default network, as well as regional brain activation using a well-validated event-related fMRI task that manipulates self-focused processing in patients with body dysmorphic and socially anxious symptoms, compared to healthy controls. We selected this clinical sample because such patients display heightened self-focused attention, and sampling individuals across these symptom dimensions will ensure greater variability on this dimension of maladaptive self-focused processing. Second, we will examine the neural correlates of self-focused processing as a predictor of treatment response. Neuroimaging data will be acquired from patients with body dysmorphic and socially anxious symptoms during two scan sessions, before and after 12 weeks of individual cognitive- behavioral therapy, and compared with healthy controls scanned twice at a 12-week interval. Finally, we will compare the prediction of treatment response between neural measures and behavioral measures of self- focused processing. We will assess the behavioral correlates of self-focused processing using a self-reference effect paradigm, and assess their relation to treatment response. If our hypotheses are borne out, we will have new targets for treatment, a method to identify promising candidates for treatment, and sensitive surrogate markers of treatment response.
As first-line interventions do not work for everyone, factors that reliably predict treatment response are greatly needed to optimize treatment outcomes and reduce healthcare costs. Neuroimaging-based predictors offer better predictive utility of treatment outcome than clinical variables alone, yet such predictors are largely lacking. The current proposal addresses this need by examining maladaptive self-focused processing and its neural correlates as a biomarker of treatment response, with the goal of identifying promising candidates for treatment and using baseline neuroimaging data to aid in individual treatment planning decisions.
| Fang, Angela; Treadway, Michael T; Hofmann, Stefan G (2017) Working hard for oneself or others: Effects of oxytocin on reward motivation in social anxiety disorder. Biol Psychol 127:157-162 |
| Fang, Angela; Wilhelm, Sabine (2016) Antidepressant use with d-Cycloserine may block fear extinction. Evid Based Ment Health 19:e5 |
