Thursday, December 26, 2024
12/26/2024
Project Summary/Abstract Cognitive behavioral therapy (CBT) is the first-line psychological treatment for anxiety and obsessive compulsive (OC) disorders, yet approximately half of patients who receive CBT fail to achieve sustained clinical remission. Such failures are widespread but poorly studied and contribute to the escalating public health burden of anxiety. To date, no reliable biomarker capable of detecting CBT non-response exists. In this R01, we seek to fill this gap by collecting a battery of clinical, behavioral, self-report, and neural measurements before, during, and after CBT in a large cohort of patients with anxiety and OC disorders and healthy controls. Using these data, we will employ state-of-the-art machine learning techniques to build a model of CBT non- response and to test the hypothesis that early changes in a specific biomarker, self-focused attention (SFA), will represent a sensitive predictor and potential mechanism of CBT non-response. Prior research from our group has identified a promising neuroimaging-based biomarker of SFA, characterized by abnormal resting state functional connectivity between regions of the default mode network (DMN) and dorsal attention network (DAN) in a transdiagnostic sample. Trait SFA showed sustained reductions by 6 weeks into treatment, which tracked with clinical improvement, suggesting potential corresponding neural changes at that time. A common limitation of prediction studies is that they typically assess predictors only at baseline, which provides limited understanding of processes contributing to non-response and leaves unaddressed the question of what can be done for those predicted to not respond. Given that for anxiety and OC disorders, substantial improvement during CBT rapidly diminishes if not achieved early in treatment, we hypothesize that early changes in DMN- DAN connectivity may represent a mechanism of non-response and contribute to an early warning system that can be used to identify individuals at risk for suboptimal CBT response. This study will first establish the reliability and construct validity of DMN-DAN connectivity as a measure of SFA, which is distinct from related cognitive constructs, such as rumination, worry, and more general attentional mechanisms, such as attentional control and orienting, in a subgroup of 50 patients and 50 matched healthy controls. Next, we will provide 12 weeks of standard CBT for 110 patients with anxiety and OC disorders. Neuroimaging data will be acquired at baseline, week 6, and post-treatment to assess changes in functional connectivity throughout treatment. As predictions of CBT response are unlikely to be a function of SFA alone, we will develop supervised machine learning models that accommodate the hypothesized DMN-DAN connectivity measure, plus other data-driven features, to predict response at post-treatment. Since our goal is not to use MRI scans clinically, the use of machine learning to identify the strongest predictors of CBT non-response from a large set of multimodal features throughout CBT may reveal potential proxy measures that do not require neuroimaging. This study will reveal potential mechanistic indicators of CBT non-response that can guide treatment selection and planning.
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