Neural functional architecture and modulation during decision making under uncertainty in individuals with generalized anxiety disorder

BACKGROUND: Recent evidence suggests that repetitive transcranial magnetic stimulation (rTMS) might be effective in treating generalized anxiety disorder (GAD). Cognitive models of GAD highlight the role of intolerance of uncertainty (IU) in precipitating and maintaining worry, and it has been hypothesized that patients with GAD exhibit decision‐making deficits under uncertain conditions. Improving understanding of the neural mechanisms underlying cognitive deficits associated with IU may lead to the identification of novel rTMS treatment targets and optimization of treatment parameters. The current report describes two interrelated studies designed to identify and verify a potential neural target for rTMS treatment of GAD. METHODS: Study I explored the integrity of prefrontal cortex (PFC) and amygdala neural networks, which underlie decision making under conditions of uncertainty, in GAD. Individuals diagnosed with GAD (n = 31) and healthy controls (n = 20) completed a functional magnetic resonance imaging (fMRI) gambling task that manipulated uncertainty using high versus low error rates. In a subsequent randomized‐controlled trial (Study II), a subset of the GAD sample (n = 16) completed the fMRI gambling task again after 30 sessions of active versus sham rTMS (1 Hz, right dorsolateral prefrontal cortex) to investigate the modulation of functional networks and symptoms. RESULTS: In Study I, participants with GAD demonstrated impairments in PFC‐PFC and PFC‐amygdala functional connectivity (FC) mostly during the high uncertainty condition. In Study II, one region of interest pair, dorsal anterior cingulate (ACC) – subgenual ACC, showed “normalization” of FC following active, but not sham, rTMS, and neural changes were associated with improvement in worry symptoms. CONCLUSIONS: These results outline a possible treatment mechanism of rTMS in GAD, and pave the way for future studies of treatment optimization.