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

Abstract

BackgroundRecent 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.MethodsStudy 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.ResultsIn 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.ConclusionsThese results outline a possible treatment mechanism of rTMS in GAD, and pave the way for future studies of treatment optimization.

Effectiveness of noninvasive brain stimulation in the treatment of anxiety disorders: a meta-analysis of sham or behaviour-controlled studies

“…We also checked that subsequent reports did not increase the J Psychiatry Neurosci 2021;46 (6) sample size, only adding secondary analysis to the original sample). [108][109][110][111][112] We excluded 1 paper because it did not include patients, 29 1 because it did not include patients with a diagnosis of an anxiety disorder, 112 1 because it did not test anxiety as an outcome measure, 113 and 1 because the full text was not available. 114 Figure 1 summarizes the selection procedure.…”

The electrophysiology correlation of the cognitive bias in anxiety under uncertainty

“…One of the main theories of Generalized Anxiety Disorder (GAD) holds that intolerance of uncertainty (IU) plays the central role of a cognitive bias that interferes with information processing (including decision-making) 17 . According to the IU model, in the case of uncertainty, excessive emotional response is conducive to the development and maintenance of anxiety 18 . A study in a large sample of 1,092 young people also showed that IU has a significant moderating effect on the relationship between daily stress and worry 19 .…”

Non-invasive brain stimulation effectiveness in anxiety disorder treatment: a meta-analysis on sham/behavior-controlled studies

“…Four records were excluded because they were conference proceedings (Assaf et al, 2017, 2014; Deppermann et al, 2013; Griffiths, O’Neill-Kerr, & De, 2019), two did not include patients (Ironside et al, 2019), one included patients but without a diagnosis of an anxiety-related disorder (Caulfield & Stern, 2020), and one did not test anxiety as an outcome measure (Heeren et al, 2017). Six studies were excluded because they did not consider a control condition, namely sham stimulation or a control group (Bystritsky et al, 2008; Clarke et al, 2019; Kumar, Singh, Parmar, Verma, & Kumar, 2018; Lu et al, 2018; Mantovani et al, 2007; White & Tavakoli, 2015), six because they involved samples already tested by one of the ten eligible papers (Assaf et al, 2018; S Deppermann et al, 2016; Saskia Deppermann et al, 2017; Diefenbach, Assaf, Goethe, Gueorguieva, & Tolin, 2016; Diefenbach et al, 2019; Praško et al, 2009). The full text of the remaining two studies was not available (He, Zheng, Cai, & Zou, 2011; Wu, Hu, Yu, Liu, & Wang, 2016).…”