A review of neuroimaging studies in generalized anxiety disorder: "So where do we stand?".

Generalized anxiety disorder (GAD) is a prevalent anxiety disorder, but is still poorly recognized in clinical practice. The aim of this review is to provide a coherent understanding of the functional neuroanatomy of GAD; second, to discuss the current theoretical cognitive models surrounding GAD; and finally to discuss the discrepancy between fundamental research and clinical practice and highlight several potential directions for future research in this domain. A systematic review of original papers investigating the neural correlates of DSM-IV and DSM-5 defined GAD samples was undertaken in Ovid literature search, PubMed, Medline, EMbase, PsycINFO, Google Scholar, and TRIP databases. Articles published between 2007 and 2018 were included. First, GAD seems to be characterized by limbic and (pre)frontal abnormalities. More specifically, GAD patients show difficulties in engaging the prefrontal cortex (PFC) and anterior cingulate cortex (ACC) during emotional regulation tasks. Second, the involved brain areas appear to be characterized by heterogeneity possibly due to a variety of experimental designs and test subjects. Third, regarding the discrimination between GAD and other anxiety disorders via fMRI, results appear to be mixed. Studies report both GAD-specific activity and an inability to differentiate between GAD and other anxiety or mood disorders. The usage of different experimental tasks, test subjects, outcome measures and experimental designs limits the possibilities of generalizing results as well as conducting meta-analytical research. Certain theoretical models of GAD describe our understanding of this disorder and form the basis for treatment interventions. However, fMRI research thus far has failed to validate these models. To bridge the gap between fundamental research and clinical practice in GAD, we propose that fMRI researchers make an effort to validate the existing cognitive model of GAD. An alternative approach could be that new models would be based on current neuroimaging research as well as convergent research methods such as Heart Rate Variability (a bottom up approach).