Commentary: Gray Matter Structural Alterations in Social Anxiety Disorder: A Voxel-Based Meta-Analysis

Stopping an action requires suppression of the primary motor cortex (M1). Inhibitory control over M1 relies on a network including the right inferior frontal cortex (rIFC) and the supplementary motor complex (SMC), but how these regions interact to exert inhibitory control over M1 is unknown. Specifically, the hierarchical position of the rIFC and SMC with respect to each other, the routes by which these regions control M1, and the causal involvement of these regions in proactive and reactive inhibition remain unclear. We used off-line repetitive TMS to perturb neural activity in the rIFC and SMC followed by fMRI to examine effects on activation in the networks involved in proactive and reactive inhibition, as assessed with a modified stop-signal task. We found repetitive TMS effects on reactive inhibition only. rIFC and SMC stimulation shortened the stop-signal RT (SSRT) and a shorter SSRT was associated with increased M1 deactivation. Furthermore, rIFC and SMC stimulation increased right striatal activation, implicating frontostriatal pathways in reactive inhibition. Finally, rIFC stimulation altered SMC activation, but SMC stimulation did not alter rIFC activation, indicating that rIFC lies upstream from SMC. These findings extend our knowledge about the functional organization of inhibitory control, an important component of executive functioning, showing that rIFC exerts reactive control over M1 via SMC and right striatum.

The ENIGMA group on Generalized Anxiety Disorder (ENIGMA‐Anxiety/GAD) is part of a broader effort to investigate anxiety disorders using imaging and genetic data across multiple sites worldwide. The group is actively conducting a mega‐analysis of a large number of brain structural scans. In this process, the group was confronted with many methodological challenges related to study planning and implementation, between‐country transfer of subject‐level data, quality control of a considerable amount of imaging data, and choices related to statistical methods and efficient use of resources. This report summarizes the background information and rationale for the various methodological decisions, as well as the approach taken to implement them. The goal is to document the approach and help guide other research groups working with large brain imaging data sets as they develop their own analytic pipelines for mega‐analyses.