Damage to the Salience Network and Interactions with the Default Mode Network

Methodology pipeline describing the techniques used in our analyses. The initial fMRI data were analyzed using a univariate approach to generate contrasts between key regressors (such as Stop > Go and Switch > Go). We then implemented the first stage of dual regression to extract subject-specific time courses for the DMN. The DMN spatial map came from an independent dataset defined by Smith et al. (2009). These time courses were then implemented in our psychophysiological interaction, where we calculated GLMs for each subject. This GLM included the DMN time course as our dependent variable. The independent variables included a constant, the time course of our seed ROI (e.g., the rAI), task time courses, and interaction time courses. The GLM generated parameter estimates for our independent variables, where we contrasted the task interactions (e.g., Stop × ROI interaction–Go × ROI interaction parameter estimates) to replicate our contrasts at the univariate level (e.g., Stop > Go). A, Schematic overview of the stop signal task. B, Schematic overview of the motor task switching paradigm.