Reduced DMN functional connectivity during conscious sedation. The DMN is shown during rest (a) and during conscious sedation (b). A 111-voxel cluster in the posterior cingulate cortex showed increased functional connectivity in the rest versus conscious sedation contrast (c). There were no regions showing significantly greater functional connectivity in the conscious sedation versus rest contrast. Other details per Figure 2.

Differential effects of sedation on connectivity in the sensory-motor and DMNs. The graph on the left shows the mean and standard deviation of the individual z-scores within the mid-cingulate cluster of the sensory-motor network that showed significantly increased connectivity in the sedated state (see Fig. 2c). The graph on the right shows the mean and standard deviation of the individual z-scores within the posterior cingulate cluster of the DMN that showed significantly decreased connectivity in the sedated state (see Fig. 3c).

Increased sensory-motor network functional connectivity during conscious sedation. The sensory-motor network is shown during rest (a) and during conscious sedation (b). A region in the mid-cingulate showed increased functional connectivity in the conscious sedation versus rest contrast (c). There were no regions showing significantly greater functional connectivity in the rest versus conscious sedation contrast. Numbers below the images refer to coordinates of the Montreal Neurological Institute template. For axial images, the left side of the image corresponds to the right side of the brain (radiologic convention). T-score bar for all three panels is shown in (c).

Automated selection of the default-mode component. Each subject’s default-mode component is selected from among their several independent components (shown here with only three components) based on the two-step process outlined earlier. Each component consists of a spatial map (colored axial images) and its corresponding time series shown beneath it. The color scale indicates the degree to which a given voxel’s time series is correlated with the overall time series of that component (with yellow–red colors indicating a positive correlation and blue colors indicating a negative correlation). First, because resting-state neural networks are driven by low-frequency oscillations, all high-frequency components (component 1 in this example) are removed using a frequency filter. The remaining low-frequency components are scored based on their spatial goodness-of-fit to a standard template of the DMN derived from a separate dataset (template not shown). The component with the highest goodness-of-fit score (component 3 here) is then entered into the group analyses. Note that all voxels of the selected component have z-scores, not just those voxels that fall within the regions defined by the standard template. An identical technique is used to select the sensory-motor network (component 2 in this example) except that the standard template is changed from the DMN to the sensory-motor network.