Figure 2: Assessing moment-to-moment brain state engagement with non-negative least squares regression.

This flowchart illustrates how beta coefficients for each brain state are concatenated across time to create a brain state timeseries. Four recurring brain states were identified using the HCP dataset: fixation, high-cognition, low-cognition and cue/transition (see Figure 3). The representative time points for each HCP-derived state were regressed from each resting-state or task-switching time point of interest in the CNP and SRPBS datasets. The inputs to this regression are one time point of interest from an independent dataset (dependent variable; either resting-state or task-based data), as well as the representative time point from each of the four recurring brain states (independent variable). After the four representative brain states were regressed from the time point of interest. We received four beta coefficients. Each coefficient indicated the contribution of each brain state to the time point of interest. For each state, we can then create a brain state time series by concatenating beta coefficients across time. Two summary measures can be generated using the brain state time series: state engagement variability and relative state engagement.