Preprocessing was performed as previously reported (21) using Statistical Parametric Mapping 12 (SPM12; Preprocessing steps included slice-time correction, realignment, segmentation of structural data, normalization of functional and structural data into the standard stereotactic Montreal Neurological Institute space, and spatial smoothing using a Gaussian kernel of 6-mm full width at half-maximum. For functional data, the three initial volumes were discarded to avoid T1 saturation effects. Motion artifact detection and rejection were performed with the artifact detection toolbox (ART toolbox): An image was defined as an outlier or artifact image if the head displacement in the x, y, or z direction was greater than 2 mm from the previous frame, if the rotational displacement was greater than 0.02 rad from the previous frame, or if the global mean intensity in the image was greater than 3 SDs from the mean image intensity for the entire resting scan. Outliers in the global mean signal intensity and motion were subsequently included as nuisance regressors within the first-level GLM so that the temporal structure of the data would not be disrupted. As a data quality measure, we calculated the number of motion outliers that were detected during preprocessing. The number of motion outlier images was similar across the groups of healthy controls (mean rank, 61), patients in MCS (mean rank, 72), and patients in UWS [mean rank, 74; Kruskal-Wallis test, H(2) = 2.5, P = 0.3], suggesting comparable datasets.

For noise reduction, we modeled the influence of noise as a voxel-specific linear combination of multiple empirically estimated noise sources by deriving principal components from noise ROIs and by including them as nuisance parameters within the GLMs (as implemented in the CONN functional connectivity toolbox, v.16.b). Specifically, the anatomical image for each participant was segmented into white matter (WM), gray matter (GM), and cerebrospinal fluid (CSF) masks (old normalize function, SPM12). To minimize partial voluming with GM, the WM and CSF masks were eroded by one voxel, which resulted in smaller masks than the original segmentations. The eroded WM and CSF masks were then used as noise ROIs. Used time series were from the unsmoothed functional volumes to avoid additional risk of contaminating WM and CSF signals with GM signals. Five principal components of the signals from WM and CSF noise ROIs were removed with regression. A temporal band-pass filter of 0.008 to 0.09 Hz was applied on the time series to restrict the analysis to low-frequency fluctuations. Residual head motion parameters (three rotation and three translation parameters and six parameters representing their first-order temporal derivatives) were further regressed out. For each sphere ROI, time series were extracted by averaging all voxels’ signal within the ROI at a given brain volume. A total of 42 network-based ROIs were selected (table S2). Concerning the positive coupling between ROIs belonging to the default mode network (DMN) and the frontoparietal (FP) network observed in some of our dynamic coordination patterns, we noted that previous articles (42, 43) using the same set of ROIs (as well as similar data preprocessing) also reported positive connectivity between them. Therefore, it is possible that the precise choice of FP ROIs introduced in these articles does not robustly represent the FP/DMN anticorrelations present in the data. Most likely, this is due to the fact that some FP ROIs are located in the proximity of the FP/DMN boundary, and the spatial extent of RSN is known to present important interperson variability, especially in higher-order heteromodal association cortices (44). However, we noted that the DMN ROIs were anticorrelated to other task-positive ROIs, as expected.

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