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Table 4. 

Correlations between Sustained Attention CPM Predictions and ANT Behavior

Predictions from Task-based Functional ConnectivityPredictions from Resting-state Functional Connectivity
Accuracy rs = .49, p = .0024* rs = .09, p = .5844 
RT variability rs = −.68, p = 9.99 × 10−6* rs = −.36, p = .0288* 
Alerting rs = −.28, p = .0899 rs = −.08, p = .6166 
Orienting rs = −.12, p = .4741 rs = −.05, p = .7782 
Executive control rs = −.34, p = .0385* rs = −.16, p = .3244 
Predictions from Task-based Functional ConnectivityPredictions from Resting-state Functional Connectivity
Accuracy rs = .49, p = .0024* rs = .09, p = .5844 
RT variability rs = −.68, p = 9.99 × 10−6* rs = −.36, p = .0288* 
Alerting rs = −.28, p = .0899 rs = −.08, p = .6166 
Orienting rs = −.12, p = .4741 rs = −.05, p = .7782 
Executive control rs = −.34, p = .0385* rs = −.16, p = .3244 

Cells show Spearman's partial correlations between predicted and observed behavioral scores, controlling for head motion. Forty-one novel participants (out of 44) who had not previously participated in Rosenberg, Finn, et al. (2016) were included in this analysis. Negative correlations between RT variability and ANT component scores were expected, given that higher sustained attention CPM predictions correspond to better attention, whereas higher variability and alerting, orienting, and executive control scores correspond to worse attention. *Significant at p < .05.

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