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Table 3. 

Correlations between Predicted and Observed Behavioral Scores

Predictions from Task-based Functional ConnectivityPredictions from Resting-state Functional Connectivity
Accuracy rs = .62, p = .001* rs = .12, p = .31 
RT variability rs = .63, p = .001* rs = .25, p = .10 
Alerting rs = .28, p = .10 rs = .31, p = .036* 
Orienting rs = .18, p = .23 rs = .19, p = .16 
Executive control rs = .35, p = .042* rs = .05, p = .42 
Predictions from Task-based Functional ConnectivityPredictions from Resting-state Functional Connectivity
Accuracy rs = .62, p = .001* rs = .12, p = .31 
RT variability rs = .63, p = .001* rs = .25, p = .10 
Alerting rs = .28, p = .10 rs = .31, p = .036* 
Orienting rs = .18, p = .23 rs = .19, p = .16 
Executive control rs = .35, p = .042* rs = .05, p = .42 

To further control for potentially confounding effects of motion, we performed Spearman's partial correlations between observed and predicted behavioral scores and motion. Correlations between observed scores and task-based predictions (left column) included maximum displacement, rotation, and mean frame-to-frame displacement during task runs. Correlations between observed scores and resting-state predictions (right column) included these three measures from both task and resting-state runs, because models were trained using task data and tested using rest. Predictions were made with general linear models with two predictors, positive and negative network strength. p values were determined with permutation testing.

*

Significant at p < .05.

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