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Table 2. 
ADHD symptoms and global modularity measures
 rpart.ppart.BF01-Reg.
Whole-brain modularity measures 
 Global modularity 0.11 0.056 0.62 
 Number of modules −0.09 0.150 1.36 
 Average module size 0.08 0.213 1.95 
 Variability in module size −0.02 0.781 3.47 
  
Whole-brain proportions of node types 
 Ultra-peripheral nodes 0.01 0.813 3.78 
 Peripheral nodes 0.06 0.363 2.29 
 Nonhub connector nodes −0.07 0.229 2.30 
 Nonhub kinless nodes −0.10 0.103 0.98 
 Provincial hubs 0.09 0.148 1.33 
 Connector hubs −0.07 0.252 2.56 
 Kinless hubs −0.07 0.255 2.58 
 rpart.ppart.BF01-Reg.
Whole-brain modularity measures 
 Global modularity 0.11 0.056 0.62 
 Number of modules −0.09 0.150 1.36 
 Average module size 0.08 0.213 1.95 
 Variability in module size −0.02 0.781 3.47 
  
Whole-brain proportions of node types 
 Ultra-peripheral nodes 0.01 0.813 3.78 
 Peripheral nodes 0.06 0.363 2.29 
 Nonhub connector nodes −0.07 0.229 2.30 
 Nonhub kinless nodes −0.10 0.103 0.98 
 Provincial hubs 0.09 0.148 1.33 
 Connector hubs −0.07 0.252 2.56 
 Kinless hubs −0.07 0.255 2.58 

Note. rpart., Pearson’s correlation coefficient for the partial correlation controlling for effects of age, sex, handedness, mean FD and FSIQ; ppart., p value of significance for the partial correlation; BF01-Reg., Bayes factor in favor of the null hypothesis (i.e., absence of correlation). Bayes factors were calculated for linear regression models predicting ADHD Index values by the respective whole-brain measure of modular network organization or whole-brain proportions of node types, respectively, whereas effects of age, sex, handedness, mean FD, and FSIQ were controlled.

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