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Table 4. 
Linear Model of Amygdala–Insula Connectivity Dissimilarity between Trustworthy and Untrustworthy Faces Predicting Neutral Face Bias Scores
 Amygdala–Insula Connectivity (No Controls)Amygdala–Insula Connectivity (Controlling for Pattern Similarity)
βSEtpβSEtp
Intercept −.433 .124 −3.498 .001 −.383 .127 −3.028 .004 
Amygdala similaritya —       .725 .762 .952 .346 
Insula similaritya —       −.548 .793 −.691 .493 
Connectivity dissimilarityb .665 .231 2.874 .006 .546 .240 2.280 .027 
R2 .152       .204       
F 8.262       3.754       
AIC 74.629       75.619       
BIC 80.242       84.975       
RMSE .495       .479       
k-fold R2 .411       .403       
k-fold RMSE .489       .490       
 Amygdala–Insula Connectivity (No Controls)Amygdala–Insula Connectivity (Controlling for Pattern Similarity)
βSEtpβSEtp
Intercept −.433 .124 −3.498 .001 −.383 .127 −3.028 .004 
Amygdala similaritya —       .725 .762 .952 .346 
Insula similaritya —       −.548 .793 −.691 .493 
Connectivity dissimilarityb .665 .231 2.874 .006 .546 .240 2.280 .027 
R2 .152       .204       
F 8.262       3.754       
AIC 74.629       75.619       
BIC 80.242       84.975       
RMSE .495       .479       
k-fold R2 .411       .403       
k-fold RMSE .489       .490       

n = 48. AIC = Akaike information criterion, BIC = Bayesian information criterion, RMSE = Root mean square error.

a

Higher values indicate greater pattern similarity between trustworthy and untrustworthy faces.

b

Absolute values, higher values indicate greater connectivity dissimilarity between trustworthy and untrustworthy faces.

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