I have a question about interpreting the direction/sign of intercepts of the change score factors. Below you can see the bivariate latent change score model I have analyzed (Imodel-fit was good). PB stands for prosocial behaviour, mPFC_CT stands for the cortical thickness of the medial prefrontal cortex, and T1 and T2 stand for the first and second measurement.
BLCS_PB_mPFC_CT<-'
T2_PB ~ 1*T1_PB # This parameter regresses T2_PB perfectly on T1_PB
dPB1 =~ 1*T2_PB # This defines the latent change score factor as measured perfectly by scores on T2_PB
dPB1 ~ 1 # This estimates the intercept (conditional mean) of the change score
T1_PB ~ 1 # This estimates the intercept (mean) of T1_PB
T2_PB ~ 0*1 # This constrains the intercept of T2_PB to 0
T2_mPFC_CT ~ 1*T1_mPFC_CT # This parameter regresses T2_mPFC_CT perfectly on T1_mPFC_CT
dmPFC_CT1 =~ 1*T2_mPFC_CT # This defines the latent change score factor as measured perfectly by scores on T2_mPFC_CT
T2_mPFC_CT ~ 0*1 # This line constrains the intercept of T2_mPFC_CT to 0
T2_mPFC_CT ~~ 0*T2_mPFC_CT # This fixes the variance of the T1_mPFC_CT to 0
dPB1 ~~ dPB1 # This estimates the variance of the change scores
T1_PB ~~ T1_PB # This estimates the variance of the T1_PB
T2_PB ~~ 0*T2_PB # This fixes the variance of the T2_PB to 0
dmPFC_CT1 ~ 1 # This estimates the intercept (conditional mean) of the change score
T1_mPFC_CT ~ 1 # This estimates the intercept (mean) of T1_mPFC_CT
dmPFC_CT1 ~~ dmPFC_CT1 # This estimates the variance of the change scores
T1_mPFC_CT ~~ T1_mPFC_CT # This estimates the variance of T1_mPFC_CT
dmPFC_CT1~T1_PB+T1_mPFC_CT # This estimates the PB to mPFC_CT coupling parameter and the PB to PB self-feedback
dPB1~T1_mPFC_CT+T1_PB # This estimates the mPFC_CT to PB coupling parameter and the mPFC_CT to mPFC_CT self-feedback
T1_PB ~~ T1_mPFC_CT # This estimates the T1_PB T1_mPFC_CT covariance
dPB1~~dmPFC_CT1 # This estimates the dPB and dmPFC_CT covariance
'
In my output all the intercepts are positive values. However, when calculating change in coritical thickness manually, I noticed that there is a decrease in change in cortical thickness. I do not understand why, according to the lavaan output, the intercept of the change in cortical thickness is positive when the change is actually negative. Notably, the within-domain coupling/self-feedback of cortical thickness (dmPFC_CT1 ~ T1_mPFC_CT) is significant and has a negative value. In addition, I read something about 'Note that if a regression parameter is included, the mean change should be interpreted conditional on the regression path' (kievit et al., 2018). I do not really understand what this 'interpreted conditional on the regression' means, but I guess this is somehow related to my question.
