Load packages

Load in and merge data

clin_dem <- read.csv("/Users/nikki/Desktop/Research/FournierLab/Datafiles/covariates_inc_dep.csv")

rois_se <- read.csv("/Users/nikki/Desktop/Research/FournierLab/Datafiles/ExtractedROI_StroopEffect_52423.csv")
rois_ca <- read.csv("/Users/nikki/Desktop/Research/FournierLab/Datafiles/ExtractedROI_CAEffect_72023.csv")

#merge together and have 2 dfs for the stroop effect (se) and conflict adaptation effect (ca)
jfk_se <- merge(clin_dem,rois_se, by.x="idnum",by.y="Subject",all.x = T)
jfk_ca <- merge(clin_dem,rois_ca, by.x="idnum",by.y="Subject",all.x = T)

#remove unneeded vars and clean up variable names
jfk_se <- jfk_se[,-c(9:43,62)]
oldnames<-colnames(jfk_se)
colnames(jfk_se) <- c(oldnames[1:14],"dACC","sgACC","IFJ","L_dAI","L_pAI","L_vAI","R_dAI","R_pAI","R_vAI","L_amyg","R_amyg","Precuneus","cond")
jfk_ca <- jfk_ca[,-c(9:43,62)]
colnames(jfk_ca) <- c(oldnames[1:14],"dACC","sgACC","IFJ","L_dAI","L_pAI","L_vAI","R_dAI","R_pAI","R_vAI","L_amyg","R_amyg","Precuneus","cond")

Simple visualization of the betas

One example here of the Precuneus within the Stroop effect conditions; can add more if its helpful

#separate the conditions
jfk_se_plot <- jfk_se
jfk_se_plot$cond <- factor(jfk_se_plot$cond,
                         levels=c(-.5,.5),
                         labels=c("con","incon"))

#plot the betas in the rois by condition
ggplot(data = jfk_se_plot, aes(x = cond, y = Precuneus, fill = cond)) +
  geom_violin(alpha = .85) +
    geom_point(alpha = .35) +
  stat_summary(geom = "point", fun = "mean",size = 4, shape = 24) +
  labs(y = "Precuneus betas", x = "condition") 

#could arrange rois in long form and facet wrap in case we wanted to see it all.

Mixed Effect Linear Models: Stroop Effect

1. Unconditional models to sense within-person variance in ROI values

Three are examined here as examples with %s ranging from 13-21% of the variance not explained bw persons

# Left Amygdala Example 1
uncond_model1_lamy <- lmer(L_amyg ~ 1 + (1|idnum), data=jfk_se)
summary(uncond_model1_lamy)

  icc_laymg <- .22/(.22+1.19)  
  icc_laymg # 16% variance 

# dACC Example 2
uncond_model1_dacc <- lmer(dACC ~ 1 + (1|idnum), data=jfk_se)
summary(uncond_model1_dacc)

  icc_dacc <- .33/(.33+1.23) 
  icc_dacc # 21% variance

# Precun Example 3
uncond_model1_precu <- lmer(Precuneus ~ 1 + (1|idnum), data=jfk_se)
summary(uncond_model1_precu)

  icc_precu <- .24/(.24+1.64) 
  icc_precu # 13% variance
tab_model(uncond_model1_lamy)
  L_amyg
Predictors Estimates CI p
(Intercept) 0.14 -0.20 – 0.48 0.417
Random Effects
σ2 0.22
τ00 idnum 1.19
ICC 0.84
N idnum 44
Observations 88
Marginal R2 / Conditional R2 0.000 / 0.844 
tab_model(uncond_model1_dacc)
  dACC
Predictors Estimates CI p
(Intercept) -0.56 -0.91 – -0.21 0.002
Random Effects
σ2 0.33
τ00 idnum 1.23
ICC 0.79
N idnum 44
Observations 88
Marginal R2 / Conditional R2 0.000 / 0.790 
tab_model(uncond_model1_precu)
  Precuneus
Predictors Estimates CI p
(Intercept) -1.36 -1.76 – -0.97 <0.001
Random Effects
σ2 0.24
τ00 idnum 1.64
ICC 0.87
N idnum 44
Observations 88
Marginal R2 / Conditional R2 0.000 / 0.871

2. Assessing the effect of condition on ROI values (including age and sex covariates)

Incongruent is coded 0.5 and Congruent is coded -0.5

cond_model2_lamy <- lmer(L_amyg ~ cond + age_years + Gender + (1|idnum), data=jfk_se)
cond_model2_ramy <- lmer(R_amyg ~ cond + age_years + Gender + (1|idnum), data=jfk_se)
cond_model2_precu<- lmer(Precuneus ~ cond + age_years + Gender + (1|idnum), data=jfk_se)
cond_model2_dacc<- lmer(dACC ~ cond + age_years + Gender + (1|idnum), data=jfk_se)
cond_model2_sgacc <- lmer(sgACC ~ cond + age_years + Gender + (1|idnum), data=jfk_se)
cond_model2_lai <- lmer(L_dAI ~ cond + age_years + Gender + (1|idnum), data=jfk_se)
cond_model2_rai <- lmer(R_dAI ~ cond + age_years + Gender + (1|idnum), data=jfk_se)
tab_model(cond_model2_lamy)
  L_amyg
Predictors Estimates CI p
(Intercept) 0.01 -1.62 – 1.65 0.986
cond -0.20 -0.39 – -0.01 0.040
age_years 0.01 -0.05 – 0.06 0.863
Gender -0.02 -0.72 – 0.69 0.963
Random Effects
σ2 0.21
τ00 idnum 1.26
ICC 0.86
N idnum 44
Observations 88
Marginal R2 / Conditional R2 0.007 / 0.861 
tab_model(cond_model2_ramy)
  R_amyg
Predictors Estimates CI p
(Intercept) 0.23 -1.45 – 1.91 0.793
cond -0.09 -0.21 – 0.04 0.169
age_years -0.00 -0.06 – 0.06 0.909
Gender 0.11 -0.61 – 0.83 0.758
Random Effects
σ2 0.09
τ00 idnum 1.39
ICC 0.94
N idnum 44
Observations 88
Marginal R2 / Conditional R2 0.004 / 0.942 
tab_model(cond_model2_precu)
  Precuneus
Predictors Estimates CI p
(Intercept) -1.94 -3.77 – -0.12 0.037
cond -0.31 -0.50 – -0.13 0.001
age_years 0.04 -0.03 – 0.10 0.269
Gender -0.64 -1.42 – 0.14 0.110
Random Effects
σ2 0.20
τ00 idnum 1.59
ICC 0.89
N idnum 44
Observations 88
Marginal R2 / Conditional R2 0.091 / 0.899 
tab_model(cond_model2_dacc)
  dACC
Predictors Estimates CI p
(Intercept) -0.73 -2.38 – 0.92 0.387
cond 0.02 -0.22 – 0.26 0.892
age_years 0.02 -0.04 – 0.08 0.558
Gender -0.49 -1.20 – 0.22 0.175
Random Effects
σ2 0.33
τ00 idnum 1.21
ICC 0.79
N idnum 44
Observations 88
Marginal R2 / Conditional R2 0.044 / 0.795 
tab_model(cond_model2_sgacc)
  sgACC
Predictors Estimates CI p
(Intercept) -1.85 -3.35 – -0.35 0.016
cond -0.33 -0.55 – -0.12 0.002
age_years 0.03 -0.02 – 0.08 0.291
Gender -0.32 -0.96 – 0.33 0.335
Random Effects
σ2 0.26
τ00 idnum 1.02
ICC 0.80
N idnum 44
Observations 88
Marginal R2 / Conditional R2 0.062 / 0.812 
tab_model(cond_model2_lai)
  L_dAI
Predictors Estimates CI p
(Intercept) 0.88 -0.82 – 2.58 0.311
cond 0.06 -0.14 – 0.26 0.562
age_years -0.01 -0.08 – 0.05 0.636
Gender 0.13 -0.60 – 0.86 0.731
Random Effects
σ2 0.23
τ00 idnum 1.36
ICC 0.85
N idnum 44
Observations 88
Marginal R2 / Conditional R2 0.008 / 0.855 
tab_model(cond_model2_rai)
  R_dAI
Predictors Estimates CI p
(Intercept) 0.03 -1.47 – 1.54 0.966
cond 0.25 0.04 – 0.45 0.019
age_years 0.01 -0.04 – 0.07 0.624
Gender 0.08 -0.57 – 0.72 0.816
Random Effects
σ2 0.24
τ00 idnum 1.03
ICC 0.81
N idnum 44
Observations 88
Marginal R2 / Conditional R2 0.018 / 0.811

3. Testing the primary model with interaction between Self consciousness and Condition

cond_model3_lamy <- lmer(L_amyg ~ cond*N_Bifact_4SC_z + age_years + Gender + (1|idnum), data=jfk_se)
## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
## Model failed to converge with max|grad| = 0.00425047 (tol = 0.002, component 1)
cond_model3_ramy <- lmer(R_amyg ~ cond*N_Bifact_4SC_z  + age_years + Gender + (1|idnum), data=jfk_se)
cond_model3_precu<- lmer(Precuneus ~ cond*N_Bifact_4SC_z + age_years + Gender + (1|idnum), data=jfk_se)
cond_model3_dacc<- lmer(dACC ~ cond*N_Bifact_4SC_z + age_years + Gender + (1|idnum), data=jfk_se)
cond_model3_sgacc <- lmer(sgACC ~ cond*N_Bifact_4SC_z + age_years + Gender + (1|idnum), data=jfk_se)
cond_model3_lai <- lmer(L_dAI ~ cond*N_Bifact_4SC_z + age_years + Gender + (1|idnum), data=jfk_se)
cond_model3_rai <- lmer(R_dAI ~ cond*N_Bifact_4SC_z + age_years + Gender + (1|idnum), data=jfk_se)
tab_model(cond_model3_lamy)
  L_amyg
Predictors Estimates CI p
(Intercept) -0.12 -1.80 – 1.56 0.888
cond -0.19 -0.38 – 0.00 0.054
N_Bifact_4SC_z -0.14 -0.50 – 0.22 0.444
age_years 0.01 -0.05 – 0.07 0.759
Gender 0.06 -0.67 – 0.79 0.870
cond * N_Bifact_4SC_z -0.06 -0.25 – 0.13 0.517
Random Effects
σ2 0.21
τ00 idnum 1.27
ICC 0.86
N idnum 44
Observations 88
Marginal R2 / Conditional R2 0.020 / 0.862 
tab_model(cond_model3_ramy)
  R_amyg
Predictors Estimates CI p
(Intercept) 0.18 -1.56 – 1.92 0.841
cond -0.09 -0.21 – 0.04 0.168
N_Bifact_4SC_z -0.05 -0.42 – 0.32 0.796
age_years -0.00 -0.06 – 0.06 0.948
Gender 0.14 -0.62 – 0.90 0.717
cond * N_Bifact_4SC_z 0.01 -0.11 – 0.14 0.824
Random Effects
σ2 0.09
τ00 idnum 1.42
ICC 0.94
N idnum 44
Observations 88
Marginal R2 / Conditional R2 0.005 / 0.942 
tab_model(cond_model3_precu)
  Precuneus
Predictors Estimates CI p
(Intercept) -2.40 -4.16 – -0.64 0.008
cond -0.30 -0.49 – -0.11 0.002
N_Bifact_4SC_z -0.47 -0.85 – -0.09 0.014
age_years 0.05 -0.01 – 0.11 0.111
Gender -0.38 -1.14 – 0.39 0.335
cond * N_Bifact_4SC_z -0.08 -0.26 – 0.11 0.416
Random Effects
σ2 0.20
τ00 idnum 1.41
ICC 0.88
N idnum 44
Observations 88
Marginal R2 / Conditional R2 0.194 / 0.899 
tab_model(cond_model3_dacc)
  dACC
Predictors Estimates CI p
(Intercept) -1.04 -2.68 – 0.60 0.213
cond 0.01 -0.23 – 0.26 0.922
N_Bifact_4SC_z -0.33 -0.68 – 0.02 0.068
age_years 0.03 -0.03 – 0.09 0.357
Gender -0.31 -1.02 – 0.41 0.397
cond * N_Bifact_4SC_z 0.03 -0.21 – 0.27 0.806
Random Effects
σ2 0.34
τ00 idnum 1.14
ICC 0.77
N idnum 44
Observations 88
Marginal R2 / Conditional R2 0.104 / 0.794 
tab_model(cond_model3_sgacc)
  sgACC
Predictors Estimates CI p
(Intercept) -2.11 -3.62 – -0.61 0.006
cond -0.32 -0.53 – -0.11 0.003
N_Bifact_4SC_z -0.27 -0.60 – 0.05 0.096
age_years 0.04 -0.02 – 0.09 0.173
Gender -0.17 -0.82 – 0.49 0.620
cond * N_Bifact_4SC_z -0.10 -0.31 – 0.11 0.365
Random Effects
σ2 0.26
τ00 idnum 0.97
ICC 0.79
N idnum 44
Observations 88
Marginal R2 / Conditional R2 0.114 / 0.815 
tab_model(cond_model3_lai)
  L_dAI
Predictors Estimates CI p
(Intercept) 0.70 -1.04 – 2.44 0.429
cond 0.04 -0.16 – 0.25 0.672
N_Bifact_4SC_z -0.18 -0.56 – 0.19 0.334
age_years -0.01 -0.07 – 0.05 0.771
Gender 0.23 -0.53 – 0.99 0.553
cond * N_Bifact_4SC_z 0.11 -0.09 – 0.31 0.284
Random Effects
σ2 0.23
τ00 idnum 1.36
ICC 0.85
N idnum 44
Observations 88
Marginal R2 / Conditional R2 0.029 / 0.859 
tab_model(cond_model3_rai)
  R_dAI
Predictors Estimates CI p
(Intercept) -0.16 -1.70 – 1.37 0.834
cond 0.23 0.02 – 0.44 0.028
N_Bifact_4SC_z -0.21 -0.53 – 0.12 0.222
age_years 0.02 -0.04 – 0.07 0.480
Gender 0.19 -0.48 – 0.86 0.577
cond * N_Bifact_4SC_z 0.09 -0.11 – 0.30 0.378
Random Effects
σ2 0.25
τ00 idnum 1.02
ICC 0.81
N idnum 44
Observations 88
Marginal R2 / Conditional R2 0.049 / 0.814

4. Testing the primary model with interaction between Self consciousness and Condition + other N factors

cond_model4_lamy <- lmer(L_amyg ~ cond*N_Bifact_4SC_z + N_Bifact_G_z + N_Bifact_2AH_z + age_years + Gender + (1|idnum), data=jfk_se)
cond_model4_ramy <- lmer(R_amyg ~ cond*N_Bifact_4SC_z + N_Bifact_G_z + N_Bifact_2AH_z + age_years + Gender + (1|idnum), data=jfk_se)
cond_model4_precu<- lmer(Precuneus ~ cond*N_Bifact_4SC_z + N_Bifact_G_z + N_Bifact_2AH_z + age_years + Gender + (1|idnum), data=jfk_se)
cond_model4_dacc<- lmer(dACC ~ cond*N_Bifact_4SC_z + N_Bifact_G_z + N_Bifact_2AH_z + age_years + Gender + (1|idnum), data=jfk_se)
cond_model4_sgacc <- lmer(sgACC ~ cond*N_Bifact_4SC_z + N_Bifact_G_z + N_Bifact_2AH_z + age_years + Gender + (1|idnum), data=jfk_se)
cond_model4_lai <- lmer(L_dAI ~ cond*N_Bifact_4SC_z + N_Bifact_G_z + N_Bifact_2AH_z + age_years + Gender + (1|idnum), data=jfk_se)
cond_model4_rai <- lmer(R_dAI ~ cond*N_Bifact_4SC_z + N_Bifact_G_z + N_Bifact_2AH_z + age_years + Gender + (1|idnum), data=jfk_se)
tab_model(cond_model4_lamy)
  L_amyg
Predictors Estimates CI p
(Intercept) -0.12 -1.92 – 1.68 0.897
cond -0.19 -0.38 – 0.00 0.054
N_Bifact_4SC_z -0.18 -0.65 – 0.29 0.460
N_Bifact_G_z -0.05 -0.96 – 0.87 0.921
N_Bifact_2AH_z -0.09 -0.45 – 0.27 0.640
age_years 0.01 -0.05 – 0.07 0.764
Gender 0.09 -0.67 – 0.85 0.818
cond * N_Bifact_4SC_z -0.06 -0.25 – 0.13 0.517
Random Effects
σ2 0.21
τ00 idnum 1.34
ICC 0.87
N idnum 44
Observations 88
Marginal R2 / Conditional R2 0.024 / 0.869 
tab_model(cond_model4_ramy)
  R_amyg
Predictors Estimates CI p
(Intercept) 0.35 -1.46 – 2.16 0.705
cond -0.09 -0.21 – 0.04 0.168
N_Bifact_4SC_z 0.15 -0.33 – 0.62 0.546
N_Bifact_G_z -0.16 -1.08 – 0.76 0.733
N_Bifact_2AH_z 0.30 -0.07 – 0.66 0.109
age_years -0.00 -0.07 – 0.06 0.919
Gender 0.07 -0.69 – 0.83 0.863
cond * N_Bifact_4SC_z 0.01 -0.11 – 0.14 0.824
Random Effects
σ2 0.09
τ00 idnum 1.40
ICC 0.94
N idnum 44
Observations 88
Marginal R2 / Conditional R2 0.059 / 0.944 
tab_model(cond_model4_precu)
  Precuneus
Predictors Estimates CI p
(Intercept) -2.41 -4.28 – -0.53 0.012
cond -0.30 -0.49 – -0.11 0.002
N_Bifact_4SC_z -0.41 -0.90 – 0.08 0.103
N_Bifact_G_z 0.09 -0.86 – 1.04 0.849
N_Bifact_2AH_z 0.15 -0.22 – 0.53 0.423
age_years 0.05 -0.01 – 0.12 0.118
Gender -0.43 -1.22 – 0.36 0.288
cond * N_Bifact_4SC_z -0.08 -0.26 – 0.11 0.416
Random Effects
σ2 0.20
τ00 idnum 1.46
ICC 0.88
N idnum 44
Observations 88
Marginal R2 / Conditional R2 0.199 / 0.903 
tab_model(cond_model4_dacc)
  dACC
Predictors Estimates CI p
(Intercept) -1.44 -3.14 – 0.25 0.095
cond 0.01 -0.23 – 0.26 0.922
N_Bifact_4SC_z -0.46 -0.90 – -0.01 0.044
N_Bifact_G_z 0.74 -0.12 – 1.60 0.094
N_Bifact_2AH_z 0.05 -0.29 – 0.39 0.765
age_years 0.03 -0.03 – 0.09 0.311
Gender -0.38 -1.09 – 0.34 0.301
cond * N_Bifact_4SC_z 0.03 -0.21 – 0.27 0.806
Random Effects
σ2 0.34
τ00 idnum 1.10
ICC 0.76
N idnum 44
Observations 88
Marginal R2 / Conditional R2 0.153 / 0.800 
   plot_model(cond_model4_dacc, type = "pred", 
          terms = c( "cond","N_Bifact_4SC_z[-1.5,1.5]")) 

tab_model(cond_model4_sgacc)
  sgACC
Predictors Estimates CI p
(Intercept) -2.28 -3.88 – -0.67 0.005
cond -0.32 -0.53 – -0.11 0.003
N_Bifact_4SC_z -0.34 -0.76 – 0.08 0.113
N_Bifact_G_z 0.28 -0.53 – 1.10 0.496
N_Bifact_2AH_z -0.01 -0.33 – 0.31 0.960
age_years 0.04 -0.02 – 0.09 0.171
Gender -0.18 -0.86 – 0.49 0.595
cond * N_Bifact_4SC_z -0.10 -0.31 – 0.11 0.365
Random Effects
σ2 0.26
τ00 idnum 1.01
ICC 0.80
N idnum 44
Observations 88
Marginal R2 / Conditional R2 0.118 / 0.822 
tab_model(cond_model4_lai)
  L_dAI
Predictors Estimates CI p
(Intercept) 0.13 -1.62 – 1.89 0.881
cond 0.04 -0.16 – 0.25 0.672
N_Bifact_4SC_z -0.40 -0.86 – 0.07 0.093
N_Bifact_G_z 1.01 0.12 – 1.91 0.026
N_Bifact_2AH_z 0.01 -0.34 – 0.36 0.947
age_years -0.01 -0.07 – 0.05 0.849
Gender 0.15 -0.59 – 0.89 0.684
cond * N_Bifact_4SC_z 0.11 -0.09 – 0.31 0.284
Random Effects
σ2 0.23
τ00 idnum 1.25
ICC 0.84
N idnum 44
Observations 88
Marginal R2 / Conditional R2 0.123 / 0.863 
tab_model(cond_model4_rai)
  R_dAI
Predictors Estimates CI p
(Intercept) -0.46 -2.07 – 1.15 0.578
cond 0.23 0.02 – 0.44 0.028
N_Bifact_4SC_z -0.31 -0.73 – 0.11 0.149
N_Bifact_G_z 0.53 -0.29 – 1.34 0.208
N_Bifact_2AH_z 0.01 -0.31 – 0.34 0.939
age_years 0.02 -0.03 – 0.08 0.445
Gender 0.15 -0.53 – 0.83 0.668
cond * N_Bifact_4SC_z 0.09 -0.11 – 0.30 0.378
Random Effects
σ2 0.25
τ00 idnum 1.03
ICC 0.81
N idnum 44
Observations 88
Marginal R2 / Conditional R2 0.078 / 0.822

5. Testing the primary model with the 3 possible N/cond interactions

cond_model5_lamy <- lmer(L_amyg ~ cond*N_Bifact_4SC_z + cond*N_Bifact_G_z + cond*N_Bifact_2AH_z + age_years + Gender + (1|idnum), data=jfk_se)
cond_model5_ramy <- lmer(R_amyg ~ cond*N_Bifact_4SC_z + cond*N_Bifact_G_z + cond*N_Bifact_2AH_z + age_years + Gender + (1|idnum), data=jfk_se)
cond_model5_precu<- lmer(Precuneus ~ cond*N_Bifact_4SC_z + cond*N_Bifact_G_z + cond*N_Bifact_2AH_z + age_years + Gender + (1|idnum), data=jfk_se)
cond_model5_dacc<- lmer(dACC ~ cond*N_Bifact_4SC_z + cond*N_Bifact_G_z + cond*N_Bifact_2AH_z + age_years + Gender + (1|idnum), data=jfk_se)
cond_model5_sgacc <- lmer(sgACC ~ cond*N_Bifact_4SC_z + cond*N_Bifact_G_z + cond*N_Bifact_2AH_z + age_years + Gender + (1|idnum), data=jfk_se)
cond_model5_lai <- lmer(L_dAI ~ cond*N_Bifact_4SC_z + cond*N_Bifact_G_z + cond*N_Bifact_2AH_z + age_years + Gender + (1|idnum), data=jfk_se)
cond_model5_rai <- lmer(R_dAI ~ cond*N_Bifact_4SC_z + cond*N_Bifact_G_z + cond*N_Bifact_2AH_z + age_years + Gender + (1|idnum), data=jfk_se)
tab_model(cond_model5_lamy)
  L_amyg
Predictors Estimates CI p
(Intercept) -0.12 -1.92 – 1.68 0.897
cond -0.19 -0.52 – 0.14 0.249
N_Bifact_4SC_z -0.18 -0.65 – 0.29 0.460
N_Bifact_G_z -0.05 -0.96 – 0.87 0.921
N_Bifact_2AH_z -0.09 -0.45 – 0.27 0.640
age_years 0.01 -0.05 – 0.07 0.764
Gender 0.09 -0.67 – 0.85 0.818
cond * N_Bifact_4SC_z -0.06 -0.31 – 0.19 0.639
cond * N_Bifact_G_z 0.01 -0.49 – 0.51 0.977
cond * N_Bifact_2AH_z 0.01 -0.19 – 0.21 0.932
Random Effects
σ2 0.22
τ00 idnum 1.33
ICC 0.86
N idnum 44
Observations 88
Marginal R2 / Conditional R2 0.024 / 0.862 
tab_model(cond_model5_ramy)
  R_amyg
Predictors Estimates CI p
(Intercept) 0.35 -1.46 – 2.16 0.705
cond -0.07 -0.28 – 0.15 0.546
N_Bifact_4SC_z 0.15 -0.33 – 0.62 0.546
N_Bifact_G_z -0.16 -1.08 – 0.76 0.733
N_Bifact_2AH_z 0.30 -0.07 – 0.66 0.109
age_years -0.00 -0.07 – 0.06 0.919
Gender 0.07 -0.69 – 0.83 0.863
cond * N_Bifact_4SC_z 0.03 -0.13 – 0.19 0.698
cond * N_Bifact_G_z -0.04 -0.37 – 0.28 0.803
cond * N_Bifact_2AH_z 0.02 -0.11 – 0.15 0.786
Random Effects
σ2 0.09
τ00 idnum 1.40
ICC 0.94
N idnum 44
Observations 88
Marginal R2 / Conditional R2 0.059 / 0.942 
tab_model(cond_model5_precu)
  Precuneus
Predictors Estimates CI p
(Intercept) -2.41 -4.28 – -0.53 0.012
cond -0.35 -0.66 – -0.04 0.027
N_Bifact_4SC_z -0.41 -0.90 – 0.08 0.103
N_Bifact_G_z 0.09 -0.86 – 1.04 0.849
N_Bifact_2AH_z 0.15 -0.22 – 0.53 0.423
age_years 0.05 -0.01 – 0.12 0.118
Gender -0.43 -1.22 – 0.36 0.288
cond * N_Bifact_4SC_z -0.03 -0.27 – 0.21 0.798
cond * N_Bifact_G_z 0.11 -0.37 – 0.59 0.650
cond * N_Bifact_2AH_z 0.14 -0.05 – 0.33 0.148
Random Effects
σ2 0.20
τ00 idnum 1.46
ICC 0.88
N idnum 44
Observations 88
Marginal R2 / Conditional R2 0.202 / 0.904 
tab_model(cond_model5_dacc)
  dACC
Predictors Estimates CI p
(Intercept) -1.44 -3.14 – 0.25 0.095
cond -0.38 -0.76 – 0.00 0.051
N_Bifact_4SC_z -0.46 -0.90 – -0.01 0.044
N_Bifact_G_z 0.74 -0.12 – 1.60 0.094
N_Bifact_2AH_z 0.05 -0.29 – 0.39 0.765
age_years 0.03 -0.03 – 0.09 0.311
Gender -0.38 -1.09 – 0.34 0.301
cond * N_Bifact_4SC_z -0.09 -0.38 – 0.20 0.547
cond * N_Bifact_G_z 0.77 0.18 – 1.36 0.010
cond * N_Bifact_2AH_z 0.10 -0.13 – 0.33 0.404
Random Effects
σ2 0.30
τ00 idnum 1.12
ICC 0.79
N idnum 44
Observations 88
Marginal R2 / Conditional R2 0.169 / 0.826 
tab_model(cond_model5_sgacc)
  sgACC
Predictors Estimates CI p
(Intercept) -2.28 -3.88 – -0.67 0.005
cond -0.57 -0.92 – -0.23 0.001
N_Bifact_4SC_z -0.34 -0.76 – 0.08 0.113
N_Bifact_G_z 0.28 -0.53 – 1.10 0.496
N_Bifact_2AH_z -0.01 -0.33 – 0.31 0.960
age_years 0.04 -0.02 – 0.09 0.171
Gender -0.18 -0.86 – 0.49 0.595
cond * N_Bifact_4SC_z -0.16 -0.42 – 0.10 0.231
cond * N_Bifact_G_z 0.50 -0.03 – 1.03 0.063
cond * N_Bifact_2AH_z 0.09 -0.11 – 0.30 0.384
Random Effects
σ2 0.24
τ00 idnum 1.02
ICC 0.81
N idnum 44
Observations 88
Marginal R2 / Conditional R2 0.126 / 0.833 
tab_model(cond_model5_lai)
  L_dAI
Predictors Estimates CI p
(Intercept) 0.13 -1.62 – 1.89 0.881
cond -0.05 -0.39 – 0.28 0.751
N_Bifact_4SC_z -0.40 -0.86 – 0.07 0.093
N_Bifact_G_z 1.01 0.12 – 1.91 0.026
N_Bifact_2AH_z 0.01 -0.34 – 0.36 0.947
age_years -0.01 -0.07 – 0.05 0.849
Gender 0.15 -0.59 – 0.89 0.684
cond * N_Bifact_4SC_z 0.12 -0.14 – 0.38 0.371
cond * N_Bifact_G_z 0.20 -0.31 – 0.72 0.443
cond * N_Bifact_2AH_z 0.11 -0.10 – 0.31 0.309
Random Effects
σ2 0.23
τ00 idnum 1.25
ICC 0.84
N idnum 44
Observations 88
Marginal R2 / Conditional R2 0.125 / 0.863 
tab_model(cond_model5_rai)
  R_dAI
Predictors Estimates CI p
(Intercept) -0.46 -2.07 – 1.15 0.578
cond 0.15 -0.21 – 0.50 0.413
N_Bifact_4SC_z -0.31 -0.73 – 0.11 0.149
N_Bifact_G_z 0.53 -0.29 – 1.34 0.208
N_Bifact_2AH_z 0.01 -0.31 – 0.34 0.939
age_years 0.02 -0.03 – 0.08 0.445
Gender 0.15 -0.53 – 0.83 0.668
cond * N_Bifact_4SC_z 0.07 -0.20 – 0.34 0.634
cond * N_Bifact_G_z 0.17 -0.37 – 0.71 0.541
cond * N_Bifact_2AH_z 0.02 -0.19 – 0.23 0.846
Random Effects
σ2 0.26
τ00 idnum 1.02
ICC 0.80
N idnum 44
Observations 88
Marginal R2 / Conditional R2 0.079 / 0.816

FDR Correction:

this is collecting all p’s from model [4] for each of the 7 ROIs

Specifically examining the 3 personality main effects and 1 int per model (4x7=28 ps)

#pull the p values from each ROI model
sum_model4_lamy_se <- summary(cond_model4_lamy)
model4_lamy_se_ps <- sum_model4_lamy_se$coefficients[c(3:5,8),5]

sum_model4_ramy_se <- summary(cond_model4_ramy)
model4_ramy_se_ps <- sum_model4_ramy_se$coefficients[c(3:5,8),5]

sum_model4_precu_se <- summary(cond_model4_precu)
model4_precu_se_ps <- sum_model4_precu_se$coefficients[c(3:5,8),5]

sum_model4_dacc_se <- summary(cond_model4_dacc)
model4_dacc_se_ps <- sum_model4_dacc_se$coefficients[c(3:5,8),5]

sum_model4_sgacc_se <- summary(cond_model4_sgacc)
model4_sgacc_se_ps <- sum_model4_sgacc_se$coefficients[c(3:5,8),5]

sum_model4_lai_se <- summary(cond_model4_lai)
model4_lai_se_ps <- sum_model4_lai_se$coefficients[c(3:5,8),5]

sum_model4_rai_se <- summary(cond_model4_rai)
model4_rai_se_ps <- sum_model4_rai_se$coefficients[c(3:5,8),5]

#concat the p values & apply the p.adjust function which uses FDR correction
model4_se_allps <- c(model4_lamy_se_ps,model4_ramy_se_ps,model4_precu_se_ps, model4_dacc_se_ps, model4_sgacc_se_ps,model4_lai_se_ps,model4_rai_se_ps)
model4_se_allps_fdr <- p.adjust(model4_se_allps, method = "fdr")

#Show first the significant uncorrected ps (ignore the labels here, or at least none of them are SC or cond:SC)
old_sig <- which(model4_se_allps < 0.05)
model4_se_allps[old_sig]
## N_Bifact_G_z 
##   0.03186147
#Show corrected values -- there are none
new_sig <- which(model4_se_allps_fdr < 0.05)
model4_se_allps[new_sig]
## named numeric(0)

Mixed Effect Linear Models: Conflict Adaptation Effect

Skipping unconditional models and going to 2. Assessing the effect of condition on ROI values (including age and sex covariates)

Incon->Incon is coded 0.5 and Con->Incon is coded -0.5

cond_model2_lamy_ca <- lmer(L_amyg ~ cond + age_years + Gender + (1|idnum), data=jfk_ca)
cond_model2_ramy_ca <- lmer(R_amyg ~ cond + age_years + Gender + (1|idnum), data=jfk_ca)
cond_model2_precu_ca<- lmer(Precuneus ~ cond + age_years + Gender + (1|idnum), data=jfk_ca)
cond_model2_dacc_ca<- lmer(dACC ~ cond + age_years + Gender + (1|idnum), data=jfk_ca)
cond_model2_sgacc_ca <- lmer(sgACC ~ cond + age_years + Gender + (1|idnum), data=jfk_ca)
cond_model2_lai_ca <- lmer(L_dAI ~ cond + age_years + Gender + (1|idnum), data=jfk_ca)
cond_model2_rai_ca <- lmer(R_dAI ~ cond + age_years + Gender + (1|idnum), data=jfk_ca)
tab_model(cond_model2_lamy_ca)
  L_amyg
Predictors Estimates CI p
(Intercept) -0.18 -1.90 – 1.53 0.834
cond 0.03 -0.17 – 0.23 0.761
age_years 0.01 -0.05 – 0.07 0.713
Gender -0.13 -0.87 – 0.61 0.729
Random Effects
σ2 0.23
τ00 idnum 1.38
ICC 0.86
N idnum 44
Observations 88
Marginal R2 / Conditional R2 0.006 / 0.861 
tab_model(cond_model2_ramy_ca)
  R_amyg
Predictors Estimates CI p
(Intercept) 0.09 -1.63 – 1.80 0.920
cond -0.06 -0.26 – 0.14 0.553
age_years 0.00 -0.06 – 0.06 0.969
Gender 0.06 -0.67 – 0.80 0.866
Random Effects
σ2 0.23
τ00 idnum 1.38
ICC 0.85
N idnum 44
Observations 88
Marginal R2 / Conditional R2 0.001 / 0.855 
tab_model(cond_model2_precu_ca)
  Precuneus
Predictors Estimates CI p
(Intercept) -2.21 -4.14 – -0.28 0.025
cond 0.34 0.12 – 0.57 0.003
age_years 0.05 -0.02 – 0.12 0.201
Gender -0.82 -1.65 – 0.00 0.051
Random Effects
σ2 0.29
τ00 idnum 1.75
ICC 0.86
N idnum 44
Observations 88
Marginal R2 / Conditional R2 0.119 / 0.875 
tab_model(cond_model2_dacc_ca)
  dACC
Predictors Estimates CI p
(Intercept) -0.52 -2.39 – 1.34 0.581
cond 0.14 -0.09 – 0.38 0.230
age_years 0.01 -0.06 – 0.08 0.732
Gender -0.55 -1.35 – 0.24 0.174
Random Effects
σ2 0.32
τ00 idnum 1.61
ICC 0.84
N idnum 44
Observations 88
Marginal R2 / Conditional R2 0.043 / 0.843 
tab_model(cond_model2_sgacc_ca)
  sgACC
Predictors Estimates CI p
(Intercept) -1.93 -3.60 – -0.27 0.023
cond 0.28 0.00 – 0.56 0.049
age_years 0.03 -0.03 – 0.09 0.327
Gender -0.50 -1.21 – 0.21 0.171
Random Effects
σ2 0.45
τ00 idnum 1.18
ICC 0.73
N idnum 44
Observations 88
Marginal R2 / Conditional R2 0.066 / 0.743 
tab_model(cond_model2_lai_ca)
  L_dAI
Predictors Estimates CI p
(Intercept) 1.00 -0.78 – 2.77 0.272
cond -0.07 -0.29 – 0.14 0.506
age_years -0.02 -0.08 – 0.05 0.621
Gender 0.04 -0.72 – 0.80 0.921
Random Effects
σ2 0.28
τ00 idnum 1.47
ICC 0.84
N idnum 44
Observations 88
Marginal R2 / Conditional R2 0.006 / 0.843 
tab_model(cond_model2_rai_ca)
  R_dAI
Predictors Estimates CI p
(Intercept) 0.43 -0.99 – 1.84 0.557
cond -0.04 -0.28 – 0.19 0.710
age_years 0.00 -0.05 – 0.05 0.886
Gender 0.05 -0.55 – 0.66 0.861
Random Effects
σ2 0.32
τ00 idnum 0.86
ICC 0.73
N idnum 44
Observations 88
Marginal R2 / Conditional R2 0.001 / 0.731

3. Testing the primary model with interaction between Self consciousness and Condition

cond_model3_lamy_ca <- lmer(L_amyg ~ cond*N_Bifact_4SC_z + age_years + Gender + (1|idnum), data=jfk_ca)
cond_model3_ramy_ca <- lmer(R_amyg ~ cond*N_Bifact_4SC_z  + age_years + Gender + (1|idnum), data=jfk_ca)
cond_model3_precu_ca<- lmer(Precuneus ~ cond*N_Bifact_4SC_z + age_years + Gender + (1|idnum), data=jfk_ca)
cond_model3_dacc_ca<- lmer(dACC ~ cond*N_Bifact_4SC_z + age_years + Gender + (1|idnum), data=jfk_ca)
cond_model3_sgacc_ca <- lmer(sgACC ~ cond*N_Bifact_4SC_z + age_years + Gender + (1|idnum), data=jfk_ca)
cond_model3_lai_ca <- lmer(L_dAI ~ cond*N_Bifact_4SC_z + age_years + Gender + (1|idnum), data=jfk_ca)
cond_model3_rai_ca <- lmer(R_dAI ~ cond*N_Bifact_4SC_z + age_years + Gender + (1|idnum), data=jfk_ca)
tab_model(cond_model3_lamy_ca)
  L_amyg
Predictors Estimates CI p
(Intercept) -0.34 -2.10 – 1.42 0.702
cond 0.02 -0.18 – 0.22 0.837
N_Bifact_4SC_z -0.17 -0.54 – 0.21 0.388
age_years 0.02 -0.05 – 0.08 0.605
Gender -0.04 -0.81 – 0.73 0.922
cond * N_Bifact_4SC_z 0.07 -0.13 – 0.26 0.510
Random Effects
σ2 0.23
τ00 idnum 1.39
ICC 0.86
N idnum 44
Observations 88
Marginal R2 / Conditional R2 0.022 / 0.862 
tab_model(cond_model3_ramy_ca)
  R_amyg
Predictors Estimates CI p
(Intercept) 0.05 -1.72 – 1.82 0.956
cond -0.10 -0.29 – 0.09 0.300
N_Bifact_4SC_z -0.04 -0.42 – 0.34 0.839
age_years 0.00 -0.06 – 0.07 0.940
Gender 0.09 -0.69 – 0.86 0.829
cond * N_Bifact_4SC_z 0.27 0.08 – 0.45 0.005
Random Effects
σ2 0.20
τ00 idnum 1.43
ICC 0.88
N idnum 44
Observations 88
Marginal R2 / Conditional R2 0.013 / 0.878 
tab_model(cond_model3_precu_ca)
  Precuneus
Predictors Estimates CI p
(Intercept) -2.69 -4.56 – -0.82 0.005
cond 0.35 0.12 – 0.58 0.003
N_Bifact_4SC_z -0.50 -0.90 – -0.10 0.015
age_years 0.06 -0.01 – 0.13 0.077
Gender -0.55 -1.37 – 0.26 0.185
cond * N_Bifact_4SC_z -0.03 -0.25 – 0.20 0.827
Random Effects
σ2 0.30
τ00 idnum 1.55
ICC 0.84
N idnum 44
Observations 88
Marginal R2 / Conditional R2 0.216 / 0.874 
 plot_model(cond_model3_precu_ca, type = "pred", 
          terms = c( "cond","N_Bifact_4SC_z[-1.5,1.5]")) 

tab_model(cond_model3_dacc_ca)
  dACC
Predictors Estimates CI p
(Intercept) -0.81 -2.69 – 1.07 0.400
cond 0.14 -0.10 – 0.38 0.250
N_Bifact_4SC_z -0.30 -0.70 – 0.11 0.152
age_years 0.02 -0.05 – 0.09 0.549
Gender -0.39 -1.21 – 0.43 0.349
cond * N_Bifact_4SC_z 0.02 -0.21 – 0.26 0.862
Random Effects
σ2 0.32
τ00 idnum 1.56
ICC 0.83
N idnum 44
Observations 88
Marginal R2 / Conditional R2 0.082 / 0.842 
tab_model(cond_model3_sgacc_ca)
  sgACC
Predictors Estimates CI p
(Intercept) -2.22 -3.89 – -0.56 0.009
cond 0.26 -0.02 – 0.55 0.068
N_Bifact_4SC_z -0.30 -0.66 – 0.05 0.097
age_years 0.04 -0.02 – 0.10 0.199
Gender -0.33 -1.06 – 0.39 0.371
cond * N_Bifact_4SC_z 0.11 -0.17 – 0.39 0.434
Random Effects
σ2 0.45
τ00 idnum 1.12
ICC 0.71
N idnum 44
Observations 88
Marginal R2 / Conditional R2 0.115 / 0.746 
tab_model(cond_model3_lai_ca)
  L_dAI
Predictors Estimates CI p
(Intercept) 0.89 -0.94 – 2.72 0.340
cond -0.09 -0.31 – 0.13 0.424
N_Bifact_4SC_z -0.11 -0.50 – 0.28 0.585
age_years -0.01 -0.08 – 0.05 0.702
Gender 0.10 -0.70 – 0.90 0.808
cond * N_Bifact_4SC_z 0.11 -0.11 – 0.33 0.317
Random Effects
σ2 0.28
τ00 idnum 1.50
ICC 0.84
N idnum 44
Observations 88
Marginal R2 / Conditional R2 0.014 / 0.847 
tab_model(cond_model3_rai_ca)
  R_dAI
Predictors Estimates CI p
(Intercept) 0.29 -1.16 – 1.74 0.695
cond -0.06 -0.30 – 0.17 0.600
N_Bifact_4SC_z -0.14 -0.45 – 0.17 0.378
age_years 0.01 -0.04 – 0.06 0.764
Gender 0.13 -0.50 – 0.77 0.684
cond * N_Bifact_4SC_z 0.13 -0.10 – 0.36 0.275
Random Effects
σ2 0.32
τ00 idnum 0.87
ICC 0.73
N idnum 44
Observations 88
Marginal R2 / Conditional R2 0.020 / 0.738

4. Testing the primary model with interaction between Self consciousness and Condition + other N factors

cond_model4_lamy_ca <- lmer(L_amyg ~ cond*N_Bifact_4SC_z + N_Bifact_G_z + N_Bifact_2AH_z + age_years + Gender + (1|idnum), data=jfk_ca)
cond_model4_ramy_ca <- lmer(R_amyg ~ cond*N_Bifact_4SC_z + N_Bifact_G_z + N_Bifact_2AH_z + age_years + Gender + (1|idnum), data=jfk_ca)
cond_model4_precu_ca<- lmer(Precuneus ~ cond*N_Bifact_4SC_z + N_Bifact_G_z + N_Bifact_2AH_z + age_years + Gender + (1|idnum), data=jfk_ca)
cond_model4_dacc_ca<- lmer(dACC ~ cond*N_Bifact_4SC_z + N_Bifact_G_z + N_Bifact_2AH_z + age_years + Gender + (1|idnum), data=jfk_ca)
cond_model4_sgacc_ca <- lmer(sgACC ~ cond*N_Bifact_4SC_z + N_Bifact_G_z + N_Bifact_2AH_z + age_years + Gender + (1|idnum), data=jfk_ca)
cond_model4_lai_ca <- lmer(L_dAI ~ cond*N_Bifact_4SC_z + N_Bifact_G_z + N_Bifact_2AH_z + age_years + Gender + (1|idnum), data=jfk_ca)
cond_model4_rai_ca <- lmer(R_dAI ~ cond*N_Bifact_4SC_z + N_Bifact_G_z + N_Bifact_2AH_z + age_years + Gender + (1|idnum), data=jfk_ca)
tab_model(cond_model4_lamy_ca)
  L_amyg
Predictors Estimates CI p
(Intercept) -0.35 -2.24 – 1.54 0.717
cond 0.02 -0.18 – 0.22 0.837
N_Bifact_4SC_z -0.20 -0.70 – 0.29 0.423
N_Bifact_G_z -0.03 -0.98 – 0.93 0.956
N_Bifact_2AH_z -0.08 -0.45 – 0.30 0.694
age_years 0.02 -0.05 – 0.08 0.613
Gender -0.01 -0.81 – 0.78 0.971
cond * N_Bifact_4SC_z 0.07 -0.13 – 0.26 0.510
Random Effects
σ2 0.23
τ00 idnum 1.46
ICC 0.86
N idnum 44
Observations 88
Marginal R2 / Conditional R2 0.024 / 0.868 
tab_model(cond_model4_ramy_ca)
  R_amyg
Predictors Estimates CI p
(Intercept) 0.23 -1.61 – 2.07 0.806
cond -0.10 -0.29 – 0.09 0.300
N_Bifact_4SC_z 0.16 -0.32 – 0.65 0.503
N_Bifact_G_z -0.17 -1.11 – 0.76 0.719
N_Bifact_2AH_z 0.31 -0.06 – 0.68 0.102
age_years 0.00 -0.06 – 0.06 0.969
Gender 0.01 -0.77 – 0.78 0.981
cond * N_Bifact_4SC_z 0.27 0.08 – 0.45 0.005
Random Effects
σ2 0.20
τ00 idnum 1.40
ICC 0.87
N idnum 44
Observations 88
Marginal R2 / Conditional R2 0.067 / 0.883 
#Shows that for the -0.5 condition [Con->Incon] there is similar amygdalar activity regardless of SC score, but in the +0.5 condition [Incon->Incon] those with lower SC show lower amyg activity than those with higher SC that show slightly increased R amyg activity
 plot_model(cond_model4_ramy_ca, type = "pred", 
          terms = c( "cond","N_Bifact_4SC_z[-1.5,1.5]")) 

tab_model(cond_model4_precu_ca)
  Precuneus
Predictors Estimates CI p
(Intercept) -2.72 -4.69 – -0.75 0.007
cond 0.35 0.12 – 0.58 0.003
N_Bifact_4SC_z -0.41 -0.92 – 0.11 0.122
N_Bifact_G_z 0.17 -0.82 – 1.17 0.732
N_Bifact_2AH_z 0.23 -0.16 – 0.63 0.243
age_years 0.06 -0.01 – 0.13 0.078
Gender -0.63 -1.46 – 0.20 0.136
cond * N_Bifact_4SC_z -0.03 -0.25 – 0.20 0.827
Random Effects
σ2 0.30
τ00 idnum 1.57
ICC 0.84
N idnum 44
Observations 88
Marginal R2 / Conditional R2 0.234 / 0.878 
tab_model(cond_model4_dacc_ca)
  dACC
Predictors Estimates CI p
(Intercept) -1.41 -3.29 – 0.46 0.140
cond 0.14 -0.10 – 0.38 0.250
N_Bifact_4SC_z -0.48 -0.97 – 0.01 0.057
N_Bifact_G_z 1.13 0.17 – 2.08 0.020
N_Bifact_2AH_z 0.11 -0.27 – 0.48 0.572
age_years 0.02 -0.04 – 0.09 0.460
Gender -0.50 -1.29 – 0.29 0.211
cond * N_Bifact_4SC_z 0.02 -0.21 – 0.26 0.862
Random Effects
σ2 0.32
τ00 idnum 1.40
ICC 0.81
N idnum 44
Observations 88
Marginal R2 / Conditional R2 0.184 / 0.847 
tab_model(cond_model4_sgacc_ca)
  sgACC
Predictors Estimates CI p
(Intercept) -2.52 -4.28 – -0.77 0.005
cond 0.26 -0.02 – 0.55 0.068
N_Bifact_4SC_z -0.39 -0.85 – 0.06 0.092
N_Bifact_G_z 0.55 -0.34 – 1.44 0.223
N_Bifact_2AH_z 0.05 -0.30 – 0.40 0.781
age_years 0.04 -0.02 – 0.10 0.182
Gender -0.39 -1.12 – 0.35 0.306
cond * N_Bifact_4SC_z 0.11 -0.17 – 0.39 0.434
Random Effects
σ2 0.45
τ00 idnum 1.14
ICC 0.71
N idnum 44
Observations 88
Marginal R2 / Conditional R2 0.139 / 0.755 
tab_model(cond_model4_lai_ca)
  L_dAI
Predictors Estimates CI p
(Intercept) 0.28 -1.55 – 2.10 0.767
cond -0.09 -0.31 – 0.13 0.424
N_Bifact_4SC_z -0.31 -0.79 – 0.17 0.203
N_Bifact_G_z 1.13 0.20 – 2.05 0.017
N_Bifact_2AH_z 0.07 -0.29 – 0.44 0.691
age_years -0.01 -0.07 – 0.05 0.774
Gender -0.00 -0.77 – 0.77 0.994
cond * N_Bifact_4SC_z 0.11 -0.11 – 0.33 0.317
Random Effects
σ2 0.28
τ00 idnum 1.34
ICC 0.83
N idnum 44
Observations 88
Marginal R2 / Conditional R2 0.127 / 0.851 
tab_model(cond_model4_rai_ca)
  R_dAI
Predictors Estimates CI p
(Intercept) -0.04 -1.56 – 1.47 0.955
cond -0.06 -0.30 – 0.17 0.600
N_Bifact_4SC_z -0.26 -0.65 – 0.14 0.207
N_Bifact_G_z 0.61 -0.16 – 1.37 0.122
N_Bifact_2AH_z 0.03 -0.28 – 0.33 0.858
age_years 0.01 -0.04 – 0.06 0.706
Gender 0.08 -0.56 – 0.72 0.805
cond * N_Bifact_4SC_z 0.13 -0.10 – 0.36 0.275
Random Effects
σ2 0.32
τ00 idnum 0.86
ICC 0.73
N idnum 44
Observations 88
Marginal R2 / Conditional R2 0.066 / 0.748

5. Testing the primary model with the 3 possible N/cond interactions

cond_model5_lamy_ca <- lmer(L_amyg ~ cond*N_Bifact_4SC_z + cond*N_Bifact_G_z + cond*N_Bifact_2AH_z + age_years + Gender + (1|idnum), data=jfk_ca)
cond_model5_ramy_ca <- lmer(R_amyg ~ cond*N_Bifact_4SC_z + cond*N_Bifact_G_z + cond*N_Bifact_2AH_z + age_years + Gender + (1|idnum), data=jfk_ca)
cond_model5_precu_ca<- lmer(Precuneus ~ cond*N_Bifact_4SC_z + cond*N_Bifact_G_z + cond*N_Bifact_2AH_z + age_years + Gender + (1|idnum), data=jfk_ca)
cond_model5_dacc_ca<- lmer(dACC ~ cond*N_Bifact_4SC_z + cond*N_Bifact_G_z + cond*N_Bifact_2AH_z + age_years + Gender + (1|idnum), data=jfk_ca)
cond_model5_sgacc_ca <- lmer(sgACC ~ cond*N_Bifact_4SC_z + cond*N_Bifact_G_z + cond*N_Bifact_2AH_z + age_years + Gender + (1|idnum), data=jfk_ca)
cond_model5_lai_ca <- lmer(L_dAI ~ cond*N_Bifact_4SC_z + cond*N_Bifact_G_z + cond*N_Bifact_2AH_z + age_years + Gender + (1|idnum), data=jfk_ca)
cond_model5_rai_ca <- lmer(R_dAI ~ cond*N_Bifact_4SC_z + cond*N_Bifact_G_z + cond*N_Bifact_2AH_z + age_years + Gender + (1|idnum), data=jfk_ca)
tab_model(cond_model5_lamy_ca)
  L_amyg
Predictors Estimates CI p
(Intercept) -0.35 -2.24 – 1.54 0.717
cond -0.17 -0.51 – 0.16 0.306
N_Bifact_4SC_z -0.20 -0.70 – 0.29 0.423
N_Bifact_G_z -0.03 -0.98 – 0.93 0.956
N_Bifact_2AH_z -0.08 -0.45 – 0.30 0.694
age_years 0.02 -0.05 – 0.08 0.613
Gender -0.01 -0.81 – 0.78 0.971
cond * N_Bifact_4SC_z -0.03 -0.28 – 0.23 0.824
cond * N_Bifact_G_z 0.38 -0.14 – 0.89 0.151
cond * N_Bifact_2AH_z -0.03 -0.23 – 0.18 0.807
Random Effects
σ2 0.23
τ00 idnum 1.46
ICC 0.87
N idnum 44
Observations 88
Marginal R2 / Conditional R2 0.027 / 0.869 
tab_model(cond_model5_ramy_ca)
  R_amyg
Predictors Estimates CI p
(Intercept) 0.23 -1.61 – 2.07 0.806
cond -0.33 -0.64 – -0.03 0.034
N_Bifact_4SC_z 0.16 -0.32 – 0.65 0.503
N_Bifact_G_z -0.17 -1.11 – 0.76 0.719
N_Bifact_2AH_z 0.31 -0.06 – 0.68 0.102
age_years 0.00 -0.06 – 0.06 0.969
Gender 0.01 -0.77 – 0.78 0.981
cond * N_Bifact_4SC_z 0.19 -0.05 – 0.42 0.114
cond * N_Bifact_G_z 0.45 -0.02 – 0.92 0.059
cond * N_Bifact_2AH_z 0.04 -0.15 – 0.22 0.679
Random Effects
σ2 0.19
τ00 idnum 1.41
ICC 0.88
N idnum 44
Observations 88
Marginal R2 / Conditional R2 0.072 / 0.888 
tab_model(cond_model5_precu_ca)
  Precuneus
Predictors Estimates CI p
(Intercept) -2.72 -4.69 – -0.75 0.007
cond 0.14 -0.24 – 0.52 0.477
N_Bifact_4SC_z -0.41 -0.92 – 0.11 0.122
N_Bifact_G_z 0.17 -0.82 – 1.17 0.732
N_Bifact_2AH_z 0.23 -0.16 – 0.63 0.243
age_years 0.06 -0.01 – 0.13 0.078
Gender -0.63 -1.46 – 0.20 0.136
cond * N_Bifact_4SC_z -0.15 -0.44 – 0.14 0.306
cond * N_Bifact_G_z 0.39 -0.19 – 0.98 0.189
cond * N_Bifact_2AH_z -0.08 -0.31 – 0.15 0.492
Random Effects
σ2 0.30
τ00 idnum 1.57
ICC 0.84
N idnum 44
Observations 88
Marginal R2 / Conditional R2 0.236 / 0.878 
tab_model(cond_model5_dacc_ca)
  dACC
Predictors Estimates CI p
(Intercept) -1.41 -3.29 – 0.46 0.140
cond -0.04 -0.44 – 0.36 0.836
N_Bifact_4SC_z -0.48 -0.97 – 0.01 0.057
N_Bifact_G_z 1.13 0.17 – 2.08 0.020
N_Bifact_2AH_z 0.11 -0.27 – 0.48 0.572
age_years 0.02 -0.04 – 0.09 0.460
Gender -0.50 -1.29 – 0.29 0.211
cond * N_Bifact_4SC_z -0.03 -0.34 – 0.27 0.832
cond * N_Bifact_G_z 0.36 -0.25 – 0.97 0.250
cond * N_Bifact_2AH_z 0.05 -0.19 – 0.29 0.684
Random Effects
σ2 0.33
τ00 idnum 1.40
ICC 0.81
N idnum 44
Observations 88
Marginal R2 / Conditional R2 0.186 / 0.846 
tab_model(cond_model5_sgacc_ca)
  sgACC
Predictors Estimates CI p
(Intercept) -2.52 -4.28 – -0.77 0.005
cond -0.06 -0.52 – 0.40 0.790
N_Bifact_4SC_z -0.39 -0.85 – 0.06 0.092
N_Bifact_G_z 0.55 -0.34 – 1.44 0.223
N_Bifact_2AH_z 0.05 -0.30 – 0.40 0.781
age_years 0.04 -0.02 – 0.10 0.182
Gender -0.39 -1.12 – 0.35 0.306
cond * N_Bifact_4SC_z -0.12 -0.47 – 0.23 0.515
cond * N_Bifact_G_z 0.61 -0.09 – 1.32 0.090
cond * N_Bifact_2AH_z -0.18 -0.46 – 0.09 0.192
Random Effects
σ2 0.43
τ00 idnum 1.15
ICC 0.73
N idnum 44
Observations 88
Marginal R2 / Conditional R2 0.149 / 0.767 
tab_model(cond_model5_lai_ca)
  L_dAI
Predictors Estimates CI p
(Intercept) 0.28 -1.55 – 2.10 0.767
cond -0.18 -0.55 – 0.19 0.334
N_Bifact_4SC_z -0.31 -0.79 – 0.17 0.203
N_Bifact_G_z 1.13 0.20 – 2.05 0.017
N_Bifact_2AH_z 0.07 -0.29 – 0.44 0.691
age_years -0.01 -0.07 – 0.05 0.774
Gender -0.00 -0.77 – 0.77 0.994
cond * N_Bifact_4SC_z 0.01 -0.27 – 0.29 0.958
cond * N_Bifact_G_z 0.16 -0.40 – 0.73 0.575
cond * N_Bifact_2AH_z -0.13 -0.36 – 0.09 0.239
Random Effects
σ2 0.28
τ00 idnum 1.34
ICC 0.83
N idnum 44
Observations 88
Marginal R2 / Conditional R2 0.129 / 0.850 
tab_model(cond_model5_rai_ca)
  R_dAI
Predictors Estimates CI p
(Intercept) -0.04 -1.56 – 1.47 0.955
cond -0.23 -0.63 – 0.17 0.264
N_Bifact_4SC_z -0.26 -0.65 – 0.14 0.207
N_Bifact_G_z 0.61 -0.16 – 1.37 0.122
N_Bifact_2AH_z 0.03 -0.28 – 0.33 0.858
age_years 0.01 -0.04 – 0.06 0.706
Gender 0.08 -0.56 – 0.72 0.805
cond * N_Bifact_4SC_z 0.06 -0.24 – 0.37 0.680
cond * N_Bifact_G_z 0.32 -0.29 – 0.93 0.308
cond * N_Bifact_2AH_z 0.01 -0.23 – 0.25 0.947
Random Effects
σ2 0.32
τ00 idnum 0.85
ICC 0.72
N idnum 44
Observations 88
Marginal R2 / Conditional R2 0.069 / 0.744

FDR Correction:

sum_model4_lamy_ca <- summary(cond_model4_lamy_ca)
model4_lamy_ca_ps <- sum_model4_lamy_ca$coefficients[c(3:5,8),5]

sum_model4_ramy_ca <- summary(cond_model4_ramy_ca)
model4_ramy_ca_ps <- sum_model4_ramy_ca$coefficients[c(3:5,8),5]

sum_model4_precu_ca <- summary(cond_model4_precu_ca)
model4_precu_ca_ps <- sum_model4_precu_ca$coefficients[c(3:5,8),5]

sum_model4_dacc_ca <- summary(cond_model4_dacc_ca)
model4_dacc_ca_ps <- sum_model4_dacc_ca$coefficients[c(3:5,8),5]

sum_model4_sgacc_ca <- summary(cond_model4_sgacc_ca)
model4_sgacc_ca_ps <- sum_model4_sgacc_ca$coefficients[c(3:5,8),5]

sum_model4_lai_ca <- summary(cond_model4_lai_ca)
model4_lai_ca_ps <- sum_model4_lai_ca$coefficients[c(3:5,8),5]

sum_model4_rai_ca <- summary(cond_model4_rai_ca)
model4_rai_ca_ps <- sum_model4_rai_ca$coefficients[c(3:5,8),5]

model4_ca_allps <- c(model4_lamy_ca_ps,model4_ramy_ca_ps,model4_precu_ca_ps, model4_dacc_ca_ps, model4_sgacc_ca_ps,model4_lai_ca_ps,model4_rai_ca_ps)
model4_ca_allps_fdr <- p.adjust(model4_ca_allps, method = "fdr")

#Show first the significant uncorrected ps (ignore the labels here, or at least none of them are SC or cond:SC)
old_sig <- which(model4_ca_allps < 0.05)
model4_ca_allps[old_sig]
## cond:N_Bifact_4SC_z        N_Bifact_G_z        N_Bifact_G_z 
##         0.006947227         0.025823994         0.022378223
#Show corrected values -- there are none
new_sig <- which(model4_ca_allps_fdr < 0.05)
model4_ca_allps[new_sig]
## named numeric(0)