Hunter Estroop fMRI: new anayses based on fMRI paired t-test results

Setup

setwd("~/Dropbox/EStroop (Hunter ERT fMRI)/Data/")
library(tidyverse)

Registered S3 method overwritten by 'rvest':
  method            from
  read_xml.response xml2
── Attaching packages ───────────────────────────────────────────────────────────────────────────────────────────── tidyverse 1.2.1 ──
✔ ggplot2 3.2.0     ✔ purrr   0.3.2
✔ tibble  2.1.3     ✔ dplyr   0.8.1
✔ tidyr   0.8.3     ✔ stringr 1.4.0
✔ readr   1.3.1     ✔ forcats 0.4.0
── Conflicts ──────────────────────────────────────────────────────────────────────────────────────────────── tidyverse_conflicts() ──
✖ .GlobalEnv::filter() masks dplyr::filter(), stats::filter()
✖ dplyr::lag()         masks stats::lag()

library(stats)
library(haven)
library(psych)

Attaching package: ‘psych’

The following objects are masked from ‘package:ggplot2’:

    %+%, alpha

library(MASS)

Attaching package: ‘MASS’

The following object is masked _by_ ‘.GlobalEnv’:

    select

The following object is masked from ‘package:dplyr’:

    select

select <- dplyr::select
filter <- dplyr::filter
EStroop_NewDataset_allSRIAdata <- read_sav("~/Dropbox/EStroop (Hunter ERT fMRI)/Data/EStroop_NewDataset_allSRIAdata.sav")
EStroop_NewDataset_allSRIAdata <- EStroop_NewDataset_allSRIAdata %>% filter(`filter_$` == 1)
tots <- select(EStroop_NewDataset_allSRIAdata, c("ERT_ID", "TOT_ACS_Focus_11", "TOT_ACS_Focus_33", "TOT_ACS_Shift_11", "TOT_ACS_Shift_33", "TOT_ACS_11", "TOT_ACS_33", "TOT_EQ_decenter_11", "TOT_EQ_decenter_33", "TOT_ERQ_Reappraisal_11", "TOT_ERQ_Reappraisal_33", "IA_T11MDD_CSR_CUR_ALL", "IA_T33MDD_CSR_CUR_ALL", "IA_ADISgad1109b","IA_ADISgad3309b")) %>% mutate(ERT_ID = as.integer(ERT_ID))
#"TOT_MDD_CSR_11", "TOT_MDD_CSR_33", "TOT_GAD_CSR_11","TOT_GAD_CSR_33"
data <- read.csv("estroop-hunter-fmri.csv")
betas <- read.csv("betas_pre_post_con4__10_54_32_roi.csv")
data <- data %>% filter(`filter_.` == 1) # filter only participants w/estroop fmri data
data <- select(data, -contains("roi")) # get rid of old ROI variables
data <- select(data, -contains("con2")) # get rid of old ROI variables
dat <- left_join(data, betas, by="ERT_ID")
dat <- left_join(dat, tots, by = "ERT_ID")
# get standardized residuals
dat$stdres_prepost_con4_10_54_32 <-stdres(lm(prepost_con4_10_54_32_POST ~ prepost_con4_10_54_32_PRE, dat))
dat$stdres_ACS_Focus <- stdres(lm(TOT_ACS_Focus_33 ~ TOT_ACS_Focus_11, data = dat))
dat$stdres_ACS_Shift <- stdres(lm(TOT_ACS_Shift_33 ~ TOT_ACS_Shift_11, data = dat))
dat$stdres_ACS_total <- stdres(lm(TOT_ACS_33 ~ TOT_ACS_11, data = dat))
dat$stdres_EQ_Decenter <- stdres(lm(TOT_EQ_decenter_33 ~ TOT_EQ_decenter_11, data = dat))
dat$stdres_ERQ_Reappraisal <- stdres(lm(TOT_ERQ_Reappraisal_33 ~ TOT_ERQ_Reappraisal_11, data = dat))
dat$stdres_GAD_CSR <- stdres(lm(IA_ADISgad3309b ~ IA_ADISgad1109b, data = dat))
dat$stdres_MDD_CSR <- stdres(lm(IA_T33MDD_CSR_CUR_ALL ~ IA_T11MDD_CSR_CUR_ALL, data = dat))

Scatterplots

Checking for outliers etc…

plot(stdres_ACS_Focus ~ stdres_prepost_con4_10_54_32, dat)

plot(stdres_ACS_Shift ~ stdres_prepost_con4_10_54_32, dat)

plot(stdres_ACS_total ~ stdres_prepost_con4_10_54_32, dat)

plot(stdres_ERQ_Reappraisal ~ stdres_prepost_con4_10_54_32, dat)

plot(stdres_EQ_Decenter ~ stdres_prepost_con4_10_54_32, dat)

plot(stdres_MDD_CSR ~ stdres_prepost_con4_10_54_32, dat)

plot(stdres_GAD_CSR ~ stdres_prepost_con4_10_54_32, dat)

Correlations

cor.test(dat$stdres_prepost_con4_10_54_32, dat$stdres_ACS_Focus)

    Pearson's product-moment correlation

data:  dat$stdres_prepost_con4_10_54_32 and dat$stdres_ACS_Focus
t = -2.4198, df = 18, p-value = 0.02633
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
 -0.76929555 -0.06777341
sample estimates:
       cor 
-0.4954355 

cor.test(dat$stdres_prepost_con4_10_54_32, dat$stdres_ACS_Shift)

    Pearson's product-moment correlation

data:  dat$stdres_prepost_con4_10_54_32 and dat$stdres_ACS_Shift
t = -2.1519, df = 18, p-value = 0.04523
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
 -0.74561432 -0.01228234
sample estimates:
       cor 
-0.4523445 

cor.test(dat$stdres_prepost_con4_10_54_32, dat$stdres_ACS_total)

    Pearson's product-moment correlation

data:  dat$stdres_prepost_con4_10_54_32 and dat$stdres_ACS_total
t = -2.6071, df = 18, p-value = 0.01783
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
 -0.7843511 -0.1054634
sample estimates:
       cor 
-0.5235502 

cor.test(dat$stdres_prepost_con4_10_54_32, dat$stdres_ERQ_Reappraisal)

    Pearson's product-moment correlation

data:  dat$stdres_prepost_con4_10_54_32 and dat$stdres_ERQ_Reappraisal
t = -0.72915, df = 18, p-value = 0.4753
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
 -0.5692333  0.2952740
sample estimates:
       cor 
-0.1693786 

cor.test(dat$stdres_prepost_con4_10_54_32, dat$stdres_EQ_Decenter)

    Pearson's product-moment correlation

data:  dat$stdres_prepost_con4_10_54_32 and dat$stdres_EQ_Decenter
t = -1.1995, df = 18, p-value = 0.2459
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
 -0.6377917  0.1937976
sample estimates:
       cor 
-0.2720544 

cor.test(dat$stdres_prepost_con4_10_54_32, dat$stdres_MDD_CSR)

    Pearson's product-moment correlation

data:  dat$stdres_prepost_con4_10_54_32 and dat$stdres_MDD_CSR
t = 0.54549, df = 18, p-value = 0.5921
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
 -0.3338359  0.5395943
sample estimates:
      cor 
0.1275239 

cor.test(dat$stdres_prepost_con4_10_54_32, dat$stdres_GAD_CSR)

    Pearson's product-moment correlation

data:  dat$stdres_prepost_con4_10_54_32 and dat$stdres_GAD_CSR
t = 1.8347, df = 18, p-value = 0.08314
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
 -0.05532568  0.71402123
sample estimates:
      cor 
0.3969126 

Testing dependent correlations

ACS-Shift

dmPFC and ACS-Shift, vs. dmPFC and EQ-Decenter

library(cocor)
?cocor
## j = brain region, k = outcome A (attentional), h = outcome B (clinical/metacog.)
# dmPFC and ACS-Shift, vs. dmPFC and EQ-Decenter
r.jk <- cor(dat$stdres_ACS_Shift, dat$stdres_prepost_con4_10_54_32)
r.jh <- cor(dat$stdres_EQ_Decenter, dat$stdres_prepost_con4_10_54_32)
r.kh <- cor(dat$stdres_ACS_Shift, dat$stdres_EQ_Decenter)
cocor.dep.groups.overlap(r.jk, r.jh, r.kh, n=20, alternative="two.sided", alpha=0.05, conf.level=0.95, null.value=0,  return.htest=FALSE, test=c("steiger1980", "zou2007"))

  Results of a comparison of two overlapping correlations based on dependent groups

Comparison between r.jk = -0.4523 and r.jh = -0.2721
Difference: r.jk - r.jh = -0.1803
Related correlation: r.kh = 0.7206
Group size: n = 20
Null hypothesis: r.jk is equal to r.jh
Alternative hypothesis: r.jk is not equal to r.jh (two-sided)
Alpha: 0.05

steiger1980: Steiger's (1980) modification of Dunn and Clark's z (1969) using average correlations
  z = -1.0837, p-value = 0.2785
  Null hypothesis retained

zou2007: Zou's (2007) confidence interval
  95% confidence interval for r.jk - r.jh: -0.5191 0.1448
  Null hypothesis retained (Interval includes 0)

dmPFC and ACS-Shift, vs. dmPFC and ERQ-Reappraisal

# dmPFC and ACS-Shift, vs. dmPFC and ERQ-Reappraisal
r.jk <- cor(dat$stdres_ACS_Shift, dat$stdres_prepost_con4_10_54_32)
r.jh <- cor(dat$stdres_ERQ_Reappraisal, dat$stdres_prepost_con4_10_54_32)
r.kh <- cor(dat$stdres_ACS_Shift, dat$stdres_ERQ_Reappraisal)
cocor.dep.groups.overlap(r.jk, r.jh, r.kh, n=20, alternative="two.sided", alpha=0.05, conf.level=0.95, null.value=0,  return.htest=FALSE, test=c("steiger1980", "zou2007"))

  Results of a comparison of two overlapping correlations based on dependent groups

Comparison between r.jk = -0.4523 and r.jh = -0.1694
Difference: r.jk - r.jh = -0.283
Related correlation: r.kh = 0.5768
Group size: n = 20
Null hypothesis: r.jk is equal to r.jh
Alternative hypothesis: r.jk is not equal to r.jh (two-sided)
Alpha: 0.05

steiger1980: Steiger's (1980) modification of Dunn and Clark's z (1969) using average correlations
  z = -1.3641, p-value = 0.1725
  Null hypothesis retained

zou2007: Zou's (2007) confidence interval
  95% confidence interval for r.jk - r.jh: -0.6726 0.1166
  Null hypothesis retained (Interval includes 0)

ACS-Focus

dmPFC and ACS-Focus, vs. dmPFC and EQ-Decenter

# dmPFC and ACS-Focus, vs. dmPFC and EQ-Decenter
r.jk <- cor(dat$stdres_ACS_Focus, dat$stdres_prepost_con4_10_54_32)
r.jh <- cor(dat$stdres_EQ_Decenter, dat$stdres_prepost_con4_10_54_32)
r.kh <- cor(dat$stdres_ACS_Focus, dat$stdres_EQ_Decenter)
cocor.dep.groups.overlap(r.jk, r.jh, r.kh, n=20, alternative="two.sided", alpha=0.05, conf.level=0.95, null.value=0,  return.htest=FALSE, test=c("steiger1980", "zou2007"))

  Results of a comparison of two overlapping correlations based on dependent groups

Comparison between r.jk = -0.4954 and r.jh = -0.2721
Difference: r.jk - r.jh = -0.2234
Related correlation: r.kh = 0.6413
Group size: n = 20
Null hypothesis: r.jk is equal to r.jh
Alternative hypothesis: r.jk is not equal to r.jh (two-sided)
Alpha: 0.05

steiger1980: Steiger's (1980) modification of Dunn and Clark's z (1969) using average correlations
  z = -1.2062, p-value = 0.2277
  Null hypothesis retained

zou2007: Zou's (2007) confidence interval
  95% confidence interval for r.jk - r.jh: -0.5966 0.1363
  Null hypothesis retained (Interval includes 0)

dmPFC and ACS-Focus, vs. dmPFC and ERQ-Reappraisal

# dmPFC and ACS-Focus, vs. dmPFC and ERQ-Reappraisal
r.jk <- cor(dat$stdres_ACS_Focus, dat$stdres_prepost_con4_10_54_32)
r.jh <- cor(dat$stdres_ERQ_Reappraisal, dat$stdres_prepost_con4_10_54_32)
r.kh <- cor(dat$stdres_ACS_Focus, dat$stdres_ERQ_Reappraisal)
cocor.dep.groups.overlap(r.jk, r.jh, r.kh, n=20, alternative="two.sided", alpha=0.05, conf.level=0.95, null.value=0,  return.htest=FALSE, test=c("steiger1980", "zou2007"))

  Results of a comparison of two overlapping correlations based on dependent groups

Comparison between r.jk = -0.4954 and r.jh = -0.1694
Difference: r.jk - r.jh = -0.3261
Related correlation: r.kh = 0.5443
Group size: n = 20
Null hypothesis: r.jk is equal to r.jh
Alternative hypothesis: r.jk is not equal to r.jh (two-sided)
Alpha: 0.05

steiger1980: Steiger's (1980) modification of Dunn and Clark's z (1969) using average correlations
  z = -1.5381, p-value = 0.1240
  Null hypothesis retained

zou2007: Zou's (2007) confidence interval
  95% confidence interval for r.jk - r.jh: -0.7260 0.0824
  Null hypothesis retained (Interval includes 0)

ACS Total

dmPFC and ACS total, vs. dmPFC and ERQ-Reappraisal

# dmPFC and ACS total, vs. dmPFC and ERQ-Reappraisal
r.jk <- cor(dat$stdres_ACS_total, dat$stdres_prepost_con4_10_54_32)
r.jh <- cor(dat$stdres_ERQ_Reappraisal, dat$stdres_prepost_con4_10_54_32)
r.kh <- cor(dat$stdres_ACS_total, dat$stdres_ERQ_Reappraisal)
cocor.dep.groups.overlap(r.jk, r.jh, r.kh, n=20, alternative="two.sided", alpha=0.05, conf.level=0.95, null.value=0,  return.htest=FALSE, test=c("steiger1980", "zou2007"))

  Results of a comparison of two overlapping correlations based on dependent groups

Comparison between r.jk = -0.5236 and r.jh = -0.1694
Difference: r.jk - r.jh = -0.3542
Related correlation: r.kh = 0.6454
Group size: n = 20
Null hypothesis: r.jk is equal to r.jh
Alternative hypothesis: r.jk is not equal to r.jh (two-sided)
Alpha: 0.05

steiger1980: Steiger's (1980) modification of Dunn and Clark's z (1969) using average correlations
  z = -1.9071, p-value = 0.0565
  Null hypothesis retained

zou2007: Zou's (2007) confidence interval
  95% confidence interval for r.jk - r.jh: -0.7196 0.0025
  Null hypothesis retained (Interval includes 0)

dmPFC and ACS total, vs. dmPFC and EQ-Decenter

# dmPFC and ACS total, vs. dmPFC and EQ-Decenter
r.jk <- cor(dat$stdres_ACS_total, dat$stdres_prepost_con4_10_54_32)
r.jh <- cor(dat$stdres_EQ_Decenter, dat$stdres_prepost_con4_10_54_32)
r.kh <- cor(dat$stdres_ACS_total, dat$stdres_EQ_Decenter)
cocor.dep.groups.overlap(r.jk, r.jh, r.kh, n=20, alternative="two.sided", alpha=0.05, conf.level=0.95, null.value=0,  return.htest=FALSE, test=c("steiger1980", "zou2007"))

  Results of a comparison of two overlapping correlations based on dependent groups

Comparison between r.jk = -0.5236 and r.jh = -0.2721
Difference: r.jk - r.jh = -0.2515
Related correlation: r.kh = 0.7694
Group size: n = 20
Null hypothesis: r.jk is equal to r.jh
Alternative hypothesis: r.jk is not equal to r.jh (two-sided)
Alpha: 0.05

steiger1980: Steiger's (1980) modification of Dunn and Clark's z (1969) using average correlations
  z = -1.7013, p-value = 0.0889
  Null hypothesis retained

zou2007: Zou's (2007) confidence interval
  95% confidence interval for r.jk - r.jh: -0.5760 0.0354
  Null hypothesis retained (Interval includes 0)

GAD vs. MDD CSR

dmPFC and GAD CSR vs. dmPFC and MDD CSR

# dmPFC and GAD CSR, vs. dmPFC and MDD CSR
r.jk <- cor(dat$stdres_GAD_CSR, dat$stdres_prepost_con4_10_54_32)
r.jh <- cor(dat$stdres_MDD_CSR, dat$stdres_prepost_con4_10_54_32)
r.kh <- cor(dat$stdres_GAD_CSR, dat$stdres_MDD_CSR)
cocor.dep.groups.overlap(r.jk, r.jh, r.kh, n=20, alternative="two.sided", alpha=0.05, conf.level=0.95, null.value=0,  return.htest=FALSE, test=c("steiger1980", "zou2007"))

  Results of a comparison of two overlapping correlations based on dependent groups

Comparison between r.jk = 0.3969 and r.jh = 0.1275
Difference: r.jk - r.jh = 0.2694
Related correlation: r.kh = 0.4432
Group size: n = 20
Null hypothesis: r.jk is equal to r.jh
Alternative hypothesis: r.jk is not equal to r.jh (two-sided)
Alpha: 0.05

steiger1980: Steiger's (1980) modification of Dunn and Clark's z (1969) using average correlations
  z = 1.1115, p-value = 0.2664
  Null hypothesis retained

zou2007: Zou's (2007) confidence interval
  95% confidence interval for r.jk - r.jh: -0.1963 0.7050
  Null hypothesis retained (Interval includes 0)

Figures

library(ggplot2)
ggplot(dat, aes(stdres_ACS_Shift, stdres_prepost_con4_10_54_32)) +
  geom_point(size=2) + 
  labs(y = "Δ dmPFC (10, 54, 32)", x = "Δ ACS-Shift") +
  ggtitle("Association between pre-post change in self-reported attentional \nshifting and change in dmPFC activation on iI vs. cI trials \n") +
  geom_smooth(method=lm, linetype="solid", color="gray59", fill="lightgray") +
  theme_light() +
  theme(axis.text = element_text(size = 13), axis.title = element_text(size=14), plot.title = element_text(size=12))

ggsave("estroop-scatter_acs_shift.png",dpi=700)

Saving 5.5 x 5.5 in image

ggplot(dat, aes(stdres_ACS_Focus, stdres_prepost_con4_10_54_32)) +
  geom_point(size=2) + 
  labs(y = "Δ dmPFC (10, 54, 32)", x = "Δ ACS-Focus") +
  ggtitle("Association between pre-post change in self-reported attentional \nfocus and change in dmPFC activation on iI vs. cI trials \n") +
  geom_smooth(method=lm, linetype="solid", color="gray59", fill="lightgray") +
  theme_light() +
  theme(axis.text = element_text(size = 13), axis.title = element_text(size=14), plot.title = element_text(size=12))

ggsave("estroop-scatter_acs_focus.png",dpi=700)

Saving 5.5 x 5.5 in image

ggplot(dat, aes(stdres_ACS_total, stdres_prepost_con4_10_54_32)) +
  geom_point(size=2) + 
  labs(y = "Δ dmPFC (10, 54, 32)", x = "Δ ACS Total") +
  ggtitle("Association between pre-post change in self-reported attentional \ncontrol and change in dmPFC activation on iI vs. cI trials \n") +
  geom_smooth(method=lm, linetype="solid", color="gray59", fill="lightgray") +
  theme_light() +
  theme(axis.text = element_text(size = 13), axis.title = element_text(size=14), plot.title = element_text(size=12))

ggsave("estroop-scatter_acs_total.png",dpi=700)

Saving 5.5 x 5.5 in image