fmri_behavioural
VivianaGreco
2023-08-24
resampled_vinsula_mask.nii.gz
No significant correlations after correcting for FDR
V_Insula.data <- read_csv("Vinsula_Bold_HRD.csv")## Rows: 72 Columns: 4
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (3): participants, contrast, mask
## dbl (1): values
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.str(V_Insula.data)## spc_tbl_ [72 × 4] (S3: spec_tbl_df/tbl_df/tbl/data.frame)
## $ participants: chr [1:72] "sub-01" "sub-01" "sub-01" "sub-01" ...
## $ contrast : chr [1:72] "contrast-Cued" "contrast-Cued-Uncued" "contrast-Uncued" "contrast-Uncued-Cued" ...
## $ mask : chr [1:72] "resampled_vinsula_mask" "resampled_vinsula_mask" "resampled_vinsula_mask" "resampled_vinsula_mask" ...
## $ values : num [1:72] -1.531 -0.128 -1.377 0.128 0.119 ...
## - attr(*, "spec")=
## .. cols(
## .. participants = col_character(),
## .. contrast = col_character(),
## .. mask = col_character(),
## .. values = col_double()
## .. )
## - attr(*, "problems")=<externalptr>B.data <- read_csv("behavData.csv")## Rows: 18 Columns: 4
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (1): participants
## dbl (3): B_CuedS2, B_Uncued_S2, B_diffS2
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.str(B.data)## spc_tbl_ [18 × 4] (S3: spec_tbl_df/tbl_df/tbl/data.frame)
## $ participants: chr [1:18] "sub-01" "sub-02" "sub-03" "sub-04" ...
## $ B_CuedS2 : num [1:18] 2.92 3.96 3.21 2.71 4 ...
## $ B_Uncued_S2 : num [1:18] 3.21 3.5 3.12 2.42 4.12 ...
## $ B_diffS2 : num [1:18] -0.2917 0.4583 0.0833 0.2917 -0.125 ...
## - attr(*, "spec")=
## .. cols(
## .. participants = col_character(),
## .. B_CuedS2 = col_double(),
## .. B_Uncued_S2 = col_double(),
## .. B_diffS2 = col_double()
## .. )
## - attr(*, "problems")=<externalptr>combined_V_Insula <- inner_join(V_Insula.data, B.data, by = "participants")
combined_V_Insula <- combined_V_Insula %>%
mutate(across(c(participants, contrast, mask), as.factor))
str(combined_V_Insula)## tibble [72 × 7] (S3: tbl_df/tbl/data.frame)
## $ participants: Factor w/ 18 levels "sub-01","sub-02",..: 1 1 1 1 2 2 2 2 3 3 ...
## $ contrast : Factor w/ 4 levels "contrast-Cued",..: 1 2 3 4 1 2 3 4 1 2 ...
## $ mask : Factor w/ 1 level "resampled_vinsula_mask": 1 1 1 1 1 1 1 1 1 1 ...
## $ values : num [1:72] -1.531 -0.128 -1.377 0.128 0.119 ...
## $ B_CuedS2 : num [1:72] 2.92 2.92 2.92 2.92 3.96 ...
## $ B_Uncued_S2 : num [1:72] 3.21 3.21 3.21 3.21 3.5 ...
## $ B_diffS2 : num [1:72] -0.292 -0.292 -0.292 -0.292 0.458 ...#
subset_1_V_Insula <- combined_V_Insula %>%
filter(contrast == "contrast-Cued")
subset_1_V_Insula <- subset_1_V_Insula[, c("values", "B_CuedS2")]
shapiro.test(subset_1_V_Insula$values) #normal##
## Shapiro-Wilk normality test
##
## data: subset_1_V_Insula$values
## W = 0.92962, p-value = 0.1912shapiro.test(subset_1_V_Insula$B_CuedS2) #normal##
## Shapiro-Wilk normality test
##
## data: subset_1_V_Insula$B_CuedS2
## W = 0.9774, p-value = 0.9193subset_1_V_Insulacorr <- corr.test(subset_1_V_Insula, use = "pairwise",method="pearson",adjust="none")
subset_1_V_Insulacorr## Call:corr.test(x = subset_1_V_Insula, use = "pairwise", method = "pearson",
## adjust = "none")
## Correlation matrix
## values B_CuedS2
## values 1.00 0.23
## B_CuedS2 0.23 1.00
## Sample Size
## [1] 18
## Probability values (Entries above the diagonal are adjusted for multiple tests.)
## values B_CuedS2
## values 0.00 0.36
## B_CuedS2 0.36 0.00
##
## To see confidence intervals of the correlations, print with the short=FALSE optionggscatter(subset_1_V_Insula, x = "values", y = "B_CuedS2",
add = "reg.line", conf.int = TRUE,
cor.coef = TRUE, cor.method = "pearson",
xlab = "Bold Cued", ylab = "B_CuedS2")
sp.subset_1_V_Insulacorr <- corr.test(subset_1_V_Insula, use = "pairwise",method="spearman",adjust="none")
sp.subset_1_V_Insulacorr## Call:corr.test(x = subset_1_V_Insula, use = "pairwise", method = "spearman",
## adjust = "none")
## Correlation matrix
## values B_CuedS2
## values 1.00 0.26
## B_CuedS2 0.26 1.00
## Sample Size
## [1] 18
## Probability values (Entries above the diagonal are adjusted for multiple tests.)
## values B_CuedS2
## values 0.00 0.29
## B_CuedS2 0.29 0.00
##
## To see confidence intervals of the correlations, print with the short=FALSE option#
subset_2_V_Insula <- combined_V_Insula %>%
filter(contrast == "contrast-Uncued")
subset_2_V_Insula <- subset_2_V_Insula[, c("values", "B_Uncued_S2")]
shapiro.test(subset_2_V_Insula$values) #normal##
## Shapiro-Wilk normality test
##
## data: subset_2_V_Insula$values
## W = 0.97524, p-value = 0.8884shapiro.test(subset_2_V_Insula$B_Uncued_S2) #normal##
## Shapiro-Wilk normality test
##
## data: subset_2_V_Insula$B_Uncued_S2
## W = 0.97342, p-value = 0.859subset_2_V_Insulacorr <- corr.test(subset_2_V_Insula, use = "pairwise",method="pearson",adjust="none")
subset_2_V_Insulacorr## Call:corr.test(x = subset_2_V_Insula, use = "pairwise", method = "pearson",
## adjust = "none")
## Correlation matrix
## values B_Uncued_S2
## values 1.00 0.06
## B_Uncued_S2 0.06 1.00
## Sample Size
## [1] 18
## Probability values (Entries above the diagonal are adjusted for multiple tests.)
## values B_Uncued_S2
## values 0.0 0.8
## B_Uncued_S2 0.8 0.0
##
## To see confidence intervals of the correlations, print with the short=FALSE optionggscatter(subset_2_V_Insula, x = "values", y = "B_Uncued_S2",
add = "reg.line", conf.int = TRUE,
cor.coef = TRUE, cor.method = "pearson",
xlab = "Bold Cued_Uncued", ylab = "B_Uncued_S2")
sp.subset_2_V_Insulacorr <- corr.test(subset_2_V_Insula, use = "pairwise",method="spearman",adjust="none")
sp.subset_2_V_Insulacorr## Call:corr.test(x = subset_2_V_Insula, use = "pairwise", method = "spearman",
## adjust = "none")
## Correlation matrix
## values B_Uncued_S2
## values 1.00 -0.05
## B_Uncued_S2 -0.05 1.00
## Sample Size
## [1] 18
## Probability values (Entries above the diagonal are adjusted for multiple tests.)
## values B_Uncued_S2
## values 0.00 0.83
## B_Uncued_S2 0.83 0.00
##
## To see confidence intervals of the correlations, print with the short=FALSE option#
subset_3_V_Insula <- combined_V_Insula %>%
filter(contrast == "contrast-Cued-Uncued")
subset_3_V_Insula <- subset_3_V_Insula[, c("values", "B_diffS2")]
shapiro.test(subset_3_V_Insula$values) #normal##
## Shapiro-Wilk normality test
##
## data: subset_3_V_Insula$values
## W = 0.9763, p-value = 0.9041shapiro.test(subset_3_V_Insula$B_diffS2) #normal##
## Shapiro-Wilk normality test
##
## data: subset_3_V_Insula$B_diffS2
## W = 0.95985, p-value = 0.5987subset_3_V_Insulacorr <- corr.test(subset_3_V_Insula, use = "pairwise",method="pearson",adjust="none")
subset_3_V_Insulacorr## Call:corr.test(x = subset_3_V_Insula, use = "pairwise", method = "pearson",
## adjust = "none")
## Correlation matrix
## values B_diffS2
## values 1.00 0.36
## B_diffS2 0.36 1.00
## Sample Size
## [1] 18
## Probability values (Entries above the diagonal are adjusted for multiple tests.)
## values B_diffS2
## values 0.00 0.15
## B_diffS2 0.15 0.00
##
## To see confidence intervals of the correlations, print with the short=FALSE optionggscatter(subset_3_V_Insula, x = "values", y = "B_diffS2",
add = "reg.line", conf.int = TRUE,
cor.coef = TRUE, cor.method = "pearson",
xlab = "Bold Cued_Uncued", ylab = "B_diffS2")
sp.subset_3_V_Insulacorr <- corr.test(subset_3_V_Insula, use = "pairwise",method="spearman",adjust="none")
sp.subset_3_V_Insulacorr## Call:corr.test(x = subset_3_V_Insula, use = "pairwise", method = "spearman",
## adjust = "none")
## Correlation matrix
## values B_diffS2
## values 1.00 0.06
## B_diffS2 0.06 1.00
## Sample Size
## [1] 18
## Probability values (Entries above the diagonal are adjusted for multiple tests.)
## values B_diffS2
## values 0.00 0.81
## B_diffS2 0.81 0.00
##
## To see confidence intervals of the correlations, print with the short=FALSE optionall_p_values.VInsula <- c(round(subset_1_V_Insulacorr$p[2], 2),
round(subset_2_V_Insulacorr$p[2], 2),
round(subset_3_V_Insulacorr$p[2], 2))
all_p_values.VInsula## [1] 0.36 0.80 0.15# Apply FDR correction to combined all p-values
adjusted_p_VInsula<- p.adjust(all_p_values.VInsula, method = "fdr")
adjusted_p_VInsula## [1] 0.54 0.80 0.45resampled_vorbitofrontal_mask.nii.gz
OFC <- read_csv("resampled_vorbitofrontal_mask.csv")## Rows: 72 Columns: 4
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (3): participants, contrast, mask
## dbl (1): values
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.str(OFC)## spc_tbl_ [72 × 4] (S3: spec_tbl_df/tbl_df/tbl/data.frame)
## $ participants: chr [1:72] "sub-01" "sub-01" "sub-01" "sub-01" ...
## $ contrast : chr [1:72] "contrast-Uncued-Cued" "contrast-Cued-Uncued" "contrast-Cued" "contrast-Uncued" ...
## $ mask : chr [1:72] "masks/resampled_vorbitofrontal_mask" "masks/resampled_vorbitofrontal_mask" "masks/resampled_vorbitofrontal_mask" "masks/resampled_vorbitofrontal_mask" ...
## $ values : num [1:72] 0.5316 -0.5316 -0.6531 0.0598 0.2876 ...
## - attr(*, "spec")=
## .. cols(
## .. participants = col_character(),
## .. contrast = col_character(),
## .. mask = col_character(),
## .. values = col_double()
## .. )
## - attr(*, "problems")=<externalptr>B.data<- read_csv("behavData.csv")## Rows: 18 Columns: 4
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (1): participants
## dbl (3): B_CuedS2, B_Uncued_S2, B_diffS2
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.str(B.data)## spc_tbl_ [18 × 4] (S3: spec_tbl_df/tbl_df/tbl/data.frame)
## $ participants: chr [1:18] "sub-01" "sub-02" "sub-03" "sub-04" ...
## $ B_CuedS2 : num [1:18] 2.92 3.96 3.21 2.71 4 ...
## $ B_Uncued_S2 : num [1:18] 3.21 3.5 3.12 2.42 4.12 ...
## $ B_diffS2 : num [1:18] -0.2917 0.4583 0.0833 0.2917 -0.125 ...
## - attr(*, "spec")=
## .. cols(
## .. participants = col_character(),
## .. B_CuedS2 = col_double(),
## .. B_Uncued_S2 = col_double(),
## .. B_diffS2 = col_double()
## .. )
## - attr(*, "problems")=<externalptr>combined_OFC <- inner_join(OFC, B.data, by = "participants")
combined_OFC <- combined_OFC %>%
mutate(across(c(participants, contrast, mask), as.factor))
str(combined_OFC)## tibble [72 × 7] (S3: tbl_df/tbl/data.frame)
## $ participants: Factor w/ 18 levels "sub-01","sub-02",..: 1 1 1 1 2 2 2 2 3 3 ...
## $ contrast : Factor w/ 4 levels "contrast-Cued",..: 4 2 1 3 1 4 2 3 4 2 ...
## $ mask : Factor w/ 1 level "masks/resampled_vorbitofrontal_mask": 1 1 1 1 1 1 1 1 1 1 ...
## $ values : num [1:72] 0.5316 -0.5316 -0.6531 0.0598 0.2876 ...
## $ B_CuedS2 : num [1:72] 2.92 2.92 2.92 2.92 3.96 ...
## $ B_Uncued_S2 : num [1:72] 3.21 3.21 3.21 3.21 3.5 ...
## $ B_diffS2 : num [1:72] -0.292 -0.292 -0.292 -0.292 0.458 ...#
subset_1_OFC <- combined_OFC %>%
filter(contrast == "contrast-Cued")
subset_1_OFC <- subset_1_OFC[, c("values", "B_CuedS2")]
shapiro.test(subset_1_OFC$values) #normal##
## Shapiro-Wilk normality test
##
## data: subset_1_OFC$values
## W = 0.92555, p-value = 0.1621shapiro.test(subset_1_OFC$B_CuedS2) #normal##
## Shapiro-Wilk normality test
##
## data: subset_1_OFC$B_CuedS2
## W = 0.9774, p-value = 0.9193subset_1_OFCcorr <- corr.test(subset_1_OFC, use = "pairwise",method="pearson",adjust="none")
subset_1_OFCcorr## Call:corr.test(x = subset_1_OFC, use = "pairwise", method = "pearson",
## adjust = "none")
## Correlation matrix
## values B_CuedS2
## values 1.00 0.18
## B_CuedS2 0.18 1.00
## Sample Size
## [1] 18
## Probability values (Entries above the diagonal are adjusted for multiple tests.)
## values B_CuedS2
## values 0.00 0.49
## B_CuedS2 0.49 0.00
##
## To see confidence intervals of the correlations, print with the short=FALSE optionggscatter(subset_1_OFC, x = "values", y = "B_CuedS2",
add = "reg.line", conf.int = TRUE,
cor.coef = TRUE, cor.method = "pearson",
xlab = "Bold Cued_Uncued", ylab = "B_CuedS2")
sp.subset_1_OFCcorr <- corr.test(subset_1_OFC, use = "pairwise",method="spearman",adjust="none")
sp.subset_1_OFCcorr## Call:corr.test(x = subset_1_OFC, use = "pairwise", method = "spearman",
## adjust = "none")
## Correlation matrix
## values B_CuedS2
## values 1.00 0.16
## B_CuedS2 0.16 1.00
## Sample Size
## [1] 18
## Probability values (Entries above the diagonal are adjusted for multiple tests.)
## values B_CuedS2
## values 0.00 0.53
## B_CuedS2 0.53 0.00
##
## To see confidence intervals of the correlations, print with the short=FALSE option#
subset_2_OFC <- combined_OFC %>%
filter(contrast == "contrast-Uncued")
subset_2_OFC## # A tibble: 18 × 7
## participants contrast mask values B_CuedS2 B_Uncued_S2 B_diffS2
## <fct> <fct> <fct> <dbl> <dbl> <dbl> <dbl>
## 1 sub-01 contrast-Uncued masks/re… 0.0598 2.92 3.21 -0.292
## 2 sub-02 contrast-Uncued masks/re… 0.242 3.96 3.5 0.458
## 3 sub-03 contrast-Uncued masks/re… 0.297 3.21 3.12 0.0833
## 4 sub-04 contrast-Uncued masks/re… -0.0818 2.71 2.42 0.292
## 5 sub-05 contrast-Uncued masks/re… 0.952 4 4.12 -0.125
## 6 sub-07 contrast-Uncued masks/re… -0.0677 2.75 3 -0.25
## 7 sub-08 contrast-Uncued masks/re… -0.00503 3.67 3.83 -0.159
## 8 sub-11 contrast-Uncued masks/re… 0.0526 2.75 2.88 -0.125
## 9 sub-12 contrast-Uncued masks/re… 0.138 4.29 4.12 0.167
## 10 sub-13 contrast-Uncued masks/re… 0.491 2.42 2.42 0
## 11 sub-14 contrast-Uncued masks/re… -0.187 2.38 2.58 -0.208
## 12 sub-16 contrast-Uncued masks/re… 0.0259 2.04 2.17 -0.125
## 13 sub-17 contrast-Uncued masks/re… 0.219 3.17 3.04 0.125
## 14 sub-18 contrast-Uncued masks/re… -0.161 3.67 3.5 0.167
## 15 sub-19 contrast-Uncued masks/re… -0.133 2.96 3.21 -0.25
## 16 sub-20 contrast-Uncued masks/re… -0.769 3.38 3.36 0.0114
## 17 sub-21 contrast-Uncued masks/re… 0.615 1.67 2.48 -0.812
## 18 sub-23 contrast-Uncued masks/re… -0.140 1.46 1.83 -0.375subset_2_OFC <- subset_2_OFC[, c("values", "B_Uncued_S2")]
shapiro.test(subset_2_OFC$values) #normal##
## Shapiro-Wilk normality test
##
## data: subset_2_OFC$values
## W = 0.9376, p-value = 0.2635shapiro.test(subset_2_OFC$B_Uncued_S2) #normal##
## Shapiro-Wilk normality test
##
## data: subset_2_OFC$B_Uncued_S2
## W = 0.97342, p-value = 0.859subset_2_OFCcorr <- corr.test(subset_2_OFC, use = "pairwise",method="pearson",adjust="none")
subset_2_OFCcorr## Call:corr.test(x = subset_2_OFC, use = "pairwise", method = "pearson",
## adjust = "none")
## Correlation matrix
## values B_Uncued_S2
## values 1.00 0.15
## B_Uncued_S2 0.15 1.00
## Sample Size
## [1] 18
## Probability values (Entries above the diagonal are adjusted for multiple tests.)
## values B_Uncued_S2
## values 0.00 0.56
## B_Uncued_S2 0.56 0.00
##
## To see confidence intervals of the correlations, print with the short=FALSE optionggscatter(subset_2_OFC, x = "values", y = "B_Uncued_S2",
add = "reg.line", conf.int = TRUE,
cor.coef = TRUE, cor.method = "pearson",
xlab = "Bold Cued_Uncued", ylab = "B_Uncued_S2")
sp.subset_2_OFCcorr <- corr.test(subset_2_OFC, use = "pairwise",method="spearman",adjust="none")
sp.subset_2_OFCcorr## Call:corr.test(x = subset_2_OFC, use = "pairwise", method = "spearman",
## adjust = "none")
## Correlation matrix
## values B_Uncued_S2
## values 1.00 0.13
## B_Uncued_S2 0.13 1.00
## Sample Size
## [1] 18
## Probability values (Entries above the diagonal are adjusted for multiple tests.)
## values B_Uncued_S2
## values 0.00 0.62
## B_Uncued_S2 0.62 0.00
##
## To see confidence intervals of the correlations, print with the short=FALSE option#
subset_3_OFC <- combined_OFC %>%
filter(contrast == "contrast-Cued-Uncued")
subset_3_OFC## # A tibble: 18 × 7
## participants contrast mask values B_CuedS2 B_Uncued_S2 B_diffS2
## <fct> <fct> <fct> <dbl> <dbl> <dbl> <dbl>
## 1 sub-01 contrast-Cued-Uncued mask… -0.532 2.92 3.21 -0.292
## 2 sub-02 contrast-Cued-Uncued mask… 0.0368 3.96 3.5 0.458
## 3 sub-03 contrast-Cued-Uncued mask… -0.343 3.21 3.12 0.0833
## 4 sub-04 contrast-Cued-Uncued mask… -0.540 2.71 2.42 0.292
## 5 sub-05 contrast-Cued-Uncued mask… -0.582 4 4.12 -0.125
## 6 sub-07 contrast-Cued-Uncued mask… 0.291 2.75 3 -0.25
## 7 sub-08 contrast-Cued-Uncued mask… 0.0159 3.67 3.83 -0.159
## 8 sub-11 contrast-Cued-Uncued mask… 0.191 2.75 2.88 -0.125
## 9 sub-12 contrast-Cued-Uncued mask… -0.359 4.29 4.12 0.167
## 10 sub-13 contrast-Cued-Uncued mask… -0.112 2.42 2.42 0
## 11 sub-14 contrast-Cued-Uncued mask… -0.521 2.38 2.58 -0.208
## 12 sub-16 contrast-Cued-Uncued mask… 0.0541 2.04 2.17 -0.125
## 13 sub-17 contrast-Cued-Uncued mask… -0.457 3.17 3.04 0.125
## 14 sub-18 contrast-Cued-Uncued mask… 0.266 3.67 3.5 0.167
## 15 sub-19 contrast-Cued-Uncued mask… -0.0594 2.96 3.21 -0.25
## 16 sub-20 contrast-Cued-Uncued mask… 0.0552 3.38 3.36 0.0114
## 17 sub-21 contrast-Cued-Uncued mask… -0.639 1.67 2.48 -0.812
## 18 sub-23 contrast-Cued-Uncued mask… -0.178 1.46 1.83 -0.375subset_3_OFC <- subset_3_OFC[, c("values", "B_diffS2")]
shapiro.test(subset_3_OFC$values) #normal##
## Shapiro-Wilk normality test
##
## data: subset_3_OFC$values
## W = 0.92178, p-value = 0.139shapiro.test(subset_3_OFC$B_diffS2) #normal##
## Shapiro-Wilk normality test
##
## data: subset_3_OFC$B_diffS2
## W = 0.95985, p-value = 0.5987subset_3_OFCcorr <- corr.test(subset_3_OFC, use = "pairwise",method="pearson",adjust="none")
subset_3_OFCcorr## Call:corr.test(x = subset_3_OFC, use = "pairwise", method = "pearson",
## adjust = "none")
## Correlation matrix
## values B_diffS2
## values 1.00 0.21
## B_diffS2 0.21 1.00
## Sample Size
## [1] 18
## Probability values (Entries above the diagonal are adjusted for multiple tests.)
## values B_diffS2
## values 0.0 0.4
## B_diffS2 0.4 0.0
##
## To see confidence intervals of the correlations, print with the short=FALSE option#library("ggpubr")
ggscatter(subset_3_OFC, x = "values", y = "B_diffS2",
add = "reg.line", conf.int = TRUE,
cor.coef = TRUE, cor.method = "pearson",
xlab = "Bold Cued_Uncued", ylab = "B_diffS2")
all_p_values.OFC <- c(round(subset_1_OFCcorr$p[2], 2),
round(subset_2_OFCcorr$p[2], 2),
round(subset_3_OFCcorr$p[2], 2))
all_p_values.OFC## [1] 0.49 0.56 0.40# Apply FDR correction to combined all p-values
adjusted_p_OFC<- p.adjust(all_p_values.OFC, method = "fdr")
adjusted_p_OFC## [1] 0.56 0.56 0.56resampled_vamygdala_mask.nii.gz
No sign correlations
amy <- read_csv("vamygdalamask.csv")## Rows: 72 Columns: 4
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (3): participants, contrast, mask
## dbl (1): values
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.str(amy)## spc_tbl_ [72 × 4] (S3: spec_tbl_df/tbl_df/tbl/data.frame)
## $ participants: chr [1:72] "sub-01" "sub-01" "sub-01" "sub-01" ...
## $ contrast : chr [1:72] "contrast-Uncued-Cued" "contrast-Cued-Uncued" "contrast-Cued" "contrast-Uncued" ...
## $ mask : chr [1:72] "masks/resampled_vamygdala_mask" "masks/resampled_vamygdala_mask" "masks/resampled_vamygdala_mask" "masks/resampled_vamygdala_mask" ...
## $ values : num [1:72] 0.366 -0.366 -1.009 -0.529 0.577 ...
## - attr(*, "spec")=
## .. cols(
## .. participants = col_character(),
## .. contrast = col_character(),
## .. mask = col_character(),
## .. values = col_double()
## .. )
## - attr(*, "problems")=<externalptr>B.data<- read_csv("behavData.csv")## Rows: 18 Columns: 4
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (1): participants
## dbl (3): B_CuedS2, B_Uncued_S2, B_diffS2
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.str(B.data)## spc_tbl_ [18 × 4] (S3: spec_tbl_df/tbl_df/tbl/data.frame)
## $ participants: chr [1:18] "sub-01" "sub-02" "sub-03" "sub-04" ...
## $ B_CuedS2 : num [1:18] 2.92 3.96 3.21 2.71 4 ...
## $ B_Uncued_S2 : num [1:18] 3.21 3.5 3.12 2.42 4.12 ...
## $ B_diffS2 : num [1:18] -0.2917 0.4583 0.0833 0.2917 -0.125 ...
## - attr(*, "spec")=
## .. cols(
## .. participants = col_character(),
## .. B_CuedS2 = col_double(),
## .. B_Uncued_S2 = col_double(),
## .. B_diffS2 = col_double()
## .. )
## - attr(*, "problems")=<externalptr>combined_amy <- inner_join(amy, B.data, by = "participants")
combined_amy <- combined_amy %>%
mutate(across(c(participants, contrast, mask), as.factor))
str(combined_amy)## tibble [72 × 7] (S3: tbl_df/tbl/data.frame)
## $ participants: Factor w/ 18 levels "sub-01","sub-02",..: 1 1 1 1 2 2 2 2 3 3 ...
## $ contrast : Factor w/ 4 levels "contrast-Cued",..: 4 2 1 3 1 4 2 3 4 2 ...
## $ mask : Factor w/ 1 level "masks/resampled_vamygdala_mask": 1 1 1 1 1 1 1 1 1 1 ...
## $ values : num [1:72] 0.366 -0.366 -1.009 -0.529 0.577 ...
## $ B_CuedS2 : num [1:72] 2.92 2.92 2.92 2.92 3.96 ...
## $ B_Uncued_S2 : num [1:72] 3.21 3.21 3.21 3.21 3.5 ...
## $ B_diffS2 : num [1:72] -0.292 -0.292 -0.292 -0.292 0.458 ...#
subset_1_amy <- combined_amy %>%
filter(contrast == "contrast-Cued")
subset_1_amy## # A tibble: 18 × 7
## participants contrast mask values B_CuedS2 B_Uncued_S2 B_diffS2
## <fct> <fct> <fct> <dbl> <dbl> <dbl> <dbl>
## 1 sub-01 contrast-Cued masks/resa… -1.01 2.92 3.21 -0.292
## 2 sub-02 contrast-Cued masks/resa… 0.577 3.96 3.5 0.458
## 3 sub-03 contrast-Cued masks/resa… 0.0491 3.21 3.12 0.0833
## 4 sub-04 contrast-Cued masks/resa… -0.630 2.71 2.42 0.292
## 5 sub-05 contrast-Cued masks/resa… 0.896 4 4.12 -0.125
## 6 sub-07 contrast-Cued masks/resa… 0.473 2.75 3 -0.25
## 7 sub-08 contrast-Cued masks/resa… 0.726 3.67 3.83 -0.159
## 8 sub-11 contrast-Cued masks/resa… 1.09 2.75 2.88 -0.125
## 9 sub-12 contrast-Cued masks/resa… 0.573 4.29 4.12 0.167
## 10 sub-13 contrast-Cued masks/resa… 1.49 2.42 2.42 0
## 11 sub-14 contrast-Cued masks/resa… -1.96 2.38 2.58 -0.208
## 12 sub-16 contrast-Cued masks/resa… 1.38 2.04 2.17 -0.125
## 13 sub-17 contrast-Cued masks/resa… 0.371 3.17 3.04 0.125
## 14 sub-18 contrast-Cued masks/resa… 0.245 3.67 3.5 0.167
## 15 sub-19 contrast-Cued masks/resa… 0.919 2.96 3.21 -0.25
## 16 sub-20 contrast-Cued masks/resa… -0.239 3.38 3.36 0.0114
## 17 sub-21 contrast-Cued masks/resa… 0.00246 1.67 2.48 -0.812
## 18 sub-23 contrast-Cued masks/resa… 0.492 1.46 1.83 -0.375subset_1_amy <- subset_1_amy[, c("values", "B_CuedS2")]
shapiro.test(subset_1_amy$values) #normal##
## Shapiro-Wilk normality test
##
## data: subset_1_amy$values
## W = 0.92494, p-value = 0.1581shapiro.test(subset_1_amy$B_CuedS2) #normal##
## Shapiro-Wilk normality test
##
## data: subset_1_amy$B_CuedS2
## W = 0.9774, p-value = 0.9193subset_1_amycorr <- corr.test(subset_1_amy, use = "pairwise",method="pearson",adjust="none")
subset_1_amycorr## Call:corr.test(x = subset_1_amy, use = "pairwise", method = "pearson",
## adjust = "none")
## Correlation matrix
## values B_CuedS2
## values 1.0 0.1
## B_CuedS2 0.1 1.0
## Sample Size
## [1] 18
## Probability values (Entries above the diagonal are adjusted for multiple tests.)
## values B_CuedS2
## values 0.0 0.7
## B_CuedS2 0.7 0.0
##
## To see confidence intervals of the correlations, print with the short=FALSE optionggscatter(subset_1_amy, x = "values", y = "B_CuedS2",
add = "reg.line", conf.int = TRUE,
cor.coef = TRUE, cor.method = "pearson",
xlab = "Bold Cued_Uncued", ylab = "B_CuedS2")
sp.subset_1_amycorr <- corr.test(subset_1_amy, use = "pairwise",method="spearman",adjust="none")
sp.subset_1_amycorr## Call:corr.test(x = subset_1_amy, use = "pairwise", method = "spearman",
## adjust = "none")
## Correlation matrix
## values B_CuedS2
## values 1.00 0.09
## B_CuedS2 0.09 1.00
## Sample Size
## [1] 18
## Probability values (Entries above the diagonal are adjusted for multiple tests.)
## values B_CuedS2
## values 0.00 0.72
## B_CuedS2 0.72 0.00
##
## To see confidence intervals of the correlations, print with the short=FALSE option#
subset_2_amy <- combined_amy %>%
filter(contrast == "contrast-Uncued")
subset_2_amy## # A tibble: 18 × 7
## participants contrast mask values B_CuedS2 B_Uncued_S2 B_diffS2
## <fct> <fct> <fct> <dbl> <dbl> <dbl> <dbl>
## 1 sub-01 contrast-Uncued masks/res… -0.529 2.92 3.21 -0.292
## 2 sub-02 contrast-Uncued masks/res… 0.0126 3.96 3.5 0.458
## 3 sub-03 contrast-Uncued masks/res… 1.16 3.21 3.12 0.0833
## 4 sub-04 contrast-Uncued masks/res… 0.100 2.71 2.42 0.292
## 5 sub-05 contrast-Uncued masks/res… 2.74 4 4.12 -0.125
## 6 sub-07 contrast-Uncued masks/res… 0.485 2.75 3 -0.25
## 7 sub-08 contrast-Uncued masks/res… 1.45 3.67 3.83 -0.159
## 8 sub-11 contrast-Uncued masks/res… 1.06 2.75 2.88 -0.125
## 9 sub-12 contrast-Uncued masks/res… 0.587 4.29 4.12 0.167
## 10 sub-13 contrast-Uncued masks/res… 0.641 2.42 2.42 0
## 11 sub-14 contrast-Uncued masks/res… -1.45 2.38 2.58 -0.208
## 12 sub-16 contrast-Uncued masks/res… 0.724 2.04 2.17 -0.125
## 13 sub-17 contrast-Uncued masks/res… 0.387 3.17 3.04 0.125
## 14 sub-18 contrast-Uncued masks/res… 0.155 3.67 3.5 0.167
## 15 sub-19 contrast-Uncued masks/res… -0.242 2.96 3.21 -0.25
## 16 sub-20 contrast-Uncued masks/res… 0.180 3.38 3.36 0.0114
## 17 sub-21 contrast-Uncued masks/res… 1.38 1.67 2.48 -0.812
## 18 sub-23 contrast-Uncued masks/res… -0.342 1.46 1.83 -0.375subset_2_amy <- subset_2_amy[, c("values", "B_Uncued_S2")]
shapiro.test(subset_2_amy$values) #normal##
## Shapiro-Wilk normality test
##
## data: subset_2_amy$values
## W = 0.96934, p-value = 0.7851shapiro.test(subset_2_amy$B_Uncued_S2) #normal##
## Shapiro-Wilk normality test
##
## data: subset_2_amy$B_Uncued_S2
## W = 0.97342, p-value = 0.859subset_2_amycorr <- corr.test(subset_2_amy, use = "pairwise",method="pearson",adjust="none")
subset_2_amycorr## Call:corr.test(x = subset_2_amy, use = "pairwise", method = "pearson",
## adjust = "none")
## Correlation matrix
## values B_Uncued_S2
## values 1.00 0.38
## B_Uncued_S2 0.38 1.00
## Sample Size
## [1] 18
## Probability values (Entries above the diagonal are adjusted for multiple tests.)
## values B_Uncued_S2
## values 0.00 0.12
## B_Uncued_S2 0.12 0.00
##
## To see confidence intervals of the correlations, print with the short=FALSE optionggscatter(subset_2_amy, x = "values", y = "B_Uncued_S2",
add = "reg.line", conf.int = TRUE,
cor.coef = TRUE, cor.method = "pearson",
xlab = "Bold Cued_Uncued", ylab = "B_Uncued_S2")
sp.subset_2_amycorr <- corr.test(subset_2_amy, use = "pairwise",method="spearman",adjust="none")
sp.subset_2_amycorr## Call:corr.test(x = subset_2_amy, use = "pairwise", method = "spearman",
## adjust = "none")
## Correlation matrix
## values B_Uncued_S2
## values 1.00 0.18
## B_Uncued_S2 0.18 1.00
## Sample Size
## [1] 18
## Probability values (Entries above the diagonal are adjusted for multiple tests.)
## values B_Uncued_S2
## values 0.00 0.46
## B_Uncued_S2 0.46 0.00
##
## To see confidence intervals of the correlations, print with the short=FALSE option#
subset_3_amy <- combined_amy %>%
filter(contrast == "contrast-Cued-Uncued")
subset_3_amy## # A tibble: 18 × 7
## participants contrast mask values B_CuedS2 B_Uncued_S2 B_diffS2
## <fct> <fct> <fct> <dbl> <dbl> <dbl> <dbl>
## 1 sub-01 contrast-Cued-Uncued mask… -0.366 2.92 3.21 -0.292
## 2 sub-02 contrast-Cued-Uncued mask… 0.433 3.96 3.5 0.458
## 3 sub-03 contrast-Cued-Uncued mask… -0.839 3.21 3.12 0.0833
## 4 sub-04 contrast-Cued-Uncued mask… -0.545 2.71 2.42 0.292
## 5 sub-05 contrast-Cued-Uncued mask… -1.36 4 4.12 -0.125
## 6 sub-07 contrast-Cued-Uncued mask… -0.0391 2.75 3 -0.25
## 7 sub-08 contrast-Cued-Uncued mask… -0.531 3.67 3.83 -0.159
## 8 sub-11 contrast-Cued-Uncued mask… 0.0149 2.75 2.88 -0.125
## 9 sub-12 contrast-Cued-Uncued mask… -0.0102 4.29 4.12 0.167
## 10 sub-13 contrast-Cued-Uncued mask… 0.629 2.42 2.42 0
## 11 sub-14 contrast-Cued-Uncued mask… -0.369 2.38 2.58 -0.208
## 12 sub-16 contrast-Cued-Uncued mask… 0.497 2.04 2.17 -0.125
## 13 sub-17 contrast-Cued-Uncued mask… -0.0127 3.17 3.04 0.125
## 14 sub-18 contrast-Cued-Uncued mask… 0.0639 3.67 3.5 0.167
## 15 sub-19 contrast-Cued-Uncued mask… 0.884 2.96 3.21 -0.25
## 16 sub-20 contrast-Cued-Uncued mask… -0.311 3.38 3.36 0.0114
## 17 sub-21 contrast-Cued-Uncued mask… -0.961 1.67 2.48 -0.812
## 18 sub-23 contrast-Cued-Uncued mask… 0.616 1.46 1.83 -0.375subset_3_amy <- subset_3_amy[, c("values", "B_diffS2")]
shapiro.test(subset_3_amy$values) #normal##
## Shapiro-Wilk normality test
##
## data: subset_3_amy$values
## W = 0.97449, p-value = 0.8766shapiro.test(subset_3_amy$B_diffS2) #normal##
## Shapiro-Wilk normality test
##
## data: subset_3_amy$B_diffS2
## W = 0.95985, p-value = 0.5987subset_3_amycorr <- corr.test(subset_3_amy, use = "pairwise",method="pearson",adjust="none")
subset_3_amycorr## Call:corr.test(x = subset_3_amy, use = "pairwise", method = "pearson",
## adjust = "none")
## Correlation matrix
## values B_diffS2
## values 1.00 0.18
## B_diffS2 0.18 1.00
## Sample Size
## [1] 18
## Probability values (Entries above the diagonal are adjusted for multiple tests.)
## values B_diffS2
## values 0.00 0.49
## B_diffS2 0.49 0.00
##
## To see confidence intervals of the correlations, print with the short=FALSE option#library("ggpubr")
ggscatter(subset_3_amy, x = "values", y = "B_diffS2",
add = "reg.line", conf.int = TRUE,
cor.coef = TRUE, cor.method = "pearson",
xlab = "Bold Cued_Uncued", ylab = "B_diffS2")
sp.subset_3_amycorr <- corr.test(subset_3_amy, use = "pairwise",method="spearman",adjust="none")
sp.subset_3_amycorr## Call:corr.test(x = subset_3_amy, use = "pairwise", method = "spearman",
## adjust = "none")
## Correlation matrix
## values B_diffS2
## values 1.00 0.06
## B_diffS2 0.06 1.00
## Sample Size
## [1] 18
## Probability values (Entries above the diagonal are adjusted for multiple tests.)
## values B_diffS2
## values 0.00 0.81
## B_diffS2 0.81 0.00
##
## To see confidence intervals of the correlations, print with the short=FALSE optionall_p_values.amy <- c(round(subset_1_amycorr$p[2], 2),
round(subset_2_amycorr$p[2], 2),
round(subset_3_amycorr$p[2], 2))
all_p_values.amy## [1] 0.70 0.12 0.49# Apply FDR correction to combined all p-values
adjusted_p_amy<- p.adjust(all_p_values.amy, method = "fdr")
adjusted_p_amy## [1] 0.70 0.36 0.70resampled_vsgcv2_mask.nii.gz
No sign correlations after correction
sgacc <- read_csv("resampled_vsgcv2_mask.csv")## Rows: 72 Columns: 4
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (3): participants, contrast, mask
## dbl (1): values
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.str(sgacc)## spc_tbl_ [72 × 4] (S3: spec_tbl_df/tbl_df/tbl/data.frame)
## $ participants: chr [1:72] "sub-01" "sub-01" "sub-01" "sub-01" ...
## $ contrast : chr [1:72] "contrast-Uncued-Cued" "contrast-Cued-Uncued" "contrast-Cued" "contrast-Uncued" ...
## $ mask : chr [1:72] "masks/resampled_vsgcv2_mask" "masks/resampled_vsgcv2_mask" "masks/resampled_vsgcv2_mask" "masks/resampled_vsgcv2_mask" ...
## $ values : num [1:72] 0.4961 -0.4961 -0.7038 -0.0491 -0.4051 ...
## - attr(*, "spec")=
## .. cols(
## .. participants = col_character(),
## .. contrast = col_character(),
## .. mask = col_character(),
## .. values = col_double()
## .. )
## - attr(*, "problems")=<externalptr>B.data <- read_csv("behavData.csv")## Rows: 18 Columns: 4
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (1): participants
## dbl (3): B_CuedS2, B_Uncued_S2, B_diffS2
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.str(B.data)## spc_tbl_ [18 × 4] (S3: spec_tbl_df/tbl_df/tbl/data.frame)
## $ participants: chr [1:18] "sub-01" "sub-02" "sub-03" "sub-04" ...
## $ B_CuedS2 : num [1:18] 2.92 3.96 3.21 2.71 4 ...
## $ B_Uncued_S2 : num [1:18] 3.21 3.5 3.12 2.42 4.12 ...
## $ B_diffS2 : num [1:18] -0.2917 0.4583 0.0833 0.2917 -0.125 ...
## - attr(*, "spec")=
## .. cols(
## .. participants = col_character(),
## .. B_CuedS2 = col_double(),
## .. B_Uncued_S2 = col_double(),
## .. B_diffS2 = col_double()
## .. )
## - attr(*, "problems")=<externalptr>combined_sgacc <- inner_join(sgacc, B.data, by = "participants")
combined_sgacc <- combined_sgacc %>%
mutate(across(c(participants, contrast, mask), as.factor))
str(combined_sgacc)## tibble [72 × 7] (S3: tbl_df/tbl/data.frame)
## $ participants: Factor w/ 18 levels "sub-01","sub-02",..: 1 1 1 1 2 2 2 2 3 3 ...
## $ contrast : Factor w/ 4 levels "contrast-Cued",..: 4 2 1 3 1 4 2 3 4 2 ...
## $ mask : Factor w/ 1 level "masks/resampled_vsgcv2_mask": 1 1 1 1 1 1 1 1 1 1 ...
## $ values : num [1:72] 0.4961 -0.4961 -0.7038 -0.0491 -0.4051 ...
## $ B_CuedS2 : num [1:72] 2.92 2.92 2.92 2.92 3.96 ...
## $ B_Uncued_S2 : num [1:72] 3.21 3.21 3.21 3.21 3.5 ...
## $ B_diffS2 : num [1:72] -0.292 -0.292 -0.292 -0.292 0.458 ...#
subset_1_sgacc <- combined_sgacc %>%
filter(contrast == "contrast-Cued")
subset_1_sgacc## # A tibble: 18 × 7
## participants contrast mask values B_CuedS2 B_Uncued_S2 B_diffS2
## <fct> <fct> <fct> <dbl> <dbl> <dbl> <dbl>
## 1 sub-01 contrast-Cued masks/resa… -0.704 2.92 3.21 -0.292
## 2 sub-02 contrast-Cued masks/resa… -0.405 3.96 3.5 0.458
## 3 sub-03 contrast-Cued masks/resa… -1.06 3.21 3.12 0.0833
## 4 sub-04 contrast-Cued masks/resa… -0.621 2.71 2.42 0.292
## 5 sub-05 contrast-Cued masks/resa… -1.04 4 4.12 -0.125
## 6 sub-07 contrast-Cued masks/resa… -0.609 2.75 3 -0.25
## 7 sub-08 contrast-Cued masks/resa… -0.409 3.67 3.83 -0.159
## 8 sub-11 contrast-Cued masks/resa… -0.294 2.75 2.88 -0.125
## 9 sub-12 contrast-Cued masks/resa… -0.00377 4.29 4.12 0.167
## 10 sub-13 contrast-Cued masks/resa… -1.58 2.42 2.42 0
## 11 sub-14 contrast-Cued masks/resa… 0.371 2.38 2.58 -0.208
## 12 sub-16 contrast-Cued masks/resa… -0.882 2.04 2.17 -0.125
## 13 sub-17 contrast-Cued masks/resa… -0.831 3.17 3.04 0.125
## 14 sub-18 contrast-Cued masks/resa… -0.181 3.67 3.5 0.167
## 15 sub-19 contrast-Cued masks/resa… -1.23 2.96 3.21 -0.25
## 16 sub-20 contrast-Cued masks/resa… -1.22 3.38 3.36 0.0114
## 17 sub-21 contrast-Cued masks/resa… -0.444 1.67 2.48 -0.812
## 18 sub-23 contrast-Cued masks/resa… -0.215 1.46 1.83 -0.375subset_1_sgacc <- subset_1_sgacc[, c("values", "B_CuedS2")]
shapiro.test(subset_1_sgacc$values) #normal##
## Shapiro-Wilk normality test
##
## data: subset_1_sgacc$values
## W = 0.99051, p-value = 0.9991shapiro.test(subset_1_sgacc$B_CuedS2) #normal##
## Shapiro-Wilk normality test
##
## data: subset_1_sgacc$B_CuedS2
## W = 0.9774, p-value = 0.9193subset_1_sgacccorr <- corr.test(subset_1_sgacc, use = "pairwise",method="pearson",adjust="none")
subset_1_sgacccorr## Call:corr.test(x = subset_1_sgacc, use = "pairwise", method = "pearson",
## adjust = "none")
## Correlation matrix
## values B_CuedS2
## values 1.00 -0.01
## B_CuedS2 -0.01 1.00
## Sample Size
## [1] 18
## Probability values (Entries above the diagonal are adjusted for multiple tests.)
## values B_CuedS2
## values 0.00 0.97
## B_CuedS2 0.97 0.00
##
## To see confidence intervals of the correlations, print with the short=FALSE optionggscatter(subset_1_sgacc, x = "values", y = "B_CuedS2",
add = "reg.line", conf.int = TRUE,
cor.coef = TRUE, cor.method = "pearson",
xlab = "Bold Cued_Uncued", ylab = "B_CuedS2")
sp.subset_1_sgacccorr <- corr.test(subset_1_sgacc, use = "pairwise",method="spearman",adjust="none")
sp.subset_1_sgacccorr## Call:corr.test(x = subset_1_sgacc, use = "pairwise", method = "spearman",
## adjust = "none")
## Correlation matrix
## values B_CuedS2
## values 1.00 0.01
## B_CuedS2 0.01 1.00
## Sample Size
## [1] 18
## Probability values (Entries above the diagonal are adjusted for multiple tests.)
## values B_CuedS2
## values 0.00 0.98
## B_CuedS2 0.98 0.00
##
## To see confidence intervals of the correlations, print with the short=FALSE option#
subset_2_sgacc <- combined_sgacc %>%
filter(contrast == "contrast-Uncued")
subset_2_sgacc## # A tibble: 18 × 7
## participants contrast mask values B_CuedS2 B_Uncued_S2 B_diffS2
## <fct> <fct> <fct> <dbl> <dbl> <dbl> <dbl>
## 1 sub-01 contrast-Uncued masks/res… -0.0491 2.92 3.21 -0.292
## 2 sub-02 contrast-Uncued masks/res… -0.396 3.96 3.5 0.458
## 3 sub-03 contrast-Uncued masks/res… -0.390 3.21 3.12 0.0833
## 4 sub-04 contrast-Uncued masks/res… -0.170 2.71 2.42 0.292
## 5 sub-05 contrast-Uncued masks/res… -0.851 4 4.12 -0.125
## 6 sub-07 contrast-Uncued masks/res… -0.185 2.75 3 -0.25
## 7 sub-08 contrast-Uncued masks/res… 0.411 3.67 3.83 -0.159
## 8 sub-11 contrast-Uncued masks/res… -0.778 2.75 2.88 -0.125
## 9 sub-12 contrast-Uncued masks/res… 0.142 4.29 4.12 0.167
## 10 sub-13 contrast-Uncued masks/res… -1.02 2.42 2.42 0
## 11 sub-14 contrast-Uncued masks/res… 0.138 2.38 2.58 -0.208
## 12 sub-16 contrast-Uncued masks/res… 0.356 2.04 2.17 -0.125
## 13 sub-17 contrast-Uncued masks/res… -0.277 3.17 3.04 0.125
## 14 sub-18 contrast-Uncued masks/res… -0.976 3.67 3.5 0.167
## 15 sub-19 contrast-Uncued masks/res… -0.907 2.96 3.21 -0.25
## 16 sub-20 contrast-Uncued masks/res… -0.853 3.38 3.36 0.0114
## 17 sub-21 contrast-Uncued masks/res… 0.115 1.67 2.48 -0.812
## 18 sub-23 contrast-Uncued masks/res… -0.0515 1.46 1.83 -0.375subset_2_sgacc <- subset_2_sgacc[, c("values", "B_Uncued_S2")]
shapiro.test(subset_2_sgacc$values) #normal##
## Shapiro-Wilk normality test
##
## data: subset_2_sgacc$values
## W = 0.92287, p-value = 0.1453shapiro.test(subset_2_sgacc$B_Uncued_S2) #normal##
## Shapiro-Wilk normality test
##
## data: subset_2_sgacc$B_Uncued_S2
## W = 0.97342, p-value = 0.859subset_2_sgacccorr <- corr.test(subset_2_sgacc, use = "pairwise",method="pearson",adjust="none")
subset_2_sgacccorr## Call:corr.test(x = subset_2_sgacc, use = "pairwise", method = "pearson",
## adjust = "none")
## Correlation matrix
## values B_Uncued_S2
## values 1.0 -0.2
## B_Uncued_S2 -0.2 1.0
## Sample Size
## [1] 18
## Probability values (Entries above the diagonal are adjusted for multiple tests.)
## values B_Uncued_S2
## values 0.00 0.44
## B_Uncued_S2 0.44 0.00
##
## To see confidence intervals of the correlations, print with the short=FALSE optionggscatter(subset_2_sgacc, x = "values", y = "B_Uncued_S2",
add = "reg.line", conf.int = TRUE,
cor.coef = TRUE, cor.method = "pearson",
xlab = "Bold Cued_Uncued", ylab = "B_Uncued_S2")
sp.subset_2_sgacccorr <- corr.test(subset_2_sgacc, use = "pairwise",method="spearman",adjust="none")
sp.subset_2_sgacccorr## Call:corr.test(x = subset_2_sgacc, use = "pairwise", method = "spearman",
## adjust = "none")
## Correlation matrix
## values B_Uncued_S2
## values 1.00 -0.15
## B_Uncued_S2 -0.15 1.00
## Sample Size
## [1] 18
## Probability values (Entries above the diagonal are adjusted for multiple tests.)
## values B_Uncued_S2
## values 0.00 0.56
## B_Uncued_S2 0.56 0.00
##
## To see confidence intervals of the correlations, print with the short=FALSE option#
subset_3_sgacc <- combined_sgacc %>%
filter(contrast == "contrast-Cued-Uncued")
subset_3_sgacc## # A tibble: 18 × 7
## participants contrast mask values B_CuedS2 B_Uncued_S2 B_diffS2
## <fct> <fct> <fct> <dbl> <dbl> <dbl> <dbl>
## 1 sub-01 contrast-Cued-Uncued mask… -0.496 2.92 3.21 -0.292
## 2 sub-02 contrast-Cued-Uncued mask… -0.0129 3.96 3.5 0.458
## 3 sub-03 contrast-Cued-Uncued mask… -0.494 3.21 3.12 0.0833
## 4 sub-04 contrast-Cued-Uncued mask… -0.336 2.71 2.42 0.292
## 5 sub-05 contrast-Cued-Uncued mask… -0.148 4 4.12 -0.125
## 6 sub-07 contrast-Cued-Uncued mask… -0.284 2.75 3 -0.25
## 7 sub-08 contrast-Cued-Uncued mask… -0.616 3.67 3.83 -0.159
## 8 sub-11 contrast-Cued-Uncued mask… 0.370 2.75 2.88 -0.125
## 9 sub-12 contrast-Cued-Uncued mask… -0.109 4.29 4.12 0.167
## 10 sub-13 contrast-Cued-Uncued mask… -0.401 2.42 2.42 0
## 11 sub-14 contrast-Cued-Uncued mask… 0.168 2.38 2.58 -0.208
## 12 sub-16 contrast-Cued-Uncued mask… -0.969 2.04 2.17 -0.125
## 13 sub-17 contrast-Cued-Uncued mask… -0.413 3.17 3.04 0.125
## 14 sub-18 contrast-Cued-Uncued mask… 0.596 3.67 3.5 0.167
## 15 sub-19 contrast-Cued-Uncued mask… -0.288 2.96 3.21 -0.25
## 16 sub-20 contrast-Cued-Uncued mask… -0.262 3.38 3.36 0.0114
## 17 sub-21 contrast-Cued-Uncued mask… -0.441 1.67 2.48 -0.812
## 18 sub-23 contrast-Cued-Uncued mask… -0.124 1.46 1.83 -0.375subset_3_sgacc <- subset_3_sgacc[, c("values", "B_diffS2")]
shapiro.test(subset_3_sgacc$values) #normal##
## Shapiro-Wilk normality test
##
## data: subset_3_sgacc$values
## W = 0.95719, p-value = 0.5483shapiro.test(subset_3_sgacc$B_diffS2) #normal##
## Shapiro-Wilk normality test
##
## data: subset_3_sgacc$B_diffS2
## W = 0.95985, p-value = 0.5987subset_3_sgacccorr <- corr.test(subset_3_sgacc, use = "pairwise",method="pearson",adjust="none")
subset_3_sgacccorr## Call:corr.test(x = subset_3_sgacc, use = "pairwise", method = "pearson",
## adjust = "none")
## Correlation matrix
## values B_diffS2
## values 1.00 0.22
## B_diffS2 0.22 1.00
## Sample Size
## [1] 18
## Probability values (Entries above the diagonal are adjusted for multiple tests.)
## values B_diffS2
## values 0.00 0.37
## B_diffS2 0.37 0.00
##
## To see confidence intervals of the correlations, print with the short=FALSE option#library("ggpubr")
ggscatter(subset_3_sgacc, x = "values", y = "B_diffS2",
add = "reg.line", conf.int = TRUE,
cor.coef = TRUE, cor.method = "pearson",
xlab = "Bold Cued_Uncued", ylab = "B_diffS2")
sp.subset_3_sgacccorr <- corr.test(subset_3_sgacc, use = "pairwise",method="spearman",adjust="none")
sp.subset_3_sgacccorr## Call:corr.test(x = subset_3_sgacc, use = "pairwise", method = "spearman",
## adjust = "none")
## Correlation matrix
## values B_diffS2
## values 1.00 0.27
## B_diffS2 0.27 1.00
## Sample Size
## [1] 18
## Probability values (Entries above the diagonal are adjusted for multiple tests.)
## values B_diffS2
## values 0.00 0.29
## B_diffS2 0.29 0.00
##
## To see confidence intervals of the correlations, print with the short=FALSE optionall_p_values.sgacc <- c(round(subset_1_sgacccorr$p[2], 2),
round(subset_2_sgacccorr$p[2], 2),
round(subset_3_sgacccorr$p[2], 2))
all_p_values.sgacc## [1] 0.97 0.44 0.37# Apply FDR correction to combined all p-values
adjusted_p_sgacc<- p.adjust(all_p_values.sgacc, method = "fdr")
adjusted_p_sgacc## [1] 0.97 0.66 0.66b_gray_thresholded.nii.gz
No sign correlations after correction
Grey <- read_csv("greyMask.csv")## Rows: 72 Columns: 4
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (3): participants, contrast, mask
## dbl (1): values
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.str(Grey)## spc_tbl_ [72 × 4] (S3: spec_tbl_df/tbl_df/tbl/data.frame)
## $ participants: chr [1:72] "sub-01" "sub-01" "sub-01" "sub-01" ...
## $ contrast : chr [1:72] "contrast-Uncued-Cued" "contrast-Cued-Uncued" "contrast-Cued" "contrast-Uncued" ...
## $ mask : chr [1:72] "masks/b_gray_thresholded" "masks/b_gray_thresholded" "masks/b_gray_thresholded" "masks/b_gray_thresholded" ...
## $ values : num [1:72] 0.7962 -0.7962 -1.1649 -0.0846 0.4954 ...
## - attr(*, "spec")=
## .. cols(
## .. participants = col_character(),
## .. contrast = col_character(),
## .. mask = col_character(),
## .. values = col_double()
## .. )
## - attr(*, "problems")=<externalptr>B.data <- read_csv("behavData.csv")## Rows: 18 Columns: 4
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (1): participants
## dbl (3): B_CuedS2, B_Uncued_S2, B_diffS2
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.str(B.data)## spc_tbl_ [18 × 4] (S3: spec_tbl_df/tbl_df/tbl/data.frame)
## $ participants: chr [1:18] "sub-01" "sub-02" "sub-03" "sub-04" ...
## $ B_CuedS2 : num [1:18] 2.92 3.96 3.21 2.71 4 ...
## $ B_Uncued_S2 : num [1:18] 3.21 3.5 3.12 2.42 4.12 ...
## $ B_diffS2 : num [1:18] -0.2917 0.4583 0.0833 0.2917 -0.125 ...
## - attr(*, "spec")=
## .. cols(
## .. participants = col_character(),
## .. B_CuedS2 = col_double(),
## .. B_Uncued_S2 = col_double(),
## .. B_diffS2 = col_double()
## .. )
## - attr(*, "problems")=<externalptr>combined_Grey <- inner_join(Grey, B.data, by = "participants")
combined_Grey <- combined_Grey %>%
mutate(across(c(participants, contrast, mask), as.factor))
str(combined_Grey)## tibble [72 × 7] (S3: tbl_df/tbl/data.frame)
## $ participants: Factor w/ 18 levels "sub-01","sub-02",..: 1 1 1 1 2 2 2 2 3 3 ...
## $ contrast : Factor w/ 4 levels "contrast-Cued",..: 4 2 1 3 1 4 2 3 4 2 ...
## $ mask : Factor w/ 1 level "masks/b_gray_thresholded": 1 1 1 1 1 1 1 1 1 1 ...
## $ values : num [1:72] 0.7962 -0.7962 -1.1649 -0.0846 0.4954 ...
## $ B_CuedS2 : num [1:72] 2.92 2.92 2.92 2.92 3.96 ...
## $ B_Uncued_S2 : num [1:72] 3.21 3.21 3.21 3.21 3.5 ...
## $ B_diffS2 : num [1:72] -0.292 -0.292 -0.292 -0.292 0.458 ...#
subset_1_Grey <- combined_Grey %>%
filter(contrast == "contrast-Cued")
subset_1_Grey## # A tibble: 18 × 7
## participants contrast mask values B_CuedS2 B_Uncued_S2 B_diffS2
## <fct> <fct> <fct> <dbl> <dbl> <dbl> <dbl>
## 1 sub-01 contrast-Cued masks/b_gra… -1.16 2.92 3.21 -0.292
## 2 sub-02 contrast-Cued masks/b_gra… 0.495 3.96 3.5 0.458
## 3 sub-03 contrast-Cued masks/b_gra… 0.0364 3.21 3.12 0.0833
## 4 sub-04 contrast-Cued masks/b_gra… 0.140 2.71 2.42 0.292
## 5 sub-05 contrast-Cued masks/b_gra… 0.817 4 4.12 -0.125
## 6 sub-07 contrast-Cued masks/b_gra… 1.09 2.75 3 -0.25
## 7 sub-08 contrast-Cued masks/b_gra… -0.182 3.67 3.83 -0.159
## 8 sub-11 contrast-Cued masks/b_gra… 1.28 2.75 2.88 -0.125
## 9 sub-12 contrast-Cued masks/b_gra… 0.179 4.29 4.12 0.167
## 10 sub-13 contrast-Cued masks/b_gra… 0.939 2.42 2.42 0
## 11 sub-14 contrast-Cued masks/b_gra… -1.31 2.38 2.58 -0.208
## 12 sub-16 contrast-Cued masks/b_gra… 0.709 2.04 2.17 -0.125
## 13 sub-17 contrast-Cued masks/b_gra… -0.0362 3.17 3.04 0.125
## 14 sub-18 contrast-Cued masks/b_gra… 1.55 3.67 3.5 0.167
## 15 sub-19 contrast-Cued masks/b_gra… 0.107 2.96 3.21 -0.25
## 16 sub-20 contrast-Cued masks/b_gra… -0.301 3.38 3.36 0.0114
## 17 sub-21 contrast-Cued masks/b_gra… 0.0182 1.67 2.48 -0.812
## 18 sub-23 contrast-Cued masks/b_gra… -0.114 1.46 1.83 -0.375subset_1_Grey <- subset_1_Grey[, c("values", "B_CuedS2")]
shapiro.test(subset_1_Grey$values) #normal##
## Shapiro-Wilk normality test
##
## data: subset_1_Grey$values
## W = 0.95546, p-value = 0.5169shapiro.test(subset_1_Grey$B_CuedS2) #normal##
## Shapiro-Wilk normality test
##
## data: subset_1_Grey$B_CuedS2
## W = 0.9774, p-value = 0.9193subset_1_Greycorr <- corr.test(subset_1_Grey, use = "pairwise",method="pearson",adjust="none")
subset_1_Greycorr## Call:corr.test(x = subset_1_Grey, use = "pairwise", method = "pearson",
## adjust = "none")
## Correlation matrix
## values B_CuedS2
## values 1.00 0.16
## B_CuedS2 0.16 1.00
## Sample Size
## [1] 18
## Probability values (Entries above the diagonal are adjusted for multiple tests.)
## values B_CuedS2
## values 0.00 0.52
## B_CuedS2 0.52 0.00
##
## To see confidence intervals of the correlations, print with the short=FALSE optionggscatter(subset_1_Grey, x = "values", y = "B_CuedS2",
add = "reg.line", conf.int = TRUE,
cor.coef = TRUE, cor.method = "pearson",
xlab = "Bold Cued_Uncued", ylab = "B_CuedS2")
sp.subset_1_Greycorr <- corr.test(subset_1_Grey, use = "pairwise",method="spearman",adjust="none")
sp.subset_1_Greycorr## Call:corr.test(x = subset_1_Grey, use = "pairwise", method = "spearman",
## adjust = "none")
## Correlation matrix
## values B_CuedS2
## values 1.00 0.16
## B_CuedS2 0.16 1.00
## Sample Size
## [1] 18
## Probability values (Entries above the diagonal are adjusted for multiple tests.)
## values B_CuedS2
## values 0.00 0.53
## B_CuedS2 0.53 0.00
##
## To see confidence intervals of the correlations, print with the short=FALSE option#
subset_2_Grey <- combined_Grey %>%
filter(contrast == "contrast-Uncued")
subset_2_Grey## # A tibble: 18 × 7
## participants contrast mask values B_CuedS2 B_Uncued_S2 B_diffS2
## <fct> <fct> <fct> <dbl> <dbl> <dbl> <dbl>
## 1 sub-01 contrast-Uncued masks/b_g… -0.0846 2.92 3.21 -0.292
## 2 sub-02 contrast-Uncued masks/b_g… 0.0962 3.96 3.5 0.458
## 3 sub-03 contrast-Uncued masks/b_g… 0.950 3.21 3.12 0.0833
## 4 sub-04 contrast-Uncued masks/b_g… 0.948 2.71 2.42 0.292
## 5 sub-05 contrast-Uncued masks/b_g… 1.30 4 4.12 -0.125
## 6 sub-07 contrast-Uncued masks/b_g… 0.873 2.75 3 -0.25
## 7 sub-08 contrast-Uncued masks/b_g… 0.0310 3.67 3.83 -0.159
## 8 sub-11 contrast-Uncued masks/b_g… 0.922 2.75 2.88 -0.125
## 9 sub-12 contrast-Uncued masks/b_g… 0.158 4.29 4.12 0.167
## 10 sub-13 contrast-Uncued masks/b_g… 0.897 2.42 2.42 0
## 11 sub-14 contrast-Uncued masks/b_g… -0.476 2.38 2.58 -0.208
## 12 sub-16 contrast-Uncued masks/b_g… 0.883 2.04 2.17 -0.125
## 13 sub-17 contrast-Uncued masks/b_g… 1.40 3.17 3.04 0.125
## 14 sub-18 contrast-Uncued masks/b_g… 0.907 3.67 3.5 0.167
## 15 sub-19 contrast-Uncued masks/b_g… -0.181 2.96 3.21 -0.25
## 16 sub-20 contrast-Uncued masks/b_g… -0.323 3.38 3.36 0.0114
## 17 sub-21 contrast-Uncued masks/b_g… 0.644 1.67 2.48 -0.812
## 18 sub-23 contrast-Uncued masks/b_g… 0.214 1.46 1.83 -0.375subset_2_Grey <- subset_2_Grey[, c("values", "B_Uncued_S2")]
shapiro.test(subset_2_Grey$values) #normal##
## Shapiro-Wilk normality test
##
## data: subset_2_Grey$values
## W = 0.91582, p-value = 0.109shapiro.test(subset_2_Grey$B_Uncued_S2) #normal##
## Shapiro-Wilk normality test
##
## data: subset_2_Grey$B_Uncued_S2
## W = 0.97342, p-value = 0.859subset_2_Greycorr <- corr.test(subset_2_Grey, use = "pairwise",method="pearson",adjust="none")
subset_2_Greycorr## Call:corr.test(x = subset_2_Grey, use = "pairwise", method = "pearson",
## adjust = "none")
## Correlation matrix
## values B_Uncued_S2
## values 1.00 -0.09
## B_Uncued_S2 -0.09 1.00
## Sample Size
## [1] 18
## Probability values (Entries above the diagonal are adjusted for multiple tests.)
## values B_Uncued_S2
## values 0.00 0.73
## B_Uncued_S2 0.73 0.00
##
## To see confidence intervals of the correlations, print with the short=FALSE optionggscatter(subset_2_Grey, x = "values", y = "B_Uncued_S2",
add = "reg.line", conf.int = TRUE,
cor.coef = TRUE, cor.method = "pearson",
xlab = "Bold Cued_Uncued", ylab = "B_Uncued_S2")
sp.subset_2_Greycorr <- corr.test(subset_2_Grey, use = "pairwise",method="spearman",adjust="none")
sp.subset_2_Greycorr## Call:corr.test(x = subset_2_Grey, use = "pairwise", method = "spearman",
## adjust = "none")
## Correlation matrix
## values B_Uncued_S2
## values 1.00 -0.14
## B_Uncued_S2 -0.14 1.00
## Sample Size
## [1] 18
## Probability values (Entries above the diagonal are adjusted for multiple tests.)
## values B_Uncued_S2
## values 0.00 0.58
## B_Uncued_S2 0.58 0.00
##
## To see confidence intervals of the correlations, print with the short=FALSE option#
subset_3_Grey <- combined_Grey %>%
filter(contrast == "contrast-Cued-Uncued")
subset_3_Grey## # A tibble: 18 × 7
## participants contrast mask values B_CuedS2 B_Uncued_S2 B_diffS2
## <fct> <fct> <fct> <dbl> <dbl> <dbl> <dbl>
## 1 sub-01 contrast-Cued-Uncued mask… -0.796 2.92 3.21 -0.292
## 2 sub-02 contrast-Cued-Uncued mask… 0.298 3.96 3.5 0.458
## 3 sub-03 contrast-Cued-Uncued mask… -0.681 3.21 3.12 0.0833
## 4 sub-04 contrast-Cued-Uncued mask… -0.597 2.71 2.42 0.292
## 5 sub-05 contrast-Cued-Uncued mask… -0.351 4 4.12 -0.125
## 6 sub-07 contrast-Cued-Uncued mask… 0.0975 2.75 3 -0.25
## 7 sub-08 contrast-Cued-Uncued mask… -0.157 3.67 3.83 -0.159
## 8 sub-11 contrast-Cued-Uncued mask… 0.251 2.75 2.88 -0.125
## 9 sub-12 contrast-Cued-Uncued mask… 0.0159 4.29 4.12 0.167
## 10 sub-13 contrast-Cued-Uncued mask… 0.0207 2.42 2.42 0
## 11 sub-14 contrast-Cued-Uncued mask… -0.613 2.38 2.58 -0.208
## 12 sub-16 contrast-Cued-Uncued mask… -0.142 2.04 2.17 -0.125
## 13 sub-17 contrast-Cued-Uncued mask… -1.06 3.17 3.04 0.125
## 14 sub-18 contrast-Cued-Uncued mask… 0.464 3.67 3.5 0.167
## 15 sub-19 contrast-Cued-Uncued mask… 0.214 2.96 3.21 -0.25
## 16 sub-20 contrast-Cued-Uncued mask… 0.0198 3.38 3.36 0.0114
## 17 sub-21 contrast-Cued-Uncued mask… -0.434 1.67 2.48 -0.812
## 18 sub-23 contrast-Cued-Uncued mask… -0.239 1.46 1.83 -0.375subset_3_Grey <- subset_3_Grey[, c("values", "B_diffS2")]
shapiro.test(subset_3_Grey$values) #normal##
## Shapiro-Wilk normality test
##
## data: subset_3_Grey$values
## W = 0.96882, p-value = 0.7753shapiro.test(subset_3_Grey$B_diffS2) #normal##
## Shapiro-Wilk normality test
##
## data: subset_3_Grey$B_diffS2
## W = 0.95985, p-value = 0.5987subset_3_Greycorr <- corr.test(subset_3_Grey, use = "pairwise",method="pearson",adjust="none")
subset_3_Greycorr## Call:corr.test(x = subset_3_Grey, use = "pairwise", method = "pearson",
## adjust = "none")
## Correlation matrix
## values B_diffS2
## values 1.00 0.17
## B_diffS2 0.17 1.00
## Sample Size
## [1] 18
## Probability values (Entries above the diagonal are adjusted for multiple tests.)
## values B_diffS2
## values 0.00 0.51
## B_diffS2 0.51 0.00
##
## To see confidence intervals of the correlations, print with the short=FALSE option#library("ggpubr")
ggscatter(subset_3_Grey, x = "values", y = "B_diffS2",
add = "reg.line", conf.int = TRUE,
cor.coef = TRUE, cor.method = "pearson",
xlab = "Bold Cued_Uncued", ylab = "B_diffS2")
sp.subset_3_Greycorr <- corr.test(subset_3_Grey, use = "pairwise",method="spearman",adjust="none")
sp.subset_3_Greycorr## Call:corr.test(x = subset_3_Grey, use = "pairwise", method = "spearman",
## adjust = "none")
## Correlation matrix
## values B_diffS2
## values 1.0 0.2
## B_diffS2 0.2 1.0
## Sample Size
## [1] 18
## Probability values (Entries above the diagonal are adjusted for multiple tests.)
## values B_diffS2
## values 0.00 0.42
## B_diffS2 0.42 0.00
##
## To see confidence intervals of the correlations, print with the short=FALSE optionall_p_values.Grey <- c(round(subset_1_Greycorr$p[2], 2),
round(subset_2_Greycorr$p[2], 2),
round(subset_3_Greycorr$p[2], 2))
all_p_values.Grey## [1] 0.52 0.73 0.51# Apply FDR correction to combined all p-values
adjusted_p_Grey<- p.adjust(all_p_values.Grey, method = "fdr")
adjusted_p_Grey## [1] 0.73 0.73 0.73mask-resampled_vinsula_mask_onesamp_ttest_results_ClusterEffEst_p-0.001_desc-binarizedCluster.nii.gz
insula2 <- read_csv("Vinsula_onsesamp.csv")## Rows: 72 Columns: 4
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (3): participants, contrast, mask
## dbl (1): values
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.str(insula2)## spc_tbl_ [72 × 4] (S3: spec_tbl_df/tbl_df/tbl/data.frame)
## $ participants: chr [1:72] "sub-01" "sub-01" "sub-01" "sub-01" ...
## $ contrast : chr [1:72] "contrast-Uncued-Cued" "contrast-Cued-Uncued" "contrast-Cued" "contrast-Uncued" ...
## $ mask : chr [1:72] "mask-resampled_vinsula_mask_onesamp_ttest_results_ClusterEffEst_p-0.001_desc-binarizedCluster" "mask-resampled_vinsula_mask_onesamp_ttest_results_ClusterEffEst_p-0.001_desc-binarizedCluster" "mask-resampled_vinsula_mask_onesamp_ttest_results_ClusterEffEst_p-0.001_desc-binarizedCluster" "mask-resampled_vinsula_mask_onesamp_ttest_results_ClusterEffEst_p-0.001_desc-binarizedCluster" ...
## $ values : num [1:72] 0.673 -0.673 -1.761 -0.862 -0.855 ...
## - attr(*, "spec")=
## .. cols(
## .. participants = col_character(),
## .. contrast = col_character(),
## .. mask = col_character(),
## .. values = col_double()
## .. )
## - attr(*, "problems")=<externalptr>B.data <- read_csv("behavData.csv")## Rows: 18 Columns: 4
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (1): participants
## dbl (3): B_CuedS2, B_Uncued_S2, B_diffS2
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.str(B.data)## spc_tbl_ [18 × 4] (S3: spec_tbl_df/tbl_df/tbl/data.frame)
## $ participants: chr [1:18] "sub-01" "sub-02" "sub-03" "sub-04" ...
## $ B_CuedS2 : num [1:18] 2.92 3.96 3.21 2.71 4 ...
## $ B_Uncued_S2 : num [1:18] 3.21 3.5 3.12 2.42 4.12 ...
## $ B_diffS2 : num [1:18] -0.2917 0.4583 0.0833 0.2917 -0.125 ...
## - attr(*, "spec")=
## .. cols(
## .. participants = col_character(),
## .. B_CuedS2 = col_double(),
## .. B_Uncued_S2 = col_double(),
## .. B_diffS2 = col_double()
## .. )
## - attr(*, "problems")=<externalptr>combined_insula2 <- inner_join(insula2, B.data, by = "participants")
combined_insula2 <- combined_insula2 %>%
mutate(across(c(participants, contrast, mask), as.factor))
str(combined_insula2)## tibble [72 × 7] (S3: tbl_df/tbl/data.frame)
## $ participants: Factor w/ 18 levels "sub-01","sub-02",..: 1 1 1 1 2 2 2 2 3 3 ...
## $ contrast : Factor w/ 4 levels "contrast-Cued",..: 4 2 1 3 1 4 2 3 4 2 ...
## $ mask : Factor w/ 1 level "mask-resampled_vinsula_mask_onesamp_ttest_results_ClusterEffEst_p-0.001_desc-binarizedCluster": 1 1 1 1 1 1 1 1 1 1 ...
## $ values : num [1:72] 0.673 -0.673 -1.761 -0.862 -0.855 ...
## $ B_CuedS2 : num [1:72] 2.92 2.92 2.92 2.92 3.96 ...
## $ B_Uncued_S2 : num [1:72] 3.21 3.21 3.21 3.21 3.5 ...
## $ B_diffS2 : num [1:72] -0.292 -0.292 -0.292 -0.292 0.458 ...#
subset_1_insula2 <- combined_insula2 %>%
filter(contrast == "contrast-Cued")
subset_1_insula2## # A tibble: 18 × 7
## participants contrast mask values B_CuedS2 B_Uncued_S2 B_diffS2
## <fct> <fct> <fct> <dbl> <dbl> <dbl> <dbl>
## 1 sub-01 contrast-Cued mask-resampl… -1.76 2.92 3.21 -0.292
## 2 sub-02 contrast-Cued mask-resampl… -0.855 3.96 3.5 0.458
## 3 sub-03 contrast-Cued mask-resampl… -1.03 3.21 3.12 0.0833
## 4 sub-04 contrast-Cued mask-resampl… -1.70 2.71 2.42 0.292
## 5 sub-05 contrast-Cued mask-resampl… 1.64 4 4.12 -0.125
## 6 sub-07 contrast-Cued mask-resampl… 2.29 2.75 3 -0.25
## 7 sub-08 contrast-Cued mask-resampl… -1.43 3.67 3.83 -0.159
## 8 sub-11 contrast-Cued mask-resampl… 4.30 2.75 2.88 -0.125
## 9 sub-12 contrast-Cued mask-resampl… 0.516 4.29 4.12 0.167
## 10 sub-13 contrast-Cued mask-resampl… -0.901 2.42 2.42 0
## 11 sub-14 contrast-Cued mask-resampl… -2.36 2.38 2.58 -0.208
## 12 sub-16 contrast-Cued mask-resampl… 1.86 2.04 2.17 -0.125
## 13 sub-17 contrast-Cued mask-resampl… -0.787 3.17 3.04 0.125
## 14 sub-18 contrast-Cued mask-resampl… 5.10 3.67 3.5 0.167
## 15 sub-19 contrast-Cued mask-resampl… 0.800 2.96 3.21 -0.25
## 16 sub-20 contrast-Cued mask-resampl… -1.84 3.38 3.36 0.0114
## 17 sub-21 contrast-Cued mask-resampl… -1.31 1.67 2.48 -0.812
## 18 sub-23 contrast-Cued mask-resampl… -2.81 1.46 1.83 -0.375subset_1_insula2 <- subset_1_insula2[, c("values", "B_CuedS2")]
shapiro.test(subset_1_insula2$values) ##
## Shapiro-Wilk normality test
##
## data: subset_1_insula2$values
## W = 0.89045, p-value = 0.03928shapiro.test(subset_1_insula2$B_CuedS2) #normal##
## Shapiro-Wilk normality test
##
## data: subset_1_insula2$B_CuedS2
## W = 0.9774, p-value = 0.9193subset_1_insula2corr <- corr.test(subset_1_insula2, use = "pairwise",method="pearson",adjust="none")
subset_1_insula2corr## Call:corr.test(x = subset_1_insula2, use = "pairwise", method = "pearson",
## adjust = "none")
## Correlation matrix
## values B_CuedS2
## values 1.00 0.27
## B_CuedS2 0.27 1.00
## Sample Size
## [1] 18
## Probability values (Entries above the diagonal are adjusted for multiple tests.)
## values B_CuedS2
## values 0.00 0.28
## B_CuedS2 0.28 0.00
##
## To see confidence intervals of the correlations, print with the short=FALSE optionggscatter(subset_1_insula2, x = "values", y = "B_CuedS2",
add = "reg.line", conf.int = TRUE,
cor.coef = TRUE, cor.method = "pearson",
xlab = "Bold Cued_Uncued", ylab = "B_CuedS2")
sp.subset_1_insula2corr <- corr.test(subset_1_insula2, use = "pairwise",method="spearman",adjust="none")
sp.subset_1_insula2corr## Call:corr.test(x = subset_1_insula2, use = "pairwise", method = "spearman",
## adjust = "none")
## Correlation matrix
## values B_CuedS2
## values 1.00 0.31
## B_CuedS2 0.31 1.00
## Sample Size
## [1] 18
## Probability values (Entries above the diagonal are adjusted for multiple tests.)
## values B_CuedS2
## values 0.00 0.21
## B_CuedS2 0.21 0.00
##
## To see confidence intervals of the correlations, print with the short=FALSE option#
subset_2_insula2 <- combined_insula2 %>%
filter(contrast == "contrast-Uncued")
subset_2_insula2## # A tibble: 18 × 7
## participants contrast mask values B_CuedS2 B_Uncued_S2 B_diffS2
## <fct> <fct> <fct> <dbl> <dbl> <dbl> <dbl>
## 1 sub-01 contrast-Uncued mask-res… -0.862 2.92 3.21 -0.292
## 2 sub-02 contrast-Uncued mask-res… 0.298 3.96 3.5 0.458
## 3 sub-03 contrast-Uncued mask-res… 0.598 3.21 3.12 0.0833
## 4 sub-04 contrast-Uncued mask-res… -0.0950 2.71 2.42 0.292
## 5 sub-05 contrast-Uncued mask-res… 2.62 4 4.12 -0.125
## 6 sub-07 contrast-Uncued mask-res… 3.43 2.75 3 -0.25
## 7 sub-08 contrast-Uncued mask-res… 0.124 3.67 3.83 -0.159
## 8 sub-11 contrast-Uncued mask-res… 4.44 2.75 2.88 -0.125
## 9 sub-12 contrast-Uncued mask-res… -0.00447 4.29 4.12 0.167
## 10 sub-13 contrast-Uncued mask-res… -0.259 2.42 2.42 0
## 11 sub-14 contrast-Uncued mask-res… -1.62 2.38 2.58 -0.208
## 12 sub-16 contrast-Uncued mask-res… 3.61 2.04 2.17 -0.125
## 13 sub-17 contrast-Uncued mask-res… -0.112 3.17 3.04 0.125
## 14 sub-18 contrast-Uncued mask-res… 5.37 3.67 3.5 0.167
## 15 sub-19 contrast-Uncued mask-res… 1.72 2.96 3.21 -0.25
## 16 sub-20 contrast-Uncued mask-res… 0.595 3.38 3.36 0.0114
## 17 sub-21 contrast-Uncued mask-res… 2.17 1.67 2.48 -0.812
## 18 sub-23 contrast-Uncued mask-res… -2.08 1.46 1.83 -0.375subset_2_insula2 <- subset_2_insula2[, c("values", "B_Uncued_S2")]
shapiro.test(subset_2_insula2$values) #normal##
## Shapiro-Wilk normality test
##
## data: subset_2_insula2$values
## W = 0.94359, p-value = 0.3334shapiro.test(subset_2_insula2$B_Uncued_S2) #normal##
## Shapiro-Wilk normality test
##
## data: subset_2_insula2$B_Uncued_S2
## W = 0.97342, p-value = 0.859subset_2_insula2corr <- corr.test(subset_2_insula2, use = "pairwise",method="pearson",adjust="none")
subset_2_insula2corr## Call:corr.test(x = subset_2_insula2, use = "pairwise", method = "pearson",
## adjust = "none")
## Correlation matrix
## values B_Uncued_S2
## values 1.00 0.18
## B_Uncued_S2 0.18 1.00
## Sample Size
## [1] 18
## Probability values (Entries above the diagonal are adjusted for multiple tests.)
## values B_Uncued_S2
## values 0.00 0.48
## B_Uncued_S2 0.48 0.00
##
## To see confidence intervals of the correlations, print with the short=FALSE optionggscatter(subset_2_insula2, x = "values", y = "B_Uncued_S2",
add = "reg.line", conf.int = TRUE,
cor.coef = TRUE, cor.method = "pearson",
xlab = "Bold Cued_Uncued", ylab = "B_Uncued_S2")
sp.subset_2_insula2corr <- corr.test(subset_2_insula2, use = "pairwise",method="spearman",adjust="none")
sp.subset_2_insula2corr## Call:corr.test(x = subset_2_insula2, use = "pairwise", method = "spearman",
## adjust = "none")
## Correlation matrix
## values B_Uncued_S2
## values 1.00 0.22
## B_Uncued_S2 0.22 1.00
## Sample Size
## [1] 18
## Probability values (Entries above the diagonal are adjusted for multiple tests.)
## values B_Uncued_S2
## values 0.00 0.39
## B_Uncued_S2 0.39 0.00
##
## To see confidence intervals of the correlations, print with the short=FALSE option#
subset_3_insula2 <- combined_insula2 %>%
filter(contrast == "contrast-Cued-Uncued")
subset_3_insula2## # A tibble: 18 × 7
## participants contrast mask values B_CuedS2 B_Uncued_S2 B_diffS2
## <fct> <fct> <fct> <dbl> <dbl> <dbl> <dbl>
## 1 sub-01 contrast-Cued-Uncued mask-… -0.673 2.92 3.21 -0.292
## 2 sub-02 contrast-Cued-Uncued mask-… -0.864 3.96 3.5 0.458
## 3 sub-03 contrast-Cued-Uncued mask-… -1.21 3.21 3.12 0.0833
## 4 sub-04 contrast-Cued-Uncued mask-… -1.18 2.71 2.42 0.292
## 5 sub-05 contrast-Cued-Uncued mask-… -0.714 4 4.12 -0.125
## 6 sub-07 contrast-Cued-Uncued mask-… -1.02 2.75 3 -0.25
## 7 sub-08 contrast-Cued-Uncued mask-… -1.15 3.67 3.83 -0.159
## 8 sub-11 contrast-Cued-Uncued mask-… -0.159 2.75 2.88 -0.125
## 9 sub-12 contrast-Cued-Uncued mask-… 0.388 4.29 4.12 0.167
## 10 sub-13 contrast-Cued-Uncued mask-… -0.479 2.42 2.42 0
## 11 sub-14 contrast-Cued-Uncued mask-… -0.540 2.38 2.58 -0.208
## 12 sub-16 contrast-Cued-Uncued mask-… -1.43 2.04 2.17 -0.125
## 13 sub-17 contrast-Cued-Uncued mask-… -0.496 3.17 3.04 0.125
## 14 sub-18 contrast-Cued-Uncued mask-… -0.254 3.67 3.5 0.167
## 15 sub-19 contrast-Cued-Uncued mask-… -0.640 2.96 3.21 -0.25
## 16 sub-20 contrast-Cued-Uncued mask-… -1.77 3.38 3.36 0.0114
## 17 sub-21 contrast-Cued-Uncued mask-… -2.57 1.67 2.48 -0.812
## 18 sub-23 contrast-Cued-Uncued mask-… -0.586 1.46 1.83 -0.375subset_3_insula2 <- subset_3_insula2[, c("values", "B_diffS2")]
shapiro.test(subset_3_insula2$values) #normal##
## Shapiro-Wilk normality test
##
## data: subset_3_insula2$values
## W = 0.94868, p-value = 0.4047shapiro.test(subset_3_insula2$B_diffS2) #normal##
## Shapiro-Wilk normality test
##
## data: subset_3_insula2$B_diffS2
## W = 0.95985, p-value = 0.5987subset_3_insula2corr <- corr.test(subset_3_insula2, use = "pairwise",method="pearson",adjust="none")
subset_3_insula2corr## Call:corr.test(x = subset_3_insula2, use = "pairwise", method = "pearson",
## adjust = "none")
## Correlation matrix
## values B_diffS2
## values 1.00 0.43
## B_diffS2 0.43 1.00
## Sample Size
## [1] 18
## Probability values (Entries above the diagonal are adjusted for multiple tests.)
## values B_diffS2
## values 0.00 0.07
## B_diffS2 0.07 0.00
##
## To see confidence intervals of the correlations, print with the short=FALSE option#library("ggpubr")
ggscatter(subset_3_insula2, x = "values", y = "B_diffS2",
add = "reg.line", conf.int = TRUE,
cor.coef = TRUE, cor.method = "pearson",
xlab = "Bold Cued_Uncued", ylab = "B_diffS2")
sp.subset_3_insula2corr <- corr.test(subset_3_insula2, use = "pairwise",method="spearman",adjust="none")
sp.subset_3_insula2corr## Call:corr.test(x = subset_3_insula2, use = "pairwise", method = "spearman",
## adjust = "none")
## Correlation matrix
## values B_diffS2
## values 1.00 0.18
## B_diffS2 0.18 1.00
## Sample Size
## [1] 18
## Probability values (Entries above the diagonal are adjusted for multiple tests.)
## values B_diffS2
## values 0.00 0.48
## B_diffS2 0.48 0.00
##
## To see confidence intervals of the correlations, print with the short=FALSE option#
all_p_values.insula2 <- c(round(subset_1_insula2corr$p[2], 2),
round(subset_2_insula2corr$p[2], 2),
round(subset_3_insula2corr$p[2], 2))
all_p_values.insula2## [1] 0.28 0.48 0.07# Apply FDR correction to combined all p-values
adjusted_p_insula2<- p.adjust(all_p_values.insula2, method = "fdr")
adjusted_p_insula2## [1] 0.42 0.48 0.21mask-resampled_vorbitofrontal_mask_onesamp_ttest_results_ClusterEffEst_p-0.001_desc-binarizedCluster.nii.gz
no sign corr
OFC2 <- read_csv("vorbitofrontal_mask_onesamp.csv")## Rows: 72 Columns: 4
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (3): participants, contrast, mask
## dbl (1): values
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.str(OFC2)## spc_tbl_ [72 × 4] (S3: spec_tbl_df/tbl_df/tbl/data.frame)
## $ participants: chr [1:72] "sub-01" "sub-01" "sub-01" "sub-01" ...
## $ contrast : chr [1:72] "contrast-Uncued-Cued" "contrast-Cued-Uncued" "contrast-Cued" "contrast-Uncued" ...
## $ mask : chr [1:72] "mask-resampled_vorbitofrontal_mask_onesamp_ttest_results_ClusterEffEst_p-0.001_desc-binarizedCluster" "mask-resampled_vorbitofrontal_mask_onesamp_ttest_results_ClusterEffEst_p-0.001_desc-binarizedCluster" "mask-resampled_vorbitofrontal_mask_onesamp_ttest_results_ClusterEffEst_p-0.001_desc-binarizedCluster" "mask-resampled_vorbitofrontal_mask_onesamp_ttest_results_ClusterEffEst_p-0.001_desc-binarizedCluster" ...
## $ values : num [1:72] 1.124 -1.124 -0.691 0.842 0.784 ...
## - attr(*, "spec")=
## .. cols(
## .. participants = col_character(),
## .. contrast = col_character(),
## .. mask = col_character(),
## .. values = col_double()
## .. )
## - attr(*, "problems")=<externalptr>B.data <- read_csv("behavData.csv")## Rows: 18 Columns: 4
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (1): participants
## dbl (3): B_CuedS2, B_Uncued_S2, B_diffS2
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.str(B.data)## spc_tbl_ [18 × 4] (S3: spec_tbl_df/tbl_df/tbl/data.frame)
## $ participants: chr [1:18] "sub-01" "sub-02" "sub-03" "sub-04" ...
## $ B_CuedS2 : num [1:18] 2.92 3.96 3.21 2.71 4 ...
## $ B_Uncued_S2 : num [1:18] 3.21 3.5 3.12 2.42 4.12 ...
## $ B_diffS2 : num [1:18] -0.2917 0.4583 0.0833 0.2917 -0.125 ...
## - attr(*, "spec")=
## .. cols(
## .. participants = col_character(),
## .. B_CuedS2 = col_double(),
## .. B_Uncued_S2 = col_double(),
## .. B_diffS2 = col_double()
## .. )
## - attr(*, "problems")=<externalptr>combined_OFC2 <- inner_join(OFC2, B.data, by = "participants")
combined_OFC2 <- combined_OFC2 %>%
mutate(across(c(participants, contrast, mask), as.factor))
str(combined_OFC2)## tibble [72 × 7] (S3: tbl_df/tbl/data.frame)
## $ participants: Factor w/ 18 levels "sub-01","sub-02",..: 1 1 1 1 2 2 2 2 3 3 ...
## $ contrast : Factor w/ 4 levels "contrast-Cued",..: 4 2 1 3 1 4 2 3 4 2 ...
## $ mask : Factor w/ 1 level "mask-resampled_vorbitofrontal_mask_onesamp_ttest_results_ClusterEffEst_p-0.001_desc-binarizedCluster": 1 1 1 1 1 1 1 1 1 1 ...
## $ values : num [1:72] 1.124 -1.124 -0.691 0.842 0.784 ...
## $ B_CuedS2 : num [1:72] 2.92 2.92 2.92 2.92 3.96 ...
## $ B_Uncued_S2 : num [1:72] 3.21 3.21 3.21 3.21 3.5 ...
## $ B_diffS2 : num [1:72] -0.292 -0.292 -0.292 -0.292 0.458 ...#
subset_1_OFC2 <- combined_OFC2 %>%
filter(contrast == "contrast-Cued")
subset_1_OFC2## # A tibble: 18 × 7
## participants contrast mask values B_CuedS2 B_Uncued_S2 B_diffS2
## <fct> <fct> <fct> <dbl> <dbl> <dbl> <dbl>
## 1 sub-01 contrast-Cued mask-resamp… -0.691 2.92 3.21 -0.292
## 2 sub-02 contrast-Cued mask-resamp… 0.784 3.96 3.5 0.458
## 3 sub-03 contrast-Cued mask-resamp… -0.199 3.21 3.12 0.0833
## 4 sub-04 contrast-Cued mask-resamp… -1.54 2.71 2.42 0.292
## 5 sub-05 contrast-Cued mask-resamp… 0.853 4 4.12 -0.125
## 6 sub-07 contrast-Cued mask-resamp… -1.31 2.75 3 -0.25
## 7 sub-08 contrast-Cued mask-resamp… -1.01 3.67 3.83 -0.159
## 8 sub-11 contrast-Cued mask-resamp… 0.511 2.75 2.88 -0.125
## 9 sub-12 contrast-Cued mask-resamp… -2.01 4.29 4.12 0.167
## 10 sub-13 contrast-Cued mask-resamp… 0.350 2.42 2.42 0
## 11 sub-14 contrast-Cued mask-resamp… -2.45 2.38 2.58 -0.208
## 12 sub-16 contrast-Cued mask-resamp… -1.98 2.04 2.17 -0.125
## 13 sub-17 contrast-Cued mask-resamp… -2.47 3.17 3.04 0.125
## 14 sub-18 contrast-Cued mask-resamp… -1.24 3.67 3.5 0.167
## 15 sub-19 contrast-Cued mask-resamp… -2.06 2.96 3.21 -0.25
## 16 sub-20 contrast-Cued mask-resamp… -2.11 3.38 3.36 0.0114
## 17 sub-21 contrast-Cued mask-resamp… -0.0457 1.67 2.48 -0.812
## 18 sub-23 contrast-Cued mask-resamp… -2.38 1.46 1.83 -0.375subset_1_OFC2 <- subset_1_OFC2[, c("values", "B_CuedS2")]
shapiro.test(subset_1_OFC2$values) #normal##
## Shapiro-Wilk normality test
##
## data: subset_1_OFC2$values
## W = 0.90315, p-value = 0.06521shapiro.test(subset_1_OFC2$B_CuedS2 ) #normal##
## Shapiro-Wilk normality test
##
## data: subset_1_OFC2$B_CuedS2
## W = 0.9774, p-value = 0.9193subset_1_OFC2corr <- corr.test(subset_1_OFC2, use = "pairwise",method="pearson",adjust="none")
subset_1_OFC2corr## Call:corr.test(x = subset_1_OFC2, use = "pairwise", method = "pearson",
## adjust = "none")
## Correlation matrix
## values B_CuedS2
## values 1.00 0.21
## B_CuedS2 0.21 1.00
## Sample Size
## [1] 18
## Probability values (Entries above the diagonal are adjusted for multiple tests.)
## values B_CuedS2
## values 0.0 0.4
## B_CuedS2 0.4 0.0
##
## To see confidence intervals of the correlations, print with the short=FALSE optionggscatter(subset_1_OFC2, x = "values", y = "B_CuedS2",
add = "reg.line", conf.int = TRUE,
cor.coef = TRUE, cor.method = "pearson",
xlab = "Bold Cued_Uncued", ylab = "B_CuedS2")
sp.subset_1_OFC2corr <- corr.test(subset_1_OFC2, use = "pairwise",method="spearman",adjust="none")
sp.subset_1_OFC2corr## Call:corr.test(x = subset_1_OFC2, use = "pairwise", method = "spearman",
## adjust = "none")
## Correlation matrix
## values B_CuedS2
## values 1.00 0.23
## B_CuedS2 0.23 1.00
## Sample Size
## [1] 18
## Probability values (Entries above the diagonal are adjusted for multiple tests.)
## values B_CuedS2
## values 0.00 0.35
## B_CuedS2 0.35 0.00
##
## To see confidence intervals of the correlations, print with the short=FALSE option#
subset_2_OFC2 <- combined_OFC2 %>%
filter(contrast == "contrast-Uncued")
subset_2_OFC2## # A tibble: 18 × 7
## participants contrast mask values B_CuedS2 B_Uncued_S2 B_diffS2
## <fct> <fct> <fct> <dbl> <dbl> <dbl> <dbl>
## 1 sub-01 contrast-Uncued mask-resa… 0.842 2.92 3.21 -0.292
## 2 sub-02 contrast-Uncued mask-resa… 2.54 3.96 3.5 0.458
## 3 sub-03 contrast-Uncued mask-resa… 0.824 3.21 3.12 0.0833
## 4 sub-04 contrast-Uncued mask-resa… -0.813 2.71 2.42 0.292
## 5 sub-05 contrast-Uncued mask-resa… 2.03 4 4.12 -0.125
## 6 sub-07 contrast-Uncued mask-resa… -0.923 2.75 3 -0.25
## 7 sub-08 contrast-Uncued mask-resa… -0.942 3.67 3.83 -0.159
## 8 sub-11 contrast-Uncued mask-resa… 1.04 2.75 2.88 -0.125
## 9 sub-12 contrast-Uncued mask-resa… -1.16 4.29 4.12 0.167
## 10 sub-13 contrast-Uncued mask-resa… 0.399 2.42 2.42 0
## 11 sub-14 contrast-Uncued mask-resa… -1.14 2.38 2.58 -0.208
## 12 sub-16 contrast-Uncued mask-resa… -0.0297 2.04 2.17 -0.125
## 13 sub-17 contrast-Uncued mask-resa… -0.608 3.17 3.04 0.125
## 14 sub-18 contrast-Uncued mask-resa… -1.25 3.67 3.5 0.167
## 15 sub-19 contrast-Uncued mask-resa… -1.68 2.96 3.21 -0.25
## 16 sub-20 contrast-Uncued mask-resa… -1.38 3.38 3.36 0.0114
## 17 sub-21 contrast-Uncued mask-resa… 1.18 1.67 2.48 -0.812
## 18 sub-23 contrast-Uncued mask-resa… -1.65 1.46 1.83 -0.375subset_2_OFC2 <- subset_2_OFC2[, c("values", "B_Uncued_S2")]
shapiro.test(subset_2_OFC2$values) #normal##
## Shapiro-Wilk normality test
##
## data: subset_2_OFC2$values
## W = 0.90506, p-value = 0.07042shapiro.test(subset_2_OFC2$B_Uncued_S2) #normal##
## Shapiro-Wilk normality test
##
## data: subset_2_OFC2$B_Uncued_S2
## W = 0.97342, p-value = 0.859subset_2_OFC2corr <- corr.test(subset_2_OFC2, use = "pairwise",method="pearson",adjust="none")
subset_2_OFC2corr## Call:corr.test(x = subset_2_OFC2, use = "pairwise", method = "pearson",
## adjust = "none")
## Correlation matrix
## values B_Uncued_S2
## values 1.00 0.16
## B_Uncued_S2 0.16 1.00
## Sample Size
## [1] 18
## Probability values (Entries above the diagonal are adjusted for multiple tests.)
## values B_Uncued_S2
## values 0.00 0.52
## B_Uncued_S2 0.52 0.00
##
## To see confidence intervals of the correlations, print with the short=FALSE optionggscatter(subset_2_OFC2, x = "values", y = "B_Uncued_S2",
add = "reg.line", conf.int = TRUE,
cor.coef = TRUE, cor.method = "pearson",
xlab = "Bold Cued_Uncued", ylab = "B_Uncued_S2")
sp.subset_2_OFC2corr <- corr.test(subset_2_OFC2, use = "pairwise",method="spearman",adjust="none")
sp.subset_2_OFC2corr## Call:corr.test(x = subset_2_OFC2, use = "pairwise", method = "spearman",
## adjust = "none")
## Correlation matrix
## values B_Uncued_S2
## values 1.00 0.01
## B_Uncued_S2 0.01 1.00
## Sample Size
## [1] 18
## Probability values (Entries above the diagonal are adjusted for multiple tests.)
## values B_Uncued_S2
## values 0.00 0.96
## B_Uncued_S2 0.96 0.00
##
## To see confidence intervals of the correlations, print with the short=FALSE optionsubset_3_OFC2 <- combined_OFC2 %>%
filter(contrast == "contrast-Cued-Uncued")
subset_3_OFC2## # A tibble: 18 × 7
## participants contrast mask values B_CuedS2 B_Uncued_S2 B_diffS2
## <fct> <fct> <fct> <dbl> <dbl> <dbl> <dbl>
## 1 sub-01 contrast-Cued-Uncued mask… -1.12 2.92 3.21 -0.292
## 2 sub-02 contrast-Cued-Uncued mask… -1.29 3.96 3.5 0.458
## 3 sub-03 contrast-Cued-Uncued mask… -0.771 3.21 3.12 0.0833
## 4 sub-04 contrast-Cued-Uncued mask… -0.533 2.71 2.42 0.292
## 5 sub-05 contrast-Cued-Uncued mask… -0.863 4 4.12 -0.125
## 6 sub-07 contrast-Cued-Uncued mask… -0.214 2.75 3 -0.25
## 7 sub-08 contrast-Cued-Uncued mask… -0.0613 3.67 3.83 -0.159
## 8 sub-11 contrast-Cued-Uncued mask… -0.403 2.75 2.88 -0.125
## 9 sub-12 contrast-Cued-Uncued mask… -0.632 4.29 4.12 0.167
## 10 sub-13 contrast-Cued-Uncued mask… -0.0423 2.42 2.42 0
## 11 sub-14 contrast-Cued-Uncued mask… -0.968 2.38 2.58 -0.208
## 12 sub-16 contrast-Cued-Uncued mask… -1.50 2.04 2.17 -0.125
## 13 sub-17 contrast-Cued-Uncued mask… -1.34 3.17 3.04 0.125
## 14 sub-18 contrast-Cued-Uncued mask… 0.0203 3.67 3.5 0.167
## 15 sub-19 contrast-Cued-Uncued mask… -0.362 2.96 3.21 -0.25
## 16 sub-20 contrast-Cued-Uncued mask… -0.509 3.38 3.36 0.0114
## 17 sub-21 contrast-Cued-Uncued mask… -0.872 1.67 2.48 -0.812
## 18 sub-23 contrast-Cued-Uncued mask… -0.564 1.46 1.83 -0.375subset_3_OFC2 <- subset_3_OFC2[, c("values", "B_diffS2")]
shapiro.test(subset_3_OFC2$values) #normal##
## Shapiro-Wilk normality test
##
## data: subset_3_OFC2$values
## W = 0.96458, p-value = 0.6918shapiro.test(subset_3_OFC2$B_diffS2) #normal##
## Shapiro-Wilk normality test
##
## data: subset_3_OFC2$B_diffS2
## W = 0.95985, p-value = 0.5987subset_3_OFC2corr <- corr.test(subset_3_OFC2, use = "pairwise",method="pearson",adjust="none")
subset_3_OFC2corr## Call:corr.test(x = subset_3_OFC2, use = "pairwise", method = "pearson",
## adjust = "none")
## Correlation matrix
## values B_diffS2
## values 1.00 -0.04
## B_diffS2 -0.04 1.00
## Sample Size
## [1] 18
## Probability values (Entries above the diagonal are adjusted for multiple tests.)
## values B_diffS2
## values 0.00 0.88
## B_diffS2 0.88 0.00
##
## To see confidence intervals of the correlations, print with the short=FALSE option#library("ggpubr")
ggscatter(subset_3_OFC2, x = "values", y = "B_diffS2",
add = "reg.line", conf.int = TRUE,
cor.coef = TRUE, cor.method = "pearson",
xlab = "Bold Cued_Uncued", ylab = "B_diffS2")
sp.subset_3_OFC2corr <- corr.test(subset_3_OFC2, use = "pairwise",method="spearman",adjust="none")
sp.subset_3_OFC2corr## Call:corr.test(x = subset_3_OFC2, use = "pairwise", method = "spearman",
## adjust = "none")
## Correlation matrix
## values B_diffS2
## values 1.00 -0.01
## B_diffS2 -0.01 1.00
## Sample Size
## [1] 18
## Probability values (Entries above the diagonal are adjusted for multiple tests.)
## values B_diffS2
## values 0.00 0.98
## B_diffS2 0.98 0.00
##
## To see confidence intervals of the correlations, print with the short=FALSE optionall_p_values.OFC2 <- c(round(subset_1_OFC2corr$p[2], 2),
round(subset_2_OFC2corr$p[2], 2),
round(subset_3_OFC2corr$p[2], 2))
all_p_values.OFC2## [1] 0.40 0.52 0.88# Apply FDR correction to combined all p-values
adjusted_p_OFC2<- p.adjust(all_p_values.OFC2, method = "fdr")
adjusted_p_OFC2## [1] 0.78 0.78 0.88mask-b_gray_thresholded_onesamp_ttest_results_ClusterEffEst_p-0.001_desc-binarizedCluster.nii.gz
No sign corr
Grey2 <- read_csv("gray_thresholded_onesamp.csv")## Rows: 72 Columns: 4
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (3): participants, contrast, mask
## dbl (1): values
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.str(Grey2)## spc_tbl_ [72 × 4] (S3: spec_tbl_df/tbl_df/tbl/data.frame)
## $ participants: chr [1:72] "sub-01" "sub-01" "sub-01" "sub-01" ...
## $ contrast : chr [1:72] "contrast-Uncued-Cued" "contrast-Cued-Uncued" "contrast-Cued" "contrast-Uncued" ...
## $ mask : chr [1:72] "mask-b_gray_thresholded_onesamp_ttest_results_ClusterEffEst_p-0.001_desc-binarizedCluster" "mask-b_gray_thresholded_onesamp_ttest_results_ClusterEffEst_p-0.001_desc-binarizedCluster" "mask-b_gray_thresholded_onesamp_ttest_results_ClusterEffEst_p-0.001_desc-binarizedCluster" "mask-b_gray_thresholded_onesamp_ttest_results_ClusterEffEst_p-0.001_desc-binarizedCluster" ...
## $ values : num [1:72] 0.8388 -0.8388 -1.1594 -0.0213 0.1929 ...
## - attr(*, "spec")=
## .. cols(
## .. participants = col_character(),
## .. contrast = col_character(),
## .. mask = col_character(),
## .. values = col_double()
## .. )
## - attr(*, "problems")=<externalptr>B.data <- read_csv("behavData.csv")## Rows: 18 Columns: 4
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (1): participants
## dbl (3): B_CuedS2, B_Uncued_S2, B_diffS2
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.str(B.data)## spc_tbl_ [18 × 4] (S3: spec_tbl_df/tbl_df/tbl/data.frame)
## $ participants: chr [1:18] "sub-01" "sub-02" "sub-03" "sub-04" ...
## $ B_CuedS2 : num [1:18] 2.92 3.96 3.21 2.71 4 ...
## $ B_Uncued_S2 : num [1:18] 3.21 3.5 3.12 2.42 4.12 ...
## $ B_diffS2 : num [1:18] -0.2917 0.4583 0.0833 0.2917 -0.125 ...
## - attr(*, "spec")=
## .. cols(
## .. participants = col_character(),
## .. B_CuedS2 = col_double(),
## .. B_Uncued_S2 = col_double(),
## .. B_diffS2 = col_double()
## .. )
## - attr(*, "problems")=<externalptr>combined_Grey2 <- inner_join(Grey2, B.data, by = "participants")
combined_Grey2 <- combined_Grey2 %>%
mutate(across(c(participants, contrast, mask), as.factor))
str(combined_Grey2)## tibble [72 × 7] (S3: tbl_df/tbl/data.frame)
## $ participants: Factor w/ 18 levels "sub-01","sub-02",..: 1 1 1 1 2 2 2 2 3 3 ...
## $ contrast : Factor w/ 4 levels "contrast-Cued",..: 4 2 1 3 1 4 2 3 4 2 ...
## $ mask : Factor w/ 1 level "mask-b_gray_thresholded_onesamp_ttest_results_ClusterEffEst_p-0.001_desc-binarizedCluster": 1 1 1 1 1 1 1 1 1 1 ...
## $ values : num [1:72] 0.8388 -0.8388 -1.1594 -0.0213 0.1929 ...
## $ B_CuedS2 : num [1:72] 2.92 2.92 2.92 2.92 3.96 ...
## $ B_Uncued_S2 : num [1:72] 3.21 3.21 3.21 3.21 3.5 ...
## $ B_diffS2 : num [1:72] -0.292 -0.292 -0.292 -0.292 0.458 ...#
subset_1_Grey2 <- combined_Grey2 %>%
filter(contrast == "contrast-Cued")
subset_1_Grey2## # A tibble: 18 × 7
## participants contrast mask values B_CuedS2 B_Uncued_S2 B_diffS2
## <fct> <fct> <fct> <dbl> <dbl> <dbl> <dbl>
## 1 sub-01 contrast-Cued mask-b_gray_… -1.16 2.92 3.21 -0.292
## 2 sub-02 contrast-Cued mask-b_gray_… 0.193 3.96 3.5 0.458
## 3 sub-03 contrast-Cued mask-b_gray_… -1.10 3.21 3.12 0.0833
## 4 sub-04 contrast-Cued mask-b_gray_… -1.98 2.71 2.42 0.292
## 5 sub-05 contrast-Cued mask-b_gray_… -1.06 4 4.12 -0.125
## 6 sub-07 contrast-Cued mask-b_gray_… -0.918 2.75 3 -0.25
## 7 sub-08 contrast-Cued mask-b_gray_… -1.71 3.67 3.83 -0.159
## 8 sub-11 contrast-Cued mask-b_gray_… -1.84 2.75 2.88 -0.125
## 9 sub-12 contrast-Cued mask-b_gray_… -2.32 4.29 4.12 0.167
## 10 sub-13 contrast-Cued mask-b_gray_… -3.31 2.42 2.42 0
## 11 sub-14 contrast-Cued mask-b_gray_… -1.42 2.38 2.58 -0.208
## 12 sub-16 contrast-Cued mask-b_gray_… -1.49 2.04 2.17 -0.125
## 13 sub-17 contrast-Cued mask-b_gray_… -1.76 3.17 3.04 0.125
## 14 sub-18 contrast-Cued mask-b_gray_… -1.44 3.67 3.5 0.167
## 15 sub-19 contrast-Cued mask-b_gray_… -1.25 2.96 3.21 -0.25
## 16 sub-20 contrast-Cued mask-b_gray_… -0.726 3.38 3.36 0.0114
## 17 sub-21 contrast-Cued mask-b_gray_… -0.873 1.67 2.48 -0.812
## 18 sub-23 contrast-Cued mask-b_gray_… -1.30 1.46 1.83 -0.375subset_1_Grey2 <- subset_1_Grey2[, c("values", "B_CuedS2")]
shapiro.test(subset_1_Grey2$values) #normal##
## Shapiro-Wilk normality test
##
## data: subset_1_Grey2$values
## W = 0.94316, p-value = 0.3279shapiro.test(subset_1_Grey2$B_CuedS2) #normal##
## Shapiro-Wilk normality test
##
## data: subset_1_Grey2$B_CuedS2
## W = 0.9774, p-value = 0.9193subset_1_Grey2corr <- corr.test(subset_1_Grey2, use = "pairwise",method="pearson",adjust="none")
subset_1_Grey2corr## Call:corr.test(x = subset_1_Grey2, use = "pairwise", method = "pearson",
## adjust = "none")
## Correlation matrix
## values B_CuedS2
## values 1.00 0.12
## B_CuedS2 0.12 1.00
## Sample Size
## [1] 18
## Probability values (Entries above the diagonal are adjusted for multiple tests.)
## values B_CuedS2
## values 0.00 0.64
## B_CuedS2 0.64 0.00
##
## To see confidence intervals of the correlations, print with the short=FALSE optionggscatter(subset_1_Grey2, x = "values", y = "B_CuedS2",
add = "reg.line", conf.int = TRUE,
cor.coef = TRUE, cor.method = "pearson",
xlab = "Bold Cued_Uncued", ylab = "B_CuedS2")
sp.subset_1_Grey2corr <- corr.test(subset_1_Grey2, use = "pairwise",method="spearman",adjust="none")
sp.subset_1_Grey2corr## Call:corr.test(x = subset_1_Grey2, use = "pairwise", method = "spearman",
## adjust = "none")
## Correlation matrix
## values B_CuedS2
## values 1.00 0.11
## B_CuedS2 0.11 1.00
## Sample Size
## [1] 18
## Probability values (Entries above the diagonal are adjusted for multiple tests.)
## values B_CuedS2
## values 0.00 0.65
## B_CuedS2 0.65 0.00
##
## To see confidence intervals of the correlations, print with the short=FALSE option#
subset_2_Grey2 <- combined_Grey2 %>%
filter(contrast == "contrast-Uncued")
subset_2_Grey2## # A tibble: 18 × 7
## participants contrast mask values B_CuedS2 B_Uncued_S2 B_diffS2
## <fct> <fct> <fct> <dbl> <dbl> <dbl> <dbl>
## 1 sub-01 contrast-Uncued mask-b_g… -0.0213 2.92 3.21 -0.292
## 2 sub-02 contrast-Uncued mask-b_g… 0.973 3.96 3.5 0.458
## 3 sub-03 contrast-Uncued mask-b_g… 0.448 3.21 3.12 0.0833
## 4 sub-04 contrast-Uncued mask-b_g… 0.00778 2.71 2.42 0.292
## 5 sub-05 contrast-Uncued mask-b_g… -0.374 4 4.12 -0.125
## 6 sub-07 contrast-Uncued mask-b_g… 0.275 2.75 3 -0.25
## 7 sub-08 contrast-Uncued mask-b_g… -0.885 3.67 3.83 -0.159
## 8 sub-11 contrast-Uncued mask-b_g… -2.11 2.75 2.88 -0.125
## 9 sub-12 contrast-Uncued mask-b_g… -1.02 4.29 4.12 0.167
## 10 sub-13 contrast-Uncued mask-b_g… -1.49 2.42 2.42 0
## 11 sub-14 contrast-Uncued mask-b_g… -0.539 2.38 2.58 -0.208
## 12 sub-16 contrast-Uncued mask-b_g… -0.429 2.04 2.17 -0.125
## 13 sub-17 contrast-Uncued mask-b_g… -0.970 3.17 3.04 0.125
## 14 sub-18 contrast-Uncued mask-b_g… -0.893 3.67 3.5 0.167
## 15 sub-19 contrast-Uncued mask-b_g… -0.156 2.96 3.21 -0.25
## 16 sub-20 contrast-Uncued mask-b_g… -0.0622 3.38 3.36 0.0114
## 17 sub-21 contrast-Uncued mask-b_g… 0.656 1.67 2.48 -0.812
## 18 sub-23 contrast-Uncued mask-b_g… -0.300 1.46 1.83 -0.375subset_2_Grey2 <- subset_2_Grey2[, c("values", "B_Uncued_S2")]
shapiro.test(subset_2_Grey2$values) #normal##
## Shapiro-Wilk normality test
##
## data: subset_2_Grey2$values
## W = 0.98277, p-value = 0.974shapiro.test(subset_2_Grey2$B_Uncued_S2) #normal##
## Shapiro-Wilk normality test
##
## data: subset_2_Grey2$B_Uncued_S2
## W = 0.97342, p-value = 0.859subset_2_Grey2corr <- corr.test(subset_2_Grey2, use = "pairwise",method="pearson",adjust="none")
subset_2_Grey2corr## Call:corr.test(x = subset_2_Grey2, use = "pairwise", method = "pearson",
## adjust = "none")
## Correlation matrix
## values B_Uncued_S2
## values 1.00 -0.03
## B_Uncued_S2 -0.03 1.00
## Sample Size
## [1] 18
## Probability values (Entries above the diagonal are adjusted for multiple tests.)
## values B_Uncued_S2
## values 0.0 0.9
## B_Uncued_S2 0.9 0.0
##
## To see confidence intervals of the correlations, print with the short=FALSE optionggscatter(subset_2_Grey2, x = "values", y = "B_Uncued_S2",
add = "reg.line", conf.int = TRUE,
cor.coef = TRUE, cor.method = "pearson",
xlab = "Bold Cued_Uncued", ylab = "B_Uncued_S2")
sp.subset_2_Grey2corr <- corr.test(subset_2_Grey2, use = "pairwise",method="spearman",adjust="none")
sp.subset_2_Grey2corr## Call:corr.test(x = subset_2_Grey2, use = "pairwise", method = "spearman",
## adjust = "none")
## Correlation matrix
## values B_Uncued_S2
## values 1.00 -0.04
## B_Uncued_S2 -0.04 1.00
## Sample Size
## [1] 18
## Probability values (Entries above the diagonal are adjusted for multiple tests.)
## values B_Uncued_S2
## values 0.00 0.86
## B_Uncued_S2 0.86 0.00
##
## To see confidence intervals of the correlations, print with the short=FALSE option#
subset_3_Grey2 <- combined_Grey2 %>%
filter(contrast == "contrast-Cued-Uncued")
subset_3_Grey2## # A tibble: 18 × 7
## participants contrast mask values B_CuedS2 B_Uncued_S2 B_diffS2
## <fct> <fct> <fct> <dbl> <dbl> <dbl> <dbl>
## 1 sub-01 contrast-Cued-Uncued mask-… -0.839 2.92 3.21 -0.292
## 2 sub-02 contrast-Cued-Uncued mask-… -0.572 3.96 3.5 0.458
## 3 sub-03 contrast-Cued-Uncued mask-… -1.14 3.21 3.12 0.0833
## 4 sub-04 contrast-Cued-Uncued mask-… -1.44 2.71 2.42 0.292
## 5 sub-05 contrast-Cued-Uncued mask-… -0.511 4 4.12 -0.125
## 6 sub-07 contrast-Cued-Uncued mask-… -0.844 2.75 3 -0.25
## 7 sub-08 contrast-Cued-Uncued mask-… -0.608 3.67 3.83 -0.159
## 8 sub-11 contrast-Cued-Uncued mask-… 0.220 2.75 2.88 -0.125
## 9 sub-12 contrast-Cued-Uncued mask-… -0.968 4.29 4.12 0.167
## 10 sub-13 contrast-Cued-Uncued mask-… -1.34 2.42 2.42 0
## 11 sub-14 contrast-Cued-Uncued mask-… -0.642 2.38 2.58 -0.208
## 12 sub-16 contrast-Cued-Uncued mask-… -0.816 2.04 2.17 -0.125
## 13 sub-17 contrast-Cued-Uncued mask-… -0.572 3.17 3.04 0.125
## 14 sub-18 contrast-Cued-Uncued mask-… -0.393 3.67 3.5 0.167
## 15 sub-19 contrast-Cued-Uncued mask-… -0.842 2.96 3.21 -0.25
## 16 sub-20 contrast-Cued-Uncued mask-… -0.475 3.38 3.36 0.0114
## 17 sub-21 contrast-Cued-Uncued mask-… -1.16 1.67 2.48 -0.812
## 18 sub-23 contrast-Cued-Uncued mask-… -0.734 1.46 1.83 -0.375subset_3_Grey2 <- subset_3_Grey2[, c("values", "B_diffS2")]
shapiro.test(subset_3_Grey2$values) #normal##
## Shapiro-Wilk normality test
##
## data: subset_3_Grey2$values
## W = 0.95173, p-value = 0.4527shapiro.test(subset_3_Grey2$B_diffS2) #normal##
## Shapiro-Wilk normality test
##
## data: subset_3_Grey2$B_diffS2
## W = 0.95985, p-value = 0.5987subset_3_Grey2corr <- corr.test(subset_3_Grey2, use = "pairwise",method="pearson",adjust="none")
subset_3_Grey2corr## Call:corr.test(x = subset_3_Grey2, use = "pairwise", method = "pearson",
## adjust = "none")
## Correlation matrix
## values B_diffS2
## values 1.00 0.05
## B_diffS2 0.05 1.00
## Sample Size
## [1] 18
## Probability values (Entries above the diagonal are adjusted for multiple tests.)
## values B_diffS2
## values 0.00 0.84
## B_diffS2 0.84 0.00
##
## To see confidence intervals of the correlations, print with the short=FALSE option#library("ggpubr")
ggscatter(subset_3_Grey2, x = "values", y = "B_diffS2",
add = "reg.line", conf.int = TRUE,
cor.coef = TRUE, cor.method = "pearson",
xlab = "Bold Cued_Uncued", ylab = "B_diffS2")
sp.subset_3_Grey2corr <- corr.test(subset_3_Grey2, use = "pairwise",method="spearman",adjust="none")
sp.subset_3_Grey2corr## Call:corr.test(x = subset_3_Grey2, use = "pairwise", method = "spearman",
## adjust = "none")
## Correlation matrix
## values B_diffS2
## values 1.00 0.14
## B_diffS2 0.14 1.00
## Sample Size
## [1] 18
## Probability values (Entries above the diagonal are adjusted for multiple tests.)
## values B_diffS2
## values 0.00 0.58
## B_diffS2 0.58 0.00
##
## To see confidence intervals of the correlations, print with the short=FALSE optionall_p_values.Grey2 <- c(round(subset_1_Grey2corr$p[2], 2),
round(subset_2_Grey2corr$p[2], 2),
round(subset_3_Grey2corr$p[2], 2))
all_p_values.Grey2## [1] 0.64 0.90 0.84# Apply FDR correction to combined all p-values
adjusted_p_Grey2<- p.adjust(all_p_values.Grey2, method = "fdr")
adjusted_p_Grey2## [1] 0.9 0.9 0.9