fmri Correlations
VivianaGreco
2023-08-18
bold betas for cued and uncued
All <- read_csv("correlations/task-arousal_maskedMeanEff.csv")## New names:
## Rows: 1872 Columns: 4
## ── Column specification
## ──────────────────────────────────────────────────────── Delimiter: "," chr
## (2): zmap, mask dbl (2): ...1, meanEffEstimate
## 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.
## • `` -> `...1`str(All)## spc_tbl_ [1,872 × 4] (S3: spec_tbl_df/tbl_df/tbl/data.frame)
## $ ...1 : num [1:1872] 0 1 2 3 4 5 6 7 8 9 ...
## $ zmap : chr [1:1872] "/cubric/collab/424_eegfmri/univariate/univariate/first_level/18/shifted-True/nm-bwc0_mmh_mod-none/sub-011220213"| __truncated__ "/cubric/collab/424_eegfmri/univariate/univariate/first_level/18/shifted-True/nm-bwc0_mmh_mod-none/sub-011220213"| __truncated__ "/cubric/collab/424_eegfmri/univariate/univariate/first_level/18/shifted-True/nm-bwc0_mmh_mod-none/sub-211020213"| __truncated__ "/cubric/collab/424_eegfmri/univariate/univariate/first_level/18/shifted-True/nm-bwc0_mmh_mod-none/sub-141020213"| __truncated__ ...
## $ mask : chr [1:1872] "/cubric/collab/424_eegfmri/univariate/univariate/masks/resampled_insular_cortex_mask.nii.gz" "/cubric/collab/424_eegfmri/univariate/univariate/masks/resampled_insular_cortex_mask.nii.gz" "/cubric/collab/424_eegfmri/univariate/univariate/masks/resampled_insular_cortex_mask.nii.gz" "/cubric/collab/424_eegfmri/univariate/univariate/masks/resampled_insular_cortex_mask.nii.gz" ...
## $ meanEffEstimate: num [1:1872] 2.6089 -0.7037 -0.1266 1.0023 -0.0321 ...
## - attr(*, "spec")=
## .. cols(
## .. ...1 = col_double(),
## .. zmap = col_character(),
## .. mask = col_character(),
## .. meanEffEstimate = col_double()
## .. )
## - attr(*, "problems")=<externalptr>All$zmap = as.factor(All$zmap)
All$mask = as.factor(All$mask)
all_levels <- levels(All$mask)
print(all_levels)## [1] "/cubric/collab/424_eegfmri/univariate/univariate/masks/b_amygdala_thresholded.nii.gz"
## [2] "/cubric/collab/424_eegfmri/univariate/univariate/masks/b_gray_thresholded.nii.gz"
## [3] "/cubric/collab/424_eegfmri/univariate/univariate/masks/b_hipp_thresholded.nii.gz"
## [4] "/cubric/collab/424_eegfmri/univariate/univariate/masks/b_mofc_thresholded.nii.gz"
## [5] "/cubric/collab/424_eegfmri/univariate/univariate/masks/resampled_bilateral_amygdala_mask.nii.gz"
## [6] "/cubric/collab/424_eegfmri/univariate/univariate/masks/resampled_bilateral_hippocampus_mask.nii.gz"
## [7] "/cubric/collab/424_eegfmri/univariate/univariate/masks/resampled_cingulate_gyrus,_anterior_division_mask.nii.gz"
## [8] "/cubric/collab/424_eegfmri/univariate/univariate/masks/resampled_cingulate_gyrus,_posterior_division_mask.nii.gz"
## [9] "/cubric/collab/424_eegfmri/univariate/univariate/masks/resampled_frontal_orbital_cortex_mask.nii.gz"
## [10] "/cubric/collab/424_eegfmri/univariate/univariate/masks/resampled_insular_cortex_mask.nii.gz"
## [11] "/cubric/collab/424_eegfmri/univariate/univariate/masks/resampled_left_amygdala_mask.nii.gz"
## [12] "/cubric/collab/424_eegfmri/univariate/univariate/masks/resampled_left_hippocampus_mask.nii.gz"
## [13] "/cubric/collab/424_eegfmri/univariate/univariate/masks/resampled_paracingulate_gyrus_mask.nii.gz"
## [14] "/cubric/collab/424_eegfmri/univariate/univariate/masks/resampled_right_amygdala_mask.nii.gz"
## [15] "/cubric/collab/424_eegfmri/univariate/univariate/masks/resampled_right_hippocampus_mask.nii.gz"
## [16] "/cubric/collab/424_eegfmri/univariate/univariate/masks/resampled_sgc_mask.nii.gz"
## [17] "/cubric/collab/424_eegfmri/univariate/univariate/masks/resampled_vamygdala_mask.nii.gz"
## [18] "/cubric/collab/424_eegfmri/univariate/univariate/masks/resampled_vinsula_mask.nii.gz"
## [19] "/cubric/collab/424_eegfmri/univariate/univariate/masks/resampled_vorbitofrontal_mask.nii.gz"
## [20] "/cubric/collab/424_eegfmri/univariate/univariate/masks/resampled_vsgc_mask.nii.gz"
## [21] "/cubric/collab/424_eegfmri/univariate/univariate/masks/resampled_vsgcv2_mask.nii.gz"
## [22] "/cubric/collab/424_eegfmri/univariate/univariate/masks/resampled_vtsgc_mask.nii.gz"
## [23] "mask-b_gray_thresholded_onesamp_ttest_results_ClusterEffEst_p-0.001_desc-binarizedCluster.nii.gz"
## [24] "mask-b_hipp_thresholded_onesamp_ttest_results_ClusterEffEst_p-0.001_desc-binarizedCluster.nii.gz"
## [25] "mask-resampled_vinsula_mask_onesamp_ttest_results_ClusterEffEst_p-0.001_desc-binarizedCluster.nii.gz"
## [26] "mask-resampled_vorbitofrontal_mask_onesamp_ttest_results_ClusterEffEst_p-0.001_desc-binarizedCluster.nii.gz"library(stringr)
# Replace multiple strings at a time
sub_number_replacements = c("06102021301" = "01", "06102021302" = "02",
"07102021301" = "03", "13102021301" = "04",
"14102021301" = "05", "20102021304" = "07", "21102021301" = "08",
"28102021301" = "11", "28102021302" = "12", "03112021304" = "13",
"24112021302" = "14", "01122021301" = "16", "08122021301" = "17",
"12012022302" = "18", "12012022301" = "19", "19012022302" = "20",
"19012022303" = "21", "26012022301" = "23")
All$zmap <- str_replace_all(All$zmap, sub_number_replacements)
All## # A tibble: 1,872 × 4
## ...1 zmap mask meanEffEstimate
## <dbl> <chr> <fct> <dbl>
## 1 0 /cubric/collab/424_eegfmri/univariate/univariate… /cub… 2.61
## 2 1 /cubric/collab/424_eegfmri/univariate/univariate… /cub… -0.704
## 3 2 /cubric/collab/424_eegfmri/univariate/univariate… /cub… -0.127
## 4 3 /cubric/collab/424_eegfmri/univariate/univariate… /cub… 1.00
## 5 4 /cubric/collab/424_eegfmri/univariate/univariate… /cub… -0.0321
## 6 5 /cubric/collab/424_eegfmri/univariate/univariate… /cub… 0.558
## 7 6 /cubric/collab/424_eegfmri/univariate/univariate… /cub… 0.216
## 8 7 /cubric/collab/424_eegfmri/univariate/univariate… /cub… 0.364
## 9 8 /cubric/collab/424_eegfmri/univariate/univariate… /cub… -0.254
## 10 9 /cubric/collab/424_eegfmri/univariate/univariate… /cub… 0.485
## # ℹ 1,862 more rowsrep2 = c("/cubric/collab/424_eegfmri/univariate/univariate/first_level/18/shifted-True/nm-bwc0_mmh_mod-none/" = "", "_zmap.nii.gz" = "")
All$zmap <- str_replace_all(All$zmap, rep2)
All## # A tibble: 1,872 × 4
## ...1 zmap mask meanEffEstimate
## <dbl> <chr> <fct> <dbl>
## 1 0 sub-16_task-arousal_contrast-Uncued /cubric/colla… 2.61
## 2 1 sub-16_task-arousal_contrast-Cued-Uncued /cubric/colla… -0.704
## 3 2 sub-08_task-arousal_contrast-Cued-Uncued /cubric/colla… -0.127
## 4 3 sub-05_task-arousal_contrast-Uncued-Cued /cubric/colla… 1.00
## 5 4 sub-07_task-arousal_contrast-Cued-Uncued /cubric/colla… -0.0321
## 6 5 sub-04_task-arousal_contrast-Uncued-Cued /cubric/colla… 0.558
## 7 6 sub-02_task-arousal_contrast-Cued /cubric/colla… 0.216
## 8 7 sub-07_task-arousal_contrast-Cued /cubric/colla… 0.364
## 9 8 sub-13_task-arousal_contrast-Uncued-Cued /cubric/colla… -0.254
## 10 9 sub-12_task-arousal_contrast-Cued /cubric/colla… 0.485
## # ℹ 1,862 more rowsrep3 = c("/cubric/collab/424_eegfmri/univariate/univariate/" = "", ".nii.gz" ="")
All$mask <- str_replace_all(All$mask, rep3)
All## # A tibble: 1,872 × 4
## ...1 zmap mask meanEffEstimate
## <dbl> <chr> <chr> <dbl>
## 1 0 sub-16_task-arousal_contrast-Uncued masks/resampl… 2.61
## 2 1 sub-16_task-arousal_contrast-Cued-Uncued masks/resampl… -0.704
## 3 2 sub-08_task-arousal_contrast-Cued-Uncued masks/resampl… -0.127
## 4 3 sub-05_task-arousal_contrast-Uncued-Cued masks/resampl… 1.00
## 5 4 sub-07_task-arousal_contrast-Cued-Uncued masks/resampl… -0.0321
## 6 5 sub-04_task-arousal_contrast-Uncued-Cued masks/resampl… 0.558
## 7 6 sub-02_task-arousal_contrast-Cued masks/resampl… 0.216
## 8 7 sub-07_task-arousal_contrast-Cued masks/resampl… 0.364
## 9 8 sub-13_task-arousal_contrast-Uncued-Cued masks/resampl… -0.254
## 10 9 sub-12_task-arousal_contrast-Cued masks/resampl… 0.485
## # ℹ 1,862 more rowsAll <- All %>%
mutate(participants = substr(zmap, 1, 6))
# Save the dataframe as a CSV file
write.csv(All, "All_subnames.csv", row.names = FALSE)resampled_vinsula_mask.nii.gz
No significant correlations after correcting for FDR
V_Insula.data <- read_csv("correlations/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>HRD.data <- read_csv("correlations/HRDdata.csv")## Rows: 18 Columns: 5
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (1): participants
## dbl (4): HRD_Cued, HRD_Uncued, HRD_Cued_Uncued, HRD_Uncued_Cued
##
## 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(HRD.data)## spc_tbl_ [18 × 5] (S3: spec_tbl_df/tbl_df/tbl/data.frame)
## $ participants : chr [1:18] "sub-01" "sub-02" "sub-03" "sub-04" ...
## $ HRD_Cued : num [1:18] -1.88 -6.37 -3.84 -2.39 -3.16 -6.58 -5.6 -4.81 -4.26 NA ...
## $ HRD_Uncued : num [1:18] -4.05 -5.43 -6.72 -3.42 -3.9 -6.38 -5.37 -6.3 -5.2 NA ...
## $ HRD_Cued_Uncued: num [1:18] 2.17 -0.94 2.88 1.03 0.74 -0.2 -0.23 1.49 0.94 NA ...
## $ HRD_Uncued_Cued: num [1:18] -2.17 0.94 -2.88 -1.03 -0.74 0.2 0.23 -1.49 -0.94 NA ...
## - attr(*, "spec")=
## .. cols(
## .. participants = col_character(),
## .. HRD_Cued = col_double(),
## .. HRD_Uncued = col_double(),
## .. HRD_Cued_Uncued = col_double(),
## .. HRD_Uncued_Cued = col_double()
## .. )
## - attr(*, "problems")=<externalptr>combined_V_Insula <- inner_join(V_Insula.data, HRD.data, by = "participants")
combined_V_Insula <- combined_V_Insula %>%
mutate(across(c(participants, contrast, mask), as.factor))
str(combined_V_Insula)## tibble [72 × 8] (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 ...
## $ HRD_Cued : num [1:72] -1.88 -1.88 -1.88 -1.88 -6.37 -6.37 -6.37 -6.37 -3.84 -3.84 ...
## $ HRD_Uncued : num [1:72] -4.05 -4.05 -4.05 -4.05 -5.43 -5.43 -5.43 -5.43 -6.72 -6.72 ...
## $ HRD_Cued_Uncued: num [1:72] 2.17 2.17 2.17 2.17 -0.94 -0.94 -0.94 -0.94 2.88 2.88 ...
## $ HRD_Uncued_Cued: num [1:72] -2.17 -2.17 -2.17 -2.17 0.94 0.94 0.94 0.94 -2.88 -2.88 ...#
subset_1_V_Insula <- combined_V_Insula %>%
filter(contrast == "contrast-Cued")
subset_1_V_Insula <- subset_1_V_Insula[, c("values", "HRD_Cued")]
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$HRD_Cued) #normal##
## Shapiro-Wilk normality test
##
## data: subset_1_V_Insula$HRD_Cued
## W = 0.90975, p-value = 0.1563subset_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 HRD_Cued
## values 1.00 -0.09
## HRD_Cued -0.09 1.00
## Sample Size
## values HRD_Cued
## values 18 14
## HRD_Cued 14 14
## Probability values (Entries above the diagonal are adjusted for multiple tests.)
## values HRD_Cued
## values 0.00 0.75
## HRD_Cued 0.75 0.00
##
## To see confidence intervals of the correlations, print with the short=FALSE optionggscatter(subset_1_V_Insula, x = "values", y = "HRD_Cued",
add = "reg.line", conf.int = TRUE,
cor.coef = TRUE, cor.method = "pearson",
xlab = "Bold Cued_Uncued", ylab = "HRD_Cued")## Warning: Removed 4 rows containing non-finite values (`stat_smooth()`).## Warning: Removed 4 rows containing non-finite values (`stat_cor()`).## Warning: Removed 4 rows containing missing values (`geom_point()`).
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 HRD_Cued
## values 1.00 -0.13
## HRD_Cued -0.13 1.00
## Sample Size
## values HRD_Cued
## values 18 14
## HRD_Cued 14 14
## Probability values (Entries above the diagonal are adjusted for multiple tests.)
## values HRD_Cued
## values 0.00 0.67
## HRD_Cued 0.67 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", "HRD_Uncued")]
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$HRD_Uncued) #normal##
## Shapiro-Wilk normality test
##
## data: subset_2_V_Insula$HRD_Uncued
## W = 0.8813, p-value = 0.06065subset_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 HRD_Uncued
## values 1.00 0.33
## HRD_Uncued 0.33 1.00
## Sample Size
## values HRD_Uncued
## values 18 14
## HRD_Uncued 14 14
## Probability values (Entries above the diagonal are adjusted for multiple tests.)
## values HRD_Uncued
## values 0.00 0.25
## HRD_Uncued 0.25 0.00
##
## To see confidence intervals of the correlations, print with the short=FALSE optionggscatter(subset_2_V_Insula, x = "values", y = "HRD_Uncued",
add = "reg.line", conf.int = TRUE,
cor.coef = TRUE, cor.method = "pearson",
xlab = "Bold Cued_Uncued", ylab = "HRD_Uncued")## Warning: Removed 4 rows containing non-finite values (`stat_smooth()`).## Warning: Removed 4 rows containing non-finite values (`stat_cor()`).## Warning: Removed 4 rows containing missing values (`geom_point()`).
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 HRD_Uncued
## values 1.00 0.25
## HRD_Uncued 0.25 1.00
## Sample Size
## values HRD_Uncued
## values 18 14
## HRD_Uncued 14 14
## Probability values (Entries above the diagonal are adjusted for multiple tests.)
## values HRD_Uncued
## values 0.00 0.38
## HRD_Uncued 0.38 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", "HRD_Cued_Uncued")]
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$HRD_Cued_Uncued) #normal##
## Shapiro-Wilk normality test
##
## data: subset_3_V_Insula$HRD_Cued_Uncued
## W = 0.95893, p-value = 0.7054subset_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 HRD_Cued_Uncued
## values 1.00 -0.53
## HRD_Cued_Uncued -0.53 1.00
## Sample Size
## values HRD_Cued_Uncued
## values 18 14
## HRD_Cued_Uncued 14 14
## Probability values (Entries above the diagonal are adjusted for multiple tests.)
## values HRD_Cued_Uncued
## values 0.00 0.05
## HRD_Cued_Uncued 0.05 0.00
##
## To see confidence intervals of the correlations, print with the short=FALSE optionggscatter(subset_3_V_Insula, x = "values", y = "HRD_Cued_Uncued",
add = "reg.line", conf.int = TRUE,
cor.coef = TRUE, cor.method = "pearson",
xlab = "Bold Cued_Uncued", ylab = "HRD_Cued_Uncued")## Warning: Removed 4 rows containing non-finite values (`stat_smooth()`).## Warning: Removed 4 rows containing non-finite values (`stat_cor()`).## Warning: Removed 4 rows containing missing values (`geom_point()`).
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 HRD_Cued_Uncued
## values 1.00 -0.49
## HRD_Cued_Uncued -0.49 1.00
## Sample Size
## values HRD_Cued_Uncued
## values 18 14
## HRD_Cued_Uncued 14 14
## Probability values (Entries above the diagonal are adjusted for multiple tests.)
## values HRD_Cued_Uncued
## values 0.00 0.08
## HRD_Cued_Uncued 0.08 0.00
##
## To see confidence intervals of the correlations, print with the short=FALSE option#
subset_4_V_Insula <- combined_V_Insula %>%
filter(contrast == "contrast-Uncued-Cued")
subset_4_V_Insula <- subset_4_V_Insula[, c("values", "HRD_Uncued_Cued")]
shapiro.test(subset_4_V_Insula$values) #normal##
## Shapiro-Wilk normality test
##
## data: subset_4_V_Insula$values
## W = 0.9763, p-value = 0.9041shapiro.test(subset_4_V_Insula$HRD_Uncued_Cued) #normal##
## Shapiro-Wilk normality test
##
## data: subset_4_V_Insula$HRD_Uncued_Cued
## W = 0.95893, p-value = 0.7054subset_4_V_Insulacorr <- corr.test(subset_4_V_Insula, use = "pairwise",method="pearson",adjust="none")
subset_4_V_Insulacorr## Call:corr.test(x = subset_4_V_Insula, use = "pairwise", method = "pearson",
## adjust = "none")
## Correlation matrix
## values HRD_Uncued_Cued
## values 1.00 -0.53
## HRD_Uncued_Cued -0.53 1.00
## Sample Size
## values HRD_Uncued_Cued
## values 18 14
## HRD_Uncued_Cued 14 14
## Probability values (Entries above the diagonal are adjusted for multiple tests.)
## values HRD_Uncued_Cued
## values 0.00 0.05
## HRD_Uncued_Cued 0.05 0.00
##
## To see confidence intervals of the correlations, print with the short=FALSE optionggscatter(subset_4_V_Insula, x = "values", y = "HRD_Uncued_Cued",
add = "reg.line", conf.int = TRUE,
cor.coef = TRUE, cor.method = "pearson",
xlab = "Bold Cued_Uncued", ylab = "HRD_Uncued_Cued")## Warning: Removed 4 rows containing non-finite values (`stat_smooth()`).## Warning: Removed 4 rows containing non-finite values (`stat_cor()`).## Warning: Removed 4 rows containing missing values (`geom_point()`).
sp.subset_4_V_Insulacorr <- corr.test(subset_4_V_Insula, use = "pairwise",method="spearman",adjust="none")
sp.subset_4_V_Insulacorr## Call:corr.test(x = subset_4_V_Insula, use = "pairwise", method = "spearman",
## adjust = "none")
## Correlation matrix
## values HRD_Uncued_Cued
## values 1.00 -0.49
## HRD_Uncued_Cued -0.49 1.00
## Sample Size
## values HRD_Uncued_Cued
## values 18 14
## HRD_Uncued_Cued 14 14
## Probability values (Entries above the diagonal are adjusted for multiple tests.)
## values HRD_Uncued_Cued
## values 0.00 0.08
## HRD_Uncued_Cued 0.08 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$p[2], 2),
round(subset_4_V_Insulacorr$p[2], 2))
all_p_values.VInsula## [1] 0.75 0.25 0.05 0.05# Apply FDR correction to combined all p-values
adjusted_p_VInsula<- p.adjust(all_p_values.VInsula, method = "fdr")
adjusted_p_VInsula## [1] 0.7500000 0.3333333 0.1000000 0.1000000resampled_vorbitofrontal_mask.nii.gz
OFC <- read_csv("correlations/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>HRD.data <- read_csv("correlations/HRDdata.csv")## Rows: 18 Columns: 5
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (1): participants
## dbl (4): HRD_Cued, HRD_Uncued, HRD_Cued_Uncued, HRD_Uncued_Cued
##
## 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(HRD.data)## spc_tbl_ [18 × 5] (S3: spec_tbl_df/tbl_df/tbl/data.frame)
## $ participants : chr [1:18] "sub-01" "sub-02" "sub-03" "sub-04" ...
## $ HRD_Cued : num [1:18] -1.88 -6.37 -3.84 -2.39 -3.16 -6.58 -5.6 -4.81 -4.26 NA ...
## $ HRD_Uncued : num [1:18] -4.05 -5.43 -6.72 -3.42 -3.9 -6.38 -5.37 -6.3 -5.2 NA ...
## $ HRD_Cued_Uncued: num [1:18] 2.17 -0.94 2.88 1.03 0.74 -0.2 -0.23 1.49 0.94 NA ...
## $ HRD_Uncued_Cued: num [1:18] -2.17 0.94 -2.88 -1.03 -0.74 0.2 0.23 -1.49 -0.94 NA ...
## - attr(*, "spec")=
## .. cols(
## .. participants = col_character(),
## .. HRD_Cued = col_double(),
## .. HRD_Uncued = col_double(),
## .. HRD_Cued_Uncued = col_double(),
## .. HRD_Uncued_Cued = col_double()
## .. )
## - attr(*, "problems")=<externalptr>combined_OFC <- inner_join(OFC, HRD.data, by = "participants")
combined_OFC <- combined_OFC %>%
mutate(across(c(participants, contrast, mask), as.factor))
str(combined_OFC)## tibble [72 × 8] (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 ...
## $ HRD_Cued : num [1:72] -1.88 -1.88 -1.88 -1.88 -6.37 -6.37 -6.37 -6.37 -3.84 -3.84 ...
## $ HRD_Uncued : num [1:72] -4.05 -4.05 -4.05 -4.05 -5.43 -5.43 -5.43 -5.43 -6.72 -6.72 ...
## $ HRD_Cued_Uncued: num [1:72] 2.17 2.17 2.17 2.17 -0.94 -0.94 -0.94 -0.94 2.88 2.88 ...
## $ HRD_Uncued_Cued: num [1:72] -2.17 -2.17 -2.17 -2.17 0.94 0.94 0.94 0.94 -2.88 -2.88 ...#
subset_1_OFC <- combined_OFC %>%
filter(contrast == "contrast-Cued")
subset_1_OFC <- subset_1_OFC[, c("values", "HRD_Cued")]
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$HRD_Cued) #normal##
## Shapiro-Wilk normality test
##
## data: subset_1_OFC$HRD_Cued
## W = 0.90975, p-value = 0.1563subset_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 HRD_Cued
## values 1.00 -0.25
## HRD_Cued -0.25 1.00
## Sample Size
## values HRD_Cued
## values 18 14
## HRD_Cued 14 14
## Probability values (Entries above the diagonal are adjusted for multiple tests.)
## values HRD_Cued
## values 0.00 0.38
## HRD_Cued 0.38 0.00
##
## To see confidence intervals of the correlations, print with the short=FALSE optionggscatter(subset_1_OFC, x = "values", y = "HRD_Cued",
add = "reg.line", conf.int = TRUE,
cor.coef = TRUE, cor.method = "pearson",
xlab = "Bold Cued_Uncued", ylab = "HRD_Cued")## Warning: Removed 4 rows containing non-finite values (`stat_smooth()`).## Warning: Removed 4 rows containing non-finite values (`stat_cor()`).## Warning: Removed 4 rows containing missing values (`geom_point()`).
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 HRD_Cued
## values 1.00 -0.43
## HRD_Cued -0.43 1.00
## Sample Size
## values HRD_Cued
## values 18 14
## HRD_Cued 14 14
## Probability values (Entries above the diagonal are adjusted for multiple tests.)
## values HRD_Cued
## values 0.00 0.12
## HRD_Cued 0.12 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 × 8
## participants contrast mask values HRD_Cued HRD_Uncued HRD_Cued_Uncued
## <fct> <fct> <fct> <dbl> <dbl> <dbl> <dbl>
## 1 sub-01 contrast-Unc… mask… 0.0598 -1.88 -4.05 2.17
## 2 sub-02 contrast-Unc… mask… 0.242 -6.37 -5.43 -0.94
## 3 sub-03 contrast-Unc… mask… 0.297 -3.84 -6.72 2.88
## 4 sub-04 contrast-Unc… mask… -0.0818 -2.39 -3.42 1.03
## 5 sub-05 contrast-Unc… mask… 0.952 -3.16 -3.9 0.74
## 6 sub-07 contrast-Unc… mask… -0.0677 -6.58 -6.38 -0.2
## 7 sub-08 contrast-Unc… mask… -0.00503 -5.6 -5.37 -0.23
## 8 sub-11 contrast-Unc… mask… 0.0526 -4.81 -6.3 1.49
## 9 sub-12 contrast-Unc… mask… 0.138 -4.26 -5.2 0.94
## 10 sub-13 contrast-Unc… mask… 0.491 NA NA NA
## 11 sub-14 contrast-Unc… mask… -0.187 -9.91 -10.0 0.14
## 12 sub-16 contrast-Unc… mask… 0.0259 -5.53 -5.4 -0.13
## 13 sub-17 contrast-Unc… mask… 0.219 -2.06 -2.97 0.91
## 14 sub-18 contrast-Unc… mask… -0.161 -10.4 -10.1 -0.3
## 15 sub-19 contrast-Unc… mask… -0.133 NA NA NA
## 16 sub-20 contrast-Unc… mask… -0.769 -4.26 -5.74 1.48
## 17 sub-21 contrast-Unc… mask… 0.615 NA NA NA
## 18 sub-23 contrast-Unc… mask… -0.140 NA NA NA
## # ℹ 1 more variable: HRD_Uncued_Cued <dbl>subset_2_OFC <- subset_2_OFC[, c("values", "HRD_Uncued")]
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$HRD_Uncued) #normal##
## Shapiro-Wilk normality test
##
## data: subset_2_OFC$HRD_Uncued
## W = 0.8813, p-value = 0.06065subset_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 HRD_Uncued
## values 1.00 0.36
## HRD_Uncued 0.36 1.00
## Sample Size
## values HRD_Uncued
## values 18 14
## HRD_Uncued 14 14
## Probability values (Entries above the diagonal are adjusted for multiple tests.)
## values HRD_Uncued
## values 0.0 0.2
## HRD_Uncued 0.2 0.0
##
## To see confidence intervals of the correlations, print with the short=FALSE optionggscatter(subset_2_OFC, x = "values", y = "HRD_Uncued",
add = "reg.line", conf.int = TRUE,
cor.coef = TRUE, cor.method = "pearson",
xlab = "Bold Cued_Uncued", ylab = "HRD_Uncued")## Warning: Removed 4 rows containing non-finite values (`stat_smooth()`).## Warning: Removed 4 rows containing non-finite values (`stat_cor()`).## Warning: Removed 4 rows containing missing values (`geom_point()`).
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 HRD_Uncued
## values 1.0 0.4
## HRD_Uncued 0.4 1.0
## Sample Size
## values HRD_Uncued
## values 18 14
## HRD_Uncued 14 14
## Probability values (Entries above the diagonal are adjusted for multiple tests.)
## values HRD_Uncued
## values 0.00 0.15
## HRD_Uncued 0.15 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 × 8
## participants contrast mask values HRD_Cued HRD_Uncued HRD_Cued_Uncued
## <fct> <fct> <fct> <dbl> <dbl> <dbl> <dbl>
## 1 sub-01 contrast-Cued… mask… -0.532 -1.88 -4.05 2.17
## 2 sub-02 contrast-Cued… mask… 0.0368 -6.37 -5.43 -0.94
## 3 sub-03 contrast-Cued… mask… -0.343 -3.84 -6.72 2.88
## 4 sub-04 contrast-Cued… mask… -0.540 -2.39 -3.42 1.03
## 5 sub-05 contrast-Cued… mask… -0.582 -3.16 -3.9 0.74
## 6 sub-07 contrast-Cued… mask… 0.291 -6.58 -6.38 -0.2
## 7 sub-08 contrast-Cued… mask… 0.0159 -5.6 -5.37 -0.23
## 8 sub-11 contrast-Cued… mask… 0.191 -4.81 -6.3 1.49
## 9 sub-12 contrast-Cued… mask… -0.359 -4.26 -5.2 0.94
## 10 sub-13 contrast-Cued… mask… -0.112 NA NA NA
## 11 sub-14 contrast-Cued… mask… -0.521 -9.91 -10.0 0.14
## 12 sub-16 contrast-Cued… mask… 0.0541 -5.53 -5.4 -0.13
## 13 sub-17 contrast-Cued… mask… -0.457 -2.06 -2.97 0.91
## 14 sub-18 contrast-Cued… mask… 0.266 -10.4 -10.1 -0.3
## 15 sub-19 contrast-Cued… mask… -0.0594 NA NA NA
## 16 sub-20 contrast-Cued… mask… 0.0552 -4.26 -5.74 1.48
## 17 sub-21 contrast-Cued… mask… -0.639 NA NA NA
## 18 sub-23 contrast-Cued… mask… -0.178 NA NA NA
## # ℹ 1 more variable: HRD_Uncued_Cued <dbl>subset_3_OFC <- subset_3_OFC[, c("values", "HRD_Cued_Uncued")]
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$HRD_Cued_Uncued) #normal##
## Shapiro-Wilk normality test
##
## data: subset_3_OFC$HRD_Cued_Uncued
## W = 0.95893, p-value = 0.7054subset_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 HRD_Cued_Uncued
## values 1.00 -0.45
## HRD_Cued_Uncued -0.45 1.00
## Sample Size
## values HRD_Cued_Uncued
## values 18 14
## HRD_Cued_Uncued 14 14
## Probability values (Entries above the diagonal are adjusted for multiple tests.)
## values HRD_Cued_Uncued
## values 0.00 0.11
## HRD_Cued_Uncued 0.11 0.00
##
## To see confidence intervals of the correlations, print with the short=FALSE option#library("ggpubr")
ggscatter(subset_3_OFC, x = "values", y = "HRD_Cued_Uncued",
add = "reg.line", conf.int = TRUE,
cor.coef = TRUE, cor.method = "pearson",
xlab = "Bold Cued_Uncued", ylab = "HRD_Cued_Uncued")## Warning: Removed 4 rows containing non-finite values (`stat_smooth()`).## Warning: Removed 4 rows containing non-finite values (`stat_cor()`).## Warning: Removed 4 rows containing missing values (`geom_point()`).
#
subset_4_OFC <- combined_OFC %>%
filter(contrast == "contrast-Uncued-Cued")
subset_4_OFC## # A tibble: 18 × 8
## participants contrast mask values HRD_Cued HRD_Uncued HRD_Cued_Uncued
## <fct> <fct> <fct> <dbl> <dbl> <dbl> <dbl>
## 1 sub-01 contrast-Uncu… mask… 0.532 -1.88 -4.05 2.17
## 2 sub-02 contrast-Uncu… mask… -0.0368 -6.37 -5.43 -0.94
## 3 sub-03 contrast-Uncu… mask… 0.343 -3.84 -6.72 2.88
## 4 sub-04 contrast-Uncu… mask… 0.540 -2.39 -3.42 1.03
## 5 sub-05 contrast-Uncu… mask… 0.582 -3.16 -3.9 0.74
## 6 sub-07 contrast-Uncu… mask… -0.291 -6.58 -6.38 -0.2
## 7 sub-08 contrast-Uncu… mask… -0.0159 -5.6 -5.37 -0.23
## 8 sub-11 contrast-Uncu… mask… -0.191 -4.81 -6.3 1.49
## 9 sub-12 contrast-Uncu… mask… 0.359 -4.26 -5.2 0.94
## 10 sub-13 contrast-Uncu… mask… 0.112 NA NA NA
## 11 sub-14 contrast-Uncu… mask… 0.521 -9.91 -10.0 0.14
## 12 sub-16 contrast-Uncu… mask… -0.0541 -5.53 -5.4 -0.13
## 13 sub-17 contrast-Uncu… mask… 0.457 -2.06 -2.97 0.91
## 14 sub-18 contrast-Uncu… mask… -0.266 -10.4 -10.1 -0.3
## 15 sub-19 contrast-Uncu… mask… 0.0594 NA NA NA
## 16 sub-20 contrast-Uncu… mask… -0.0552 -4.26 -5.74 1.48
## 17 sub-21 contrast-Uncu… mask… 0.639 NA NA NA
## 18 sub-23 contrast-Uncu… mask… 0.178 NA NA NA
## # ℹ 1 more variable: HRD_Uncued_Cued <dbl>subset_4_OFC <- subset_4_OFC[, c("values", "HRD_Uncued_Cued")]
shapiro.test(subset_4_OFC$values) #normal##
## Shapiro-Wilk normality test
##
## data: subset_4_OFC$values
## W = 0.92178, p-value = 0.139shapiro.test(subset_4_OFC$HRD_Uncued_Cued) #normal##
## Shapiro-Wilk normality test
##
## data: subset_4_OFC$HRD_Uncued_Cued
## W = 0.95893, p-value = 0.7054subset_4_OFCcorr <- corr.test(subset_4_OFC, use = "pairwise",method="pearson",adjust="none")
subset_4_OFCcorr## Call:corr.test(x = subset_4_OFC, use = "pairwise", method = "pearson",
## adjust = "none")
## Correlation matrix
## values HRD_Uncued_Cued
## values 1.00 -0.45
## HRD_Uncued_Cued -0.45 1.00
## Sample Size
## values HRD_Uncued_Cued
## values 18 14
## HRD_Uncued_Cued 14 14
## Probability values (Entries above the diagonal are adjusted for multiple tests.)
## values HRD_Uncued_Cued
## values 0.00 0.11
## HRD_Uncued_Cued 0.11 0.00
##
## To see confidence intervals of the correlations, print with the short=FALSE optionggscatter(subset_4_OFC, x = "values", y = "HRD_Uncued_Cued",
add = "reg.line", conf.int = TRUE,
cor.coef = TRUE, cor.method = "pearson",
xlab = "Bold Cued_Uncued", ylab = "HRD_Uncued_Cued")## Warning: Removed 4 rows containing non-finite values (`stat_smooth()`).## Warning: Removed 4 rows containing non-finite values (`stat_cor()`).## Warning: Removed 4 rows containing missing values (`geom_point()`).
sp.subset_4_OFCcorr <- corr.test(subset_2_OFC, use = "pairwise",method="spearman",adjust="none")
sp.subset_4_OFCcorr## Call:corr.test(x = subset_2_OFC, use = "pairwise", method = "spearman",
## adjust = "none")
## Correlation matrix
## values HRD_Uncued
## values 1.0 0.4
## HRD_Uncued 0.4 1.0
## Sample Size
## values HRD_Uncued
## values 18 14
## HRD_Uncued 14 14
## Probability values (Entries above the diagonal are adjusted for multiple tests.)
## values HRD_Uncued
## values 0.00 0.15
## HRD_Uncued 0.15 0.00
##
## To see confidence intervals of the correlations, print with the short=FALSE optionall_p_values.OFC <- c(round(subset_1_OFCcorr$p[2], 2),
round(subset_2_OFCcorr$p[2], 2),
round(subset_3_OFCcorr$p[2], 2),
round(subset_4_OFCcorr$p[2], 2))
all_p_values.OFC## [1] 0.38 0.20 0.11 0.11# Apply FDR correction to combined all p-values
adjusted_p_OFC<- p.adjust(all_p_values.OFC, method = "fdr")
adjusted_p_OFC## [1] 0.3800000 0.2666667 0.2200000 0.2200000resampled_vamygdala_mask.nii.gz
No sign correlations
amy <- read_csv("correlations/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>HRD.data <- read_csv("correlations/HRDdata.csv")## Rows: 18 Columns: 5
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (1): participants
## dbl (4): HRD_Cued, HRD_Uncued, HRD_Cued_Uncued, HRD_Uncued_Cued
##
## 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(HRD.data)## spc_tbl_ [18 × 5] (S3: spec_tbl_df/tbl_df/tbl/data.frame)
## $ participants : chr [1:18] "sub-01" "sub-02" "sub-03" "sub-04" ...
## $ HRD_Cued : num [1:18] -1.88 -6.37 -3.84 -2.39 -3.16 -6.58 -5.6 -4.81 -4.26 NA ...
## $ HRD_Uncued : num [1:18] -4.05 -5.43 -6.72 -3.42 -3.9 -6.38 -5.37 -6.3 -5.2 NA ...
## $ HRD_Cued_Uncued: num [1:18] 2.17 -0.94 2.88 1.03 0.74 -0.2 -0.23 1.49 0.94 NA ...
## $ HRD_Uncued_Cued: num [1:18] -2.17 0.94 -2.88 -1.03 -0.74 0.2 0.23 -1.49 -0.94 NA ...
## - attr(*, "spec")=
## .. cols(
## .. participants = col_character(),
## .. HRD_Cued = col_double(),
## .. HRD_Uncued = col_double(),
## .. HRD_Cued_Uncued = col_double(),
## .. HRD_Uncued_Cued = col_double()
## .. )
## - attr(*, "problems")=<externalptr>combined_amy <- inner_join(amy, HRD.data, by = "participants")
combined_amy <- combined_amy %>%
mutate(across(c(participants, contrast, mask), as.factor))
str(combined_amy)## tibble [72 × 8] (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 ...
## $ HRD_Cued : num [1:72] -1.88 -1.88 -1.88 -1.88 -6.37 -6.37 -6.37 -6.37 -3.84 -3.84 ...
## $ HRD_Uncued : num [1:72] -4.05 -4.05 -4.05 -4.05 -5.43 -5.43 -5.43 -5.43 -6.72 -6.72 ...
## $ HRD_Cued_Uncued: num [1:72] 2.17 2.17 2.17 2.17 -0.94 -0.94 -0.94 -0.94 2.88 2.88 ...
## $ HRD_Uncued_Cued: num [1:72] -2.17 -2.17 -2.17 -2.17 0.94 0.94 0.94 0.94 -2.88 -2.88 ...#
subset_1_amy <- combined_amy %>%
filter(contrast == "contrast-Cued")
subset_1_amy## # A tibble: 18 × 8
## participants contrast mask values HRD_Cued HRD_Uncued HRD_Cued_Uncued
## <fct> <fct> <fct> <dbl> <dbl> <dbl> <dbl>
## 1 sub-01 contrast-Cued mask… -1.01 -1.88 -4.05 2.17
## 2 sub-02 contrast-Cued mask… 0.577 -6.37 -5.43 -0.94
## 3 sub-03 contrast-Cued mask… 0.0491 -3.84 -6.72 2.88
## 4 sub-04 contrast-Cued mask… -0.630 -2.39 -3.42 1.03
## 5 sub-05 contrast-Cued mask… 0.896 -3.16 -3.9 0.74
## 6 sub-07 contrast-Cued mask… 0.473 -6.58 -6.38 -0.2
## 7 sub-08 contrast-Cued mask… 0.726 -5.6 -5.37 -0.23
## 8 sub-11 contrast-Cued mask… 1.09 -4.81 -6.3 1.49
## 9 sub-12 contrast-Cued mask… 0.573 -4.26 -5.2 0.94
## 10 sub-13 contrast-Cued mask… 1.49 NA NA NA
## 11 sub-14 contrast-Cued mask… -1.96 -9.91 -10.0 0.14
## 12 sub-16 contrast-Cued mask… 1.38 -5.53 -5.4 -0.13
## 13 sub-17 contrast-Cued mask… 0.371 -2.06 -2.97 0.91
## 14 sub-18 contrast-Cued mask… 0.245 -10.4 -10.1 -0.3
## 15 sub-19 contrast-Cued mask… 0.919 NA NA NA
## 16 sub-20 contrast-Cued mask… -0.239 -4.26 -5.74 1.48
## 17 sub-21 contrast-Cued mask… 0.00246 NA NA NA
## 18 sub-23 contrast-Cued mask… 0.492 NA NA NA
## # ℹ 1 more variable: HRD_Uncued_Cued <dbl>subset_1_amy <- subset_1_amy[, c("values", "HRD_Cued")]
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$HRD_Cued) #normal##
## Shapiro-Wilk normality test
##
## data: subset_1_amy$HRD_Cued
## W = 0.90975, p-value = 0.1563subset_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 HRD_Cued
## values 1.00 0.14
## HRD_Cued 0.14 1.00
## Sample Size
## values HRD_Cued
## values 18 14
## HRD_Cued 14 14
## Probability values (Entries above the diagonal are adjusted for multiple tests.)
## values HRD_Cued
## values 0.00 0.63
## HRD_Cued 0.63 0.00
##
## To see confidence intervals of the correlations, print with the short=FALSE optionggscatter(subset_1_amy, x = "values", y = "HRD_Cued",
add = "reg.line", conf.int = TRUE,
cor.coef = TRUE, cor.method = "pearson",
xlab = "Bold Cued_Uncued", ylab = "HRD_Cued")## Warning: Removed 4 rows containing non-finite values (`stat_smooth()`).## Warning: Removed 4 rows containing non-finite values (`stat_cor()`).## Warning: Removed 4 rows containing missing values (`geom_point()`).
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 HRD_Cued
## values 1.00 -0.17
## HRD_Cued -0.17 1.00
## Sample Size
## values HRD_Cued
## values 18 14
## HRD_Cued 14 14
## Probability values (Entries above the diagonal are adjusted for multiple tests.)
## values HRD_Cued
## values 0.00 0.57
## HRD_Cued 0.57 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 × 8
## participants contrast mask values HRD_Cued HRD_Uncued HRD_Cued_Uncued
## <fct> <fct> <fct> <dbl> <dbl> <dbl> <dbl>
## 1 sub-01 contrast-Uncu… mask… -0.529 -1.88 -4.05 2.17
## 2 sub-02 contrast-Uncu… mask… 0.0126 -6.37 -5.43 -0.94
## 3 sub-03 contrast-Uncu… mask… 1.16 -3.84 -6.72 2.88
## 4 sub-04 contrast-Uncu… mask… 0.100 -2.39 -3.42 1.03
## 5 sub-05 contrast-Uncu… mask… 2.74 -3.16 -3.9 0.74
## 6 sub-07 contrast-Uncu… mask… 0.485 -6.58 -6.38 -0.2
## 7 sub-08 contrast-Uncu… mask… 1.45 -5.6 -5.37 -0.23
## 8 sub-11 contrast-Uncu… mask… 1.06 -4.81 -6.3 1.49
## 9 sub-12 contrast-Uncu… mask… 0.587 -4.26 -5.2 0.94
## 10 sub-13 contrast-Uncu… mask… 0.641 NA NA NA
## 11 sub-14 contrast-Uncu… mask… -1.45 -9.91 -10.0 0.14
## 12 sub-16 contrast-Uncu… mask… 0.724 -5.53 -5.4 -0.13
## 13 sub-17 contrast-Uncu… mask… 0.387 -2.06 -2.97 0.91
## 14 sub-18 contrast-Uncu… mask… 0.155 -10.4 -10.1 -0.3
## 15 sub-19 contrast-Uncu… mask… -0.242 NA NA NA
## 16 sub-20 contrast-Uncu… mask… 0.180 -4.26 -5.74 1.48
## 17 sub-21 contrast-Uncu… mask… 1.38 NA NA NA
## 18 sub-23 contrast-Uncu… mask… -0.342 NA NA NA
## # ℹ 1 more variable: HRD_Uncued_Cued <dbl>subset_2_amy <- subset_2_amy[, c("values", "HRD_Uncued")]
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$HRD_Uncued) #normal##
## Shapiro-Wilk normality test
##
## data: subset_2_amy$HRD_Uncued
## W = 0.8813, p-value = 0.06065subset_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 HRD_Uncued
## values 1.00 0.39
## HRD_Uncued 0.39 1.00
## Sample Size
## values HRD_Uncued
## values 18 14
## HRD_Uncued 14 14
## Probability values (Entries above the diagonal are adjusted for multiple tests.)
## values HRD_Uncued
## values 0.00 0.17
## HRD_Uncued 0.17 0.00
##
## To see confidence intervals of the correlations, print with the short=FALSE optionggscatter(subset_2_amy, x = "values", y = "HRD_Uncued",
add = "reg.line", conf.int = TRUE,
cor.coef = TRUE, cor.method = "pearson",
xlab = "Bold Cued_Uncued", ylab = "HRD_Uncued")## Warning: Removed 4 rows containing non-finite values (`stat_smooth()`).## Warning: Removed 4 rows containing non-finite values (`stat_cor()`).## Warning: Removed 4 rows containing missing values (`geom_point()`).
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 HRD_Uncued
## values 1.00 0.12
## HRD_Uncued 0.12 1.00
## Sample Size
## values HRD_Uncued
## values 18 14
## HRD_Uncued 14 14
## Probability values (Entries above the diagonal are adjusted for multiple tests.)
## values HRD_Uncued
## values 0.00 0.69
## HRD_Uncued 0.69 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 × 8
## participants contrast mask values HRD_Cued HRD_Uncued HRD_Cued_Uncued
## <fct> <fct> <fct> <dbl> <dbl> <dbl> <dbl>
## 1 sub-01 contrast-Cued… mask… -0.366 -1.88 -4.05 2.17
## 2 sub-02 contrast-Cued… mask… 0.433 -6.37 -5.43 -0.94
## 3 sub-03 contrast-Cued… mask… -0.839 -3.84 -6.72 2.88
## 4 sub-04 contrast-Cued… mask… -0.545 -2.39 -3.42 1.03
## 5 sub-05 contrast-Cued… mask… -1.36 -3.16 -3.9 0.74
## 6 sub-07 contrast-Cued… mask… -0.0391 -6.58 -6.38 -0.2
## 7 sub-08 contrast-Cued… mask… -0.531 -5.6 -5.37 -0.23
## 8 sub-11 contrast-Cued… mask… 0.0149 -4.81 -6.3 1.49
## 9 sub-12 contrast-Cued… mask… -0.0102 -4.26 -5.2 0.94
## 10 sub-13 contrast-Cued… mask… 0.629 NA NA NA
## 11 sub-14 contrast-Cued… mask… -0.369 -9.91 -10.0 0.14
## 12 sub-16 contrast-Cued… mask… 0.497 -5.53 -5.4 -0.13
## 13 sub-17 contrast-Cued… mask… -0.0127 -2.06 -2.97 0.91
## 14 sub-18 contrast-Cued… mask… 0.0639 -10.4 -10.1 -0.3
## 15 sub-19 contrast-Cued… mask… 0.884 NA NA NA
## 16 sub-20 contrast-Cued… mask… -0.311 -4.26 -5.74 1.48
## 17 sub-21 contrast-Cued… mask… -0.961 NA NA NA
## 18 sub-23 contrast-Cued… mask… 0.616 NA NA NA
## # ℹ 1 more variable: HRD_Uncued_Cued <dbl>subset_3_amy <- subset_3_amy[, c("values", "HRD_Cued_Uncued")]
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$HRD_Cued_Uncued) #normal##
## Shapiro-Wilk normality test
##
## data: subset_3_amy$HRD_Cued_Uncued
## W = 0.95893, p-value = 0.7054subset_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 HRD_Cued_Uncued
## values 1.00 -0.48
## HRD_Cued_Uncued -0.48 1.00
## Sample Size
## values HRD_Cued_Uncued
## values 18 14
## HRD_Cued_Uncued 14 14
## Probability values (Entries above the diagonal are adjusted for multiple tests.)
## values HRD_Cued_Uncued
## values 0.00 0.08
## HRD_Cued_Uncued 0.08 0.00
##
## To see confidence intervals of the correlations, print with the short=FALSE option#library("ggpubr")
ggscatter(subset_3_amy, x = "values", y = "HRD_Cued_Uncued",
add = "reg.line", conf.int = TRUE,
cor.coef = TRUE, cor.method = "pearson",
xlab = "Bold Cued_Uncued", ylab = "HRD_Cued_Uncued")## Warning: Removed 4 rows containing non-finite values (`stat_smooth()`).## Warning: Removed 4 rows containing non-finite values (`stat_cor()`).## Warning: Removed 4 rows containing missing values (`geom_point()`).
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 HRD_Cued_Uncued
## values 1.00 -0.41
## HRD_Cued_Uncued -0.41 1.00
## Sample Size
## values HRD_Cued_Uncued
## values 18 14
## HRD_Cued_Uncued 14 14
## Probability values (Entries above the diagonal are adjusted for multiple tests.)
## values HRD_Cued_Uncued
## values 0.00 0.15
## HRD_Cued_Uncued 0.15 0.00
##
## To see confidence intervals of the correlations, print with the short=FALSE option#
subset_4_amy <- combined_amy %>%
filter(contrast == "contrast-Uncued-Cued")
subset_4_amy## # A tibble: 18 × 8
## participants contrast mask values HRD_Cued HRD_Uncued HRD_Cued_Uncued
## <fct> <fct> <fct> <dbl> <dbl> <dbl> <dbl>
## 1 sub-01 contrast-Uncu… mask… 0.366 -1.88 -4.05 2.17
## 2 sub-02 contrast-Uncu… mask… -0.433 -6.37 -5.43 -0.94
## 3 sub-03 contrast-Uncu… mask… 0.839 -3.84 -6.72 2.88
## 4 sub-04 contrast-Uncu… mask… 0.545 -2.39 -3.42 1.03
## 5 sub-05 contrast-Uncu… mask… 1.36 -3.16 -3.9 0.74
## 6 sub-07 contrast-Uncu… mask… 0.0391 -6.58 -6.38 -0.2
## 7 sub-08 contrast-Uncu… mask… 0.531 -5.6 -5.37 -0.23
## 8 sub-11 contrast-Uncu… mask… -0.0149 -4.81 -6.3 1.49
## 9 sub-12 contrast-Uncu… mask… 0.0102 -4.26 -5.2 0.94
## 10 sub-13 contrast-Uncu… mask… -0.629 NA NA NA
## 11 sub-14 contrast-Uncu… mask… 0.369 -9.91 -10.0 0.14
## 12 sub-16 contrast-Uncu… mask… -0.497 -5.53 -5.4 -0.13
## 13 sub-17 contrast-Uncu… mask… 0.0127 -2.06 -2.97 0.91
## 14 sub-18 contrast-Uncu… mask… -0.0639 -10.4 -10.1 -0.3
## 15 sub-19 contrast-Uncu… mask… -0.884 NA NA NA
## 16 sub-20 contrast-Uncu… mask… 0.311 -4.26 -5.74 1.48
## 17 sub-21 contrast-Uncu… mask… 0.961 NA NA NA
## 18 sub-23 contrast-Uncu… mask… -0.616 NA NA NA
## # ℹ 1 more variable: HRD_Uncued_Cued <dbl>subset_4_amy <- subset_4_amy[, c("values", "HRD_Uncued_Cued")]
shapiro.test(subset_4_amy$values) #normal##
## Shapiro-Wilk normality test
##
## data: subset_4_amy$values
## W = 0.97449, p-value = 0.8766shapiro.test(subset_4_amy$HRD_Uncued_Cued) #normal##
## Shapiro-Wilk normality test
##
## data: subset_4_amy$HRD_Uncued_Cued
## W = 0.95893, p-value = 0.7054subset_4_amycorr <- corr.test(subset_4_amy, use = "pairwise",method="pearson",adjust="none")
subset_4_amycorr## Call:corr.test(x = subset_4_amy, use = "pairwise", method = "pearson",
## adjust = "none")
## Correlation matrix
## values HRD_Uncued_Cued
## values 1.00 -0.48
## HRD_Uncued_Cued -0.48 1.00
## Sample Size
## values HRD_Uncued_Cued
## values 18 14
## HRD_Uncued_Cued 14 14
## Probability values (Entries above the diagonal are adjusted for multiple tests.)
## values HRD_Uncued_Cued
## values 0.00 0.08
## HRD_Uncued_Cued 0.08 0.00
##
## To see confidence intervals of the correlations, print with the short=FALSE optionggscatter(subset_4_amy, x = "values", y = "HRD_Uncued_Cued",
add = "reg.line", conf.int = TRUE,
cor.coef = TRUE, cor.method = "pearson",
xlab = "Bold Cued_Uncued", ylab = "HRD_Uncued_Cued")## Warning: Removed 4 rows containing non-finite values (`stat_smooth()`).## Warning: Removed 4 rows containing non-finite values (`stat_cor()`).## Warning: Removed 4 rows containing missing values (`geom_point()`).
sp.subset_4_amycorr <- corr.test(subset_4_amy, use = "pairwise",method="spearman",adjust="none")
sp.subset_4_amycorr## Call:corr.test(x = subset_4_amy, use = "pairwise", method = "spearman",
## adjust = "none")
## Correlation matrix
## values HRD_Uncued_Cued
## values 1.00 -0.41
## HRD_Uncued_Cued -0.41 1.00
## Sample Size
## values HRD_Uncued_Cued
## values 18 14
## HRD_Uncued_Cued 14 14
## Probability values (Entries above the diagonal are adjusted for multiple tests.)
## values HRD_Uncued_Cued
## values 0.00 0.15
## HRD_Uncued_Cued 0.15 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$p[2], 2),
round(subset_4_amycorr$p[2], 2))
all_p_values.amy## [1] 0.63 0.17 0.08 0.08# Apply FDR correction to combined all p-values
adjusted_p_amy<- p.adjust(all_p_values.amy, method = "fdr")
adjusted_p_amy## [1] 0.6300000 0.2266667 0.1600000 0.1600000resampled_vsgcv2_mask.nii.gz
No sign correlations after correction
sgacc <- read_csv("correlations/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>HRD.data <- read_csv("correlations/HRDdata.csv")## Rows: 18 Columns: 5
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (1): participants
## dbl (4): HRD_Cued, HRD_Uncued, HRD_Cued_Uncued, HRD_Uncued_Cued
##
## 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(HRD.data)## spc_tbl_ [18 × 5] (S3: spec_tbl_df/tbl_df/tbl/data.frame)
## $ participants : chr [1:18] "sub-01" "sub-02" "sub-03" "sub-04" ...
## $ HRD_Cued : num [1:18] -1.88 -6.37 -3.84 -2.39 -3.16 -6.58 -5.6 -4.81 -4.26 NA ...
## $ HRD_Uncued : num [1:18] -4.05 -5.43 -6.72 -3.42 -3.9 -6.38 -5.37 -6.3 -5.2 NA ...
## $ HRD_Cued_Uncued: num [1:18] 2.17 -0.94 2.88 1.03 0.74 -0.2 -0.23 1.49 0.94 NA ...
## $ HRD_Uncued_Cued: num [1:18] -2.17 0.94 -2.88 -1.03 -0.74 0.2 0.23 -1.49 -0.94 NA ...
## - attr(*, "spec")=
## .. cols(
## .. participants = col_character(),
## .. HRD_Cued = col_double(),
## .. HRD_Uncued = col_double(),
## .. HRD_Cued_Uncued = col_double(),
## .. HRD_Uncued_Cued = col_double()
## .. )
## - attr(*, "problems")=<externalptr>combined_sgacc <- inner_join(sgacc, HRD.data, by = "participants")
combined_sgacc <- combined_sgacc %>%
mutate(across(c(participants, contrast, mask), as.factor))
str(combined_sgacc)## tibble [72 × 8] (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 ...
## $ HRD_Cued : num [1:72] -1.88 -1.88 -1.88 -1.88 -6.37 -6.37 -6.37 -6.37 -3.84 -3.84 ...
## $ HRD_Uncued : num [1:72] -4.05 -4.05 -4.05 -4.05 -5.43 -5.43 -5.43 -5.43 -6.72 -6.72 ...
## $ HRD_Cued_Uncued: num [1:72] 2.17 2.17 2.17 2.17 -0.94 -0.94 -0.94 -0.94 2.88 2.88 ...
## $ HRD_Uncued_Cued: num [1:72] -2.17 -2.17 -2.17 -2.17 0.94 0.94 0.94 0.94 -2.88 -2.88 ...#
subset_1_sgacc <- combined_sgacc %>%
filter(contrast == "contrast-Cued")
subset_1_sgacc## # A tibble: 18 × 8
## participants contrast mask values HRD_Cued HRD_Uncued HRD_Cued_Uncued
## <fct> <fct> <fct> <dbl> <dbl> <dbl> <dbl>
## 1 sub-01 contrast-Cued mask… -0.704 -1.88 -4.05 2.17
## 2 sub-02 contrast-Cued mask… -0.405 -6.37 -5.43 -0.94
## 3 sub-03 contrast-Cued mask… -1.06 -3.84 -6.72 2.88
## 4 sub-04 contrast-Cued mask… -0.621 -2.39 -3.42 1.03
## 5 sub-05 contrast-Cued mask… -1.04 -3.16 -3.9 0.74
## 6 sub-07 contrast-Cued mask… -0.609 -6.58 -6.38 -0.2
## 7 sub-08 contrast-Cued mask… -0.409 -5.6 -5.37 -0.23
## 8 sub-11 contrast-Cued mask… -0.294 -4.81 -6.3 1.49
## 9 sub-12 contrast-Cued mask… -0.00377 -4.26 -5.2 0.94
## 10 sub-13 contrast-Cued mask… -1.58 NA NA NA
## 11 sub-14 contrast-Cued mask… 0.371 -9.91 -10.0 0.14
## 12 sub-16 contrast-Cued mask… -0.882 -5.53 -5.4 -0.13
## 13 sub-17 contrast-Cued mask… -0.831 -2.06 -2.97 0.91
## 14 sub-18 contrast-Cued mask… -0.181 -10.4 -10.1 -0.3
## 15 sub-19 contrast-Cued mask… -1.23 NA NA NA
## 16 sub-20 contrast-Cued mask… -1.22 -4.26 -5.74 1.48
## 17 sub-21 contrast-Cued mask… -0.444 NA NA NA
## 18 sub-23 contrast-Cued mask… -0.215 NA NA NA
## # ℹ 1 more variable: HRD_Uncued_Cued <dbl>subset_1_sgacc <- subset_1_sgacc[, c("values", "HRD_Cued")]
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$HRD_Cued) #normal##
## Shapiro-Wilk normality test
##
## data: subset_1_sgacc$HRD_Cued
## W = 0.90975, p-value = 0.1563subset_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 HRD_Cued
## values 1.00 -0.63
## HRD_Cued -0.63 1.00
## Sample Size
## values HRD_Cued
## values 18 14
## HRD_Cued 14 14
## Probability values (Entries above the diagonal are adjusted for multiple tests.)
## values HRD_Cued
## values 0.00 0.02
## HRD_Cued 0.02 0.00
##
## To see confidence intervals of the correlations, print with the short=FALSE optionggscatter(subset_1_sgacc, x = "values", y = "HRD_Cued",
add = "reg.line", conf.int = TRUE,
cor.coef = TRUE, cor.method = "pearson",
xlab = "Bold Cued_Uncued", ylab = "HRD_Cued")## Warning: Removed 4 rows containing non-finite values (`stat_smooth()`).## Warning: Removed 4 rows containing non-finite values (`stat_cor()`).## Warning: Removed 4 rows containing missing values (`geom_point()`).
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 HRD_Cued
## values 1.00 -0.58
## HRD_Cued -0.58 1.00
## Sample Size
## values HRD_Cued
## values 18 14
## HRD_Cued 14 14
## Probability values (Entries above the diagonal are adjusted for multiple tests.)
## values HRD_Cued
## values 0.00 0.03
## HRD_Cued 0.03 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 × 8
## participants contrast mask values HRD_Cued HRD_Uncued HRD_Cued_Uncued
## <fct> <fct> <fct> <dbl> <dbl> <dbl> <dbl>
## 1 sub-01 contrast-Uncu… mask… -0.0491 -1.88 -4.05 2.17
## 2 sub-02 contrast-Uncu… mask… -0.396 -6.37 -5.43 -0.94
## 3 sub-03 contrast-Uncu… mask… -0.390 -3.84 -6.72 2.88
## 4 sub-04 contrast-Uncu… mask… -0.170 -2.39 -3.42 1.03
## 5 sub-05 contrast-Uncu… mask… -0.851 -3.16 -3.9 0.74
## 6 sub-07 contrast-Uncu… mask… -0.185 -6.58 -6.38 -0.2
## 7 sub-08 contrast-Uncu… mask… 0.411 -5.6 -5.37 -0.23
## 8 sub-11 contrast-Uncu… mask… -0.778 -4.81 -6.3 1.49
## 9 sub-12 contrast-Uncu… mask… 0.142 -4.26 -5.2 0.94
## 10 sub-13 contrast-Uncu… mask… -1.02 NA NA NA
## 11 sub-14 contrast-Uncu… mask… 0.138 -9.91 -10.0 0.14
## 12 sub-16 contrast-Uncu… mask… 0.356 -5.53 -5.4 -0.13
## 13 sub-17 contrast-Uncu… mask… -0.277 -2.06 -2.97 0.91
## 14 sub-18 contrast-Uncu… mask… -0.976 -10.4 -10.1 -0.3
## 15 sub-19 contrast-Uncu… mask… -0.907 NA NA NA
## 16 sub-20 contrast-Uncu… mask… -0.853 -4.26 -5.74 1.48
## 17 sub-21 contrast-Uncu… mask… 0.115 NA NA NA
## 18 sub-23 contrast-Uncu… mask… -0.0515 NA NA NA
## # ℹ 1 more variable: HRD_Uncued_Cued <dbl>subset_2_sgacc <- subset_2_sgacc[, c("values", "HRD_Uncued")]
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$HRD_Uncued) #normal##
## Shapiro-Wilk normality test
##
## data: subset_2_sgacc$HRD_Uncued
## W = 0.8813, p-value = 0.06065subset_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 HRD_Uncued
## values 1.00 0.15
## HRD_Uncued 0.15 1.00
## Sample Size
## values HRD_Uncued
## values 18 14
## HRD_Uncued 14 14
## Probability values (Entries above the diagonal are adjusted for multiple tests.)
## values HRD_Uncued
## values 0.00 0.62
## HRD_Uncued 0.62 0.00
##
## To see confidence intervals of the correlations, print with the short=FALSE optionggscatter(subset_2_sgacc, x = "values", y = "HRD_Uncued",
add = "reg.line", conf.int = TRUE,
cor.coef = TRUE, cor.method = "pearson",
xlab = "Bold Cued_Uncued", ylab = "HRD_Uncued")## Warning: Removed 4 rows containing non-finite values (`stat_smooth()`).## Warning: Removed 4 rows containing non-finite values (`stat_cor()`).## Warning: Removed 4 rows containing missing values (`geom_point()`).
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 HRD_Uncued
## values 1.00 0.28
## HRD_Uncued 0.28 1.00
## Sample Size
## values HRD_Uncued
## values 18 14
## HRD_Uncued 14 14
## Probability values (Entries above the diagonal are adjusted for multiple tests.)
## values HRD_Uncued
## values 0.00 0.33
## HRD_Uncued 0.33 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 × 8
## participants contrast mask values HRD_Cued HRD_Uncued HRD_Cued_Uncued
## <fct> <fct> <fct> <dbl> <dbl> <dbl> <dbl>
## 1 sub-01 contrast-Cued… mask… -0.496 -1.88 -4.05 2.17
## 2 sub-02 contrast-Cued… mask… -0.0129 -6.37 -5.43 -0.94
## 3 sub-03 contrast-Cued… mask… -0.494 -3.84 -6.72 2.88
## 4 sub-04 contrast-Cued… mask… -0.336 -2.39 -3.42 1.03
## 5 sub-05 contrast-Cued… mask… -0.148 -3.16 -3.9 0.74
## 6 sub-07 contrast-Cued… mask… -0.284 -6.58 -6.38 -0.2
## 7 sub-08 contrast-Cued… mask… -0.616 -5.6 -5.37 -0.23
## 8 sub-11 contrast-Cued… mask… 0.370 -4.81 -6.3 1.49
## 9 sub-12 contrast-Cued… mask… -0.109 -4.26 -5.2 0.94
## 10 sub-13 contrast-Cued… mask… -0.401 NA NA NA
## 11 sub-14 contrast-Cued… mask… 0.168 -9.91 -10.0 0.14
## 12 sub-16 contrast-Cued… mask… -0.969 -5.53 -5.4 -0.13
## 13 sub-17 contrast-Cued… mask… -0.413 -2.06 -2.97 0.91
## 14 sub-18 contrast-Cued… mask… 0.596 -10.4 -10.1 -0.3
## 15 sub-19 contrast-Cued… mask… -0.288 NA NA NA
## 16 sub-20 contrast-Cued… mask… -0.262 -4.26 -5.74 1.48
## 17 sub-21 contrast-Cued… mask… -0.441 NA NA NA
## 18 sub-23 contrast-Cued… mask… -0.124 NA NA NA
## # ℹ 1 more variable: HRD_Uncued_Cued <dbl>subset_3_sgacc <- subset_3_sgacc[, c("values", "HRD_Cued_Uncued")]
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$HRD_Cued_Uncued) #normal##
## Shapiro-Wilk normality test
##
## data: subset_3_sgacc$HRD_Cued_Uncued
## W = 0.95893, p-value = 0.7054subset_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 HRD_Cued_Uncued
## values 1.00 -0.17
## HRD_Cued_Uncued -0.17 1.00
## Sample Size
## values HRD_Cued_Uncued
## values 18 14
## HRD_Cued_Uncued 14 14
## Probability values (Entries above the diagonal are adjusted for multiple tests.)
## values HRD_Cued_Uncued
## values 0.00 0.57
## HRD_Cued_Uncued 0.57 0.00
##
## To see confidence intervals of the correlations, print with the short=FALSE option#library("ggpubr")
ggscatter(subset_3_sgacc, x = "values", y = "HRD_Cued_Uncued",
add = "reg.line", conf.int = TRUE,
cor.coef = TRUE, cor.method = "pearson",
xlab = "Bold Cued_Uncued", ylab = "HRD_Cued_Uncued")## Warning: Removed 4 rows containing non-finite values (`stat_smooth()`).## Warning: Removed 4 rows containing non-finite values (`stat_cor()`).## Warning: Removed 4 rows containing missing values (`geom_point()`).
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 HRD_Cued_Uncued
## values 1.0 -0.2
## HRD_Cued_Uncued -0.2 1.0
## Sample Size
## values HRD_Cued_Uncued
## values 18 14
## HRD_Cued_Uncued 14 14
## Probability values (Entries above the diagonal are adjusted for multiple tests.)
## values HRD_Cued_Uncued
## values 0.00 0.49
## HRD_Cued_Uncued 0.49 0.00
##
## To see confidence intervals of the correlations, print with the short=FALSE option#
subset_4_sgacc <- combined_sgacc %>%
filter(contrast == "contrast-Uncued-Cued")
subset_4_sgacc## # A tibble: 18 × 8
## participants contrast mask values HRD_Cued HRD_Uncued HRD_Cued_Uncued
## <fct> <fct> <fct> <dbl> <dbl> <dbl> <dbl>
## 1 sub-01 contrast-Uncu… mask… 0.496 -1.88 -4.05 2.17
## 2 sub-02 contrast-Uncu… mask… 0.0129 -6.37 -5.43 -0.94
## 3 sub-03 contrast-Uncu… mask… 0.494 -3.84 -6.72 2.88
## 4 sub-04 contrast-Uncu… mask… 0.336 -2.39 -3.42 1.03
## 5 sub-05 contrast-Uncu… mask… 0.148 -3.16 -3.9 0.74
## 6 sub-07 contrast-Uncu… mask… 0.284 -6.58 -6.38 -0.2
## 7 sub-08 contrast-Uncu… mask… 0.616 -5.6 -5.37 -0.23
## 8 sub-11 contrast-Uncu… mask… -0.370 -4.81 -6.3 1.49
## 9 sub-12 contrast-Uncu… mask… 0.109 -4.26 -5.2 0.94
## 10 sub-13 contrast-Uncu… mask… 0.401 NA NA NA
## 11 sub-14 contrast-Uncu… mask… -0.168 -9.91 -10.0 0.14
## 12 sub-16 contrast-Uncu… mask… 0.969 -5.53 -5.4 -0.13
## 13 sub-17 contrast-Uncu… mask… 0.413 -2.06 -2.97 0.91
## 14 sub-18 contrast-Uncu… mask… -0.596 -10.4 -10.1 -0.3
## 15 sub-19 contrast-Uncu… mask… 0.288 NA NA NA
## 16 sub-20 contrast-Uncu… mask… 0.262 -4.26 -5.74 1.48
## 17 sub-21 contrast-Uncu… mask… 0.441 NA NA NA
## 18 sub-23 contrast-Uncu… mask… 0.124 NA NA NA
## # ℹ 1 more variable: HRD_Uncued_Cued <dbl>subset_4_sgacc <- subset_4_sgacc[, c("values", "HRD_Uncued_Cued")]
shapiro.test(subset_4_sgacc$values) #normal##
## Shapiro-Wilk normality test
##
## data: subset_4_sgacc$values
## W = 0.95719, p-value = 0.5483shapiro.test(subset_4_sgacc$HRD_Uncued_Cued) #normal##
## Shapiro-Wilk normality test
##
## data: subset_4_sgacc$HRD_Uncued_Cued
## W = 0.95893, p-value = 0.7054subset_4_sgacccorr <- corr.test(subset_4_sgacc, use = "pairwise",method="pearson",adjust="none")
subset_4_sgacccorr## Call:corr.test(x = subset_4_sgacc, use = "pairwise", method = "pearson",
## adjust = "none")
## Correlation matrix
## values HRD_Uncued_Cued
## values 1.00 -0.17
## HRD_Uncued_Cued -0.17 1.00
## Sample Size
## values HRD_Uncued_Cued
## values 18 14
## HRD_Uncued_Cued 14 14
## Probability values (Entries above the diagonal are adjusted for multiple tests.)
## values HRD_Uncued_Cued
## values 0.00 0.57
## HRD_Uncued_Cued 0.57 0.00
##
## To see confidence intervals of the correlations, print with the short=FALSE optionggscatter(subset_4_sgacc, x = "values", y = "HRD_Uncued_Cued",
add = "reg.line", conf.int = TRUE,
cor.coef = TRUE, cor.method = "pearson",
xlab = "Bold Cued_Uncued", ylab = "HRD_Uncued_Cued")## Warning: Removed 4 rows containing non-finite values (`stat_smooth()`).## Warning: Removed 4 rows containing non-finite values (`stat_cor()`).## Warning: Removed 4 rows containing missing values (`geom_point()`).
sp.subset_4_sgacccorr <- corr.test(subset_4_sgacc, use = "pairwise",method="spearman",adjust="none")
sp.subset_4_sgacccorr## Call:corr.test(x = subset_4_sgacc, use = "pairwise", method = "spearman",
## adjust = "none")
## Correlation matrix
## values HRD_Uncued_Cued
## values 1.0 -0.2
## HRD_Uncued_Cued -0.2 1.0
## Sample Size
## values HRD_Uncued_Cued
## values 18 14
## HRD_Uncued_Cued 14 14
## Probability values (Entries above the diagonal are adjusted for multiple tests.)
## values HRD_Uncued_Cued
## values 0.00 0.49
## HRD_Uncued_Cued 0.49 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$p[2], 2),
round(subset_4_sgacccorr$p[2], 2))
all_p_values.sgacc## [1] 0.02 0.62 0.57 0.57# Apply FDR correction to combined all p-values
adjusted_p_sgacc<- p.adjust(all_p_values.sgacc, method = "fdr")
adjusted_p_sgacc## [1] 0.08 0.62 0.62 0.62b_gray_thresholded.nii.gz
No sign correlations after correction
Grey <- read_csv("correlations/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>HRD.data <- read_csv("correlations/HRDdata.csv")## Rows: 18 Columns: 5
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (1): participants
## dbl (4): HRD_Cued, HRD_Uncued, HRD_Cued_Uncued, HRD_Uncued_Cued
##
## 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(HRD.data)## spc_tbl_ [18 × 5] (S3: spec_tbl_df/tbl_df/tbl/data.frame)
## $ participants : chr [1:18] "sub-01" "sub-02" "sub-03" "sub-04" ...
## $ HRD_Cued : num [1:18] -1.88 -6.37 -3.84 -2.39 -3.16 -6.58 -5.6 -4.81 -4.26 NA ...
## $ HRD_Uncued : num [1:18] -4.05 -5.43 -6.72 -3.42 -3.9 -6.38 -5.37 -6.3 -5.2 NA ...
## $ HRD_Cued_Uncued: num [1:18] 2.17 -0.94 2.88 1.03 0.74 -0.2 -0.23 1.49 0.94 NA ...
## $ HRD_Uncued_Cued: num [1:18] -2.17 0.94 -2.88 -1.03 -0.74 0.2 0.23 -1.49 -0.94 NA ...
## - attr(*, "spec")=
## .. cols(
## .. participants = col_character(),
## .. HRD_Cued = col_double(),
## .. HRD_Uncued = col_double(),
## .. HRD_Cued_Uncued = col_double(),
## .. HRD_Uncued_Cued = col_double()
## .. )
## - attr(*, "problems")=<externalptr>combined_Grey <- inner_join(Grey, HRD.data, by = "participants")
combined_Grey <- combined_Grey %>%
mutate(across(c(participants, contrast, mask), as.factor))
str(combined_Grey)## tibble [72 × 8] (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 ...
## $ HRD_Cued : num [1:72] -1.88 -1.88 -1.88 -1.88 -6.37 -6.37 -6.37 -6.37 -3.84 -3.84 ...
## $ HRD_Uncued : num [1:72] -4.05 -4.05 -4.05 -4.05 -5.43 -5.43 -5.43 -5.43 -6.72 -6.72 ...
## $ HRD_Cued_Uncued: num [1:72] 2.17 2.17 2.17 2.17 -0.94 -0.94 -0.94 -0.94 2.88 2.88 ...
## $ HRD_Uncued_Cued: num [1:72] -2.17 -2.17 -2.17 -2.17 0.94 0.94 0.94 0.94 -2.88 -2.88 ...#
subset_1_Grey <- combined_Grey %>%
filter(contrast == "contrast-Cued")
subset_1_Grey## # A tibble: 18 × 8
## participants contrast mask values HRD_Cued HRD_Uncued HRD_Cued_Uncued
## <fct> <fct> <fct> <dbl> <dbl> <dbl> <dbl>
## 1 sub-01 contrast-Cued masks… -1.16 -1.88 -4.05 2.17
## 2 sub-02 contrast-Cued masks… 0.495 -6.37 -5.43 -0.94
## 3 sub-03 contrast-Cued masks… 0.0364 -3.84 -6.72 2.88
## 4 sub-04 contrast-Cued masks… 0.140 -2.39 -3.42 1.03
## 5 sub-05 contrast-Cued masks… 0.817 -3.16 -3.9 0.74
## 6 sub-07 contrast-Cued masks… 1.09 -6.58 -6.38 -0.2
## 7 sub-08 contrast-Cued masks… -0.182 -5.6 -5.37 -0.23
## 8 sub-11 contrast-Cued masks… 1.28 -4.81 -6.3 1.49
## 9 sub-12 contrast-Cued masks… 0.179 -4.26 -5.2 0.94
## 10 sub-13 contrast-Cued masks… 0.939 NA NA NA
## 11 sub-14 contrast-Cued masks… -1.31 -9.91 -10.0 0.14
## 12 sub-16 contrast-Cued masks… 0.709 -5.53 -5.4 -0.13
## 13 sub-17 contrast-Cued masks… -0.0362 -2.06 -2.97 0.91
## 14 sub-18 contrast-Cued masks… 1.55 -10.4 -10.1 -0.3
## 15 sub-19 contrast-Cued masks… 0.107 NA NA NA
## 16 sub-20 contrast-Cued masks… -0.301 -4.26 -5.74 1.48
## 17 sub-21 contrast-Cued masks… 0.0182 NA NA NA
## 18 sub-23 contrast-Cued masks… -0.114 NA NA NA
## # ℹ 1 more variable: HRD_Uncued_Cued <dbl>subset_1_Grey <- subset_1_Grey[, c("values", "HRD_Cued")]
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$HRD_Cued) #normal##
## Shapiro-Wilk normality test
##
## data: subset_1_Grey$HRD_Cued
## W = 0.90975, p-value = 0.1563subset_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 HRD_Cued
## values 1.00 -0.21
## HRD_Cued -0.21 1.00
## Sample Size
## values HRD_Cued
## values 18 14
## HRD_Cued 14 14
## Probability values (Entries above the diagonal are adjusted for multiple tests.)
## values HRD_Cued
## values 0.00 0.47
## HRD_Cued 0.47 0.00
##
## To see confidence intervals of the correlations, print with the short=FALSE optionggscatter(subset_1_Grey, x = "values", y = "HRD_Cued",
add = "reg.line", conf.int = TRUE,
cor.coef = TRUE, cor.method = "pearson",
xlab = "Bold Cued_Uncued", ylab = "HRD_Cued")## Warning: Removed 4 rows containing non-finite values (`stat_smooth()`).## Warning: Removed 4 rows containing non-finite values (`stat_cor()`).## Warning: Removed 4 rows containing missing values (`geom_point()`).
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 HRD_Cued
## values 1.00 -0.34
## HRD_Cued -0.34 1.00
## Sample Size
## values HRD_Cued
## values 18 14
## HRD_Cued 14 14
## Probability values (Entries above the diagonal are adjusted for multiple tests.)
## values HRD_Cued
## values 0.00 0.24
## HRD_Cued 0.24 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 × 8
## participants contrast mask values HRD_Cued HRD_Uncued HRD_Cued_Uncued
## <fct> <fct> <fct> <dbl> <dbl> <dbl> <dbl>
## 1 sub-01 contrast-Uncu… mask… -0.0846 -1.88 -4.05 2.17
## 2 sub-02 contrast-Uncu… mask… 0.0962 -6.37 -5.43 -0.94
## 3 sub-03 contrast-Uncu… mask… 0.950 -3.84 -6.72 2.88
## 4 sub-04 contrast-Uncu… mask… 0.948 -2.39 -3.42 1.03
## 5 sub-05 contrast-Uncu… mask… 1.30 -3.16 -3.9 0.74
## 6 sub-07 contrast-Uncu… mask… 0.873 -6.58 -6.38 -0.2
## 7 sub-08 contrast-Uncu… mask… 0.0310 -5.6 -5.37 -0.23
## 8 sub-11 contrast-Uncu… mask… 0.922 -4.81 -6.3 1.49
## 9 sub-12 contrast-Uncu… mask… 0.158 -4.26 -5.2 0.94
## 10 sub-13 contrast-Uncu… mask… 0.897 NA NA NA
## 11 sub-14 contrast-Uncu… mask… -0.476 -9.91 -10.0 0.14
## 12 sub-16 contrast-Uncu… mask… 0.883 -5.53 -5.4 -0.13
## 13 sub-17 contrast-Uncu… mask… 1.40 -2.06 -2.97 0.91
## 14 sub-18 contrast-Uncu… mask… 0.907 -10.4 -10.1 -0.3
## 15 sub-19 contrast-Uncu… mask… -0.181 NA NA NA
## 16 sub-20 contrast-Uncu… mask… -0.323 -4.26 -5.74 1.48
## 17 sub-21 contrast-Uncu… mask… 0.644 NA NA NA
## 18 sub-23 contrast-Uncu… mask… 0.214 NA NA NA
## # ℹ 1 more variable: HRD_Uncued_Cued <dbl>subset_2_Grey <- subset_2_Grey[, c("values", "HRD_Uncued")]
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$HRD_Uncued) #normal##
## Shapiro-Wilk normality test
##
## data: subset_2_Grey$HRD_Uncued
## W = 0.8813, p-value = 0.06065subset_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 HRD_Uncued
## values 1.0 0.3
## HRD_Uncued 0.3 1.0
## Sample Size
## values HRD_Uncued
## values 18 14
## HRD_Uncued 14 14
## Probability values (Entries above the diagonal are adjusted for multiple tests.)
## values HRD_Uncued
## values 0.00 0.29
## HRD_Uncued 0.29 0.00
##
## To see confidence intervals of the correlations, print with the short=FALSE optionggscatter(subset_2_Grey, x = "values", y = "HRD_Uncued",
add = "reg.line", conf.int = TRUE,
cor.coef = TRUE, cor.method = "pearson",
xlab = "Bold Cued_Uncued", ylab = "HRD_Uncued")## Warning: Removed 4 rows containing non-finite values (`stat_smooth()`).## Warning: Removed 4 rows containing non-finite values (`stat_cor()`).## Warning: Removed 4 rows containing missing values (`geom_point()`).
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 HRD_Uncued
## values 1.00 0.32
## HRD_Uncued 0.32 1.00
## Sample Size
## values HRD_Uncued
## values 18 14
## HRD_Uncued 14 14
## Probability values (Entries above the diagonal are adjusted for multiple tests.)
## values HRD_Uncued
## values 0.00 0.27
## HRD_Uncued 0.27 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 × 8
## participants contrast mask values HRD_Cued HRD_Uncued HRD_Cued_Uncued
## <fct> <fct> <fct> <dbl> <dbl> <dbl> <dbl>
## 1 sub-01 contrast-Cued… mask… -0.796 -1.88 -4.05 2.17
## 2 sub-02 contrast-Cued… mask… 0.298 -6.37 -5.43 -0.94
## 3 sub-03 contrast-Cued… mask… -0.681 -3.84 -6.72 2.88
## 4 sub-04 contrast-Cued… mask… -0.597 -2.39 -3.42 1.03
## 5 sub-05 contrast-Cued… mask… -0.351 -3.16 -3.9 0.74
## 6 sub-07 contrast-Cued… mask… 0.0975 -6.58 -6.38 -0.2
## 7 sub-08 contrast-Cued… mask… -0.157 -5.6 -5.37 -0.23
## 8 sub-11 contrast-Cued… mask… 0.251 -4.81 -6.3 1.49
## 9 sub-12 contrast-Cued… mask… 0.0159 -4.26 -5.2 0.94
## 10 sub-13 contrast-Cued… mask… 0.0207 NA NA NA
## 11 sub-14 contrast-Cued… mask… -0.613 -9.91 -10.0 0.14
## 12 sub-16 contrast-Cued… mask… -0.142 -5.53 -5.4 -0.13
## 13 sub-17 contrast-Cued… mask… -1.06 -2.06 -2.97 0.91
## 14 sub-18 contrast-Cued… mask… 0.464 -10.4 -10.1 -0.3
## 15 sub-19 contrast-Cued… mask… 0.214 NA NA NA
## 16 sub-20 contrast-Cued… mask… 0.0198 -4.26 -5.74 1.48
## 17 sub-21 contrast-Cued… mask… -0.434 NA NA NA
## 18 sub-23 contrast-Cued… mask… -0.239 NA NA NA
## # ℹ 1 more variable: HRD_Uncued_Cued <dbl>subset_3_Grey <- subset_3_Grey[, c("values", "HRD_Cued_Uncued")]
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$HRD_Cued_Uncued) #normal##
## Shapiro-Wilk normality test
##
## data: subset_3_Grey$HRD_Cued_Uncued
## W = 0.95893, p-value = 0.7054subset_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 HRD_Cued_Uncued
## values 1.00 -0.51
## HRD_Cued_Uncued -0.51 1.00
## Sample Size
## values HRD_Cued_Uncued
## values 18 14
## HRD_Cued_Uncued 14 14
## Probability values (Entries above the diagonal are adjusted for multiple tests.)
## values HRD_Cued_Uncued
## values 0.00 0.06
## HRD_Cued_Uncued 0.06 0.00
##
## To see confidence intervals of the correlations, print with the short=FALSE option#library("ggpubr")
ggscatter(subset_3_Grey, x = "values", y = "HRD_Cued_Uncued",
add = "reg.line", conf.int = TRUE,
cor.coef = TRUE, cor.method = "pearson",
xlab = "Bold Cued_Uncued", ylab = "HRD_Cued_Uncued")## Warning: Removed 4 rows containing non-finite values (`stat_smooth()`).## Warning: Removed 4 rows containing non-finite values (`stat_cor()`).## Warning: Removed 4 rows containing missing values (`geom_point()`).
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 HRD_Cued_Uncued
## values 1.0 -0.5
## HRD_Cued_Uncued -0.5 1.0
## Sample Size
## values HRD_Cued_Uncued
## values 18 14
## HRD_Cued_Uncued 14 14
## Probability values (Entries above the diagonal are adjusted for multiple tests.)
## values HRD_Cued_Uncued
## values 0.00 0.07
## HRD_Cued_Uncued 0.07 0.00
##
## To see confidence intervals of the correlations, print with the short=FALSE option#
subset_4_Grey <- combined_Grey %>%
filter(contrast == "contrast-Uncued-Cued")
subset_4_Grey## # A tibble: 18 × 8
## participants contrast mask values HRD_Cued HRD_Uncued HRD_Cued_Uncued
## <fct> <fct> <fct> <dbl> <dbl> <dbl> <dbl>
## 1 sub-01 contrast-Uncu… mask… 0.796 -1.88 -4.05 2.17
## 2 sub-02 contrast-Uncu… mask… -0.298 -6.37 -5.43 -0.94
## 3 sub-03 contrast-Uncu… mask… 0.681 -3.84 -6.72 2.88
## 4 sub-04 contrast-Uncu… mask… 0.597 -2.39 -3.42 1.03
## 5 sub-05 contrast-Uncu… mask… 0.351 -3.16 -3.9 0.74
## 6 sub-07 contrast-Uncu… mask… -0.0975 -6.58 -6.38 -0.2
## 7 sub-08 contrast-Uncu… mask… 0.157 -5.6 -5.37 -0.23
## 8 sub-11 contrast-Uncu… mask… -0.251 -4.81 -6.3 1.49
## 9 sub-12 contrast-Uncu… mask… -0.0159 -4.26 -5.2 0.94
## 10 sub-13 contrast-Uncu… mask… -0.0207 NA NA NA
## 11 sub-14 contrast-Uncu… mask… 0.613 -9.91 -10.0 0.14
## 12 sub-16 contrast-Uncu… mask… 0.142 -5.53 -5.4 -0.13
## 13 sub-17 contrast-Uncu… mask… 1.06 -2.06 -2.97 0.91
## 14 sub-18 contrast-Uncu… mask… -0.464 -10.4 -10.1 -0.3
## 15 sub-19 contrast-Uncu… mask… -0.214 NA NA NA
## 16 sub-20 contrast-Uncu… mask… -0.0198 -4.26 -5.74 1.48
## 17 sub-21 contrast-Uncu… mask… 0.434 NA NA NA
## 18 sub-23 contrast-Uncu… mask… 0.239 NA NA NA
## # ℹ 1 more variable: HRD_Uncued_Cued <dbl>subset_4_Grey <- subset_4_Grey[, c("values", "HRD_Uncued_Cued")]
shapiro.test(subset_4_Grey$values) #normal##
## Shapiro-Wilk normality test
##
## data: subset_4_Grey$values
## W = 0.96882, p-value = 0.7753shapiro.test(subset_4_Grey$HRD_Uncued_Cued) #normal##
## Shapiro-Wilk normality test
##
## data: subset_4_Grey$HRD_Uncued_Cued
## W = 0.95893, p-value = 0.7054subset_4_Greycorr <- corr.test(subset_4_Grey, use = "pairwise",method="pearson",adjust="none")
subset_4_Greycorr## Call:corr.test(x = subset_4_Grey, use = "pairwise", method = "pearson",
## adjust = "none")
## Correlation matrix
## values HRD_Uncued_Cued
## values 1.00 -0.51
## HRD_Uncued_Cued -0.51 1.00
## Sample Size
## values HRD_Uncued_Cued
## values 18 14
## HRD_Uncued_Cued 14 14
## Probability values (Entries above the diagonal are adjusted for multiple tests.)
## values HRD_Uncued_Cued
## values 0.00 0.06
## HRD_Uncued_Cued 0.06 0.00
##
## To see confidence intervals of the correlations, print with the short=FALSE optionggscatter(subset_4_Grey, x = "values", y = "HRD_Uncued_Cued",
add = "reg.line", conf.int = TRUE,
cor.coef = TRUE, cor.method = "pearson",
xlab = "Bold Cued_Uncued", ylab = "HRD_Uncued_Cued")## Warning: Removed 4 rows containing non-finite values (`stat_smooth()`).## Warning: Removed 4 rows containing non-finite values (`stat_cor()`).## Warning: Removed 4 rows containing missing values (`geom_point()`).
sp.subset_4_Greycorr <- corr.test(subset_4_Grey, use = "pairwise",method="spearman",adjust="none")
sp.subset_4_Greycorr## Call:corr.test(x = subset_4_Grey, use = "pairwise", method = "spearman",
## adjust = "none")
## Correlation matrix
## values HRD_Uncued_Cued
## values 1.0 -0.5
## HRD_Uncued_Cued -0.5 1.0
## Sample Size
## values HRD_Uncued_Cued
## values 18 14
## HRD_Uncued_Cued 14 14
## Probability values (Entries above the diagonal are adjusted for multiple tests.)
## values HRD_Uncued_Cued
## values 0.00 0.07
## HRD_Uncued_Cued 0.07 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$p[2], 2),
round(subset_4_Greycorr$p[2], 2))
all_p_values.Grey## [1] 0.47 0.29 0.06 0.06# Apply FDR correction to combined all p-values
adjusted_p_Grey<- p.adjust(all_p_values.Grey, method = "fdr")
adjusted_p_Grey## [1] 0.4700000 0.3866667 0.1200000 0.1200000mask-resampled_vinsula_mask_onesamp_ttest_results_ClusterEffEst_p-0.001_desc-binarizedCluster.nii.gz
insula2 <- read_csv("correlations/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>HRD.data <- read_csv("correlations/HRDdata.csv")## Rows: 18 Columns: 5
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (1): participants
## dbl (4): HRD_Cued, HRD_Uncued, HRD_Cued_Uncued, HRD_Uncued_Cued
##
## 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(HRD.data)## spc_tbl_ [18 × 5] (S3: spec_tbl_df/tbl_df/tbl/data.frame)
## $ participants : chr [1:18] "sub-01" "sub-02" "sub-03" "sub-04" ...
## $ HRD_Cued : num [1:18] -1.88 -6.37 -3.84 -2.39 -3.16 -6.58 -5.6 -4.81 -4.26 NA ...
## $ HRD_Uncued : num [1:18] -4.05 -5.43 -6.72 -3.42 -3.9 -6.38 -5.37 -6.3 -5.2 NA ...
## $ HRD_Cued_Uncued: num [1:18] 2.17 -0.94 2.88 1.03 0.74 -0.2 -0.23 1.49 0.94 NA ...
## $ HRD_Uncued_Cued: num [1:18] -2.17 0.94 -2.88 -1.03 -0.74 0.2 0.23 -1.49 -0.94 NA ...
## - attr(*, "spec")=
## .. cols(
## .. participants = col_character(),
## .. HRD_Cued = col_double(),
## .. HRD_Uncued = col_double(),
## .. HRD_Cued_Uncued = col_double(),
## .. HRD_Uncued_Cued = col_double()
## .. )
## - attr(*, "problems")=<externalptr>combined_insula2 <- inner_join(insula2, HRD.data, by = "participants")
combined_insula2 <- combined_insula2 %>%
mutate(across(c(participants, contrast, mask), as.factor))
str(combined_insula2)## tibble [72 × 8] (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 ...
## $ HRD_Cued : num [1:72] -1.88 -1.88 -1.88 -1.88 -6.37 -6.37 -6.37 -6.37 -3.84 -3.84 ...
## $ HRD_Uncued : num [1:72] -4.05 -4.05 -4.05 -4.05 -5.43 -5.43 -5.43 -5.43 -6.72 -6.72 ...
## $ HRD_Cued_Uncued: num [1:72] 2.17 2.17 2.17 2.17 -0.94 -0.94 -0.94 -0.94 2.88 2.88 ...
## $ HRD_Uncued_Cued: num [1:72] -2.17 -2.17 -2.17 -2.17 0.94 0.94 0.94 0.94 -2.88 -2.88 ...#
subset_1_insula2 <- combined_insula2 %>%
filter(contrast == "contrast-Cued")
subset_1_insula2## # A tibble: 18 × 8
## participants contrast mask values HRD_Cued HRD_Uncued HRD_Cued_Uncued
## <fct> <fct> <fct> <dbl> <dbl> <dbl> <dbl>
## 1 sub-01 contrast-Cued mask-r… -1.76 -1.88 -4.05 2.17
## 2 sub-02 contrast-Cued mask-r… -0.855 -6.37 -5.43 -0.94
## 3 sub-03 contrast-Cued mask-r… -1.03 -3.84 -6.72 2.88
## 4 sub-04 contrast-Cued mask-r… -1.70 -2.39 -3.42 1.03
## 5 sub-05 contrast-Cued mask-r… 1.64 -3.16 -3.9 0.74
## 6 sub-07 contrast-Cued mask-r… 2.29 -6.58 -6.38 -0.2
## 7 sub-08 contrast-Cued mask-r… -1.43 -5.6 -5.37 -0.23
## 8 sub-11 contrast-Cued mask-r… 4.30 -4.81 -6.3 1.49
## 9 sub-12 contrast-Cued mask-r… 0.516 -4.26 -5.2 0.94
## 10 sub-13 contrast-Cued mask-r… -0.901 NA NA NA
## 11 sub-14 contrast-Cued mask-r… -2.36 -9.91 -10.0 0.14
## 12 sub-16 contrast-Cued mask-r… 1.86 -5.53 -5.4 -0.13
## 13 sub-17 contrast-Cued mask-r… -0.787 -2.06 -2.97 0.91
## 14 sub-18 contrast-Cued mask-r… 5.10 -10.4 -10.1 -0.3
## 15 sub-19 contrast-Cued mask-r… 0.800 NA NA NA
## 16 sub-20 contrast-Cued mask-r… -1.84 -4.26 -5.74 1.48
## 17 sub-21 contrast-Cued mask-r… -1.31 NA NA NA
## 18 sub-23 contrast-Cued mask-r… -2.81 NA NA NA
## # ℹ 1 more variable: HRD_Uncued_Cued <dbl>subset_1_insula2 <- subset_1_insula2[, c("values", "HRD_Cued")]
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$HRD_Cued) #normal##
## Shapiro-Wilk normality test
##
## data: subset_1_insula2$HRD_Cued
## W = 0.90975, p-value = 0.1563subset_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 HRD_Cued
## values 1.00 -0.35
## HRD_Cued -0.35 1.00
## Sample Size
## values HRD_Cued
## values 18 14
## HRD_Cued 14 14
## Probability values (Entries above the diagonal are adjusted for multiple tests.)
## values HRD_Cued
## values 0.00 0.21
## HRD_Cued 0.21 0.00
##
## To see confidence intervals of the correlations, print with the short=FALSE optionggscatter(subset_1_insula2, x = "values", y = "HRD_Cued",
add = "reg.line", conf.int = TRUE,
cor.coef = TRUE, cor.method = "pearson",
xlab = "Bold Cued_Uncued", ylab = "HRD_Cued")## Warning: Removed 4 rows containing non-finite values (`stat_smooth()`).## Warning: Removed 4 rows containing non-finite values (`stat_cor()`).## Warning: Removed 4 rows containing missing values (`geom_point()`).
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 HRD_Cued
## values 1.0 -0.3
## HRD_Cued -0.3 1.0
## Sample Size
## values HRD_Cued
## values 18 14
## HRD_Cued 14 14
## Probability values (Entries above the diagonal are adjusted for multiple tests.)
## values HRD_Cued
## values 0.0 0.3
## HRD_Cued 0.3 0.0
##
## 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 × 8
## participants contrast mask values HRD_Cued HRD_Uncued HRD_Cued_Uncued
## <fct> <fct> <fct> <dbl> <dbl> <dbl> <dbl>
## 1 sub-01 contrast-Unc… mask… -0.862 -1.88 -4.05 2.17
## 2 sub-02 contrast-Unc… mask… 0.298 -6.37 -5.43 -0.94
## 3 sub-03 contrast-Unc… mask… 0.598 -3.84 -6.72 2.88
## 4 sub-04 contrast-Unc… mask… -0.0950 -2.39 -3.42 1.03
## 5 sub-05 contrast-Unc… mask… 2.62 -3.16 -3.9 0.74
## 6 sub-07 contrast-Unc… mask… 3.43 -6.58 -6.38 -0.2
## 7 sub-08 contrast-Unc… mask… 0.124 -5.6 -5.37 -0.23
## 8 sub-11 contrast-Unc… mask… 4.44 -4.81 -6.3 1.49
## 9 sub-12 contrast-Unc… mask… -0.00447 -4.26 -5.2 0.94
## 10 sub-13 contrast-Unc… mask… -0.259 NA NA NA
## 11 sub-14 contrast-Unc… mask… -1.62 -9.91 -10.0 0.14
## 12 sub-16 contrast-Unc… mask… 3.61 -5.53 -5.4 -0.13
## 13 sub-17 contrast-Unc… mask… -0.112 -2.06 -2.97 0.91
## 14 sub-18 contrast-Unc… mask… 5.37 -10.4 -10.1 -0.3
## 15 sub-19 contrast-Unc… mask… 1.72 NA NA NA
## 16 sub-20 contrast-Unc… mask… 0.595 -4.26 -5.74 1.48
## 17 sub-21 contrast-Unc… mask… 2.17 NA NA NA
## 18 sub-23 contrast-Unc… mask… -2.08 NA NA NA
## # ℹ 1 more variable: HRD_Uncued_Cued <dbl>subset_2_insula2 <- subset_2_insula2[, c("values", "HRD_Uncued")]
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$HRD_Uncued) #normal##
## Shapiro-Wilk normality test
##
## data: subset_2_insula2$HRD_Uncued
## W = 0.8813, p-value = 0.06065subset_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 HRD_Uncued
## values 1.00 -0.28
## HRD_Uncued -0.28 1.00
## Sample Size
## values HRD_Uncued
## values 18 14
## HRD_Uncued 14 14
## Probability values (Entries above the diagonal are adjusted for multiple tests.)
## values HRD_Uncued
## values 0.00 0.33
## HRD_Uncued 0.33 0.00
##
## To see confidence intervals of the correlations, print with the short=FALSE optionggscatter(subset_2_insula2, x = "values", y = "HRD_Uncued",
add = "reg.line", conf.int = TRUE,
cor.coef = TRUE, cor.method = "pearson",
xlab = "Bold Cued_Uncued", ylab = "HRD_Uncued")## Warning: Removed 4 rows containing non-finite values (`stat_smooth()`).## Warning: Removed 4 rows containing non-finite values (`stat_cor()`).## Warning: Removed 4 rows containing missing values (`geom_point()`).
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 HRD_Uncued
## values 1.00 -0.45
## HRD_Uncued -0.45 1.00
## Sample Size
## values HRD_Uncued
## values 18 14
## HRD_Uncued 14 14
## Probability values (Entries above the diagonal are adjusted for multiple tests.)
## values HRD_Uncued
## values 0.00 0.11
## HRD_Uncued 0.11 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 × 8
## participants contrast mask values HRD_Cued HRD_Uncued HRD_Cued_Uncued
## <fct> <fct> <fct> <dbl> <dbl> <dbl> <dbl>
## 1 sub-01 contrast-Cued-… mask… -0.673 -1.88 -4.05 2.17
## 2 sub-02 contrast-Cued-… mask… -0.864 -6.37 -5.43 -0.94
## 3 sub-03 contrast-Cued-… mask… -1.21 -3.84 -6.72 2.88
## 4 sub-04 contrast-Cued-… mask… -1.18 -2.39 -3.42 1.03
## 5 sub-05 contrast-Cued-… mask… -0.714 -3.16 -3.9 0.74
## 6 sub-07 contrast-Cued-… mask… -1.02 -6.58 -6.38 -0.2
## 7 sub-08 contrast-Cued-… mask… -1.15 -5.6 -5.37 -0.23
## 8 sub-11 contrast-Cued-… mask… -0.159 -4.81 -6.3 1.49
## 9 sub-12 contrast-Cued-… mask… 0.388 -4.26 -5.2 0.94
## 10 sub-13 contrast-Cued-… mask… -0.479 NA NA NA
## 11 sub-14 contrast-Cued-… mask… -0.540 -9.91 -10.0 0.14
## 12 sub-16 contrast-Cued-… mask… -1.43 -5.53 -5.4 -0.13
## 13 sub-17 contrast-Cued-… mask… -0.496 -2.06 -2.97 0.91
## 14 sub-18 contrast-Cued-… mask… -0.254 -10.4 -10.1 -0.3
## 15 sub-19 contrast-Cued-… mask… -0.640 NA NA NA
## 16 sub-20 contrast-Cued-… mask… -1.77 -4.26 -5.74 1.48
## 17 sub-21 contrast-Cued-… mask… -2.57 NA NA NA
## 18 sub-23 contrast-Cued-… mask… -0.586 NA NA NA
## # ℹ 1 more variable: HRD_Uncued_Cued <dbl>subset_3_insula2 <- subset_3_insula2[, c("values", "HRD_Cued_Uncued")]
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$HRD_Cued_Uncued) #normal##
## Shapiro-Wilk normality test
##
## data: subset_3_insula2$HRD_Cued_Uncued
## W = 0.95893, p-value = 0.7054subset_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 HRD_Cued_Uncued
## values 1.00 -0.03
## HRD_Cued_Uncued -0.03 1.00
## Sample Size
## values HRD_Cued_Uncued
## values 18 14
## HRD_Cued_Uncued 14 14
## Probability values (Entries above the diagonal are adjusted for multiple tests.)
## values HRD_Cued_Uncued
## values 0.00 0.91
## HRD_Cued_Uncued 0.91 0.00
##
## To see confidence intervals of the correlations, print with the short=FALSE option#library("ggpubr")
ggscatter(subset_3_insula2, x = "values", y = "HRD_Cued_Uncued",
add = "reg.line", conf.int = TRUE,
cor.coef = TRUE, cor.method = "pearson",
xlab = "Bold Cued_Uncued", ylab = "HRD_Cued_Uncued")## Warning: Removed 4 rows containing non-finite values (`stat_smooth()`).## Warning: Removed 4 rows containing non-finite values (`stat_cor()`).## Warning: Removed 4 rows containing missing values (`geom_point()`).
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 HRD_Cued_Uncued
## values 1.00 -0.05
## HRD_Cued_Uncued -0.05 1.00
## Sample Size
## values HRD_Cued_Uncued
## values 18 14
## HRD_Cued_Uncued 14 14
## Probability values (Entries above the diagonal are adjusted for multiple tests.)
## values HRD_Cued_Uncued
## values 0.00 0.86
## HRD_Cued_Uncued 0.86 0.00
##
## To see confidence intervals of the correlations, print with the short=FALSE option#
subset_4_insula2 <- combined_insula2 %>%
filter(contrast == "contrast-Uncued-Cued")
subset_4_insula2## # A tibble: 18 × 8
## participants contrast mask values HRD_Cued HRD_Uncued HRD_Cued_Uncued
## <fct> <fct> <fct> <dbl> <dbl> <dbl> <dbl>
## 1 sub-01 contrast-Uncue… mask… 0.673 -1.88 -4.05 2.17
## 2 sub-02 contrast-Uncue… mask… 0.864 -6.37 -5.43 -0.94
## 3 sub-03 contrast-Uncue… mask… 1.21 -3.84 -6.72 2.88
## 4 sub-04 contrast-Uncue… mask… 1.18 -2.39 -3.42 1.03
## 5 sub-05 contrast-Uncue… mask… 0.714 -3.16 -3.9 0.74
## 6 sub-07 contrast-Uncue… mask… 1.02 -6.58 -6.38 -0.2
## 7 sub-08 contrast-Uncue… mask… 1.15 -5.6 -5.37 -0.23
## 8 sub-11 contrast-Uncue… mask… 0.159 -4.81 -6.3 1.49
## 9 sub-12 contrast-Uncue… mask… -0.388 -4.26 -5.2 0.94
## 10 sub-13 contrast-Uncue… mask… 0.479 NA NA NA
## 11 sub-14 contrast-Uncue… mask… 0.540 -9.91 -10.0 0.14
## 12 sub-16 contrast-Uncue… mask… 1.43 -5.53 -5.4 -0.13
## 13 sub-17 contrast-Uncue… mask… 0.496 -2.06 -2.97 0.91
## 14 sub-18 contrast-Uncue… mask… 0.254 -10.4 -10.1 -0.3
## 15 sub-19 contrast-Uncue… mask… 0.640 NA NA NA
## 16 sub-20 contrast-Uncue… mask… 1.77 -4.26 -5.74 1.48
## 17 sub-21 contrast-Uncue… mask… 2.57 NA NA NA
## 18 sub-23 contrast-Uncue… mask… 0.586 NA NA NA
## # ℹ 1 more variable: HRD_Uncued_Cued <dbl>subset_4_insula2 <- subset_4_insula2[, c("values", "HRD_Uncued_Cued")]
shapiro.test(subset_4_insula2$values) #normal##
## Shapiro-Wilk normality test
##
## data: subset_4_insula2$values
## W = 0.94868, p-value = 0.4047shapiro.test(subset_4_insula2$HRD_Uncued_Cued) #normal##
## Shapiro-Wilk normality test
##
## data: subset_4_insula2$HRD_Uncued_Cued
## W = 0.95893, p-value = 0.7054subset_4_insula2corr <- corr.test(subset_4_insula2, use = "pairwise",method="pearson",adjust="none")
subset_4_insula2corr## Call:corr.test(x = subset_4_insula2, use = "pairwise", method = "pearson",
## adjust = "none")
## Correlation matrix
## values HRD_Uncued_Cued
## values 1.00 -0.03
## HRD_Uncued_Cued -0.03 1.00
## Sample Size
## values HRD_Uncued_Cued
## values 18 14
## HRD_Uncued_Cued 14 14
## Probability values (Entries above the diagonal are adjusted for multiple tests.)
## values HRD_Uncued_Cued
## values 0.00 0.91
## HRD_Uncued_Cued 0.91 0.00
##
## To see confidence intervals of the correlations, print with the short=FALSE optionggscatter(subset_4_insula2, x = "values", y = "HRD_Uncued_Cued",
add = "reg.line", conf.int = TRUE,
cor.coef = TRUE, cor.method = "pearson",
xlab = "Bold Cued_Uncued", ylab = "HRD_Uncued_Cued")## Warning: Removed 4 rows containing non-finite values (`stat_smooth()`).## Warning: Removed 4 rows containing non-finite values (`stat_cor()`).## Warning: Removed 4 rows containing missing values (`geom_point()`).
sp.subset_4_insula2corr <- corr.test(subset_4_insula2, use = "pairwise",method="spearman",adjust="none")
sp.subset_4_insula2corr## Call:corr.test(x = subset_4_insula2, use = "pairwise", method = "spearman",
## adjust = "none")
## Correlation matrix
## values HRD_Uncued_Cued
## values 1.00 -0.05
## HRD_Uncued_Cued -0.05 1.00
## Sample Size
## values HRD_Uncued_Cued
## values 18 14
## HRD_Uncued_Cued 14 14
## Probability values (Entries above the diagonal are adjusted for multiple tests.)
## values HRD_Uncued_Cued
## values 0.00 0.86
## HRD_Uncued_Cued 0.86 0.00
##
## To see confidence intervals of the correlations, print with the short=FALSE optionall_p_values.insula2 <- c(round(subset_1_insula2corr$p[2], 2),
round(subset_2_insula2corr$p[2], 2),
round(subset_3_insula2corr$p[2], 2),
round(subset_4_insula2corr$p[2], 2))
all_p_values.insula2## [1] 0.21 0.33 0.91 0.91# Apply FDR correction to combined all p-values
adjusted_p_insula2<- p.adjust(all_p_values.insula2, method = "fdr")
adjusted_p_insula2## [1] 0.66 0.66 0.91 0.91mask-resampled_vorbitofrontal_mask_onesamp_ttest_results_ClusterEffEst_p-0.001_desc-binarizedCluster.nii.gz
no sign corr
OFC2 <- read_csv("correlations/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>HRD.data <- read_csv("correlations/HRDdata.csv")## Rows: 18 Columns: 5
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (1): participants
## dbl (4): HRD_Cued, HRD_Uncued, HRD_Cued_Uncued, HRD_Uncued_Cued
##
## 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(HRD.data)## spc_tbl_ [18 × 5] (S3: spec_tbl_df/tbl_df/tbl/data.frame)
## $ participants : chr [1:18] "sub-01" "sub-02" "sub-03" "sub-04" ...
## $ HRD_Cued : num [1:18] -1.88 -6.37 -3.84 -2.39 -3.16 -6.58 -5.6 -4.81 -4.26 NA ...
## $ HRD_Uncued : num [1:18] -4.05 -5.43 -6.72 -3.42 -3.9 -6.38 -5.37 -6.3 -5.2 NA ...
## $ HRD_Cued_Uncued: num [1:18] 2.17 -0.94 2.88 1.03 0.74 -0.2 -0.23 1.49 0.94 NA ...
## $ HRD_Uncued_Cued: num [1:18] -2.17 0.94 -2.88 -1.03 -0.74 0.2 0.23 -1.49 -0.94 NA ...
## - attr(*, "spec")=
## .. cols(
## .. participants = col_character(),
## .. HRD_Cued = col_double(),
## .. HRD_Uncued = col_double(),
## .. HRD_Cued_Uncued = col_double(),
## .. HRD_Uncued_Cued = col_double()
## .. )
## - attr(*, "problems")=<externalptr>combined_OFC2 <- inner_join(OFC2, HRD.data, by = "participants")
combined_OFC2 <- combined_OFC2 %>%
mutate(across(c(participants, contrast, mask), as.factor))
str(combined_OFC2)## tibble [72 × 8] (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 ...
## $ HRD_Cued : num [1:72] -1.88 -1.88 -1.88 -1.88 -6.37 -6.37 -6.37 -6.37 -3.84 -3.84 ...
## $ HRD_Uncued : num [1:72] -4.05 -4.05 -4.05 -4.05 -5.43 -5.43 -5.43 -5.43 -6.72 -6.72 ...
## $ HRD_Cued_Uncued: num [1:72] 2.17 2.17 2.17 2.17 -0.94 -0.94 -0.94 -0.94 2.88 2.88 ...
## $ HRD_Uncued_Cued: num [1:72] -2.17 -2.17 -2.17 -2.17 0.94 0.94 0.94 0.94 -2.88 -2.88 ...#
subset_1_OFC2 <- combined_OFC2 %>%
filter(contrast == "contrast-Cued")
subset_1_OFC2## # A tibble: 18 × 8
## participants contrast mask values HRD_Cued HRD_Uncued HRD_Cued_Uncued
## <fct> <fct> <fct> <dbl> <dbl> <dbl> <dbl>
## 1 sub-01 contrast-Cued mask-… -0.691 -1.88 -4.05 2.17
## 2 sub-02 contrast-Cued mask-… 0.784 -6.37 -5.43 -0.94
## 3 sub-03 contrast-Cued mask-… -0.199 -3.84 -6.72 2.88
## 4 sub-04 contrast-Cued mask-… -1.54 -2.39 -3.42 1.03
## 5 sub-05 contrast-Cued mask-… 0.853 -3.16 -3.9 0.74
## 6 sub-07 contrast-Cued mask-… -1.31 -6.58 -6.38 -0.2
## 7 sub-08 contrast-Cued mask-… -1.01 -5.6 -5.37 -0.23
## 8 sub-11 contrast-Cued mask-… 0.511 -4.81 -6.3 1.49
## 9 sub-12 contrast-Cued mask-… -2.01 -4.26 -5.2 0.94
## 10 sub-13 contrast-Cued mask-… 0.350 NA NA NA
## 11 sub-14 contrast-Cued mask-… -2.45 -9.91 -10.0 0.14
## 12 sub-16 contrast-Cued mask-… -1.98 -5.53 -5.4 -0.13
## 13 sub-17 contrast-Cued mask-… -2.47 -2.06 -2.97 0.91
## 14 sub-18 contrast-Cued mask-… -1.24 -10.4 -10.1 -0.3
## 15 sub-19 contrast-Cued mask-… -2.06 NA NA NA
## 16 sub-20 contrast-Cued mask-… -2.11 -4.26 -5.74 1.48
## 17 sub-21 contrast-Cued mask-… -0.0457 NA NA NA
## 18 sub-23 contrast-Cued mask-… -2.38 NA NA NA
## # ℹ 1 more variable: HRD_Uncued_Cued <dbl>subset_1_OFC2 <- subset_1_OFC2[, c("values", "HRD_Cued")]
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$HRD_Cued) #normal##
## Shapiro-Wilk normality test
##
## data: subset_1_OFC2$HRD_Cued
## W = 0.90975, p-value = 0.1563subset_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 HRD_Cued
## values 1.00 0.13
## HRD_Cued 0.13 1.00
## Sample Size
## values HRD_Cued
## values 18 14
## HRD_Cued 14 14
## Probability values (Entries above the diagonal are adjusted for multiple tests.)
## values HRD_Cued
## values 0.00 0.65
## HRD_Cued 0.65 0.00
##
## To see confidence intervals of the correlations, print with the short=FALSE optionggscatter(subset_1_OFC2, x = "values", y = "HRD_Cued",
add = "reg.line", conf.int = TRUE,
cor.coef = TRUE, cor.method = "pearson",
xlab = "Bold Cued_Uncued", ylab = "HRD_Cued")## Warning: Removed 4 rows containing non-finite values (`stat_smooth()`).## Warning: Removed 4 rows containing non-finite values (`stat_cor()`).## Warning: Removed 4 rows containing missing values (`geom_point()`).
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 HRD_Cued
## values 1.00 0.02
## HRD_Cued 0.02 1.00
## Sample Size
## values HRD_Cued
## values 18 14
## HRD_Cued 14 14
## Probability values (Entries above the diagonal are adjusted for multiple tests.)
## values HRD_Cued
## values 0.00 0.94
## HRD_Cued 0.94 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 × 8
## participants contrast mask values HRD_Cued HRD_Uncued HRD_Cued_Uncued
## <fct> <fct> <fct> <dbl> <dbl> <dbl> <dbl>
## 1 sub-01 contrast-Uncu… mask… 0.842 -1.88 -4.05 2.17
## 2 sub-02 contrast-Uncu… mask… 2.54 -6.37 -5.43 -0.94
## 3 sub-03 contrast-Uncu… mask… 0.824 -3.84 -6.72 2.88
## 4 sub-04 contrast-Uncu… mask… -0.813 -2.39 -3.42 1.03
## 5 sub-05 contrast-Uncu… mask… 2.03 -3.16 -3.9 0.74
## 6 sub-07 contrast-Uncu… mask… -0.923 -6.58 -6.38 -0.2
## 7 sub-08 contrast-Uncu… mask… -0.942 -5.6 -5.37 -0.23
## 8 sub-11 contrast-Uncu… mask… 1.04 -4.81 -6.3 1.49
## 9 sub-12 contrast-Uncu… mask… -1.16 -4.26 -5.2 0.94
## 10 sub-13 contrast-Uncu… mask… 0.399 NA NA NA
## 11 sub-14 contrast-Uncu… mask… -1.14 -9.91 -10.0 0.14
## 12 sub-16 contrast-Uncu… mask… -0.0297 -5.53 -5.4 -0.13
## 13 sub-17 contrast-Uncu… mask… -0.608 -2.06 -2.97 0.91
## 14 sub-18 contrast-Uncu… mask… -1.25 -10.4 -10.1 -0.3
## 15 sub-19 contrast-Uncu… mask… -1.68 NA NA NA
## 16 sub-20 contrast-Uncu… mask… -1.38 -4.26 -5.74 1.48
## 17 sub-21 contrast-Uncu… mask… 1.18 NA NA NA
## 18 sub-23 contrast-Uncu… mask… -1.65 NA NA NA
## # ℹ 1 more variable: HRD_Uncued_Cued <dbl>subset_2_OFC2 <- subset_2_OFC2[, c("values", "HRD_Uncued")]
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$HRD_Uncued) #normal##
## Shapiro-Wilk normality test
##
## data: subset_2_OFC2$HRD_Uncued
## W = 0.8813, p-value = 0.06065subset_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 HRD_Uncued
## values 1.0 0.3
## HRD_Uncued 0.3 1.0
## Sample Size
## values HRD_Uncued
## values 18 14
## HRD_Uncued 14 14
## Probability values (Entries above the diagonal are adjusted for multiple tests.)
## values HRD_Uncued
## values 0.00 0.29
## HRD_Uncued 0.29 0.00
##
## To see confidence intervals of the correlations, print with the short=FALSE optionggscatter(subset_2_OFC2, x = "values", y = "HRD_Uncued",
add = "reg.line", conf.int = TRUE,
cor.coef = TRUE, cor.method = "pearson",
xlab = "Bold Cued_Uncued", ylab = "HRD_Uncued")## Warning: Removed 4 rows containing non-finite values (`stat_smooth()`).## Warning: Removed 4 rows containing non-finite values (`stat_cor()`).## Warning: Removed 4 rows containing missing values (`geom_point()`).
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 HRD_Uncued
## values 1.0 0.3
## HRD_Uncued 0.3 1.0
## Sample Size
## values HRD_Uncued
## values 18 14
## HRD_Uncued 14 14
## Probability values (Entries above the diagonal are adjusted for multiple tests.)
## values HRD_Uncued
## values 0.0 0.3
## HRD_Uncued 0.3 0.0
##
## To see confidence intervals of the correlations, print with the short=FALSE option#
subset_3_OFC2 <- combined_OFC2 %>%
filter(contrast == "contrast-Cued-Uncued")
subset_3_OFC2## # A tibble: 18 × 8
## participants contrast mask values HRD_Cued HRD_Uncued HRD_Cued_Uncued
## <fct> <fct> <fct> <dbl> <dbl> <dbl> <dbl>
## 1 sub-01 contrast-Cued… mask… -1.12 -1.88 -4.05 2.17
## 2 sub-02 contrast-Cued… mask… -1.29 -6.37 -5.43 -0.94
## 3 sub-03 contrast-Cued… mask… -0.771 -3.84 -6.72 2.88
## 4 sub-04 contrast-Cued… mask… -0.533 -2.39 -3.42 1.03
## 5 sub-05 contrast-Cued… mask… -0.863 -3.16 -3.9 0.74
## 6 sub-07 contrast-Cued… mask… -0.214 -6.58 -6.38 -0.2
## 7 sub-08 contrast-Cued… mask… -0.0613 -5.6 -5.37 -0.23
## 8 sub-11 contrast-Cued… mask… -0.403 -4.81 -6.3 1.49
## 9 sub-12 contrast-Cued… mask… -0.632 -4.26 -5.2 0.94
## 10 sub-13 contrast-Cued… mask… -0.0423 NA NA NA
## 11 sub-14 contrast-Cued… mask… -0.968 -9.91 -10.0 0.14
## 12 sub-16 contrast-Cued… mask… -1.50 -5.53 -5.4 -0.13
## 13 sub-17 contrast-Cued… mask… -1.34 -2.06 -2.97 0.91
## 14 sub-18 contrast-Cued… mask… 0.0203 -10.4 -10.1 -0.3
## 15 sub-19 contrast-Cued… mask… -0.362 NA NA NA
## 16 sub-20 contrast-Cued… mask… -0.509 -4.26 -5.74 1.48
## 17 sub-21 contrast-Cued… mask… -0.872 NA NA NA
## 18 sub-23 contrast-Cued… mask… -0.564 NA NA NA
## # ℹ 1 more variable: HRD_Uncued_Cued <dbl>subset_3_OFC2 <- subset_3_OFC2[, c("values", "HRD_Cued_Uncued")]
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$HRD_Cued_Uncued) #normal##
## Shapiro-Wilk normality test
##
## data: subset_3_OFC2$HRD_Cued_Uncued
## W = 0.95893, p-value = 0.7054subset_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 HRD_Cued_Uncued
## values 1.00 -0.06
## HRD_Cued_Uncued -0.06 1.00
## Sample Size
## values HRD_Cued_Uncued
## values 18 14
## HRD_Cued_Uncued 14 14
## Probability values (Entries above the diagonal are adjusted for multiple tests.)
## values HRD_Cued_Uncued
## values 0.00 0.83
## HRD_Cued_Uncued 0.83 0.00
##
## To see confidence intervals of the correlations, print with the short=FALSE option#library("ggpubr")
ggscatter(subset_3_OFC2, x = "values", y = "HRD_Cued_Uncued",
add = "reg.line", conf.int = TRUE,
cor.coef = TRUE, cor.method = "pearson",
xlab = "Bold Cued_Uncued", ylab = "HRD_Cued_Uncued")## Warning: Removed 4 rows containing non-finite values (`stat_smooth()`).## Warning: Removed 4 rows containing non-finite values (`stat_cor()`).## Warning: Removed 4 rows containing missing values (`geom_point()`).
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 HRD_Cued_Uncued
## values 1.0 -0.1
## HRD_Cued_Uncued -0.1 1.0
## Sample Size
## values HRD_Cued_Uncued
## values 18 14
## HRD_Cued_Uncued 14 14
## Probability values (Entries above the diagonal are adjusted for multiple tests.)
## values HRD_Cued_Uncued
## values 0.00 0.74
## HRD_Cued_Uncued 0.74 0.00
##
## To see confidence intervals of the correlations, print with the short=FALSE option#
subset_4_OFC2 <- combined_OFC2 %>%
filter(contrast == "contrast-Uncued-Cued")
subset_4_OFC2## # A tibble: 18 × 8
## participants contrast mask values HRD_Cued HRD_Uncued HRD_Cued_Uncued
## <fct> <fct> <fct> <dbl> <dbl> <dbl> <dbl>
## 1 sub-01 contrast-Uncu… mask… 1.12 -1.88 -4.05 2.17
## 2 sub-02 contrast-Uncu… mask… 1.29 -6.37 -5.43 -0.94
## 3 sub-03 contrast-Uncu… mask… 0.771 -3.84 -6.72 2.88
## 4 sub-04 contrast-Uncu… mask… 0.533 -2.39 -3.42 1.03
## 5 sub-05 contrast-Uncu… mask… 0.863 -3.16 -3.9 0.74
## 6 sub-07 contrast-Uncu… mask… 0.214 -6.58 -6.38 -0.2
## 7 sub-08 contrast-Uncu… mask… 0.0613 -5.6 -5.37 -0.23
## 8 sub-11 contrast-Uncu… mask… 0.403 -4.81 -6.3 1.49
## 9 sub-12 contrast-Uncu… mask… 0.632 -4.26 -5.2 0.94
## 10 sub-13 contrast-Uncu… mask… 0.0423 NA NA NA
## 11 sub-14 contrast-Uncu… mask… 0.968 -9.91 -10.0 0.14
## 12 sub-16 contrast-Uncu… mask… 1.50 -5.53 -5.4 -0.13
## 13 sub-17 contrast-Uncu… mask… 1.34 -2.06 -2.97 0.91
## 14 sub-18 contrast-Uncu… mask… -0.0203 -10.4 -10.1 -0.3
## 15 sub-19 contrast-Uncu… mask… 0.362 NA NA NA
## 16 sub-20 contrast-Uncu… mask… 0.509 -4.26 -5.74 1.48
## 17 sub-21 contrast-Uncu… mask… 0.872 NA NA NA
## 18 sub-23 contrast-Uncu… mask… 0.564 NA NA NA
## # ℹ 1 more variable: HRD_Uncued_Cued <dbl>subset_4_OFC2 <- subset_4_OFC2[, c("values", "HRD_Uncued_Cued")]
shapiro.test(subset_4_OFC2$values) #normal##
## Shapiro-Wilk normality test
##
## data: subset_4_OFC2$values
## W = 0.96458, p-value = 0.6918shapiro.test(subset_4_OFC2$HRD_Uncued_Cued) #normal##
## Shapiro-Wilk normality test
##
## data: subset_4_OFC2$HRD_Uncued_Cued
## W = 0.95893, p-value = 0.7054subset_4_OFC2corr <- corr.test(subset_4_OFC2, use = "pairwise",method="pearson",adjust="none")
subset_4_OFC2corr## Call:corr.test(x = subset_4_OFC2, use = "pairwise", method = "pearson",
## adjust = "none")
## Correlation matrix
## values HRD_Uncued_Cued
## values 1.00 -0.06
## HRD_Uncued_Cued -0.06 1.00
## Sample Size
## values HRD_Uncued_Cued
## values 18 14
## HRD_Uncued_Cued 14 14
## Probability values (Entries above the diagonal are adjusted for multiple tests.)
## values HRD_Uncued_Cued
## values 0.00 0.83
## HRD_Uncued_Cued 0.83 0.00
##
## To see confidence intervals of the correlations, print with the short=FALSE optionggscatter(subset_4_OFC2, x = "values", y = "HRD_Uncued_Cued",
add = "reg.line", conf.int = TRUE,
cor.coef = TRUE, cor.method = "pearson",
xlab = "Bold Cued_Uncued", ylab = "HRD_Uncued_Cued")## Warning: Removed 4 rows containing non-finite values (`stat_smooth()`).## Warning: Removed 4 rows containing non-finite values (`stat_cor()`).## Warning: Removed 4 rows containing missing values (`geom_point()`).
sp.subset_4_OFC2corr <- corr.test(subset_4_OFC2, use = "pairwise",method="spearman",adjust="none")
sp.subset_4_OFC2corr## Call:corr.test(x = subset_4_OFC2, use = "pairwise", method = "spearman",
## adjust = "none")
## Correlation matrix
## values HRD_Uncued_Cued
## values 1.0 -0.1
## HRD_Uncued_Cued -0.1 1.0
## Sample Size
## values HRD_Uncued_Cued
## values 18 14
## HRD_Uncued_Cued 14 14
## Probability values (Entries above the diagonal are adjusted for multiple tests.)
## values HRD_Uncued_Cued
## values 0.00 0.74
## HRD_Uncued_Cued 0.74 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$p[2], 2),
round(subset_4_OFC2corr$p[2], 2))
all_p_values.OFC2## [1] 0.65 0.29 0.83 0.83# Apply FDR correction to combined all p-values
adjusted_p_OFC2<- p.adjust(all_p_values.OFC2, method = "fdr")
adjusted_p_OFC2## [1] 0.83 0.83 0.83 0.83mask-b_gray_thresholded_onesamp_ttest_results_ClusterEffEst_p-0.001_desc-binarizedCluster.nii.gz
No sign corr
Grey2 <- read_csv("correlations/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>HRD.data <- read_csv("correlations/HRDdata.csv")## Rows: 18 Columns: 5
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (1): participants
## dbl (4): HRD_Cued, HRD_Uncued, HRD_Cued_Uncued, HRD_Uncued_Cued
##
## 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(HRD.data)## spc_tbl_ [18 × 5] (S3: spec_tbl_df/tbl_df/tbl/data.frame)
## $ participants : chr [1:18] "sub-01" "sub-02" "sub-03" "sub-04" ...
## $ HRD_Cued : num [1:18] -1.88 -6.37 -3.84 -2.39 -3.16 -6.58 -5.6 -4.81 -4.26 NA ...
## $ HRD_Uncued : num [1:18] -4.05 -5.43 -6.72 -3.42 -3.9 -6.38 -5.37 -6.3 -5.2 NA ...
## $ HRD_Cued_Uncued: num [1:18] 2.17 -0.94 2.88 1.03 0.74 -0.2 -0.23 1.49 0.94 NA ...
## $ HRD_Uncued_Cued: num [1:18] -2.17 0.94 -2.88 -1.03 -0.74 0.2 0.23 -1.49 -0.94 NA ...
## - attr(*, "spec")=
## .. cols(
## .. participants = col_character(),
## .. HRD_Cued = col_double(),
## .. HRD_Uncued = col_double(),
## .. HRD_Cued_Uncued = col_double(),
## .. HRD_Uncued_Cued = col_double()
## .. )
## - attr(*, "problems")=<externalptr>combined_Grey2 <- inner_join(Grey2, HRD.data, by = "participants")
combined_Grey2 <- combined_Grey2 %>%
mutate(across(c(participants, contrast, mask), as.factor))
str(combined_Grey2)## tibble [72 × 8] (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 ...
## $ HRD_Cued : num [1:72] -1.88 -1.88 -1.88 -1.88 -6.37 -6.37 -6.37 -6.37 -3.84 -3.84 ...
## $ HRD_Uncued : num [1:72] -4.05 -4.05 -4.05 -4.05 -5.43 -5.43 -5.43 -5.43 -6.72 -6.72 ...
## $ HRD_Cued_Uncued: num [1:72] 2.17 2.17 2.17 2.17 -0.94 -0.94 -0.94 -0.94 2.88 2.88 ...
## $ HRD_Uncued_Cued: num [1:72] -2.17 -2.17 -2.17 -2.17 0.94 0.94 0.94 0.94 -2.88 -2.88 ...#
subset_1_Grey2 <- combined_Grey2 %>%
filter(contrast == "contrast-Cued")
subset_1_Grey2## # A tibble: 18 × 8
## participants contrast mask values HRD_Cued HRD_Uncued HRD_Cued_Uncued
## <fct> <fct> <fct> <dbl> <dbl> <dbl> <dbl>
## 1 sub-01 contrast-Cued mask-b… -1.16 -1.88 -4.05 2.17
## 2 sub-02 contrast-Cued mask-b… 0.193 -6.37 -5.43 -0.94
## 3 sub-03 contrast-Cued mask-b… -1.10 -3.84 -6.72 2.88
## 4 sub-04 contrast-Cued mask-b… -1.98 -2.39 -3.42 1.03
## 5 sub-05 contrast-Cued mask-b… -1.06 -3.16 -3.9 0.74
## 6 sub-07 contrast-Cued mask-b… -0.918 -6.58 -6.38 -0.2
## 7 sub-08 contrast-Cued mask-b… -1.71 -5.6 -5.37 -0.23
## 8 sub-11 contrast-Cued mask-b… -1.84 -4.81 -6.3 1.49
## 9 sub-12 contrast-Cued mask-b… -2.32 -4.26 -5.2 0.94
## 10 sub-13 contrast-Cued mask-b… -3.31 NA NA NA
## 11 sub-14 contrast-Cued mask-b… -1.42 -9.91 -10.0 0.14
## 12 sub-16 contrast-Cued mask-b… -1.49 -5.53 -5.4 -0.13
## 13 sub-17 contrast-Cued mask-b… -1.76 -2.06 -2.97 0.91
## 14 sub-18 contrast-Cued mask-b… -1.44 -10.4 -10.1 -0.3
## 15 sub-19 contrast-Cued mask-b… -1.25 NA NA NA
## 16 sub-20 contrast-Cued mask-b… -0.726 -4.26 -5.74 1.48
## 17 sub-21 contrast-Cued mask-b… -0.873 NA NA NA
## 18 sub-23 contrast-Cued mask-b… -1.30 NA NA NA
## # ℹ 1 more variable: HRD_Uncued_Cued <dbl>subset_1_Grey2 <- subset_1_Grey2[, c("values", "HRD_Cued")]
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$HRD_Cued) #normal##
## Shapiro-Wilk normality test
##
## data: subset_1_Grey2$HRD_Cued
## W = 0.90975, p-value = 0.1563subset_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 HRD_Cued
## values 1.00 -0.16
## HRD_Cued -0.16 1.00
## Sample Size
## values HRD_Cued
## values 18 14
## HRD_Cued 14 14
## Probability values (Entries above the diagonal are adjusted for multiple tests.)
## values HRD_Cued
## values 0.00 0.58
## HRD_Cued 0.58 0.00
##
## To see confidence intervals of the correlations, print with the short=FALSE optionggscatter(subset_1_Grey2, x = "values", y = "HRD_Cued",
add = "reg.line", conf.int = TRUE,
cor.coef = TRUE, cor.method = "pearson",
xlab = "Bold Cued_Uncued", ylab = "HRD_Cued")## Warning: Removed 4 rows containing non-finite values (`stat_smooth()`).## Warning: Removed 4 rows containing non-finite values (`stat_cor()`).## Warning: Removed 4 rows containing missing values (`geom_point()`).
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 HRD_Cued
## values 1.00 -0.21
## HRD_Cued -0.21 1.00
## Sample Size
## values HRD_Cued
## values 18 14
## HRD_Cued 14 14
## Probability values (Entries above the diagonal are adjusted for multiple tests.)
## values HRD_Cued
## values 0.00 0.46
## HRD_Cued 0.46 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 × 8
## participants contrast mask values HRD_Cued HRD_Uncued HRD_Cued_Uncued
## <fct> <fct> <fct> <dbl> <dbl> <dbl> <dbl>
## 1 sub-01 contrast-Unc… mask… -0.0213 -1.88 -4.05 2.17
## 2 sub-02 contrast-Unc… mask… 0.973 -6.37 -5.43 -0.94
## 3 sub-03 contrast-Unc… mask… 0.448 -3.84 -6.72 2.88
## 4 sub-04 contrast-Unc… mask… 0.00778 -2.39 -3.42 1.03
## 5 sub-05 contrast-Unc… mask… -0.374 -3.16 -3.9 0.74
## 6 sub-07 contrast-Unc… mask… 0.275 -6.58 -6.38 -0.2
## 7 sub-08 contrast-Unc… mask… -0.885 -5.6 -5.37 -0.23
## 8 sub-11 contrast-Unc… mask… -2.11 -4.81 -6.3 1.49
## 9 sub-12 contrast-Unc… mask… -1.02 -4.26 -5.2 0.94
## 10 sub-13 contrast-Unc… mask… -1.49 NA NA NA
## 11 sub-14 contrast-Unc… mask… -0.539 -9.91 -10.0 0.14
## 12 sub-16 contrast-Unc… mask… -0.429 -5.53 -5.4 -0.13
## 13 sub-17 contrast-Unc… mask… -0.970 -2.06 -2.97 0.91
## 14 sub-18 contrast-Unc… mask… -0.893 -10.4 -10.1 -0.3
## 15 sub-19 contrast-Unc… mask… -0.156 NA NA NA
## 16 sub-20 contrast-Unc… mask… -0.0622 -4.26 -5.74 1.48
## 17 sub-21 contrast-Unc… mask… 0.656 NA NA NA
## 18 sub-23 contrast-Unc… mask… -0.300 NA NA NA
## # ℹ 1 more variable: HRD_Uncued_Cued <dbl>subset_2_Grey2 <- subset_2_Grey2[, c("values", "HRD_Uncued")]
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$HRD_Uncued) #normal##
## Shapiro-Wilk normality test
##
## data: subset_2_Grey2$HRD_Uncued
## W = 0.8813, p-value = 0.06065subset_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 HRD_Uncued
## values 1.00 0.11
## HRD_Uncued 0.11 1.00
## Sample Size
## values HRD_Uncued
## values 18 14
## HRD_Uncued 14 14
## Probability values (Entries above the diagonal are adjusted for multiple tests.)
## values HRD_Uncued
## values 0.0 0.7
## HRD_Uncued 0.7 0.0
##
## To see confidence intervals of the correlations, print with the short=FALSE optionggscatter(subset_2_Grey2, x = "values", y = "HRD_Uncued",
add = "reg.line", conf.int = TRUE,
cor.coef = TRUE, cor.method = "pearson",
xlab = "Bold Cued_Uncued", ylab = "HRD_Uncued")## Warning: Removed 4 rows containing non-finite values (`stat_smooth()`).## Warning: Removed 4 rows containing non-finite values (`stat_cor()`).## Warning: Removed 4 rows containing missing values (`geom_point()`).
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 HRD_Uncued
## values 1.00 -0.07
## HRD_Uncued -0.07 1.00
## Sample Size
## values HRD_Uncued
## values 18 14
## HRD_Uncued 14 14
## Probability values (Entries above the diagonal are adjusted for multiple tests.)
## values HRD_Uncued
## values 0.00 0.82
## HRD_Uncued 0.82 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 × 8
## participants contrast mask values HRD_Cued HRD_Uncued HRD_Cued_Uncued
## <fct> <fct> <fct> <dbl> <dbl> <dbl> <dbl>
## 1 sub-01 contrast-Cued-… mask… -0.839 -1.88 -4.05 2.17
## 2 sub-02 contrast-Cued-… mask… -0.572 -6.37 -5.43 -0.94
## 3 sub-03 contrast-Cued-… mask… -1.14 -3.84 -6.72 2.88
## 4 sub-04 contrast-Cued-… mask… -1.44 -2.39 -3.42 1.03
## 5 sub-05 contrast-Cued-… mask… -0.511 -3.16 -3.9 0.74
## 6 sub-07 contrast-Cued-… mask… -0.844 -6.58 -6.38 -0.2
## 7 sub-08 contrast-Cued-… mask… -0.608 -5.6 -5.37 -0.23
## 8 sub-11 contrast-Cued-… mask… 0.220 -4.81 -6.3 1.49
## 9 sub-12 contrast-Cued-… mask… -0.968 -4.26 -5.2 0.94
## 10 sub-13 contrast-Cued-… mask… -1.34 NA NA NA
## 11 sub-14 contrast-Cued-… mask… -0.642 -9.91 -10.0 0.14
## 12 sub-16 contrast-Cued-… mask… -0.816 -5.53 -5.4 -0.13
## 13 sub-17 contrast-Cued-… mask… -0.572 -2.06 -2.97 0.91
## 14 sub-18 contrast-Cued-… mask… -0.393 -10.4 -10.1 -0.3
## 15 sub-19 contrast-Cued-… mask… -0.842 NA NA NA
## 16 sub-20 contrast-Cued-… mask… -0.475 -4.26 -5.74 1.48
## 17 sub-21 contrast-Cued-… mask… -1.16 NA NA NA
## 18 sub-23 contrast-Cued-… mask… -0.734 NA NA NA
## # ℹ 1 more variable: HRD_Uncued_Cued <dbl>subset_3_Grey2 <- subset_3_Grey2[, c("values", "HRD_Cued_Uncued")]
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$HRD_Cued_Uncued) #normal##
## Shapiro-Wilk normality test
##
## data: subset_3_Grey2$HRD_Cued_Uncued
## W = 0.95893, p-value = 0.7054subset_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 HRD_Cued_Uncued
## values 1.00 -0.18
## HRD_Cued_Uncued -0.18 1.00
## Sample Size
## values HRD_Cued_Uncued
## values 18 14
## HRD_Cued_Uncued 14 14
## Probability values (Entries above the diagonal are adjusted for multiple tests.)
## values HRD_Cued_Uncued
## values 0.00 0.54
## HRD_Cued_Uncued 0.54 0.00
##
## To see confidence intervals of the correlations, print with the short=FALSE option#library("ggpubr")
ggscatter(subset_3_Grey2, x = "values", y = "HRD_Cued_Uncued",
add = "reg.line", conf.int = TRUE,
cor.coef = TRUE, cor.method = "pearson",
xlab = "Bold Cued_Uncued", ylab = "HRD_Cued_Uncued")## Warning: Removed 4 rows containing non-finite values (`stat_smooth()`).## Warning: Removed 4 rows containing non-finite values (`stat_cor()`).## Warning: Removed 4 rows containing missing values (`geom_point()`).
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 HRD_Cued_Uncued
## values 1.00 -0.27
## HRD_Cued_Uncued -0.27 1.00
## Sample Size
## values HRD_Cued_Uncued
## values 18 14
## HRD_Cued_Uncued 14 14
## Probability values (Entries above the diagonal are adjusted for multiple tests.)
## values HRD_Cued_Uncued
## values 0.00 0.36
## HRD_Cued_Uncued 0.36 0.00
##
## To see confidence intervals of the correlations, print with the short=FALSE option#
subset_4_Grey2 <- combined_Grey2 %>%
filter(contrast == "contrast-Uncued-Cued")
subset_4_Grey2## # A tibble: 18 × 8
## participants contrast mask values HRD_Cued HRD_Uncued HRD_Cued_Uncued
## <fct> <fct> <fct> <dbl> <dbl> <dbl> <dbl>
## 1 sub-01 contrast-Uncue… mask… 0.839 -1.88 -4.05 2.17
## 2 sub-02 contrast-Uncue… mask… 0.572 -6.37 -5.43 -0.94
## 3 sub-03 contrast-Uncue… mask… 1.14 -3.84 -6.72 2.88
## 4 sub-04 contrast-Uncue… mask… 1.44 -2.39 -3.42 1.03
## 5 sub-05 contrast-Uncue… mask… 0.511 -3.16 -3.9 0.74
## 6 sub-07 contrast-Uncue… mask… 0.844 -6.58 -6.38 -0.2
## 7 sub-08 contrast-Uncue… mask… 0.608 -5.6 -5.37 -0.23
## 8 sub-11 contrast-Uncue… mask… -0.220 -4.81 -6.3 1.49
## 9 sub-12 contrast-Uncue… mask… 0.968 -4.26 -5.2 0.94
## 10 sub-13 contrast-Uncue… mask… 1.34 NA NA NA
## 11 sub-14 contrast-Uncue… mask… 0.642 -9.91 -10.0 0.14
## 12 sub-16 contrast-Uncue… mask… 0.816 -5.53 -5.4 -0.13
## 13 sub-17 contrast-Uncue… mask… 0.572 -2.06 -2.97 0.91
## 14 sub-18 contrast-Uncue… mask… 0.393 -10.4 -10.1 -0.3
## 15 sub-19 contrast-Uncue… mask… 0.842 NA NA NA
## 16 sub-20 contrast-Uncue… mask… 0.475 -4.26 -5.74 1.48
## 17 sub-21 contrast-Uncue… mask… 1.16 NA NA NA
## 18 sub-23 contrast-Uncue… mask… 0.734 NA NA NA
## # ℹ 1 more variable: HRD_Uncued_Cued <dbl>subset_4_Grey2 <- subset_4_Grey2[, c("values", "HRD_Uncued_Cued")]
shapiro.test(subset_4_Grey2$values) #normal##
## Shapiro-Wilk normality test
##
## data: subset_4_Grey2$values
## W = 0.95173, p-value = 0.4527shapiro.test(subset_4_Grey2$HRD_Uncued_Cued) #normal##
## Shapiro-Wilk normality test
##
## data: subset_4_Grey2$HRD_Uncued_Cued
## W = 0.95893, p-value = 0.7054subset_4_Grey2corr <- corr.test(subset_4_Grey2, use = "pairwise",method="pearson",adjust="none")
subset_4_Grey2corr## Call:corr.test(x = subset_4_Grey2, use = "pairwise", method = "pearson",
## adjust = "none")
## Correlation matrix
## values HRD_Uncued_Cued
## values 1.00 -0.18
## HRD_Uncued_Cued -0.18 1.00
## Sample Size
## values HRD_Uncued_Cued
## values 18 14
## HRD_Uncued_Cued 14 14
## Probability values (Entries above the diagonal are adjusted for multiple tests.)
## values HRD_Uncued_Cued
## values 0.00 0.54
## HRD_Uncued_Cued 0.54 0.00
##
## To see confidence intervals of the correlations, print with the short=FALSE optionggscatter(subset_4_Grey2, x = "values", y = "HRD_Uncued_Cued",
add = "reg.line", conf.int = TRUE,
cor.coef = TRUE, cor.method = "pearson",
xlab = "Bold Cued_Uncued", ylab = "HRD_Uncued_Cued")## Warning: Removed 4 rows containing non-finite values (`stat_smooth()`).## Warning: Removed 4 rows containing non-finite values (`stat_cor()`).## Warning: Removed 4 rows containing missing values (`geom_point()`).
sp.subset_4_Grey2corr <- corr.test(subset_4_Grey2, use = "pairwise",method="spearman",adjust="none")
sp.subset_4_Grey2corr## Call:corr.test(x = subset_4_Grey2, use = "pairwise", method = "spearman",
## adjust = "none")
## Correlation matrix
## values HRD_Uncued_Cued
## values 1.00 -0.27
## HRD_Uncued_Cued -0.27 1.00
## Sample Size
## values HRD_Uncued_Cued
## values 18 14
## HRD_Uncued_Cued 14 14
## Probability values (Entries above the diagonal are adjusted for multiple tests.)
## values HRD_Uncued_Cued
## values 0.00 0.36
## HRD_Uncued_Cued 0.36 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$p[2], 2),
round(subset_4_Grey2corr$p[2], 2))
all_p_values.Grey2## [1] 0.58 0.70 0.54 0.54# Apply FDR correction to combined all p-values
adjusted_p_Grey2<- p.adjust(all_p_values.Grey2, method = "fdr")
adjusted_p_Grey2## [1] 0.7 0.7 0.7 0.7