Data: feedback fMRI task

As an example, we read the Feedback task fMRI region of interest data using the read_csv() function from the readr package. This data comes shipped with the NeuroUp package in two forms: as an R dataset that you can directly call using feedback and as a csv file in the extdata/ folder. Here, we load the data from the csv-file to mimic the expected use-case that you have region of interest data in a similar format:

# load feedback.csv data located at local system 
feedback_csv <- (system.file("extdata/feedback.csv", package = "neuroUp"))

# use read_csv function to load csv-file as tibble
feedback_data <- readr::read_csv(feedback_csv)
#> Rows: 271 Columns: 4
#> ── Column specification ────────────────────────────────────────────────────────
#> Delimiter: ","
#> dbl (4): participant_id, age, mfg_learning, mfg_application
#> 
#> 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.

When you have a csv-file locally you can load it directly like this:

taskname_data <- readr::read_csv("path/to/filename.csv")

Estimate raw differences and Cohen’s d

estim_diff allows you to determine the sample size required to estimate differences in raw means and Cohen’s d’s for multiple sample sizes with a certain precision. Precision is presented in the form of a 95% highest density credible interval (HDCI), that is, the narrowest possible interval that is believed to contain the true value of the parameter of interest with a probability of .95 . The narrower this interval, the higher the precision with which the parameter is estimated.

In this example, we are interested in the contrast between the learning phase and the application phase in the atlas-based middle frontal gyrus during the feedback task. We use an existing dataset with a sample size of N = 271, and for all sample sizes between a minimum (e.g., N = 20) and maximum sample size of the sample at hand the estim_diff() function will compute the HDCI. To account for the arbitrary order of the participant in a data set, this is done for a set number of permutations (50 by default), of the participants in the data set. For each sample size, the average HDCI of all permutations is also calculated.

The following arguments need to be provided:

data should be a dataframe with the data to be analyzed (feedback_data in our example)
vars_of_interest should be a vector containing the names of the variables to be compared on their means (c("mfg_learning", "mfg_application") in our example)
sample_size is the range of sample size to be used (20:271 in our example)
k is the number of permutations to be used for each sample size. We will use a smaller number (20) than the default (50) to reduce the build-time of this vignette
name is an optional title of the dataset or variables to be displayed with the figures. We will use "Feedback middle frontal gyrus" in our example

We provide these arguments to the estim_diff() function and store it in a new object called feedback_estim. Before we do so, we set the seed value to make sure we get the same results when re-running the function.

set.seed(1234)

feedback_estim <- estim_diff(feedback_data,
                             c("mfg_learning", "mfg_application"), 20:271,
                             20, "Feedback middle frontal gyrus")

Explore `estim_diff()` output

The estim_diff() function provides several outputs that we can call to explore the results of the estimations. We will display the different outputs below.

fig_diff: scatterplot with HDCI’s for difference in raw means

First, we will plot fig_diff, which returns a scatterplot for the difference in raw means, where for five different sample sizes, 10 out of the total number of HDCI’s computed are displayed (in light blue). The average estimate with credible interval summarizing the total number of HDCIs for each sample size are plotted in reddish purple

feedback_estim$fig_diff

fig_nozero: barplot with proportion permutations not containing zero for difference in raw means

The second plot we will display is fig_nozero, which returns a barplot where for each of the five sample sizes the proportion of permutations not containing zero is displayed for the difference in raw means:

feedback_estim$fig_nozero

fig_cohens_d: scatterplot with HDCI’s for difference in raw means

Next, we will plot fig_cohens_d, which returns a scatterplot for Cohen’s d, where for five different sample sizes, 10 out of the total number of HDCI’s computed are displayed (in light blue). The average estimate with credible interval summarizing the total number of HDCIs for each sample size are plotted in reddish purple:

feedback_estim$fig_cohens_d

fig_d_nozero: barplot with proportion permutations not containing zero for difference in raw means

The final plot we will display is fig_d_nozero, which returns a barplot where for each of the five sample sizes the proportion of permutations not containing zero is displayed for Cohen’s d:

feedback_estim$fig_d_nozero

tbl_select & tbl_total: tibbles with estimates

The estim_diff() function also returns two tibbles with the values on which the previous plots are based.

tbl_select returns a tibble containing estimates of the difference in raw means and of Cohen’s d with associated SD, SE, 95% CI, and width of the 95% CI (lower, upper) for five different sample sizes (starting with the minimum sample size, then 1/5th parts of the total dataset). This is the summary data used to plot the figures:

feedback_estim$tbl_select
#> # A tibble: 55 × 13
#>    N     estimate variance stdev sterror lower upper cohens_d d_lower d_upper
#>    <fct>    <dbl>    <dbl> <dbl>   <dbl> <dbl> <dbl>    <dbl>   <dbl>   <dbl>
#>  1 20        3.21     3.07  1.75  0.392   2.45  3.98     1.89    1.24    2.54
#>  2 70        2.82     2.32  1.52  0.182   2.46  3.18     1.89    1.50    2.28
#>  3 120       2.89     2.41  1.55  0.142   2.61  3.17     1.88    1.64    2.13
#>  4 170       2.67     2.21  1.49  0.114   2.45  2.89     1.80    1.54    2.05
#>  5 271       2.83     2.32  1.52  0.0925  2.65  3.01     1.87    1.70    2.04
#>  6 20        2.97     2.60  1.61  0.360   2.27  3.68     1.91    1.49    2.34
#>  7 70        2.92     2.87  1.69  0.202   2.52  3.32     1.75    1.41    2.09
#>  8 120       2.68     2.44  1.56  0.143   2.40  2.96     1.75    1.48    2.02
#>  9 170       2.94     2.41  1.55  0.119   2.71  3.18     1.92    1.67    2.16
#> 10 271       2.83     2.32  1.52  0.0925  2.65  3.01     1.86    1.67    2.04
#> # ℹ 45 more rows
#> # ℹ 3 more variables: permutation <fct>, nozero <dbl>, d_nozero <dbl>

tbl_total returns a tibble containing estimates of the difference in raw means and of Cohen’s d with associated SD, SE, 95% CI, and width of the 95% CI (lower, upper) for all sample sizes, including the permutation number. This is the total (large) table with all the estimates for all requested sample sizes and permutations:

feedback_estim$tbl_total
#> # A tibble: 5,040 × 11
#>        N estimate variance stdev sterror lower upper cohens_d d_lower d_upper
#>    <int>    <dbl>    <dbl> <dbl>   <dbl> <dbl> <dbl>    <dbl>   <dbl>   <dbl>
#>  1    20     3.03     1.93  1.39   0.311  2.42  3.64     2.28   1.44     3.12
#>  2    21     3.24     3.68  1.92   0.419  2.42  4.06     1.80   0.754    2.85
#>  3    22     2.80     1.64  1.28   0.273  2.27  3.34     2.25   1.57     2.94
#>  4    23     2.84     2.53  1.59   0.331  2.19  3.49     1.84   1.25     2.43
#>  5    24     3.01     3.50  1.87   0.382  2.27  3.76     1.68   1.16     2.20
#>  6    25     2.71     2.20  1.48   0.296  2.13  3.29     1.86   1.28     2.44
#>  7    26     3.19     2.19  1.48   0.290  2.62  3.76     2.14   1.49     2.79
#>  8    27     2.75     2.60  1.61   0.310  2.14  3.36     1.82   1.19     2.45
#>  9    28     2.80     1.48  1.22   0.230  2.35  3.25     2.37   1.81     2.93
#> 10    29     2.67     2.36  1.54   0.285  2.11  3.23     1.80   1.35     2.24
#> # ℹ 5,030 more rows
#> # ℹ 1 more variable: permutation <int>

Estimate correlations

Data: gambling fMRI task

As an example, we read the Gambling task fMRI region of interest data using the read_csv() function from the readr package. Just like the Feedback data above, this data comes shipped with the NeuroUp package in two forms: as an R dataset that you can directly call using gambling and as a csv file in the extdata/ folder. Again, we load the data from the csv-file to mimic the expected use-case that you have region of interest data in a similar format:

# load gambling.csv data located at local system 
gambling_csv <- (system.file("extdata/gambling.csv", package = "neuroUp"))

# use read_csv function to load csv-file as tibble
gambling_data <- readr::read_csv(gambling_csv)
#> Rows: 221 Columns: 5
#> ── Column specification ────────────────────────────────────────────────────────
#> Delimiter: ","
#> dbl (5): participant_id, age, lnacc_self_win, lnacc_self_loss, lnacc_self_wi...
#> 
#> 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.

Estimate correlations: `estim_corr`

estim_corr allows you to determine the sample size required to estimate Pearson correlations for multiple sample sizes with a certain precision. Precision is presented in the form of a 95% highest density credible interval (HDCI), that is, the narrowest possible interval that is believed to contain the true value of the parameter of interest with a probability of .95 . The narrower this interval, the higher the precision with which the parameter is estimated.

In this example, we are interested in the correlation between age and activity in the anatomical mask of the left nucleus accumbens during the gambling task (winning for self > losing for self contrast). We use an existing dataset with a sample size of N = 221, and for all sample sizes between a minimum (e.g., N = 20) and maximum sample size of the sample at hand the estim_corr() function will compute the HDCI. To account for the arbitrary order of the participant in a data set, this is done for a set number of permutations (50 by default), of the participants in the data set. For each sample size, the average HDCI of all permutations is also calculated.

The following arguments need to be provided:

data should be a dataframe with the data to be analyzed (gambling_data in our example)
vars_of_interest should be a vector containing the names of the variables to be correlated (c("lnacc_self_winvsloss", "age") in our example)
sample_size is the range of sample size to be used (20:221 in our example)
k is the number of permutations to be used for each sample size. We will use a smaller number (20) than the default (50) to reduce the build-time of this vignette
name is an optional title of the dataset or variables to be displayed with the figures. We will use "Gambling NAcc correlation w/ age" in our example

We provide these arguments to the estim_corr() function and store it in a new object called gambling_estim. Before we do so, we set the seed value to make sure we get the same results when re-running the function.

set.seed(1234)

gambling_estim <- estim_corr(gambling_data,
                             c("lnacc_self_winvsloss", "age"), 20:221,
                             20, "Gambling NAcc correlation w/ age")

Explore `estim_corr()` output

The estim_corr() function provides several outputs that we can call to explore the results of the estimations. We will display the different outputs below.

fig_corr: scatterplot with HDCI’s for correlations

First, we will plot fig_corr, which returns a scatterplot for the difference in raw means, where for five different sample sizes, 10 out of the total number of HDCI’s computed are displayed (in green). The average estimate with credible interval summarizing the total number of HDCIs for each sample size are plotted in orange:

gambling_estim$fig_corr

fig_nozero: barplot with proportion permutations not containing zero for the correlations

The second plot we will display is fig_corr_nozero, which returns a barplot where for each of the five sample sizes the proportion of permutations not containing zero is displayed for the correlations:

gambling_estim$fig_corr_nozero

tbl_select & tbl_total: tibbles with estimates of the correlations

The estim_corr() function also returns two tibbles with the values on which the previous plots are based.

tbl_select returns a tibble containing estimates of the Pearson’s correlation between two correlated variables with associated SD, SE, 95% CI, and width of the 95% CI (lower, upper) for five different sample sizes (starting with the minimum sample size, then 1/5th parts of the total dataset). This is the summary data used to plot the figures:

gambling_estim$tbl_select
#> # A tibble: 55 × 6
#>    N     correlation   lower  upper permutation nozero
#>    <fct>       <dbl>   <dbl>  <dbl> <fct>        <dbl>
#>  1 20        0.0708  -0.384  0.498  1               NA
#>  2 60        0.130   -0.128  0.372  1               NA
#>  3 100      -0.00247 -0.199  0.194  1               NA
#>  4 140      -0.0982  -0.260  0.0688 1               NA
#>  5 221      -0.0405  -0.172  0.0920 1               NA
#>  6 20       -0.242   -0.618  0.225  2               NA
#>  7 60        0.233   -0.0224 0.460  2               NA
#>  8 100      -0.0800  -0.272  0.118  2               NA
#>  9 140      -0.0827  -0.245  0.0844 2               NA
#> 10 221      -0.0405  -0.172  0.0920 2               NA
#> # ℹ 45 more rows

tbl_total returns a tibble containing estimates of the Pearson’s correlation between two correlated variables with associated SD, SE, 95% CI, and width of the 95% CI (lower, upper) for all sample sizes, including the permutation number. This is the total (large) table with all the estimates for all requested sample sizes and permutations:

gambling_estim$tbl_total
#> # A tibble: 4,040 × 5
#>        N correlation   lower  upper permutation
#>    <int>       <dbl>   <dbl>  <dbl>       <int>
#>  1    20      0.0708 -0.384  0.498            1
#>  2    21     -0.377  -0.696  0.0649           1
#>  3    22     -0.176  -0.556  0.266            1
#>  4    23     -0.246  -0.597  0.185            1
#>  5    24     -0.180  -0.544  0.241            1
#>  6    25      0.0508 -0.351  0.437            1
#>  7    26     -0.229  -0.566  0.174            1
#>  8    27     -0.119  -0.478  0.273            1
#>  9    28      0.288  -0.0950 0.597            1
#> 10    29      0.0859 -0.290  0.439            1
#> # ℹ 4,030 more rows

Introduction to neuroUp