gambling

A dataset containing the parameter estimates of
the anatomical mask of the left nucleus accumbens
(Harvard-Oxford subcortical atlas; thresholded at 40%; 28 voxels included),
with one value for the mean activation during winning and one value for the
mean activation during losing for all participants.

datasets

Calculate the precision in mean differences (raw or Cohen's D) and
correlation coefficients for different sample sizes. Uses permutations of the
collected functional magnetic resonance imaging (fMRI) region of interest data.
Method described in Klapwijk, Jongerling, Hoijtink and Crone (2024)
<doi:10.31234/osf.io/cz32t>.

Eduard Klapwijk

neuroUp

Plan Sample Size for Task fMRI Research using Bayesian Updating

Herbert Hoijtink

Joran Jongerling

gambling function

A data frame with 221 rows and 5 variables:<dl>
<dt>participant_id</dt>
<dd>unique id for every participant</dd><dt>age</dt>
<dd>age in years (11.94-28.46)</dd><dt>lnacc_self_win</dt>
<dd>parameter estimates for the left nucleas accumbens during
winning (-2.78-3.41)</dd><dt>lnacc_self_loss</dt>
<dd>parameter estimates for the left nucleas accumbens during
losing (-3.84-3.28)</dd><dt>lnacc_self_winvsloss</dt>
<dd>parameter estimates for the left nucleas accumbens for
the contrast winning versus losing (-2.60-4.47)</dd>
</dl>

Format

Gambling task fMRI region of interest data — gambling

A data frame with 221 rows and 5 variables:<dl>
<dt>participant_id</dt>
<dd>unique id for every participant</dd>

<dt>age</dt>
<dd>age in years (11.94-28.46)</dd>

<dt>lnacc_self_win</dt>
<dd>parameter estimates for the left nucleas accumbens during
winning (-2.78-3.41)</dd>

<dt>lnacc_self_loss</dt>
<dd>parameter estimates for the left nucleas accumbens during
losing (-3.84-3.28)</dd>

<dt>lnacc_self_winvsloss</dt>
<dd>parameter estimates for the left nucleas accumbens for
the contrast winning versus losing (-2.60-4.47)</dd>


</dl>

gambling: Gambling task fMRI region of interest data

Description

Usage

Arguments

Format