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As a FTP we used a multivariate regressor (see Methods) that can predict the time until the next relapse with a resolution of a few days.
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Instead of including home country fix effects, targeted regressions are run using "coming from EU member states" as additional regressor (see Table 5).
Since the selective reminding task permitted separate calculation of older and recent correctly retrieved names two different regression analyses were run, one for each regressor (see Results section).
Given that the statistical model included a parametric regressor coding for Go response time (that had a low correlation with the stop-signal probability regressor, see Methods), these stop-signal anticipation-related activations were not confounded by trial-to-trial variations in response speed.
In addition to the regressors of interest we included a series of nuisance regressors (see above).
In order to eliminate any possible visual activations induced by eye blinks or changes in pupil size, we also modeled those parameters with 2 additional regressors (see also Ruff et al. 2006, 2008), ensuring that any variance due to those eye parameters was partialed out in the GLM analysis and so could not contribute to significant results for the other regressors of interest.
A multivariate regressor approach detailed below was used to relate changes in EPI intensity to differences in task characteristics [66].
We then analyzed all phenotypes using a multivariate approach (see figure 4).
These variables were excluded from the multivariate analyses (see below).
The PDC is a multivariate spectral measure used to determine the directed influences between any given pair of signals in a multivariate data set (see also Methods S5).
quad end{aligned} (21 It is also easy to directly verify that the coefficient formulas result in (22), the fundamental linear MMSE property, i.e., the uncorrelatedness of the errors with the data regressor Z, see Wikipedia 2013.
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CEO of Professional Science Editing for Scientists @ prosciediting.com