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Two contrasts of interest were: LE versus HE, HES (effects of energy density) and HE versus HES (method of increasing energy density).
Next, we computed two contrasts of interest: all food pictures > non-food pictures; high-energy food pictures > non-food pictures.
Second, we tested for voxels that showed higher or lower activity in two contrasts of interest (difficult-to-easy: incon_CON > con_CON; easy-to-difficult: con_INCON > incon_INCON).
To demonstrate this point, we divided the deprived cortex ROI into two sub sections – SI and M1 (while excluding all voxels along the central sulcus, see below), and calculated mean beta values within each sub-region for the two contrasts of interest (hand and arm movements vs. feet movements).
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For each subject, the value of the maximum contrast of parameter estimate (COPE) was extracted for the four structures and the four contrasts of interest, using the FSL Featquery tool in FSL.
Three contrasts of interest were computed and tested.
Subsequently, we performed moderated t-tests over the four contrasts of interest.
Of these, a total of 733 probes showed differential gene expression signals between at least one of the three contrasts of interest, namely PvH, PvS and HvS.
For each of the five contrasts of interest a single model was used to examine correlations with the selected scores from the PANSS.
For each subject, one contrast of interest was computed: lexical (related+unrelated) vs. baseline.
This story gives us two contrasting ways of thinking of liberty.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com