With nilearn, is it possible to run 1st level analysis in native epi space, but do the 2nd level in standard space?
Assuming you are doing a standard voxel-level GLM, then you will need your 1st level maps in standard space, otherwise you won’t have voxel-wise correspondance across subjects. You can of course still run 1st level maps in native space if helps for visualization purposes.
Thanks @Steven. In nilearn,
compute_contrast() has several types of output: ‘
effect_variance’. Which of these should I bring to standard space?
effect_size is what should be input into your second level maps
Thanks @Steven. By the way, what are the other images used for? I suppose they are most useful for second level analysis?
stat is the t-stat images, which relate to effect sizes, but should not normally be used as input to second level analyses.
z_score is the normalized t-stat image.
p_value is the significance based on these statistics.
effect_variance relates to the error.
Indeed, you can output these images for the second level analyses. The stats/p-val images may be thresholded to find “significant” activation. But the input into second level models should usually be effect size maps from your 1st level models.
Hi @Steven, in most tutorials, the maps brought to the second level are called “
cmaps” (for example, here)
Are these the maps generated with
They are typically first-level effect_size maps.