With nilearn, is it possible to run 1st level analysis in native epi space, but do the 2nd level in standard space?

Hi,

Assuming you are doing a standard voxel-level GLM, then you will need your 1st level maps in standard space, otherwise you wonβt have voxel-wise correspondance across subjects. You can of course still run 1st level maps in native space if helps for visualization purposes.

Steven

Thanks @Steven. In nilearn, `compute_contrast()`

has several types of output: β`z_score`

β, β`stat`

β, β`p_value`

β, β`effect_size`

β, β`effect_variance`

β. 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 βsignificantβ 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 β`cmaps`

β (for example, here)

Are these the maps generated with `second_level_model.compute_contrast(output_type="effect_size")`

?

They are typically first-level effect_size maps.