Second level analysis from native epi maps with nilearn?

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

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Hi,

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.

Steven

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Thanks @Steven. In nilearn, compute_contrast() has several types of output: ‘z_score’, ‘stat’, ‘p_value’, ‘effect_size’, ‘effect_variance’. Which of these should I bring to standard space?

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effect_size is what should be input into your second level maps

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Thanks @Steven. By the way, what are the other images used for? I suppose they are most useful for second level analysis?

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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.

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Hi @Steven, in most tutorials, the maps brought to the second level are called “cmaps” (for example, here)
Are these the maps generated with second_level_model.compute_contrast(output_type="effect_size")?

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They are typically first-level effect_size maps.