I have a question concerning the strategy to adopt with feature selection.
We ran a fMRI experiment with young adults (25-35) and adolescents (14-17). In this experiment, subjects were asked to observe social and transitive actions, we were interested in the development of the action observation network especially during adolescence.
We work with several a-proiri (functional) ROI that came from a previous meta-analysis ran in the team. Then we conducted univariate and multivariate analyses in those ROIs.
Each ROI are transformed from the MNI-space to the native space of each of my subject in order to make MVPA classification. However, I have ROIs with different number of voxels and I think it could biais the results.
First, I masked each ROI with the mask of grey matter (obtain from my segmentation; CAT12) in order to consider only voxels containing grey matter in the classification. But now, I want to make comparable my analyses across my subjects. Is it important to have the same number of voxels for each ROI? for each subject?
My question is: What is usually used in the litterature in order to compare ROIs with comparable voxels number across participants?
Do I have to select arbitrarily a number of voxels equally for each subject and ROI with the risk of supressing valuable information that is more widespread in certain ROI (e.g., Lateral Occipital Cortex)?
Is there a way to select the same number of voxels in each ROIs based on their “importance” on the classifier and how can I do that with nilearn?
Thank you very much for your helpful answers!