In group ICA, when do spatial ICA, apart from extracted spatial component there are also corresponding temporal component representing some common / average time courses presenting in all subjects. Is there a way to get similar temporal component in canICA in nilearn? (I didn’t find it)
From this paper comparing different ICA versions, probably CanICA can only give spatial maps?
after fitting the model, the CanICA.components_ attributes will give you the spatial components while CanICA.transform(data) will project the data in the reduced representation (shape : (n_samples, n_components)), which I guess, is what you’re looking for.
Thanks for your response! After trying out, I think this CanICA.transform(data) is related to what I need. However, the returned list has n_subject entries, each with a (n_temporal, n_component) array, which means for each subject, it has a unique temporal component corresponds to one spatial component? If I want a common temporal component among all subjects corresponding to a spatial component when do spatial CanICA, is it done by simply averaging all the temporal components?
Hi @neo –
Just to add on: yes, you would expect to see a unique subject-level temporal component (time series) for each group-level spatial component (brain map). The idea is that this shared spatial component differentially contributes to each subject’s overall activity pattern.
If instead you wanted a shared group-level temporal component, you would then expect to see unique subject-level spatial components. This is temporal ICA.
Neither temporal nor spatial ICA will give you shared spatial components with a shared temporal component. This would suggest that all of your subjects had the same activity patterns !