Hi everyone,

I was hoping to create a functional connectivity matrix using nilearn, where each index of the matrix corresponds to the correlation strength between a pair of voxels between two ROI’s. So, I have ROI 1 (i/e PFC) and ROI 2 (i/e PPC) and each voxel would correspond to the correlation strength between each possible pair of voxels between the two ROI’s.

Would it be easiest to combine the two ROI’s (so a PFC + PPC ROI) and a connectivity matrix of all the voxels within the ROI?

Any tips to go about this? Thanks so much!

Hi,

You can extract the voxel-level time courses for each of the ROIs with a `NiftiMasker`

object, which will give you a set of time courses per ROI. You can then input these time courses to the `ConnectomeMeasure`

that is relevant to you. You probably want to use a concatenation of all the time courses to compute the cross-ROI connectivity term.

Does that answer your question ?

Best,

Bertrand

It does - thank you! Sorry for the late response.

I was wondering - do you have an idea of how to mark voxel coordinates on the resulting connectivity matrix?

You mean, when plotting the connectome as a matrix ? You can use Computing a connectome with sparse inverse covariance - Nilearn

HTH,

Bertrand