dorianps

What are A_1 and A_2 in this case, different components?

I am a bit confused, maybe I should read more. My understanding is that, no matter what sparseness we choose (positive or negative, signed vs. unsigned weights), the canonical correlation...

Ok, good to know we are on the same page. But by this example, any relationship between A and B columns can be found simply by knowing the weight signs...

Maybe we can have a chat about this approach. So far I have allowed LESYMAP to search for negative sparseness (aka signed weights) and things seem to work well (also...

And this is the code I use to orient the weights of voxels in the correct relationship with behavior, related to this topic. https://github.com/dorianps/LESYMAP/blob/master/R/lsm_sccan.R#L211-L214

Thanks for following up. In the LESYMAP case we have (e.g.) 100,000 columns on one side (voxels) and 1 column on the other side (behavior), which should at least resolve...

I assume the problem with example you showed is that all inds2 voxels should be negatively correlated with behavior, most of which are but not all. Got the point. SCCAN...

If I remember correctly, these cases benefit by adding a brain+skull registration mask with the -f argument: -f: Brain extraction registration mask Mask used for registration to limit the metric...

Hey Phil, looks like you are including a bit of the edge of the removed face, which may confuse the registration, right? On Thu, Sep 30, 2021 at 9:56 PM...

I see. The skull edge is good to have actually. I only thought the anterior area by the face, which is included in the mask, may be awkward to register....