👋 will this package support your connectomics with 🦓CEBRA approach?

[MMathisLab]

Hey @timonmerk et al, firstly thanks SO MUCH for using CEBRA in your awesome paper, https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10543023/pdf/nihpp-rs3212709v1.pdf. I am currently working on teaching material, and I was wondering if you have a demo notebook, or such, for how you used CEBRA in your paper?

" In brief, voxel-wise correlations between decoding performance and whole-brain connectivity maps seeded from channel MNI coordinates were calculated to identify an optimal connectomic template fingerprint for movement decoding (so called connectomic decoding network map) across all subjects (Fig. 2i). This allows for an optimized a priori channel selection in realtime, by identifying the individual recording channel that has most network overlap with the optimal template. Finally, we have transformed neural features from the selected channel into a lower dimensional embedding. For this, a five-layer convolutional neural network with a temporal filter length of 1 s was trained using the InfoNCE (Noise-Contrastive Estimation) contrastive loss function. The resulting embeddings showed exceptionally high consistency across subjects as investigated with linear identifiability"

i.e., j and k here:

Sorry if I missed it in the code, but a quick serach doesn't have cebra in this code base! Not a worry if you are waiting for formal publication, etc -- totally get it -- just figured I'd ask 🥰. Thanks again for using CEBRA -- truly one of my fav. uses so far 🦓🚀