Thank you 😊 That’s a brilliant idea, I’ll be in touch!

So kind, thank you Valerie! 😊

Thank you for taking the time to check this out, and for sharing your inspiring work for the community to benefit from and build upon! 😊

Have I missed your favorite subcortical/cerebellar/brainstem atlas?? 🥲
Feel free to create an issue suggesting one, or create your own vectorized map (& hopefully share with other researchers via repo PR 🙏) following the guide in the preprint + project website!
Repo: github.com/anniegbryant...

These plots can be readily integrated with cortical surface visualizations created using the fantastic ggseg package (github.com/ggseg/ggseg) led by @drmowinckels.io, as shown here with a GABAA-a1 PET map from Hansen et al. 2026: doi.org/10.1007/s004...

Live your best data viz life below the cortical surface, with pre-vectorized atlases ready to apply to subcortical/cerebellar stats from neuroimaging, transcriptomics, cytoarchitecture, etc 💅 demo'ed here on neurotransmitter PET receptor maps from Justine Hansen & co in @misicbata.bsky.social group

Thank you all for the great chats about this work and the inspiring ideas for where to go next with this project!

And a big thank you to @bendfulcher.bsky.social and @macshine.bsky.social for your guidance and support throughout this project, as with all others of my PhD 😊

Big shout-out to @jlizier.bsky.social, whose patience, dedication, and guidance in supervising me through this project have been invaluable 🙏 Thank you also to our other fab co-authors for their insightful input as always: Oliver Cliff, @macshine.bsky.social, and @bendfulcher.bsky.social!

I learned so much through working on this piece! My goal with this is to share a beginner-friendly and intuitive guide for all my fellow visual learners out there, that also doesn’t shy away from the maths and important nitty-gritty details 😊

Unbiased opinion, but I’m really interested in how we can get the most bang for our info theory buck in computational neuroscience 🧠 So, we demo the implementation and interpretation of all 11 measures with an illustrative BOLD fMRI case study:

We organized 11 measures into 6 categories according to temporal dependency and the type(s) of dynamical properties each measure captures. These measures are summarized in one large ‘pocket reference’ schematic, and we then zoom in to discuss the ‘what/how/why’ of each measure individually 🔎