Many of you asked how we create those pretty whole-brain plots with the cortical outline. We just made it available as a Python package: pni-lab.github.io/quickbrain/

The brain’s “default mode” and “action mode” networks are two sides of the same attractor. Encoding a macro-scale Bayesian prior that biases processing toward internal or external drive.

Looking forward to the colloquium in Bochum next Wednesday! www.ini.rub.de/events/

Our work with Karl Friston on Self-Orthogonalizing Attractor Neural Networks is now out in Neurocomputing! What does this theoretical model mean for our understanding of the brain? I’ve mapped out the key neuroscience implications below. Read the thread for a neuroscience walk-through ↓

Really exciting to see my pTFCE work re-implemented in Python and taken even further by an independent group. Great to know pTFCE is in good hands! github.com/Don-Yin/pytfce arxiv.org/abs/2603.11344

Out in @elife.bsky.social: Functional connectivity-based attractor dynamics of the human brain in rest, task, and disease doi.org/10.7554/eLif...

Brain attractors are approximately orthogonal to each other, suggesting that the brain may function as a self-orthogonalizing attractor network. Check out our revised manuscript about functional connectivity-based brain attractor dynamics in @elife.bsky.social. Link in comment!

Replicability of BWAS with functional and structural MRI has been hotly debated. But what about DWI? Our new paper - first authored by @rkotikalapudi.bsky.social - shows that multivariate DWI models of trait-like phenotypes can be replicable, even with moderate sample sizes. 🔗 Link in comment!

Serious concerns about a new cortical biomarker for pain sensitivity jamanetwork.com/journals/jam... We (with @tspisak.bsky.social, @christianbuchel.bsky.social) published a commentary on Chowdhury, Bi et al. (2025, JAMA Neurology) raising serious concerns about their reported results. 👇 1/13

As many of you know, I’ve been fascinated by brain attractor dynamics lately. Thrilled to share a new preprint on their link to orthogonal neural representations, co-authored with Karl Friston: arxiv.org/abs/2505.22749 - with implications for both neuroscience & AI! First in a series - stay tuned!

🚨 New paper out in GigaScience! To avoid common pitfalls in multivariate modeling: combine external validation with pre-registration — freeze your model before testing. For the pros: decide on the fly when to stop training! First-authored by the brilliant @ggallitto.bsky.social