Tamas Spisak
@tspisak.bsky.social
Neuroscience Professor at UK Essen, Germany
💐✨ Huge congrats to Dr. Balint Kincses ✨💐 He has received the IFORES Career Kickstart grant for "Layers of pain: uncovering the mechanisms in the cortical integration of bottom-up and top-down processes in pain perception and its modulation using ultra-high field fMRI" @elh-institute.bsky.social
More than words: Experience shapes #placebo pain relief through additional brain pathways 🧠 We're excited to share our latest publication in @natcomms.nature.com! 🤩 🔗 Read the paper: rdcu.be/fuxmn #neuroskyence #PsychSciSky #Neuroscience #PainResearch #BrainImaging #TeamScience #Reproducibility
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.
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
GitHub - Don-Yin/pytfce: Fast probabilistic Threshold-Free Cluster Enhancement in Python
Fast probabilistic Threshold-Free Cluster Enhancement in Python - Don-Yin/pytfce
github.com
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
Concern About Predictive Performance of a Pain Sensitivity Biomarker
To the Editor Chowdhury et al1 evaluated a biomarker for pain sensitivity, combining peak alpha frequency and corticomotor excitability. The authors report outstanding performance (validation set area...
jamanetwork.com
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!
Self-orthogonalizing attractor neural networks emerging from the free energy principle
Attractor dynamics are a hallmark of many complex systems, including the brain. Understanding how such self-organizing dynamics emerge from first principles is crucial for advancing our understanding ...
arxiv.org
🚨 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
GigaScience Journal@gigascience.bsky.social · last yr.A new approach for transparent reporting of prospective predictive modeling studies involving preregistration of machine learning models. External validation of machine learning models—registered models and adaptive sample splitting doi.org/10.1093/giga...
