Latest work at #EUSIPCO25!🚀
We bring Topological Signal Processing to the brain 🧠 — showing that edge-based approaches outperform classical GSP in task decoding.

Great collab with M.Nurisso & @lordgrilo.bsky.social 🙌
Paper 👉 eusipco2025.org/wp-content/u...

#Neuroimaging #SignalProcessing #TSP

12/ One standout: topological scaffolds. They act as integrators — capturing synergy in nodes, and structure-function coupling at edges. They're central in linking connectivity patterns to behavior.

11/ Even dopamine levels relate: lower dopamine -> more flexible task-related reconfiguration.

10/ HOIs also help explain how the brain reconfigures during different tasks. Social/emotional tasks show more synergy, while sensorimotor ones lean on redundancy.

8/🧠 For task decoding, though, classic FC is still one of the top methods.

7/ When it comes to applications, HOIs show their power:

🎯 For brain fingerprinting (ID'ing individuals), HOI metrics outperform classical functional connectivity (FC)

6/ But there's more.

These metrics also reflect the brain’s neurochemistry:
• Redundant metrics correlate with metabolic maps
• Synergistic & topological metrics align with receptor distributions

5/ Despite different math, rank differences of all HOI metrics align with the brain's core hierarchy: from sensory (unimodal) to associative (transmodal) cortex.

This "HOI axis" reflects fundamental computational principles embedded in the brain’s layout. 🧭

4/ First big insight: HOI metrics fall into 3 categories:

🔴 Redundant: capture overlapping info (e.g., within sensory networks)
🔵 Synergistic: capture integrative info (e.g., across systems)
🟣 Topological: bridge the two, identifying mesoscale structures

🧠 Want to understand how different higher-order methods compare in brain connectivity analysis?

Check out our new preprint — a fantastic collab with past & present NPL members @lordgrilo.bsky.social :
🔗 www.biorxiv.org/content/10.1...
A thread🧵