Andrea Santoro

Renormalization in the brain?
With @andreasantoro.bsky.social and @jesseba.bsky.social we're organizing a workshop at @cosynemeeting.bsky.social (Lisbon, March 16) on how coarse-graining principles from physics can explain neural computation across scales. nplresearch.github.io/neurorenorm2...

La matematica del sapore, si può indovinare l’origine di una ricetta dalla combinazione dei suoi ingredienti? Uno studio di migliaia di ricette da tutto il mondo mostra come diverse cucine seguano schemi ricorrenti. E come questi influenzino le nostre preferenze alimentari

🇮🇹 Un articolo di Davide Risso su Wired Italia racconta il nostro lavoro su reti di ingredienti e combinazioni per caratterizzare cucine del mondo:

www.wired.it/article/mate...

@wired.it @saumitranetsci.bsky.social @andreasantoro.bsky.social

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

Next week (on the 15th and 16th), with @yasserroudi.bsky.social and Federico Turkheimer we will organize a workshop on the "The Topology of Human and Synthetic Minds"

Please do join us!
(Registration is free but required for access/catering!)

lordgrilo.github.io/topology-nat...

🚨🚨 New preprint just dropped! In collaboration with @colltoaction.bsky.social, Cliff Joslyn, @fralotito.bsky.social, Audun Myers, Joshua Pickard, Brenda Praggastis, and Przemysław Szufel, we define a new data sharing standard for higher-order networks. arxiv.org/abs/2507.11520

Thrilled to finally share a huge piece of my PhD project!
Our paper “The Topological Architecture of Brain Identity” is now out on bioRxiv: biorxiv.org/content/10.1... 🧵 1/n

Huge thanks for this fun collaboration to past and present members of the NPLab — Matteo Neri, @poetz.bsky.social, Davide Orsenigo, Matteo Diano, @marilyngatica.bsky.social, @lordgrilo.bsky.social 🙌🧠

14/ This work takes an important step toward a unified roadmap for navigating the complex, fragmented landscape of higher-order brain connectivity.

13/ In short:

✅ HOIs provide richer, biologically grounded insights into brain function
✅ The research landscape is now organized — synergy, redundancy, topology
✅ HOIs better capture traits; FC better captures states
✅ Tools are available for broader use (HOI toolbox, TDA code, etc.)

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.

9/ This shows a key trade-off:

🔍 If you want to understand who a person is (trait-level): use HOIs
⚡ If you want to know what they're doing (state-level): FC is often enough

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

3/ Yet with all these new tools — PhiID, O-information, PED, persistent homology, triangles, scaffolds — no one knew how they compared, what they captured, or when to use which.

This paper tackles this directly with a comprehensive comparison across 10 HOI metrics.

2/ Brain models often focus on pairs of regions, but real interactions might involve groups acting together

HOI methods aim to capture this, from detecting synergy and redundancy using different information-theoretic approaches to mapping topological cycles in brain networks.

🧵 1/ Over the past years, brain connectivity research has moved beyond simple pairwise interactions. A wide range of higher-order interaction (HOI) methods — from information theory to topological data analysis — have emerged.

But the field is quite fragmented. Let's dive in. 🧠

🧠 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🧵

The topology of synergy: linking topological and information-theoretic approaches to higher-order interactions in complex systems The study of irreducible higher-order interactions has become a core topic of study in complex systems. Two of the most well-developed frameworks, topological data analysis and multivariate informatio...

New pre-print just dropped, with @doctorjosh.bsky.social, @alicepatania.bsky.social, and Pedro Mediano.

In it we ask: when an algebraic topologist and an information theorist talk about "higher-order" structure, are they talking about the same thing?

A 🧵1/n

arxiv.org/abs/2504.10140

Connectome architecture favours within-module diffusion and between-module routing.

Really cool multi-species work from Caio Seguin on module-defined communication policies in connectomes!

www.biorxiv.org/content/10.1...

Pandemic monitoring with global aircraft-based wastewater surveillance networks - Nature Medicine By simulating the implementation of airport-based wastewater surveillance sites at the global level, a modeling study shows how this early warning system would perform in identifying sources of pandem...

Wastewater from airplane toilets?
We introduce a global Aircraft-Based Wastewater Surveillance Network (WWSN) for pandemic monitoring in Nature Medicine 🔗 doi.org/10.1038/s415...

Aircraft-based wastewater surveillance allows for real-time, non-invasive monitoring of global pathogen spread
Short 🧵

Thrilled to announce that I've been awarded a #MSCA fellowship! I'll continue my work at CENTAI in Turin with @lordgrilo, while collaborating with the NERV team in Paris with @DeVicoFallani at the intersection of neuroscience & higher-order interactions. Thanks to everyone who supported me!

Also! The symposium on higher-order interactions, chaired by @manishsaggar.bsky.social and @lordgrilo.bsky.social, was accepted.

Absolute must-attend if you're at OHBM 2025, with talks from: @popeme.bsky.social, @andreasantoro.bsky.social, and @novelli-leo.bsky.social!

@ohbmofficial.bsky.social

And here we go! First position on my ERC grant is officially open. Fully funded 3.5 years PhD position at NetSI London working with me and the @nplab.bsky.social on modeling and reconstructing higher-order systems.
Application deadline: 16th February 2025.
Bring your A game!

Higher-order connectomics of human brain function reveals local topological signatures of task decoding, individual identification, and behavior - Nature Communications Here, the authors perform a higher-order analysis of fMRI data, revealing that accounting for group interactions greatly enhances task decoding, brain fingerprinting, and brain-behavior associations c...

Higher-order connectomics of human brain function reveals local topological signatures of task decoding, individual identification, and behavior www.nature.com/articles/s41...

9/n This topological perspective parallels ongoing research efforts using information-theoretic approaches, emphasizing the importance of considering complex, multi-region interactions to truly understand brain function. 🌌