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Andrea Santoro

@andreasantoro.bsky.social
176 followers 168 following 36 posts

Researcher @ ISI Foundation - MSCA Fellow

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Andrea Santoro @andreasantoro.bsky.social · 06/10/2026
7/9 Valence did not reliably transfer. But the relative geometry of affective states stayed aligned across datasets, and connectivity preserved it substantially better than regional BOLD activity.
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Andrea Santoro @andreasantoro.bsky.social · 06/10/2026
4/9 🎯 Fine-grained emotion: the homological scaffold best tracked recurrent emotional states in 13 of 14 films. It also most accurately predicted the full 50-feature affective profile.
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Andrea Santoro @andreasantoro.bsky.social · 06/10/2026
1/9 🧠🎬 How does the brain represent emotion as it continuously unfolds? Our new preprint finds a division of labour across neural scales: more complex is not always better, and the most informative view of brain activity depends on how finely emotion is described. Link in 🧵
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Andrea Santoro @andreasantoro.bsky.social · 09/09/2025
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
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Andrea Santoro @andreasantoro.bsky.social · 27/06/2025
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.
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Andrea Santoro @andreasantoro.bsky.social · 27/06/2025
11/ Even dopamine levels relate: lower dopamine -> more flexible task-related reconfiguration.
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Andrea Santoro @andreasantoro.bsky.social · 27/06/2025
10/ HOIs also help explain how the brain reconfigures during different tasks. Social/emotional tasks show more synergy, while sensorimotor ones lean on redundancy.
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Andrea Santoro @andreasantoro.bsky.social · 27/06/2025
8/🧠 For task decoding, though, classic FC is still one of the top methods.
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Andrea Santoro @andreasantoro.bsky.social · 27/06/2025
7/ When it comes to applications, HOIs show their power: 🎯 For brain fingerprinting (ID'ing individuals), HOI metrics outperform classical functional connectivity (FC)
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Andrea Santoro @andreasantoro.bsky.social · 27/06/2025
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
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Andrea Santoro @andreasantoro.bsky.social · 27/06/2025
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. 🧭
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Andrea Santoro @andreasantoro.bsky.social · 27/06/2025
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
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Andrea Santoro @andreasantoro.bsky.social · 27/06/2025
🧠 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🧵
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