24/04/2026, PhD in Cognitive and Brain Sciences, cum laude 🎓 @cimecunitrento.bsky.social @iitalk.bsky.social What a wonderful journey life is. I am so grateful that I’m still out of words..I’ll let these pictures speak. And yes, my lab really did build me a cardboard Iconeus machine!!

📢 New preprint from the lab🧠 ▶️doi.org/10.64898/2026.03.12.710517 What does fMRI connectivity actually reflect at the neural level? The natural intuition is: more neural activity = more connectivity! Using cortical perturbations we show this is not necessarily the case: sometimes less is more! 👇🧵

1️⃣We find that cortical excitability inversely relates to fMRI connectivity. 2️⃣These changes are predicted by low-frequency (<4 Hz) LFP coherence. 3️⃣Biophysical modeling shows that these effects arise from the interaction between local firing changes and shared slow brain-wide LFP power fluctuations.

🧠What if increasing neuronal firing could actually reduce fMRI connectivity? 📡What drives long-range fMRI connectivity? I’m very excited to share that my PhD work is now out as a preprint Check this out! 👇 doi.org/10.64898/202...

Finally, huge credit to soon-to-be-PhD @chiarapepe.bsky.social who led this work with exceptional rigor and vision, and to @jcmariani.bsky.social who co-led and shaped the project throughout. And grateful to @erc.europa.eu and @iitalk.bsky.social for supporting this research! 14/14

If you’d like to play with these data or run your own analyses, all datasets (fUSI + fMRI) and the full analysis code are openly available here zenodo.org/records/1848... 🚀 13/n

Take-home message: you can map the mouse functional connectome easily and conveniently with transcranial fUSI. As a long-time fMRI user, I’m genuinely struck by how much lower the technical and infrastructural barrier is compared with MRI! 12/n

Best part: the state transitions aren’t random! They form a structured grammar that converges onto three stable attractor-like co-activation modes, matching hallmark fMRI dynamics. 12/n

But static maps are only half the story. Resting brain activity moves through states. fUSI captures this too: activity falls into recurring co-activation patterns, organized into paired opposites (CAP/anti-CAPs). 11/n

But we didn’t stop at one correlation number. We found that structure–function coupling is best understood as four major axes, i.e., 4 dominant “wiring-to-function motifs” shaping the whole connectome 10/n

Next question (the fun one): are these fUSI networks actually grounded in anatomical wiring? Yes: functional connectivity aligns with the structural connectome, strongly and systematically 9/n

But fUSI isn’t just “fMRI-lite”. It has its own signature: connectivity patterns are way stronger and reproducible in cortical regions than fMRI, but are more focal and less robust subcortically (so depth sensitivity matters!) 8/n

Head-to-head against matched rsfMRI, the agreement is very encouraging: we recovered the same large-scale networks with highly similar spatial topographies. This cross-modal convergence is the key “trust test” for fUSI as a connectomics tool! 7/n

First payoff: fUSI cleanly recovers the familiar “cast of characters” in functional connectome mapping, including a default-mode network. The connectivity matrix also shows clear modular organization and left–right symmetry, exactly what you’d expect from a connectome-scale signal 6/n

Next challenge: resting-state networks in rodents are fragile. We thus identified a very simple and reliable protocol that maintains long-range connectivity while preserving slow, intrinsic arousal fluctuations (tracked via pupil dynamics) 5/n

To obtain large-scale connectome level coverage, we cycled across multiple brain planes repeatedly, fast enough to rebuild a brain-wide picture over time 4/n

We devised a non-invasive, transcranial protocol (no cranial window), and recorded spontaneous activity to reconstruct whole-brain functional connectivity from those fluctuations. We did so using an fMRI-inspired image preprocessing and analyses pipeline tuned for multi-slice fUSI 3/n

fUSI is an emerging hemodynamic imaging modality that’s quiet, portable, fast, and high-resolution. However, to date the application of fUSI in rodents has mostly mapped evoked activity. So we asked: can fUSI recover the same large-scale network structure we trust from rsfMRI? 2/n

🚨Do we need a large, expensive MRI scanner to map whole-brain networks in the mouse? Not necessarily! In our new preprint ▶️ tinyurl.com/3a2h4r9f we show that transcranial functional ultrasound (fUSI) can map connectome-scale networks with striking agreement to fMRI 🧵👇

🐭🧠 Can fUSI truly map canonical mouse resting-state networks — and how does it compare to fMRI? I’m excited to share the work of my PhD in our new preprint!👉 doi.org/10.64898/202... Go check it out — it’s time to expand your neuroimaging toolkit 🧠🚀

Best Last-Year PhD Abstract Award in LifeTech! 🎉 A big thank you to the Office for Higher Education of @iitalk.bsky.social for organizing such a great initiative for PhD students. Keep on fUSI everyone! 〰️✨

It’s out! 🐭🧠 I had the privilege of contributing to this work while taking my first steps in science at the Center for Molecular Neurobiology (ZMNH, Hamburg). It’s so rewarding to see it finally published and to read the story that came out of it. Huge congratulations to everyone involved!

🚨 Excited to share the latest preprint form the lab ➡️https://tinyurl.com/32d3be9f Here we tackle a long-standing chicken-or-egg 🐣🥚question in #autism and developmental neuroscience ➡️ Is excitation–inhibition (E:I) imbalance a "cause" or a "consequence" of #autism? Check out what we found! 🧵1/n

Working link to the preprint 👇 www.biorxiv.org/content/10.1... See thread below for a looong explainer

🧠 🐭 Our new perspective paper is out! @gozziale.bsky.social Rodent functional neuroimaging is rapidly evolving — driven by recent breakthroughs and a shared vision for the future. We highlight key challenges, opportunities, and next steps for the field. 🔗 Check it out direct.mit.edu/imag/article...