With deep gratitude to my extraordinary co-authors @MariannaAngiolelli, @FukaiTomoki, @GustavoDeco, @pierpasorre.bsky.social, and @davemomi.bsky.social
—this work would not have been possible without them.
Supported by the MSCA grant @ec.europa.eu and @upf.edu—more to come!

Key message: variability is not just noise—it reflects structured brain dynamics that can be harnessed for more reliable, state-aware stimulation protocols.

Preprint: doi.org/10.1101/2025...
Code: github.com/grabuffo/Sta...

7/n

Not all networks behave the same. Sensorimotor regions showed stronger state-dependence than higher-order association areas, revealing a hierarchy—especially in SEEG analyses. 6/n

Conditioning stimulation on favorable pre-stimulus states reduced response variability by over 20%. This shows that brain-state monitoring can make interventions more reproducible, offering a concrete step toward refined closed-loop and precision neuromodulation. 5/n

Whole-brain context matters. When predictions are based only on activity close to the stimulation site, accuracy is limited. Expanding to include wider pre-stimulus dynamics across the whole brain consistently improves predictability of outcomes. 4/n

Across 125 candidate metrics, measures of synchronization, connectivity, and spatiotemporal signal diversity consistently predicted responses—sometimes explaining up to 80% of the variability within a session. 3/n

We analyzed a rare dataset: 36 patients, ~320 sessions, and more than >10,000 stimulations, combining intracranial and high-density EEG recordings. We asked: which pre-stimulus features are most reliable in predicting stimulation outcomes? 2/n

🙏 Armelle Lokossou, Zengmin Li, Abolfazl Ziaee-Mehr, Meysam Hashemi, Pascale Quilichini, A. Ghestem, O. Arab, M. Esclapez, Parul Verma, Ashish Raj, @gozziale.bsky.social , @pierpasorre.bsky.social , Kai-Hsiang Chuang, Adriana Perles-Barbacaru, Angèle Viola, Viktor Jirsa, Christophe Bernard 🔥