Kick off of #Cortico2025 with @dngman.bsky.social who came to Lyon by bike from Basel !! 🚲

#StandUpForScience

#StandUpForScience in Lyon 🧪

Bingo

Check the supplementary figure of his recent paper: (direct.mit.edu/imag/article...)
This also goes to show how superlets are far superior than Morlet wavelets for identifying these phenomenon

Others? Not so much. 😅 Some subjects needed a bit more tweaking—We had to play around with parameters, rerun iterations, and double-check we weren’t over-cleaning real neural activity. The classic battle: artifact removal vs. preserving the signal.

To tackle this, we turned to MEEGKIT’s Zapline for noise removal—applying it iteratively to clean up the signal. For some subjects, it worked like a charm! 🎉 The 24 Hz bump was gone, leaving us with cleaner beta activity
-> github.com/nbara/python...

So, we went back to the raw data thinking maybe the superlets we use for the burst detection spotted something our initial Welch-based PSD analysis didn't. Turns out… every single subject has this 24Hz bump in their PSDs 😑 Which is SLIGHTLY annoying when you're focusing on activity in the beta range

This week, we put our beta burst detection algorithm to the test on an open (preprocessed) dataset. Everything seemed fine… until we checked the distribution of the frequency span of the extracted bursts. And there it was—a weird, unexpected 24 Hz peak.⬇️

I thought that with LLMs, predatory emails were supposed to become more difficult to spot...

✨Evidence of a Christmas spirit network in the brain: functional MRI study✨by Hougaard and colleagues 🎄
www.bmj.com/content/351/...

Special mention to the participants section

@mmellon.bsky.social presenting his new project using #fNIRS #neuroskyence #NeuroDay

NeuroDay today in #Lyon - great opportunity to meet the entire neuroscience community 🧠