Quentin Moreau
@quentinmoreau.bsky.social
350 followers 370 following 26 posts
Cognitive and Social Neuroscientist in Lyon 🧠- MEEG and Beta Bursts 💥 Postdoc at the DANC Lab 👨🏻‍🎓 - Basketball fan 🏀 https://www.danclab.com/member/member17/
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Reposted by Quentin Moreau
danclab.bsky.social
It's been a while since our last laminar MEG paper, but we're back! This time we push beyond deep versus superficial distinctions and go whole hog. Check it out- lots more exciting stuff to come! 🧠📈
maciekszul.bsky.social
🚨🚨🚨PREPRINT ALERT🚨🚨🚨
Neural dynamics across cortical layers are key to brain computations - but non-invasively, we’ve been limited to rough "deep vs. superficial" distinctions. What if we told you that it is possible to achieve full (TRUE!) laminar (I, II, III, IV, V, VI) precision with MEG!
Overview of the simulation strategy and analysis. a) Pial and white matter boundaries
surfaces are extracted from anatomical MRI volumes. b) Intermediate equidistant surfaces are
generated between the pial and white matter surfaces (labeled as superficial (S) and deep (D)
respectively). c) Surfaces are downsampled together, maintaining vertex correspondence across
layers. Dipole orientations are constrained using vectors linking corresponding vertices (link vectors).
d) The thickness of cortical laminae varies across the cortical depth (70–72), which is evenly sampled
by the equidistant source surface layers. e) Each colored line represents the model evidence (relative
to the worst model, ΔF) over source layer models, for a signal simulated at a particular layer (the
simulated layer is indicated by the line color). The source layer model with the maximal ΔF is
indicated by “˄”. f) Result matrix summarizing ΔF across simulated source locations, with peak
relative model evidence marked with “˄”. g) Error is calculated from the result matrix as the absolute
distance in mm or layers from the simulated source (*) to the peak ΔF (˄). h) Bias is calculated as the
relative position of a peak ΔF(˄) to a simulated source (*) in layers or mm.
Reposted by Quentin Moreau
maciekszul.bsky.social
Great thanks to coauthors and collaborators: Ishita Agarwal, Quentin Moreau (@quentinmoreau.bsky.social), Bassem Hiba, Sven Bestmann (@armlabucl.bsky.social), Gareth Barnes, and James Bonaiuto (@danclab.bsky.social). It was a huge amount of work and cluster time, but it’s out now!
Reposted by Quentin Moreau
maciekszul.bsky.social
🚨🚨🚨PREPRINT ALERT🚨🚨🚨
Neural dynamics across cortical layers are key to brain computations - but non-invasively, we’ve been limited to rough "deep vs. superficial" distinctions. What if we told you that it is possible to achieve full (TRUE!) laminar (I, II, III, IV, V, VI) precision with MEG!
Overview of the simulation strategy and analysis. a) Pial and white matter boundaries
surfaces are extracted from anatomical MRI volumes. b) Intermediate equidistant surfaces are
generated between the pial and white matter surfaces (labeled as superficial (S) and deep (D)
respectively). c) Surfaces are downsampled together, maintaining vertex correspondence across
layers. Dipole orientations are constrained using vectors linking corresponding vertices (link vectors).
d) The thickness of cortical laminae varies across the cortical depth (70–72), which is evenly sampled
by the equidistant source surface layers. e) Each colored line represents the model evidence (relative
to the worst model, ΔF) over source layer models, for a signal simulated at a particular layer (the
simulated layer is indicated by the line color). The source layer model with the maximal ΔF is
indicated by “˄”. f) Result matrix summarizing ΔF across simulated source locations, with peak
relative model evidence marked with “˄”. g) Error is calculated from the result matrix as the absolute
distance in mm or layers from the simulated source (*) to the peak ΔF (˄). h) Bias is calculated as the
relative position of a peak ΔF(˄) to a simulated source (*) in layers or mm.
Reposted by Quentin Moreau
scnl-agliotilab.bsky.social
PhD Call 📢📢📢
Are you interested in a PhD in Cognitive and Social Neuroscience? Join us! We are booking for highly motivated and inspired future researchers! Waiting for you in Rome!

🗓️ Deadline 19th June

Useful links👇🏻👇🏻👇🏻
phd.uniroma1.it/web/concorso...

phd.uniroma1.it/web/pagina.a...
Reposted by Quentin Moreau
felixbigand.bsky.social
EEG of the Dancing Brain - new paper out! 🧠🕺

Thrilled to share our latest work on disentangling the neural basis of real-time social dance!!

www.jneurosci.org/content/45/2...

A full @giacomonovembre.bsky.social NPA Lab production 🎬

#dance #socialinteraction #hyperscanning #motioncapture
Reposted by Quentin Moreau
giacomonovembre.bsky.social
Amazing work, @felixbigand.bsky.social ! And thanks to all our lab collaborators: Sara Abalde, @robertabianco.bsky.social and @trinhnguyen.bsky.social !
Reposted by Quentin Moreau
ljubapi.bsky.social
You wouldn’t drink from a glass if a fish is IN the glass, or would not grab a chair if one is already sitting ON it.
How do we represent relations between things? And how does it relate to object and action recognition?
New paper out osf.io/preprints/ps...
@mmellon.bsky.social
Reposted by Quentin Moreau
danclab.bsky.social
DANClab is at #NeuroFrance2025 in full force! @quentinmoreau.bsky.social @hollyrayson.bsky.social
Tomorrow at 9h30 I'll give a talk about the cool methods we've developed to analyze beta bursts
quentinmoreau.bsky.social
Kick off of #Cortico2025 with @dngman.bsky.social who came to Lyon by bike from Basel !! 🚲
Reposted by Quentin Moreau
sfnjournals.bsky.social
New in #eNeuro from Pesci, Moreau, Era, and Candidi: How people interact with and process the movements of avatars differs depending on whether avatars look like people. eneuro.org/lookup/DOI/10.1523/ENEURO.0390-24.2025
Reposted by Quentin Moreau
Here are six examples of how foreign visitors to the US with no criminal record are being treated:

1. Mahmoud Khalil, a green card holding student with no criminal record married to an American is abducted by ICE and is still in detention over a week later.
www.nytimes.com/2025/03/19/n...
......
quentinmoreau.bsky.social
Cool video explaining what we do in the lab! And congrats again @danclab.bsky.social for this well deserved award !
isc-mj.bsky.social
Congratulations to Dr. Bonaiuto (@danclab.bsky.social) for his CNRS distinction #médailleduCNRS. Learn more about the research he is conducting with his team in our institute :

youtu.be/sNOGnCUhGnc

#neuroscience
James Bonaiuto : les rythmes cérébraux et les troubles moteurs l Talents CNRS
YouTube video by CNRS
youtu.be
Reposted by Quentin Moreau
danclab.bsky.social
It was an honor to be awarded this cool medal from @cnrs.fr @cnrs-rhoneauvergne.bsky.social. Sorry my French was only good enough to say "le mieux c'est que je vous montre" with a thick American accent! I was hoping to get dubbed, but they went with French subtitles
isc-mj.bsky.social
Congratulations to Dr. Bonaiuto (@danclab.bsky.social) for his CNRS distinction #médailleduCNRS. Learn more about the research he is conducting with his team in our institute :

youtu.be/sNOGnCUhGnc

#neuroscience
James Bonaiuto : les rythmes cérébraux et les troubles moteurs l Talents CNRS
YouTube video by CNRS
youtu.be
quentinmoreau.bsky.social
#StandUpForScience in Lyon 🧪
quentinmoreau.bsky.social
Moral of the story? Always check your data. 🔍 Even preprocessed, "clean" datasets can hide unexpected artifacts that mess with your analysis. If you have any good guesses about the origin of this 24 Hz noise, we’d love to hear them!
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ALT: a black background with a lot of green dots on it
media.tenor.com
quentinmoreau.bsky.social
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
quentinmoreau.bsky.social
This isn’t our first rodeo with weird EEG artifacts. 🎢 Sotirios Papadopoulos from our lab also ran into strong, unexpected noise in a shared EEG datasets. Different data, same headache. 🤦‍♂️
quentinmoreau.bsky.social
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.
quentinmoreau.bsky.social
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...
quentinmoreau.bsky.social
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
quentinmoreau.bsky.social
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.⬇️