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Holly Rayson

@hollyrayson.bsky.social

Postdoctoral researcher at the CNRS. Interested in early neurocognitive development, effects of social experience, and emergence of socioemotional and sensorimotor skills.

3 Posts111 Follower189 Folgt
Holly Rayson@hollyrayson.bsky.social · 2mo ago

I am happy to bring your attention to an upcoming workshop I am co-organising on early social experience, neuropsychological development, and mental health. This will be held in Sicily on September 2nd-5th 2026: centromajorana.it/earlydevelop...

Early Neuropsychological Development and Plasticity: linking early social experience to mental health outcomes

'Ettore Majorana' Foundation and International Centre for Scientific Culture Schools

centromajorana.it

Maciek Szul@maciekszul.bsky.social · 8mo ago

An accessible overview on why beta band activity is best seen as a diverse set of events and not an oscillation, along with cortico-subcortical network responsible for specific waveform shape. @hollyrayson.bsky.social @quentinmoreau.bsky.social Solène Gailhard @danclab.bsky.social

Beta Burst Waveform Diversity: A Window onto Cortical Computation - Holly Rayson, Quentin Moreau, Solene Gailhard, Maciej J. Szul, James J. Bonaiuto, 2025

Neural activity in the beta band is increasingly recognized to occur not as sustained oscillations but as transient burst-like events. These beta bursts are div...

journals.sagepub.com

Maciek Szul@maciekszul.bsky.social · last yr.

🚨🚨🚨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.
Decision, Action, and Neural Computation (DANC) lab@danclab.bsky.social · last yr.

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! 🧠📈

Maciek Szul@maciekszul.bsky.social · last yr.

🚨🚨🚨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.
Holly Rayson@hollyrayson.bsky.social · 2y ago

Check out our new preprint! It's especially useful for looking at rhythmicity in infant EEG signals 🧠👶

Decision, Action, and Neural Computation (DANC) lab@danclab.bsky.social · 2y ago

We're happy to announce the release of a preprint presenting lagged Hilbert autocoherence for determining signal rhythmicity at different frequencies. Fantastic work by Siqi Zhang, @maciekszul.bsky.social, and some others who have not made the leap over here. 🧠📈 🧠💻 www.biorxiv.org/content/10.1...