jan-sch.bsky.social
Jan Schalla
@jan-sch.bsky.social
73 followers 280 following 1 posts
Interested in how the brain integrates endogenous and exogenous stimuli and how brain activity is changed by that. PhD student at the Heinrich Heine University working on PD in the research group clinical neuroscience
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Reposted by Jan Schalla
braincomms.bsky.social
Brain Communications @braincomms.bsky.social · 19d
Kohl et al. report that sensorimotor network dynamics can distinguish patients with Parkinson’s disease from controls and highlight the importance of network context of motor cortical activations.
buff.ly/489DYfB
#Parkinsons
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Reposted by Jan Schalla
kohliver.bsky.social
Oliver Kohl @kohliver.bsky.social · Aug 28
Hurrah!
New paper out doi.org/10.1093/brai...!

We show that sensorimotor network dynamics are altered in Parkinson’s disease and highlight the importance of looking at motor cortical activity within the broader brain network context to better understand pathophysiological changes underlying PD.
Changes in sensorimotor network dynamics in resting-state recordings in Parkinson’s disease
Kohl et al. report that sensorimotor network dynamics extracted from magnetoencephalogram recordings can distinguish patients with Parkinson’s disease from
doi.org
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Reposted by Jan Schalla
hubschmidf.bsky.social
Fabrice Hubschmid @hubschmidf.bsky.social · Jul 21
Really glad that my first paper is now out in @painthejournal.bsky.social 😁🧠!
painthejournal.bsky.social
PAIN @painthejournal.bsky.social · Jul 21
Hubschmid et al. find that monetary rewards increase sensory signal strength leading to enhanced pain discrimination. Mechanistically, this process seems to be driven by learning from positive prediction errors. Learn more in #PAIN bit.ly/4lWzNjb
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Reposted by Jan Schalla
pirtho.bsky.social
Thomas Pirenne @pirtho.bsky.social · Jul 17
I’m sure you’ve always wondered how reliable Granger causality of source reconstructed MEG really is. Better yet, you surely wonder how to improve it. You’re in luck, I’ve just arrived at @meguki2025.bsky.social to discuss it. All jokes aside, meet me at poster 38 if you’re interested. 👋🏼🧠
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jan-sch.bsky.social
Jan Schalla @jan-sch.bsky.social · Jul 17
Just arrived at @meguki2025.bsky.social excited to present results on head movement reduction in people with Parkinson‘s using individual head casts. Additionally we can replicate prior findings on laminar source reconstruction! If you want to find out more, do not hesitate to find me at poster 45!
Poster 45 of MEGUKI 2025 from Jan Schalla.
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Reposted by Jan Schalla
maciekszul.bsky.social
Maciek Szul @maciekszul.bsky.social · Jun 2
🚨🚨🚨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.
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Reposted by Jan Schalla
imagingneurosci.bsky.social
Imaging Neuroscience @imagingneurosci.bsky.social · Apr 11
New paper in Imaging Neuroscience by Vaishali Balaji, Alfons Schnitzler, and Joachim Lange:

Modulating somatosensory alpha oscillations using short-period transcranial alternating current stimulation

doi.org/10.1162/imag...
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