Alina Studenova

Last day at #MBB2026
Keynote by Julian Thayer on "Breathing is Fundamental: A Mind/Brain/Body Perspective".

Amazing talk by Ulrike Bingel
on expectations "Mind over matter: how expectations shape health and treatment outcomes" #MBB2026 Interestingly, nocebo effects are easier to induce than placebo effects🤔

Absolutely beautiful and comprehensive #keynote by #SonjaKotz on #time and #rhythm processing from a #comparative and #translational #MindBrainBody perspective @ MBB Symposium in Berlin! ✨

band-lab.com
@maastrichtu.bsky.social

#MBBS2026

Slides of the talk with samples and the task

Slide with the results across the 4 samples

Slide with discussions about the brain gut results

Slide with results about pre and post-stimulus oscillations

So many inspiring talks (here by @saramehrhof.bsky.social, Sameer Alladin, @studenova.bsky.social) and discussions on the first day of the #MindBrainBody Symposium 2026!

#MBB2026

@studenova.bsky.social surprised the audience of the #MindBrainBody Symposium with an “embodied presentation” in how absolute post-stimulus alpha amplitude better explains trial-by-trial perception across 5 datasets and 3 somatosensory tasks

#MBBS2026

Today I'm at MindBrainBody symposium #MBB2026 Keynote by Karl J. Friston "The physics of affordance"

An interesting way to look at white matter is to measure fiber stiffness using Magnetic Resonance Elastography (MRE). This study shows that stiffness changes with age, more so stiffness parallel to the main fiber direction. Cool!🤩 Combining MRE with DTI surely provides a more comprehensive picture.

👍I agree that decomposition of some sort will improve SNR. Especially, for P50 in my example, as it's a small ERP.

Evoked responses in individual participants rarely look like an average over all participants. Does it mean we should have collected more trials? And what if the average from one dataset doesn't look like the average from another dataset? Should we collect more participants? #brainmovies

In this study, a total of 75 neurons in the locus coeruleus (LC) were recorded from 2 monkeys performing a perception decision-making task. My main takeaway is that neuron responses were heterogeneous: there are subpopulations that serve different functions. Cool!👏 But what to do if LC is 4 voxels?

I had been thinking about ways to analyze beta bursts in MEG data. And then I found this paper. Here, oscillatory bursts in mouse V1 were analyzed across 3 dimensions: time, space, and frequency. Results revealed visual-feature-specific burst classes. Amazing!🤩 Exactly what I needed.

Alina Studenova - in-consciousness-we-trust Recently, I have witnessed a discussion during one of the talks in our department. The issue was about the study of motor imagery. The experiment went as follows: participants imagined a movement; the...

I read another book. “In Consciousness We Trust: The Cognitive Neuroscience of Subjective Experience” by Hakwan Lau. Finally, the book about consciousness (=subjective experience) that I can comprehend. Read my opinion here
www.alinastudenova.com/home/blog/bo...

Such a cool and rigorous paper by @studenova.bsky.social !
bsky.app/profile/stud...

Another nice example of merging empirical data analysis with computational modelling to gain insights into brain biophysics.

I thank the wonderful co-authors.😍
Felix Ströckens, Luke J. Edwards @lukejoeledwards.bsky.social, Anna-Lena Stroh @lenastroh.bsky.social, Saskia Helbling @saskiahelbling.bsky.social, Burkhard Maess, Kerrin J. Pine, Katrin Amunts, Evgeniya Kirilina, Nikolaus Weiskopf, Arno Villringer, Vadim Nikulin

So now I'm also interested in between- vs. within-variability in structure and function. What are the factors that influence each kind of variability? (6/6)

We speculate that positive correlations across regions are driven by the total number of neurons and the negative correlation across participants—by circuits involving inhibitory interneurons. We did some simulations with TVB to support this idea. (5/6)

Meanwhile, across participants, alpha power correlated *negatively* with myelin and iron estimates from a mid-cortical component. (4/6)

Across regions, using data from different cohorts and different modalities, we found that alpha power correlated positively with the thickness of layer IV and with myelin and iron estimates from the mid-cortical component. (3/6)

Structure was obtained from the post-mortem brain and high-resolution MRI (in-vivo histology). Dynamics were obtained from EEG and MEG.
We looked for relations from two angles: across regions and across participants. (2/6)

How dynamics arise from the structure is my biggest interest. In this study, we started with a small step and asked how structure constrains dynamics. Spoiler: would that it were so simple… (1/6)

Proprioceptive neurons are found in layer 2/3 of the mouse S1. They are involved in movement execution. This paper shows that without those neurons, the movement will be executed anyway. But the trajectory becomes more stereotyped and less variable. Cool!🤩 Noise in behavioral estimates is not noise.

This paper suggests that in visual statistical learning, associations are better captured when predictability is high in both directions. Dual Factor Heuristic best explains learning effects. Interesting!👍 If a meeting is always on Monday, do Mondays predict meetings or do meetings predict Mondays?

underappreciated concept 🙂

for more dipoles: neighboring dipoles have different orientations & can have different stimulus preferences. so even though topographies look similar, subtle differences remain. maybe that's why it is possible to decode information from relatively few sensors?

The angle of the dipole obviously matters. If active dipoles in two conditions are slightly misaligned, a difference can be observed. However, this difference is not driven by differences in amplitudes, but only by differences in orientations. Here, I simulated such a scenario.
#brainmovies

Processing speed, attention, and working memory correlate with both P300 and intelligence, and therefore may explain the association. I wish more studies would control for domain-general capacities.

A meta-analysis showing a small but significant association between P300 amplitude and latency and general cognitive abilities (intelligence). The larger and earlier the P300, the more intelligent the person. The elephant in the room, which the authors also acknowledge, is domain-general capacities.

In y/n detection tasks, the internal signal and noise distributions have unequal variance. Instead of using d', we should be using dₐ. dₐ is usually estimated from confidence ratings, but this paper shows (in visual tasks) that it's possible to get dₐ from reaction times. Good news!👏 Will surely try

Woop woop 🎉 Excited to share that our paper has been accepted in Imaging Neuroscience!

Can ventral striatal reward signals — originating from deep within the brain — be reconstructed from scalp EEG?

We tried to answer this question using deep learning.

More soon 👀 Stay tuned.

With each of repetition runs,
Here comes magnetic resonance.
#brainrhymes