Valentin Riedl

However, I do agree that press titles stating that “40% of fMRI-cases were false” are wrong; yet, this is not our “headline result”. BOLD-fMRI remains the best method we have for studying the human brain. But we do question the uniform assumption of a generic response function across the cortex.

In sum, your simulation illustrates that noise multiplies but uses non-realistic parameters, ignores the validation of an established hemodynamic model, several biological prerequisits, and all subsequent validation steps of our initial finding.

Finally, we offer a biological mechanism explaining the lack of CBF-changes using an independent measurement of OEF, during different brain states of rest and task activation. In short, our study goes well beyond Fig.3b.

Instead, your model output is implausible: Your noise-free correlation (post 9) assumes CBF changes way beyond physiological measurements (>5x higher than ever measured) showing that your model parameters are implausible.

Our result (Fig.3b) is not an arbitrary correlation between two random signals but a replication and validation of an established biophysical model of the BOLD signal. We did not report an arbitrary mismatch without biological plausibility.

Your error-plot (post 10) produces 40% error-voxels, but without any reference to brain space. Your error-voxels are randomly distributed, which ignores the spatial clustering we observe in our main and replication sample, which, in contrast, adds biological plausibility to our finding.

Both, your BOLD- and CBF-data are simulated by the same random term d_real. …i’m not an expert here, but both imaging signals have their own physiological signal structure, yet your error propagates stronger when based on the same structure as in your sim.

MRI-data are noisy, but your simulation uses error-terms and SNRs beyond real data quality (i’d guess your CBF signal is around 5x weaker than imaging data, the real T2* changes are around 5x higher), so sure, you’ll easily (intentionally?) get more noise propagation.

Hey Alex, here’s a short response as co-author of the original paper. Your simulation is statistically interesting, but ignores several physiological prerequisites that render it biologically implausible and therefore not related to our measured data.

yes, that‘s exactly what i meant, high-frequency bands power is only a small portion of total activity, and, interestingly, the authors only find reduced HFB power in 2/8 regions related to DMN (fig.2)

Thanks Nicolas! unfortunately, very much we couldn’t cite (space limits 🫣), but right, that’s relevant work and we currently look into glucose (not oxygen like here) metabolism where it’ll better fit

Thanks Vadim! The electrophys. evidence i would know of (but you may have sth specific in mind?) are rather selective, showing reduced synchrony (not amplitude), reductions in certain frequency bands (not global) or from few neurons (vs entire systems)… we capture very broad and global reductions

8:
Huge congrats to Samira on her epic PhD-work!
And thanks to our colleagues @gabocas.bsky.social, Beija, Jessica, and Christine,
my hosting institutions FAU @fau.de & TUM
and the support from @erc.europa.eu

7:
Samira, @samomat.bsky.social, has collated all data and analysis code here:
data: openneuro.org/datasets/ds0...
code: github.com/Neuroenerget...

6:
Still, varying hemodynamic responses may offer new insights:

-Does CBF regulation only kick in after the oxygen buffer is used?
-Does OEF regulation indicate different signaling strategies or cell type metabolism?
- Does oxygen availability indicate disease susceptibility?

5:
BOLD-fMRI remains the most effective method for studying human brain activity.

Yet, we might have to reconsider the regional interpretation of BOLD-signal changes in relation to neuronal activity.

4:
In summary, we identified varying oxygen extraction as a novel hemodynamic response type to neuronal activity, leading to paradoxically inverse BOLD signal responses, particularly in the Default Mode Network.

3:
Most voxels in the Default Mode Network (DMN) exhibited a paradoxical negative BOLD response to increased metabolism due to higher oxygen extraction instead of decreased blood flow.

Hemodynamic response in the brain

2:
We found inconsistent hemodynamic responses via blood flow (CBF) across the cortex and even within the same voxels, depending on task type and baseline oxygen extraction fraction (OEF).

Multiparametric, quantitative fMRI

1:
The BOLD signal is a complex representation of various hemodynamic processes. We used quantitative fMRI to measure all hemodynamic factors contributing to positive and negative BOLD signal changes.

BOLD signal changes can oppose oxygen metabolism across the human cortex, Nature Neuroscience

fMRI signals “up,” but neural metabolism might be going “down.”

In our @natneuro.nature.com paper, we demonstrate that about 40% of voxels with robust BOLD responses exhibit opposite oxygen metabolism, revealing two distinct hemodynamic modes.

rdcu.be/eUPO8
funds @erc.europa.eu
#neuroskyence 🧵:

What started as a spinoff project for Madeleine's PhD became one of the most striking indications that glucose levels play an important role in regulating everyday stress responses. This shows the potential of biosensors to evaluate whether metabolism alters stress reactivity #neuroskyence 🩺

What if complex life began when evolution hit a search bottleneck?

Across 6,500+ species, 🧬 length follow a scale-invariant law. At eukaryote origins, proteins plateau while 🧬 keep growing as noncoding regulatory DNA. Phase transition?

www.pnas.org/doi/10.1073/...

👉 manlius.substack.com

One of the more provocative and important articles I've read in a while: A call for a "map" of neuroscience understanding and relationships between domains. apertureneuro.org/article/1388...

The Energetic Collapse of the Alzheimer's Brain: Metabolic Inflexibility Across Cells and Networks Metabolic inflexibility in Alzheimer's disease. Schematic illustrating the biphasic trajectory of metabolic activity relative to canonical Alzheimer's disease (AD) biomarkers. In the presymptomatic p...

Our new review is out today!

𝗧𝗵𝗲 𝗘𝗻𝗲𝗿𝗴𝗲𝘁𝗶𝗰 𝗖𝗼𝗹𝗹𝗮𝗽𝘀𝗲 𝗼𝗳 𝘁𝗵𝗲 𝗔𝗹𝘇𝗵𝗲𝗶𝗺𝗲𝗿’𝘀 𝗕𝗿𝗮𝗶𝗻: 𝗠𝗲𝘁𝗮𝗯𝗼𝗹𝗶𝗰 𝗜𝗻𝗳𝗹𝗲𝘅𝗶𝗯𝗶𝗹𝗶𝘁𝘆 𝗔𝗰𝗿𝗼𝘀𝘀 𝗖𝗲𝗹𝗹𝘀 𝗮𝗻𝗱 𝗡𝗲𝘁𝘄𝗼𝗿𝗸𝘀

We argue that Alzheimer’s disease is not just a problem of brain hypometabolism, but a disorder of metabolic inflexibility.

onlinelibrary.wiley.com/doi/10.1111/...

Cover for the book "Creating Communication and Media Research Labs: A Blueprint for Success". Edited by Chad Edwards, Autumn Edwards, and Patric R. Spence. Published by Palgrave Pivot.

🧵 What does it take to build a small, scrappy, and successful communication neuroscience lab? Our lab, @gongxuanjun.bsky.social, @rachaelkee.bsky.social, Allyson Snyder, Ziyu Zhao, and I put out heads together to answer this question. Here's what we came up with: link.springer.com/chapter/10.1...

Fast hierarchical processing of orthographic and semantic parafoveal information during natural reading - Nature Communications Combining MEG, eye-tracking, and representational similarity analysis, this study shows that readers rapidly and sequentially extract orthographic and semantic information from upcoming words before fixation, supporting efficient reading.

Ever wonder how you read so fast? Your brain gets a head start—processing the next word before your eyes move. Our MEG + eye tracking study out in Nature Communications study from @thechbh.bsky.social reveals orthographic & semantic previews predicting reading speed www.nature.com/articles/s41...

Hello! It has been a while, but for the Hector Fellow Academy I created a small project at the interface of neuroscience and music. In short, it is like a neuro-jukebox of projects of friends and colleagues integrating music and art. I thought it would be nice to share here
cng-lab.github.io/kiosk

Mitochondrial origins of the pressure to sleep - Nature Research on Drosophila neurons shows links between the need to sleep and aerobic metabolism, indicating that the pressure to sleep may have a mitochondrial origin.

Nature research paper: Mitochondrial origins of the pressure to sleep

go.nature.com/40oC7Y4

Event structure sculpts neural population dynamics in the lateral entorhinal cortex Our experience of the world is a continuous stream of events that must be segmented and organized at multiple timescales. The neural mechanisms underlying this process remain unknown. In this work, we...

Your brain doesn’t just passively track time ⏳ - it structures it.
In @Science.org we show that activity in 🧠 memory circuits (LEC) drifts constantly, but makes sharp jumps at key moments, segmenting life into meaningful events. (1/2)

👉 www.science.org/doi/10.1126/...