I didn’t expect a short video about an octopus to stay with me this long.
At first, it looks like a simple, almost playful interaction. But the more you watch, the more something deeper emerges: a learning process unfolding between two completely different forms of intelligence.
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The supply of blood to brain tissue is thought to depend on the overall neural activity in that tissue, and this dependence is thought to differ across brain regions and across brain states. However, studies supporting these views have measured neural activity as a bulk quantity and related it to blood supply following disparate events in different regions. Here we measure fluctuations in neuronal activity and blood volume across the mouse brain, and find that their relationship is consistent across brain states and brain regions but differs in two opposing brainwide neural populations. Functional ultrasound imaging (fUSI) revealed that whisking, a marker of arousal, is associated with brainwide fluctuations in blood volume. Simultaneous fUSI and Neuropixels recordings showed that neurons that increase activity with whisking have distinct haemodynamic response functions compared with those that decrease activity. Their summed contributions predicted blood volume across states.Brainwide Neuropixels recordings revealed that these opposing populations coexist in the entire brain. Their differing contributions to blood volume largely explain the apparent differences in blood volume fluctuations across regions. The mouse brain thus contains two neural populations with opposite relations to brain state and distinct relationships to blood supply, which together account for brainwide fluctuations in blood volume.
How does blood flow relate to brain activity? We discovered that it reflects two neural populations affected oppositely by arousal. Together, they explain neurovascular coupling in all brain regions and brain states!
Out today in Nature: rdcu.be/fdC2A
@uclbrainscience.bsky.social
🤯
This looks like a significant discovery from Doris Tao's lab:
Rapid concerted switching of the neural code in the inferotemporal cortex
@nature.com
"..our findings indicate that there is a previously unknown mechanism for neural representation:.."
www.nature.com/articles/s41...
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...
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...