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Proceedings of the National Academy of Sciences (PNAS), a peer reviewed journal of the National Academy of Sciences (NAS) - an authoritative source of high-impact, original research that broadly spans...

Out now in @pnas.org We present a new metabolism-weighted brain connectome that incorporates each region’s energy expenditure into the mapping of brain connectivity. www.pnas.org/doi/10.1073/...

pnas.org

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Reposted by Valentin Riedl

Naomi Klein @naomiaklein.bsky.social · 02/07/2026

Everybody in the "climate space" (which means everyone on earth...) needs to understand these mind-blowing figures. Google's "total electricity consumption jumped from 31 terawatt hours (TWh) in 2024 to 43 TWh in 2025." In ONE year. The AI corporate arms race is eating the world.

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Reposted by Valentin Riedl

Ye Lab @liye-tsri.bsky.social · 02/07/2026

Our new preprint is out!! The human brain runs on ~20 watts. Computers and modern AI systems require vastly higher energy. This gap raises a fundamental question: how does the brain compute so efficiently? In our new study, we take a critical step toward measuring and understanding this directly 1/3

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These findings establish a unifying principle: brain regions most essential for cognition bear the greatest metabolic burden, and this cost may underlie their susceptibility to neurodegeneration. funded by @erc.europa.eu, congrats @MahnazAshrafi, @laurafraticelli.bsky.social, @gabocas.bsky.social

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Using simultaneous positron emission tomography (PET)-MRI in healthy volunteers, we show that energetically most active hubs anchor networks for complex cognition, exhibit molecular signatures of intense synaptic activity, and are disproportionately vulnerable to Alzheimer’s disease.

Energetically active brain hubs are highly susceptible to neurodegeneration

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The regions of the brain exhibit unequal energy consumption. Specifically, areas associated with functions such as memory and attention are metabolic hotspots of intense neural activity. Yet brain connectivity analyses ignore this energetic dimension, treating all regions as functionally equivalent.

Cortical distribution of metabolically-weighted brain hubs

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Valentin Riedl @vavatin.bsky.social · 17/06/2026

Out now in @pnas.org We present a new metabolism-weighted brain connectome that incorporates each region’s energy expenditure into the mapping of brain connectivity. www.pnas.org/doi/10.1073/...

pnas.org

PNAS

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it‘s hot again in Bordeaux 😎 and Wednesday is neuroenergetics day: find Mahnaz‘s oral presentation and Samira‘s poster dates below and drop by for discussions! #ohbm2026 @samomat.bsky.social, @mahnazAshrafi

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Ali Maximilian Erturk @erturklab.bsky.social · 13/06/2026

See our science at Deep Piction, how we use it to develop new precision medicine.

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Valentin Riedl @vavatin.bsky.social · 12/05/2026

my point was that expecting sign CMRO2 might miss variance in hemodyn responses…therefore our regression analyses

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Valentin Riedl @vavatin.bsky.social · 12/05/2026

Hi Ole, thanks for starting this discussion. Our control analyses, specifically Fig. S8, cover the same masking approach

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3. A qBOLD significance mask is only helpful if BOLD reflects a canonical response rooted in identical underlying mechanisms, but questioning that assumption is the point of our work.

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1. their binominal test requires consistent signal in >80% of subjects. how many BOLD studies would survive the same requirement? 🕳️ 2. despite their sceptical title, they find ~90% of discordant BOLD among significant CMRO2 voxels (Fig. 3), basically replicating our own BOLD-negative findings. 🧐

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well, ‚been debunked” feels like a pretty strong conclusion to draw from the preprint (but i might be biased 🫣). let me raise just a few issues: We actually ran a similar analysis (see Fig. S8) but the preprint applies a stricter statistical threshold to (fair enough) noisy qBOLD data. Yet:

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Hey Cyril, there's a BOLD-fMRI study by Mujica-Parodi using a ketogenic diet paradigm: www.pnas.org/doi/10.1073/... We also applied qBOLD during hypoglycemia and observed only CBF, but no metabolic changes. Unfortunately, didn't survive a long review process recently.-) biorxiv.org/lookup/doi/1...

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Goltermann, Huth & Büchel take a more skeptical view, arguing via conservative statistical thresholding that standard BOLD remains sufficient. Interestingly, though, they also found discordant negative BOLD in ~90% of significant CMRO₂ voxels, Fig.3 🤔 biorxiv.org/content/10.64898/2026.04.21.719913v1

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practiCal fMRI @practicalfmri.bsky.social · 09/05/2026

Samira's work made the cover of @natneuro.nature.com this month 🎉 🔗 nature.com/neuro/volumes/29/issues/5 I also want to highlight two recent comments that engage with our quantitative approach to interpreting the BOLD signal; both are worth reading (see below).

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Multiplexed magnetic resonance imaging (MRx). www.nature.com/articles/s41... 22 measures incl. T1, T2, main metabolites in 14 min. A technical tour de force, but I'm not sure where/how it will find clinical applications.

nature.com

Multiplexed magnetic resonance imaging - Nature

A new approach to magnetic resonance imaging, ‘multiplexed magnetic resonance imaging’, is reported, which enables high-resolution simultaneous multiparametric mapping of multiple molecules in standar...

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Manlio De Domenico @manlius.bsky.social · 19/04/2026

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. 🧵 1/

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Agnès Landemard @agnesland.bsky.social · 15/04/2026

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

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.

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Hugo Spiers @hugospiers.bsky.social · 14/04/2026

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...

nature.com

Rapid concerted switching of the neural code in the inferotemporal cortex - Nature

Face cells in the macaque inferotemporal cortex are initially able to detect faces and then rapidly switch to a face-specific neural code to discriminate between different face identities.

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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.

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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.

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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.

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Valentin Riedl @vavatin.bsky.social · 17/12/2025

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.

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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)

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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

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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

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

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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?

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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.

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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.

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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.

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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).

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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.

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