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

Maybe I should have made the timescale of the effect more clear - we are talking about tens of seconds before behavior, not preparatory signals. We find that the mouse is in a state that makes it more likely to switch (a bit like sleep pressure making you increasingly more likely to sleep!)

Our question here was to see if spontaneous events are more likely to happen when animals are in a specific internal/arousal brain state or not. The spontaneous aspect is assumed - it refers to events being uninstructed and not triggered by external inputs (as far as we can measure)!

The control trace in the decoding plot is addressing that, it is made using pseudo events (data from same time points in other sessions when no event happened). Does that answer your question?

How do the basal ganglia turn what you see into what you do?
New preprint w/ @kenneth-harris.bsky.social, @flickerfusion.bsky.social & @carandinilab.net: we recorded across striatum, GPe & SNr in a Go/NoGo task. Striatum encodes which stimulus, GPe & SNr encode action. 🧵
biorxiv.org/content/10.6...

Here we inhibited the cells. For Vglut neurons, given previous results showing that they promote behavior, I would expect the opposite. We also see a strong pupil constriction which hints at a cholinergic effect. Data will tell!!

Cholinergic cells are our guess.. Dissecting the role of different cell types in MS is our next step! Regarding the brain activity, what is maybe surprising is the timescale of the effect? Of course preparatory signals are known. Here we focus more on slow changes in internal state.

8/8. This work builds on inspiring studies showing that internal states shape spontaneous behavior over long timescales.🕜

Huge thanks to co-authors, institutions, and funders for making this possible! 🙏

@uni-goettingen.de @mpiforbi.bsky.social @dfg.de @ekfstiftung.bsky.social @mbexc.bsky.social

7/8. In short:
~10 s before a spontaneous behavioral switch:
↑ decodability
↓ medial septum activity (and other regions)

Our interpretation: internal drive gradually builds up, creating a transition-prone brain state that makes switching more likely.

6/8. To test causality, we used optogenetics 💡 to inhibit the medial septum, mimicking the decrease seen with fUS seconds before behavior initiation. The result: MS inhibition made transitions to egress, running, and grooming more likely.

5/8. So what drives this predictability before behavioral transitions? We identified a set of regions whose hemodynamic signal decreased seconds before egress and running, with one potential key player: the medial septum (MS).

4/8. As expected, whole-brain fUSi revealed distinct activity patterns associated with spontaneous egress, grooming, and running. But what happens before behavior initiation? We found that whole-brain signals predicted both egress and running several seconds before onset! ⏳

3/8 To test this, we used whole-brain fUSi in head-fixed mice across two contexts: a virtual burrow and a running wheel. In both, mice spent most of their time in quiet wakefulness, but spontaneously initiated egress (exiting the burrow), grooming, or running. 🐭

a cartoon mouse is standing next to a hole in the floor ALT: a cartoon mouse is standing next to a hole in the floor

2/8. Paulina Wanken 🤩, the outstanding student who led this project, asked a simple question: when animals change behavior without external cues, are those transitions random, or does the brain enter a specific internal state first? 🤔

1/8. New preprint! ✨

How spontaneous is spontaneous behavior? 🧠🐭

We found that whole-brain fUSi signals predicted spontaneous behavioral transitions seconds in advance. Inhibiting one node of this transition-prone state, the medial septum, facilitated switching!

www.biorxiv.org/content/10.6...

Wait… localized norepinephrine transients in the awake visual cortex?!
Who would have guessed this neuromodulatory signal is that spatially precise, right where visual processing is happening. Brain state control just got a lot more local. @ruedigersarah.bsky.social www.nature.com/articles/s41...

1/8 New preprint alert!

How are signals from the heart encoded in the brain?
What could be the functional implications of cardioception?

We found that neurons in the posterior insular cortex are precisely tuned to heartbeats, and that this cardio-insular coupling supports emotion coding in mice.

Thanks Peter! We were super happy to see that you also found a big difference between LC opto and natural arousal at the cellular level in hippocampus. We mentioned it extensively in the paper discussion! The inhibition that you see during optoLC is also in line with the decrease in fUSi we see.

8/8. Last but not least: Jose Maria is looking for a postdoc in computational neuroscience 🧠💻

Don’t miss the chance to recruit him — he comes highly recommended! 🚀

7/8. In sum: arousal engages a brain-wide hemodynamic wave independent of noradrenergic tone!

Huge thanks to all the coauthors, institutions, and funders for making this possible! 🙏

@uni-goettingen.de, @mpiforbi.bsky.social, @dfg.de, @ekfstiftung.bsky.social, @mbexc.bsky.social

6/8. We then tested an obvious suspect: noradrenaline.

Twist: bidirectional optogenetic manipulations of the locus coeruleus shifted baseline fUSi signals, but the arousal wave remained intact.

This suggests that other neuromodulators, such as acetylcholine, may play a bigger role. 🤔

5/8. This wave propagated from subcortex → cortex.

We recovered it from low-dimensional components of resting-state signals. These components predicted both spontaneous and evoked responses in a test set!

The arousal wave is a latent state that explains a large fraction of brain-wide signals ⚡

4/8. We used functional ultrasound imaging (fUSi) + pupillometry in awake mice 🐭 to track whole-brain signals around spontaneous pupil dilations and after arousing stimuli (air puffs).

Result: a robust spatiotemporal "arousal wave" 🧠🌊

ALT:

3/8. 🧠👀 Arousal shapes physiology, behavior, perception, and task performance. It also drives brain-wide activity, often more strongly than sensory or task variables.

But what is the spatiotemporal structure of this effect? And is it shared between spontaneous and sensory-driven arousal?

2/8. This is the first output of our lab’s effort to understand spontaneous behavior and neuromodulation at the brain-wide level.

This was a fantastic project to see come together, led by Jose Maria Martinez de Paz, with a key contribution from @johannalmayer.bsky.social.

Now to the results👇

1/8. New preprint!

Using fUSi in head-fixed mice🐭, we found that arousal events trigger a brain-wide wave of activity 🌊🧠.

Surprisingly, this pattern was preserved during opto manipulations of the locus coeruleus, pointing to a minor role for noradrenergic tone.

www.biorxiv.org/content/10.6...

1/7 🧠 My journey into development begins with this work and question: how does the brain's spatial navigation system develop? We found that the neural networks for spatial navigation (tori and rings) are preconfigured and only later anchor gradually to the world with experience! 🧵

Compact deep neural network models of the visual cortex Nature - Parsimonious deep neural network models can be used for prediction of visual neuron responses.

DNN models of the brain are getting bigger. Are we replacing one complicated system in vivo with another in silico?

In new work, we seek the *smallest* DNN models of visual cortex, balancing prediction with parsimony.

It turns out these compact models are surprisingly small!

rdcu.be/e5H8G

Visual motion and landmark position align with heading direction in the zebrafish interpeduncular nucleus - Nature Communications How are various visual signals integrated in the vertebrate brain for navigation? Here authors show that different spatial signals are topographically organized and align to one another in the zebrafi...

(1/n) We are excited to share our new paper in Nature Communications, by Hagar Lavian (@hlavian.bsky.social) and team, revealing how the zebrafish brain integrates visual navigation signals! www.nature.com/articles/s41...

Environmental Novelty Modulates Rapid Cortical Plasticity During Navigation In novel environments, animals quickly learn to navigate, and position-correlated spatial representations rapidly emerge in both the retrosplenial cortex (RSC) and primary visual cortex (V1). However,...

How does the brain balance learning new things without overwriting what it already knows? Our new paper tackles this long-standing stability–plasticity dilemma during active navigation. With Tony Drinnenberg from the Deisseroth Lab (@deisseroth.bsky.social)
doi.org/10.1101/2025...