Congrats very elegant study!

Going from neural activity to blood flow just became easier! Two brainwide populations, each with its neurovascular coupling. (But going backwards... is now a tad more complicated.)

By @agnesland.bsky.social & team.
Thanks @intlbrainlab.bsky.social @wellcometrust.bsky.social @simonsfoundation.org

White matter pathways mediating dorsolateral prefrontal TMS therapy for depression

New @natneuro.nature.com paper led by Caio Seguin, Robin Cash, and Andrew Zalesky.

We map (indirect) pathways from DLPFC to SGC and link individual variation with response efficacy.

www.nature.com/articles/s41...

✨Excited to share that our new preprint, "Mapping developmental patterns of intrinsic timescale", is now available on bioRxiv!!
📚 doi.org/10.64898/202...

New paper out 🎉

Awake fUSI is powerful, but motion can strongly bias the data, even in head-fixed experiments.

In this paper, we tried to systematically characterize those artifacts, benchmark denoising strategies, and turn that into practical recommendations for awake fUSI of mouse brains.

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Sobering experience clearing dad’s emeritus office at UBC. A lifetime of work dumped into recycling bins, including much of the early history of radio astronomy in the USA. Says a lot about how (little) our academic legacies will be valued. The guy had 3 Nature papers before 1971!!

We develop a new TMS targeting algorithm and test it in an open label trial in a treatment-resistant depression population with high comorbidities. Preprints by @rubykong92.bsky.social Phern-Chern Tor
1. doi.org/10.1101/2025...
2. doi.org/10.64898/202...

Our new approach ...

JOB ALERT: PhD opening in my lab!

@cimecunitrento.bsky.social
in Italy, as part of an Italian FIS3 starting grant.

The project will use advanced analysis methods of MEG data to investigate how our world's naturalistic hierarchical structure facilitates predictive neural processing.

Opportunities and pitfalls of data contextualization in neuroimaging - Nature Reviews Neuroscience Despite rapid exploitation of the opportunities that contextualization of brain maps affords, potential limitations have received little attention. In this Roadmap, Royer et al. provide practical guid...

Correlating brain maps across datasets is everywhere in neuroimaging. Here we ask: when you contextualize a brain map against genes, metabolism, or connectivity... What can you really conclude? How can we do better? We explore these questions here: tinyurl.com/2dudkevc

Updated preprint: doi.org/10.1101/2025...

We have improved DELSSOME and showed that we can accelerate the estimation of two new biophysical models. By collating 12,005 individuals, we derive normative trajectories of cortical E/I ratio across the lifespan ...

🧵 I gave Claude two things: a short paper (doi.org/10.1073/pnas...) and a raw behavioural dataset with 3 lines of variable descriptions.

Then I asked it to fit three computational RL models described only by equations in the manuscript. No code, no toolbox, no guidance on the fitting procedure. 1/3

How do we define "good" fMRI data? Especially with resting state, there are circularity risks if we evaluate data quality as showing the networks we expect to see. Javier Gonzalez-Castillo (& me & others) developed pBOLD, a new metric that uses multi-echo info. www.biorxiv.org/content/10.6... 1/8

New paper in Molecular Psychiatry:

In patients with anxiety + depression, targeting a novel “anxiosomatic” circuit (dmPFC) outperforms standard dlPFC for anxiety—and is equally effective for depression.

Free link: rdcu.be/faL22
Full link: lnkd.in/e4WZTncu

But the bigger story is the pipeline
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Convergent transcriptomic and connectomic controllers of information integration and its anaesthetic breakdown across mammalian brains - Nature Human Behaviour Luppi et al. identify transcriptomic and connectomic controllers of information integration and its breakdown induced by anaesthesia in humans, macaques, marmosets and mice.

What makes brains (un)conscious? We provide new answers—and a universal mammalian blueprint for information processing—in a cross-species study of humans, macaques, marmosets & mice. Exploring convergent breakdown of integration in:

www.nature.com/articles/s41...

Led by @loopyluppi.bsky.social

Do you censor high motion frames in fMRI? In two preprints by @twktan.bsky.social @mandymejia.bsky.social, we find that we may be censoring too much!

doi.org/10.64898/202...
arxiv.org/html/2603.07...

Strict censoring leads to worse personalized TMS targets than no censoring, even with high motion!

Attention fluctuates over time and across contexts—how is this reflected in the brain?🧠 Fitting a dynamical systems model to fMRI data, we show that the geometry of neural dynamics along the attractor landscape reflects changes in attention. Out in @natcomms.nature.com
www.nature.com/articles/s41...

I said “oh no” out loud when ryan gosling didn’t balance the centrifuge

Convergent transcriptomic and connectomic controllers of information integration and its anaesthetic breakdown across mammalian brains - Nature Human Behaviour Luppi et al. identify transcriptomic and connectomic controllers of information integration and its breakdown induced by anaesthesia in humans, macaques, marmosets and mice.

The Mammalian Architecture of Information Integration🧠🧬

For #BrainAwarenessWeek, excited to share our latest work about #Neuroscience of #Consciousness in @nathumbehav.nature.com

www.nature.com/articles/s41... 🧵👇

What a great thread! Saw this teased at OHBM last year - eager to dig into this manuscript.

interareal coupling, especially in the low-frequency range. So our results are consistent with the idea that activity-dependent changes in variability/covariation may extend to interareal coupling, but not sure we can say whether the underlying mechanisms are the same. Worth further testing for sure

Great point, I agree this is ultimately an empirical question. We don’t directly measure trial-by-trial variability here (we’re in a steady-state, post-perturbation regime rather than task-evoked activity). What we do see is that, across perturbations, changes in firing/excitability track changes in

Have not fully digested, but if it comes from Alessandro, you know it will be good!

🙇🙇 😅

So the phenomenon you report seems related, but likely different from what we describe here. In our case, the effect is more about changes in interareal coupling linked to cortical excitability, rather than trial-to-trial variability per se. Related territory, but probably distinct mechanisms!

If I understand correctly your paper seems to refer more specifically to the reduction of variability during task engagement, especially in early phases of task execution. This kind og quenching of spiking variability often leads to reduced co-variability, and thus also reduced noise correlations

The structures and functions of correlations in neural population codes - Nature Reviews Neuroscience In this Review, Panzeri, Moroni, Safaai and Harvey explain how the levels and structures of correlations among the activity of neurons in a population shape information encoding, transmission and read...

thanks @mwcole.bsky.social 🙇 I had missed your cool paper! Actually, noise correlations (during spontaneous activity or task after regressing out co-tuning/task-variable effects) generally tend to go down as firing rate goes up see here
doi.org/10.1038/s415...
www.nature.com/articles/nat...

Many thanks are due to all the authors and collaborators, especially @dasasgue.bsky.social who led this project throghout, #Stefano_panzeri for his computational insight. And many thanks to @erc.europa.eu, #SFARI and @iitalk.bsky.social for funding this work. Comments and suggestions are welcome!

They may also have implications for brain stimulation. For example: if we increase excitability in a cortical area (with TMS) we may see a decrease in its fMRI connectivity. What we like here is that these are testable hypotheses: and so we will soon see if (any of) this holds in humans!

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We believe our results may (partly) reframe how we interpret fMRI connectivity
▶️ fMRI connectivity ≠direct communication strength
▶️ fMRI connectivity is supported by distributed slow neuronal coupling
▶️ Hyper/hypoconnectivity (eg., in brain disorders) may reflect cortical hypo/hyperexcitability
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Thus our work suggests that
1️⃣ cortical excitability inversely modulates fMRI connectivity
2️⃣ fMRI coupling rests on distributed, slow neuronal fluctuations (i.e. QPPs, CAPs, neuromodulation pulses..)
3️⃣cortical excitability gates local coupling by weakening or facilitating that slow synchrony

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