Bin Wan

✨ New preprint ✨

Introducing cuBNM, a toolbox we developed to run brain simulations *much* more efficiently on GPUs 🎉🚀

Preprint: www.biorxiv.org/content/10.1...
Documentation: cubnm.readthedocs.io/en/stable/
GitHub: github.com/amnsbr/cubnm

1/n 🧵

congrats！Valerie

It's a wrap! BIG thanks to all speakers, chairs, attendees, and our organizing committee - particularly our superstar local organizers Johan and Saurabh - for making our gradient dreams come true for the sixth year in a row ⭐ 🌈 🧠 🚀

woooowoooo

🎉🎉#OHBM2025 is just around the corner! Remember to visit the SP-SIG stall next to the registration desk and add stickers to your badge describing your research interests!

I finally defended my thesis! Thanks all! I started my PhD during covid! I am really grateful that my supervisor @sofievalk.bsky.social picked me to be her first PhD student when I was naive to neuroimage. I have been so lucky to stay in this happy and respectful academic environment so far!

Congrats bro!

Benchmarking methods for mapping functional connectivity in the brain - Nature Methods In this Analysis, Liu et al. benchmark more than 200 pairwise statistics for functional brain connectivity in tasks such as hub mapping, distance relationships, structure–function coupling and behavio...

An Analysis of 200 pairwise statistics for functional brain connectivity in tasks such as hub mapping, distance relationships, structure-function coupling and behavior prediction highlights their effectiveness for neurophysiological applications.

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

Adolescent maturation of cortical excitation-inhibition ratio based on individualized biophysical network modeling Individualized simulations reveal a decrease in excitation-inhibition ratio in association areas throughout adolescence.

🧠⚖️📉 How does the cortical excitation-inhibition ratio mature during adolescence?
We asked this in our new paper just out in #ScienceAdvances ✨
“Adolescent maturation of cortical excitation-inhibition ratio based on individualized biophysical network modeling”
📄 www.science.org/doi/full/10....
🧵⤵️

Merchant et al. perform a meta-analytic investigation of neurocognitive systems involved in real-time social interaction doi.org/10.52294/001...

@fmri-today.bsky.social @mallarchak.bsky.social @ohbmofficial.bsky.social

Overview of the simulation strategy and analysis. a) Pial and white matter boundaries surfaces are extracted from anatomical MRI volumes. b) Intermediate equidistant surfaces are generated between the pial and white matter surfaces (labeled as superficial (S) and deep (D) respectively). c) Surfaces are downsampled together, maintaining vertex correspondence across layers. Dipole orientations are constrained using vectors linking corresponding vertices (link vectors). d) The thickness of cortical laminae varies across the cortical depth (70–72), which is evenly sampled by the equidistant source surface layers. e) Each colored line represents the model evidence (relative to the worst model, ΔF) over source layer models, for a signal simulated at a particular layer (the simulated layer is indicated by the line color). The source layer model with the maximal ΔF is indicated by “˄”. f) Result matrix summarizing ΔF across simulated source locations, with peak relative model evidence marked with “˄”. g) Error is calculated from the result matrix as the absolute distance in mm or layers from the simulated source (*) to the peak ΔF (˄). h) Bias is calculated as the relative position of a peak ΔF(˄) to a simulated source (*) in layers or mm.

🚨🚨🚨PREPRINT ALERT🚨🚨🚨
Neural dynamics across cortical layers are key to brain computations - but non-invasively, we’ve been limited to rough "deep vs. superficial" distinctions. What if we told you that it is possible to achieve full (TRUE!) laminar (I, II, III, IV, V, VI) precision with MEG!

Juicy

🎨🧑‍🎨 Looking for a tool to visualize subcortical/thalamic data in 2D? Check out this python-based package I put together (subcortex-visualization on PyPI), plus a guide for creating your own custom atlas meshes and vector graphics! All feedback/tips welcome 😊

anniegbryant.github.io/subcortex_vi...

See our GWAS of grapical topology🤟

As always, it was great fun to connect with our ENIGMA Consortium community last night at #SOBP2025 in Toronto - thanks to all who joined us! Cheers to science and the next SOBP conference🍻🧠

in press @natcomms.nature.com 🌟

"Multimodal gradients unify local and global cortical organization"

7T MRI + cytoarchitectonics reveal a sensory-paralimbic axis of areal specialization & integration

led by superstar Yezhou Wang & a terrific team of friends & colleagues

▶️ doi.org/10.1038/s414...

that‘s pretty

Multimodal precision MRI of the individual human brain at ultra-high fields - Scientific Data Scientific Data - Multimodal precision MRI of the individual human brain at ultra-high fields

Our open-source multimodal precision MRI dataset is officially out! 🧲🧠🧲

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

osf.io/mhq3f/

@borismontreal.bsky.social @jroyer.bsky.social @themindwanders.bsky.social @jordandekraker.bsky.social

Genetic, transcriptomic, metabolic, and neuropsychiatric underpinnings of cortical functional gradients Functional gradients capture the organization of functional activity in the cerebral cortex, delineating transitions from sensory to higher-order association areas. While group-level gradient patterns...

forget the most important thing 😱
www.medrxiv.org/content/10.1...

Genetic, transcriptomic, metabolic, and neuropsychiatric underpinnings of cortical functional gradients Functional gradients capture the organization of functional activity in the cerebral cortex, delineating transitions from sensory to higher-order association areas. While group-level gradient patterns...

here is the link: www.medrxiv.org/content/10.1...

Thanks for the coauthors! Yuankai He, Varun Warrier, @johnalexandra.bsky.social, Matthias Kirschner, @sbe.bsky.social, @raibethlehem.bsky.social, @sofievalk.bsky.social (3/3).

1) The heritability was robust across samples of different ages
2) GWAS identified 16 genes, involved in metabolic process
3) The genes were further validated using Allen Brain
4) Individual protective and risk metabolic makers showed reversed correlations
5) SCZ, PTSD, and BP won the game
(2/3)

Happy to share my last PhD work "Genetic, transcriptomic, metabolic, and neuropsychiatric underpinnings of cortical functional gradients". We used UKB, HCP, and QTAB to analyze the genetics of functional gradients. The results are so good, consistent, and inspiring! (1/3)

GINNA, a 33 resting-state networks atlas with meta-analytic decoding-based cognitive characterization - Communications Biology The GINNA brain atlas offers a comprehensive framework for exploring the functional significance of 33 resting-state networks and their relationship to human cognition, using task-based meta-analytic ...

🧠 The GINNA brain atlas offers a comprehensive framework for exploring the functional significance of 33 resting-state networks & their relationship to human cognition.
@imn-bordeaux.bsky.social @achetyl.bsky.social

www.nature.com/articles/s42...

For all the thalamus lovers❤️‍🔥 : Excited to share that our Paper is out now✨Huge thanks to @sofievalk.bsky.social and all collaborators @meikehettwer.bsky.social, @wanb.bsky.social, @amnsbr.bsky.social, @jroyer.bsky.social, @borismontreal.bsky.social, @linaschaare.bsky.social, Seyma Bayrak

Geometric influences on the regional organization of the mammalian brain The mammalian brain is comprised of anatomically and functionally distinct regions. Substantial work over the past century has pursued the generation of ever-more accurate maps of regional boundaries,...

For my first-ever bluesky post, I'm really excited to share our new preprint "Geometric influences on the regional organization of the mammalian brain" with @alexfornito.bsky.social and a superstar 17-person team! (1/n)

www.biorxiv.org/content/10.1...

The architecture of the human default mode network explored through cytoarchitecture, wiring and signal flow - Nature Neuroscience The default mode network (DMN) is implicated in cognition and behavior. Here, the authors show that the DMN is cytoarchitecturally heterogeneous, it contains regions receptive to input from the sensory cortex and a core relatively insulated from environmental input, and it uniquely balances its output across sensory hierarchies.

Out in @natureneuro.bsky.social today 🥂

Cytoarchitecture, wiring and signal flow of the human default mode network

Combining 3D histology, 7T MRI, and connectomics to explore DMN structure-function associations

Led by Casey Paquola, @themindwanders.bsky.social & a terrific team of colleagues 🙏

this old paper used 5-year follow-up data, compared changes between SZ and their families and found reduction in SZ is faster. But only some brief results. doi.org/10.1192/bjp....

Brains minimize energy consumption while maximizing computation. In humans, this trade-off is reconciled towards complex behaviors & hence relatively high energy use

Review & synthesis with @sharnajamadar.bsky.social @annabhlr.bsky.social & Hamish Deery

tinyurl.com/47c9n65w
osf.io/preprints/os...

🥳🥳