Shaoshi Zhang
neuroscience, computational models | Computational Brain Imaging Group | Huge fan of Metroidvania and Edward Hopper.
- Reposted by Shaoshi Zhang@nichols.bsky.social collaborated with researchers at the National University of Singapore on a recent study published in @nature.com on how longer duration fMRI brain scans reduce costs and improve prediction accuracy for AI models. Read more about the study below 👇
- 1/11 Excited to share our @Naturestudy led by @leonooi.bsky.social @csabaorban.bsky.social @shaoshiz.bsky.social AI performance is known to scale with logarithm of sample size (Kaplan 2020), but in many domains, sample size can be # participants or # measurements... doi.org/10.1038/s415...
- Reposted by Shaoshi ZhangJust dropped in @natcomms.nature.com: we show that re-engaging a thalamic–ventral tegmental circuit with deep brain stimulation can reignite consciousness in patients with severe brain injury. Work led by Aaron Warren, with @andreashorn.org @foxmdphd.bsky.social @ others! tinyurl.com/4kz8j89b
- Reposted by Shaoshi ZhangWhat a fantastic effort. Truly inspiring to see brilliant people dig deeply into these meta scientific issues. This is the best time to be doing neuroimaging.
- 1/11 Excited to share our @Naturestudy led by @leonooi.bsky.social @csabaorban.bsky.social @shaoshiz.bsky.social AI performance is known to scale with logarithm of sample size (Kaplan 2020), but in many domains, sample size can be # participants or # measurements... doi.org/10.1038/s415...
- Reposted by Shaoshi ZhangI'm so proud to see this great paper finally published in @nature.com!
- Reposted by Shaoshi ZhangOur Nature paper on the hashtag#scaling hashtag#behavior and economics of hashtag#machine hashtag#learning predictions in high-dimensional brain scans is out ! Congrats to the whole team. www.nature.com/articles/s41...
- Reposted by Shaoshi ZhangReally nice study, and extends some of the ideas developed in this paper pubmed.ncbi.nlm.nih.gov/32673043/
- 1/11 Excited to share our @Naturestudy led by @leonooi.bsky.social @csabaorban.bsky.social @shaoshiz.bsky.social AI performance is known to scale with logarithm of sample size (Kaplan 2020), but in many domains, sample size can be # participants or # measurements... doi.org/10.1038/s415...
- Reposted by Shaoshi ZhangA super important and well designed study. Curious if those who took such interest in the original "BWAS needs impossibly huge n" will pay any attention to it
- 1/11 Excited to share our @Naturestudy led by @leonooi.bsky.social @csabaorban.bsky.social @shaoshiz.bsky.social AI performance is known to scale with logarithm of sample size (Kaplan 2020), but in many domains, sample size can be # participants or # measurements... doi.org/10.1038/s415...
- Reposted by Shaoshi ZhangThis new Yeo Lab tool should immediately and permanently replace sample-size-only power calculations for functional MRI. www.nature.com/articles/s41...
- 8/ In contrast to standard power calculations, our results suggest that jointly optimizing sample size and scan time can boost prediction accuracy while cutting costs. For more complex study design, you can check out our calculator: thomasyeolab.github.io/OptimalScanT...
- Reposted by Shaoshi ZhangJust incredible results from a massive effort— moves the field forward. Bravo!!!
- 1/11 Excited to share our @Naturestudy led by @leonooi.bsky.social @csabaorban.bsky.social @shaoshiz.bsky.social AI performance is known to scale with logarithm of sample size (Kaplan 2020), but in many domains, sample size can be # participants or # measurements... doi.org/10.1038/s415...
- Reposted by Shaoshi ZhangNature research paper: Longer scans boost prediction and cut costs in brain-wide association studies go.nature.com/3IME4aA
- Reposted by Shaoshi ZhangBig congrats to @bttyeo.bsky.social and team on this impressive and important work!
- 1/11 Excited to share our @Naturestudy led by @leonooi.bsky.social @csabaorban.bsky.social @shaoshiz.bsky.social AI performance is known to scale with logarithm of sample size (Kaplan 2020), but in many domains, sample size can be # participants or # measurements... doi.org/10.1038/s415...
- Reposted by Shaoshi ZhangFor me, this work is a classic @ohbmofficial.bsky.social story: In 2023 I wasn't working with @bttyeo.bsky.social but I overheard him at his poster pointing to some accuracy curves saying "I don't why they have this particular shape". That kicked off the collab that led to these results.
- Reposted by Shaoshi ZhangEveryone should try out the Trandiagnostic Connectome Project (TCP) dataset! Openly available on @openneuro.bsky.social
- V useful paper by @bttyeo.bsky.social @leonooi.bsky.social @csabaorban.bsky.social @shaoshiz.bsky.social in @nature.com. Scan longer if you want to predict behav using fMRI and save $. Great use of the TCP data: (pmc.ncbi.nlm.nih.gov/articles/PMC...).
- Reposted by Shaoshi ZhangV useful paper by @bttyeo.bsky.social @leonooi.bsky.social @csabaorban.bsky.social @shaoshiz.bsky.social in @nature.com. Scan longer if you want to predict behav using fMRI and save $. Great use of the TCP data: (pmc.ncbi.nlm.nih.gov/articles/PMC...).
- 1/11 Excited to share our @Naturestudy led by @leonooi.bsky.social @csabaorban.bsky.social @shaoshiz.bsky.social AI performance is known to scale with logarithm of sample size (Kaplan 2020), but in many domains, sample size can be # participants or # measurements... doi.org/10.1038/s415...
- Reposted by Shaoshi ZhangSuper thankful to @bttyeo.bsky.social @csabaorban.bsky.social and @shaoshiz.bsky.social for pouring in all the effort to make this work possible!
- 1/11 Excited to share our @Naturestudy led by @leonooi.bsky.social @csabaorban.bsky.social @shaoshiz.bsky.social AI performance is known to scale with logarithm of sample size (Kaplan 2020), but in many domains, sample size can be # participants or # measurements... doi.org/10.1038/s415...
- 🚨Thrilled to share our latest work just published in @nature.com where we looked into the optimal fMRI scan time for brain-wide association studies (BWAS) 🧠⏱️! Full thread below👇:
- 1/11 Excited to share our @Naturestudy led by @leonooi.bsky.social @csabaorban.bsky.social @shaoshiz.bsky.social AI performance is known to scale with logarithm of sample size (Kaplan 2020), but in many domains, sample size can be # participants or # measurements... doi.org/10.1038/s415...
- Special shoutout to @csabaorban.bsky.social and @leonooi.bsky.social for co-leading this work! Huge thanks to @nfranzme.bsky.social , @sebroemer.bsky.social and all our collaborators for contributing their invaluable datasets! Truly an amazing joint effort! ❤️
- 🔗 Check out our online calculator for future study designs and other more interactive features!👉 thomasyeolab.github.io/OptimalScanT...
- Reposted by Shaoshi ZhangHow does the human brain coordinate hierarchical cortical development? Our work in Nature Neuroscience identifies a role for thalamocortical structural connectivity in the expression of hierarchical periods of cortical plasticity & environmental receptivity in youth 🧵 www.nature.com/articles/s41...
- Reposted by Shaoshi ZhangCheck out our latest open data release. n=240, most with a dsm-5 dx with extensive phenotying (~100 scales/subscale), rest and task functional imaging. See @carrisacocuzza.bsky.social's thread below for deets and links 👇🏾👇🏾👇🏾
- 🚨 Dataset & Manuscript alert! 🚨 The Transdiagnostic Connectome Project (TCP) manuscript is now available @natureportfolio.nature.com Scientific Data! 🎉 www.nature.com/articles/s41... 🧵1👇
- Reposted by Shaoshi ZhangI love the work, not only because it speed up FIC models a lot, but also how it saves poor students from grad student descent 🤣🤣
- While the world burns, we cook up a new preprint! doi.org/10.1101/2025... Biophysical modeling is a key tool to derive mechanistic insights into the brain. These models are governed by biologically meaningful parameters (unlike artificial neural networks), but the dirty secret ... 1/N
- Reposted by Shaoshi ZhangCan deep learning help us solve dynamical systems problems, particularly those used in neural mass models? Check out this preprint to read about the perks...
- While the world burns, we cook up a new preprint! doi.org/10.1101/2025... Biophysical modeling is a key tool to derive mechanistic insights into the brain. These models are governed by biologically meaningful parameters (unlike artificial neural networks), but the dirty secret ... 1/N
- Check our latest preprint led by the amazing @tianchu.bsky.social and @tianfang.bsky.social where we speed up the tedious parameter optimization process for biophysical modelling
- While the world burns, we cook up a new preprint! doi.org/10.1101/2025... Biophysical modeling is a key tool to derive mechanistic insights into the brain. These models are governed by biologically meaningful parameters (unlike artificial neural networks), but the dirty secret ... 1/N
