Shaoshi Zhang
@shaoshiz.bsky.social
neuroscience, computational models | Computational Brain Imaging Group | Huge fan of Metroidvania and Edward Hopper.
Pinned
Shaoshi Zhang
@shaoshiz.bsky.social
· Jul 17
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...
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👇:
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...
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...
July 22, 2025 at 3:47 PM
@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 👇
Reposted by Shaoshi Zhang
Just 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
A human brain network linked to restoration of consciousness after deep brain stimulation - Nature Communications
In people with severe brain injuries, stimulation restored consciousness by engaging a deep brain circuit for wakefulness—revealing a target that may also guide treatment in stroke and epilepsy.
www.nature.com
July 21, 2025 at 9:39 PM
Just 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 Zhang
What 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.
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...
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...
July 17, 2025 at 2:18 PM
What 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.
This is the best time to be doing neuroimaging.
Reposted by Shaoshi Zhang
I'm so proud to see this great paper finally published in @nature.com!
9/ Check out the actual study for many more analyses, e.g., phenotypic variation, scan parameters, signal to noise ratio, etc! doi.org/10.1038/s415...
Thank you to the editor @meharpist.bsky.social and anonymous reviewers for the many helpful suggestions, which greatly improved the study.
Thank you to the editor @meharpist.bsky.social and anonymous reviewers for the many helpful suggestions, which greatly improved the study.
Longer scans boost prediction and cut costs in brain-wide association studies - Nature
Although the number of participants is important for phenotypic prediction accuracy in brain-wide association studies using functional MRI, scanning for at least 30 min offers the greatest cost effect...
doi.org
July 17, 2025 at 7:54 PM
I'm so proud to see this great paper finally published in @nature.com!
Reposted by Shaoshi Zhang
Our 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...
Congrats to the whole team.
www.nature.com/articles/s41...
July 16, 2025 at 3:40 PM
Our 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...
Congrats to the whole team.
www.nature.com/articles/s41...
Reposted by Shaoshi Zhang
Really 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...
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...
July 17, 2025 at 2:46 PM
Really nice study, and extends some of the ideas developed in this paper pubmed.ncbi.nlm.nih.gov/32673043/
Reposted by Shaoshi Zhang
A 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...
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...
July 17, 2025 at 11:28 AM
A 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
Reposted by Shaoshi Zhang
This new Yeo Lab tool should immediately and permanently replace sample-size-only power calculations for functional MRI.
www.nature.com/articles/s41...
www.nature.com/articles/s41...
July 17, 2025 at 11:16 AM
This new Yeo Lab tool should immediately and permanently replace sample-size-only power calculations for functional MRI.
www.nature.com/articles/s41...
www.nature.com/articles/s41...
Reposted by Shaoshi Zhang
Just 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...
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...
July 17, 2025 at 2:11 PM
Just incredible results from a massive effort— moves the field forward. Bravo!!!
Reposted by Shaoshi Zhang
Nature research paper: Longer scans boost prediction and cut costs in brain-wide association studies
go.nature.com/3IME4aA
go.nature.com/3IME4aA
Longer scans boost prediction and cut costs in brain-wide association studies - Nature
Although the number of participants is important for phenotypic prediction accuracy in brain-wide association studies using functional MRI, scanning for at least 30 min offers the greatest cost effectiveness.
go.nature.com
July 17, 2025 at 10:35 AM
Nature research paper: Longer scans boost prediction and cut costs in brain-wide association studies
go.nature.com/3IME4aA
go.nature.com/3IME4aA
Reposted by Shaoshi Zhang
Big 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...
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...
July 17, 2025 at 5:17 AM
Big congrats to @bttyeo.bsky.social and team on this impressive and important work!
Reposted by Shaoshi Zhang
For 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.
3/11 Tom's model explains empirical prediction accuracies well across 76 phenotypes from 9 resting-fMRI & task-fMRI datasets (R2 = 0.89), spanning many scanners, acquisitions, racial groups, disorders & ages.
Does this mean that we should collect large datasets with short scans?
Does this mean that we should collect large datasets with short scans?
July 17, 2025 at 6:34 AM
For 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 Zhang
Everyone 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...).
Great use of the TCP data: (pmc.ncbi.nlm.nih.gov/articles/PMC...).
July 17, 2025 at 3:24 AM
Everyone should try out the Trandiagnostic Connectome Project (TCP) dataset! Openly available on @openneuro.bsky.social
Reposted by Shaoshi Zhang
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...).
Great use of the TCP data: (pmc.ncbi.nlm.nih.gov/articles/PMC...).
July 17, 2025 at 1:52 AM
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...).
Great use of the TCP data: (pmc.ncbi.nlm.nih.gov/articles/PMC...).
Reposted by Shaoshi Zhang
Super 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...
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...
July 17, 2025 at 2:14 AM
Super thankful to @bttyeo.bsky.social @csabaorban.bsky.social and @shaoshiz.bsky.social for pouring in all the effort to make this work possible!
🚨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...
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...
July 17, 2025 at 2:22 AM
🚨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👇:
Reposted by Shaoshi Zhang
How 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...
July 8, 2025 at 12:00 AM
How 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 Zhang
Check 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👇
www.nature.com/articles/s41...
🧵1👇
The Transdiagnostic Connectome Project: an open dataset for studying brain-behavior relationships in psychiatry - Scientific Data
Scientific Data - The Transdiagnostic Connectome Project: an open dataset for studying brain-behavior relationships in psychiatry
www.nature.com
June 4, 2025 at 11:45 PM
Check 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 👇🏾👇🏾👇🏾
Reposted by Shaoshi Zhang
I 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
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
May 16, 2025 at 4:03 AM
I love the work, not only because it speed up FIC models a lot, but also how it saves poor students from grad student descent 🤣🤣
Reposted by Shaoshi Zhang
Can 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
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
April 20, 2025 at 12:29 PM
Can deep learning help us solve dynamical systems problems, particularly those used in neural mass models? Check out this preprint to read about the perks...
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
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
April 11, 2025 at 5:27 AM
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
Reposted by Shaoshi Zhang
🚨 Predicting Alzheimer's Progression 🚨 A thread 🧵
1/ Accurate prediction of Alzheimer’s progression is critical for early intervention. How can we make predictions more precise and generalizable? 🧠✨
📝 Read the preprint led by @chen-zhang.bsky.social : doi.org/10.1101/2024...
1/ Accurate prediction of Alzheimer’s progression is critical for early intervention. How can we make predictions more precise and generalizable? 🧠✨
📝 Read the preprint led by @chen-zhang.bsky.social : doi.org/10.1101/2024...
November 20, 2024 at 2:30 AM
🚨 Predicting Alzheimer's Progression 🚨 A thread 🧵
1/ Accurate prediction of Alzheimer’s progression is critical for early intervention. How can we make predictions more precise and generalizable? 🧠✨
📝 Read the preprint led by @chen-zhang.bsky.social : doi.org/10.1101/2024...
1/ Accurate prediction of Alzheimer’s progression is critical for early intervention. How can we make predictions more precise and generalizable? 🧠✨
📝 Read the preprint led by @chen-zhang.bsky.social : doi.org/10.1101/2024...