Lightnews — Scholar-powered news

Reposted by Shaoshi Zhang

Oxford Population Health @oxpop.bsky.social · Jul 22

@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 👇

Thomas Yeo @bttyeo.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...

3 6

Reposted by Shaoshi Zhang

John Rolston @rolstonjohn.bsky.social · Jul 21

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

18 27

Reposted by Shaoshi Zhang

Brenden Tervo-Clemmens @tervoclemmensb.bsky.social · Jul 17

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.

Thomas Yeo @bttyeo.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...

6 15

Reposted by Shaoshi Zhang

Mary Elizabeth Sutherland @meharpist.bsky.social · Jul 17

I'm so proud to see this great paper finally published in @nature.com!

Thomas Yeo @bttyeo.bsky.social · Jul 17

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.

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

9 24

Reposted by Shaoshi Zhang

danilobzdok @danilobzdok.bsky.social · Jul 16

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...

1 3 15

Reposted by Shaoshi Zhang

Laurence Hunt @lhuntneuro.bsky.social · Jul 17

Really nice study, and extends some of the ideas developed in this paper pubmed.ncbi.nlm.nih.gov/32673043/

Thomas Yeo @bttyeo.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...

2 9

Reposted by Shaoshi Zhang

Jim Thompson @jimthommo.bsky.social · Jul 17

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

Thomas Yeo @bttyeo.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...

2 2 8

Reposted by Shaoshi Zhang

Nico Dosenbach @ndosenbach.bsky.social · Jul 17

This new Yeo Lab tool should immediately and permanently replace sample-size-only power calculations for functional MRI.

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

3 16 57

Reposted by Shaoshi Zhang

Ted Satterthwaite @ted-satterthwaite.bsky.social · Jul 17

Just incredible results from a massive effort— moves the field forward. Bravo!!!

Thomas Yeo @bttyeo.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...

1 4 16

Reposted by Shaoshi Zhang

Nature @nature.com · Jul 17

Nature research paper: Longer scans boost prediction and cut costs in brain-wide association studies

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

8 32

Reposted by Shaoshi Zhang

Alex Fornito @alexfornito.bsky.social · Jul 17

Big congrats to @bttyeo.bsky.social and team on this impressive and important work!

Thomas Yeo @bttyeo.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...

1 2 13

Reposted by Shaoshi Zhang

Thomas Nichols @nichols.bsky.social · Jul 17

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.

Thomas Yeo @bttyeo.bsky.social · Jul 17

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?

9 33

Reposted by Shaoshi Zhang

Thomas Yeo @bttyeo.bsky.social · Jul 17

Everyone should try out the Trandiagnostic Connectome Project (TCP) dataset! Openly available on @openneuro.bsky.social

Sidhant Chopra @sidchop.bsky.social · Jul 17

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...).

5 19

Reposted by Shaoshi Zhang

Sidhant Chopra @sidchop.bsky.social · Jul 17

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...).

6 14

Reposted by Shaoshi Zhang

Leon @leonooi.bsky.social · Jul 17

Super thankful to @bttyeo.bsky.social @csabaorban.bsky.social and @shaoshiz.bsky.social for pouring in all the effort to make this work possible!

Thomas Yeo @bttyeo.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...

5 9

Shaoshi Zhang @shaoshiz.bsky.social · Jul 17

🔗 Check out our online calculator for future study designs and other more interactive features!👉 thomasyeolab.github.io/OptimalScanT...

Ooi2025 Optimal Scan Time Calculator

thomasyeolab.github.io

1

Shaoshi Zhang @shaoshiz.bsky.social · Jul 17

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! ❤️

1 1 2

Shaoshi Zhang @shaoshiz.bsky.social · Jul 17

🚨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👇:

Thomas Yeo @bttyeo.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...

1 8 14

Reposted by Shaoshi Zhang

Valerie Jill Sydnor @valeriejsydnor.bsky.social · Jul 8

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...

4 44 120

Reposted by Shaoshi Zhang

Sidhant Chopra @sidchop.bsky.social · Jun 4

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 👇🏾👇🏾👇🏾

Carrisa V. Cocuzza, PhD (she/her) @carrisacocuzza.bsky.social · Jun 4

🚨 Dataset & Manuscript alert! 🚨 The Transdiagnostic Connectome Project (TCP) manuscript is now available @natureportfolio.nature.com Scientific Data! 🎉

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

7 10

Reposted by Shaoshi Zhang

Feilong Ma @feilong.bsky.social · May 16

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 🤣🤣

Thomas Yeo @bttyeo.bsky.social · Apr 11

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

2 4

Reposted by Shaoshi Zhang

Sina Mansour L. @sinamansourl.bsky.social · Apr 20

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...

Thomas Yeo @bttyeo.bsky.social · Apr 11

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

2 3

Shaoshi Zhang @shaoshiz.bsky.social · Apr 11

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

Thomas Yeo @bttyeo.bsky.social · Apr 11

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

1 1

Reposted by Shaoshi Zhang

Thomas Yeo @bttyeo.bsky.social · Nov 20

🚨 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 9 20