Shaoshi Zhang
@ZShaoshi
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neuroscience, computational models | Computational Brain Imaging Group | Huge fan of Metroidvania and Edward Hopper.
Singapore
Joined May 2020
Thrilled to share our latest work just published in @Nature 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 @Leon_Oo1 @csabaorban @ZShaoshi. It is well-known that AI performance scales with logarithm of sample size (Kaplan, McCandlish 2020), but in many domains, sample size can be # participants or # measurements.
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RT @mgkumar138: Amazing work from @bttyeo on leveraging compute scaling laws to improve predictions using neuroimaging data!.
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RT @Oxford_NDPH: @ten_photos collaborated with researchers at the National University of Singapore on a recent study published in @Nature o….
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RT @washumedicine: “This is a gamechanger for the field.” A study co-authored by professor of neurology @ndosenbach with @NUSingapore shows….
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RT @Nature: Nature research paper: Longer scans boost prediction and cut costs in brain-wide association studies.
nature.com
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...
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RT @ten_photos: For me, this work is a classic @OHBM story: In 2023 I wasn't working with @bttyeo but I overheard him at his poster pointin….
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RT @anlijuncn: Can AI reveal the risk and co-pathology of multiple neurodegenerative diseases from just a single blood sample? We explored….
medrxiv.org
Co-pathology is a common feature of neurodegenerative diseases that complicates diagnosis, treatment and clinical management. However, sensitive, specific and scalable biomarkers for in vivo pathol...
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RT @Leon_Oo1: Super thankful to @bttyeo @csabaorban and @ZShaoshi for pouring in all the effort to make this work possible!.
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RT @DavidRen555: Proud to be part of this exciting @Nature study! It's time to embrace longer fMRI scan durations!.
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RT @SidChop: V useful paper by @bttyeo @Leon_Oo1 & @csabaorban out in @Nature. Scan for longer if you want to predict behaviour using fMRI….
pmc.ncbi.nlm.nih.gov
An important aim in psychiatry is the establishment of valid and reliable associations linking profiles of brain functioning to clinically relevant symptoms and behaviors across patient populations....
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Checkout our online calculator for future study designs and other more interactive features here 👉
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Special shoutout to @csabaorban and @Leon_Oo1 for co-leading this work! Huge thanks to @nfranzme, @SebRoemer and all our collaborators for contributing their invaluable datasets! Truly an amazing joint effort!.
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RT @marcelomattar: Thrilled to see our TinyRNN paper in @nature! We show how tiny RNNs predict choices of individual subjects accurately wh….
nature.com
Nature - Modelling biological decision-making with tiny recurrent neural networks enables more accurate predictions of animal choices than classical cognitive models and offers insights into the...
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Check out our latest preprint led by the amazing @tianchuzeng and @t___fang where we speed up the tedious parameter optimization process for biophysical modelling🔥👇.
While the world burns, we cook up a new preprint! Biophysical modeling is a key tool to derive mechanistic insights into the brain. These models are governed by biologically meaningful parameters (unlike deep neural nets), but the dirty secret . 1/N
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RT @HolmesLab_BHI: New paper by Lydia Qu @laurant_lydia et. al out now in Nature @NatMentHealth 📰. Here, we show that predictive network fe….
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